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Merge branch 'master' into vdimir/multiple_joins_with_storage_join

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vdimir 2024-08-19 11:06:06 +02:00 committed by GitHub
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1574 changed files with 55222 additions and 17506 deletions
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3
.github/PULL_REQUEST_TEMPLATE.md vendored
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@ -59,6 +59,9 @@ At a minimum, the following information should be added (but add more as needed)
- [ ] <!---ci_exclude_tsan|msan|ubsan|coverage--> Exclude: All with TSAN, MSAN, UBSAN, Coverage
- [ ] <!---ci_exclude_aarch64|release|debug--> Exclude: All with aarch64, release, debug
---
- [ ] <!---ci_include_fuzzer--> Run only fuzzers related jobs (libFuzzer fuzzers, AST fuzzers, etc.)
- [ ] <!---ci_exclude_ast--> Exclude: AST fuzzers
---
- [ ] <!---do_not_test--> Do not test
- [ ] <!---woolen_wolfdog--> Woolen Wolfdog
- [ ] <!---upload_all--> Upload binaries for special builds

18
.github/actions/debug/action.yml vendored Normal file
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@ -0,0 +1,18 @@
name: DebugInfo
description: Prints workflow debug info
runs:
using: "composite"
steps:
- name: Print envs
shell: bash
run: |
echo "::group::Envs"
env
echo "::endgroup::"
- name: Print Event.json
shell: bash
run: |
echo "::group::Event.json"
python3 -m json.tool "$GITHUB_EVENT_PATH"
echo "::endgroup::"

109
.github/workflows/auto_releases.yml vendored Normal file
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@ -0,0 +1,109 @@
name: AutoReleases
env:
PYTHONUNBUFFERED: 1
concurrency:
group: autoreleases
on:
# schedule:
# - cron: '0 9 * * *'
workflow_dispatch:
inputs:
dry-run:
description: 'Dry run'
required: false
default: true
type: boolean
jobs:
AutoReleaseInfo:
runs-on: [self-hosted, style-checker-aarch64]
outputs:
data: ${{ steps.info.outputs.AUTO_RELEASE_PARAMS }}
dry_run: ${{ steps.info.outputs.DRY_RUN }}
steps:
- name: Debug Info
uses: ./.github/actions/debug
- name: Set envs
run: |
cat >> "$GITHUB_ENV" << 'EOF'
ROBOT_CLICKHOUSE_SSH_KEY<<RCSK
${{secrets.ROBOT_CLICKHOUSE_SSH_KEY}}
RCSK
EOF
echo "DRY_RUN=true" >> "$GITHUB_ENV"
- name: Check out repository code
uses: ClickHouse/checkout@v1
- name: Prepare Info
id: info
run: |
cd "$GITHUB_WORKSPACE/tests/ci"
python3 auto_release.py --prepare
echo "::group::Auto Release Info"
python3 -m json.tool /tmp/autorelease_info.json
echo "::endgroup::"
{
echo 'AUTO_RELEASE_PARAMS<<EOF'
cat /tmp/autorelease_info.json
echo 'EOF'
} >> "$GITHUB_ENV"
{
echo 'AUTO_RELEASE_PARAMS<<EOF'
cat /tmp/autorelease_info.json
echo 'EOF'
} >> "$GITHUB_OUTPUT"
echo "DRY_RUN=true" >> "$GITHUB_OUTPUT"
- name: Post Release Branch statuses
run: |
cd "$GITHUB_WORKSPACE/tests/ci"
python3 auto_release.py --post-status
- name: Clean up
uses: ./.github/actions/clean
Release_0:
needs: AutoReleaseInfo
name: Release ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[0].release_branch }}
if: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[0] && fromJson(needs.AutoReleaseInfo.outputs.data).releases[0].ready }}
uses: ./.github/workflows/create_release.yml
with:
ref: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[0].commit_sha }}
type: patch
dry-run: ${{ needs.AutoReleaseInfo.outputs.dry_run }}
#
# Release_1:
# needs: [AutoReleaseInfo, Release_0]
# name: Release ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[1].release_branch }}
# if: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[1] && fromJson(needs.AutoReleaseInfo.outputs.data).releases[1].ready }}
# uses: ./.github/workflows/create_release.yml
# with:
# ref: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[1].commit_sha }}
# type: patch
# dry-run: ${{ env.DRY_RUN }}
#
# Release_2:
# needs: [AutoReleaseInfo, Release_1]
# name: Release ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[2].release_branch }}
# if: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[0] && fromJson(needs.AutoReleaseInfo.outputs.data).releases[2].ready }}
# uses: ./.github/workflow/create_release.yml
# with:
# ref: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[0].commit_sha }}
# type: patch
# dry-run: ${{ env.DRY_RUN }}
#
# Release_3:
# needs: [AutoReleaseInfo, Release_2]
# name: Release ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[3].release_branch }}
# if: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[3] && fromJson(needs.AutoReleaseInfo.outputs.data).releases[3].ready }}
# uses: ./.github/workflow/create_release.yml
# with:
# ref: ${{ fromJson(needs.AutoReleaseInfo.outputs.data).releases[3].commit_sha }}
# type: patch
# dry-run: ${{ env.DRY_RUN }}
# - name: Post Slack Message
# if: ${{ !cancelled() }}
# run: |
# cd "$GITHUB_WORKSPACE/tests/ci"
# python3 auto_release.py --post-auto-release-complete --wf-status ${{ job.status }}

24
.github/workflows/create_release.yml vendored
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@ -2,6 +2,7 @@ name: CreateRelease
concurrency:
group: release
'on':
workflow_dispatch:
inputs:
@ -26,6 +27,26 @@ concurrency:
required: false
default: false
type: boolean
workflow_call:
inputs:
ref:
description: 'Git reference (branch or commit sha) from which to create the release'
required: true
type: string
type:
description: 'The type of release: "new" for a new release or "patch" for a patch release'
required: true
type: string
only-repo:
description: 'Run only repos updates including docker (repo-recovery, tests)'
required: false
default: false
type: boolean
dry-run:
description: 'Dry run'
required: false
default: false
type: boolean
jobs:
CreateRelease:
@ -101,6 +122,7 @@ jobs:
--volume=".:/wd" --workdir="/wd" \
clickhouse/style-test \
./tests/ci/changelog.py -v --debug-helpers \
--gh-user-or-token ${{ secrets.ROBOT_CLICKHOUSE_COMMIT_TOKEN }} \
--jobs=5 \
--output="./docs/changelogs/${{ env.RELEASE_TAG }}.md" ${{ env.RELEASE_TAG }}
git add ./docs/changelogs/${{ env.RELEASE_TAG }}.md
@ -129,9 +151,9 @@ jobs:
if: ${{ inputs.type == 'patch' && ! inputs.only-repo }}
shell: bash
run: |
python3 ./tests/ci/create_release.py --set-progress-completed
git reset --hard HEAD
git checkout "$GITHUB_REF_NAME"
python3 ./tests/ci/create_release.py --set-progress-completed
- name: Create GH Release
if: ${{ inputs.type == 'patch' && ! inputs.only-repo }}
shell: bash

6
.gitmodules vendored
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@ -230,9 +230,6 @@
[submodule "contrib/minizip-ng"]
path = contrib/minizip-ng
url = https://github.com/zlib-ng/minizip-ng
[submodule "contrib/annoy"]
path = contrib/annoy
url = https://github.com/ClickHouse/annoy
[submodule "contrib/qpl"]
path = contrib/qpl
url = https://github.com/intel/qpl
@ -348,9 +345,6 @@
[submodule "contrib/FP16"]
path = contrib/FP16
url = https://github.com/Maratyszcza/FP16.git
[submodule "contrib/robin-map"]
path = contrib/robin-map
url = https://github.com/Tessil/robin-map.git
[submodule "contrib/aklomp-base64"]
path = contrib/aklomp-base64
url = https://github.com/aklomp/base64.git

31
CMakeLists.txt
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@ -187,14 +187,6 @@ else ()
set(NO_WHOLE_ARCHIVE --no-whole-archive)
endif ()
if (NOT CMAKE_BUILD_TYPE_UC STREQUAL "RELEASE")
# Can be lld or ld-lld or lld-13 or /path/to/lld.
if (LINKER_NAME MATCHES "lld")
set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--gdb-index")
message (STATUS "Adding .gdb-index via --gdb-index linker option.")
endif ()
endif()
if (NOT (SANITIZE_COVERAGE OR WITH_COVERAGE)
AND (CMAKE_BUILD_TYPE_UC STREQUAL "RELEASE"
OR CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO"
@ -330,17 +322,21 @@ if (DISABLE_OMIT_FRAME_POINTER)
set (CMAKE_ASM_FLAGS_ADD "${CMAKE_ASM_FLAGS_ADD} -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer")
endif()
# Before you start hating your debugger because it refuses to show variables ('<optimized out>'), try building with -DDEBUG_O_LEVEL="0"
# https://stackoverflow.com/questions/63386189/whats-the-difference-between-a-compilers-o0-option-and-og-option/63386263#63386263
set(DEBUG_O_LEVEL "g" CACHE STRING "The -Ox level used for debug builds")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${COMPILER_FLAGS} ${CMAKE_CXX_FLAGS_ADD}")
set (CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -O3 ${DEBUG_INFO_FLAGS} ${CMAKE_CXX_FLAGS_ADD}")
set (CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -Og ${DEBUG_INFO_FLAGS} ${CMAKE_CXX_FLAGS_ADD}")
set (CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O${DEBUG_O_LEVEL} ${DEBUG_INFO_FLAGS} ${CMAKE_CXX_FLAGS_ADD}")
set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${COMPILER_FLAGS} ${CMAKE_C_FLAGS_ADD}")
set (CMAKE_C_FLAGS_RELWITHDEBINFO "${CMAKE_C_FLAGS_RELWITHDEBINFO} -O3 ${DEBUG_INFO_FLAGS} ${CMAKE_C_FLAGS_ADD}")
set (CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} -Og ${DEBUG_INFO_FLAGS} ${CMAKE_C_FLAGS_ADD}")
set (CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} -O${DEBUG_O_LEVEL} ${DEBUG_INFO_FLAGS} ${CMAKE_C_FLAGS_ADD}")
set (CMAKE_ASM_FLAGS "${CMAKE_ASM_FLAGS} ${COMPILER_FLAGS} ${CMAKE_ASM_FLAGS_ADD}")
set (CMAKE_ASM_FLAGS_RELWITHDEBINFO "${CMAKE_ASM_FLAGS_RELWITHDEBINFO} -O3 ${DEBUG_INFO_FLAGS} ${CMAKE_ASM_FLAGS_ADD}")
set (CMAKE_ASM_FLAGS_DEBUG "${CMAKE_ASM_FLAGS_DEBUG} -Og ${DEBUG_INFO_FLAGS} ${CMAKE_ASM_FLAGS_ADD}")
set (CMAKE_ASM_FLAGS_DEBUG "${CMAKE_ASM_FLAGS_DEBUG} -O${DEBUG_O_LEVEL} ${DEBUG_INFO_FLAGS} ${CMAKE_ASM_FLAGS_ADD}")
if (OS_DARWIN)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libc++")
@ -402,7 +398,7 @@ if ((NOT OS_LINUX AND NOT OS_ANDROID) OR (CMAKE_BUILD_TYPE_UC STREQUAL "DEBUG")
set(ENABLE_GWP_ASAN OFF)
endif ()
option (ENABLE_FIU "Enable Fiu" ON)
option (ENABLE_LIBFIU "Enable libfiu" ON)
option(WERROR "Enable -Werror compiler option" ON)
@ -428,12 +424,17 @@ if (NOT SANITIZE)
set (CMAKE_POSITION_INDEPENDENT_CODE OFF)
endif()
if (OS_LINUX AND NOT (ARCH_AARCH64 OR ARCH_S390X) AND NOT SANITIZE)
# Slightly more efficient code can be generated
# It's disabled for ARM because otherwise ClickHouse cannot run on Android.
if (NOT OS_ANDROID AND OS_LINUX AND NOT ARCH_S390X AND NOT SANITIZE)
# Using '-no-pie' builds executables with fixed addresses, resulting in slightly more efficient code
# and keeping binary addresses constant even with ASLR enabled.
# Disabled on Android as it requires PIE: https://source.android.com/docs/security/enhancements#android-5
# Disabled on IBM S390X due to build issues with 'no-pie'
# Disabled with sanitizers to avoid issues with maximum relocation size: https://github.com/ClickHouse/ClickHouse/pull/49145
set (CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -fno-pie")
set (CMAKE_C_FLAGS_RELWITHDEBINFO "${CMAKE_C_FLAGS_RELWITHDEBINFO} -fno-pie")
set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -no-pie -Wl,-no-pie")
else ()
message (WARNING "ClickHouse is built as PIE, system.trace_log will contain invalid addresses after server restart.")
endif ()
if (ENABLE_TESTS)

5
base/base/CMakeLists.txt
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@ -1,4 +1,4 @@
add_compile_options($<$<OR:$<COMPILE_LANGUAGE:C>,$<COMPILE_LANGUAGE:CXX>>:${COVERAGE_FLAGS}>)
add_compile_options("$<$<OR:$<COMPILE_LANGUAGE:C>,$<COMPILE_LANGUAGE:CXX>>:${COVERAGE_FLAGS}>")
if (USE_CLANG_TIDY)
set (CMAKE_CXX_CLANG_TIDY "${CLANG_TIDY_PATH}")
@ -8,6 +8,8 @@ endif ()
# when instantiated from JSON.cpp. Try again when libcxx(abi) and Clang are upgraded to 16.
set (CMAKE_CXX_STANDARD 20)
configure_file(GitHash.cpp.in GitHash.generated.cpp)
set (SRCS
argsToConfig.cpp
cgroupsv2.cpp
@ -33,6 +35,7 @@ set (SRCS
safeExit.cpp
throwError.cpp
Numa.cpp
GitHash.generated.cpp
)
add_library (common ${SRCS})

0
src/Daemon/GitHash.cpp.in → base/base/GitHash.cpp.in
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46
base/base/cgroupsv2.cpp
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@ -27,27 +27,6 @@ bool cgroupsV2Enabled()
#endif
}
bool cgroupsV2MemoryControllerEnabled()
{
#if defined(OS_LINUX)
chassert(cgroupsV2Enabled());
/// According to https://docs.kernel.org/admin-guide/cgroup-v2.html, file "cgroup.controllers" defines which controllers are available
/// for the current + child cgroups. The set of available controllers can be restricted from level to level using file
/// "cgroups.subtree_control". It is therefore sufficient to check the bottom-most nested "cgroup.controllers" file.
fs::path cgroup_dir = cgroupV2PathOfProcess();
if (cgroup_dir.empty())
return false;
std::ifstream controllers_file(cgroup_dir / "cgroup.controllers");
if (!controllers_file.is_open())
return false;
std::string controllers;
std::getline(controllers_file, controllers);
return controllers.find("memory") != std::string::npos;
#else
return false;
#endif
}
fs::path cgroupV2PathOfProcess()
{
#if defined(OS_LINUX)
@ -71,3 +50,28 @@ fs::path cgroupV2PathOfProcess()
return {};
#endif
}
std::optional<std::string> getCgroupsV2PathContainingFile([[maybe_unused]] std::string_view file_name)
{
#if defined(OS_LINUX)
if (!cgroupsV2Enabled())
return {};
fs::path current_cgroup = cgroupV2PathOfProcess();
if (current_cgroup.empty())
return {};
/// Return the bottom-most nested file. If there is no such file at the current
/// level, try again at the parent level as settings are inherited.
while (current_cgroup != default_cgroups_mount.parent_path())
{
const auto path = current_cgroup / file_name;
if (fs::exists(path))
return {current_cgroup};
current_cgroup = current_cgroup.parent_path();
}
return {};
#else
return {};
#endif
}

9
base/base/cgroupsv2.h
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@ -1,6 +1,7 @@
#pragma once
#include <filesystem>
#include <string_view>
#if defined(OS_LINUX)
/// I think it is possible to mount the cgroups hierarchy somewhere else (e.g. when in containers).
@ -11,11 +12,11 @@ static inline const std::filesystem::path default_cgroups_mount = "/sys/fs/cgrou
/// Is cgroups v2 enabled on the system?
bool cgroupsV2Enabled();
/// Is the memory controller of cgroups v2 enabled on the system?
/// Assumes that cgroupsV2Enabled() is enabled.
bool cgroupsV2MemoryControllerEnabled();
/// Detects which cgroup v2 the process belongs to and returns the filesystem path to the cgroup.
/// Returns an empty path the cgroup cannot be determined.
/// Assumes that cgroupsV2Enabled() is enabled.
std::filesystem::path cgroupV2PathOfProcess();
/// Returns the most nested cgroup dir containing the specified file.
/// If cgroups v2 is not enabled - returns an empty optional.
std::optional<std::string> getCgroupsV2PathContainingFile([[maybe_unused]] std::string_view file_name);

3
base/base/getMemoryAmount.cpp
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@ -19,9 +19,6 @@ std::optional<uint64_t> getCgroupsV2MemoryLimit()
if (!cgroupsV2Enabled())
return {};
if (!cgroupsV2MemoryControllerEnabled())
return {};
std::filesystem::path current_cgroup = cgroupV2PathOfProcess();
if (current_cgroup.empty())
return {};

4
base/poco/Net/include/Poco/Net/HTTPServerSession.h
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@ -58,6 +58,10 @@ namespace Net
void setKeepAliveTimeout(Poco::Timespan keepAliveTimeout);
size_t getKeepAliveTimeout() const { return _keepAliveTimeout.totalSeconds(); }
size_t getMaxKeepAliveRequests() const { return _maxKeepAliveRequests; }
private:
bool _firstRequest;
Poco::Timespan _keepAliveTimeout;

21
base/poco/Net/src/HTTPServerSession.cpp
View File

@ -19,11 +19,11 @@ namespace Poco {
namespace Net {
HTTPServerSession::HTTPServerSession(const StreamSocket& socket, HTTPServerParams::Ptr pParams):
HTTPSession(socket, pParams->getKeepAlive()),
_firstRequest(true),
_keepAliveTimeout(pParams->getKeepAliveTimeout()),
_maxKeepAliveRequests(pParams->getMaxKeepAliveRequests())
HTTPServerSession::HTTPServerSession(const StreamSocket & socket, HTTPServerParams::Ptr pParams)
: HTTPSession(socket, pParams->getKeepAlive())
, _firstRequest(true)
, _keepAliveTimeout(pParams->getKeepAliveTimeout())
, _maxKeepAliveRequests(pParams->getMaxKeepAliveRequests())
{
setTimeout(pParams->getTimeout());
}
@ -52,11 +52,12 @@ bool HTTPServerSession::hasMoreRequests()
}
else if (_maxKeepAliveRequests != 0 && getKeepAlive())
{
if (_maxKeepAliveRequests > 0)
--_maxKeepAliveRequests;
return buffered() > 0 || socket().poll(_keepAliveTimeout, Socket::SELECT_READ);
}
else return false;
if (_maxKeepAliveRequests > 0)
--_maxKeepAliveRequests;
return buffered() > 0 || socket().poll(_keepAliveTimeout, Socket::SELECT_READ);
}
else
return false;
}

10
cmake/autogenerated_versions.txt
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@ -2,11 +2,11 @@
# NOTE: VERSION_REVISION has nothing common with DBMS_TCP_PROTOCOL_VERSION,
# only DBMS_TCP_PROTOCOL_VERSION should be incremented on protocol changes.
SET(VERSION_REVISION 54489)
SET(VERSION_REVISION 54490)
SET(VERSION_MAJOR 24)
SET(VERSION_MINOR 8)
SET(VERSION_MINOR 9)
SET(VERSION_PATCH 1)
SET(VERSION_GITHASH 3f8b27d7accd2b5ec4afe7d0dd459115323304af)
SET(VERSION_DESCRIBE v24.8.1.1-testing)
SET(VERSION_STRING 24.8.1.1)
SET(VERSION_GITHASH e02b434d2fc0c4fbee29ca675deab7474d274608)
SET(VERSION_DESCRIBE v24.9.1.1-testing)
SET(VERSION_STRING 24.9.1.1)
# end of autochange

16
cmake/limit_jobs.cmake
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@ -42,19 +42,9 @@ endif ()
# But use 2 parallel jobs, since:
# - this is what llvm does
# - and I've verfied that lld-11 does not use all available CPU time (in peak) while linking one binary
if (CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO" AND ENABLE_THINLTO)
if (ARCH_AARCH64)
# aarch64 builds start to often fail with OOMs (reason not yet clear), for now let's limit the concurrency
message(STATUS "ThinLTO provides its own parallel linking - limiting parallel link jobs to 1.")
set (PARALLEL_LINK_JOBS 1)
if (LINKER_NAME MATCHES "lld")
math(EXPR LTO_JOBS ${NUMBER_OF_LOGICAL_CORES}/4)
set (CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFO "${CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFO} -Wl,--thinlto-jobs=${LTO_JOBS}")
endif()
elseif (PARALLEL_LINK_JOBS GREATER 2)
message(STATUS "ThinLTO provides its own parallel linking - limiting parallel link jobs to 2.")
set (PARALLEL_LINK_JOBS 2)
endif ()
if (CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO" AND ENABLE_THINLTO AND PARALLEL_LINK_JOBS GREATER 2)
message(STATUS "ThinLTO provides its own parallel linking - limiting parallel link jobs to 2.")
set (PARALLEL_LINK_JOBS 2)
endif()
message(STATUS "Building sub-tree with ${PARALLEL_COMPILE_JOBS} compile jobs and ${PARALLEL_LINK_JOBS} linker jobs (system: ${NUMBER_OF_LOGICAL_CORES} cores, ${TOTAL_PHYSICAL_MEMORY} MB RAM, 'OFF' means the native core count).")

4
cmake/sanitize.cmake
View File

@ -57,8 +57,8 @@ option(WITH_COVERAGE "Instrumentation for code coverage with default implementat
if (WITH_COVERAGE)
message (STATUS "Enabled instrumentation for code coverage")
set(COVERAGE_FLAGS "SHELL:-fprofile-instr-generate -fcoverage-mapping")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fprofile-instr-generate -fcoverage-mapping")
set (COVERAGE_FLAGS -fprofile-instr-generate -fcoverage-mapping)
set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fprofile-instr-generate -fcoverage-mapping")
endif()
option (SANITIZE_COVERAGE "Instrumentation for code coverage with custom callbacks" OFF)

10
contrib/CMakeLists.txt vendored
View File

@ -71,7 +71,6 @@ add_contrib (zlib-ng-cmake zlib-ng)
add_contrib (bzip2-cmake bzip2)
add_contrib (minizip-ng-cmake minizip-ng)
add_contrib (snappy-cmake snappy)
add_contrib (rocksdb-cmake rocksdb)
add_contrib (thrift-cmake thrift)
# parquet/arrow/orc
add_contrib (arrow-cmake arrow) # requires: snappy, thrift, double-conversion
@ -148,6 +147,7 @@ add_contrib (hive-metastore-cmake hive-metastore) # requires: thrift, avro, arro
add_contrib (cppkafka-cmake cppkafka)
add_contrib (libpqxx-cmake libpqxx)
add_contrib (libpq-cmake libpq)
add_contrib (rocksdb-cmake rocksdb) # requires: jemalloc, snappy, zlib, lz4, zstd, liburing
add_contrib (nuraft-cmake NuRaft)
add_contrib (fast_float-cmake fast_float)
add_contrib (idna-cmake idna)
@ -179,7 +179,7 @@ else()
message(STATUS "Not using QPL")
endif ()
if (OS_LINUX AND ARCH_AMD64)
if (OS_LINUX AND ARCH_AMD64 AND NOT NO_SSE3_OR_HIGHER)
option (ENABLE_QATLIB "Enable Intel® QuickAssist Technology Library (QATlib)" ${ENABLE_LIBRARIES})
elseif(ENABLE_QATLIB)
message (${RECONFIGURE_MESSAGE_LEVEL} "QATLib is only supported on x86_64")
@ -205,14 +205,12 @@ add_contrib (morton-nd-cmake morton-nd)
if (ARCH_S390X)
add_contrib(crc32-s390x-cmake crc32-s390x)
endif()
add_contrib (annoy-cmake annoy)
option(ENABLE_USEARCH "Enable USearch (Approximate Neighborhood Search, HNSW) support" ${ENABLE_LIBRARIES})
option(ENABLE_USEARCH "Enable USearch" ${ENABLE_LIBRARIES})
if (ENABLE_USEARCH)
add_contrib (FP16-cmake FP16)
add_contrib (robin-map-cmake robin-map)
add_contrib (SimSIMD-cmake SimSIMD)
add_contrib (usearch-cmake usearch) # requires: FP16, robin-map, SimdSIMD
add_contrib (usearch-cmake usearch) # requires: FP16, SimdSIMD
else ()
message(STATUS "Not using USearch")
endif ()

4
contrib/QAT-ZSTD-Plugin-cmake/CMakeLists.txt
View File

@ -27,7 +27,7 @@ if (ENABLE_QAT_OUT_OF_TREE_BUILD)
${QAT_AL_INCLUDE_DIR}
${QAT_USDM_INCLUDE_DIR}
${ZSTD_LIBRARY_DIR})
target_compile_definitions(_qatzstd_plugin PRIVATE -DDEBUGLEVEL=0 PUBLIC -DENABLE_ZSTD_QAT_CODEC)
target_compile_definitions(_qatzstd_plugin PRIVATE -DDEBUGLEVEL=0)
add_library (ch_contrib::qatzstd_plugin ALIAS _qatzstd_plugin)
else () # In-tree build
message(STATUS "Intel QATZSTD in-tree build")
@ -78,7 +78,7 @@ else () # In-tree build
${QAT_USDM_INCLUDE_DIR}
${ZSTD_LIBRARY_DIR}
${LIBQAT_HEADER_DIR})
target_compile_definitions(_qatzstd_plugin PRIVATE -DDEBUGLEVEL=0 PUBLIC -DENABLE_ZSTD_QAT_CODEC -DINTREE)
target_compile_definitions(_qatzstd_plugin PRIVATE -DDEBUGLEVEL=0 PUBLIC -DINTREE)
target_include_directories(_qatzstd_plugin SYSTEM PUBLIC $<BUILD_INTERFACE:${QATZSTD_SRC_DIR}> $<INSTALL_INTERFACE:include>)
add_library (ch_contrib::qatzstd_plugin ALIAS _qatzstd_plugin)
endif ()

2
contrib/SimSIMD vendored

@ -1 +1 @@
Subproject commit de2cb75b9e9e3389d5e1e51fd9f8ed151f3c17cf
Subproject commit 91a76d1ac519b3b9dc8957734a3dabd985f00c26

1
contrib/annoy vendored

@ -1 +0,0 @@
Subproject commit f2ac8e7b48f9a9cf676d3b58286e5455aba8e956

24
contrib/annoy-cmake/CMakeLists.txt
View File

@ -1,24 +0,0 @@
option(ENABLE_ANNOY "Enable Annoy index support" ${ENABLE_LIBRARIES})
# Annoy index should be disabled with undefined sanitizer. Because of memory storage optimizations
# (https://github.com/ClickHouse/annoy/blob/9d8a603a4cd252448589e84c9846f94368d5a289/src/annoylib.h#L442-L463)
# UBSan fails and leads to crash. Simmilar issue is already opened in Annoy repo
# https://github.com/spotify/annoy/issues/456
# Problem with aligment can lead to errors like
# (https://stackoverflow.com/questions/46790550/c-undefined-behavior-strict-aliasing-rule-or-incorrect-alignment)
# or will lead to crash on arm https://developer.arm.com/documentation/ka003038/latest
# This issues should be resolved before annoy became non-experimental (--> setting "allow_experimental_annoy_index")
if ((NOT ENABLE_ANNOY) OR (SANITIZE STREQUAL "undefined") OR (ARCH_AARCH64))
message (STATUS "Not using annoy")
return()
endif()
set(ANNOY_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/annoy")
set(ANNOY_SOURCE_DIR "${ANNOY_PROJECT_DIR}/src")
add_library(_annoy INTERFACE)
target_include_directories(_annoy SYSTEM INTERFACE ${ANNOY_SOURCE_DIR})
add_library(ch_contrib::annoy ALIAS _annoy)
target_compile_definitions(_annoy INTERFACE ENABLE_ANNOY)
target_compile_definitions(_annoy INTERFACE ANNOYLIB_MULTITHREADED_BUILD)

27
contrib/libfiu-cmake/CMakeLists.txt
View File

@ -1,20 +1,21 @@
if (NOT ENABLE_FIU)
message (STATUS "Not using fiu")
if (NOT ENABLE_LIBFIU)
message (STATUS "Not using libfiu")
return ()
endif ()
set(FIU_DIR "${ClickHouse_SOURCE_DIR}/contrib/libfiu/")
set(LIBFIU_DIR "${ClickHouse_SOURCE_DIR}/contrib/libfiu/")
set(FIU_SOURCES
${FIU_DIR}/libfiu/fiu.c
${FIU_DIR}/libfiu/fiu-rc.c
${FIU_DIR}/libfiu/backtrace.c
${FIU_DIR}/libfiu/wtable.c
set(LIBFIU_SOURCES
${LIBFIU_DIR}/libfiu/fiu.c
${LIBFIU_DIR}/libfiu/fiu-rc.c
${LIBFIU_DIR}/libfiu/backtrace.c
${LIBFIU_DIR}/libfiu/wtable.c
)
set(FIU_HEADERS "${FIU_DIR}/libfiu")
set(LIBFIU_HEADERS "${LIBFIU_DIR}/libfiu")
add_library(_fiu ${FIU_SOURCES})
target_compile_definitions(_fiu PUBLIC DUMMY_BACKTRACE)
target_include_directories(_fiu PUBLIC ${FIU_HEADERS})
add_library(ch_contrib::fiu ALIAS _fiu)
add_library(_libfiu ${LIBFIU_SOURCES})
target_compile_definitions(_libfiu PUBLIC DUMMY_BACKTRACE)
target_compile_definitions(_libfiu PUBLIC FIU_ENABLE)
target_include_directories(_libfiu PUBLIC ${LIBFIU_HEADERS})
add_library(ch_contrib::libfiu ALIAS _libfiu)

2
contrib/libprotobuf-mutator vendored

@ -1 +1 @@
Subproject commit 1f95f8083066f5b38fd2db172e7e7f9aa7c49d2d
Subproject commit b922c8ab9004ef9944982e4f165e2747b13223fa

2
contrib/librdkafka vendored

@ -1 +1 @@
Subproject commit 2d2aab6f5b79db1cfca15d7bf0dee75d00d82082
Subproject commit 39d4ed49ccf3406e2bf825d5d7b0903b5a290782

2
contrib/libunwind vendored

@ -1 +1 @@
Subproject commit a89d904befea07814628c6ce0b44083c4e149c62
Subproject commit 601db0b0e03018c01710470a37703b618f9cf08b

4
contrib/qpl-cmake/CMakeLists.txt
View File

@ -728,10 +728,6 @@ add_library(_qpl STATIC ${LIB_DEPS})
target_include_directories(_qpl
PUBLIC $<BUILD_INTERFACE:${QPL_PROJECT_DIR}/include/> $<INSTALL_INTERFACE:include>)
target_compile_definitions(_qpl
PUBLIC -DENABLE_QPL_COMPRESSION)
target_link_libraries(_qpl
PRIVATE ch_contrib::accel-config)

1
contrib/robin-map vendored

@ -1 +0,0 @@
Subproject commit 851a59e0e3063ee0e23089062090a73fd3de482d

1
contrib/robin-map-cmake/CMakeLists.txt
View File

@ -1 +0,0 @@
# See contrib/usearch-cmake/CMakeLists.txt

2
contrib/rocksdb vendored

@ -1 +1 @@
Subproject commit 49ce8a1064dd1ad89117899839bf136365e49e79
Subproject commit 5f003e4a22d2e48e37c98d9620241237cd30dd24

44
contrib/rocksdb-cmake/CMakeLists.txt
View File

@ -5,36 +5,38 @@ if (NOT ENABLE_ROCKSDB OR NO_SSE3_OR_HIGHER) # assumes SSE4.2 and PCLMUL
return()
endif()
# not in original build system, otherwise xxHash.cc fails to compile with ClickHouse C++23 default
set (CMAKE_CXX_STANDARD 20)
# Always disable jemalloc for rocksdb by default because it introduces non-standard jemalloc APIs
option(WITH_JEMALLOC "build with JeMalloc" OFF)
option(WITH_LIBURING "build with liburing" OFF) # TODO could try to enable this conditionally, depending on ClickHouse's ENABLE_LIBURING
# ClickHouse cannot be compiled without snappy, lz4, zlib, zstd
option(WITH_SNAPPY "build with SNAPPY" ON)
option(WITH_LZ4 "build with lz4" ON)
option(WITH_ZLIB "build with zlib" ON)
option(WITH_ZSTD "build with zstd" ON)
if(WITH_SNAPPY)
if (ENABLE_JEMALLOC AND OS_LINUX) # gives compile errors with jemalloc enabled for rocksdb on non-Linux
add_definitions(-DROCKSDB_JEMALLOC -DJEMALLOC_NO_DEMANGLE)
list (APPEND THIRDPARTY_LIBS ch_contrib::jemalloc)
endif ()
if (ENABLE_LIBURING)
add_definitions(-DROCKSDB_IOURING_PRESENT)
list (APPEND THIRDPARTY_LIBS ch_contrib::liburing)
endif ()
if (WITH_SNAPPY)
add_definitions(-DSNAPPY)
list(APPEND THIRDPARTY_LIBS ch_contrib::snappy)
endif()
if(WITH_ZLIB)
if (WITH_ZLIB)
add_definitions(-DZLIB)
list(APPEND THIRDPARTY_LIBS ch_contrib::zlib)
endif()
if(WITH_LZ4)
if (WITH_LZ4)
add_definitions(-DLZ4)
list(APPEND THIRDPARTY_LIBS ch_contrib::lz4)
endif()
if(WITH_ZSTD)
if (WITH_ZSTD)
add_definitions(-DZSTD)
list(APPEND THIRDPARTY_LIBS ch_contrib::zstd)
endif()
@ -88,6 +90,7 @@ set(SOURCES
${ROCKSDB_SOURCE_DIR}/cache/sharded_cache.cc
${ROCKSDB_SOURCE_DIR}/cache/tiered_secondary_cache.cc
${ROCKSDB_SOURCE_DIR}/db/arena_wrapped_db_iter.cc
${ROCKSDB_SOURCE_DIR}/db/attribute_group_iterator_impl.cc
${ROCKSDB_SOURCE_DIR}/db/blob/blob_contents.cc
${ROCKSDB_SOURCE_DIR}/db/blob/blob_fetcher.cc
${ROCKSDB_SOURCE_DIR}/db/blob/blob_file_addition.cc
@ -104,6 +107,7 @@ set(SOURCES
${ROCKSDB_SOURCE_DIR}/db/blob/prefetch_buffer_collection.cc
${ROCKSDB_SOURCE_DIR}/db/builder.cc
${ROCKSDB_SOURCE_DIR}/db/c.cc
${ROCKSDB_SOURCE_DIR}/db/coalescing_iterator.cc
${ROCKSDB_SOURCE_DIR}/db/column_family.cc
${ROCKSDB_SOURCE_DIR}/db/compaction/compaction.cc
${ROCKSDB_SOURCE_DIR}/db/compaction/compaction_iterator.cc
@ -124,6 +128,7 @@ set(SOURCES
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_write.cc
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_compaction_flush.cc
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_files.cc
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_follower.cc
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_open.cc
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_debug.cc
${ROCKSDB_SOURCE_DIR}/db/db_impl/db_impl_experimental.cc
@ -181,6 +186,7 @@ set(SOURCES
${ROCKSDB_SOURCE_DIR}/env/env_encryption.cc
${ROCKSDB_SOURCE_DIR}/env/file_system.cc
${ROCKSDB_SOURCE_DIR}/env/file_system_tracer.cc
${ROCKSDB_SOURCE_DIR}/env/fs_on_demand.cc
${ROCKSDB_SOURCE_DIR}/env/fs_remap.cc
${ROCKSDB_SOURCE_DIR}/env/mock_env.cc
${ROCKSDB_SOURCE_DIR}/env/unique_id_gen.cc
@ -368,6 +374,7 @@ set(SOURCES
${ROCKSDB_SOURCE_DIR}/utilities/persistent_cache/volatile_tier_impl.cc
${ROCKSDB_SOURCE_DIR}/utilities/simulator_cache/cache_simulator.cc
${ROCKSDB_SOURCE_DIR}/utilities/simulator_cache/sim_cache.cc
${ROCKSDB_SOURCE_DIR}/utilities/table_properties_collectors/compact_for_tiering_collector.cc
${ROCKSDB_SOURCE_DIR}/utilities/table_properties_collectors/compact_on_deletion_collector.cc
${ROCKSDB_SOURCE_DIR}/utilities/trace/file_trace_reader_writer.cc
${ROCKSDB_SOURCE_DIR}/utilities/trace/replayer_impl.cc
@ -388,6 +395,7 @@ set(SOURCES
${ROCKSDB_SOURCE_DIR}/utilities/transactions/write_prepared_txn_db.cc
${ROCKSDB_SOURCE_DIR}/utilities/transactions/write_unprepared_txn.cc
${ROCKSDB_SOURCE_DIR}/utilities/transactions/write_unprepared_txn_db.cc
${ROCKSDB_SOURCE_DIR}/utilities/types_util.cc
${ROCKSDB_SOURCE_DIR}/utilities/ttl/db_ttl_impl.cc
${ROCKSDB_SOURCE_DIR}/utilities/wal_filter.cc
${ROCKSDB_SOURCE_DIR}/utilities/write_batch_with_index/write_batch_with_index.cc
@ -418,14 +426,18 @@ if(HAS_ARMV8_CRC)
endif(HAS_ARMV8_CRC)
list(APPEND SOURCES
"${ROCKSDB_SOURCE_DIR}/port/port_posix.cc"
"${ROCKSDB_SOURCE_DIR}/env/env_posix.cc"
"${ROCKSDB_SOURCE_DIR}/env/fs_posix.cc"
"${ROCKSDB_SOURCE_DIR}/env/io_posix.cc")
${ROCKSDB_SOURCE_DIR}/port/port_posix.cc
${ROCKSDB_SOURCE_DIR}/env/env_posix.cc
${ROCKSDB_SOURCE_DIR}/env/fs_posix.cc
${ROCKSDB_SOURCE_DIR}/env/io_posix.cc)
add_library(_rocksdb ${SOURCES})
add_library(ch_contrib::rocksdb ALIAS _rocksdb)
target_link_libraries(_rocksdb PRIVATE ${THIRDPARTY_LIBS} ${SYSTEM_LIBS})
# Not in the native build system but useful anyways:
# Make all functions in xxHash.h inline. Beneficial for performance: https://github.com/Cyan4973/xxHash/tree/v0.8.2#build-modifiers
target_compile_definitions (_rocksdb PRIVATE XXH_INLINE_ALL)
# SYSTEM is required to overcome some issues
target_include_directories(_rocksdb SYSTEM BEFORE INTERFACE "${ROCKSDB_SOURCE_DIR}/include")

2
contrib/usearch vendored

@ -1 +1 @@
Subproject commit 30810452bec5d3d3aa0931bb5d761e2f09aa6356
Subproject commit e21a5778a0d4469ddaf38c94b7be0196bb701ee4

21
contrib/usearch-cmake/CMakeLists.txt
View File

@ -1,17 +1,22 @@
set(USEARCH_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/usearch")
set(USEARCH_SOURCE_DIR "${USEARCH_PROJECT_DIR}/include")
set(FP16_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/FP16")
set(ROBIN_MAP_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/robin-map")
set(SIMSIMD_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/SimSIMD-map")
set(SIMSIMD_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/SimSIMD")
set(USEARCH_PROJECT_DIR "${ClickHouse_SOURCE_DIR}/contrib/usearch")
add_library(_usearch INTERFACE)
target_include_directories(_usearch SYSTEM INTERFACE
${FP16_PROJECT_DIR}/include
${ROBIN_MAP_PROJECT_DIR}/include
${SIMSIMD_PROJECT_DIR}/include
${USEARCH_SOURCE_DIR})
${USEARCH_PROJECT_DIR}/include)
target_compile_definitions(_usearch INTERFACE USEARCH_USE_FP16LIB)
# target_compile_definitions(_usearch INTERFACE USEARCH_USE_SIMSIMD)
# ^^ simsimd is not enabled at the moment. Reasons:
# - Vectorization is important for raw scans but not so much for HNSW. We use usearch only for HNSW.
# - Simsimd does compile-time dispatch (choice of SIMD kernels determined by capabilities of the build machine) or dynamic dispatch (SIMD
# kernels chosen at runtime based on cpuid instruction). Since current builds are limited to SSE 4.2 (x86) and NEON (ARM), the speedup of
# the former would be moderate compared to AVX-512 / SVE. The latter is at the moment too fragile with respect to portability across x86
# and ARM machines ... certain conbinations of quantizations / distance functions / SIMD instructions are not implemented at the moment.
add_library(ch_contrib::usearch ALIAS _usearch)
target_compile_definitions(_usearch INTERFACE ENABLE_USEARCH)

2
docker/keeper/Dockerfile
View File

@ -34,7 +34,7 @@ RUN arch=${TARGETARCH:-amd64} \
# lts / testing / prestable / etc
ARG REPO_CHANNEL="stable"
ARG REPOSITORY="https://packages.clickhouse.com/tgz/${REPO_CHANNEL}"
ARG VERSION="24.7.2.13"
ARG VERSION="24.7.3.42"
ARG PACKAGES="clickhouse-keeper"
ARG DIRECT_DOWNLOAD_URLS=""

3
docker/packager/binary-builder/build.sh
View File

@ -108,7 +108,8 @@ if [ -n "$MAKE_DEB" ]; then
bash -x /build/packages/build
fi
mv ./programs/clickhouse* /output || mv ./programs/*_fuzzer /output
mv ./programs/clickhouse* /output ||:
mv ./programs/*_fuzzer /output ||:
[ -x ./programs/self-extracting/clickhouse ] && mv ./programs/self-extracting/clickhouse /output
[ -x ./programs/self-extracting/clickhouse-stripped ] && mv ./programs/self-extracting/clickhouse-stripped /output
[ -x ./programs/self-extracting/clickhouse-keeper ] && mv ./programs/self-extracting/clickhouse-keeper /output

26
docker/packager/cctools/Dockerfile
View File

@ -1,3 +1,5 @@
# docker build -t clickhouse/cctools .
# This is a hack to significantly reduce the build time of the clickhouse/binary-builder
# It's based on the assumption that we don't care of the cctools version so much
# It event does not depend on the clickhouse/fasttest in the `docker/images.json`
@ -30,5 +32,29 @@ RUN git clone https://github.com/tpoechtrager/cctools-port.git \
&& cd ../.. \
&& rm -rf cctools-port
#
# GDB
#
# ld from binutils is 2.38, which has the following error:
#
# DWARF error: invalid or unhandled FORM value: 0x23
#
ENV LD=ld.lld-${LLVM_VERSION}
ARG GDB_VERSION=15.1
RUN apt-get update \
&& apt-get install --yes \
libgmp-dev \
libmpfr-dev \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/* /var/cache/debconf /tmp/*
RUN wget https://sourceware.org/pub/gdb/releases/gdb-$GDB_VERSION.tar.gz \
&& tar -xvf gdb-$GDB_VERSION.tar.gz \
&& cd gdb-$GDB_VERSION \
&& ./configure --prefix=/opt/gdb \
&& make -j $(nproc) \
&& make install \
&& rm -fr gdb-$GDB_VERSION gdb-$GDB_VERSION.tar.gz
FROM scratch
COPY --from=builder /cctools /cctools
COPY --from=builder /opt/gdb /opt/gdb

2
docker/server/Dockerfile.alpine
View File

@ -32,7 +32,7 @@ RUN arch=${TARGETARCH:-amd64} \
# lts / testing / prestable / etc
ARG REPO_CHANNEL="stable"
ARG REPOSITORY="https://packages.clickhouse.com/tgz/${REPO_CHANNEL}"
ARG VERSION="24.7.2.13"
ARG VERSION="24.7.3.42"
ARG PACKAGES="clickhouse-client clickhouse-server clickhouse-common-static"
ARG DIRECT_DOWNLOAD_URLS=""

2
docker/server/Dockerfile.ubuntu
View File

@ -28,7 +28,7 @@ RUN sed -i "s|http://archive.ubuntu.com|${apt_archive}|g" /etc/apt/sources.list
ARG REPO_CHANNEL="stable"
ARG REPOSITORY="deb [signed-by=/usr/share/keyrings/clickhouse-keyring.gpg] https://packages.clickhouse.com/deb ${REPO_CHANNEL} main"
ARG VERSION="24.7.2.13"
ARG VERSION="24.7.3.42"
ARG PACKAGES="clickhouse-client clickhouse-server clickhouse-common-static"
#docker-official-library:off

2
docker/test/fasttest/Dockerfile
View File

@ -83,7 +83,7 @@ RUN arch=${TARGETARCH:-amd64} \
# Give suid to gdb to grant it attach permissions
# chmod 777 to make the container user independent
RUN chmod u+s /usr/bin/gdb \
RUN chmod u+s /opt/gdb/bin/gdb \
&& mkdir -p /var/lib/clickhouse \
&& chmod 777 /var/lib/clickhouse

4
docker/test/integration/base/Dockerfile
View File

@ -11,7 +11,6 @@ RUN apt-get update \
curl \
default-jre \
g++ \
gdb \
iproute2 \
krb5-user \
libicu-dev \
@ -73,3 +72,6 @@ maxClientCnxns=80' > /opt/zookeeper/conf/zoo.cfg && \
ENV TZ=Etc/UTC
RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
COPY --from=clickhouse/cctools:0d6b90a7a490 /opt/gdb /opt/gdb
ENV PATH="/opt/gdb/bin:${PATH}"

3
docker/test/integration/runner/Dockerfile
View File

@ -30,7 +30,6 @@ RUN apt-get update \
luajit \
libssl-dev \
libcurl4-openssl-dev \
gdb \
default-jdk \
software-properties-common \
libkrb5-dev \
@ -87,6 +86,8 @@ COPY modprobe.sh /usr/local/bin/modprobe
COPY dockerd-entrypoint.sh /usr/local/bin/
COPY misc/ /misc/
COPY --from=clickhouse/cctools:0d6b90a7a490 /opt/gdb /opt/gdb
ENV PATH="/opt/gdb/bin:${PATH}"
# Same options as in test/base/Dockerfile
# (in case you need to override them in tests)

4
docker/test/performance-comparison/Dockerfile
View File

@ -9,7 +9,6 @@ RUN apt-get update \
curl \
dmidecode \
g++ \
gdb \
git \
gnuplot \
imagemagick \
@ -42,6 +41,9 @@ RUN pip3 --no-cache-dir install -r requirements.txt
COPY run.sh /
COPY --from=clickhouse/cctools:0d6b90a7a490 /opt/gdb /opt/gdb
ENV PATH="/opt/gdb/bin:${PATH}"
CMD ["bash", "/run.sh"]
# docker run --network=host --volume <workspace>:/workspace --volume=<output>:/output -e PR_TO_TEST=<> -e SHA_TO_TEST=<> clickhouse/performance-comparison

25
docker/test/stateful/run.sh
View File

@ -232,15 +232,26 @@ function run_tests()
set +e
TEST_ARGS=(
-j 2
--testname
--shard
--zookeeper
--check-zookeeper-session
--no-stateless
--hung-check
--print-time
--capture-client-stacktrace
"${ADDITIONAL_OPTIONS[@]}"
"$SKIP_TESTS_OPTION"
)
if [[ -n "$USE_PARALLEL_REPLICAS" ]] && [[ "$USE_PARALLEL_REPLICAS" -eq 1 ]]; then
clickhouse-test --client="clickhouse-client --allow_experimental_parallel_reading_from_replicas=1 --parallel_replicas_for_non_replicated_merge_tree=1 \
--max_parallel_replicas=100 --cluster_for_parallel_replicas='parallel_replicas'" \
-j 2 --testname --shard --zookeeper --check-zookeeper-session --no-stateless --no-parallel-replicas --hung-check --print-time "${ADDITIONAL_OPTIONS[@]}" \
"$SKIP_TESTS_OPTION" 2>&1 | ts '%Y-%m-%d %H:%M:%S' | tee test_output/test_result.txt
else
clickhouse-test -j 2 --testname --shard --zookeeper --check-zookeeper-session --no-stateless --hung-check --print-time "${ADDITIONAL_OPTIONS[@]}" \
"$SKIP_TESTS_OPTION" 2>&1 | ts '%Y-%m-%d %H:%M:%S' | tee test_output/test_result.txt
TEST_ARGS+=(
--client="clickhouse-client --allow_experimental_parallel_reading_from_replicas=1 --parallel_replicas_for_non_replicated_merge_tree=1 --max_parallel_replicas=100 --cluster_for_parallel_replicas='parallel_replicas'"
--no-parallel-replicas
)
fi
clickhouse-test "${TEST_ARGS[@]}" 2>&1 | ts '%Y-%m-%d %H:%M:%S' | tee test_output/test_result.txt
set -e
}

4
docker/test/stateless/Dockerfile
View File

@ -69,8 +69,8 @@ ENV MAX_RUN_TIME=0
# Unrelated to vars in setup_minio.sh, but should be the same there
# to have the same binaries for local running scenario
ARG MINIO_SERVER_VERSION=2022-01-03T18-22-58Z
ARG MINIO_CLIENT_VERSION=2022-01-05T23-52-51Z
ARG MINIO_SERVER_VERSION=2024-08-03T04-33-23Z
ARG MINIO_CLIENT_VERSION=2024-07-31T15-58-33Z
ARG TARGETARCH
# Download Minio-related binaries

80
docker/test/stateless/run.sh
View File

@ -54,8 +54,6 @@ source /utils.lib
/usr/share/clickhouse-test/config/install.sh
./setup_minio.sh stateless
./mc admin trace clickminio > /test_output/minio.log &
MC_ADMIN_PID=$!
./setup_hdfs_minicluster.sh
@ -176,6 +174,55 @@ done
setup_logs_replication
attach_gdb_to_clickhouse
# create tables for minio log webhooks
clickhouse-client --query "CREATE TABLE minio_audit_logs
(
log String,
event_time DateTime64(9) MATERIALIZED parseDateTime64BestEffortOrZero(trim(BOTH '\"' FROM JSONExtractRaw(log, 'time')), 9, 'UTC')
)
ENGINE = MergeTree
ORDER BY tuple()"
clickhouse-client --query "CREATE TABLE minio_server_logs
(
log String,
event_time DateTime64(9) MATERIALIZED parseDateTime64BestEffortOrZero(trim(BOTH '\"' FROM JSONExtractRaw(log, 'time')), 9, 'UTC')
)
ENGINE = MergeTree
ORDER BY tuple()"
# create minio log webhooks for both audit and server logs
# use async inserts to avoid creating too many parts
./mc admin config set clickminio logger_webhook:ch_server_webhook endpoint="http://localhost:8123/?async_insert=1&wait_for_async_insert=0&async_insert_busy_timeout_min_ms=5000&async_insert_busy_timeout_max_ms=5000&async_insert_max_query_number=1000&async_insert_max_data_size=10485760&query=INSERT%20INTO%20minio_server_logs%20FORMAT%20LineAsString" queue_size=1000000 batch_size=500
./mc admin config set clickminio audit_webhook:ch_audit_webhook endpoint="http://localhost:8123/?async_insert=1&wait_for_async_insert=0&async_insert_busy_timeout_min_ms=5000&async_insert_busy_timeout_max_ms=5000&async_insert_max_query_number=1000&async_insert_max_data_size=10485760&query=INSERT%20INTO%20minio_audit_logs%20FORMAT%20LineAsString" queue_size=1000000 batch_size=500
max_retries=100
retry=1
while [ $retry -le $max_retries ]; do
echo "clickminio restart attempt $retry:"
output=$(./mc admin service restart clickminio --wait --json 2>&1 | jq -r .status)
echo "Output of restart status: $output"
expected_output="success
success"
if [ "$output" = "$expected_output" ]; then
echo "Restarted clickminio successfully."
break
fi
sleep 1
retry=$((retry + 1))
done
if [ $retry -gt $max_retries ]; then
echo "Failed to restart clickminio after $max_retries attempts."
fi
./mc admin trace clickminio > /test_output/minio.log &
MC_ADMIN_PID=$!
function fn_exists() {
declare -F "$1" > /dev/null;
}
@ -264,11 +311,22 @@ function run_tests()
TIMEOUT=$((MAX_RUN_TIME - 800 > 8400 ? 8400 : MAX_RUN_TIME - 800))
START_TIME=${SECONDS}
set +e
timeout --preserve-status --signal TERM --kill-after 60m ${TIMEOUT}s \
clickhouse-test --testname --shard --zookeeper --check-zookeeper-session --hung-check --print-time \
--no-drop-if-fail --test-runs "$NUM_TRIES" "${ADDITIONAL_OPTIONS[@]}" 2>&1 \
| ts '%Y-%m-%d %H:%M:%S' \
| tee -a test_output/test_result.txt
TEST_ARGS=(
--testname
--shard
--zookeeper
--check-zookeeper-session
--hung-check
--print-time
--no-drop-if-fail
--capture-client-stacktrace
--test-runs "$NUM_TRIES"
"${ADDITIONAL_OPTIONS[@]}"
)
timeout --preserve-status --signal TERM --kill-after 60m ${TIMEOUT}s clickhouse-test "${TEST_ARGS[@]}" 2>&1 \
| ts '%Y-%m-%d %H:%M:%S' \
| tee -a test_output/test_result.txt
set -e
DURATION=$((SECONDS - START_TIME))
@ -328,6 +386,14 @@ do
fi
done
# collect minio audit and server logs
# wait for minio to flush its batch if it has any
sleep 1
clickhouse-client -q "SYSTEM FLUSH ASYNC INSERT QUEUE"
clickhouse-client -q "SELECT log FROM minio_audit_logs ORDER BY event_time INTO OUTFILE '/test_output/minio_audit_logs.jsonl.zst' FORMAT JSONEachRow"
clickhouse-client -q "SELECT log FROM minio_server_logs ORDER BY event_time INTO OUTFILE '/test_output/minio_server_logs.jsonl.zst' FORMAT JSONEachRow"
# Stop server so we can safely read data with clickhouse-local.
# Why do we read data with clickhouse-local?
# Because it's the simplest way to read it when server has crashed.

17
docker/test/stateless/setup_minio.sh
View File

@ -59,8 +59,8 @@ find_os() {
download_minio() {
local os
local arch
local minio_server_version=${MINIO_SERVER_VERSION:-2022-09-07T22-25-02Z}
local minio_client_version=${MINIO_CLIENT_VERSION:-2022-08-28T20-08-11Z}
local minio_server_version=${MINIO_SERVER_VERSION:-2024-08-03T04-33-23Z}
local minio_client_version=${MINIO_CLIENT_VERSION:-2024-07-31T15-58-33Z}
os=$(find_os)
arch=$(find_arch)
@ -82,10 +82,10 @@ setup_minio() {
local test_type=$1
./mc alias set clickminio http://localhost:11111 clickhouse clickhouse
./mc admin user add clickminio test testtest
./mc admin policy set clickminio readwrite user=test
./mc admin policy attach clickminio readwrite --user=test
./mc mb --ignore-existing clickminio/test
if [ "$test_type" = "stateless" ]; then
./mc policy set public clickminio/test
./mc anonymous set public clickminio/test
fi
}
@ -99,10 +99,9 @@ upload_data() {
# iterating over globs will cause redundant file variable to be
# a path to a file, not a filename
# shellcheck disable=SC2045
for file in $(ls "${data_path}"); do
echo "${file}";
./mc cp "${data_path}"/"${file}" clickminio/test/"${file}";
done
if [ -d "${data_path}" ]; then
./mc cp --recursive "${data_path}"/ clickminio/test/
fi
}
setup_aws_credentials() {
@ -148,4 +147,4 @@ main() {
setup_aws_credentials
}
main "$@"
main "$@"

7
docker/test/upgrade/run.sh
View File

@ -129,6 +129,7 @@ configure
# Check that all new/changed setting were added in settings changes history.
# Some settings can be different for builds with sanitizers, so we check
# Also the automatic value of 'max_threads' and similar was displayed as "'auto(...)'" in previous versions instead of "auto(...)".
# settings changes only for non-sanitizer builds.
IS_SANITIZED=$(clickhouse-local --query "SELECT value LIKE '%-fsanitize=%' FROM system.build_options WHERE name = 'CXX_FLAGS'")
if [ "${IS_SANITIZED}" -eq "0" ]
@ -145,7 +146,9 @@ then
old_settings.value AS old_value
FROM new_settings
LEFT JOIN old_settings ON new_settings.name = old_settings.name
WHERE (new_settings.value != old_settings.value) AND (name NOT IN (
WHERE (new_value != old_value)
AND NOT (startsWith(new_value, 'auto(') AND old_value LIKE '%auto(%')
AND (name NOT IN (
SELECT arrayJoin(tupleElement(changes, 'name'))
FROM
(
@ -177,7 +180,7 @@ then
if [ -s changed_settings.txt ]
then
mv changed_settings.txt /test_output/
echo -e "Changed settings are not reflected in settings changes history (see changed_settings.txt)$FAIL$(head_escaped /test_output/changed_settings.txt)" >> /test_output/test_results.tsv
echo -e "Changed settings are not reflected in the settings changes history (see changed_settings.txt)$FAIL$(head_escaped /test_output/changed_settings.txt)" >> /test_output/test_results.tsv
else
echo -e "There are no changed settings or they are reflected in settings changes history$OK" >> /test_output/test_results.tsv
fi

4
docker/test/util/Dockerfile
View File

@ -44,7 +44,6 @@ RUN apt-get update \
bash \
bsdmainutils \
build-essential \
gdb \
git \
gperf \
moreutils \
@ -58,3 +57,6 @@ RUN apt-get update \
&& rm -rf /var/lib/apt/lists/* /var/cache/debconf /tmp/*
COPY process_functional_tests_result.py /
COPY --from=clickhouse/cctools:0d6b90a7a490 /opt/gdb /opt/gdb
ENV PATH="/opt/gdb/bin:${PATH}"

29
docs/changelogs/v24.3.7.30-lts.md Normal file
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@ -0,0 +1,29 @@
---
sidebar_position: 1
sidebar_label: 2024
---
# 2024 Changelog
### ClickHouse release v24.3.7.30-lts (c8a28cf4331) FIXME as compared to v24.3.6.48-lts (b2d33c3c45d)
#### Improvement
* Backported in [#68103](https://github.com/ClickHouse/ClickHouse/issues/68103): Distinguish booleans and integers while parsing values for custom settings: ``` SET custom_a = true; SET custom_b = 1; ```. [#62206](https://github.com/ClickHouse/ClickHouse/pull/62206) ([Vitaly Baranov](https://github.com/vitlibar)).
#### Bug Fix (user-visible misbehavior in an official stable release)
* Backported in [#67931](https://github.com/ClickHouse/ClickHouse/issues/67931): Fixing the `Not-ready Set` error after the `PREWHERE` optimization for StorageMerge. [#65057](https://github.com/ClickHouse/ClickHouse/pull/65057) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
* Backported in [#68062](https://github.com/ClickHouse/ClickHouse/issues/68062): Fix boolean literals in query sent to external database (for engines like `PostgreSQL`). [#66282](https://github.com/ClickHouse/ClickHouse/pull/66282) ([vdimir](https://github.com/vdimir)).
* Backported in [#67812](https://github.com/ClickHouse/ClickHouse/issues/67812): Only relevant to the experimental Variant data type. Fix crash with Variant + AggregateFunction type. [#67122](https://github.com/ClickHouse/ClickHouse/pull/67122) ([Kruglov Pavel](https://github.com/Avogar)).
* Backported in [#67848](https://github.com/ClickHouse/ClickHouse/issues/67848): Fixes [#66026](https://github.com/ClickHouse/ClickHouse/issues/66026). Avoid unresolved table function arguments traversal in `ReplaceTableNodeToDummyVisitor`. [#67522](https://github.com/ClickHouse/ClickHouse/pull/67522) ([Dmitry Novik](https://github.com/novikd)).
* Backported in [#68271](https://github.com/ClickHouse/ClickHouse/issues/68271): Fix inserting into stream like engines (Kafka, RabbitMQ, NATS) through HTTP interface. [#67554](https://github.com/ClickHouse/ClickHouse/pull/67554) ([János Benjamin Antal](https://github.com/antaljanosbenjamin)).
* Backported in [#67806](https://github.com/ClickHouse/ClickHouse/issues/67806): Fix reloading SQL UDFs with UNION. Previously, restarting the server could make UDF invalid. [#67665](https://github.com/ClickHouse/ClickHouse/pull/67665) ([Antonio Andelic](https://github.com/antonio2368)).
* Backported in [#67834](https://github.com/ClickHouse/ClickHouse/issues/67834): Fix potential stack overflow in `JSONMergePatch` function. Renamed this function from `jsonMergePatch` to `JSONMergePatch` because the previous name was wrong. The previous name is still kept for compatibility. Improved diagnostic of errors in the function. This closes [#67304](https://github.com/ClickHouse/ClickHouse/issues/67304). [#67756](https://github.com/ClickHouse/ClickHouse/pull/67756) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
* Backported in [#68206](https://github.com/ClickHouse/ClickHouse/issues/68206): Fix wrong `count()` result when there is non-deterministic function in predicate. [#67922](https://github.com/ClickHouse/ClickHouse/pull/67922) ([János Benjamin Antal](https://github.com/antaljanosbenjamin)).
* Backported in [#68089](https://github.com/ClickHouse/ClickHouse/issues/68089): Fixed the calculation of the maximum thread soft limit in containerized environments where the usable CPU count is limited. [#67963](https://github.com/ClickHouse/ClickHouse/pull/67963) ([Robert Schulze](https://github.com/rschu1ze)).
* Backported in [#68120](https://github.com/ClickHouse/ClickHouse/issues/68120): Fixed skipping of untouched parts in mutations with new analyzer. Previously with enabled analyzer data in part could be rewritten by mutation even if mutation doesn't affect this part according to predicate. [#68052](https://github.com/ClickHouse/ClickHouse/pull/68052) ([Anton Popov](https://github.com/CurtizJ)).
#### NOT FOR CHANGELOG / INSIGNIFICANT
* Update version after release. [#67676](https://github.com/ClickHouse/ClickHouse/pull/67676) ([robot-clickhouse](https://github.com/robot-clickhouse)).
* Backported in [#68074](https://github.com/ClickHouse/ClickHouse/issues/68074): Add an explicit error for `ALTER MODIFY SQL SECURITY` on non-view tables. [#67953](https://github.com/ClickHouse/ClickHouse/pull/67953) ([pufit](https://github.com/pufit)).

37
docs/changelogs/v24.7.3.42-stable.md Normal file
View File

@ -0,0 +1,37 @@
---
sidebar_position: 1
sidebar_label: 2024
---
# 2024 Changelog
### ClickHouse release v24.7.3.42-stable (63730bc4293) FIXME as compared to v24.7.2.13-stable (6e41f601b2f)
#### Bug Fix (user-visible misbehavior in an official stable release)
* Backported in [#67969](https://github.com/ClickHouse/ClickHouse/issues/67969): Fixed reading of subcolumns after `ALTER ADD COLUMN` query. [#66243](https://github.com/ClickHouse/ClickHouse/pull/66243) ([Anton Popov](https://github.com/CurtizJ)).
* Backported in [#67637](https://github.com/ClickHouse/ClickHouse/issues/67637): Fix for occasional deadlock in Context::getDDLWorker. [#66843](https://github.com/ClickHouse/ClickHouse/pull/66843) ([Alexander Gololobov](https://github.com/davenger)).
* Backported in [#67820](https://github.com/ClickHouse/ClickHouse/issues/67820): Fix possible deadlock on query cancel with parallel replicas. [#66905](https://github.com/ClickHouse/ClickHouse/pull/66905) ([Nikita Taranov](https://github.com/nickitat)).
* Backported in [#67881](https://github.com/ClickHouse/ClickHouse/issues/67881): Correctly parse file name/URI containing `::` if it's not an archive. [#67433](https://github.com/ClickHouse/ClickHouse/pull/67433) ([Antonio Andelic](https://github.com/antonio2368)).
* Backported in [#67713](https://github.com/ClickHouse/ClickHouse/issues/67713): Fix reloading SQL UDFs with UNION. Previously, restarting the server could make UDF invalid. [#67665](https://github.com/ClickHouse/ClickHouse/pull/67665) ([Antonio Andelic](https://github.com/antonio2368)).
* Backported in [#67995](https://github.com/ClickHouse/ClickHouse/issues/67995): Validate experimental/suspicious data types in ALTER ADD/MODIFY COLUMN. [#67911](https://github.com/ClickHouse/ClickHouse/pull/67911) ([Kruglov Pavel](https://github.com/Avogar)).
#### Critical Bug Fix (crash, LOGICAL_ERROR, data loss, RBAC)
* Backported in [#67818](https://github.com/ClickHouse/ClickHouse/issues/67818): Only relevant to the experimental Variant data type. Fix crash with Variant + AggregateFunction type. [#67122](https://github.com/ClickHouse/ClickHouse/pull/67122) ([Kruglov Pavel](https://github.com/Avogar)).
* Backported in [#67766](https://github.com/ClickHouse/ClickHouse/issues/67766): Fix crash of `uniq` and `uniqTheta ` with `tuple()` argument. Closes [#67303](https://github.com/ClickHouse/ClickHouse/issues/67303). [#67306](https://github.com/ClickHouse/ClickHouse/pull/67306) ([flynn](https://github.com/ucasfl)).
* Backported in [#67854](https://github.com/ClickHouse/ClickHouse/issues/67854): Fixes [#66026](https://github.com/ClickHouse/ClickHouse/issues/66026). Avoid unresolved table function arguments traversal in `ReplaceTableNodeToDummyVisitor`. [#67522](https://github.com/ClickHouse/ClickHouse/pull/67522) ([Dmitry Novik](https://github.com/novikd)).
* Backported in [#67840](https://github.com/ClickHouse/ClickHouse/issues/67840): Fix potential stack overflow in `JSONMergePatch` function. Renamed this function from `jsonMergePatch` to `JSONMergePatch` because the previous name was wrong. The previous name is still kept for compatibility. Improved diagnostic of errors in the function. This closes [#67304](https://github.com/ClickHouse/ClickHouse/issues/67304). [#67756](https://github.com/ClickHouse/ClickHouse/pull/67756) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
#### NOT FOR CHANGELOG / INSIGNIFICANT
* Backported in [#67518](https://github.com/ClickHouse/ClickHouse/issues/67518): Split slow test 03036_dynamic_read_subcolumns. [#66954](https://github.com/ClickHouse/ClickHouse/pull/66954) ([Nikita Taranov](https://github.com/nickitat)).
* Backported in [#67516](https://github.com/ClickHouse/ClickHouse/issues/67516): Split 01508_partition_pruning_long. [#66983](https://github.com/ClickHouse/ClickHouse/pull/66983) ([Nikita Taranov](https://github.com/nickitat)).
* Backported in [#67529](https://github.com/ClickHouse/ClickHouse/issues/67529): Reduce max time of 00763_long_lock_buffer_alter_destination_table. [#67185](https://github.com/ClickHouse/ClickHouse/pull/67185) ([Raúl Marín](https://github.com/Algunenano)).
* Backported in [#67643](https://github.com/ClickHouse/ClickHouse/issues/67643): [Green CI] Fix potentially flaky test_mask_sensitive_info integration test. [#67506](https://github.com/ClickHouse/ClickHouse/pull/67506) ([Alexey Katsman](https://github.com/alexkats)).
* Backported in [#67609](https://github.com/ClickHouse/ClickHouse/issues/67609): Fix test_zookeeper_config_load_balancing after adding the xdist worker name to the instance. [#67590](https://github.com/ClickHouse/ClickHouse/pull/67590) ([Pablo Marcos](https://github.com/pamarcos)).
* Backported in [#67871](https://github.com/ClickHouse/ClickHouse/issues/67871): Fix 02434_cancel_insert_when_client_dies. [#67600](https://github.com/ClickHouse/ClickHouse/pull/67600) ([vdimir](https://github.com/vdimir)).
* Backported in [#67704](https://github.com/ClickHouse/ClickHouse/issues/67704): Fix 02910_bad_logs_level_in_local in fast tests. [#67603](https://github.com/ClickHouse/ClickHouse/pull/67603) ([Raúl Marín](https://github.com/Algunenano)).
* Backported in [#67689](https://github.com/ClickHouse/ClickHouse/issues/67689): Fix 01605_adaptive_granularity_block_borders. [#67605](https://github.com/ClickHouse/ClickHouse/pull/67605) ([Nikita Taranov](https://github.com/nickitat)).
* Backported in [#67827](https://github.com/ClickHouse/ClickHouse/issues/67827): Try fix 03143_asof_join_ddb_long. [#67620](https://github.com/ClickHouse/ClickHouse/pull/67620) ([Nikita Taranov](https://github.com/nickitat)).
* Backported in [#67892](https://github.com/ClickHouse/ClickHouse/issues/67892): Revert "Merge pull request [#66510](https://github.com/ClickHouse/ClickHouse/issues/66510) from canhld94/fix_trivial_count_non_deterministic_func". [#67800](https://github.com/ClickHouse/ClickHouse/pull/67800) ([János Benjamin Antal](https://github.com/antaljanosbenjamin)).

28
docs/en/development/contrib.md
View File

@ -27,23 +27,23 @@ Avoid dumping copies of external code into the library directory.
Instead create a Git submodule to pull third-party code from an external upstream repository.
All submodules used by ClickHouse are listed in the `.gitmodule` file.
If the library can be used as-is (the default case), you can reference the upstream repository directly.
If the library needs patching, create a fork of the upstream repository in the [ClickHouse organization on GitHub](https://github.com/ClickHouse).
- If the library can be used as-is (the default case), you can reference the upstream repository directly.
- If the library needs patching, create a fork of the upstream repository in the [ClickHouse organization on GitHub](https://github.com/ClickHouse).
In the latter case, we aim to isolate custom patches as much as possible from upstream commits.
To that end, create a branch with prefix `clickhouse/` from the branch or tag you want to integrate, e.g. `clickhouse/master` (for branch `master`) or `clickhouse/release/vX.Y.Z` (for tag `release/vX.Y.Z`).
This ensures that pulls from the upstream repository into the fork will leave custom `clickhouse/` branches unaffected.
Submodules in `contrib/` must only track `clickhouse/` branches of forked third-party repositories.
To that end, create a branch with prefix `ClickHouse/` from the branch or tag you want to integrate, e.g. `ClickHouse/2024_2` (for branch `2024_2`) or `ClickHouse/release/vX.Y.Z` (for tag `release/vX.Y.Z`).
Avoid following upstream development branches `master`/ `main` / `dev` (i.e., prefix branches `ClickHouse/master` / `ClickHouse/main` / `ClickHouse/dev` in the fork repository).
Such branches are moving targets which make proper versioning harder.
"Prefix branches" ensure that pulls from the upstream repository into the fork will leave custom `ClickHouse/` branches unaffected.
Submodules in `contrib/` must only track `ClickHouse/` branches of forked third-party repositories.
Patches are only applied against `clickhouse/` branches of external libraries.
For that, push the patch as a branch with `clickhouse/`, e.g. `clickhouse/fix-some-desaster`.
Then create a PR from the new branch against the custom tracking branch with `clickhouse/` prefix, (e.g. `clickhouse/master` or `clickhouse/release/vX.Y.Z`) and merge the patch.
Patches are only applied against `ClickHouse/` branches of external libraries.
There are two ways to do that:
- you like to make a new fix against a `ClickHouse/`-prefix branch in the forked repository, e.g. a sanitizer fix. In that case, push the fix as a branch with `ClickHouse/` prefix, e.g. `ClickHouse/fix-sanitizer-disaster`. Then create a PR from the new branch against the custom tracking branch, e.g. `ClickHouse/2024_2 <-- ClickHouse/fix-sanitizer-disaster` and merge the PR.
- you update the submodule and need to re-apply earlier patches. In this case, re-creating old PRs is overkill. Instead, simply cherry-pick older commits into the new `ClickHouse/` branch (corresponding to the new version). Feel free to squash commits of PRs that had multiple commits. In the best case, we did contribute custom patches back to upstream and can omit patches in the new version.
Once the submodule has been updated, bump the submodule in ClickHouse to point to the new hash in the fork.
Create patches of third-party libraries with the official repository in mind and consider contributing the patch back to the upstream repository.
This makes sure that others will also benefit from the patch and it will not be a maintenance burden for the ClickHouse team.
To pull upstream changes into the submodule, you can use two methods:
- (less work but less clean): merge upstream `master` into the corresponding `clickhouse/` tracking branch in the forked repository. You will need to resolve merge conflicts with previous custom patches. This method can be used when the `clickhouse/` branch tracks an upstream development branch like `master`, `main`, `dev`, etc.
- (more work but cleaner): create a new branch with `clickhouse/` prefix from the upstream commit or tag you like to integrate. Then re-apply all existing patches using new PRs (or squash them into a single PR). This method can be used when the `clickhouse/` branch tracks a specific upstream version branch or tag. It is cleaner in the sense that custom patches and upstream changes are better isolated from each other.
Once the submodule has been updated, bump the submodule in ClickHouse to point to the new hash in the fork.

4
docs/en/development/tests.md
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@ -14,7 +14,7 @@ Each functional test sends one or multiple queries to the running ClickHouse ser
Tests are located in `queries` directory. There are two subdirectories: `stateless` and `stateful`. Stateless tests run queries without any preloaded test data - they often create small synthetic datasets on the fly, within the test itself. Stateful tests require preloaded test data from ClickHouse and it is available to general public.
Each test can be one of two types: `.sql` and `.sh`. `.sql` test is the simple SQL script that is piped to `clickhouse-client --multiquery`. `.sh` test is a script that is run by itself. SQL tests are generally preferable to `.sh` tests. You should use `.sh` tests only when you have to test some feature that cannot be exercised from pure SQL, such as piping some input data into `clickhouse-client` or testing `clickhouse-local`.
Each test can be one of two types: `.sql` and `.sh`. `.sql` test is the simple SQL script that is piped to `clickhouse-client`. `.sh` test is a script that is run by itself. SQL tests are generally preferable to `.sh` tests. You should use `.sh` tests only when you have to test some feature that cannot be exercised from pure SQL, such as piping some input data into `clickhouse-client` or testing `clickhouse-local`.
:::note
A common mistake when testing data types `DateTime` and `DateTime64` is assuming that the server uses a specific time zone (e.g. "UTC"). This is not the case, time zones in CI test runs
@ -38,7 +38,7 @@ For more options, see `tests/clickhouse-test --help`. You can simply run all tes
### Adding a New Test
To add new test, create a `.sql` or `.sh` file in `queries/0_stateless` directory, check it manually and then generate `.reference` file in the following way: `clickhouse-client --multiquery < 00000_test.sql > 00000_test.reference` or `./00000_test.sh > ./00000_test.reference`.
To add new test, create a `.sql` or `.sh` file in `queries/0_stateless` directory, check it manually and then generate `.reference` file in the following way: `clickhouse-client < 00000_test.sql > 00000_test.reference` or `./00000_test.sh > ./00000_test.reference`.
Tests should use (create, drop, etc) only tables in `test` database that is assumed to be created beforehand; also tests can use temporary tables.

1
docs/en/engines/table-engines/index.md
View File

@ -61,6 +61,7 @@ Engines in the family:
- [RabbitMQ](../../engines/table-engines/integrations/rabbitmq.md)
- [PostgreSQL](../../engines/table-engines/integrations/postgresql.md)
- [S3Queue](../../engines/table-engines/integrations/s3queue.md)
- [TimeSeries](../../engines/table-engines/integrations/time-series.md)
### Special Engines {#special-engines}

38
docs/en/engines/table-engines/integrations/kafka.md
View File

@ -251,6 +251,44 @@ The number of rows in one Kafka message depends on whether the format is row-bas
- For row-based formats the number of rows in one Kafka message can be controlled by setting `kafka_max_rows_per_message`.
- For block-based formats we cannot divide block into smaller parts, but the number of rows in one block can be controlled by general setting [max_block_size](../../../operations/settings/settings.md#setting-max_block_size).
## Experimental engine to store committed offsets in ClickHouse Keeper
If `allow_experimental_kafka_offsets_storage_in_keeper` is enabled, then two more settings can be specified to the Kafka table engine:
- `kafka_keeper_path` specifies the path to the table in ClickHouse Keeper
- `kafka_replica_name` specifies the replica name in ClickHouse Keeper
Either both of the settings must be specified or neither of them. When both of them are specified, then a new, experimental Kafka engine will be used. The new engine doesn't depend on storing the committed offsets in Kafka, but stores them in ClickHouse Keeper. It still tries to commit the offsets to Kafka, but it only depends on those offsets when the table is created. In any other circumstances (table is restarted, or recovered after some error) the offsets stored in ClickHouse Keeper will be used as an offset to continue consuming messages from. Apart from the committed offset, it also stores how many messages were consumed in the last batch, so if the insert fails, the same amount of messages will be consumed, thus enabling deduplication if necessary.
Example:
``` sql
CREATE TABLE experimental_kafka (key UInt64, value UInt64)
ENGINE = Kafka('localhost:19092', 'my-topic', 'my-consumer', 'JSONEachRow')
SETTINGS
kafka_keeper_path = '/clickhouse/{database}/experimental_kafka',
kafka_replica_name = 'r1'
SETTINGS allow_experimental_kafka_offsets_storage_in_keeper=1;
```
Or to utilize the `uuid` and `replica` macros similarly to ReplicatedMergeTree:
``` sql
CREATE TABLE experimental_kafka (key UInt64, value UInt64)
ENGINE = Kafka('localhost:19092', 'my-topic', 'my-consumer', 'JSONEachRow')
SETTINGS
kafka_keeper_path = '/clickhouse/{database}/{uuid}',
kafka_replica_name = '{replica}'
SETTINGS allow_experimental_kafka_offsets_storage_in_keeper=1;
```
### Known limitations
As the new engine is experimental, it is not production ready yet. There are few known limitations of the implementation:
- The biggest limitation is the engine doesn't support direct reading. Reading from the engine using materialized views and writing to the engine work, but direct reading doesn't. As a result, all direct `SELECT` queries will fail.
- Rapidly dropping and recreating the table or specifying the same ClickHouse Keeper path to different engines might cause issues. As best practice you can use the `{uuid}` in `kafka_keeper_path` to avoid clashing paths.
- To make repeatable reads, messages cannot be consumed from multiple partitions on a single thread. On the other hand, the Kafka consumers have to be polled regularly to keep them alive. As a result of these two objectives, we decided to only allow creating multiple consumers if `kafka_thread_per_consumer` is enabled, otherwise it is too complicated to avoid issues regarding polling consumers regularly.
- Consumers created by the new storage engine do not show up in [`system.kafka_consumers`](../../../operations/system-tables/kafka_consumers.md) table.
**See Also**
- [Virtual columns](../../../engines/table-engines/index.md#table_engines-virtual_columns)

295
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@ -0,0 +1,295 @@
---
slug: /en/engines/table-engines/special/time_series
sidebar_position: 60
sidebar_label: TimeSeries
---
# TimeSeries Engine [Experimental]
A table engine storing time series, i.e. a set of values associated with timestamps and tags (or labels):
```
metric_name1[tag1=value1, tag2=value2, ...] = {timestamp1: value1, timestamp2: value2, ...}
metric_name2[...] = ...
```
:::info
This is an experimental feature that may change in backwards-incompatible ways in the future releases.
Enable usage of the TimeSeries table engine
with [allow_experimental_time_series_table](../../../operations/settings/settings.md#allow-experimental-time-series-table) setting.
Input the command `set allow_experimental_time_series_table = 1`.
:::
## Syntax {#syntax}
``` sql
CREATE TABLE name [(columns)] ENGINE=TimeSeries
[SETTINGS var1=value1, ...]
[DATA db.data_table_name | DATA ENGINE data_table_engine(arguments)]
[TAGS db.tags_table_name | TAGS ENGINE tags_table_engine(arguments)]
[METRICS db.metrics_table_name | METRICS ENGINE metrics_table_engine(arguments)]
```
## Usage {#usage}
It's easier to start with everything set by default (it's allowed to create a `TimeSeries` table without specifying a list of columns):
``` sql
CREATE TABLE my_table ENGINE=TimeSeries
```
Then this table can be used with the following protocols (a port must be assigned in the server configuration):
- [prometheus remote-write](../../../interfaces/prometheus.md#remote-write)
- [prometheus remote-read](../../../interfaces/prometheus.md#remote-read)
## Target tables {#target-tables}
A `TimeSeries` table doesn't have its own data, everything is stored in its target tables.
This is similar to how a [materialized view](../../../sql-reference/statements/create/view#materialized-view) works,
with the difference that a materialized view has one target table
whereas a `TimeSeries` table has three target tables named [data]{#data-table}, [tags]{#tags-table], and [metrics]{#metrics-table}.
The target tables can be either specified explicitly in the `CREATE TABLE` query
or the `TimeSeries` table engine can generate inner target tables automatically.
The target tables are the following:
1. The _data_ table {#data-table} contains time series associated with some identifier.
The _data_ table must have columns:
| Name | Mandatory? | Default type | Possible types | Description |
|---|---|---|---|---|
| `id` | [x] | `UUID` | any | Identifies a combination of a metric names and tags |
| `timestamp` | [x] | `DateTime64(3)` | `DateTime64(X)` | A time point |
| `value` | [x] | `Float64` | `Float32` or `Float64` | A value associated with the `timestamp` |
2. The _tags_ table {#tags-table} contains identifiers calculated for each combination of a metric name and tags.
The _tags_ table must have columns:
| Name | Mandatory? | Default type | Possible types | Description |
|---|---|---|---|---|
| `id` | [x] | `UUID` | any (must match the type of `id` in the [data]{#data-table} table) | An `id` identifies a combination of a metric name and tags. The DEFAULT expression specifies how to calculate such an identifier |
| `metric_name` | [x] | `LowCardinality(String)` | `String` or `LowCardinality(String)` | The name of a metric |
| `<tag_value_column>` | [ ] | `String` | `String` or `LowCardinality(String)` or `LowCardinality(Nullable(String))` | The value of a specific tag, the tag's name and the name of a corresponding column are specified in the [tags_to_columns](#settings) setting |
| `tags` | [x] | `Map(LowCardinality(String), String)` | `Map(String, String)` or `Map(LowCardinality(String), String)` or `Map(LowCardinality(String), LowCardinality(String))` | Map of tags excluding the tag `__name__` containing the name of a metric and excluding tags with names enumerated in the [tags_to_columns](#settings) setting |
| `all_tags` | [ ] | `Map(String, String)` | `Map(String, String)` or `Map(LowCardinality(String), String)` or `Map(LowCardinality(String), LowCardinality(String))` | Ephemeral column, each row is a map of all the tags excluding only the tag `__name__` containing the name of a metric. The only purpose of that column is to be used while calculating `id` |
| `min_time` | [ ] | `Nullable(DateTime64(3))` | `DateTime64(X)` or `Nullable(DateTime64(X))` | Minimum timestamp of time series with that `id`. The column is created if [store_min_time_and_max_time](#settings) is `true` |
| `max_time` | [ ] | `Nullable(DateTime64(3))` | `DateTime64(X)` or `Nullable(DateTime64(X))` | Maximum timestamp of time series with that `id`. The column is created if [store_min_time_and_max_time](#settings) is `true` |
3. The _metrics_ table {#metrics-table} contains some information about metrics been collected, the types of those metrics and their descriptions.
The _metrics_ table must have columns:
| Name | Mandatory? | Default type | Possible types | Description |
|---|---|---|---|---|
| `metric_family_name` | [x] | `String` | `String` or `LowCardinality(String)` | The name of a metric family |
| `type` | [x] | `String` | `String` or `LowCardinality(String)` | The type of a metric family, one of "counter", "gauge", "summary", "stateset", "histogram", "gaugehistogram" |
| `unit` | [x] | `String` | `String` or `LowCardinality(String)` | The unit used in a metric |
| `help` | [x] | `String` | `String` or `LowCardinality(String)` | The description of a metric |
Any row inserted into a `TimeSeries` table will be in fact stored in those three target tables.
A `TimeSeries` table contains all those columns from the [data]{#data-table}, [tags]{#tags-table}, [metrics]{#metrics-table} tables.
## Creation {#creation}
There are multiple ways to create a table with the `TimeSeries` table engine.
The simplest statement
``` sql
CREATE TABLE my_table ENGINE=TimeSeries
```
will actually create the following table (you can see that by executing `SHOW CREATE TABLE my_table`):
``` sql
CREATE TABLE my_table
(
`id` UUID DEFAULT reinterpretAsUUID(sipHash128(metric_name, all_tags)),
`timestamp` DateTime64(3),
`value` Float64,
`metric_name` LowCardinality(String),
`tags` Map(LowCardinality(String), String),
`all_tags` Map(String, String),
`min_time` Nullable(DateTime64(3)),
`max_time` Nullable(DateTime64(3)),
`metric_family_name` String,
`type` String,
`unit` String,
`help` String
)
ENGINE = TimeSeries
DATA ENGINE = MergeTree ORDER BY (id, timestamp)
DATA INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
TAGS ENGINE = AggregatingMergeTree PRIMARY KEY metric_name ORDER BY (metric_name, id)
TAGS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
METRICS ENGINE = ReplacingMergeTree ORDER BY metric_family_name
METRICS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
So the columns were generated automatically and also there are three inner UUIDs in this statement -
one per each inner target table that was created.
(Inner UUIDs are not shown normally until setting
[show_table_uuid_in_table_create_query_if_not_nil](../../../operations/settings/settings#show_table_uuid_in_table_create_query_if_not_nil)
is set.)
Inner target tables have names like `.inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`,
`.inner_id.tags.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`, `.inner_id.metrics.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
and each target table has columns which is a subset of the columns of the main `TimeSeries` table:
``` sql
CREATE TABLE default.`.inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
`id` UUID,
`timestamp` DateTime64(3),
`value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp)
```
``` sql
CREATE TABLE default.`.inner_id.tags.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
`id` UUID DEFAULT reinterpretAsUUID(sipHash128(metric_name, all_tags)),
`metric_name` LowCardinality(String),
`tags` Map(LowCardinality(String), String),
`all_tags` Map(String, String) EPHEMERAL,
`min_time` SimpleAggregateFunction(min, Nullable(DateTime64(3))),
`max_time` SimpleAggregateFunction(max, Nullable(DateTime64(3)))
)
ENGINE = AggregatingMergeTree
PRIMARY KEY metric_name
ORDER BY (metric_name, id)
```
``` sql
CREATE TABLE default.`.inner_id.metrics.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
`metric_family_name` String,
`type` String,
`unit` String,
`help` String
)
ENGINE = ReplacingMergeTree
ORDER BY metric_family_name
```
## Adjusting types of columns {#adjusting-column-types}
You can adjust the types of almost any column of the inner target tables by specifying them explicitly
while defining the main table. For example,
``` sql
CREATE TABLE my_table
(
timestamp DateTime64(6)
) ENGINE=TimeSeries
```
will make the inner [data]{#data-table} table store timestamp in microseconds instead of milliseconds:
``` sql
CREATE TABLE default.`.inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
`id` UUID,
`timestamp` DateTime64(6),
`value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp)
```
## The `id` column {#id-column}
The `id` column contains identifiers, every identifier is calculated for a combination of a metric name and tags.
The DEFAULT expression for the `id` column is an expression which will be used to calculate such identifiers.
Both the type of the `id` column and that expression can be adjusted by specifying them explicitly:
``` sql
CREATE TABLE my_table
(
id UInt64 DEFAULT sipHash64(metric_name, all_tags)
) ENGINE=TimeSeries
```
## The `tags` and `all_tags` columns {#tags-and-all-tags}
There are two columns containing maps of tags - `tags` and `all_tags`. In this example they mean the same, however they can be different
if setting `tags_to_columns` is used. This setting allows to specify that a specific tag should be stored in a separate column instead of storing
in a map inside the `tags` column:
``` sql
CREATE TABLE my_table ENGINE=TimeSeries SETTINGS = {'instance': 'instance', 'job': 'job'}
```
This statement will add columns
```
`instance` String,
`job` String
```
to the definition of both `my_table` and its inner [tags]{#tags-table} target table. In this case the `tags` column will not contain tags `instance` and `job`,
but the `all_tags` column will contain them. The `all_tags` column is ephemeral and its only purpose to be used in the DEFAULT expression
for the `id` column.
The types of columns can be adjusted by specifying them explicitly:
``` sql
CREATE TABLE my_table (instance LowCardinality(String), job LowCardinality(Nullable(String)))
ENGINE=TimeSeries SETTINGS = {'instance': 'instance', 'job': 'job'}
```
## Table engines of inner target tables {#inner-table-engines}
By default inner target tables use the following table engines:
- the [data]{#data-table} table uses [MergeTree](../mergetree-family/mergetree);
- the [tags]{#tags-table} table uses [AggregatingMergeTree](../mergetree-family/aggregatingmergetree) because the same data is often inserted multiple times to this table so we need a way
to remove duplicates, and also because it's required to do aggregation for columns `min_time` and `max_time`;
- the [metrics]{#metrics-table} table uses [ReplacingMergeTree](../mergetree-family/replacingmergetree) because the same data is often inserted multiple times to this table so we need a way
to remove duplicates.
Other table engines also can be used for inner target tables if it's specified so:
``` sql
CREATE TABLE my_table ENGINE=TimeSeries
DATA ENGINE=ReplicatedMergeTree
TAGS ENGINE=ReplicatedAggregatingMergeTree
METRICS ENGINE=ReplicatedReplacingMergeTree
```
## External target tables {#external-target-tables}
It's possible to make a `TimeSeries` table use a manually created table:
``` sql
CREATE TABLE data_for_my_table
(
`id` UUID,
`timestamp` DateTime64(3),
`value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp);
CREATE TABLE tags_for_my_table ...
CREATE TABLE metrics_for_my_table ...
CREATE TABLE my_table ENGINE=TimeSeries DATA data_for_my_table TAGS tags_for_my_table METRICS metrics_for_my_table;
```
## Settings {#settings}
Here is a list of settings which can be specified while defining a `TimeSeries` table:
| Name | Type | Default | Description |
|---|---|---|---|
| `tags_to_columns` | Map | {} | Map specifying which tags should be put to separate columns in the [tags]{#tags-table} table. Syntax: `{'tag1': 'column1', 'tag2' : column2, ...}` |
| `use_all_tags_column_to_generate_id` | Bool | true | When generating an expression to calculate an identifier of a time series, this flag enables using the `all_tags` column in that calculation |
| `store_min_time_and_max_time` | Bool | true | If set to true then the table will store `min_time` and `max_time` for each time series |
| `aggregate_min_time_and_max_time` | Bool | true | When creating an inner target `tags` table, this flag enables using `SimpleAggregateFunction(min, Nullable(DateTime64(3)))` instead of just `Nullable(DateTime64(3))` as the type of the `min_time` column, and the same for the `max_time` column |
| `filter_by_min_time_and_max_time` | Bool | true | If set to true then the table will use the `min_time` and `max_time` columns for filtering time series |
# Functions {#functions}
Here is a list of functions supporting a `TimeSeries` table as an argument:
- [timeSeriesData](../../../sql-reference/table-functions/timeSeriesData.md)
- [timeSeriesTags](../../../sql-reference/table-functions/timeSeriesTags.md)
- [timeSeriesMetrics](../../../sql-reference/table-functions/timeSeriesMetrics.md)

243
docs/en/engines/table-engines/mergetree-family/annindexes.md
View File

@ -17,85 +17,121 @@ In terms of SQL, the nearest neighborhood problem can be expressed as follows:
``` sql
SELECT *
FROM table_with_ann_index
FROM table
ORDER BY Distance(vectors, Point)
LIMIT N
```
`vectors` contains N-dimensional values of type [Array(Float32)](../../../sql-reference/data-types/array.md), for example embeddings.
Function `Distance` computes the distance between two vectors. Often, the Euclidean (L2) distance is chosen as distance function but [other
distance functions](/docs/en/sql-reference/functions/distance-functions.md) are also possible. `Point` is the reference point, e.g. `(0.17,
0.33, ...)`, and `N` limits the number of search results.
`vectors` contains N-dimensional values of type [Array(Float32)](../../../sql-reference/data-types/array.md) or Array(Float64), for example
embeddings. Function `Distance` computes the distance between two vectors. Often, the Euclidean (L2) distance is chosen as distance function
but [other distance functions](/docs/en/sql-reference/functions/distance-functions.md) are also possible. `Point` is the reference point,
e.g. `(0.17, 0.33, ...)`, and `N` limits the number of search results.
An alternative formulation of the nearest neighborhood search problem looks as follows:
This query returns the top-`N` closest points to the reference point. Parameter `N` limits the number of returned values which is useful for
situations where `MaxDistance` is difficult to determine in advance.
``` sql
SELECT *
FROM table_with_ann_index
WHERE Distance(vectors, Point) < MaxDistance
LIMIT N
```
While the first query returns the top-`N` closest points to the reference point, the second query returns all points closer to the reference
point than a maximally allowed radius `MaxDistance`. Parameter `N` limits the number of returned values which is useful for situations where
`MaxDistance` is difficult to determine in advance.
With brute force search, both queries are expensive (linear in the number of points) because the distance between all points in `vectors` and
With brute force search, the query is expensive (linear in the number of points) because the distance between all points in `vectors` and
`Point` must be computed. To speed this process up, Approximate Nearest Neighbor Search Indexes (ANN indexes) store a compact representation
of the search space (using clustering, search trees, etc.) which allows to compute an approximate answer much quicker (in sub-linear time).
# Creating and Using ANN Indexes {#creating_using_ann_indexes}
# Creating and Using Vector Similarity Indexes
Syntax to create an ANN index over an [Array(Float32)](../../../sql-reference/data-types/array.md) column:
Syntax to create a vector similarity index over an [Array(Float32)](../../../sql-reference/data-types/array.md) column:
```sql
CREATE TABLE table_with_ann_index
CREATE TABLE table
(
`id` Int64,
`vectors` Array(Float32),
INDEX [ann_index_name vectors TYPE [ann_index_type]([ann_index_parameters]) [GRANULARITY [N]]
id Int64,
vectors Array(Float32),
INDEX index_name vectors TYPE vector_similarity(method, distance_function[, quantization, connectivity, expansion_add, expansion_search]) [GRANULARITY N]
)
ENGINE = MergeTree
ORDER BY id;
```
Parameters:
- `method`: Supports currently only `hnsw`.
- `distance_function`: either `L2Distance` (the [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance) - the length of a
line between two points in Euclidean space), or `cosineDistance` (the [cosine
distance](https://en.wikipedia.org/wiki/Cosine_similarity#Cosine_distance)- the angle between two non-zero vectors).
- `quantization`: either `f32`, `f16`, or `i8` for storing the vector with reduced precision (optional, default: `f32`)
- `m`: the number of neighbors per graph node (optional, default: 16)
- `ef_construction`: (optional, default: 128)
- `ef_search`: (optional, default: 64)
Value 0 for parameters `m`, `ef_construction`, and `ef_search` refers to the default value.
Example:
```sql
CREATE TABLE table
(
id Int64,
vectors Array(Float32),
INDEX idx vectors TYPE vector_similarity('hnsw', 'L2Distance') -- Alternative syntax: TYPE vector_similarity(hnsw, L2Distance)
)
ENGINE = MergeTree
ORDER BY id;
```
Vector similarity indexes are based on the [USearch library](https://github.com/unum-cloud/usearch), which implements the [HNSW
algorithm](https://arxiv.org/abs/1603.09320), i.e., a hierarchical graph where each point represents a vector and the edges represent
similarity. Such hierarchical structures can be very efficient on large collections. They may often fetch 0.05% or less data from the
overall dataset, while still providing 99% recall. This is especially useful when working with high-dimensional vectors, that are expensive
to load and compare. The library also has several hardware-specific SIMD optimizations to accelerate further distance computations on modern
Arm (NEON and SVE) and x86 (AVX2 and AVX-512) CPUs and OS-specific optimizations to allow efficient navigation around immutable persistent
files, without loading them into RAM.
USearch indexes are currently experimental, to use them you first need to `SET allow_experimental_vector_similarity_index = 1`.
Vector similarity indexes currently support two distance functions:
- `L2Distance`, also called Euclidean distance, is the length of a line segment between two points in Euclidean space
([Wikipedia](https://en.wikipedia.org/wiki/Euclidean_distance)).
- `cosineDistance`, also called cosine similarity, is the cosine of the angle between two (non-zero) vectors
([Wikipedia](https://en.wikipedia.org/wiki/Cosine_similarity)).
Vector similarity indexes allows storing the vectors in reduced precision formats. Supported scalar kinds are `f64`, `f32`, `f16` or `i8`.
If no scalar kind was specified during index creation, `f16` is used as default.
For normalized data, `L2Distance` is usually a better choice, otherwise `cosineDistance` is recommended to compensate for scale. If no
distance function was specified during index creation, `L2Distance` is used as default.
:::note
All arrays must have same length. To avoid errors, you can use a
[CONSTRAINT](/docs/en/sql-reference/statements/create/table.md#constraints), for example, `CONSTRAINT constraint_name_1 CHECK
length(vectors) = 256`. Also, empty `Arrays` and unspecified `Array` values in INSERT statements (i.e. default values) are not supported.
:::
:::note
The vector similarity index currently does not work with per-table, non-default `index_granularity` settings (see
[here](https://github.com/ClickHouse/ClickHouse/pull/51325#issuecomment-1605920475)). If necessary, the value must be changed in config.xml.
:::
ANN indexes are built during column insertion and merge. As a result, `INSERT` and `OPTIMIZE` statements will be slower than for ordinary
tables. ANNIndexes are ideally used only with immutable or rarely changed data, respectively when are far more read requests than write
requests.
ANN indexes support two types of queries:
- ORDER BY queries:
ANN indexes support these queries:
``` sql
SELECT *
FROM table_with_ann_index
FROM table
[WHERE ...]
ORDER BY Distance(vectors, Point)
LIMIT N
```
- WHERE queries:
``` sql
SELECT *
FROM table_with_ann_index
WHERE Distance(vectors, Point) < MaxDistance
LIMIT N
```
:::tip
To avoid writing out large vectors, you can use [query
parameters](/docs/en/interfaces/cli.md#queries-with-parameters-cli-queries-with-parameters), e.g.
```bash
clickhouse-client --param_vec='hello' --query="SELECT * FROM table_with_ann_index WHERE L2Distance(vectors, {vec: Array(Float32)}) < 1.0"
clickhouse-client --param_vec='hello' --query="SELECT * FROM table WHERE L2Distance(vectors, {vec: Array(Float32)}) < 1.0"
```
:::
**Restrictions**: Queries that contain both a `WHERE Distance(vectors, Point) < MaxDistance` and an `ORDER BY Distance(vectors, Point)`
clause cannot use ANN indexes. Also, the approximate algorithms used to determine the nearest neighbors require a limit, hence queries
without `LIMIT` clause cannot utilize ANN indexes. Also, ANN indexes are only used if the query has a `LIMIT` value smaller than setting
**Restrictions**: Approximate algorithms used to determine the nearest neighbors require a limit, hence queries without `LIMIT` clause
cannot utilize ANN indexes. Also, ANN indexes are only used if the query has a `LIMIT` value smaller than setting
`max_limit_for_ann_queries` (default: 1 million rows). This is a safeguard to prevent large memory allocations by external libraries for
approximate neighbor search.
@ -122,128 +158,3 @@ brute-force distance calculation over all rows of the granules. With a small `GR
equally good, only the processing performance differs. It is generally recommended to use a large `GRANULARITY` for ANN indexes and fall
back to a smaller `GRANULARITY` values only in case of problems like excessive memory consumption of the ANN structures. If no `GRANULARITY`
was specified for ANN indexes, the default value is 100 million.
# Available ANN Indexes {#available_ann_indexes}
- [Annoy](/docs/en/engines/table-engines/mergetree-family/annindexes.md#annoy-annoy)
- [USearch](/docs/en/engines/table-engines/mergetree-family/annindexes.md#usearch-usearch)
## Annoy {#annoy}
Annoy indexes are currently experimental, to use them you first need to `SET allow_experimental_annoy_index = 1`. They are also currently
disabled on ARM due to memory safety problems with the algorithm.
This type of ANN index is based on the [Annoy library](https://github.com/spotify/annoy) which recursively divides the space into random
linear surfaces (lines in 2D, planes in 3D etc.).
<div class='vimeo-container'>
<iframe src="//www.youtube.com/embed/QkCCyLW0ehU"
width="640"
height="360"
frameborder="0"
allow="autoplay;
fullscreen;
picture-in-picture"
allowfullscreen>
</iframe>
</div>
Syntax to create an Annoy index over an [Array(Float32)](../../../sql-reference/data-types/array.md) column:
```sql
CREATE TABLE table_with_annoy_index
(
id Int64,
vectors Array(Float32),
INDEX [ann_index_name] vectors TYPE annoy([Distance[, NumTrees]]) [GRANULARITY N]
)
ENGINE = MergeTree
ORDER BY id;
```
Annoy currently supports two distance functions:
- `L2Distance`, also called Euclidean distance, is the length of a line segment between two points in Euclidean space
([Wikipedia](https://en.wikipedia.org/wiki/Euclidean_distance)).
- `cosineDistance`, also called cosine similarity, is the cosine of the angle between two (non-zero) vectors
([Wikipedia](https://en.wikipedia.org/wiki/Cosine_similarity)).
For normalized data, `L2Distance` is usually a better choice, otherwise `cosineDistance` is recommended to compensate for scale. If no
distance function was specified during index creation, `L2Distance` is used as default.
Parameter `NumTrees` is the number of trees which the algorithm creates (default if not specified: 100). Higher values of `NumTree` mean
more accurate search results but slower index creation / query times (approximately linearly) as well as larger index sizes.
:::note
All arrays must have same length. To avoid errors, you can use a
[CONSTRAINT](/docs/en/sql-reference/statements/create/table.md#constraints), for example, `CONSTRAINT constraint_name_1 CHECK
length(vectors) = 256`. Also, empty `Arrays` and unspecified `Array` values in INSERT statements (i.e. default values) are not supported.
:::
The creation of Annoy indexes (whenever a new part is build, e.g. at the end of a merge) is a relatively slow process. You can increase
setting `max_threads_for_annoy_index_creation` (default: 4) which controls how many threads are used to create an Annoy index. Please be
careful with this setting, it is possible that multiple indexes are created in parallel in which case there can be overparallelization.
Setting `annoy_index_search_k_nodes` (default: `NumTrees * LIMIT`) determines how many tree nodes are inspected during SELECTs. Larger
values mean more accurate results at the cost of longer query runtime:
```sql
SELECT *
FROM table_name
ORDER BY L2Distance(vectors, Point)
LIMIT N
SETTINGS annoy_index_search_k_nodes=100;
```
:::note
The Annoy index currently does not work with per-table, non-default `index_granularity` settings (see
[here](https://github.com/ClickHouse/ClickHouse/pull/51325#issuecomment-1605920475)). If necessary, the value must be changed in config.xml.
:::
## USearch {#usearch}
This type of ANN index is based on the [USearch library](https://github.com/unum-cloud/usearch), which implements the [HNSW
algorithm](https://arxiv.org/abs/1603.09320), i.e., builds a hierarchical graph where each point represents a vector and the edges represent
similarity. Such hierarchical structures can be very efficient on large collections. They may often fetch 0.05% or less data from the
overall dataset, while still providing 99% recall. This is especially useful when working with high-dimensional vectors,
that are expensive to load and compare. The library also has several hardware-specific SIMD optimizations to accelerate further
distance computations on modern Arm (NEON and SVE) and x86 (AVX2 and AVX-512) CPUs and OS-specific optimizations to allow efficient
navigation around immutable persistent files, without loading them into RAM.
<div class='vimeo-container'>
<iframe src="//www.youtube.com/embed/UMrhB3icP9w"
width="640"
height="360"
frameborder="0"
allow="autoplay;
fullscreen;
picture-in-picture"
allowfullscreen>
</iframe>
</div>
Syntax to create an USearch index over an [Array](../../../sql-reference/data-types/array.md) column:
```sql
CREATE TABLE table_with_usearch_index
(
id Int64,
vectors Array(Float32),
INDEX [ann_index_name] vectors TYPE usearch([Distance[, ScalarKind]]) [GRANULARITY N]
)
ENGINE = MergeTree
ORDER BY id;
```
USearch currently supports two distance functions:
- `L2Distance`, also called Euclidean distance, is the length of a line segment between two points in Euclidean space
([Wikipedia](https://en.wikipedia.org/wiki/Euclidean_distance)).
- `cosineDistance`, also called cosine similarity, is the cosine of the angle between two (non-zero) vectors
([Wikipedia](https://en.wikipedia.org/wiki/Cosine_similarity)).
USearch allows storing the vectors in reduced precision formats. Supported scalar kinds are `f64`, `f32`, `f16` or `i8`. If no scalar kind
was specified during index creation, `f16` is used as default.
For normalized data, `L2Distance` is usually a better choice, otherwise `cosineDistance` is recommended to compensate for scale. If no
distance function was specified during index creation, `L2Distance` is used as default.

2
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View File

@ -1005,7 +1005,7 @@ They can be used for prewhere optimization only if we enable `set allow_statisti
## Column-level Settings {#column-level-settings}
Certain MergeTree settings can be override at column level:
Certain MergeTree settings can be overridden at column level:
- `max_compress_block_size` — Maximum size of blocks of uncompressed data before compressing for writing to a table.
- `min_compress_block_size` — Minimum size of blocks of uncompressed data required for compression when writing the next mark.

160
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View File

@ -0,0 +1,160 @@
---
slug: /en/interfaces/prometheus
sidebar_position: 19
sidebar_label: Prometheus protocols
---
# Prometheus protocols
## Exposing metrics {#expose}
:::note
ClickHouse Cloud does not currently support connecting to Prometheus. To be notified when this feature is supported, please contact support@clickhouse.com.
:::
ClickHouse can expose its own metrics for scraping from Prometheus:
```xml
<prometheus>
<port>9363</port>
<endpoint>/metrics</endpoint>
<metrics>true</metrics>
<asynchronous_metrics>true</asynchronous_metrics>
<events>true</events>
<errors>true</errors>
</prometheus>
Section `<prometheus.handlers>` can be used to make more extended handlers.
This section is similar to [<http_handlers>](/en/interfaces/http) but works for prometheus protocols:
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/metrics</url>
<handler>
<type>expose_metrics</type>
<metrics>true</metrics>
<asynchronous_metrics>true</asynchronous_metrics>
<events>true</events>
<errors>true</errors>
</handler>
</my_rule_1>
</handlers>
</prometheus>
```
Settings:
| Name | Default | Description |
|---|---|---|---|
| `port` | none | Port for serving the exposing metrics protocol. |
| `endpoint` | `/metrics` | HTTP endpoint for scraping metrics by prometheus server. Starts with `/`. Should not be used with the `<handlers>` section. |
| `url` / `headers` / `method` | none | Filters used to find a matching handler for a request. Similar to the fields with the same names in the [<http_handlers>](/en/interfaces/http) section. |
| `metrics` | true | Expose metrics from the [system.metrics](/en/operations/system-tables/metrics) table. |
| `asynchronous_metrics` | true | Expose current metrics values from the [system.asynchronous_metrics](/en/operations/system-tables/asynchronous_metrics) table. |
| `events` | true | Expose metrics from the [system.events](/en/operations/system-tables/events) table. |
| `errors` | true | Expose the number of errors by error codes occurred since the last server restart. This information could be obtained from the [system.errors](/en/operations/system-tables/errors) as well. |
Check (replace `127.0.0.1` with the IP addr or hostname of your ClickHouse server):
```bash
curl 127.0.0.1:9363/metrics
```
## Remote-write protocol {#remote-write}
ClickHouse supports the [remote-write](https://prometheus.io/docs/specs/remote_write_spec/) protocol.
Data are received by this protocol and written to a [TimeSeries](/en/engines/table-engines/special/time_series) table
(which should be created beforehand).
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/write</url>
<handler>
<type>remote_write</type>
<database>db_name</database>
<table>time_series_table</table>
</handler>
</my_rule_1>
</handlers>
</prometheus>
```
Settings:
| Name | Default | Description |
|---|---|---|---|
| `port` | none | Port for serving the `remote-write` protocol. |
| `url` / `headers` / `method` | none | Filters used to find a matching handler for a request. Similar to the fields with the same names in the [<http_handlers>](/en/interfaces/http) section. |
| `table` | none | The name of a [TimeSeries](/en/engines/table-engines/special/time_series) table to write data received by the `remote-write` protocol. This name can optionally contain the name of a database too. |
| `database` | none | The name of a database where the table specified in the `table` setting is located if it's not specified in the `table` setting. |
## Remote-read protocol {#remote-read}
ClickHouse supports the [remote-read](https://prometheus.io/docs/prometheus/latest/querying/remote_read_api/) protocol.
Data are read from a [TimeSeries](/en/engines/table-engines/special/time_series) table and sent via this protocol.
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/read</url>
<handler>
<type>remote_read</type>
<database>db_name</database>
<table>time_series_table</table>
</handler>
</my_rule_1>
</handlers>
</prometheus>
```
Settings:
| Name | Default | Description |
|---|---|---|---|
| `port` | none | Port for serving the `remote-read` protocol. |
| `url` / `headers` / `method` | none | Filters used to find a matching handler for a request. Similar to the fields with the same names in the [<http_handlers>](/en/interfaces/http) section. |
| `table` | none | The name of a [TimeSeries](/en/engines/table-engines/special/time_series) table to read data to send by the `remote-read` protocol. This name can optionally contain the name of a database too. |
| `database` | none | The name of a database where the table specified in the `table` setting is located if it's not specified in the `table` setting. |
## Configuration for multiple protocols {#multiple-protocols}
Multiple protocols can be specified together in one place:
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/metrics</url>
<handler>
<type>expose_metrics</type>
<metrics>true</metrics>
<asynchronous_metrics>true</asynchronous_metrics>
<events>true</events>
<errors>true</errors>
</handler>
</my_rule_1>
<my_rule_2>
<url>/write</url>
<handler>
<type>remote_write</type>
<table>db_name.time_series_table</table>
</handler>
</my_rule_2>
<my_rule_3>
<url>/read</url>
<handler>
<type>remote_read</type>
<table>db_name.time_series_table</table>
</handler>
</my_rule_3>
</handlers>
</prometheus>
```

93
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View File

@ -359,13 +359,14 @@ DESC format(JSONEachRow, '{"int" : 42, "float" : 42.42, "string" : "Hello, World
Dates, DateTimes:
```sql
DESC format(JSONEachRow, '{"date" : "2022-01-01", "datetime" : "2022-01-01 00:00:00"}')
DESC format(JSONEachRow, '{"date" : "2022-01-01", "datetime" : "2022-01-01 00:00:00", "datetime64" : "2022-01-01 00:00:00.000"}')
```
```response
┌─name─────┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ date │ Nullable(Date) │ │ │ │ │ │
│ datetime │ Nullable(DateTime64(9)) │ │ │ │ │ │
└──────────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
┌─name───────┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ date │ Nullable(Date) │ │ │ │ │ │
│ datetime │ Nullable(DateTime) │ │ │ │ │ │
│ datetime64 │ Nullable(DateTime64(9)) │ │ │ │ │ │
└────────────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
Arrays:
@ -759,12 +760,13 @@ DESC format(CSV, 'Hello world!,World hello!')
Dates, DateTimes:
```sql
DESC format(CSV, '"2020-01-01","2020-01-01 00:00:00"')
DESC format(CSV, '"2020-01-01","2020-01-01 00:00:00","2022-01-01 00:00:00.000"')
```
```response
┌─name─┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ c1 │ Nullable(Date) │ │ │ │ │ │
│ c2 │ Nullable(DateTime64(9)) │ │ │ │ │ │
│ c2 │ Nullable(DateTime) │ │ │ │ │ │
│ c3 │ Nullable(DateTime64(9)) │ │ │ │ │ │
└──────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
@ -956,12 +958,13 @@ DESC format(TSKV, 'int=42 float=42.42 bool=true string=Hello,World!\n')
Dates, DateTimes:
```sql
DESC format(TSV, '2020-01-01 2020-01-01 00:00:00')
DESC format(TSV, '2020-01-01 2020-01-01 00:00:00 2022-01-01 00:00:00.000')
```
```response
┌─name─┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ c1 │ Nullable(Date) │ │ │ │ │ │
│ c2 │ Nullable(DateTime64(9)) │ │ │ │ │ │
│ c2 │ Nullable(DateTime) │ │ │ │ │ │
│ c3 │ Nullable(DateTime64(9)) │ │ │ │ │ │
└──────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
@ -1126,12 +1129,13 @@ DESC format(Values, $$(42, 42.42, true, 'Hello,World!')$$)
Dates, DateTimes:
```sql
DESC format(Values, $$('2020-01-01', '2020-01-01 00:00:00')$$)
```
DESC format(Values, $$('2020-01-01', '2020-01-01 00:00:00', '2022-01-01 00:00:00.000')$$)
```
```response
┌─name─┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ c1 │ Nullable(Date) │ │ │ │ │ │
│ c2 │ Nullable(DateTime64(9)) │ │ │ │ │ │
│ c2 │ Nullable(DateTime) │ │ │ │ │ │
│ c3 │ Nullable(DateTime64(9)) │ │ │ │ │ │
└──────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
@ -1504,8 +1508,8 @@ DESC format(JSONEachRow, $$
#### input_format_try_infer_datetimes
If enabled, ClickHouse will try to infer type `DateTime64` from string fields in schema inference for text formats.
If all fields from a column in sample data were successfully parsed as datetimes, the result type will be `DateTime64(9)`,
If enabled, ClickHouse will try to infer type `DateTime` or `DateTime64` from string fields in schema inference for text formats.
If all fields from a column in sample data were successfully parsed as datetimes, the result type will be `DateTime` or `DateTime64(9)` (if any datetime had fractional part),
if at least one field was not parsed as datetime, the result type will be `String`.
Enabled by default.
@ -1513,39 +1517,66 @@ Enabled by default.
**Examples**
```sql
SET input_format_try_infer_datetimes = 0
SET input_format_try_infer_datetimes = 0;
DESC format(JSONEachRow, $$
{"datetime" : "2021-01-01 00:00:00.000"}
{"datetime" : "2022-01-01 00:00:00.000"}
{"datetime" : "2021-01-01 00:00:00", "datetime64" : "2021-01-01 00:00:00.000"}
{"datetime" : "2022-01-01 00:00:00", "datetime64" : "2022-01-01 00:00:00.000"}
$$)
```
```response
┌─name─────┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(String) │ │ │ │ │ │
└──────────┴──────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
┌─name───────┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(String) │ │ │ │ │ │
│ datetime64 │ Nullable(String) │ │ │ │ │ │
└────────────┴──────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
```sql
SET input_format_try_infer_datetimes = 1
SET input_format_try_infer_datetimes = 1;
DESC format(JSONEachRow, $$
{"datetime" : "2021-01-01 00:00:00.000"}
{"datetime" : "2022-01-01 00:00:00.000"}
{"datetime" : "2021-01-01 00:00:00", "datetime64" : "2021-01-01 00:00:00.000"}
{"datetime" : "2022-01-01 00:00:00", "datetime64" : "2022-01-01 00:00:00.000"}
$$)
```
```response
┌─name─────┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(DateTime64(9)) │ │ │ │ │ │
└──────────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
┌─name───────┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(DateTime) │ │ │ │ │ │
│ datetime64 │ Nullable(DateTime64(9)) │ │ │ │ │ │
└────────────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
```sql
DESC format(JSONEachRow, $$
{"datetime" : "2021-01-01 00:00:00.000"}
{"datetime" : "unknown"}
{"datetime" : "2021-01-01 00:00:00", "datetime64" : "2021-01-01 00:00:00.000"}
{"datetime" : "unknown", "datetime64" : "unknown"}
$$)
```
```response
┌─name─────┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(String) │ │ │ │ │ │
└──────────┴──────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
┌─name───────┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(String) │ │ │ │ │ │
│ datetime64 │ Nullable(String) │ │ │ │ │ │
└────────────┴──────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
#### input_format_try_infer_datetimes_only_datetime64
If enabled, ClickHouse will always infer `DateTime64(9)` when `input_format_try_infer_datetimes` is enabled even if datetime values don't contain fractional part.
Disabled by default.
**Examples**
```sql
SET input_format_try_infer_datetimes = 1;
SET input_format_try_infer_datetimes_only_datetime64 = 1;
DESC format(JSONEachRow, $$
{"datetime" : "2021-01-01 00:00:00", "datetime64" : "2021-01-01 00:00:00.000"}
{"datetime" : "2022-01-01 00:00:00", "datetime64" : "2022-01-01 00:00:00.000"}
$$)
```
```text
┌─name───────┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ datetime │ Nullable(DateTime64(9)) │ │ │ │ │ │
│ datetime64 │ Nullable(DateTime64(9)) │ │ │ │ │ │
└────────────┴─────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
Note: Parsing datetimes during schema inference respect setting [date_time_input_format](/docs/en/operations/settings/settings-formats.md#date_time_input_format)

26
docs/en/interfaces/third-party/gui.md vendored
View File

@ -10,7 +10,7 @@ sidebar_label: Visual Interfaces
### ch-ui {#ch-ui}
[ch-ui](https://github.com/caioricciuti/ch-ui) is a simple React.js app interface for ClickHouse databases, designed for executing queries and visualizing data. Built with React and the ClickHouse client for web, it offers a sleek and user-friendly UI for easy database interactions.
[ch-ui](https://github.com/caioricciuti/ch-ui) is a simple React.js app interface for ClickHouse databases designed for executing queries and visualizing data. Built with React and the ClickHouse client for web, it offers a sleek and user-friendly UI for easy database interactions.
Features:
@ -25,7 +25,7 @@ Web interface for ClickHouse in the [Tabix](https://github.com/tabixio/tabix) pr
Features:
- Works with ClickHouse directly from the browser, without the need to install additional software.
- Works with ClickHouse directly from the browser without the need to install additional software.
- Query editor with syntax highlighting.
- Auto-completion of commands.
- Tools for graphical analysis of query execution.
@ -63,7 +63,7 @@ Features:
- Table list with filtering and metadata.
- Table preview with filtering and sorting.
- Read-only queries execution.
- Read-only query execution.
### Redash {#redash}
@ -75,23 +75,23 @@ Features:
- Powerful editor of queries.
- Database explorer.
- Visualization tools, that allow you to represent data in different forms.
- Visualization tool that allows you to represent data in different forms.
### Grafana {#grafana}
[Grafana](https://grafana.com/grafana/plugins/grafana-clickhouse-datasource/) is a platform for monitoring and visualization.
"Grafana allows you to query, visualize, alert on and understand your metrics no matter where they are stored. Create, explore, and share dashboards with your team and foster a data driven culture. Trusted and loved by the community" &mdash; grafana.com.
"Grafana allows you to query, visualize, alert on and understand your metrics no matter where they are stored. Create, explore, and share dashboards with your team and foster a data-driven culture. Trusted and loved by the community" &mdash; grafana.com.
ClickHouse datasource plugin provides a support for ClickHouse as a backend database.
ClickHouse data source plugin provides support for ClickHouse as a backend database.
### qryn (#qryn)
### qryn {#qryn}
[qryn](https://metrico.in) is a polyglot, high-performance observability stack for ClickHouse _(formerly cLoki)_ with native Grafana integrations allowing users to ingest and analyze logs, metrics and telemetry traces from any agent supporting Loki/LogQL, Prometheus/PromQL, OTLP/Tempo, Elastic, InfluxDB and many more.
Features:
- Built in Explore UI and LogQL CLI for querying, extracting and visualizing data
- Built-in Explore UI and LogQL CLI for querying, extracting and visualizing data
- Native Grafana APIs support for querying, processing, ingesting, tracing and alerting without plugins
- Powerful pipeline to dynamically search, filter and extract data from logs, events, traces and beyond
- Ingestion and PUSH APIs transparently compatible with LogQL, PromQL, InfluxDB, Elastic and many more
@ -139,7 +139,7 @@ Features:
### DBM {#dbm}
[DBM](https://dbm.incubator.edurt.io/) DBM is a visual management tool for ClickHouse!
[DBM](https://github.com/devlive-community/dbm) DBM is a visual management tool for ClickHouse!
Features:
@ -151,7 +151,7 @@ Features:
- Support custom query
- Support multiple data sources management(connection test, monitoring)
- Support monitor (processor, connection, query)
- Support migrate data
- Support migrating data
### Bytebase {#bytebase}
@ -169,7 +169,7 @@ Features:
### Zeppelin-Interpreter-for-ClickHouse {#zeppelin-interpreter-for-clickhouse}
[Zeppelin-Interpreter-for-ClickHouse](https://github.com/SiderZhang/Zeppelin-Interpreter-for-ClickHouse) is a [Zeppelin](https://zeppelin.apache.org) interpreter for ClickHouse. Compared with JDBC interpreter, it can provide better timeout control for long running queries.
[Zeppelin-Interpreter-for-ClickHouse](https://github.com/SiderZhang/Zeppelin-Interpreter-for-ClickHouse) is a [Zeppelin](https://zeppelin.apache.org) interpreter for ClickHouse. Compared with the JDBC interpreter, it can provide better timeout control for long-running queries.
### ClickCat {#clickcat}
@ -179,7 +179,7 @@ Features:
- An online SQL editor which can run your SQL code without any installing.
- You can observe all processes and mutations. For those unfinished processes, you can kill them in ui.
- The Metrics contains Cluster Analysis,Data Analysis,Query Analysis.
- The Metrics contain Cluster Analysis, Data Analysis, and Query Analysis.
### ClickVisual {#clickvisual}
@ -332,7 +332,7 @@ Learn more about the product at [TABLUM.IO](https://tablum.io/)
### CKMAN {#ckman}
[CKMAN] (https://www.github.com/housepower/ckman) is a tool for managing and monitoring ClickHouse clusters!
[CKMAN](https://www.github.com/housepower/ckman) is a tool for managing and monitoring ClickHouse clusters!
Features:

22
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View File

@ -307,8 +307,22 @@ SELECT dictGet('dict', 'B', 2);
## Named collections for accessing PostgreSQL database
The description of parameters see [postgresql](../sql-reference/table-functions/postgresql.md).
The description of parameters see [postgresql](../sql-reference/table-functions/postgresql.md). Additionally, there are aliases:
- `username` for `user`
- `db` for `database`.
Parameter `addresses_expr` is used in a collection instead of `host:port`. The parameter is optional, because there are other optional ones: `host`, `hostname`, `port`. The following pseudo code explains the priority:
```sql
CASE
WHEN collection['addresses_expr'] != '' THEN collection['addresses_expr']
WHEN collection['host'] != '' THEN collection['host'] || ':' || if(collection['port'] != '', collection['port'], '5432')
WHEN collection['hostname'] != '' THEN collection['hostname'] || ':' || if(collection['port'] != '', collection['port'], '5432')
END
```
Example of creation:
```sql
CREATE NAMED COLLECTION mypg AS
user = 'pguser',
@ -316,7 +330,7 @@ password = 'jw8s0F4',
host = '127.0.0.1',
port = 5432,
database = 'test',
schema = 'test_schema',
schema = 'test_schema'
```
Example of configuration:
@ -369,6 +383,10 @@ SELECT * FROM mypgtable;
└───┘
```
:::note
PostgreSQL copies data from the named collection when the table is being created. A change in the collection does not affect the existing tables.
:::
### Example of using named collections with database with engine PostgreSQL
```sql

12
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View File

@ -143,6 +143,18 @@ value can be specified at session, profile or query level using setting [query_c
Entries in the query cache are compressed by default. This reduces the overall memory consumption at the cost of slower writes into / reads
from the query cache. To disable compression, use setting [query_cache_compress_entries](settings/settings.md#query-cache-compress-entries).
Sometimes it is useful to keep multiple results for the same query cached. This can be achieved using setting
[query_cache_tag](settings/settings.md#query-cache-tag) that acts as as a label (or namespace) for a query cache entries. The query cache
considers results of the same query with different tags different.
Example for creating three different query cache entries for the same query:
```sql
SELECT 1 SETTINGS use_query_cache = true; -- query_cache_tag is implicitly '' (empty string)
SELECT 1 SETTINGS use_query_cache = true, query_cache_tag = 'tag 1';
SELECT 1 SETTINGS use_query_cache = true, query_cache_tag = 'tag 2';
```
ClickHouse reads table data in blocks of [max_block_size](settings/settings.md#setting-max_block_size) rows. Due to filtering, aggregation,
etc., result blocks are typically much smaller than 'max_block_size' but there are also cases where they are much bigger. Setting
[query_cache_squash_partial_results](settings/settings.md#query-cache-squash-partial-results) (enabled by default) controls if result blocks

52
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View File

@ -1400,6 +1400,16 @@ The number of seconds that ClickHouse waits for incoming requests before closing
<keep_alive_timeout>10</keep_alive_timeout>
```
## max_keep_alive_requests {#max-keep-alive-requests}
Maximal number of requests through a single keep-alive connection until it will be closed by ClickHouse server. Default to 10000.
**Example**
``` xml
<max_keep_alive_requests>10</max_keep_alive_requests>
```
## listen_host {#listen_host}
Restriction on hosts that requests can come from. If you want the server to answer all of them, specify `::`.
@ -2112,48 +2122,6 @@ The trailing slash is mandatory.
<path>/var/lib/clickhouse/</path>
```
## Prometheus {#prometheus}
:::note
ClickHouse Cloud does not currently support connecting to Prometheus. To be notified when this feature is supported, please contact support@clickhouse.com.
:::
Exposing metrics data for scraping from [Prometheus](https://prometheus.io).
Settings:
- `endpoint` – HTTP endpoint for scraping metrics by prometheus server. Start from ‘/’.
- `port` – Port for `endpoint`.
- `metrics` – Expose metrics from the [system.metrics](../../operations/system-tables/metrics.md#system_tables-metrics) table.
- `events` – Expose metrics from the [system.events](../../operations/system-tables/events.md#system_tables-events) table.
- `asynchronous_metrics` – Expose current metrics values from the [system.asynchronous_metrics](../../operations/system-tables/asynchronous_metrics.md#system_tables-asynchronous_metrics) table.
- `errors` - Expose the number of errors by error codes occurred since the last server restart. This information could be obtained from the [system.errors](../../operations/system-tables/asynchronous_metrics.md#system_tables-errors) as well.
**Example**
``` xml
<clickhouse>
<listen_host>0.0.0.0</listen_host>
<http_port>8123</http_port>
<tcp_port>9000</tcp_port>
<!-- highlight-start -->
<prometheus>
<endpoint>/metrics</endpoint>
<port>9363</port>
<metrics>true</metrics>
<events>true</events>
<asynchronous_metrics>true</asynchronous_metrics>
<errors>true</errors>
</prometheus>
<!-- highlight-end -->
</clickhouse>
```
Check (replace `127.0.0.1` with the IP addr or hostname of your ClickHouse server):
```bash
curl 127.0.0.1:9363/metrics
```
## query_log {#query-log}
Setting for logging queries received with the [log_queries=1](../../operations/settings/settings.md) setting.

24
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View File

@ -1041,3 +1041,27 @@ Compression rates of LZ4 or ZSTD improve on average by 20-40%.
This setting works best for tables with no primary key or a low-cardinality primary key, i.e. a table with only few distinct primary key values.
High-cardinality primary keys, e.g. involving timestamp columns of type `DateTime64`, are not expected to benefit from this setting.
## lightweight_mutation_projection_mode
By default, lightweight delete `DELETE` does not work for tables with projections. This is because rows in a projection may be affected by a `DELETE` operation. So the default value would be `throw`.
However, this option can change the behavior. With the value either `drop` or `rebuild`, deletes will work with projections. `drop` would delete the projection so it might be fast in the current query as projection gets deleted but slow in future queries as no projection attached.
`rebuild` would rebuild the projection which might affect the performance of the current query, but might speedup for future queries. A good thing is that these options would only work in the part level,
which means projections in the part that don't get touched would stay intact instead of triggering any action like drop or rebuild.
Possible values:
- throw, drop, rebuild
Default value: throw
## deduplicate_merge_projection_mode
Whether to allow create projection for the table with non-classic MergeTree, that is not (Replicated, Shared) MergeTree. If allowed, what is the action when merge projections, either drop or rebuild. So classic MergeTree would ignore this setting.
It also controls `OPTIMIZE DEDUPLICATE` as well, but has effect on all MergeTree family members. Similar to the option `lightweight_mutation_projection_mode`, it is also part level.
Possible values:
- throw, drop, rebuild
Default value: throw

47
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View File

@ -1800,6 +1800,17 @@ Possible values:
Default value: `0`.
## query_cache_tag {#query-cache-tag}
A string which acts as a label for [query cache](../query-cache.md) entries.
The same queries with different tags are considered different by the query cache.
Possible values:
- Any string
Default value: `''`
## query_cache_max_size_in_bytes {#query-cache-max-size-in-bytes}
The maximum amount of memory (in bytes) the current user may allocate in the [query cache](../query-cache.md). 0 means unlimited.
@ -5609,6 +5620,19 @@ Minimal size of block to compress in CROSS JOIN. Zero value means - disable this
Default value: `1GiB`.
## use_json_alias_for_old_object_type
When enabled, `JSON` data type alias will be used to create an old [Object('json')](../../sql-reference/data-types/json.md) type instead of the new [JSON](../../sql-reference/data-types/newjson.md) type.
This setting requires server restart to take effect when changed.
Default value: `false`.
## type_json_skip_duplicated_paths
When enabled, ClickHouse will skip duplicated paths during parsing of [JSON](../../sql-reference/data-types/newjson.md) object. Only the value of the first occurrence of each path will be inserted.
Default value: `false`
## restore_replace_external_engines_to_null
For testing purposes. Replaces all external engines to Null to not initiate external connections.
@ -5626,3 +5650,26 @@ Default value: `False`
Disable all insert and mutations (alter table update / alter table delete / alter table drop partition). Set to true, can make this node focus on reading queries.
Default value: `false`.
## use_hive_partitioning
When enabled, ClickHouse will detect Hive-style partitioning in path (`/name=value/`) in file-like table engines [File](../../engines/table-engines/special/file.md#hive-style-partitioning)/[S3](../../engines/table-engines/integrations/s3.md#hive-style-partitioning)/[URL](../../engines/table-engines/special/url.md#hive-style-partitioning)/[HDFS](../../engines/table-engines/integrations/hdfs.md#hive-style-partitioning)/[AzureBlobStorage](../../engines/table-engines/integrations/azureBlobStorage.md#hive-style-partitioning) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.
Default value: `false`.
## allow_experimental_time_series_table {#allow-experimental-time-series-table}
Allows creation of tables with the [TimeSeries](../../engines/table-engines/integrations/time-series.md) table engine.
Possible values:
- 0 — the [TimeSeries](../../engines/table-engines/integrations/time-series.md) table engine is disabled.
- 1 — the [TimeSeries](../../engines/table-engines/integrations/time-series.md) table engine is enabled.
Default value: `0`.
## create_if_not_exists
Enable `IF NOT EXISTS` for `CREATE` statement by default. If either this setting or `IF NOT EXISTS` is specified and a table with the provided name already exists, no exception will be thrown.
Default value: `false`.

1
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View File

@ -24,6 +24,7 @@ Columns:
- `num_rebalance_revocations`, (UInt64) - number of times the consumer was revoked its partitions
- `num_rebalance_assignments`, (UInt64) - number of times the consumer was assigned to Kafka cluster
- `is_currently_used`, (UInt8) - consumer is in use
- `last_used`, (UInt64) - last time this consumer was in use, unix time in microseconds
- `rdkafka_stat` (String) - library internal statistic. See https://github.com/ClickHouse/librdkafka/blob/master/STATISTICS.md . Set `statistics_interval_ms` to 0 disable, default is 3000 (once in three seconds).
Example:

2
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View File

@ -9,6 +9,7 @@ Columns:
- `query` ([String](../../sql-reference/data-types/string.md)) — Query string.
- `result_size` ([UInt64](../../sql-reference/data-types/int-uint.md#uint-ranges)) — Size of the query cache entry.
- `tag` ([LowCardinality(String)](../../sql-reference/data-types/lowcardinality.md)) — Tag of the query cache entry.
- `stale` ([UInt8](../../sql-reference/data-types/int-uint.md)) — If the query cache entry is stale.
- `shared` ([UInt8](../../sql-reference/data-types/int-uint.md)) — If the query cache entry is shared between multiple users.
- `compressed` ([UInt8](../../sql-reference/data-types/int-uint.md)) — If the query cache entry is compressed.
@ -26,6 +27,7 @@ Row 1:
──────
query: SELECT 1 SETTINGS use_query_cache = 1
result_size: 128
tag:
stale: 0
shared: 0
compressed: 1

2
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View File

@ -3,7 +3,7 @@ slug: /en/operations/system-tables/trace_log
---
# trace_log
Contains stack traces collected by the sampling query profiler.
Contains stack traces collected by the [sampling query profiler](../../operations/optimizing-performance/sampling-query-profiler.md).
ClickHouse creates this table when the [trace_log](../../operations/server-configuration-parameters/settings.md#server_configuration_parameters-trace_log) server configuration section is set. Also see settings: [query_profiler_real_time_period_ns](../../operations/settings/settings.md#query_profiler_real_time_period_ns), [query_profiler_cpu_time_period_ns](../../operations/settings/settings.md#query_profiler_cpu_time_period_ns), [memory_profiler_step](../../operations/settings/settings.md#memory_profiler_step),
[memory_profiler_sample_probability](../../operations/settings/settings.md#memory_profiler_sample_probability), [trace_profile_events](../../operations/settings/settings.md#trace_profile_events).

3
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View File

@ -17,7 +17,8 @@ Columns:
- `duration_ms` ([UInt64](../../sql-reference/data-types/int-uint.md)) — How long the last refresh attempt took.
- `next_refresh_time` ([DateTime](../../sql-reference/data-types/datetime.md)) — Time at which the next refresh is scheduled to start.
- `remaining_dependencies` ([Array(String)](../../sql-reference/data-types/array.md)) — If the view has [refresh dependencies](../../sql-reference/statements/create/view.md#refresh-dependencies), this array contains the subset of those dependencies that are not satisfied for the current refresh yet. If `status = 'WaitingForDependencies'`, a refresh is ready to start as soon as these dependencies are fulfilled.
- `exception` ([String](../../sql-reference/data-types/string.md)) — if `last_refresh_result = 'Exception'`, i.e. the last refresh attempt failed, this column contains the corresponding error message and stack trace.
- `exception` ([String](../../sql-reference/data-types/string.md)) — if `last_refresh_result = 'Error'`, i.e. the last refresh attempt failed, this column contains the corresponding error message and stack trace.
- `retry` ([UInt64](../../sql-reference/data-types/int-uint.md)) — If nonzero, the current or next refresh is a retry (see `refresh_retries` refresh setting), and `retry` is the 1-based index of that retry.
- `refresh_count` ([UInt64](../../sql-reference/data-types/int-uint.md)) — Number of successful refreshes since last server restart or table creation.
- `progress` ([Float64](../../sql-reference/data-types/float.md)) — Progress of the current refresh, between 0 and 1.
- `read_rows` ([UInt64](../../sql-reference/data-types/int-uint.md)) — Number of rows read by the current refresh so far.

102
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View File

@ -12,57 +12,59 @@ This specification describes the binary format that can be used for binary encod
The table below describes how each data type is represented in binary format. Each data type encoding consist of 1 byte that indicates the type and some optional additional information.
`var_uint` in the binary encoding means that the size is encoded using Variable-Length Quantity compression.
| ClickHouse data type | Binary encoding |
|--------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `Nothing` | `0x00` |
| `UInt8` | `0x01` |
| `UInt16` | `0x02` |
| `UInt32` | `0x03` |
| `UInt64` | `0x04` |
| `UInt128` | `0x05` |
| `UInt256` | `0x06` |
| `Int8` | `0x07` |
| `Int16` | `0x08` |
| `Int32` | `0x09` |
| `Int64` | `0x0A` |
| `Int128` | `0x0B` |
| `Int256` | `0x0C` |
| `Float32` | `0x0D` |
| `Float64` | `0x0E` |
| `Date` | `0x0F` |
| `Date32` | `0x10` |
| `DateTime` | `0x11` |
| `DateTime(time_zone)` | `0x12<var_uint_time_zone_name_size><time_zone_name_data>` |
| `DateTime64(P)` | `0x13<uint8_precision>` |
| `DateTime64(P, time_zone)` | `0x14<uint8_precision><var_uint_time_zone_name_size><time_zone_name_data>` |
| `String` | `0x15` |
| `FixedString(N)` | `0x16<var_uint_size>` |
| `Enum8` | `0x17<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><int8_value_1>...<var_uint_name_size_N><name_data_N><int8_value_N>` |
| `Enum16` | `0x18<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><int16_little_endian_value_1>...><var_uint_name_size_N><name_data_N><int16_little_endian_value_N>` |
| `Decimal32(P, S)` | `0x19<uint8_precision><uint8_scale>` |
| `Decimal64(P, S)` | `0x1A<uint8_precision><uint8_scale>` |
| `Decimal128(P, S)` | `0x1B<uint8_precision><uint8_scale>` |
| `Decimal256(P, S)` | `0x1C<uint8_precision><uint8_scale>` |
| `UUID` | `0x1D` |
| `Array(T)` | `0x1E<nested_type_encoding>` |
| `Tuple(T1, ..., TN)` | `0x1F<var_uint_number_of_elements><nested_type_encoding_1>...<nested_type_encoding_N>` |
| `Tuple(name1 T1, ..., nameN TN)` | `0x20<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><nested_type_encoding_1>...<var_uint_name_size_N><name_data_N><nested_type_encoding_N>` |
| `Set` | `0x21` |
| `Interval` | `0x22<interval_kind>` (see [interval kind binary encoding](#interval-kind-binary-encoding)) |
| `Nullable(T)` | `0x23<nested_type_encoding>` |
| `Function` | `0x24<var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N><return_type_encoding>` |
| `AggregateFunction(function_name(param_1, ..., param_N), arg_T1, ..., arg_TN)` | `0x25<var_uint_version><var_uint_function_name_size><function_name_data><var_uint_number_of_parameters><param_1>...<param_N><var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N>` (see [aggregate function parameter binary encoding](#aggregate-function-parameter-binary-encoding)) |
| `LowCardinality(T)` | `0x26<nested_type_encoding>` |
| `Map(K, V)` | `0x27<key_type_encoding><value_type_encoding>` |
| `IPv4` | `0x28` |
| `IPv6` | `0x29` |
| `Variant(T1, ..., TN)` | `0x2A<var_uint_number_of_variants><variant_type_encoding_1>...<variant_type_encoding_N>` |
| `Dynamic(max_types=N)` | `0x2B<uint8_max_types>` |
| `Custom type` (`Ring`, `Polygon`, etc) | `0x2C<var_uint_type_name_size><type_name_data>` |
| `Bool` | `0x2D` |
| `SimpleAggregateFunction(function_name(param_1, ..., param_N), arg_T1, ..., arg_TN)` | `0x2E<var_uint_function_name_size><function_name_data><var_uint_number_of_parameters><param_1>...<param_N><var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N>` (see [aggregate function parameter binary encoding](#aggregate-function-parameter-binary-encoding)) |
| `Nested(name1 T1, ..., nameN TN)` | `0x2F<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><nested_type_encoding_1>...<var_uint_name_size_N><name_data_N><nested_type_encoding_N>` |
| ClickHouse data type | Binary encoding |
|-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `Nothing` | `0x00` |
| `UInt8` | `0x01` |
| `UInt16` | `0x02` |
| `UInt32` | `0x03` |
| `UInt64` | `0x04` |
| `UInt128` | `0x05` |
| `UInt256` | `0x06` |
| `Int8` | `0x07` |
| `Int16` | `0x08` |
| `Int32` | `0x09` |
| `Int64` | `0x0A` |
| `Int128` | `0x0B` |
| `Int256` | `0x0C` |
| `Float32` | `0x0D` |
| `Float64` | `0x0E` |
| `Date` | `0x0F` |
| `Date32` | `0x10` |
| `DateTime` | `0x11` |
| `DateTime(time_zone)` | `0x12<var_uint_time_zone_name_size><time_zone_name_data>` |
| `DateTime64(P)` | `0x13<uint8_precision>` |
| `DateTime64(P, time_zone)` | `0x14<uint8_precision><var_uint_time_zone_name_size><time_zone_name_data>` |
| `String` | `0x15` |
| `FixedString(N)` | `0x16<var_uint_size>` |
| `Enum8` | `0x17<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><int8_value_1>...<var_uint_name_size_N><name_data_N><int8_value_N>` |
| `Enum16` | `0x18<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><int16_little_endian_value_1>...><var_uint_name_size_N><name_data_N><int16_little_endian_value_N>` |
| `Decimal32(P, S)` | `0x19<uint8_precision><uint8_scale>` |
| `Decimal64(P, S)` | `0x1A<uint8_precision><uint8_scale>` |
| `Decimal128(P, S)` | `0x1B<uint8_precision><uint8_scale>` |
| `Decimal256(P, S)` | `0x1C<uint8_precision><uint8_scale>` |
| `UUID` | `0x1D` |
| `Array(T)` | `0x1E<nested_type_encoding>` |
| `Tuple(T1, ..., TN)` | `0x1F<var_uint_number_of_elements><nested_type_encoding_1>...<nested_type_encoding_N>` |
| `Tuple(name1 T1, ..., nameN TN)` | `0x20<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><nested_type_encoding_1>...<var_uint_name_size_N><name_data_N><nested_type_encoding_N>` |
| `Set` | `0x21` |
| `Interval` | `0x22<interval_kind>` (see [interval kind binary encoding](#interval-kind-binary-encoding)) |
| `Nullable(T)` | `0x23<nested_type_encoding>` |
| `Function` | `0x24<var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N><return_type_encoding>` |
| `AggregateFunction(function_name(param_1, ..., param_N), arg_T1, ..., arg_TN)` | `0x25<var_uint_version><var_uint_function_name_size><function_name_data><var_uint_number_of_parameters><param_1>...<param_N><var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N>` (see [aggregate function parameter binary encoding](#aggregate-function-parameter-binary-encoding)) |
| `LowCardinality(T)` | `0x26<nested_type_encoding>` |
| `Map(K, V)` | `0x27<key_type_encoding><value_type_encoding>` |
| `IPv4` | `0x28` |
| `IPv6` | `0x29` |
| `Variant(T1, ..., TN)` | `0x2A<var_uint_number_of_variants><variant_type_encoding_1>...<variant_type_encoding_N>` |
| `Dynamic(max_types=N)` | `0x2B<uint8_max_types>` |
| `Custom type` (`Ring`, `Polygon`, etc) | `0x2C<var_uint_type_name_size><type_name_data>` |
| `Bool` | `0x2D` |
| `SimpleAggregateFunction(function_name(param_1, ..., param_N), arg_T1, ..., arg_TN)` | `0x2E<var_uint_function_name_size><function_name_data><var_uint_number_of_parameters><param_1>...<param_N><var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N>` (see [aggregate function parameter binary encoding](#aggregate-function-parameter-binary-encoding)) |
| `Nested(name1 T1, ..., nameN TN)` | `0x2F<var_uint_number_of_elements><var_uint_name_size_1><name_data_1><nested_type_encoding_1>...<var_uint_name_size_N><name_data_N><nested_type_encoding_N>` |
| `JSON(max_dynamic_paths=N, max_dynamic_types=M, path Type, SKIP skip_path, SKIP REGEXP skip_path_regexp)` | `0x30<uint8_serialization_version><var_int_max_dynamic_paths><uint8_max_dynamic_types><var_uint_number_of_typed_paths><var_uint_path_name_size_1><path_name_data_1><encoded_type_1>...<var_uint_number_of_skip_paths><var_uint_skip_path_size_1><skip_path_data_1>...<var_uint_number_of_skip_path_regexps><var_uint_skip_path_regexp_size_1><skip_path_data_regexp_1>...` |
For type `JSON` byte `uint8_serialization_version` indicates the version of the serialization. Right now the version is always 0 but can change in future if new arguments will be introduced for `JSON` type.
### Interval kind binary encoding

123
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View File

@ -14,7 +14,7 @@ To declare a column of `Dynamic` type, use the following syntax:
<column_name> Dynamic(max_types=N)
```
Where `N` is an optional parameter between `1` and `255` indicating how many different data types can be stored inside a column with type `Dynamic` across single block of data that is stored separately (for example across single data part for MergeTree table). If this limit is exceeded, all new types will be converted to type `String`. Default value of `max_types` is `32`.
Where `N` is an optional parameter between `0` and `254` indicating how many different data types can be stored as separate subcolumns inside a column with type `Dynamic` across single block of data that is stored separately (for example across single data part for MergeTree table). If this limit is exceeded, all values with new types will be stored together in a special shared data structure in binary form. Default value of `max_types` is `32`.
:::note
The Dynamic data type is an experimental feature. To use it, set `allow_experimental_dynamic_type = 1`.
@ -224,41 +224,43 @@ SELECT d::Dynamic(max_types=5) as d2, dynamicType(d2) FROM test;
└───────┴────────────────┘
```
If `K < N`, then the values with the rarest types are converted to `String`:
If `K < N`, then the values with the rarest types will be inserted into a single special subcolumn, but still will be accessible:
```text
CREATE TABLE test (d Dynamic(max_types=4)) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), (43), ('42.42'), (true), ([1, 2, 3]);
SELECT d, dynamicType(d), d::Dynamic(max_types=2) as d2, dynamicType(d2) FROM test;
SELECT d, dynamicType(d), d::Dynamic(max_types=2) as d2, dynamicType(d2), isDynamicElementInSharedData(d2) FROM test;
```
```text
┌─d───────┬─dynamicType(d)─┬─d2──────┬─dynamicType(d2)─┐
│ ᴺᵁᴸᴸ │ None │ ᴺᵁᴸᴸ │ None │
│ 42 │ Int64 │ 42 │ Int64 │
│ 43 │ Int64 │ 43 │ Int64 │
│ 42.42 │ String │ 42.42 │ String │
│ true │ Bool │ true │ String │
│ [1,2,3] │ Array(Int64) │ [1,2,3] │ String │
└─────────┴────────────────┴─────────┴─────────────────┘
┌─d───────┬─dynamicType(d)─┬─d2──────┬─dynamicType(d2)─┬─isDynamicElementInSharedData(d2)─┐
│ ᴺᵁᴸᴸ │ None │ ᴺᵁᴸᴸ │ None │ false │
│ 42 │ Int64 │ 42 │ Int64 │ false │
│ 43 │ Int64 │ 43 │ Int64 │ false │
│ 42.42 │ String │ 42.42 │ String │ false │
│ true │ Bool │ true │ Bool │ true │
│ [1,2,3] │ Array(Int64) │ [1,2,3] │ Array(Int64) │ true │
└─────────┴────────────────┴─────────┴─────────────────┴──────────────────────────────────┘
```
If `K=1`, all types are converted to `String`:
Functions `isDynamicElementInSharedData` returns `true` for rows that are stored in a special shared data structure inside `Dynamic` and as we can see, resulting column contains only 2 types that are not stored in shared data structure.
If `K=0`, all types will be inserted into single special subcolumn:
```text
CREATE TABLE test (d Dynamic(max_types=4)) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), (43), ('42.42'), (true), ([1, 2, 3]);
SELECT d, dynamicType(d), d::Dynamic(max_types=1) as d2, dynamicType(d2) FROM test;
SELECT d, dynamicType(d), d::Dynamic(max_types=0) as d2, dynamicType(d2), isDynamicElementInSharedData(d2) FROM test;
```
```text
┌─d───────┬─dynamicType(d)─┬─d2──────┬─dynamicType(d2)─┐
│ ᴺᵁᴸᴸ │ None │ ᴺᵁᴸᴸ │ None │
│ 42 │ Int64 │ 42 │ String │
│ 43 │ Int64 │ 43 │ String │
│ 42.42 │ String │ 42.42 │ String │
│ true │ Bool │ true │ String │
│ [1,2,3] │ Array(Int64) │ [1,2,3] │ String │
└─────────┴────────────────┴─────────┴─────────────────┘
┌─d───────┬─dynamicType(d)─┬─d2──────┬─dynamicType(d2)─┬─isDynamicElementInSharedData(d2)─┐
│ ᴺᵁᴸᴸ │ None │ ᴺᵁᴸᴸ │ None │ false │
│ 42 │ Int64 │ 42 │ Int64 │ true │
│ 43 │ Int64 │ 43 │ Int64 │ true │
│ 42.42 │ String │ 42.42 │ String │ true │
│ true │ Bool │ true │ Bool │ true │
│ [1,2,3] │ Array(Int64) │ [1,2,3] │ Array(Int64) │ true │
└─────────┴────────────────┴─────────┴─────────────────┴──────────────────────────────────┘
```
## Reading Dynamic type from the data
@ -411,17 +413,17 @@ SELECT d, dynamicType(d) FROM test ORDER by d;
## Reaching the limit in number of different data types stored inside Dynamic
`Dynamic` data type can store only limited number of different data types inside. By default, this limit is 32, but you can change it in type declaration using syntax `Dynamic(max_types=N)` where N is between 1 and 255 (due to implementation details, it's impossible to have more than 255 different data types inside Dynamic).
When the limit is reached, all new data types inserted to `Dynamic` column will be casted to `String` and stored as `String` values.
`Dynamic` data type can store only limited number of different data types as separate subcolumns. By default, this limit is 32, but you can change it in type declaration using syntax `Dynamic(max_types=N)` where N is between 0 and 254 (due to implementation details, it's impossible to have more than 254 different data types that can be stored as separate subcolumns inside Dynamic).
When the limit is reached, all new data types inserted to `Dynamic` column will be inserted into a single shared data structure that stores values with different data types in binary form.
Let's see what happens when the limit is reached in different scenarios.
### Reaching the limit during data parsing
During parsing of `Dynamic` values from the data, when the limit is reached for current block of data, all new values will be inserted as `String` values:
During parsing of `Dynamic` values from the data, when the limit is reached for current block of data, all new values will be inserted into shared data structure:
```sql
SELECT d, dynamicType(d) FROM format(JSONEachRow, 'd Dynamic(max_types=3)', '
SELECT d, dynamicType(d), isDynamicElementInSharedData(d) FROM format(JSONEachRow, 'd Dynamic(max_types=3)', '
{"d" : 42}
{"d" : [1, 2, 3]}
{"d" : "Hello, World!"}
@ -432,22 +434,22 @@ SELECT d, dynamicType(d) FROM format(JSONEachRow, 'd Dynamic(max_types=3)', '
```
```text
┌─d──────────────────────────┬─dynamicType(d)─┐
│ 42 │ Int64 │
│ [1,2,3] │ Array(Int64) │
│ Hello, World! │ String │
│ 2020-01-01 │ String │
│ ["str1", "str2", "str3"] │ String │
│ {"a" : 1, "b" : [1, 2, 3]} │ String │
└────────────────────────────┴────────────────┘
┌─d──────────────────────┬─dynamicType(d)─────────────────┬─isDynamicElementInSharedData(d)─┐
│ 42 │ Int64 │ false │
│ [1,2,3] │ Array(Int64) │ false │
│ Hello, World! │ String │ false │
│ 2020-01-01 │ Date │ true │
│ ['str1','str2','str3'] │ Array(String) │ true │
│ (1,[1,2,3]) │ Tuple(a Int64, b Array(Int64)) │ true │
└────────────────────────┴────────────────────────────────┴─────────────────────────────────┘
```
As we can see, after inserting 3 different data types `Int64`, `Array(Int64)` and `String` all new types were converted to `String`.
As we can see, after inserting 3 different data types `Int64`, `Array(Int64)` and `String` all new types were inserted into special shared data structure.
### During merges of data parts in MergeTree table engines
During merge of several data parts in MergeTree table the `Dynamic` column in the resulting data part can reach the limit of different data types inside and won't be able to store all types from source parts.
In this case ClickHouse chooses what types will remain after merge and what types will be casted to `String`. In most cases ClickHouse tries to keep the most frequent types and cast the rarest types to `String`, but it depends on the implementation.
During merge of several data parts in MergeTree table the `Dynamic` column in the resulting data part can reach the limit of different data types that can be stored in separate subcolumns inside and won't be able to store all types as subcolumns from source parts.
In this case ClickHouse chooses what types will remain as separate subcolumns after merge and what types will be inserted into shared data structure. In most cases ClickHouse tries to keep the most frequent types and store the rarest types in shared data structure, but it depends on the implementation.
Let's see an example of such merge. First, let's create a table with `Dynamic` column, set the limit of different data types to `3` and insert values with `5` different types:
@ -463,17 +465,17 @@ INSERT INTO test SELECT number, 'str_' || toString(number) FROM numbers(1);
Each insert will create a separate data pert with `Dynamic` column containing single type:
```sql
SELECT count(), dynamicType(d), _part FROM test GROUP BY _part, dynamicType(d) ORDER BY _part;
SELECT count(), dynamicType(d), isDynamicElementInSharedData(d), _part FROM test GROUP BY _part, dynamicType(d), isDynamicElementInSharedData(d) ORDER BY _part, count();
```
```text
┌─count()─┬─dynamicType(d)──────┬─_part─────┐
│ 5 │ UInt64 │ all_1_1_0 │
│ 4 │ Array(UInt64) │ all_2_2_0 │
│ 3 │ Date │ all_3_3_0 │
│ 2 │ Map(UInt64, UInt64) │ all_4_4_0 │
│ 1 │ String │ all_5_5_0 │
└─────────┴─────────────────────┴───────────┘
┌─count()─┬─dynamicType(d)──────┬─isDynamicElementInSharedData(d)─┬─_part─────┐
│ 5 │ UInt64 │ false │ all_1_1_0 │
│ 4 │ Array(UInt64) │ false │ all_2_2_0 │
│ 3 │ Date │ false │ all_3_3_0 │
│ 2 │ Map(UInt64, UInt64) │ false │ all_4_4_0 │
│ 1 │ String │ false │ all_5_5_0 │
└─────────┴─────────────────────┴─────────────────────────────────┴───────────┘
```
Now, let's merge all parts into one and see what will happen:
@ -481,18 +483,20 @@ Now, let's merge all parts into one and see what will happen:
```sql
SYSTEM START MERGES test;
OPTIMIZE TABLE test FINAL;
SELECT count(), dynamicType(d), _part FROM test GROUP BY _part, dynamicType(d) ORDER BY _part;
SELECT count(), dynamicType(d), isDynamicElementInSharedData(d), _part FROM test GROUP BY _part, dynamicType(d), isDynamicElementInSharedData(d) ORDER BY _part, count() desc;
```
```text
┌─count()─┬─dynamicType(d)─┬─_part─────┐
│ 5 │ UInt64 │ all_1_5_2 │
│ 6 │ String │ all_1_5_2 │
│ 4 │ Array(UInt64) │ all_1_5_2 │
└─────────┴────────────────┴───────────┘
┌─count()─┬─dynamicType(d)──────┬─isDynamicElementInSharedData(d)─┬─_part─────┐
│ 5 │ UInt64 │ false │ all_1_5_2 │
│ 4 │ Array(UInt64) │ false │ all_1_5_2 │
│ 3 │ Date │ false │ all_1_5_2 │
│ 2 │ Map(UInt64, UInt64) │ true │ all_1_5_2 │
│ 1 │ String │ true │ all_1_5_2 │
└─────────┴─────────────────────┴─────────────────────────────────┴───────────┘
```
As we can see, ClickHouse kept the most frequent types `UInt64` and `Array(UInt64)` and casted all other types to `String`.
As we can see, ClickHouse kept the most frequent types `UInt64` and `Array(UInt64)` as subcolumns and inserted all other types into shared data.
## JSONExtract functions with Dynamic
@ -509,22 +513,23 @@ SELECT JSONExtract('{"a" : [1, 2, 3]}', 'a', 'Dynamic') AS dynamic, dynamicType(
```
```sql
SELECT JSONExtract('{"obj" : {"a" : 42, "b" : "Hello", "c" : [1,2,3]}}', 'obj', 'Map(String, Variant(UInt32, String, Array(UInt32)))') AS map_of_dynamics, mapApply((k, v) -> (k, variantType(v)), map_of_dynamics) AS map_of_dynamic_types```
SELECT JSONExtract('{"obj" : {"a" : 42, "b" : "Hello", "c" : [1,2,3]}}', 'obj', 'Map(String, Dynamic)') AS map_of_dynamics, mapApply((k, v) -> (k, dynamicType(v)), map_of_dynamics) AS map_of_dynamic_types
```
```text
┌─map_of_dynamics──────────────────┬─map_of_dynamic_types────────────────────────────┐
│ {'a':42,'b':'Hello','c':[1,2,3]} │ {'a':'UInt32','b':'String','c':'Array(UInt32)'} │
└──────────────────────────────────┴─────────────────────────────────────────────────┘
┌─map_of_dynamics──────────────────┬─map_of_dynamic_types────────────────────────────────────┐
│ {'a':42,'b':'Hello','c':[1,2,3]} │ {'a':'Int64','b':'String','c':'Array(Nullable(Int64))'} │
└──────────────────────────────────┴─────────────────────────────────────────────────────────┘
```
```sql
SELECT JSONExtractKeysAndValues('{"a" : 42, "b" : "Hello", "c" : [1,2,3]}', 'Variant(UInt32, String, Array(UInt32))') AS dynamics, arrayMap(x -> (x.1, variantType(x.2)), dynamics) AS dynamic_types```
SELECT JSONExtractKeysAndValues('{"a" : 42, "b" : "Hello", "c" : [1,2,3]}', 'Dynamic') AS dynamics, arrayMap(x -> (x.1, dynamicType(x.2)), dynamics) AS dynamic_types```
```
```text
┌─dynamics───────────────────────────────┬─dynamic_types─────────────────────────────────────────┐
│ [('a',42),('b','Hello'),('c',[1,2,3])] │ [('a','UInt32'),('b','String'),('c','Array(UInt32)')] │
└────────────────────────────────────────┴───────────────────────────────────────────────────────┘
┌─dynamics───────────────────────────────┬─dynamic_types─────────────────────────────────────────────────┐
│ [('a',42),('b','Hello'),('c',[1,2,3])] │ [('a','Int64'),('b','String'),('c','Array(Nullable(Int64))')] │
└────────────────────────────────────────┴───────────────────────────────────────────────────────────────┘
```
### Binary output format

42
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View File

@ -52,6 +52,48 @@ Result:
└───────────────────────────────┴───────────────┘
```
## LineString
`LineString` is a line stored as an array of points: [Array](array.md)([Point](#point)).
**Example**
Query:
```sql
CREATE TABLE geo_linestring (l LineString) ENGINE = Memory();
INSERT INTO geo_linestring VALUES([(0, 0), (10, 0), (10, 10), (0, 10)]);
SELECT l, toTypeName(l) FROM geo_linestring;
```
Result:
``` text
┌─r─────────────────────────────┬─toTypeName(r)─┐
│ [(0,0),(10,0),(10,10),(0,10)] │ LineString │
└───────────────────────────────┴───────────────┘
```
## MultiLineString
`MultiLineString` is multiple lines stored as an array of `LineString`: [Array](array.md)([LineString](#linestring)).
**Example**
Query:
```sql
CREATE TABLE geo_multilinestring (l MultiLineString) ENGINE = Memory();
INSERT INTO geo_multilinestring VALUES([[(0, 0), (10, 0), (10, 10), (0, 10)], [(1, 1), (2, 2), (3, 3)]]);
SELECT l, toTypeName(l) FROM geo_multilinestring;
```
Result:
``` text
┌─l───────────────────────────────────────────────────┬─toTypeName(l)───┐
│ [[(0,0),(10,0),(10,10),(0,10)],[(1,1),(2,2),(3,3)]] │ MultiLineString │
└─────────────────────────────────────────────────────┴─────────────────┘
```
## Polygon
`Polygon` is a polygon with holes stored as an array of rings: [Array](array.md)([Ring](#ring)). First element of outer array is the outer shape of polygon and all the following elements are holes.

3
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View File

@ -19,7 +19,8 @@ ClickHouse data types include:
- **Boolean**: ClickHouse has a [`Boolean` type](./boolean.md)
- **Strings**: [`String`](./string.md) and [`FixedString`](./fixedstring.md)
- **Dates**: use [`Date`](./date.md) and [`Date32`](./date32.md) for days, and [`DateTime`](./datetime.md) and [`DateTime64`](./datetime64.md) for instances in time
- **JSON**: the [`JSON` object](./json.md) stores a JSON document in a single column
- **Object**: the [`Object`](./json.md) stores a JSON document in a single column (deprecated)
- **JSON**: the [`JSON` object](./newjson.md) stores a JSON document in a single column
- **UUID**: a performant option for storing [`UUID` values](./uuid.md)
- **Low cardinality types**: use an [`Enum`](./enum.md) when you have a handful of unique values, or use [`LowCardinality`](./lowcardinality.md) when you have up to 10,000 unique values of a column
- **Arrays**: any column can be defined as an [`Array` of values](./array.md)

4
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View File

@ -13,7 +13,7 @@ keywords: [object, data type]
Stores JavaScript Object Notation (JSON) documents in a single column.
`JSON` is an alias for `Object('json')`.
`JSON` can be used as an alias to `Object('json')` when setting [use_json_alias_for_old_object_type](../../operations/settings/settings.md#usejsonaliasforoldobjecttype) is enabled.
## Example
@ -79,5 +79,5 @@ SELECT * FROM json FORMAT JSONEachRow
## Related Content
- [Using JSON in ClickHouse](/docs/en/integrations/data-formats/json)
- [Using JSON in ClickHouse](/en/integrations/data-formats/json/overview)
- [Getting Data Into ClickHouse - Part 2 - A JSON detour](https://clickhouse.com/blog/getting-data-into-clickhouse-part-2-json)

516
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View File

@ -0,0 +1,516 @@
---
slug: /en/sql-reference/data-types/newjson
sidebar_position: 63
sidebar_label: JSON
keywords: [json, data type]
---
# JSON
Stores JavaScript Object Notation (JSON) documents in a single column.
:::note
This feature is experimental and is not production-ready. If you need to work with JSON documents, consider using [this guide](/docs/en/integrations/data-formats/json/overview) instead.
If you want to use JSON type, set `allow_experimental_json_type = 1`.
:::
To declare a column of `JSON` type, use the following syntax:
``` sql
<column_name> JSON(max_dynamic_paths=N, max_dynamic_types=M, some.path TypeName, SKIP path.to.skip, SKIP REGEXP 'paths_regexp')
```
Where:
- `max_dynamic_paths` is an optional parameter indicating how many paths can be stored separately as subcolumns across single block of data that is stored separately (for example across single data part for MergeTree table). If this limit is exceeded, all other paths will be stored together in a single structure. Default value of `max_dynamic_paths` is `1024`.
- `max_dynamic_types` is an optional parameter between `1` and `255` indicating how many different data types can be stored inside a single path column with type `Dynamic` across single block of data that is stored separately (for example across single data part for MergeTree table). If this limit is exceeded, all new types will be converted to type `String`. Default value of `max_dynamic_types` is `32`.
- `some.path TypeName` is an optional type hint for particular path in the JSON. Such paths will be always stored as subcolumns with specified type.
- `SKIP path.to.skip` is an optional hint for particular path that should be skipped during JSON parsing. Such paths will never be stored in the JSON column. If specified path is a nested JSON object, the whole nested object will be skipped.
- `SKIP REGEXP 'path_regexp'` is an optional hint with a regular expression that is used to skip paths during JSON parsing. All paths that match this regular expression will never be stored in the JSON column.
## Creating JSON
Using `JSON` type in table column definition:
```sql
CREATE TABLE test (json JSON) ENGINE = Memory;
INSERT INTO test VALUES ('{"a" : {"b" : 42}, "c" : [1, 2, 3]}'), ('{"f" : "Hello, World!"}'), ('{"a" : {"b" : 43, "e" : 10}, "c" : [4, 5, 6]}');
SELECT json FROM test;
```
```text
┌─json────────────────────────────────────────┐
│ {"a":{"b":"42"},"c":["1","2","3"]} │
│ {"f":"Hello, World!"} │
│ {"a":{"b":"43","e":"10"},"c":["4","5","6"]} │
└─────────────────────────────────────────────┘
```
```sql
CREATE TABLE test (json JSON(a.b UInt32, SKIP a.e)) ENGINE = Memory;
INSERT INTO test VALUES ('{"a" : {"b" : 42}, "c" : [1, 2, 3]}'), ('{"f" : "Hello, World!"}'), ('{"a" : {"b" : 43, "e" : 10}, "c" : [4, 5, 6]}');
SELECT json FROM test;
```
```text
┌─json──────────────────────────────┐
│ {"a":{"b":42},"c":[1,2,3]} │
│ {"a":{"b":0},"f":"Hello, World!"} │
│ {"a":{"b":43},"c":[4,5,6]} │
└───────────────────────────────────┘
```
Using CAST from 'String':
```sql
SELECT '{"a" : {"b" : 42},"c" : [1, 2, 3], "d" : "Hello, World!"}'::JSON as json;
```
```text
┌─json───────────────────────────────────────────┐
│ {"a":{"b":42},"c":[1,2,3],"d":"Hello, World!"} │
└────────────────────────────────────────────────┘
```
CAST from named `Tuple`, `Map` and `Object('json')` to `JSON` type will be supported later.
## Reading JSON paths as subcolumns
JSON type supports reading every path as a separate subcolumn. If type of the requested path was not specified in the JSON type declaration, the subcolumn of the path will always have type [Dynamic](/docs/en/sql-reference/data-types/dynamic.md).
For example:
```sql
CREATE TABLE test (json JSON(a.b UInt32, SKIP a.e)) ENGINE = Memory;
INSERT INTO test VALUES ('{"a" : {"b" : 42, "g" : 42.42}, "c" : [1, 2, 3], "d" : "2020-01-01"}'), ('{"f" : "Hello, World!", "d" : "2020-01-02"}'), ('{"a" : {"b" : 43, "e" : 10, "g" : 43.43}, "c" : [4, 5, 6]}');
SELECT json FROM test;
```
```text
┌─json──────────────────────────────────────────────────┐
│ {"a":{"b":42,"g":42.42},"c":[1,2,3],"d":"2020-01-01"} │
│ {"a":{"b":0},"d":"2020-01-02","f":"Hello, World!"} │
│ {"a":{"b":43,"g":43.43},"c":[4,5,6]} │
└───────────────────────────────────────────────────────┘
```
```sql
SELECT json.a.b, json.a.g, json.c, json.d FROM test;
```
```text
┌─json.a.b─┬─json.a.g─┬─json.c──┬─json.d─────┐
│ 42 │ 42.42 │ [1,2,3] │ 2020-01-01 │
│ 0 │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ 2020-01-02 │
│ 43 │ 43.43 │ [4,5,6] │ ᴺᵁᴸᴸ │
└──────────┴──────────┴─────────┴────────────┘
```
If the requested path wasn't found in the data, it will be filled with `NULL` values:
```sql
SELECT json.non.existing.path FROM test;
```
```text
┌─json.non.existing.path─┐
│ ᴺᵁᴸᴸ │
│ ᴺᵁᴸᴸ │
│ ᴺᵁᴸᴸ │
└────────────────────────┘
```
Let's check the data types of returned subcolumns:
```sql
SELECT toTypeName(json.a.b), toTypeName(json.a.g), toTypeName(json.c), toTypeName(json.d) FROM test;
```
```text
┌─toTypeName(json.a.b)─┬─toTypeName(json.a.g)─┬─toTypeName(json.c)─┬─toTypeName(json.d)─┐
│ UInt32 │ Dynamic │ Dynamic │ Dynamic │
│ UInt32 │ Dynamic │ Dynamic │ Dynamic │
│ UInt32 │ Dynamic │ Dynamic │ Dynamic │
└──────────────────────┴──────────────────────┴────────────────────┴────────────────────┘
```
As we can see, for `a.b` the type is `UInt32` as we specified in the JSON type declaration, and for all other subcolumns the type is `Dynamic`.
It is also possible to read subcolumns of a `Dynamic` type using special syntax `json.some.path.:TypeName`:
```sql
select json.a.g.:Float64, dynamicType(json.a.g), json.d.:Date, dynamicType(json.d) FROM test;
```
```text
┌─json.a.g.:`Float64`─┬─dynamicType(json.a.g)─┬─json.d.:`Date`─┬─dynamicType(json.d)─┐
│ 42.42 │ Float64 │ 2020-01-01 │ Date │
│ ᴺᵁᴸᴸ │ None │ 2020-01-02 │ Date │
│ 43.43 │ Float64 │ ᴺᵁᴸᴸ │ None │
└─────────────────────┴───────────────────────┴────────────────┴─────────────────────┘
```
`Dynamic` subcolumns can be casted to any data type. In this case the exception will be thrown if internal type inside `Dynamic` cannot be casted to the requested type:
```sql
select json.a.g::UInt64 as uint FROM test;
```
```text
┌─uint─┐
│ 42 │
│ 0 │
│ 43 │
└──────┘
```
```sql
select json.a.g::UUID as float FROM test;
```
```text
Received exception:
Code: 48. DB::Exception: Conversion between numeric types and UUID is not supported. Probably the passed UUID is unquoted: while executing 'FUNCTION CAST(__table1.json.a.g :: 2, 'UUID'_String :: 1) -> CAST(__table1.json.a.g, 'UUID'_String) UUID : 0'. (NOT_IMPLEMENTED)
```
## Reading JSON sub-objects as subcolumns
JSON type supports reading nested objects as subcolumns with type `JSON` using special syntax `json.^some.path`:
```sql
CREATE TABLE test (json JSON) ENGINE = Memory;
INSERT INTO test VALUES ('{"a" : {"b" : {"c" : 42, "g" : 42.42}}, "c" : [1, 2, 3], "d" : {"e" : {"f" : {"g" : "Hello, World", "h" : [1, 2, 3]}}}}'), ('{"f" : "Hello, World!", "d" : {"e" : {"f" : {"h" : [4, 5, 6]}}}}'), ('{"a" : {"b" : {"c" : 43, "e" : 10, "g" : 43.43}}, "c" : [4, 5, 6]}');
SELECT json FROM test;
```
```text
┌─json────────────────────────────────────────────────────────────────────────────────────────┐
│ {"a":{"b":{"c":42,"g":42.42}},"c":[1,2,3],"d":{"e":{"f":{"g":"Hello, World","h":[1,2,3]}}}} │
│ {"d":{"e":{"f":{"h":[4,5,6]}}},"f":"Hello, World!"} │
│ {"a":{"b":{"c":43,"e":10,"g":43.43}},"c":[4,5,6]} │
└─────────────────────────────────────────────────────────────────────────────────────────────┘
```
```sql
SELECT json.^a.b, json.^d.e.f FROM test;
```
```text
┌─json.^`a`.b───────────────┬─json.^`d`.e.f────────────────────┐
│ {"c":42,"g":42.42} │ {"g":"Hello, World","h":[1,2,3]} │
│ {} │ {"h":[4,5,6]} │
│ {"c":43,"e":10,"g":43.43} │ {} │
└───────────────────────────┴──────────────────────────────────┘
```
:::note
Reading sub-objects as subcolumns may be inefficient, as this may require almost full scan of the JSON data.
:::
## Types inference for paths
During JSON parsing ClickHouse tries to detect the most appropriate data type for each JSON path. It works similar to [automatic schema inference from input data](/docs/en/interfaces/schema-inference.md) and controlled by the same settings:
- [input_format_try_infer_integers](/docs/en/interfaces/schema-inference.md#inputformattryinferintegers)
- [input_format_try_infer_dates](/docs/en/interfaces/schema-inference.md#inputformattryinferdates)
- [input_format_try_infer_datetimes](/docs/en/interfaces/schema-inference.md#inputformattryinferdatetimes)
- [schema_inference_make_columns_nullable](/docs/en/interfaces/schema-inference.md#schemainferencemakecolumnsnullable)
- [input_format_json_try_infer_numbers_from_strings](/docs/en/interfaces/schema-inference.md#inputformatjsontryinfernumbersfromstrings)
- [input_format_json_infer_incomplete_types_as_strings](/docs/en/interfaces/schema-inference.md#inputformatjsoninferincompletetypesasstrings)
- [input_format_json_read_numbers_as_strings](/docs/en/interfaces/schema-inference.md#inputformatjsonreadnumbersasstrings)
- [input_format_json_read_bools_as_strings](/docs/en/interfaces/schema-inference.md#inputformatjsonreadboolsasstrings)
- [input_format_json_read_bools_as_numbers](/docs/en/interfaces/schema-inference.md#inputformatjsonreadboolsasnumbers)
- [input_format_json_read_arrays_as_strings](/docs/en/interfaces/schema-inference.md#inputformatjsonreadarraysasstrings)
Let's see some examples:
```sql
SELECT JSONAllPathsWithTypes('{"a" : "2020-01-01", "b" : "2020-01-01 10:00:00"}'::JSON) AS paths_with_types settings input_format_try_infer_dates=1, input_format_try_infer_datetimes=1;
```
```text
┌─paths_with_types─────────────────┐
│ {'a':'Date','b':'DateTime64(9)'} │
└──────────────────────────────────┘
```
```sql
SELECT JSONAllPathsWithTypes('{"a" : "2020-01-01", "b" : "2020-01-01 10:00:00"}'::JSON) AS paths_with_types settings input_format_try_infer_dates=0, input_format_try_infer_datetimes=0;
```
```text
┌─paths_with_types────────────┐
│ {'a':'String','b':'String'} │
└─────────────────────────────┘
```
```sql
SELECT JSONAllPathsWithTypes('{"a" : [1, 2, 3]}'::JSON) AS paths_with_types settings schema_inference_make_columns_nullable=1;
```
```text
┌─paths_with_types───────────────┐
│ {'a':'Array(Nullable(Int64))'} │
└────────────────────────────────┘
```
```sql
SELECT JSONAllPathsWithTypes('{"a" : [1, 2, 3]}'::JSON) AS paths_with_types settings schema_inference_make_columns_nullable=0;
```
```text
┌─paths_with_types─────┐
│ {'a':'Array(Int64)'} │
└──────────────────────┘
```
## Handling arrays of JSON objects
JSON paths that contains an array of objects are parsed as type `Array(JSON)` and inserted into `Dynamic` column for this path. To read an array of objects you can extract it from `Dynamic` column as a subcolumn:
```sql
CREATE TABLE test (json JSON) ENGINE = Memory;
INSERT INTO test VALUES
('{"a" : {"b" : [{"c" : 42, "d" : "Hello", "f" : [[{"g" : 42.42}]], "k" : {"j" : 1000}}, {"c" : 43}, {"e" : [1, 2, 3], "d" : "My", "f" : [[{"g" : 43.43, "h" : "2020-01-01"}]], "k" : {"j" : 2000}}]}}'),
('{"a" : {"b" : [1, 2, 3]}}'),
('{"a" : {"b" : [{"c" : 44, "f" : [[{"h" : "2020-01-02"}]]}, {"e" : [4, 5, 6], "d" : "World", "f" : [[{"g" : 44.44}]], "k" : {"j" : 3000}}]}}');
SELECT json FROM test;
```
```text3
┌─json────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ {"a":{"b":[{"c":"42","d":"Hello","f":[[{"g":42.42}]],"k":{"j":"1000"}},{"c":"43"},{"d":"My","e":["1","2","3"],"f":[[{"g":43.43,"h":"2020-01-01"}]],"k":{"j":"2000"}}]}} │
│ {"a":{"b":["1","2","3"]}} │
│ {"a":{"b":[{"c":"44","f":[[{"h":"2020-01-02"}]]},{"d":"World","e":["4","5","6"],"f":[[{"g":44.44}]],"k":{"j":"3000"}}]}} │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```
```sql
SELECT json.a.b, dynamicType(json.a.b) FROM test;
```
```text
┌─json.a.b──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬─dynamicType(json.a.b)────────────────────────────────────┐
│ ['{"c":"42","d":"Hello","f":[[{"g":42.42}]],"k":{"j":"1000"}}','{"c":"43"}','{"d":"My","e":["1","2","3"],"f":[[{"g":43.43,"h":"2020-01-01"}]],"k":{"j":"2000"}}'] │ Array(JSON(max_dynamic_types=16, max_dynamic_paths=256)) │
│ [1,2,3] │ Array(Nullable(Int64)) │
│ ['{"c":"44","f":[[{"h":"2020-01-02"}]]}','{"d":"World","e":["4","5","6"],"f":[[{"g":44.44}]],"k":{"j":"3000"}}'] │ Array(JSON(max_dynamic_types=16, max_dynamic_paths=256)) │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴──────────────────────────────────────────────────────────┘
```
As you can notice, the `max_dynamic_types/max_dynamic_paths` parameters of the nested `JSON` type were reduced compared to the default values. It's needed to avoid number of subcolumns to grow uncontrolled on nested arrays of JSON objects.
Let's try to read subcolumns from this nested `JSON` column:
```sql
SELECT json.a.b.:`Array(JSON)`.c, json.a.b.:`Array(JSON)`.f, json.a.b.:`Array(JSON)`.d FROM test;
```
```text
┌─json.a.b.:`Array(JSON)`.c─┬─json.a.b.:`Array(JSON)`.f───────────────────────────────────┬─json.a.b.:`Array(JSON)`.d─┐
│ [42,43,NULL] │ [[['{"g":42.42}']],NULL,[['{"g":43.43,"h":"2020-01-01"}']]] │ ['Hello',NULL,'My'] │
│ [] │ [] │ [] │
│ [44,NULL] │ [[['{"h":"2020-01-02"}']],[['{"g":44.44}']]] │ [NULL,'World'] │
└───────────────────────────┴─────────────────────────────────────────────────────────────┴───────────────────────────┘
```
We can avoid writing `Array(JSON)` subcolumn name using special syntax:
```sql
SELECT json.a.b[].c, json.a.b[].f, json.a.b[].d FROM test;
```
```text
┌─json.a.b.:`Array(JSON)`.c─┬─json.a.b.:`Array(JSON)`.f───────────────────────────────────┬─json.a.b.:`Array(JSON)`.d─┐
│ [42,43,NULL] │ [[['{"g":42.42}']],NULL,[['{"g":43.43,"h":"2020-01-01"}']]] │ ['Hello',NULL,'My'] │
│ [] │ [] │ [] │
│ [44,NULL] │ [[['{"h":"2020-01-02"}']],[['{"g":44.44}']]] │ [NULL,'World'] │
└───────────────────────────┴─────────────────────────────────────────────────────────────┴───────────────────────────┘
```
The number of `[]` after path indicates the array level. `json.path[][]` will be transformed to `json.path.:Array(Array(JSON))`
Let's check the paths and types inside our `Array(JSON)`:
```sql
SELECT DISTINCT arrayJoin(JSONAllPathsWithTypes(arrayJoin(json.a.b[]))) FROM test;
```
```text
┌─arrayJoin(JSONAllPathsWithTypes(arrayJoin(json.a.b.:`Array(JSON)`)))──┐
│ ('c','Int64') │
│ ('d','String') │
│ ('f','Array(Array(JSON(max_dynamic_types=8, max_dynamic_paths=64)))') │
│ ('k.j','Int64') │
│ ('e','Array(Nullable(Int64))') │
└───────────────────────────────────────────────────────────────────────┘
```
Let's read subcolumns from `Array(JSON)` column:
```sql
SELECT json.a.b[].c.:Int64, json.a.b[].f[][].g.:Float64, json.a.b[].f[][].h.:Date FROM test;
```
```text
┌─json.a.b.:`Array(JSON)`.c.:`Int64`─┬─json.a.b.:`Array(JSON)`.f.:`Array(Array(JSON))`.g.:`Float64`─┬─json.a.b.:`Array(JSON)`.f.:`Array(Array(JSON))`.h.:`Date`─┐
│ [42,43,NULL] │ [[[42.42]],[],[[43.43]]] │ [[[NULL]],[],[['2020-01-01']]] │
│ [] │ [] │ [] │
│ [44,NULL] │ [[[NULL]],[[44.44]]] │ [[['2020-01-02']],[[NULL]]] │
└────────────────────────────────────┴──────────────────────────────────────────────────────────────┴───────────────────────────────────────────────────────────┘
```
We can also read sub-object subcolumns from nested `JSON` column:
```sql
SELECT json.a.b[].^k FROM test
```
```text
┌─json.a.b.:`Array(JSON)`.^`k`─────────┐
│ ['{"j":"1000"}','{}','{"j":"2000"}'] │
│ [] │
│ ['{}','{"j":"3000"}'] │
└──────────────────────────────────────┘
```
## Reading JSON type from the data
All text formats (JSONEachRow, TSV, CSV, CustomSeparated, Values, etc) supports reading `JSON` type.
Examples:
```sql
SELECT json FROM format(JSONEachRow, 'json JSON(a.b.c UInt32, SKIP a.b.d, SKIP d.e, SKIP REGEXP \'b.*\')', '
{"json" : {"a" : {"b" : {"c" : 1, "d" : [0, 1]}}, "b" : "2020-01-01", "c" : 42, "d" : {"e" : {"f" : ["s1", "s2"]}, "i" : [1, 2, 3]}}}
{"json" : {"a" : {"b" : {"c" : 2, "d" : [2, 3]}}, "b" : [1, 2, 3], "c" : null, "d" : {"e" : {"g" : 43}, "i" : [4, 5, 6]}}}
{"json" : {"a" : {"b" : {"c" : 3, "d" : [4, 5]}}, "b" : {"c" : 10}, "e" : "Hello, World!"}}
{"json" : {"a" : {"b" : {"c" : 4, "d" : [6, 7]}}, "c" : 43}}
{"json" : {"a" : {"b" : {"c" : 5, "d" : [8, 9]}}, "b" : {"c" : 11, "j" : [1, 2, 3]}, "d" : {"e" : {"f" : ["s3", "s4"], "g" : 44}, "h" : "2020-02-02 10:00:00"}}}
')
```
```text
┌─json──────────────────────────────────────────────────────────┐
│ {"a":{"b":{"c":1}},"c":"42","d":{"i":["1","2","3"]}} │
│ {"a":{"b":{"c":2}},"d":{"i":["4","5","6"]}} │
│ {"a":{"b":{"c":3}},"e":"Hello, World!"} │
│ {"a":{"b":{"c":4}},"c":"43"} │
│ {"a":{"b":{"c":5}},"d":{"h":"2020-02-02 10:00:00.000000000"}} │
└───────────────────────────────────────────────────────────────┘
```
For text formats like CSV/TSV/etc `JSON` is parsed from a string containing JSON object
```sql
SELECT json FROM format(TSV, 'json JSON(a.b.c UInt32, SKIP a.b.d, SKIP REGEXP \'b.*\')',
'{"a" : {"b" : {"c" : 1, "d" : [0, 1]}}, "b" : "2020-01-01", "c" : 42, "d" : {"e" : {"f" : ["s1", "s2"]}, "i" : [1, 2, 3]}}
{"a" : {"b" : {"c" : 2, "d" : [2, 3]}}, "b" : [1, 2, 3], "c" : null, "d" : {"e" : {"g" : 43}, "i" : [4, 5, 6]}}
{"a" : {"b" : {"c" : 3, "d" : [4, 5]}}, "b" : {"c" : 10}, "e" : "Hello, World!"}
{"a" : {"b" : {"c" : 4, "d" : [6, 7]}}, "c" : 43}
{"a" : {"b" : {"c" : 5, "d" : [8, 9]}}, "b" : {"c" : 11, "j" : [1, 2, 3]}, "d" : {"e" : {"f" : ["s3", "s4"], "g" : 44}, "h" : "2020-02-02 10:00:00"}}')
```
```text
┌─json──────────────────────────────────────────────────────────┐
│ {"a":{"b":{"c":1}},"c":"42","d":{"i":["1","2","3"]}} │
│ {"a":{"b":{"c":2}},"d":{"i":["4","5","6"]}} │
│ {"a":{"b":{"c":3}},"e":"Hello, World!"} │
│ {"a":{"b":{"c":4}},"c":"43"} │
│ {"a":{"b":{"c":5}},"d":{"h":"2020-02-02 10:00:00.000000000"}} │
└───────────────────────────────────────────────────────────────┘
```
## Reaching the limit of dynamic paths inside JSON
`JSON` data type can store only limited number of paths as separate subcolumns inside. By default, this limit is 1024, but you can change it in type declaration using parameter `max_dynamic_paths`.
When the limit is reached, all new paths inserted to `JSON` column will be stored in a single shared data structure. It's still possible to read such paths as subcolumns, but it will require reading the whole
shared data structure to extract the values of this path. This limit is needed to avoid the enormous number of different subcolumns that can make the table unusable.
Let's see what happens when the limit is reached in different scenarios.
### Reaching the limit during data parsing
During parsing of `JSON` object from the data, when the limit is reached for current block of data, all new paths will be stored in a shared data structure. We can check it using introspection functions `JSONDynamicPaths, JSONSharedDataPaths`:
```sql
SELECT json, JSONDynamicPaths(json), JSONSharedDataPaths(json) FROM format(JSONEachRow, 'json JSON(max_dynamic_paths=3)', '
{"json" : {"a" : {"b" : 42}, "c" : [1, 2, 3]}}
{"json" : {"a" : {"b" : 43}, "d" : "2020-01-01"}}
{"json" : {"a" : {"b" : 44}, "c" : [4, 5, 6]}}
{"json" : {"a" : {"b" : 43}, "d" : "2020-01-02", "e" : "Hello", "f" : {"g" : 42.42}}}
{"json" : {"a" : {"b" : 43}, "c" : [7, 8, 9], "f" : {"g" : 43.43}, "h" : "World"}}
')
```
```text
┌─json───────────────────────────────────────────────────────────┬─JSONDynamicPaths(json)─┬─JSONSharedDataPaths(json)─┐
│ {"a":{"b":"42"},"c":["1","2","3"]} │ ['a.b','c','d'] │ [] │
│ {"a":{"b":"43"},"d":"2020-01-01"} │ ['a.b','c','d'] │ [] │
│ {"a":{"b":"44"},"c":["4","5","6"]} │ ['a.b','c','d'] │ [] │
│ {"a":{"b":"43"},"d":"2020-01-02","e":"Hello","f":{"g":42.42}} │ ['a.b','c','d'] │ ['e','f.g'] │
│ {"a":{"b":"43"},"c":["7","8","9"],"f":{"g":43.43},"h":"World"} │ ['a.b','c','d'] │ ['f.g','h'] │
└────────────────────────────────────────────────────────────────┴────────────────────────┴───────────────────────────┘
```
As we can see, after inserting paths `e` and `f.g` the limit was reached and we inserted them into shared data structure.
### During merges of data parts in MergeTree table engines
During merge of several data parts in MergeTree table the `JSON` column in the resulting data part can reach the limit of dynamic paths won't be able to store all paths from source parts as subcolumns.
In this case ClickHouse chooses what paths will remain as subcolumns after merge and what types will be stored in the shared data structure. In most cases ClickHouse tries to keep paths that contains
the largest number of non-null values and move the rarest paths to the shared data structure, but it depends on the implementation.
Let's see an example of such merge. First, let's create a table with `JSON` column, set the limit of dynamic paths to `3` and insert values with `5` different paths:
```sql
CREATE TABLE test (id UInt64, json JSON(max_dynamic_paths=3)) engine=MergeTree ORDER BY id;
SYSTEM STOP MERGES test;
INSERT INTO test SELECT number, formatRow('JSONEachRow', number as a) FROM numbers(5);
INSERT INTO test SELECT number, formatRow('JSONEachRow', number as b) FROM numbers(4);
INSERT INTO test SELECT number, formatRow('JSONEachRow', number as c) FROM numbers(3);
INSERT INTO test SELECT number, formatRow('JSONEachRow', number as d) FROM numbers(2);
INSERT INTO test SELECT number, formatRow('JSONEachRow', number as e) FROM numbers(1);
```
Each insert will create a separate data pert with `JSON` column containing single path:
```sql
SELECT count(), JSONDynamicPaths(json) AS dynamic_paths, JSONSharedDataPaths(json) AS shared_data_paths, _part FROM test GROUP BY _part, dynamic_paths, shared_data_paths ORDER BY _part ASC
```
```text
┌─count()─┬─dynamic_paths─┬─shared_data_paths─┬─_part─────┐
│ 5 │ ['a'] │ [] │ all_1_1_0 │
│ 4 │ ['b'] │ [] │ all_2_2_0 │
│ 3 │ ['c'] │ [] │ all_3_3_0 │
│ 2 │ ['d'] │ [] │ all_4_4_0 │
│ 1 │ ['e'] │ [] │ all_5_5_0 │
└─────────┴───────────────┴───────────────────┴───────────┘
```
Now, let's merge all parts into one and see what will happen:
```sql
SYSTEM START MERGES test;
OPTIMIZE TABLE test FINAL;
SELECT count(), dynamicType(d), _part FROM test GROUP BY _part, dynamicType(d) ORDER BY _part;
```
```text
┌─count()─┬─dynamic_paths─┬─shared_data_paths─┬─_part─────┐
│ 1 │ ['a','b','c'] │ ['e'] │ all_1_5_2 │
│ 2 │ ['a','b','c'] │ ['d'] │ all_1_5_2 │
│ 12 │ ['a','b','c'] │ [] │ all_1_5_2 │
└─────────┴───────────────┴───────────────────┴───────────┘
```
As we can see, ClickHouse kept the most frequent paths `a`, `b` and `c` and moved paths `e` and `d` to shared data structure.
## Introspection functions
There are several functions that can help to inspect the content of the JSON column: [JSONAllPaths](../functions/json-functions.md#jsonallpaths), [JSONAllPathsWithTypes](../functions/json-functions.md#jsonallpathswithtypes), [JSONDynamicPaths](../functions/json-functions.md#jsondynamicpaths), [JSONDynamicPathsWithTypes](../functions/json-functions.md#jsondynamicpathswithtypes), [JSONSharedDataPaths](../functions/json-functions.md#jsonshareddatapaths), [JSONSharedDataPathsWithTypes](../functions/json-functions.md#jsonshareddatapathswithtypes).
## Tips for better usage of the JSON type
Before creating `JSON` column and loading data into it, consider the following tips:
- Investigate your data and specify as many path hints with types as you can. It will make the storage and the reading much more efficient.
- Think about what paths you will need and what paths you will never need. Specify paths that you won't need in the SKIP section and SKIP REGEXP if needed. It will improve the storage.
- Don't set `max_dynamic_paths` parameter to very high values, it can make the storage and reading less efficient.

42
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View File

@ -6,11 +6,13 @@ title: "Functions for Working with Polygons"
## WKT
Returns a WKT (Well Known Text) geometric object from various [Geo Data Types](../../data-types/geo.md). Supported WKT objects are:
Returns a WKT (Well Known Text) geometric object from various [Geo Data Types](../../data-types/geo.md). Supported WKT objects are:
- POINT
- POLYGON
- MULTIPOLYGON
- LINESTRING
- MULTILINESTRING
**Syntax**
@ -26,12 +28,16 @@ WKT(geo_data)
- [Ring](../../data-types/geo.md#ring)
- [Polygon](../../data-types/geo.md#polygon)
- [MultiPolygon](../../data-types/geo.md#multipolygon)
- [LineString](../../data-types/geo.md#linestring)
- [MultiLineString](../../data-types/geo.md#multilinestring)
**Returned value**
- WKT geometric object `POINT` is returned for a Point.
- WKT geometric object `POLYGON` is returned for a Polygon
- WKT geometric object `MULTIPOLYGON` is returned for a MultiPolygon.
- WKT geometric object `MULTIPOLYGON` is returned for a MultiPolygon.
- WKT geometric object `LINESTRING` is returned for a LineString.
- WKT geometric object `MULTILINESTRING` is returned for a MultiLineString.
**Examples**
@ -84,7 +90,7 @@ SELECT
### Input parameters
String starting with `MULTIPOLYGON`
String starting with `MULTIPOLYGON`
### Returned value
@ -170,6 +176,34 @@ SELECT readWKTLineString('LINESTRING (1 1, 2 2, 3 3, 1 1)');
[(1,1),(2,2),(3,3),(1,1)]
```
## readWKTMultiLineString
Parses a Well-Known Text (WKT) representation of a MultiLineString geometry and returns it in the internal ClickHouse format.
### Syntax
```sql
readWKTMultiLineString(wkt_string)
```
### Arguments
- `wkt_string`: The input WKT string representing a MultiLineString geometry.
### Returned value
The function returns a ClickHouse internal representation of the multilinestring geometry.
### Example
```sql
SELECT readWKTMultiLineString('MULTILINESTRING ((1 1, 2 2, 3 3), (4 4, 5 5, 6 6))');
```
```response
[[(1,1),(2,2),(3,3)],[(4,4),(5,5),(6,6)]]
```
## readWKTRing
Parses a Well-Known Text (WKT) representation of a Polygon geometry and returns a ring (closed linestring) in the internal ClickHouse format.
@ -219,7 +253,7 @@ UInt8, 0 for false, 1 for true
## polygonsDistanceSpherical
Calculates the minimal distance between two points where one point belongs to the first polygon and the second to another polygon. Spherical means that coordinates are interpreted as coordinates on a pure and ideal sphere, which is not true for the Earth. Using this type of coordinate system speeds up execution, but of course is not precise.
Calculates the minimal distance between two points where one point belongs to the first polygon and the second to another polygon. Spherical means that coordinates are interpreted as coordinates on a pure and ideal sphere, which is not true for the Earth. Using this type of coordinate system speeds up execution, but of course is not precise.
### Example

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View File

@ -1155,3 +1155,207 @@ SELECT jsonMergePatch('{"a":1}', '{"name": "joey"}', '{"name": "tom"}', '{"name"
│ {"a":1,"name":"zoey"} │
└───────────────────────┘
```
### JSONAllPaths
Returns the list of all paths stored in each row in [JSON](../data-types/newjson.md) column.
**Syntax**
``` sql
JSONAllPaths(json)
```
**Arguments**
- `json` — [JSON](../data-types/newjson.md).
**Returned value**
- An array of paths. [Array(String)](../data-types/array.md).
**Example**
``` sql
CREATE TABLE test (json JSON(max_dynamic_paths=1)) ENGINE = Memory;
INSERT INTO test FORMAT JSONEachRow {"json" : {"a" : 42}}, {"json" : {"b" : "Hello"}}, {"json" : {"a" : [1, 2, 3], "c" : "2020-01-01"}}
SELECT json, JSONAllPaths(json) FROM test;
```
```text
┌─json─────────────────────────────────┬─JSONAllPaths(json)─┐
│ {"a":"42"} │ ['a'] │
│ {"b":"Hello"} │ ['b'] │
│ {"a":["1","2","3"],"c":"2020-01-01"} │ ['a','c'] │
└──────────────────────────────────────┴────────────────────┘
```
### JSONAllPathsWithTypes
Returns the map of all paths and their data types stored in each row in [JSON](../data-types/newjson.md) column.
**Syntax**
``` sql
JSONAllPathsWithTypes(json)
```
**Arguments**
- `json` — [JSON](../data-types/newjson.md).
**Returned value**
- An array of paths. [Map(String, String)](../data-types/array.md).
**Example**
``` sql
CREATE TABLE test (json JSON(max_dynamic_paths=1)) ENGINE = Memory;
INSERT INTO test FORMAT JSONEachRow {"json" : {"a" : 42}}, {"json" : {"b" : "Hello"}}, {"json" : {"a" : [1, 2, 3], "c" : "2020-01-01"}}
SELECT json, JSONAllPathsWithTypes(json) FROM test;
```
```text
┌─json─────────────────────────────────┬─JSONAllPathsWithTypes(json)───────────────┐
│ {"a":"42"} │ {'a':'Int64'} │
│ {"b":"Hello"} │ {'b':'String'} │
│ {"a":["1","2","3"],"c":"2020-01-01"} │ {'a':'Array(Nullable(Int64))','c':'Date'} │
└──────────────────────────────────────┴───────────────────────────────────────────┘
```
### JSONDynamicPaths
Returns the list of dynamic paths that are stored as separate subcolumns in [JSON](../data-types/newjson.md) column.
**Syntax**
``` sql
JSONDynamicPaths(json)
```
**Arguments**
- `json` — [JSON](../data-types/newjson.md).
**Returned value**
- An array of paths. [Array(String)](../data-types/array.md).
**Example**
``` sql
CREATE TABLE test (json JSON(max_dynamic_paths=1)) ENGINE = Memory;
INSERT INTO test FORMAT JSONEachRow {"json" : {"a" : 42}}, {"json" : {"b" : "Hello"}}, {"json" : {"a" : [1, 2, 3], "c" : "2020-01-01"}}
SELECT json, JSONDynamicPaths(json) FROM test;
```
```text
┌─json─────────────────────────────────┬─JSONDynamicPaths(json)─┐
| {"a":"42"} │ ['a'] │
│ {"b":"Hello"} │ [] │
│ {"a":["1","2","3"],"c":"2020-01-01"} │ ['a'] │
└──────────────────────────────────────┴────────────────────────┘
```
### JSONDynamicPathsWithTypes
Returns the map of dynamic paths that are stored as separate subcolumns and their types in each row in [JSON](../data-types/newjson.md) column.
**Syntax**
``` sql
JSONAllPathsWithTypes(json)
```
**Arguments**
- `json` — [JSON](../data-types/newjson.md).
**Returned value**
- An array of paths. [Map(String, String)](../data-types/array.md).
**Example**
``` sql
CREATE TABLE test (json JSON(max_dynamic_paths=1)) ENGINE = Memory;
INSERT INTO test FORMAT JSONEachRow {"json" : {"a" : 42}}, {"json" : {"b" : "Hello"}}, {"json" : {"a" : [1, 2, 3], "c" : "2020-01-01"}}
SELECT json, JSONDynamicPathsWithTypes(json) FROM test;
```
```text
┌─json─────────────────────────────────┬─JSONDynamicPathsWithTypes(json)─┐
│ {"a":"42"} │ {'a':'Int64'} │
│ {"b":"Hello"} │ {} │
│ {"a":["1","2","3"],"c":"2020-01-01"} │ {'a':'Array(Nullable(Int64))'} │
└──────────────────────────────────────┴─────────────────────────────────┘
```
### JSONSharedDataPaths
Returns the list of paths that are stored in shared data structure in [JSON](../data-types/newjson.md) column.
**Syntax**
``` sql
JSONSharedDataPaths(json)
```
**Arguments**
- `json` — [JSON](../data-types/newjson.md).
**Returned value**
- An array of paths. [Array(String)](../data-types/array.md).
**Example**
``` sql
CREATE TABLE test (json JSON(max_dynamic_paths=1)) ENGINE = Memory;
INSERT INTO test FORMAT JSONEachRow {"json" : {"a" : 42}}, {"json" : {"b" : "Hello"}}, {"json" : {"a" : [1, 2, 3], "c" : "2020-01-01"}}
SELECT json, JSONSharedDataPaths(json) FROM test;
```
```text
┌─json─────────────────────────────────┬─JSONSharedDataPaths(json)─┐
│ {"a":"42"} │ [] │
│ {"b":"Hello"} │ ['b'] │
│ {"a":["1","2","3"],"c":"2020-01-01"} │ ['c'] │
└──────────────────────────────────────┴───────────────────────────┘
```
### JSONSharedDataPathsWithTypes
Returns the map of paths that are stored in shared data structure and their types in each row in [JSON](../data-types/newjson.md) column.
**Syntax**
``` sql
JSONSharedDataPathsWithTypes(json)
```
**Arguments**
- `json` — [JSON](../data-types/newjson.md).
**Returned value**
- An array of paths. [Map(String, String)](../data-types/array.md).
**Example**
``` sql
CREATE TABLE test (json JSON(max_dynamic_paths=1)) ENGINE = Memory;
INSERT INTO test FORMAT JSONEachRow {"json" : {"a" : 42}}, {"json" : {"b" : "Hello"}}, {"json" : {"a" : [1, 2, 3], "c" : "2020-01-01"}}
SELECT json, JSONSharedDataPathsWithTypes(json) FROM test;
```
```text
┌─json─────────────────────────────────┬─JSONSharedDataPathsWithTypes(json)─┐
│ {"a":"42"} │ {} │
│ {"b":"Hello"} │ {'b':'String'} │
│ {"a":["1","2","3"],"c":"2020-01-01"} │ {'c':'Date'} │
└──────────────────────────────────────┴────────────────────────────────────┘
```

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@ -4189,3 +4189,94 @@ Result:
│ 32 │
└─────────────────────────────┘
```
## getSubcolumn
Takes a table expression or identifier and constant string with the name of the sub-column, and returns the requested sub-column extracted from the expression.
**Syntax**
```sql
getSubcolumn(col_name, subcol_name)
```
**Arguments**
- `col_name` — Table expression or identifier. [Expression](../syntax.md/#expressions), [Identifier](../syntax.md/#identifiers).
- `subcol_name` — The name of the sub-column. [String](../data-types/string.md).
**Returned value**
- Returns the extracted sub-column.
**Example**
Query:
```sql
CREATE TABLE t_arr (arr Array(Tuple(subcolumn1 UInt32, subcolumn2 String))) ENGINE = MergeTree ORDER BY tuple();
INSERT INTO t_arr VALUES ([(1, 'Hello'), (2, 'World')]), ([(3, 'This'), (4, 'is'), (5, 'subcolumn')]);
SELECT getSubcolumn(arr, 'subcolumn1'), getSubcolumn(arr, 'subcolumn2') FROM t_arr;
```
Result:
```response
┌─getSubcolumn(arr, 'subcolumn1')─┬─getSubcolumn(arr, 'subcolumn2')─┐
1. │ [1,2] │ ['Hello','World'] │
2. │ [3,4,5] │ ['This','is','subcolumn'] │
└─────────────────────────────────┴─────────────────────────────────┘
```
## getTypeSerializationStreams
Enumerates stream paths of a data type.
:::note
This function is intended for use by developers.
:::
**Syntax**
```sql
getTypeSerializationStreams(col)
```
**Arguments**
- `col` — Column or string representation of a data-type from which the data type will be detected.
**Returned value**
- Returns an array with all the serialization sub-stream paths.[Array](../data-types/array.md)([String](../data-types/string.md)).
**Examples**
Query:
```sql
SELECT getTypeSerializationStreams(tuple('a', 1, 'b', 2));
```
Result:
```response
┌─getTypeSerializationStreams(('a', 1, 'b', 2))─────────────────────────────────────────────────────────────────────────┐
1. │ ['{TupleElement(1), Regular}','{TupleElement(2), Regular}','{TupleElement(3), Regular}','{TupleElement(4), Regular}'] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```
Query:
```sql
SELECT getTypeSerializationStreams('Map(String, Int64)');
```
Result:
```response
┌─getTypeSerializationStreams('Map(String, Int64)')────────────────────────────────────────────────────────────────┐
1. │ ['{ArraySizes}','{ArrayElements, TupleElement(keys), Regular}','{ArrayElements, TupleElement(values), Regular}'] │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```

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@ -9,6 +9,7 @@ The following operations with [partitions](/docs/en/engines/table-engines/merget
- [DETACH PARTITION\|PART](#detach-partitionpart) — Moves a partition or part to the `detached` directory and forget it.
- [DROP PARTITION\|PART](#drop-partitionpart) — Deletes a partition or part.
- [DROP DETACHED PARTITION\|PART](#drop-detached-partitionpart) - Delete a part or all parts of a partition from `detached`.
- [FORGET PARTITION](#forget-partition) — Deletes a partition metadata from zookeeper if it's empty.
- [ATTACH PARTITION\|PART](#attach-partitionpart) — Adds a partition or part from the `detached` directory to the table.
- [ATTACH PARTITION FROM](#attach-partition-from) — Copies the data partition from one table to another and adds.
@ -68,7 +69,7 @@ ALTER TABLE mt DROP PART 'all_4_4_0';
## DROP DETACHED PARTITION\|PART
``` sql
ALTER TABLE table_name [ON CLUSTER cluster] DROP DETACHED PARTITION|PART partition_expr
ALTER TABLE table_name [ON CLUSTER cluster] DROP DETACHED PARTITION|PART ALL|partition_expr
```
Removes the specified part or all parts of the specified partition from `detached`.

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@ -8,26 +8,28 @@ sidebar_label: STATISTICS
The following operations are available:
- `ALTER TABLE [db].table ADD STATISTICS (columns list) TYPE (type list)` - Adds statistic description to tables metadata.
- `ALTER TABLE [db].table ADD STATISTICS [IF NOT EXISTS] (column list) TYPE (type list)` - Adds statistic description to tables metadata.
- `ALTER TABLE [db].table MODIFY STATISTICS (columns list) TYPE (type list)` - Modifies statistic description to tables metadata.
- `ALTER TABLE [db].table MODIFY STATISTICS (column list) TYPE (type list)` - Modifies statistic description to tables metadata.
- `ALTER TABLE [db].table DROP STATISTICS (columns list)` - Removes statistics from the metadata of the specified columns and deletes all statistics objects in all parts for the specified columns.
- `ALTER TABLE [db].table DROP STATISTICS [IF EXISTS] (column list)` - Removes statistics from the metadata of the specified columns and deletes all statistics objects in all parts for the specified columns.
- `ALTER TABLE [db].table CLEAR STATISTICS (columns list)` - Deletes all statistics objects in all parts for the specified columns. Statistics objects can be rebuild using `ALTER TABLE MATERIALIZE STATISTICS`.
- `ALTER TABLE [db].table CLEAR STATISTICS [IF EXISTS] (column list)` - Deletes all statistics objects in all parts for the specified columns. Statistics objects can be rebuild using `ALTER TABLE MATERIALIZE STATISTICS`.
- `ALTER TABLE [db.]table MATERIALIZE STATISTICS (columns list)` - Rebuilds the statistic for columns. Implemented as a [mutation](../../../sql-reference/statements/alter/index.md#mutations).
- `ALTER TABLE [db.]table MATERIALIZE STATISTICS [IF EXISTS] (column list)` - Rebuilds the statistic for columns. Implemented as a [mutation](../../../sql-reference/statements/alter/index.md#mutations).
The first two commands are lightweight in a sense that they only change metadata or remove files.
Also, they are replicated, syncing statistics metadata via ZooKeeper.
There is an example adding two statistics types to two columns:
## Example:
Adding two statistics types to two columns:
```
ALTER TABLE t1 MODIFY STATISTICS c, d TYPE TDigest, Uniq;
```
:::note
Statistic manipulation is supported only for tables with [`*MergeTree`](../../../engines/table-engines/mergetree-family/mergetree.md) engine (including [replicated](../../../engines/table-engines/mergetree-family/replication.md) variants).
Statistic are supported only for [`*MergeTree`](../../../engines/table-engines/mergetree-family/mergetree.md) engine tables (including [replicated](../../../engines/table-engines/mergetree-family/replication.md) variants).
:::

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@ -13,8 +13,8 @@ Creates a new view. Views can be [normal](#normal-view), [materialized](#materia
Syntax:
``` sql
CREATE [OR REPLACE] VIEW [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster_name]
[DEFINER = { user | CURRENT_USER }] [SQL SECURITY { DEFINER | INVOKER | NONE }]
CREATE [OR REPLACE] VIEW [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster_name]
[DEFINER = { user | CURRENT_USER }] [SQL SECURITY { DEFINER | INVOKER | NONE }]
AS SELECT ...
[COMMENT 'comment']
```
@ -55,8 +55,8 @@ SELECT * FROM view(column1=value1, column2=value2 ...)
## Materialized View
``` sql
CREATE MATERIALIZED VIEW [IF NOT EXISTS] [db.]table_name [ON CLUSTER] [TO[db.]name] [ENGINE = engine] [POPULATE]
[DEFINER = { user | CURRENT_USER }] [SQL SECURITY { DEFINER | INVOKER | NONE }]
CREATE MATERIALIZED VIEW [IF NOT EXISTS] [db.]table_name [ON CLUSTER] [TO[db.]name] [ENGINE = engine] [POPULATE]
[DEFINER = { user | CURRENT_USER }] [SQL SECURITY { DEFINER | INVOKER | NONE }]
AS SELECT ...
[COMMENT 'comment']
```
@ -92,7 +92,7 @@ Given that `POPULATE` works like `CREATE TABLE ... AS SELECT ...` it has limitat
- It is not supported with Replicated database
- It is not supported in ClickHouse cloud
Instead a separate `INSERT ... SELECT` can be used.
Instead a separate `INSERT ... SELECT` can be used.
:::
A `SELECT` query can contain `DISTINCT`, `GROUP BY`, `ORDER BY`, `LIMIT`. Note that the corresponding conversions are performed independently on each block of inserted data. For example, if `GROUP BY` is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won’t be further aggregated. The exception is when using an `ENGINE` that independently performs data aggregation, such as `SummingMergeTree`.
@ -110,7 +110,7 @@ To delete a view, use [DROP VIEW](../../../sql-reference/statements/drop.md#drop
`DEFINER` and `SQL SECURITY` allow you to specify which ClickHouse user to use when executing the view's underlying query.
`SQL SECURITY` has three legal values: `DEFINER`, `INVOKER`, or `NONE`. You can specify any existing user or `CURRENT_USER` in the `DEFINER` clause.
The following table will explain which rights are required for which user in order to select from view.
The following table will explain which rights are required for which user in order to select from view.
Note that regardless of the SQL security option, in every case it is still required to have `GRANT SELECT ON <view>` in order to read from it.
| SQL security option | View | Materialized View |
@ -130,7 +130,7 @@ If `DEFINER`/`SQL SECURITY` aren't specified, the default values are used:
If a view is attached without `DEFINER`/`SQL SECURITY` specified, the default value is `SQL SECURITY NONE` for the materialized view and `SQL SECURITY INVOKER` for the normal view.
To change SQL security for an existing view, use
To change SQL security for an existing view, use
```sql
ALTER TABLE MODIFY SQL SECURITY { DEFINER | INVOKER | NONE } [DEFINER = { user | CURRENT_USER }]
```
@ -161,6 +161,8 @@ CREATE MATERIALIZED VIEW [IF NOT EXISTS] [db.]table_name
REFRESH EVERY|AFTER interval [OFFSET interval]
RANDOMIZE FOR interval
DEPENDS ON [db.]name [, [db.]name [, ...]]
SETTINGS name = value [, name = value [, ...]]
[APPEND]
[TO[db.]name] [(columns)] [ENGINE = engine] [EMPTY]
AS SELECT ...
[COMMENT 'comment']
@ -170,18 +172,23 @@ where `interval` is a sequence of simple intervals:
number SECOND|MINUTE|HOUR|DAY|WEEK|MONTH|YEAR
```
Periodically runs the corresponding query and stores its result in a table, atomically replacing the table's previous contents.
Periodically runs the corresponding query and stores its result in a table.
* If the query says `APPEND`, each refresh inserts rows into the table without deleting existing rows. The insert is not atomic, just like a regular INSERT SELECT.
* Otherwise each refresh atomically replaces the table's previous contents.
Differences from regular non-refreshable materialized views:
* No insert trigger. I.e. when new data is inserted into the table specified in SELECT, it's *not* automatically pushed to the refreshable materialized view. The periodic refresh runs the entire query and replaces the entire table.
* No insert trigger. I.e. when new data is inserted into the table specified in SELECT, it's *not* automatically pushed to the refreshable materialized view. The periodic refresh runs the entire query.
* No restrictions on the SELECT query. Table functions (e.g. `url()`), views, UNION, JOIN, are all allowed.
:::note
The settings in the `REFRESH ... SETTINGS` part of the query are refresh settings (e.g. `refresh_retries`), distinct from regular settings (e.g. `max_threads`). Regular settings can be specified using `SETTINGS` at the end of the query.
:::
:::note
Refreshable materialized views are a work in progress. Setting `allow_experimental_refreshable_materialized_view = 1` is required for creating one. Current limitations:
* not compatible with Replicated database or table engines
* It is not supported in ClickHouse Cloud
* require [Atomic database engine](../../../engines/database-engines/atomic.md),
* no retries for failed refresh - we just skip to the next scheduled refresh time,
* no limit on number of concurrent refreshes.
:::
@ -246,15 +253,22 @@ A few more examples:
`DEPENDS ON` only works between refreshable materialized views. Listing a regular table in the `DEPENDS ON` list will prevent the view from ever refreshing (dependencies can be removed with `ALTER`, see below).
:::
### Settings
Available refresh settings:
* `refresh_retries` - How many times to retry if refresh query fails with an exception. If all retries fail, skip to the next scheduled refresh time. 0 means no retries, -1 means infinite retries. Default: 0.
* `refresh_retry_initial_backoff_ms` - Delay before the first retry, if `refresh_retries` is not zero. Each subsequent retry doubles the delay, up to `refresh_retry_max_backoff_ms`. Default: 100 ms.
* `refresh_retry_max_backoff_ms` - Limit on the exponential growth of delay between refresh attempts. Default: 60000 ms (1 minute).
### Changing Refresh Parameters {#changing-refresh-parameters}
To change refresh parameters:
```
ALTER TABLE [db.]name MODIFY REFRESH EVERY|AFTER ... [RANDOMIZE FOR ...] [DEPENDS ON ...]
ALTER TABLE [db.]name MODIFY REFRESH EVERY|AFTER ... [RANDOMIZE FOR ...] [DEPENDS ON ...] [SETTINGS ...]
```
:::note
This replaces refresh schedule *and* dependencies. If the table had a `DEPENDS ON`, doing a `MODIFY REFRESH` without `DEPENDS ON` will remove the dependencies.
This replaces *all* refresh parameters at once: schedule, dependencies, settings, and APPEND-ness. E.g. if the table had a `DEPENDS ON`, doing a `MODIFY REFRESH` without `DEPENDS ON` will remove the dependencies.
:::
### Other operations
@ -263,6 +277,10 @@ The status of all refreshable materialized views is available in table [`system.
To manually stop, start, trigger, or cancel refreshes use [`SYSTEM STOP|START|REFRESH|CANCEL VIEW`](../system.md#refreshable-materialized-views).
:::note
Fun fact: the refresh query is allowed to read from the view that's being refreshed, seeing pre-refresh version of the data. This means you can implement Conway's game of life: https://pastila.nl/?00021a4b/d6156ff819c83d490ad2dcec05676865#O0LGWTO7maUQIA4AcGUtlA==
:::
## Window View [Experimental]
:::info

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@ -38,8 +38,7 @@ If you anticipate frequent deletes, consider using a [custom partitioning key](/
### Lightweight `DELETE`s with projections
By default, `DELETE` does not work for tables with projections. This is because rows in a projection may be affected by a `DELETE` operation and may require the projection to be rebuilt, negatively affecting `DELETE` performance.
However, there is an option to change this behavior. By changing setting `lightweight_mutation_projection_mode = 'drop'`, deletes will work with projections.
By default, `DELETE` does not work for tables with projections. This is because rows in a projection may be affected by a `DELETE` operation. But there is a [MergeTree setting](https://clickhouse.com/docs/en/operations/settings/merge-tree-settings) `lightweight_mutation_projection_mode` can change the behavior.
## Performance considerations when using lightweight `DELETE`

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@ -186,7 +186,7 @@ Otherwise, you'll get `INVALID_JOIN_ON_EXPRESSION`.
:::
Clickhouse currently supports `ALL INNER/LEFT/RIGHT/FULL JOIN` with inequality conditions in addition to equality conditions. The inequality conditions are supported only for `hash` and `grace_hash` join algorithms. The inequality conditions are not supported with `join_use_nulls`.
Clickhouse currently supports `ALL/ANY/SEMI/ANTI INNER/LEFT/RIGHT/FULL JOIN` with inequality conditions in addition to equality conditions. The inequality conditions are supported only for `hash` and `grace_hash` join algorithms. The inequality conditions are not supported with `join_use_nulls`.
**Example**

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@ -400,7 +400,7 @@ SYSTEM SYNC REPLICA [ON CLUSTER cluster_name] [db.]replicated_merge_tree_family_
After running this statement the `[db.]replicated_merge_tree_family_table_name` fetches commands from the common replicated log into its own replication queue, and then the query waits till the replica processes all of the fetched commands. The following modifiers are supported:
- If a `STRICT` modifier was specified then the query waits for the replication queue to become empty. The `STRICT` version may never succeed if new entries constantly appear in the replication queue.
- If a `LIGHTWEIGHT` modifier was specified then the query waits only for `GET_PART`, `ATTACH_PART`, `DROP_RANGE`, `REPLACE_RANGE` and `DROP_PART` entries to be processed.
- If a `LIGHTWEIGHT` modifier was specified then the query waits only for `GET_PART`, `ATTACH_PART`, `DROP_RANGE`, `REPLACE_RANGE` and `DROP_PART` entries to be processed.
Additionally, the LIGHTWEIGHT modifier supports an optional FROM 'srcReplicas' clause, where 'srcReplicas' is a comma-separated list of source replica names. This extension allows for more targeted synchronization by focusing only on replication tasks originating from the specified source replicas.
- If a `PULL` modifier was specified then the query pulls new replication queue entries from ZooKeeper, but does not wait for anything to be processed.
@ -526,6 +526,10 @@ Trigger an immediate out-of-schedule refresh of a given view.
SYSTEM REFRESH VIEW [db.]name
```
### REFRESH VIEW
Wait for the currently running refresh to complete. If the refresh fails, throws an exception. If no refresh is running, completes immediately, throwing an exception if previous refresh failed.
### STOP VIEW, STOP VIEWS
Disable periodic refreshing of the given view or all refreshable views. If a refresh is in progress, cancel it too.

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@ -77,3 +77,16 @@ SELECT count(*) FROM azureBlobStorage('DefaultEndpointsProtocol=https;AccountNam
**See Also**
- [AzureBlobStorage Table Engine](/docs/en/engines/table-engines/integrations/azureBlobStorage.md)
## Hive-style partitioning {#hive-style-partitioning}
When setting `use_hive_partitioning` is set to 1, ClickHouse will detect Hive-style partitioning in the path (`/name=value/`) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.
**Example**
Use virtual column, created with Hive-style partitioning
``` sql
SET use_hive_partitioning = 1;
SELECT * from azureBlobStorage(config, storage_account_url='...', container='...', blob_path='http://data/path/date=*/country=*/code=*/*.parquet') where _date > '2020-01-01' and _country = 'Netherlands' and _code = 42;
```

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@ -103,7 +103,7 @@ LIMIT 2;
└─────────┴─────────┴─────────┘
```
### Inserting data from a file into a table:
### Inserting data from a file into a table
``` sql
INSERT INTO FUNCTION
@ -206,6 +206,19 @@ SELECT count(*) FROM file('big_dir/**/file002', 'CSV', 'name String, value UInt3
- `_size` — Size of the file in bytes. Type: `Nullable(UInt64)`. If the file size is unknown, the value is `NULL`.
- `_time` — Last modified time of the file. Type: `Nullable(DateTime)`. If the time is unknown, the value is `NULL`.
## Hive-style partitioning {#hive-style-partitioning}
When setting `use_hive_partitioning` is set to 1, ClickHouse will detect Hive-style partitioning in the path (`/name=value/`) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.
**Example**
Use virtual column, created with Hive-style partitioning
``` sql
SET use_hive_partitioning = 1;
SELECT * from file('data/path/date=*/country=*/code=*/*.parquet') where _date > '2020-01-01' and _country = 'Netherlands' and _code = 42;
```
## Settings {#settings}
- [engine_file_empty_if_not_exists](/docs/en/operations/settings/settings.md#engine-file-empty_if-not-exists) - allows to select empty data from a file that doesn't exist. Disabled by default.

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@ -0,0 +1,36 @@
---
slug: /en/sql-reference/table-functions/fuzzQuery
sidebar_position: 75
sidebar_label: fuzzQuery
---
# fuzzQuery
Perturbs the given query string with random variations.
``` sql
fuzzQuery(query[, max_query_length[, random_seed]])
```
**Arguments**
- `query` (String) - The source query to perform the fuzzing on.
- `max_query_length` (UInt64) - A maximum length the query can get during the fuzzing process.
- `random_seed` (UInt64) - A random seed for producing stable results.
**Returned Value**
A table object with a single column containing perturbed query strings.
## Usage Example
``` sql
SELECT * FROM fuzzQuery('SELECT materialize(\'a\' AS key) GROUP BY key') LIMIT 2;
```
```
┌─query──────────────────────────────────────────────────────────┐
1. │ SELECT 'a' AS key GROUP BY key │
2. │ EXPLAIN PIPELINE compact = true SELECT 'a' AS key GROUP BY key │
└────────────────────────────────────────────────────────────────┘
```

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@ -44,6 +44,7 @@ LIMIT 2
Paths may use globbing. Files must match the whole path pattern, not only the suffix or prefix.
- `*` — Represents arbitrarily many characters except `/` but including the empty string.
- `**` — Represents all files inside a folder recursively.
- `?` — Represents an arbitrary single character.
- `{some_string,another_string,yet_another_one}` — Substitutes any of strings `'some_string', 'another_string', 'yet_another_one'`. The strings can contain the `/` symbol.
- `{N..M}` — Represents any number `>= N` and `<= M`.
@ -99,6 +100,19 @@ FROM hdfs('hdfs://hdfs1:9000/big_dir/file{0..9}{0..9}{0..9}', 'CSV', 'name Strin
- `_size` — Size of the file in bytes. Type: `Nullable(UInt64)`. If the size is unknown, the value is `NULL`.
- `_time` — Last modified time of the file. Type: `Nullable(DateTime)`. If the time is unknown, the value is `NULL`.
## Hive-style partitioning {#hive-style-partitioning}
When setting `use_hive_partitioning` is set to 1, ClickHouse will detect Hive-style partitioning in the path (`/name=value/`) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.
**Example**
Use virtual column, created with Hive-style partitioning
``` sql
SET use_hive_partitioning = 1;
SELECT * from HDFS('hdfs://hdfs1:9000/data/path/date=*/country=*/code=*/*.parquet') where _date > '2020-01-01' and _country = 'Netherlands' and _code = 42;
```
## Storage Settings {#storage-settings}
- [hdfs_truncate_on_insert](/docs/en/operations/settings/settings.md#hdfs_truncate_on_insert) - allows to truncate file before insert into it. Disabled by default.

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@ -274,6 +274,19 @@ FROM s3(
- `_size` — Size of the file in bytes. Type: `Nullable(UInt64)`. If the file size is unknown, the value is `NULL`. In case of archive shows uncompressed file size of the file inside the archive.
- `_time` — Last modified time of the file. Type: `Nullable(DateTime)`. If the time is unknown, the value is `NULL`.
## Hive-style partitioning {#hive-style-partitioning}
When setting `use_hive_partitioning` is set to 1, ClickHouse will detect Hive-style partitioning in the path (`/name=value/`) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.
**Example**
Use virtual column, created with Hive-style partitioning
``` sql
SET use_hive_partitioning = 1;
SELECT * from s3('s3://data/path/date=*/country=*/code=*/*.parquet') where _date > '2020-01-01' and _country = 'Netherlands' and _code = 42;
```
## Storage Settings {#storage-settings}
- [s3_truncate_on_insert](/docs/en/operations/settings/settings.md#s3_truncate_on_insert) - allows to truncate file before insert into it. Disabled by default.

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@ -0,0 +1,28 @@
---
slug: /en/sql-reference/table-functions/timeSeriesData
sidebar_position: 145
sidebar_label: timeSeriesData
---
# timeSeriesData
`timeSeriesData(db_name.time_series_table)` - Returns the [data](../../engines/table-engines/integrations/time-series.md#data-table) table
used by table `db_name.time_series_table` which table engine is [TimeSeries](../../engines/table-engines/integrations/time-series.md):
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries DATA data_table
```
The function also works if the _data_ table is inner:
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries DATA INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
The following queries are equivalent:
``` sql
SELECT * FROM timeSeriesData(db_name.time_series_table);
SELECT * FROM timeSeriesData('db_name.time_series_table');
SELECT * FROM timeSeriesData('db_name', 'time_series_table');
```

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@ -0,0 +1,28 @@
---
slug: /en/sql-reference/table-functions/timeSeriesMetrics
sidebar_position: 145
sidebar_label: timeSeriesMetrics
---
# timeSeriesMetrics
`timeSeriesMetrics(db_name.time_series_table)` - Returns the [metrics](../../engines/table-engines/integrations/time-series.md#metrics-table) table
used by table `db_name.time_series_table` which table engine is [TimeSeries](../../engines/table-engines/integrations/time-series.md):
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries METRICS metrics_table
```
The function also works if the _metrics_ table is inner:
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries METRICS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
The following queries are equivalent:
``` sql
SELECT * FROM timeSeriesMetrics(db_name.time_series_table);
SELECT * FROM timeSeriesMetrics('db_name.time_series_table');
SELECT * FROM timeSeriesMetrics('db_name', 'time_series_table');
```

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@ -0,0 +1,28 @@
---
slug: /en/sql-reference/table-functions/timeSeriesTags
sidebar_position: 145
sidebar_label: timeSeriesTags
---
# timeSeriesTags
`timeSeriesTags(db_name.time_series_table)` - Returns the [tags](../../engines/table-engines/integrations/time-series.md#tags-table) table
used by table `db_name.time_series_table` which table engine is [TimeSeries](../../engines/table-engines/integrations/time-series.md):
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries TAGS tags_table
```
The function also works if the _tags_ table is inner:
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries TAGS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
The following queries are equivalent:
``` sql
SELECT * FROM timeSeriesTags(db_name.time_series_table);
SELECT * FROM timeSeriesTags('db_name.time_series_table');
SELECT * FROM timeSeriesTags('db_name', 'time_series_table');
```

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@ -55,6 +55,19 @@ Character `|` inside patterns is used to specify failover addresses. They are it
- `_size` — Size of the resource in bytes. Type: `Nullable(UInt64)`. If the size is unknown, the value is `NULL`.
- `_time` — Last modified time of the file. Type: `Nullable(DateTime)`. If the time is unknown, the value is `NULL`.
## Hive-style partitioning {#hive-style-partitioning}
When setting `use_hive_partitioning` is set to 1, ClickHouse will detect Hive-style partitioning in the path (`/name=value/`) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.
**Example**
Use virtual column, created with Hive-style partitioning
``` sql
SET use_hive_partitioning = 1;
SELECT * from url('http://data/path/date=*/country=*/code=*/*.parquet') where _date > '2020-01-01' and _country = 'Netherlands' and _code = 42;
```
## Storage Settings {#storage-settings}
- [engine_url_skip_empty_files](/docs/en/operations/settings/settings.md#engine_url_skip_empty_files) - allows to skip empty files while reading. Disabled by default.

29
docs/ru/operations/named-collections.md
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@ -146,7 +146,30 @@ SELECT dictGet('dict', 'B', 2);
## Пример использования именованных соединений с базой данных PostgreSQL
Описание параметров смотрите [postgresql](../sql-reference/table-functions/postgresql.md).
Описание параметров смотрите [postgresql](../sql-reference/table-functions/postgresql.md). Дополнительно есть алиасы:
- `username` для `user`
- `db` для `database`.
Параметр `addresses_expr` используется в коллекции вместо `host:port`. Параметр опционален, потому что есть так же другие: `host`, `hostname`, `port`. Следующий псевдокод показывает приоритет:
```sql
CASE
WHEN collection['addresses_expr'] != '' THEN collection['addresses_expr']
WHEN collection['host'] != '' THEN collection['host'] || ':' || if(collection['port'] != '', collection['port'], '5432')
WHEN collection['hostname'] != '' THEN collection['hostname'] || ':' || if(collection['port'] != '', collection['port'], '5432')
END
```
Пример создания:
```sql
CREATE NAMED COLLECTION mypg AS
user = 'pguser',
password = 'jw8s0F4',
host = '127.0.0.1',
port = 5432,
database = 'test',
schema = 'test_schema'
```
Пример конфигурации:
```xml
@ -199,6 +222,10 @@ SELECT * FROM mypgtable;
└───┘
```
:::note
PostgreSQL копирует данные из named collection при создании таблицы. Изменения в коллекции не влияют на существующие таблицы.
:::
### Пример использования именованных соединений базой данных с движком PostgreSQL
```sql

1
docs/ru/sql-reference/table-functions/file.md
View File

@ -81,6 +81,7 @@ SELECT * FROM file('test.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 U
Обрабатываться будут те и только те файлы, которые существуют в файловой системе и удовлетворяют всему шаблону пути.
- `*` — заменяет любое количество любых символов кроме `/`, включая отсутствие символов.
- `**` — Заменяет любое количество любых символов, включая `/`, то есть осуществляет рекурсивный поиск по вложенным директориям.
- `?` — заменяет ровно один любой символ.
- `{some_string,another_string,yet_another_one}` — заменяет любую из строк `'some_string', 'another_string', 'yet_another_one'`. Эти строки также могут содержать символ `/`.
- `{N..M}` — заменяет любое число в интервале от `N` до `M` включительно (может содержать ведущие нули).

1
docs/ru/sql-reference/table-functions/hdfs.md
View File

@ -47,6 +47,7 @@ LIMIT 2
- `*` — Заменяет любое количество любых символов (кроме `/`), включая отсутствие символов.
- `**` — Заменяет любое количество любых символов, включая `/`, то есть осуществляет рекурсивный поиск по вложенным директориям.
- `?` — Заменяет ровно один любой символ.
- `{some_string,another_string,yet_another_one}` — Заменяет любую из строк `'some_string', 'another_string', 'yet_another_one'`. Эти строки также могут содержать символ `/`.
- `{N..M}` — Заменяет любое число в интервале от `N` до `M` включительно (может содержать ведущие нули).

16
docs/zh/operations/external-authenticators/kerberos.md
View File

@ -23,30 +23,30 @@ slug: /zh/operations/external-authenticators/kerberos
示例 (进入 `config.xml`):
```xml
<yandex>
<clickhouse>
<!- ... -->
<kerberos />
</yandex>
</clickhouse>
```
主体规范:
```xml
<yandex>
<clickhouse>
<!- ... -->
<kerberos>
<principal>HTTP/clickhouse.example.com@EXAMPLE.COM</principal>
</kerberos>
</yandex>
</clickhouse>
```
按领域过滤:
```xml
<yandex>
<clickhouse>
<!- ... -->
<kerberos>
<realm>EXAMPLE.COM</realm>
</kerberos>
</yandex>
</clickhouse>
```
!!! warning "注意"
@ -74,7 +74,7 @@ Kerberos主体名称格式通常遵循以下模式:
示例 (进入 `users.xml`):
```
<yandex>
<clickhouse>
<!- ... -->
<users>
<!- ... -->
@ -85,7 +85,7 @@ Kerberos主体名称格式通常遵循以下模式:
</kerberos>
</my_user>
</users>
</yandex>
</clickhouse>
```
!!! warning "警告"

2
programs/CMakeLists.txt
View File

@ -1,4 +1,4 @@
add_compile_options($<$<OR:$<COMPILE_LANGUAGE:C>,$<COMPILE_LANGUAGE:CXX>>:${COVERAGE_FLAGS}>)
add_compile_options("$<$<OR:$<COMPILE_LANGUAGE:C>,$<COMPILE_LANGUAGE:CXX>>:${COVERAGE_FLAGS}>")
if (USE_CLANG_TIDY)
set (CMAKE_CXX_CLANG_TIDY "${CLANG_TIDY_PATH}")

52
programs/benchmark/Benchmark.cpp
View File

@ -75,6 +75,8 @@ public:
const String & default_database_,
const String & user_,
const String & password_,
const String & proto_send_chunked_,
const String & proto_recv_chunked_,
const String & quota_key_,
const String & stage,
bool randomize_,
@ -128,7 +130,9 @@ public:
connections.emplace_back(std::make_unique<ConnectionPool>(
concurrency,
cur_host, cur_port,
default_database_, user_, password_, quota_key_,
default_database_, user_, password_,
proto_send_chunked_, proto_recv_chunked_,
quota_key_,
/* cluster_= */ "",
/* cluster_secret_= */ "",
/* client_name_= */ std::string(DEFAULT_CLIENT_NAME),
@ -662,6 +666,50 @@ int mainEntryClickHouseBenchmark(int argc, char ** argv)
Strings hosts = options.count("host") ? options["host"].as<Strings>() : Strings({"localhost"});
String proto_send_chunked {"notchunked"};
String proto_recv_chunked {"notchunked"};
if (options.count("proto_caps"))
{
std::string proto_caps_str = options["proto_caps"].as<std::string>();
std::vector<std::string_view> proto_caps;
splitInto<','>(proto_caps, proto_caps_str);
for (auto cap_str : proto_caps)
{
std::string direction;
if (cap_str.starts_with("send_"))
{
direction = "send";
cap_str = cap_str.substr(std::string_view("send_").size());
}
else if (cap_str.starts_with("recv_"))
{
direction = "recv";
cap_str = cap_str.substr(std::string_view("recv_").size());
}
if (cap_str != "chunked" && cap_str != "notchunked" && cap_str != "chunked_optional" && cap_str != "notchunked_optional")
throw Exception(ErrorCodes::BAD_ARGUMENTS, "proto_caps option is incorrect ({})", proto_caps_str);
if (direction.empty())
{
proto_send_chunked = cap_str;
proto_recv_chunked = cap_str;
}
else
{
if (direction == "send")
proto_send_chunked = cap_str;
else
proto_recv_chunked = cap_str;
}
}
}
Benchmark benchmark(
options["concurrency"].as<unsigned>(),
options["delay"].as<double>(),
@ -673,6 +721,8 @@ int mainEntryClickHouseBenchmark(int argc, char ** argv)
options["database"].as<std::string>(),
options["user"].as<std::string>(),
options["password"].as<std::string>(),
proto_send_chunked,
proto_recv_chunked,
options["quota_key"].as<std::string>(),
options["stage"].as<std::string>(),
options.count("randomize"),

5
programs/client/Client.cpp
View File

@ -223,7 +223,7 @@ std::vector<String> Client::loadWarningMessages()
size_t rows = packet.block.rows();
for (size_t i = 0; i < rows; ++i)
messages.emplace_back(column[i].get<String>());
messages.emplace_back(column[i].safeGet<String>());
}
continue;
@ -1164,6 +1164,9 @@ void Client::processOptions(const OptionsDescription & options_description,
/// (There is no need to copy the context because clickhouse-client has no background tasks so it won't use that context in parallel.)
client_context = global_context;
initClientContext();
/// Allow to pass-through unknown settings to the server.
client_context->getAccessControl().allowAllSettings();
}

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