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Merge remote-tracking branch 'upstream/master' into fix-projection-merge

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This commit is contained in:
jsc0218 2024-08-14 15:12:50 +00:00
commit 50a42cfee6
1028 changed files with 33630 additions and 14064 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

2
.github/workflows/create_release.yml vendored
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@ -129,9 +129,9 @@ 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

2
.github/workflows/jepsen.yml vendored
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@ -67,7 +67,7 @@ jobs:
if: ${{ !cancelled() }}
run: |
export WORKFLOW_RESULT_FILE="/tmp/workflow_results.json"
cat >> "$WORKFLOW_RESULT_FILE" << 'EOF'
cat > "$WORKFLOW_RESULT_FILE" << 'EOF'
${{ toJson(needs) }}
EOF
python3 ./tests/ci/ci_buddy.py --check-wf-status

3
.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

21
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 "${CMAKE_EXE_LINKER_FLAGS} -Wl,--gdb-index")
message (STATUS "Adding .gdb-index via --gdb-index linker option.")
endif ()
endif()
if (NOT (SANITIZE_COVERAGE OR WITH_COVERAGE)
AND (CMAKE_BUILD_TYPE_UC STREQUAL "RELEASE"
OR CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO"
@ -402,7 +394,7 @@ if ((NOT OS_LINUX AND NOT OS_ANDROID) OR (CMAKE_BUILD_TYPE_UC STREQUAL "DEBUG")
set(ENABLE_GWP_ASAN OFF)
endif ()
option (ENABLE_FIU "Enable Fiu" ON)
option (ENABLE_LIBFIU "Enable libfiu" ON)
option(WERROR "Enable -Werror compiler option" ON)
@ -428,12 +420,17 @@ if (NOT SANITIZE)
set (CMAKE_POSITION_INDEPENDENT_CODE OFF)
endif()
if (OS_LINUX AND NOT (ARCH_AARCH64 OR ARCH_S390X) AND NOT SANITIZE)
# Slightly more efficient code can be generated
# It's disabled for ARM because otherwise ClickHouse cannot run on Android.
if (NOT OS_ANDROID AND OS_LINUX AND NOT ARCH_S390X AND NOT SANITIZE)
# Using '-no-pie' builds executables with fixed addresses, resulting in slightly more efficient code
# and keeping binary addresses constant even with ASLR enabled.
# Disabled on Android as it requires PIE: https://source.android.com/docs/security/enhancements#android-5
# Disabled on IBM S390X due to build issues with 'no-pie'
# Disabled with sanitizers to avoid issues with maximum relocation size: https://github.com/ClickHouse/ClickHouse/pull/49145
set (CMAKE_CXX_FLAGS_RELWITHDEBINFO "${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)

2
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}")

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
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@ -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

4
cmake/sanitize.cmake
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@ -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)

7
contrib/CMakeLists.txt vendored
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@ -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")
@ -205,9 +205,8 @@ 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)

4
contrib/QAT-ZSTD-Plugin-cmake/CMakeLists.txt
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@ -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 ()

1
contrib/annoy vendored

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

24
contrib/annoy-cmake/CMakeLists.txt
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@ -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)

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")

9
contrib/usearch-cmake/CMakeLists.txt
View File

@ -1,9 +1,7 @@
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)
@ -11,7 +9,6 @@ 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)
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 "$@"
main "$@"

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}"

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.

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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.

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@ -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
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@ -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)

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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)

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@ -17,7 +17,7 @@ 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
```
@ -27,75 +27,109 @@ Function `Distance` computes the distance between two vectors. Often, the Euclid
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)
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 +156,3 @@ 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>
```

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
docs/en/operations/server-configuration-parameters/settings.md
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.

11
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View File

@ -1041,3 +1041,14 @@ 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.
### 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.
Possible values:
- throw, drop, rebuild
Default value: throw

28
docs/en/operations/settings/settings.md
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.
@ -5626,3 +5637,20 @@ 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`.

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
docs/en/operations/system-tables/query_cache.md
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
docs/en/operations/system-tables/trace_log.md
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).

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

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@ -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.

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@ -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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@ -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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@ -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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@ -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 │
└────────────────────────────────────────────────────────────────┘
```

14
docs/en/sql-reference/table-functions/hdfs.md
View File

@ -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.

13
docs/en/sql-reference/table-functions/s3.md
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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.

28
docs/en/sql-reference/table-functions/timeSeriesData.md Normal file
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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');
```

28
docs/en/sql-reference/table-functions/timeSeriesMetrics.md Normal file
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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');
```

28
docs/en/sql-reference/table-functions/timeSeriesTags.md Normal file
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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');
```

13
docs/en/sql-reference/table-functions/url.md
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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.

2
docs/en/sql-reference/window-functions/lagInFrame.md
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@ -1,7 +1,7 @@
---
slug: /en/sql-reference/window-functions/lagInFrame
sidebar_label: lagInFrame
sidebar_position: 8
sidebar_position: 9
---
# lagInFrame

2
docs/en/sql-reference/window-functions/leadInFrame.md
View File

@ -1,7 +1,7 @@
---
slug: /en/sql-reference/window-functions/leadInFrame
sidebar_label: leadInFrame
sidebar_position: 9
sidebar_position: 10
---
# leadInFrame

72
docs/en/sql-reference/window-functions/percent_rank.md Normal file
View File

@ -0,0 +1,72 @@
---
slug: /en/sql-reference/window-functions/percent_rank
sidebar_label: percent_rank
sidebar_position: 8
---
# percent_rank
returns the relative rank (i.e. percentile) of rows within a window partition.
**Syntax**
Alias: `percentRank` (case-sensitive)
```sql
percent_rank (column_name)
OVER ([[PARTITION BY grouping_column] [ORDER BY sorting_column]
[RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING]] | [window_name])
FROM table_name
WINDOW window_name as ([PARTITION BY grouping_column] [ORDER BY sorting_column] RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
```
The default and required window frame definition is `RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`.
For more detail on window function syntax see: [Window Functions - Syntax](./index.md/#syntax).
**Example**
Query:
```sql
CREATE TABLE salaries
(
`team` String,
`player` String,
`salary` UInt32,
`position` String
)
Engine = Memory;
INSERT INTO salaries FORMAT Values
('Port Elizabeth Barbarians', 'Gary Chen', 195000, 'F'),
('New Coreystad Archdukes', 'Charles Juarez', 190000, 'F'),
('Port Elizabeth Barbarians', 'Michael Stanley', 150000, 'D'),
('New Coreystad Archdukes', 'Scott Harrison', 150000, 'D'),
('Port Elizabeth Barbarians', 'Robert George', 195000, 'M'),
('South Hampton Seagulls', 'Douglas Benson', 150000, 'M'),
('South Hampton Seagulls', 'James Henderson', 140000, 'M');
```
```sql
SELECT player, salary,
percent_rank() OVER (ORDER BY salary DESC) AS percent_rank
FROM salaries;
```
Result:
```response
┌─player──────────┬─salary─┬───────percent_rank─┐
1. │ Gary Chen │ 195000 │ 0 │
2. │ Robert George │ 195000 │ 0 │
3. │ Charles Juarez │ 190000 │ 0.3333333333333333 │
4. │ Michael Stanley │ 150000 │ 0.5 │
5. │ Scott Harrison │ 150000 │ 0.5 │
6. │ Douglas Benson │ 150000 │ 0.5 │
7. │ James Henderson │ 140000 │ 1 │
└─────────────────┴────────┴────────────────────┘
```

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"),

2
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;

5
programs/client/Client.h
View File

@ -11,7 +11,10 @@ class Client : public ClientApplicationBase
public:
using Arguments = ClientApplicationBase::Arguments;
Client() = default;
Client()
{
fuzzer = QueryFuzzer(randomSeed(), &std::cout, &std::cerr);
}
void initialize(Poco::Util::Application & self) override;

15
programs/client/clickhouse-client.xml
View File

@ -38,6 +38,21 @@
<production>{display_name} \e[1;31m:)\e[0m </production> <!-- if it matched to the substring "production" in the server display name -->
</prompt_by_server_display_name>
<!-- Chunked capabilities for native protocol by client.
Can be enabled separately for send and receive channels.
Supported modes:
- chunked - client will only work with server supporting chunked protocol;
- chunked_optional - client prefer server to enable chunked protocol, but can switch to notchunked if server does not support this;
- notchunked - client will only work with server supporting notchunked protocol (current default);
- notchunked_optional - client prefer server notchunked protocol, but can switch to chunked if server does not support this.
-->
<!--
<proto_caps>
<send>chunked_optional</send>
<recv>chunked_optional</recv>
</proto_caps>
-->
<!--
Settings adjustable via command-line parameters
can take their defaults from that config file, see examples:

6
programs/keeper-client/Commands.cpp
View File

@ -95,7 +95,7 @@ void SetCommand::execute(const ASTKeeperQuery * query, KeeperClient * client) co
client->zookeeper->set(
client->getAbsolutePath(query->args[0].safeGet<String>()),
query->args[1].safeGet<String>(),
static_cast<Int32>(query->args[2].get<Int32>()));
static_cast<Int32>(query->args[2].safeGet<Int32>()));
}
bool CreateCommand::parse(IParser::Pos & pos, std::shared_ptr<ASTKeeperQuery> & node, Expected & expected) const
@ -494,7 +494,7 @@ void RMCommand::execute(const ASTKeeperQuery * query, KeeperClient * client) con
{
Int32 version{-1};
if (query->args.size() == 2)
version = static_cast<Int32>(query->args[1].get<Int32>());
version = static_cast<Int32>(query->args[1].safeGet<Int32>());
client->zookeeper->remove(client->getAbsolutePath(query->args[0].safeGet<String>()), version);
}
@ -549,7 +549,7 @@ void ReconfigCommand::execute(const DB::ASTKeeperQuery * query, DB::KeeperClient
String leaving;
String new_members;
auto operation = query->args[0].get<ReconfigCommand::Operation>();
auto operation = query->args[0].safeGet<ReconfigCommand::Operation>();
switch (operation)
{
case static_cast<UInt8>(ReconfigCommand::Operation::ADD):

5
programs/keeper/Keeper.cpp
View File

@ -38,7 +38,7 @@
#include <Server/HTTP/HTTPServer.h>
#include <Server/HTTPHandlerFactory.h>
#include <Server/KeeperReadinessHandler.h>
#include <Server/PrometheusMetricsWriter.h>
#include <Server/PrometheusRequestHandlerFactory.h>
#include <Server/TCPServer.h>
#include "Core/Defines.h"
@ -509,14 +509,13 @@ try
auto address = socketBindListen(socket, listen_host, port);
socket.setReceiveTimeout(my_http_context->getReceiveTimeout());
socket.setSendTimeout(my_http_context->getSendTimeout());
auto metrics_writer = std::make_shared<KeeperPrometheusMetricsWriter>(config, "prometheus", async_metrics);
servers->emplace_back(
listen_host,
port_name,
"Prometheus: http://" + address.toString(),
std::make_unique<HTTPServer>(
std::move(my_http_context),
createPrometheusMainHandlerFactory(*this, config_getter(), metrics_writer, "PrometheusHandler-factory"),
createKeeperPrometheusHandlerFactory(*this, config_getter(), async_metrics, "PrometheusHandler-factory"),
server_pool,
socket,
http_params));

2
programs/library-bridge/LibraryBridge.cpp
View File

@ -27,7 +27,7 @@ std::string LibraryBridge::bridgeName() const
LibraryBridge::HandlerFactoryPtr LibraryBridge::getHandlerFactoryPtr(ContextPtr context) const
{
return std::make_shared<LibraryBridgeHandlerFactory>("LibraryRequestHandlerFactory", keep_alive_timeout, context);
return std::make_shared<LibraryBridgeHandlerFactory>("LibraryRequestHandlerFactory", context);
}
}

10
programs/library-bridge/LibraryBridgeHandlerFactory.cpp
View File

@ -9,12 +9,10 @@ namespace DB
{
LibraryBridgeHandlerFactory::LibraryBridgeHandlerFactory(
const std::string & name_,
size_t keep_alive_timeout_,
ContextPtr context_)
: WithContext(context_)
, log(getLogger(name_))
, name(name_)
, keep_alive_timeout(keep_alive_timeout_)
{
}
@ -26,17 +24,17 @@ std::unique_ptr<HTTPRequestHandler> LibraryBridgeHandlerFactory::createRequestHa
if (request.getMethod() == Poco::Net::HTTPRequest::HTTP_GET)
{
if (uri.getPath() == "/extdict_ping")
return std::make_unique<ExternalDictionaryLibraryBridgeExistsHandler>(keep_alive_timeout, getContext());
return std::make_unique<ExternalDictionaryLibraryBridgeExistsHandler>(getContext());
else if (uri.getPath() == "/catboost_ping")
return std::make_unique<CatBoostLibraryBridgeExistsHandler>(keep_alive_timeout, getContext());
return std::make_unique<CatBoostLibraryBridgeExistsHandler>(getContext());
}
if (request.getMethod() == Poco::Net::HTTPRequest::HTTP_POST)
{
if (uri.getPath() == "/extdict_request")
return std::make_unique<ExternalDictionaryLibraryBridgeRequestHandler>(keep_alive_timeout, getContext());
return std::make_unique<ExternalDictionaryLibraryBridgeRequestHandler>(getContext());
else if (uri.getPath() == "/catboost_request")
return std::make_unique<CatBoostLibraryBridgeRequestHandler>(keep_alive_timeout, getContext());
return std::make_unique<CatBoostLibraryBridgeRequestHandler>(getContext());
}
return nullptr;

2
programs/library-bridge/LibraryBridgeHandlerFactory.h
View File

@ -13,7 +13,6 @@ class LibraryBridgeHandlerFactory : public HTTPRequestHandlerFactory, WithContex
public:
LibraryBridgeHandlerFactory(
const std::string & name_,
size_t keep_alive_timeout_,
ContextPtr context_);
std::unique_ptr<HTTPRequestHandler> createRequestHandler(const HTTPServerRequest & request) override;
@ -21,7 +20,6 @@ public:
private:
LoggerPtr log;
const std::string name;
const size_t keep_alive_timeout;
};
}

33
programs/library-bridge/LibraryBridgeHandlers.cpp
View File

@ -87,10 +87,8 @@ static void writeData(Block data, OutputFormatPtr format)
}
ExternalDictionaryLibraryBridgeRequestHandler::ExternalDictionaryLibraryBridgeRequestHandler(size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, keep_alive_timeout(keep_alive_timeout_)
, log(getLogger("ExternalDictionaryLibraryBridgeRequestHandler"))
ExternalDictionaryLibraryBridgeRequestHandler::ExternalDictionaryLibraryBridgeRequestHandler(ContextPtr context_)
: WithContext(context_), log(getLogger("ExternalDictionaryLibraryBridgeRequestHandler"))
{
}
@ -137,7 +135,7 @@ void ExternalDictionaryLibraryBridgeRequestHandler::handleRequest(HTTPServerRequ
const String & dictionary_id = params.get("dictionary_id");
LOG_TRACE(log, "Library method: '{}', dictionary id: {}", method, dictionary_id);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD, keep_alive_timeout);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD);
try
{
@ -374,10 +372,8 @@ void ExternalDictionaryLibraryBridgeRequestHandler::handleRequest(HTTPServerRequ
}
ExternalDictionaryLibraryBridgeExistsHandler::ExternalDictionaryLibraryBridgeExistsHandler(size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, keep_alive_timeout(keep_alive_timeout_)
, log(getLogger("ExternalDictionaryLibraryBridgeExistsHandler"))
ExternalDictionaryLibraryBridgeExistsHandler::ExternalDictionaryLibraryBridgeExistsHandler(ContextPtr context_)
: WithContext(context_), log(getLogger("ExternalDictionaryLibraryBridgeExistsHandler"))
{
}
@ -401,7 +397,7 @@ void ExternalDictionaryLibraryBridgeExistsHandler::handleRequest(HTTPServerReque
String res = library_handler ? "1" : "0";
setResponseDefaultHeaders(response, keep_alive_timeout);
setResponseDefaultHeaders(response);
LOG_TRACE(log, "Sending ping response: {} (dictionary id: {})", res, dictionary_id);
response.sendBuffer(res.data(), res.size());
}
@ -412,11 +408,8 @@ void ExternalDictionaryLibraryBridgeExistsHandler::handleRequest(HTTPServerReque
}
CatBoostLibraryBridgeRequestHandler::CatBoostLibraryBridgeRequestHandler(
size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, keep_alive_timeout(keep_alive_timeout_)
, log(getLogger("CatBoostLibraryBridgeRequestHandler"))
CatBoostLibraryBridgeRequestHandler::CatBoostLibraryBridgeRequestHandler(ContextPtr context_)
: WithContext(context_), log(getLogger("CatBoostLibraryBridgeRequestHandler"))
{
}
@ -455,7 +448,7 @@ void CatBoostLibraryBridgeRequestHandler::handleRequest(HTTPServerRequest & requ
const String & method = params.get("method");
LOG_TRACE(log, "Library method: '{}'", method);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD, keep_alive_timeout);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD);
try
{
@ -617,10 +610,8 @@ void CatBoostLibraryBridgeRequestHandler::handleRequest(HTTPServerRequest & requ
}
CatBoostLibraryBridgeExistsHandler::CatBoostLibraryBridgeExistsHandler(size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, keep_alive_timeout(keep_alive_timeout_)
, log(getLogger("CatBoostLibraryBridgeExistsHandler"))
CatBoostLibraryBridgeExistsHandler::CatBoostLibraryBridgeExistsHandler(ContextPtr context_)
: WithContext(context_), log(getLogger("CatBoostLibraryBridgeExistsHandler"))
{
}
@ -634,7 +625,7 @@ void CatBoostLibraryBridgeExistsHandler::handleRequest(HTTPServerRequest & reque
String res = "1";
setResponseDefaultHeaders(response, keep_alive_timeout);
setResponseDefaultHeaders(response);
LOG_TRACE(log, "Sending ping response: {}", res);
response.sendBuffer(res.data(), res.size());
}

12
programs/library-bridge/LibraryBridgeHandlers.h
View File

@ -18,14 +18,13 @@ namespace DB
class ExternalDictionaryLibraryBridgeRequestHandler : public HTTPRequestHandler, WithContext
{
public:
ExternalDictionaryLibraryBridgeRequestHandler(size_t keep_alive_timeout_, ContextPtr context_);
explicit ExternalDictionaryLibraryBridgeRequestHandler(ContextPtr context_);
void handleRequest(HTTPServerRequest & request, HTTPServerResponse & response, const ProfileEvents::Event & write_event) override;
private:
static constexpr auto FORMAT = "RowBinary";
const size_t keep_alive_timeout;
LoggerPtr log;
};
@ -34,12 +33,11 @@ private:
class ExternalDictionaryLibraryBridgeExistsHandler : public HTTPRequestHandler, WithContext
{
public:
ExternalDictionaryLibraryBridgeExistsHandler(size_t keep_alive_timeout_, ContextPtr context_);
explicit ExternalDictionaryLibraryBridgeExistsHandler(ContextPtr context_);
void handleRequest(HTTPServerRequest & request, HTTPServerResponse & response, const ProfileEvents::Event & write_event) override;
private:
const size_t keep_alive_timeout;
LoggerPtr log;
};
@ -63,12 +61,11 @@ private:
class CatBoostLibraryBridgeRequestHandler : public HTTPRequestHandler, WithContext
{
public:
CatBoostLibraryBridgeRequestHandler(size_t keep_alive_timeout_, ContextPtr context_);
explicit CatBoostLibraryBridgeRequestHandler(ContextPtr context_);
void handleRequest(HTTPServerRequest & request, HTTPServerResponse & response, const ProfileEvents::Event & write_event) override;
private:
const size_t keep_alive_timeout;
LoggerPtr log;
};
@ -77,12 +74,11 @@ private:
class CatBoostLibraryBridgeExistsHandler : public HTTPRequestHandler, WithContext
{
public:
CatBoostLibraryBridgeExistsHandler(size_t keep_alive_timeout_, ContextPtr context_);
explicit CatBoostLibraryBridgeExistsHandler(ContextPtr context_);
void handleRequest(HTTPServerRequest & request, HTTPServerResponse & response, const ProfileEvents::Event & write_event) override;
private:
const size_t keep_alive_timeout;
LoggerPtr log;
};

23
programs/local/LocalServer.cpp
View File

@ -14,7 +14,6 @@
#include <Databases/registerDatabases.h>
#include <Databases/DatabaseFilesystem.h>
#include <Databases/DatabaseMemory.h>
#include <Databases/DatabaseAtomic.h>
#include <Databases/DatabasesOverlay.h>
#include <Storages/System/attachSystemTables.h>
#include <Storages/System/attachInformationSchemaTables.h>
@ -51,6 +50,7 @@
#include <Dictionaries/registerDictionaries.h>
#include <Disks/registerDisks.h>
#include <Formats/registerFormats.h>
#include <boost/algorithm/string/replace.hpp>
#include <boost/program_options/options_description.hpp>
#include <base/argsToConfig.h>
#include <filesystem>
@ -143,7 +143,7 @@ void LocalServer::initialize(Poco::Util::Application & self)
if (fs::exists(config_path))
{
ConfigProcessor config_processor(config_path, false, true);
ConfigProcessor config_processor(config_path);
ConfigProcessor::setConfigPath(fs::path(config_path).parent_path());
auto loaded_config = config_processor.loadConfig();
getClientConfiguration().add(loaded_config.configuration.duplicate(), PRIO_DEFAULT, false);
@ -216,12 +216,12 @@ static DatabasePtr createMemoryDatabaseIfNotExists(ContextPtr context, const Str
return system_database;
}
static DatabasePtr createClickHouseLocalDatabaseOverlay(const String & name_, ContextPtr context)
static DatabasePtr createClickHouseLocalDatabaseOverlay(const String & name_, ContextPtr context_)
{
auto overlay = std::make_shared<DatabasesOverlay>(name_, context);
overlay->registerNextDatabase(std::make_shared<DatabaseAtomic>(name_, fs::weakly_canonical(context->getPath()), UUIDHelpers::generateV4(), context));
overlay->registerNextDatabase(std::make_shared<DatabaseFilesystem>(name_, "", context));
return overlay;
auto databaseCombiner = std::make_shared<DatabasesOverlay>(name_, context_);
databaseCombiner->registerNextDatabase(std::make_shared<DatabaseFilesystem>(name_, "", context_));
databaseCombiner->registerNextDatabase(std::make_shared<DatabaseMemory>(name_, context_));
return databaseCombiner;
}
/// If path is specified and not empty, will try to setup server environment and load existing metadata
@ -367,7 +367,7 @@ std::string LocalServer::getInitialCreateTableQuery()
else
table_structure = "(" + table_structure + ")";
return fmt::format("CREATE TEMPORARY TABLE {} {} ENGINE = File({}, {});",
return fmt::format("CREATE TABLE {} {} ENGINE = File({}, {});",
table_name, table_structure, data_format, table_file);
}
@ -761,12 +761,7 @@ void LocalServer::processConfig()
DatabaseCatalog::instance().initializeAndLoadTemporaryDatabase();
std::string default_database = server_settings.default_database;
{
DatabasePtr database = createClickHouseLocalDatabaseOverlay(default_database, global_context);
if (UUID uuid = database->getUUID(); uuid != UUIDHelpers::Nil)
DatabaseCatalog::instance().addUUIDMapping(uuid);
DatabaseCatalog::instance().attachDatabase(default_database, database);
}
DatabaseCatalog::instance().attachDatabase(default_database, createClickHouseLocalDatabaseOverlay(default_database, global_context));
global_context->setCurrentDatabase(default_database);
if (getClientConfiguration().has("path"))

1
programs/obfuscator/Obfuscator.cpp
View File

@ -1307,6 +1307,7 @@ try
throw ErrnoException(ErrorCodes::CANNOT_SEEK_THROUGH_FILE, "Input must be seekable file (it will be read twice)");
SingleReadBufferIterator read_buffer_iterator(std::move(file));
schema_columns = readSchemaFromFormat(input_format, {}, read_buffer_iterator, context_const);
}
else

5
programs/odbc-bridge/ColumnInfoHandler.cpp
View File

@ -202,10 +202,7 @@ void ODBCColumnsInfoHandler::handleRequest(HTTPServerRequest & request, HTTPServ
if (columns.empty())
throw Exception(ErrorCodes::UNKNOWN_TABLE, "Columns definition was not returned");
WriteBufferFromHTTPServerResponse out(
response,
request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD,
keep_alive_timeout);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD);
try
{
writeStringBinary(columns.toString(), out);

8
programs/odbc-bridge/ColumnInfoHandler.h
View File

@ -15,18 +15,12 @@ namespace DB
class ODBCColumnsInfoHandler : public HTTPRequestHandler, WithContext
{
public:
ODBCColumnsInfoHandler(size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, log(getLogger("ODBCColumnsInfoHandler"))
, keep_alive_timeout(keep_alive_timeout_)
{
}
explicit ODBCColumnsInfoHandler(ContextPtr context_) : WithContext(context_), log(getLogger("ODBCColumnsInfoHandler")) { }
void handleRequest(HTTPServerRequest & request, HTTPServerResponse & response, const ProfileEvents::Event & write_event) override;
private:
LoggerPtr log;
size_t keep_alive_timeout;
};
}

2
programs/odbc-bridge/IdentifierQuoteHandler.cpp
View File

@ -74,7 +74,7 @@ void IdentifierQuoteHandler::handleRequest(HTTPServerRequest & request, HTTPServ
auto identifier = getIdentifierQuote(std::move(connection));
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD, keep_alive_timeout);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD);
try
{
writeStringBinary(identifier, out);

8
programs/odbc-bridge/IdentifierQuoteHandler.h
View File

@ -14,18 +14,12 @@ namespace DB
class IdentifierQuoteHandler : public HTTPRequestHandler, WithContext
{
public:
IdentifierQuoteHandler(size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, log(getLogger("IdentifierQuoteHandler"))
, keep_alive_timeout(keep_alive_timeout_)
{
}
explicit IdentifierQuoteHandler(ContextPtr context_) : WithContext(context_), log(getLogger("IdentifierQuoteHandler")) { }
void handleRequest(HTTPServerRequest & request, HTTPServerResponse & response, const ProfileEvents::Event & write_event) override;
private:
LoggerPtr log;
size_t keep_alive_timeout;
};
}

2
programs/odbc-bridge/MainHandler.cpp
View File

@ -132,7 +132,7 @@ void ODBCHandler::handleRequest(HTTPServerRequest & request, HTTPServerResponse
return;
}
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD, keep_alive_timeout);
WriteBufferFromHTTPServerResponse out(response, request.getMethod() == Poco::Net::HTTPRequest::HTTP_HEAD);
try
{

3
programs/odbc-bridge/MainHandler.h
View File

@ -20,12 +20,10 @@ class ODBCHandler : public HTTPRequestHandler, WithContext
{
public:
ODBCHandler(
size_t keep_alive_timeout_,
ContextPtr context_,
const String & mode_)
: WithContext(context_)
, log(getLogger("ODBCHandler"))
, keep_alive_timeout(keep_alive_timeout_)
, mode(mode_)
{
}
@ -35,7 +33,6 @@ public:
private:
LoggerPtr log;
size_t keep_alive_timeout;
String mode;
static inline std::mutex mutex;

2
programs/odbc-bridge/ODBCBridge.cpp
View File

@ -27,7 +27,7 @@ std::string ODBCBridge::bridgeName() const
ODBCBridge::HandlerFactoryPtr ODBCBridge::getHandlerFactoryPtr(ContextPtr context) const
{
return std::make_shared<ODBCBridgeHandlerFactory>("ODBCRequestHandlerFactory-factory", keep_alive_timeout, context);
return std::make_shared<ODBCBridgeHandlerFactory>("ODBCRequestHandlerFactory-factory", context);
}
}

19
programs/odbc-bridge/ODBCHandlerFactory.cpp
View File

@ -9,11 +9,8 @@
namespace DB
{
ODBCBridgeHandlerFactory::ODBCBridgeHandlerFactory(const std::string & name_, size_t keep_alive_timeout_, ContextPtr context_)
: WithContext(context_)
, log(getLogger(name_))
, name(name_)
, keep_alive_timeout(keep_alive_timeout_)
ODBCBridgeHandlerFactory::ODBCBridgeHandlerFactory(const std::string & name_, ContextPtr context_)
: WithContext(context_), log(getLogger(name_)), name(name_)
{
}
@ -23,33 +20,33 @@ std::unique_ptr<HTTPRequestHandler> ODBCBridgeHandlerFactory::createRequestHandl
LOG_TRACE(log, "Request URI: {}", uri.toString());
if (uri.getPath() == "/ping" && request.getMethod() == Poco::Net::HTTPRequest::HTTP_GET)
return std::make_unique<PingHandler>(keep_alive_timeout);
return std::make_unique<PingHandler>();
if (request.getMethod() == Poco::Net::HTTPRequest::HTTP_POST)
{
if (uri.getPath() == "/columns_info")
#if USE_ODBC
return std::make_unique<ODBCColumnsInfoHandler>(keep_alive_timeout, getContext());
return std::make_unique<ODBCColumnsInfoHandler>(getContext());
#else
return nullptr;
#endif
else if (uri.getPath() == "/identifier_quote")
#if USE_ODBC
return std::make_unique<IdentifierQuoteHandler>(keep_alive_timeout, getContext());
return std::make_unique<IdentifierQuoteHandler>(getContext());
#else
return nullptr;
#endif
else if (uri.getPath() == "/schema_allowed")
#if USE_ODBC
return std::make_unique<SchemaAllowedHandler>(keep_alive_timeout, getContext());
return std::make_unique<SchemaAllowedHandler>(getContext());
#else
return nullptr;
#endif
else if (uri.getPath() == "/write")
return std::make_unique<ODBCHandler>(keep_alive_timeout, getContext(), "write");
return std::make_unique<ODBCHandler>(getContext(), "write");
else
return std::make_unique<ODBCHandler>(keep_alive_timeout, getContext(), "read");
return std::make_unique<ODBCHandler>(getContext(), "read");
}
return nullptr;
}

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