thevar1able/ClickHouse
1
0
Fork 0
mirror of https://github.com/ClickHouse/ClickHouse.git synced 2024-11-25 09:02:00 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'upstream/master' into HEAD

Browse Source
This commit is contained in:
Anton Popov 2024-01-23 14:03:34 +00:00
commit bceda4cd29
641 changed files with 19091 additions and 5961 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.github/workflows/jepsen.yml vendored
View File

@ -8,13 +8,13 @@ on: # yamllint disable-line rule:truthy
schedule:
- cron: '0 */6 * * *'
workflow_dispatch:
workflow_call:
jobs:
KeeperJepsenRelease:
uses: ./.github/workflows/reusable_simple_job.yml
with:
test_name: Jepsen keeper check
runner_type: style-checker
report_required: true
run_command: |
python3 jepsen_check.py keeper
# ServerJepsenRelease:

10
.github/workflows/master.yml vendored
View File

@ -15,6 +15,8 @@ jobs:
outputs:
data: ${{ steps.runconfig.outputs.CI_DATA }}
steps:
- name: DebugInfo
uses: hmarr/debug-action@a701ed95a46e6f2fb0df25e1a558c16356fae35a
- name: Check out repository code
uses: ClickHouse/checkout@v1
with:
@ -33,11 +35,9 @@ jobs:
- name: PrepareRunConfig
id: runconfig
run: |
echo "::group::configure CI run"
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --configure --rebuild-all-binaries --outfile ${{ runner.temp }}/ci_run_data.json
echo "::endgroup::"
echo "::group::CI run configure results"
echo "::group::CI configuration"
python3 -m json.tool ${{ runner.temp }}/ci_run_data.json
echo "::endgroup::"
@ -255,9 +255,9 @@ jobs:
run_command: |
cd "$GITHUB_WORKSPACE/tests/ci"
python3 docker_server.py --release-type head \
--image-repo clickhouse/clickhouse-server --image-path docker/server
--image-repo clickhouse/clickhouse-server --image-path docker/server --allow-build-reuse
python3 docker_server.py --release-type head \
--image-repo clickhouse/clickhouse-keeper --image-path docker/keeper
--image-repo clickhouse/clickhouse-keeper --image-path docker/keeper --allow-build-reuse
############################################################################################
##################################### BUILD REPORTER #######################################
############################################################################################

28
.github/workflows/pull_request.yml vendored
View File

@ -22,6 +22,8 @@ jobs:
outputs:
data: ${{ steps.runconfig.outputs.CI_DATA }}
steps:
- name: DebugInfo
uses: hmarr/debug-action@a701ed95a46e6f2fb0df25e1a558c16356fae35a
- name: Check out repository code
uses: ClickHouse/checkout@v1
with:
@ -44,11 +46,9 @@ jobs:
- name: PrepareRunConfig
id: runconfig
run: |
echo "::group::configure CI run"
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --configure --outfile ${{ runner.temp }}/ci_run_data.json
echo "::endgroup::"
echo "::group::CI run configure results"
echo "::group::CI configuration"
python3 -m json.tool ${{ runner.temp }}/ci_run_data.json
echo "::endgroup::"
@ -67,6 +67,7 @@ jobs:
DOCKER_TAG=$(echo '${{ toJson(fromJson(steps.runconfig.outputs.CI_DATA).docker_data.images) }}' | tr -d '\n')
export DOCKER_TAG=$DOCKER_TAG
python3 ./tests/ci/style_check.py --no-push
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --infile ${{ runner.temp }}/ci_run_data.json --post --job-name 'Style check'
BuildDockers:
needs: [RunConfig]
if: ${{ !failure() && !cancelled() }}
@ -796,7 +797,7 @@ jobs:
test_name: Unit tests (asan)
runner_type: fuzzer-unit-tester
data: ${{ needs.RunConfig.outputs.data }}
UnitTestsReleaseClang:
UnitTestsRelease:
needs: [RunConfig, BuilderBinRelease]
if: ${{ !failure() && !cancelled() }}
uses: ./.github/workflows/reusable_test.yml
@ -923,7 +924,7 @@ jobs:
- UnitTestsTsan
- UnitTestsMsan
- UnitTestsUBsan
- UnitTestsReleaseClang
- UnitTestsRelease
- CompatibilityCheckX86
- CompatibilityCheckAarch64
- SQLancerTestRelease
@ -966,13 +967,20 @@ jobs:
#############################################################################################
###################################### JEPSEN TESTS #########################################
#############################################################################################
# This is special test NOT INCLUDED in FinishCheck
# When it's skipped, all dependent tasks will be skipped too.
# DO NOT add it there
Jepsen:
# This is special test NOT INCLUDED in FinishCheck
# When it's skipped, all dependent tasks will be skipped too.
# DO NOT add it there
if: ${{ !failure() && !cancelled() && contains(github.event.pull_request.labels.*.name, 'jepsen-test') }}
# we need concurrency as the job uses dedicated instances in the cloud
concurrency:
group: jepsen
if: ${{ !failure() && !cancelled() }}
needs: [RunConfig, BuilderBinRelease]
uses: ./.github/workflows/jepsen.yml
uses: ./.github/workflows/reusable_test.yml
with:
test_name: ClickHouse Keeper Jepsen
runner_type: style-checker
data: ${{ needs.RunConfig.outputs.data }}
#############################################################################################
####################################### libFuzzer ###########################################
#############################################################################################

10
.github/workflows/reusable_build.yml vendored
View File

@ -58,6 +58,7 @@ jobs:
- name: Apply sparse checkout for contrib # in order to check that it doesn't break build
# This step is done in GITHUB_WORKSPACE,
# because it's broken in REPO_COPY for some reason
# See also update-submodules.sh
if: ${{ env.BUILD_SPARSE_CHECKOUT == 'true' }}
run: |
rm -rf "$GITHUB_WORKSPACE/contrib" && echo 'removed'
@ -72,12 +73,15 @@ jobs:
- name: Pre
run: |
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --infile ${{ toJson(inputs.data) }} --pre --job-name '${{inputs.build_name}}'
- name: Build
- name: Run
run: |
python3 "$GITHUB_WORKSPACE/tests/ci/build_check.py" "$BUILD_NAME"
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" \
--infile ${{ toJson(inputs.data) }} \
--job-name "$BUILD_NAME" \
--run
- name: Post
# it still be build report to upload for failed build job
if: always()
if: ${{ !cancelled() }}
run: |
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --infile ${{ toJson(inputs.data) }} --post --job-name '${{inputs.build_name}}'
- name: Mark as done

18
.github/workflows/reusable_simple_job.yml vendored
View File

@ -34,12 +34,16 @@ name: Simple job
working-directory:
description: sets custom working directory
type: string
default: ""
default: "$GITHUB_WORKSPACE/tests/ci"
git_ref:
description: commit to use, merge commit for pr or head
required: false
type: string
default: ${{ github.event.after }} # no merge commit
report_required:
description: set to true if job report with the commit status required
type: boolean
default: false
secrets:
secret_envs:
description: if given, it's passed to the environments
@ -58,6 +62,8 @@ jobs:
env:
GITHUB_JOB_OVERRIDDEN: ${{inputs.test_name}}
steps:
- name: DebugInfo
uses: hmarr/debug-action@a701ed95a46e6f2fb0df25e1a558c16356fae35a
- name: Check out repository code
uses: ClickHouse/checkout@v1
with:
@ -79,12 +85,12 @@ jobs:
job_type: test
- name: Run
run: |
if [ -n '${{ inputs.working-directory }}' ]; then
cd "${{ inputs.working-directory }}"
else
cd "$GITHUB_WORKSPACE/tests/ci"
fi
cd "${{ inputs.working-directory }}"
${{ inputs.run_command }}
- name: Post
if: ${{ inputs.report_required }}
run: |
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --post --job-name '${{inputs.test_name}}'
- name: Clean
if: always()
uses: ./.github/actions/clean

21
.github/workflows/reusable_test.yml vendored
View File

@ -38,7 +38,7 @@ name: Testing workflow
working-directory:
description: sets custom working directory
type: string
default: ""
default: "$GITHUB_WORKSPACE/tests/ci"
secrets:
secret_envs:
description: if given, it's passed to the environments
@ -96,19 +96,14 @@ jobs:
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --infile ${{ toJson(inputs.data) }} --pre --job-name '${{inputs.test_name}}'
- name: Run
run: |
if [ -n "${{ inputs.working-directory }}" ]; then
cd "${{ inputs.working-directory }}"
else
cd "$GITHUB_WORKSPACE/tests/ci"
fi
if [ -n "$(echo '${{ inputs.run_command }}' | tr -d '\n')" ]; then
echo "Running command from workflow input"
${{ inputs.run_command }}
else
echo "Running command from job config"
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --infile ${{ toJson(inputs.data) }} --run --job-name '${{inputs.test_name}}'
fi
cd "${{ inputs.working-directory }}"
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" \
--infile ${{ toJson(inputs.data) }} \
--job-name '${{inputs.test_name}}' \
--run \
--run-command '''${{inputs.run_command}}'''
- name: Post run
if: ${{ !cancelled() }}
run: |
python3 "$GITHUB_WORKSPACE/tests/ci/ci.py" --infile ${{ toJson(inputs.data) }} --post --job-name '${{inputs.test_name}}'
- name: Mark as done

15
.gitmessage
View File

@ -1,9 +1,18 @@
## To avoid merge commit in CI run (add a leading space to apply):
#no-merge-commit
### CI modificators (add a leading space to apply):
## Running specified job (add a leading space to apply):
## To avoid a merge commit in CI:
#no_merge_commit
## To discard CI cache:
#no_ci_cache
## To run specified set of tests in CI:
#ci_set_<SET_NAME>
#ci_set_reduced
## To run specified job in CI:
#job_<JOB NAME>
#job_stateless_tests_release
#job_package_debug

7
base/poco/Foundation/include/Poco/BufferedStreamBuf.h
View File

@ -26,6 +26,11 @@
#include "Poco/StreamUtil.h"
namespace DB
{
class ReadBufferFromIStream;
}
namespace Poco
{
@ -120,6 +125,8 @@ protected:
openmode getMode() const { return _mode; }
private:
friend class DB::ReadBufferFromIStream;
virtual int readFromDevice(char_type * /*buffer*/, std::streamsize /*length*/) { return 0; }
virtual int writeToDevice(const char_type * /*buffer*/, std::streamsize /*length*/) { return 0; }

2
contrib/avro vendored

@ -1 +1 @@
Subproject commit 2fb8a8a6ec0eab9109b68abf3b4857e8c476b918
Subproject commit d43acc84d3d455b016f847d6666fbc3cd27f16a9

2
contrib/boost-cmake/CMakeLists.txt
View File

@ -44,12 +44,14 @@ set (SRCS_IOSTREAMS
"${LIBRARY_DIR}/libs/iostreams/src/gzip.cpp"
"${LIBRARY_DIR}/libs/iostreams/src/mapped_file.cpp"
"${LIBRARY_DIR}/libs/iostreams/src/zlib.cpp"
"${LIBRARY_DIR}/libs/iostreams/src/zstd.cpp"
)
add_library (_boost_iostreams ${SRCS_IOSTREAMS})
add_library (boost::iostreams ALIAS _boost_iostreams)
target_include_directories (_boost_iostreams PRIVATE ${LIBRARY_DIR})
target_link_libraries (_boost_iostreams PRIVATE ch_contrib::zlib)
target_link_libraries (_boost_iostreams PRIVATE ch_contrib::zstd)
# program_options

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

@ -34,9 +34,9 @@ if (OS_LINUX)
# avoid spurious latencies and additional work associated with
# MADV_DONTNEED. See
# https://github.com/ClickHouse/ClickHouse/issues/11121 for motivation.
set (JEMALLOC_CONFIG_MALLOC_CONF "percpu_arena:percpu,oversize_threshold:0,muzzy_decay_ms:5000,dirty_decay_ms:5000")
set (JEMALLOC_CONFIG_MALLOC_CONF "percpu_arena:percpu,oversize_threshold:0,muzzy_decay_ms:0,dirty_decay_ms:5000")
else()
set (JEMALLOC_CONFIG_MALLOC_CONF "oversize_threshold:0,muzzy_decay_ms:5000,dirty_decay_ms:5000")
set (JEMALLOC_CONFIG_MALLOC_CONF "oversize_threshold:0,muzzy_decay_ms:0,dirty_decay_ms:5000")
endif()
# CACHE variable is empty to allow changing defaults without the necessity
# to purge cache

6
contrib/update-submodules.sh vendored
View File

@ -6,9 +6,15 @@ SCRIPT_DIR=$(dirname "${SCRIPT_PATH}")
GIT_DIR=$(git -C "$SCRIPT_DIR" rev-parse --show-toplevel)
cd $GIT_DIR
# Exclude from contribs some garbage subdirs that we don't need.
# It reduces the checked out files size about 3 times and therefore speeds up indexing in IDEs and searching.
# NOTE .git/ still contains everything that we don't check out (although, it's compressed)
# See also https://git-scm.com/docs/git-sparse-checkout
contrib/sparse-checkout/setup-sparse-checkout.sh
git submodule init
git submodule sync
# NOTE: do not use --remote for `git submodule update`[1] command, since the submodule references to the specific commit SHA1 in the subproject.
# It may cause unexpected behavior. Instead you need to commit a new SHA1 for a submodule.
#

30
docker/server/entrypoint.sh
View File

@ -49,17 +49,10 @@ CLICKHOUSE_PASSWORD="${CLICKHOUSE_PASSWORD:-}"
CLICKHOUSE_DB="${CLICKHOUSE_DB:-}"
CLICKHOUSE_ACCESS_MANAGEMENT="${CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT:-0}"
for dir in "$DATA_DIR" \
"$ERROR_LOG_DIR" \
"$LOG_DIR" \
"$TMP_DIR" \
"$USER_PATH" \
"$FORMAT_SCHEMA_PATH" \
"${DISKS_PATHS[@]}" \
"${DISKS_METADATA_PATHS[@]}"
do
function create_directory_and_do_chown() {
local dir=$1
# check if variable not empty
[ -z "$dir" ] && continue
[ -z "$dir" ] && return
# ensure directories exist
if [ "$DO_CHOWN" = "1" ]; then
mkdir="mkdir"
@ -81,6 +74,23 @@ do
chown -R "$USER:$GROUP" "$dir"
fi
fi
}
create_directory_and_do_chown "$DATA_DIR"
# Change working directory to $DATA_DIR in case there're paths relative to $DATA_DIR, also avoids running
# clickhouse-server at root directory.
cd "$DATA_DIR"
for dir in "$ERROR_LOG_DIR" \
"$LOG_DIR" \
"$TMP_DIR" \
"$USER_PATH" \
"$FORMAT_SCHEMA_PATH" \
"${DISKS_PATHS[@]}" \
"${DISKS_METADATA_PATHS[@]}"
do
create_directory_and_do_chown "$dir"
done
# if clickhouse user is defined - create it (user "default" already exists out of box)

5
docker/test/fuzzer/run-fuzzer.sh
View File

@ -242,7 +242,7 @@ quit
--create-query-fuzzer-runs=50 \
--queries-file $(ls -1 ch/tests/queries/0_stateless/*.sql | sort -R) \
$NEW_TESTS_OPT \
> >(tail -n 100000 > fuzzer.log) \
> fuzzer.log \
2>&1 &
fuzzer_pid=$!
echo "Fuzzer pid is $fuzzer_pid"
@ -390,6 +390,7 @@ rg --text -F '<Fatal>' server.log > fatal.log ||:
dmesg -T > dmesg.log ||:
zstd --threads=0 server.log
zstd --threads=0 fuzzer.log
cat > report.html <<EOF ||:
<!DOCTYPE html>
@ -413,7 +414,7 @@ p.links a { padding: 5px; margin: 3px; background: #FFF; line-height: 2; white-s
<h1>AST Fuzzer for PR <a href="https://github.com/ClickHouse/ClickHouse/pull/${PR_TO_TEST}">#${PR_TO_TEST}</a> @ ${SHA_TO_TEST}</h1>
<p class="links">
<a href="run.log">run.log</a>
<a href="fuzzer.log">fuzzer.log</a>
<a href="fuzzer.log.zst">fuzzer.log.zst</a>
<a href="server.log.zst">server.log.zst</a>
<a href="main.log">main.log</a>
<a href="dmesg.log">dmesg.log</a>

58
docker/test/stateless/run.sh
View File

@ -99,6 +99,16 @@ if [[ -n "$USE_DATABASE_REPLICATED" ]] && [[ "$USE_DATABASE_REPLICATED" -eq 1 ]]
> /etc/clickhouse-server2/config.d/filesystem_caches_path.xml.tmp
mv /etc/clickhouse-server2/config.d/filesystem_caches_path.xml.tmp /etc/clickhouse-server2/config.d/filesystem_caches_path.xml
sudo cat /etc/clickhouse-server1/config.d/filesystem_caches_path.xml \
| sed "s|<custom_cached_disks_base_directory replace=\"replace\">/var/lib/clickhouse/filesystem_caches/</custom_cached_disks_base_directory>|<custom_cached_disks_base_directory replace=\"replace\">/var/lib/clickhouse/filesystem_caches_1/</custom_cached_disks_base_directory>|" \
> /etc/clickhouse-server1/config.d/filesystem_caches_path.xml.tmp
mv /etc/clickhouse-server1/config.d/filesystem_caches_path.xml.tmp /etc/clickhouse-server1/config.d/filesystem_caches_path.xml
sudo cat /etc/clickhouse-server2/config.d/filesystem_caches_path.xml \
| sed "s|<custom_cached_disks_base_directory replace=\"replace\">/var/lib/clickhouse/filesystem_caches/</custom_cached_disks_base_directory>|<custom_cached_disks_base_directory replace=\"replace\">/var/lib/clickhouse/filesystem_caches_2/</custom_cached_disks_base_directory>|" \
> /etc/clickhouse-server2/config.d/filesystem_caches_path.xml.tmp
mv /etc/clickhouse-server2/config.d/filesystem_caches_path.xml.tmp /etc/clickhouse-server2/config.d/filesystem_caches_path.xml
mkdir -p /var/run/clickhouse-server1
sudo chown clickhouse:clickhouse /var/run/clickhouse-server1
sudo -E -u clickhouse /usr/bin/clickhouse server --config /etc/clickhouse-server1/config.xml --daemon \
@ -235,6 +245,20 @@ clickhouse-client -q "system flush logs" ||:
# stop logs replication to make it possible to dump logs tables via clickhouse-local
stop_logs_replication
# Try to get logs while server is running
successfuly_saved=0
for table in query_log zookeeper_log trace_log transactions_info_log
do
clickhouse-client -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.tsv.zst
successfuly_saved=$?
if [[ -n "$USE_DATABASE_REPLICATED" ]] && [[ "$USE_DATABASE_REPLICATED" -eq 1 ]]; then
clickhouse-client -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.1.tsv.zst
successfuly_saved=$((successfuly_saved | $?))
clickhouse-client -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.2.tsv.zst
successfuly_saved=$((successfuly_saved | $?))
fi
done
# Stop server so we can safely read data with clickhouse-local.
# Why do we read data with clickhouse-local?
# Because it's the simplest way to read it when server has crashed.
@ -254,21 +278,25 @@ if [[ -n "$USE_S3_STORAGE_FOR_MERGE_TREE" ]] && [[ "$USE_S3_STORAGE_FOR_MERGE_TR
data_path_config="--config-file=/etc/clickhouse-server/config.xml"
fi
# Compress tables.
#
# NOTE:
# - that due to tests with s3 storage we cannot use /var/lib/clickhouse/data
# directly
# - even though ci auto-compress some files (but not *.tsv) it does this only
# for files >64MB, we want this files to be compressed explicitly
for table in query_log zookeeper_log trace_log transactions_info_log
do
clickhouse-local "$data_path_config" --only-system-tables -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.tsv.zst ||:
if [[ -n "$USE_DATABASE_REPLICATED" ]] && [[ "$USE_DATABASE_REPLICATED" -eq 1 ]]; then
clickhouse-local --path /var/lib/clickhouse1/ --only-system-tables -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.1.tsv.zst ||:
clickhouse-local --path /var/lib/clickhouse2/ --only-system-tables -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.2.tsv.zst ||:
fi
done
# If server crashed dump system logs with clickhouse-local
if [ $successfuly_saved -ne 0 ]; then
# Compress tables.
#
# NOTE:
# - that due to tests with s3 storage we cannot use /var/lib/clickhouse/data
# directly
# - even though ci auto-compress some files (but not *.tsv) it does this only
# for files >64MB, we want this files to be compressed explicitly
for table in query_log zookeeper_log trace_log transactions_info_log
do
clickhouse-local "$data_path_config" --only-system-tables -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.tsv.zst ||:
if [[ -n "$USE_DATABASE_REPLICATED" ]] && [[ "$USE_DATABASE_REPLICATED" -eq 1 ]]; then
clickhouse-local --path /var/lib/clickhouse1/ --only-system-tables -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.1.tsv.zst ||:
clickhouse-local --path /var/lib/clickhouse2/ --only-system-tables -q "select * from system.$table format TSVWithNamesAndTypes" | zstd --threads=0 > /test_output/$table.2.tsv.zst ||:
fi
done
fi
# Also export trace log in flamegraph-friendly format.
for trace_type in CPU Memory Real

64
docker/test/upgrade/run.sh
View File

@ -56,6 +56,9 @@ echo "ATTACH DATABASE system ENGINE=Ordinary" > /var/lib/clickhouse/metadata/sys
# Install previous release packages
install_packages previous_release_package_folder
# Save old settings from system table for settings changes check
clickhouse-local -q "select * from system.settings format Native" > old_settings.native
# Initial run without S3 to create system.*_log on local file system to make it
# available for dump via clickhouse-local
configure
@ -78,6 +81,7 @@ remove_keeper_config "create_if_not_exists" "[01]"
rm /etc/clickhouse-server/config.d/merge_tree.xml
rm /etc/clickhouse-server/config.d/enable_wait_for_shutdown_replicated_tables.xml
rm /etc/clickhouse-server/config.d/zero_copy_destructive_operations.xml
rm /etc/clickhouse-server/config.d/storage_conf_02963.xml
rm /etc/clickhouse-server/users.d/nonconst_timezone.xml
rm /etc/clickhouse-server/users.d/s3_cache_new.xml
rm /etc/clickhouse-server/users.d/replicated_ddl_entry.xml
@ -117,6 +121,7 @@ sudo chgrp clickhouse /etc/clickhouse-server/config.d/s3_storage_policy_by_defau
rm /etc/clickhouse-server/config.d/merge_tree.xml
rm /etc/clickhouse-server/config.d/enable_wait_for_shutdown_replicated_tables.xml
rm /etc/clickhouse-server/config.d/zero_copy_destructive_operations.xml
rm /etc/clickhouse-server/config.d/storage_conf_02963.xml
rm /etc/clickhouse-server/users.d/nonconst_timezone.xml
rm /etc/clickhouse-server/users.d/s3_cache_new.xml
rm /etc/clickhouse-server/users.d/replicated_ddl_entry.xml
@ -150,6 +155,63 @@ install_packages package_folder
export ZOOKEEPER_FAULT_INJECTION=1
configure
# Check that all new/changed setting were added in settings changes history.
# Some settings can be different for builds with sanitizers, so we check
# settings changes only for non-sanitizer builds.
IS_SANITIZED=$(clickhouse-local --query "SELECT value LIKE '%-fsanitize=%' FROM system.build_options WHERE name = 'CXX_FLAGS'")
if [ "${IS_SANITIZED}" -eq "0" ]
then
clickhouse-local -q "select * from system.settings format Native" > new_settings.native
clickhouse-local -nmq "
CREATE TABLE old_settings AS file('old_settings.native');
CREATE TABLE new_settings AS file('new_settings.native');
SELECT
name,
new_settings.value AS new_value,
old_settings.value AS old_value
FROM new_settings
LEFT JOIN old_settings ON new_settings.name = old_settings.name
WHERE (new_settings.value != old_settings.value) AND (name NOT IN (
SELECT arrayJoin(tupleElement(changes, 'name'))
FROM system.settings_changes
WHERE version = extract(version(), '^(?:\\d+\\.\\d+)')
))
SETTINGS join_use_nulls = 1
INTO OUTFILE 'changed_settings.txt'
FORMAT PrettyCompactNoEscapes;
SELECT name
FROM new_settings
WHERE (name NOT IN (
SELECT name
FROM old_settings
)) AND (name NOT IN (
SELECT arrayJoin(tupleElement(changes, 'name'))
FROM system.settings_changes
WHERE version = extract(version(), '^(?:\\d+\\.\\d+)')
))
INTO OUTFILE 'new_settings.txt'
FORMAT PrettyCompactNoEscapes;
"
if [ -s changed_settings.txt ]
then
mv changed_settings.txt /test_output/
echo -e "Changed settings are not reflected in settings changes history (see changed_settings.txt)$FAIL$(head_escaped /test_output/changed_settings.txt)" >> /test_output/test_results.tsv
else
echo -e "There are no changed settings or they are reflected in settings changes history$OK" >> /test_output/test_results.tsv
fi
if [ -s new_settings.txt ]
then
mv new_settings.txt /test_output/
echo -e "New settings are not reflected in settings changes history (see new_settings.txt)$FAIL$(head_escaped /test_output/new_settings.txt)" >> /test_output/test_results.tsv
else
echo -e "There are no new settings or they are reflected in settings changes history$OK" >> /test_output/test_results.tsv
fi
fi
# Just in case previous version left some garbage in zk
sudo cat /etc/clickhouse-server/config.d/lost_forever_check.xml \
| sed "s|>1<|>0<|g" \
@ -255,6 +317,8 @@ clickhouse-local --structure "test String, res String, time Nullable(Float32), d
(test like '%Fatal message%') DESC,
(test like '%Error message%') DESC,
(test like '%previous release%') DESC,
(test like '%Changed settings%') DESC,
(test like '%New settings%') DESC,
rowNumberInAllBlocks()
LIMIT 1" < /test_output/test_results.tsv > /test_output/check_status.tsv || echo "failure\tCannot parse test_results.tsv" > /test_output/check_status.tsv
[ -s /test_output/check_status.tsv ] || echo -e "success\tNo errors found" > /test_output/check_status.tsv

35
docs/en/engines/table-engines/integrations/mysql.md
View File

@ -16,7 +16,7 @@ CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1] [TTL expr1],
name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2] [TTL expr2],
...
) ENGINE = MySQL('host:port', 'database', 'table', 'user', 'password'[, replace_query, 'on_duplicate_clause'])
) ENGINE = MySQL({host:port, database, table, user, password[, replace_query, on_duplicate_clause] | named_collection[, option=value [,..]]})
SETTINGS
[ connection_pool_size=16, ]
[ connection_max_tries=3, ]
@ -42,23 +42,17 @@ The MySQL Table Engine is currently not available on the ClickHouse builds for M
**Engine Parameters**
- `host:port` — MySQL server address.
- `database` — Remote database name.
- `table` — Remote table name.
- `user` — MySQL user.
- `password` — User password.
- `replace_query` — Flag that converts `INSERT INTO` queries to `REPLACE INTO`. If `replace_query=1`, the query is substituted.
- `on_duplicate_clause` — The `ON DUPLICATE KEY on_duplicate_clause` expression that is added to the `INSERT` query.
Example: `INSERT INTO t (c1,c2) VALUES ('a', 2) ON DUPLICATE KEY UPDATE c2 = c2 + 1`, where `on_duplicate_clause` is `UPDATE c2 = c2 + 1`. See the [MySQL documentation](https://dev.mysql.com/doc/refman/8.0/en/insert-on-duplicate.html) to find which `on_duplicate_clause` you can use with the `ON DUPLICATE KEY` clause.
To specify `on_duplicate_clause` you need to pass `0` to the `replace_query` parameter. If you simultaneously pass `replace_query = 1` and `on_duplicate_clause`, ClickHouse generates an exception.
Arguments also can be passed using [named collections](/docs/en/operations/named-collections.md). In this case `host` and `port` should be specified separately. This approach is recommended for production environment.
Simple `WHERE` clauses such as `=, !=, >, >=, <, <=` are executed on the MySQL server.
The rest of the conditions and the `LIMIT` sampling constraint are executed in ClickHouse only after the query to MySQL finishes.
@ -71,7 +65,7 @@ CREATE TABLE test_replicas (id UInt32, name String, age UInt32, money UInt32) EN
## Usage Example {#usage-example}
Table in MySQL:
Create table in MySQL:
``` text
mysql> CREATE TABLE `test`.`test` (
@ -94,7 +88,7 @@ mysql> select * from test;
1 row in set (0,00 sec)
```
Table in ClickHouse, retrieving data from the MySQL table created above:
Create table in ClickHouse using plain arguments:
``` sql
CREATE TABLE mysql_table
@ -105,6 +99,25 @@ CREATE TABLE mysql_table
ENGINE = MySQL('localhost:3306', 'test', 'test', 'bayonet', '123')
```
Or using [named collections](/docs/en/operations/named-collections.md):
```sql
CREATE NAMED COLLECTION creds AS
host = 'localhost',
port = 3306,
database = 'test',
user = 'bayonet',
password = '123';
CREATE TABLE mysql_table
(
`float_nullable` Nullable(Float32),
`int_id` Int32
)
ENGINE = MySQL(creds, table='test')
```
Retrieving data from MySQL table:
``` sql
SELECT * FROM mysql_table
```

28
docs/en/engines/table-engines/integrations/postgresql.md
View File

@ -16,7 +16,7 @@ CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
name1 type1 [DEFAULT|MATERIALIZED|ALIAS expr1] [TTL expr1],
name2 type2 [DEFAULT|MATERIALIZED|ALIAS expr2] [TTL expr2],
...
) ENGINE = PostgreSQL('host:port', 'database', 'table', 'user', 'password'[, `schema`]);
) ENGINE = PostgreSQL({host:port, database, table, user, password[, schema, [, on_conflict]] | named_collection[, option=value [,..]]})
```
See a detailed description of the [CREATE TABLE](../../../sql-reference/statements/create/table.md#create-table-query) query.
@ -35,31 +35,25 @@ The table structure can differ from the original PostgreSQL table structure:
- `user` — PostgreSQL user.
- `password` — User password.
- `schema` — Non-default table schema. Optional.
- `on conflict ...` — example: `ON CONFLICT DO NOTHING`. Optional. Note: adding this option will make insertion less efficient.
- `on_conflict` — Conflict resolution strategy. Example: `ON CONFLICT DO NOTHING`. Optional. Note: adding this option will make insertion less efficient.
or via config (since version 21.11):
[Named collections](/docs/en/operations/named-collections.md) (available since version 21.11) are recommended for production environment. Here is an example:
```
<named_collections>
<postgres1>
<host></host>
<port></port>
<user></user>
<password></password>
<table></table>
</postgres1>
<postgres2>
<host></host>
<port></port>
<user></user>
<password></password>
</postgres2>
<postgres_creds>
<host>localhost</host>
<port>5432</port>
<user>postgres</user>
<password>****</password>
<schema>schema1</schema>
</postgres_creds>
</named_collections>
```
Some parameters can be overridden by key value arguments:
``` sql
SELECT * FROM postgresql(postgres1, schema='schema1', table='table1');
SELECT * FROM postgresql(postgres_creds, table='table1');
```
## Implementation Details {#implementation-details}

45
docs/en/engines/table-engines/integrations/redis.md
View File

@ -16,30 +16,32 @@ CREATE TABLE [IF NOT EXISTS] [db.]table_name
name1 [type1],
name2 [type2],
...
) ENGINE = Redis(host:port[, db_index[, password[, pool_size]]]) PRIMARY KEY(primary_key_name);
) ENGINE = Redis({host:port[, db_index[, password[, pool_size]]] | named_collection[, option=value [,..]] })
PRIMARY KEY(primary_key_name);
```
**Engine Parameters**
- `host:port` — Redis server address, you can ignore port and default Redis port 6379 will be used.
- `db_index` — Redis db index range from 0 to 15, default is 0.
- `password` — User password, default is blank string.
- `pool_size` — Redis max connection pool size, default is 16.
- `primary_key_name` - any column name in the column list.
- `primary` must be specified, it supports only one column in the primary key. The primary key will be serialized in binary as a Redis key.
:::note Serialization
`PRIMARY KEY` supports only one column. The primary key will be serialized in binary as a Redis key.
Columns other than the primary key will be serialized in binary as Redis value in corresponding order.
:::
- columns other than the primary key will be serialized in binary as Redis value in corresponding order.
Arguments also can be passed using [named collections](/docs/en/operations/named-collections.md). In this case `host` and `port` should be specified separately. This approach is recommended for production environment. At this moment, all parameters passed using named collections to redis are required.
- queries with key equals or in filtering will be optimized to multi keys lookup from Redis. If queries without filtering key full table scan will happen which is a heavy operation.
:::note Filtering
Queries with `key equals` or `in filtering` will be optimized to multi keys lookup from Redis. If queries without filtering key full table scan will happen which is a heavy operation.
:::
## Usage Example {#usage-example}
Create a table in ClickHouse which allows to read data from Redis:
Create a table in ClickHouse using `Redis` engine with plain arguments:
``` sql
CREATE TABLE redis_table
@ -52,6 +54,31 @@ CREATE TABLE redis_table
ENGINE = Redis('redis1:6379') PRIMARY KEY(key);
```
Or using [named collections](/docs/en/operations/named-collections.md):
```
<named_collections>
<redis_creds>
<host>localhost</host>
<port>6379</port>
<password>****</password>
<pool_size>16</pool_size>
<db_index>s0</db_index>
</redis_creds>
</named_collections>
```
```sql
CREATE TABLE redis_table
(
`key` String,
`v1` UInt32,
`v2` String,
`v3` Float32
)
ENGINE = Redis(redis_creds) PRIMARY KEY(key);
```
Insert:
```sql

61
docs/en/engines/table-engines/mergetree-family/mergetree.md
View File

@ -39,8 +39,8 @@ If you need to update rows frequently, we recommend using the [`ReplacingMergeTr
``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
name1 [type1] [[NOT] NULL] [DEFAULT|MATERIALIZED|ALIAS|EPHEMERAL expr1] [COMMENT ...] [CODEC(codec1)] [STATISTIC(stat1)] [TTL expr1] [PRIMARY KEY],
name2 [type2] [[NOT] NULL] [DEFAULT|MATERIALIZED|ALIAS|EPHEMERAL expr2] [COMMENT ...] [CODEC(codec2)] [STATISTIC(stat2)] [TTL expr2] [PRIMARY KEY],
name1 [type1] [[NOT] NULL] [DEFAULT|MATERIALIZED|ALIAS|EPHEMERAL expr1] [COMMENT ...] [CODEC(codec1)] [STATISTIC(stat1)] [TTL expr1] [PRIMARY KEY] [SETTINGS (name = value, ...)],
name2 [type2] [[NOT] NULL] [DEFAULT|MATERIALIZED|ALIAS|EPHEMERAL expr2] [COMMENT ...] [CODEC(codec2)] [STATISTIC(stat2)] [TTL expr2] [PRIMARY KEY] [SETTINGS (name = value, ...)],
...
INDEX index_name1 expr1 TYPE type1(...) [GRANULARITY value1],
INDEX index_name2 expr2 TYPE type2(...) [GRANULARITY value2],
@ -56,7 +56,7 @@ ORDER BY expr
[DELETE|TO DISK 'xxx'|TO VOLUME 'xxx' [, ...] ]
[WHERE conditions]
[GROUP BY key_expr [SET v1 = aggr_func(v1) [, v2 = aggr_func(v2) ...]] ] ]
[SETTINGS name=value, ...]
[SETTINGS name = value, ...]
```
For a description of parameters, see the [CREATE query description](/docs/en/sql-reference/statements/create/table.md).
@ -620,7 +620,7 @@ The `TTL` clause can’t be used for key columns.
#### Creating a table with `TTL`:
``` sql
CREATE TABLE example_table
CREATE TABLE tab
(
d DateTime,
a Int TTL d + INTERVAL 1 MONTH,
@ -635,7 +635,7 @@ ORDER BY d;
#### Adding TTL to a column of an existing table
``` sql
ALTER TABLE example_table
ALTER TABLE tab
MODIFY COLUMN
c String TTL d + INTERVAL 1 DAY;
```
@ -643,7 +643,7 @@ ALTER TABLE example_table
#### Altering TTL of the column
``` sql
ALTER TABLE example_table
ALTER TABLE tab
MODIFY COLUMN
c String TTL d + INTERVAL 1 MONTH;
```
@ -681,7 +681,7 @@ If a column is not part of the `GROUP BY` expression and is not set explicitly i
#### Creating a table with `TTL`:
``` sql
CREATE TABLE example_table
CREATE TABLE tab
(
d DateTime,
a Int
@ -697,7 +697,7 @@ TTL d + INTERVAL 1 MONTH DELETE,
#### Altering `TTL` of the table:
``` sql
ALTER TABLE example_table
ALTER TABLE tab
MODIFY TTL d + INTERVAL 1 DAY;
```
@ -1366,7 +1366,7 @@ In this sample configuration:
The statistic declaration is in the columns section of the `CREATE` query for tables from the `*MergeTree*` Family when we enable `set allow_experimental_statistic = 1`.
``` sql
CREATE TABLE example_table
CREATE TABLE tab
(
a Int64 STATISTIC(tdigest),
b Float64
@ -1378,8 +1378,8 @@ ORDER BY a
We can also manipulate statistics with `ALTER` statements.
```sql
ALTER TABLE example_table ADD STATISTIC b TYPE tdigest;
ALTER TABLE example_table DROP STATISTIC a TYPE tdigest;
ALTER TABLE tab ADD STATISTIC b TYPE tdigest;
ALTER TABLE tab DROP STATISTIC a TYPE tdigest;
```
These lightweight statistics aggregate information about distribution of values in columns.
@ -1390,3 +1390,42 @@ They can be used for query optimization when we enable `set allow_statistic_opti
- `tdigest`
Stores distribution of values from numeric columns in [TDigest](https://github.com/tdunning/t-digest) sketch.
## Column-level Settings {#column-level-settings}
Certain MergeTree settings can be override 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.
Example:
```sql
CREATE TABLE tab
(
id Int64,
document String SETTINGS (min_compress_block_size = 16777216, max_compress_block_size = 16777216)
)
ENGINE = MergeTree
ORDER BY id
```
Column-level settings can be modified or removed using [ALTER MODIFY COLUMN](/docs/en/sql-reference/statements/alter/column.md), for example:
- Remove `SETTINGS` from column declaration:
```sql
ALTER TABLE tab MODIFY COLUMN document REMOVE SETTINGS;
```
- Modify a setting:
```sql
ALTER TABLE tab MODIFY COLUMN document MODIFY SETTING min_compress_block_size = 8192;
```
- Reset one or more settings, also removes the setting declaration in the column expression of the table's CREATE query.
```sql
ALTER TABLE tab MODIFY COLUMN document RESET SETTING min_compress_block_size;
```

207
docs/en/operations/allocation-profiling.md Normal file
View File

@ -0,0 +1,207 @@
---
slug: /en/operations/allocation-profiling
sidebar_label: "Allocation profiling"
title: "Allocation profiling"
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Allocation profiling
ClickHouse uses [jemalloc](https://github.com/jemalloc/jemalloc) as its global allocator that comes with some tools for allocation sampling and profiling.
To make allocation profiling more convenient, `SYSTEM` commands are provided along 4LW commands in Keeper.
## Sampling allocations and flushing heap profiles
If we want to sample and profile allocations in `jemalloc`, we need to start ClickHouse/Keeper with profiling enabled using environment variable `MALLOC_CONF`.
```sh
MALLOC_CONF=background_thread:true,prof:true
```
`jemalloc` will sample allocation and store the information internally.
We can tell `jemalloc` to flush current profile by running:
<Tabs groupId="binary">
<TabItem value="clickhouse" label="ClickHouse">
SYSTEM JEMALLOC FLUSH PROFILE
</TabItem>
<TabItem value="keeper" label="Keeper">
echo jmfp | nc localhost 9181
</TabItem>
</Tabs>
By default, heap profile file will be generated in `/tmp/jemalloc_clickhouse._pid_._seqnum_.heap` where `_pid_` is the PID of ClickHouse and `_seqnum_` is the global sequence number for the current heap profile.
For Keeper, the default file is `/tmp/jemalloc_keeper._pid_._seqnum_.heap` following the same rules.
A different location can be defined by appending the `MALLOC_CONF` environment variable with `prof_prefix` option.
For example, if we want to generate profiles in `/data` folder where the prefix for filename will be `my_current_profile` we can run ClickHouse/Keeper with following environment variable:
```sh
MALLOC_CONF=background_thread:true,prof:true,prof_prefix:/data/my_current_profile
```
Generated file will append to prefix PID and sequence number.
## Analyzing heap profiles
After we generated heap profiles, we need to analyze them.
For that, we need to use `jemalloc`'s tool called [jeprof](https://github.com/jemalloc/jemalloc/blob/dev/bin/jeprof.in) which can be installed in multiple ways:
- installing `jemalloc` using system's package manager
- cloning [jemalloc repo](https://github.com/jemalloc/jemalloc) and running autogen.sh from the root folder that will provide you with `jeprof` script inside the `bin` folder
:::note
`jeprof` uses `addr2line` to generate stacktraces which can be really slow.
If that’s the case, we recommend installing an [alternative implementation](https://github.com/gimli-rs/addr2line) of the tool.
```
git clone https://github.com/gimli-rs/addr2line
cd addr2line
cargo b --examples -r
cp ./target/release/examples/addr2line path/to/current/addr2line
```
:::
There are many different formats to generate from the heap profile using `jeprof`.
We recommend to run `jeprof --help` to check usage and many different options the tool provides.
In general, `jeprof` command will look like this:
```sh
jeprof path/to/binary path/to/heap/profile --output_format [ > output_file]
```
If we want to compare which allocations happened between 2 profiles we can set the base argument:
```sh
jeprof path/to/binary --base path/to/first/heap/profile path/to/second/heap/profile --output_format [ > output_file]
```
For example:
- if we want to generate a text file with each procedure written per line:
```sh
jeprof path/to/binary path/to/heap/profile --text > result.txt
```
- if we want to generate a PDF file with call-graph:
```sh
jeprof path/to/binary path/to/heap/profile --pdf > result.pdf
```
### Generating flame graph
`jeprof` allows us to generate collapsed stacks for building flame graphs.
We need to use `--collapsed` argument:
```sh
jeprof path/to/binary path/to/heap/profile --collapsed > result.collapsed
```
After that, we can use many different tools to visualize collapsed stacks.
Most popular would be [FlameGraph](https://github.com/brendangregg/FlameGraph) which contains a script called `flamegraph.pl`:
```sh
cat result.collapsed | /path/to/FlameGraph/flamegraph.pl --color=mem --title="Allocation Flame Graph" --width 2400 > result.svg
```
Another interesting tool is [speedscope](https://www.speedscope.app/) that allows you to analyze collected stacks in a more interactive way.
## Controlling allocation profiler during runtime
If ClickHouse/Keeper were started with enabled profiler, they support additional commands for disabling/enabling allocation profiling during runtime.
Using those commands, it's easier to profile only specific intervals.
Disable profiler:
<Tabs groupId="binary">
<TabItem value="clickhouse" label="ClickHouse">
SYSTEM JEMALLOC DISABLE PROFILE
</TabItem>
<TabItem value="keeper" label="Keeper">
echo jmdp | nc localhost 9181
</TabItem>
</Tabs>
Enable profiler:
<Tabs groupId="binary">
<TabItem value="clickhouse" label="ClickHouse">
SYSTEM JEMALLOC ENABLE PROFILE
</TabItem>
<TabItem value="keeper" label="Keeper">
echo jmep | nc localhost 9181
</TabItem>
</Tabs>
It's also possible to control the initial state of the profiler by setting `prof_active` option which is enabled by default.
For example, if we don't want to sample allocations during startup but only after we enable the profiler, we can start ClickHouse/Keeper with following environment variable:
```sh
MALLOC_CONF=background_thread:true,prof:true,prof_active:false
```
and enable profiler at a later point.
## Additional options for profiler
`jemalloc` has many different options available related to profiler which can be controlled by modifying `MALLOC_CONF` environment variable.
For example, interval between allocation samples can be controlled with `lg_prof_sample`.
If you want to dump heap profile every N bytes you can enable it using `lg_prof_interval`.
We recommend to check `jemalloc`s [reference page](https://jemalloc.net/jemalloc.3.html) for such options.
## Other resources
ClickHouse/Keeper expose `jemalloc` related metrics in many different ways.
:::warning Warning
It's important to be aware that none of these metrics are synchronized with each other and values may drift.
:::
### System table `asynchronous_metrics`
```sql
SELECT *
FROM system.asynchronous_metrics
WHERE metric ILIKE '%jemalloc%'
FORMAT Vertical
```
[Reference](/en/operations/system-tables/asynchronous_metrics)
### System table `jemalloc_bins`
Contains information about memory allocations done via jemalloc allocator in different size classes (bins) aggregated from all arenas.
[Reference](/en/operations/system-tables/jemalloc_bins)
### Prometheus
All `jemalloc` related metrics from `asynchronous_metrics` are also exposed using Prometheus endpoint in both ClickHouse and Keeper.
[Reference](/en/operations/server-configuration-parameters/settings#prometheus)
### `jmst` 4LW command in Keeper
Keeper supports `jmst` 4LW command which returns [basic allocator statistics](https://github.com/jemalloc/jemalloc/wiki/Use-Case%3A-Basic-Allocator-Statistics).
Example:
```sh
echo jmst | nc localhost 9181
```

14
docs/en/operations/cluster-discovery.md
View File

@ -65,6 +65,20 @@ With Cluster Discovery, rather than defining each node explicitly, you simply sp
<cluster_name>
<discovery>
<path>/clickhouse/discovery/cluster_name</path>
<!-- # Optional configuration parameters: -->
<!-- ## Authentication credentials to access all other nodes in cluster: -->
<!-- <user>user1</user> -->
<!-- <password>pass123</password> -->
<!-- ### Alternatively to password, interserver secret may be used: -->
<!-- <secret>secret123</secret> -->
<!-- ## Shard for current node (see below): -->
<!-- <shard>1</shard> -->
<!-- ## Observer mode (see below): -->
<!-- <observer/> -->
</discovery>
</cluster_name>
</remote_servers>

9
docs/en/operations/named-collections.md
View File

@ -5,9 +5,9 @@ sidebar_label: "Named collections"
title: "Named collections"
---
Named collections provide a way to store collections of key-value pairs to be
Named collections provide a way to store collections of key-value pairs to be
used to configure integrations with external sources. You can use named collections with
dictionaries, tables, table functions, and object storage.
dictionaries, tables, table functions, and object storage.
Named collections can be configured with DDL or in configuration files and are applied
when ClickHouse starts. They simplify the creation of objects and the hiding of credentials
@ -64,7 +64,7 @@ To manage named collections with DDL a user must have the `named_control_collect
```
:::tip
In the above example the `password_sha256_hex` value is the hexadecimal representation of the SHA256 hash of the password. This configuration for the user `default` has the attribute `replace=true` as in the default configuration has a plain text `password` set, and it is not possible to have both plain text and sha256 hex passwords set for a user.
In the above example the `password_sha256_hex` value is the hexadecimal representation of the SHA256 hash of the password. This configuration for the user `default` has the attribute `replace=true` as in the default configuration has a plain text `password` set, and it is not possible to have both plain text and sha256 hex passwords set for a user.
:::
## Storing named collections in configuration files
@ -296,7 +296,6 @@ host = '127.0.0.1',
port = 5432,
database = 'test',
schema = 'test_schema',
connection_pool_size = 8
```
Example of configuration:
@ -310,7 +309,6 @@ Example of configuration:
<port>5432</port>
<database>test</database>
<schema>test_schema</schema>
<connection_pool_size>8</connection_pool_size>
</mypg>
</named_collections>
</clickhouse>
@ -445,4 +443,3 @@ SELECT dictGet('dict', 'b', 1);
│ a │
└─────────────────────────┘
```

7
docs/en/operations/server-configuration-parameters/settings.md
View File

@ -2866,3 +2866,10 @@ This also allows a mix of resolver types can be used.
### disable_tunneling_for_https_requests_over_http_proxy {#disable_tunneling_for_https_requests_over_http_proxy}
By default, tunneling (i.e, `HTTP CONNECT`) is used to make `HTTPS` requests over `HTTP` proxy. This setting can be used to disable it.
## max_materialized_views_count_for_table {#max_materialized_views_count_for_table}
A limit on the number of materialized views attached to a table.
Note that only directly dependent views are considered here, and the creation of one view on top of another view is not considered.
Default value: `0`.

6
docs/en/operations/settings/query-complexity.md
View File

@ -172,7 +172,7 @@ If you set `timeout_before_checking_execution_speed `to 0, ClickHouse will use c
## timeout_overflow_mode {#timeout-overflow-mode}
What to do if the query is run longer than `max_execution_time`: `throw` or `break`. By default, `throw`.
What to do if the query is run longer than `max_execution_time` or the estimated running time is longer than `max_estimated_execution_time`: `throw` or `break`. By default, `throw`.
# max_execution_time_leaf
@ -214,6 +214,10 @@ A maximum number of execution bytes per second. Checked on every data block when
Checks that execution speed is not too slow (no less than ‘min_execution_speed’), after the specified time in seconds has expired.
## max_estimated_execution_time {#max_estimated_execution_time}
Maximum query estimate execution time in seconds. Checked on every data block when ‘timeout_before_checking_execution_speed’ expires.
## max_columns_to_read {#max-columns-to-read}
A maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.

8
docs/en/operations/settings/settings-formats.md
View File

@ -1597,7 +1597,13 @@ Result:
Use ANSI escape sequences to paint colors in Pretty formats.
Enabled by default.
possible values:
- `0` — Disabled. Pretty formats do not use ANSI escape sequences.
- `1` — Enabled. Pretty formats will use ANSI escape sequences except for `NoEscapes` formats.
- `auto` - Enabled if `stdout` is a terminal except for `NoEscapes` formats.
Default value is `auto`.
### output_format_pretty_grid_charset {#output_format_pretty_grid_charset}

2
docs/en/operations/system-tables/asynchronous_metric_log.md
View File

@ -10,7 +10,7 @@ Columns:
- `hostname` ([LowCardinality(String)](../../sql-reference/data-types/string.md)) — Hostname of the server executing the query.
- `event_date` ([Date](../../sql-reference/data-types/date.md)) — Event date.
- `event_time` ([DateTime](../../sql-reference/data-types/datetime.md)) — Event time.
- `name` ([String](../../sql-reference/data-types/string.md)) — Metric name.
- `metric` ([String](../../sql-reference/data-types/string.md)) — Metric name.
- `value` ([Float64](../../sql-reference/data-types/float.md)) — Metric value.
**Example**

2
docs/en/operations/system-tables/replication_queue.md
View File

@ -49,7 +49,7 @@ Columns:
- `last_attempt_time` ([DateTime](../../sql-reference/data-types/datetime.md)) — Date and time when the task was last attempted.
- `num_postponed` ([UInt32](../../sql-reference/data-types/int-uint.md)) — The number of postponed tasks.
- `num_postponed` ([UInt32](../../sql-reference/data-types/int-uint.md)) — The number of times the action was postponed.
- `postpone_reason` ([String](../../sql-reference/data-types/string.md)) — The reason why the task was postponed.

1
docs/en/sql-reference/aggregate-functions/reference/index.md
View File

@ -88,6 +88,7 @@ ClickHouse-specific aggregate functions:
- [quantileTDigestWeighted](/docs/en/sql-reference/aggregate-functions/reference/quantiletdigestweighted.md)
- [quantileBFloat16](/docs/en/sql-reference/aggregate-functions/reference/quantilebfloat16.md#quantilebfloat16)
- [quantileBFloat16Weighted](/docs/en/sql-reference/aggregate-functions/reference/quantilebfloat16.md#quantilebfloat16weighted)
- [quantileDDSketch](/docs/en/sql-reference/aggregate-functions/reference/quantileddsketch.md#quantileddsketch)
- [simpleLinearRegression](/docs/en/sql-reference/aggregate-functions/reference/simplelinearregression.md)
- [stochasticLinearRegression](/docs/en/sql-reference/aggregate-functions/reference/stochasticlinearregression.md)
- [stochasticLogisticRegression](/docs/en/sql-reference/aggregate-functions/reference/stochasticlogisticregression.md)

1
docs/en/sql-reference/aggregate-functions/reference/median.md
View File

@ -18,6 +18,7 @@ Functions:
- `medianTDigest` — Alias for [quantileTDigest](../../../sql-reference/aggregate-functions/reference/quantiletdigest.md#quantiletdigest).
- `medianTDigestWeighted` — Alias for [quantileTDigestWeighted](../../../sql-reference/aggregate-functions/reference/quantiletdigestweighted.md#quantiletdigestweighted).
- `medianBFloat16` — Alias for [quantileBFloat16](../../../sql-reference/aggregate-functions/reference/quantilebfloat16.md#quantilebfloat16).
- `medianDDSketch` — Alias for [quantileDDSketch](../../../sql-reference/aggregate-functions/reference/quantileddsketch.md#quantileddsketch).
**Example**

61
docs/en/sql-reference/aggregate-functions/reference/quantileddsketch.md Normal file
View File

@ -0,0 +1,61 @@
---
slug: /en/sql-reference/aggregate-functions/reference/quantileddsketch
sidebar_position: 211
title: quantileDDSketch
---
Computes an approximate [quantile](https://en.wikipedia.org/wiki/Quantile) of a sample with relative-error guarantees. It works by building a [DDSketch](https://www.vldb.org/pvldb/vol12/p2195-masson.pdf).
**Syntax**
``` sql
quantileDDsketch[relative_accuracy, (level)](expr)
```
**Arguments**
- `expr` — Column with numeric data. [Integer](../../../sql-reference/data-types/int-uint.md), [Float](../../../sql-reference/data-types/float.md).
**Parameters**
- `relative_accuracy` — Relative accuracy of the quantile. Possible values are in the range from 0 to 1. [Float](../../../sql-reference/data-types/float.md). The size of the sketch depends on the range of the data and the relative accuracy. The larger the range and the smaller the relative accuracy, the larger the sketch. The rough memory size of the of the sketch is `log(max_value/min_value)/relative_accuracy`. The recommended value is 0.001 or higher.
- `level` — Level of quantile. Optional. Possible values are in the range from 0 to 1. Default value: 0.5. [Float](../../../sql-reference/data-types/float.md).
**Returned value**
- Approximate quantile of the specified level.
Type: [Float64](../../../sql-reference/data-types/float.md#float32-float64).
**Example**
Input table has an integer and a float columns:
``` text
┌─a─┬─────b─┐
│ 1 │ 1.001 │
│ 2 │ 1.002 │
│ 3 │ 1.003 │
│ 4 │ 1.004 │
└───┴───────┘
```
Query to calculate 0.75-quantile (third quartile):
``` sql
SELECT quantileDDSketch(0.01, 0.75)(a), quantileDDSketch(0.01, 0.75)(b) FROM example_table;
```
Result:
``` text
┌─quantileDDSketch(0.01, 0.75)(a)─┬─quantileDDSketch(0.01, 0.75)(b)─┐
│ 2.974233423476717 │ 1.01 │
└─────────────────────────────────┴─────────────────────────────────┘
```
**See Also**
- [median](../../../sql-reference/aggregate-functions/reference/median.md#median)
- [quantiles](../../../sql-reference/aggregate-functions/reference/quantiles.md#quantiles)

2
docs/en/sql-reference/aggregate-functions/reference/quantiles.md
View File

@ -9,7 +9,7 @@ sidebar_position: 201
Syntax: `quantiles(level1, level2, …)(x)`
All the quantile functions also have corresponding quantiles functions: `quantiles`, `quantilesDeterministic`, `quantilesTiming`, `quantilesTimingWeighted`, `quantilesExact`, `quantilesExactWeighted`, `quantileInterpolatedWeighted`, `quantilesTDigest`, `quantilesBFloat16`. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.
All the quantile functions also have corresponding quantiles functions: `quantiles`, `quantilesDeterministic`, `quantilesTiming`, `quantilesTimingWeighted`, `quantilesExact`, `quantilesExactWeighted`, `quantileInterpolatedWeighted`, `quantilesTDigest`, `quantilesBFloat16`, `quantilesDDSketch`. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.
## quantilesExactExclusive

4
docs/en/sql-reference/data-types/float.md
View File

@ -35,8 +35,8 @@ Types are equivalent to types of C:
Aliases:
- `Float32` — `FLOAT`.
- `Float64` — `DOUBLE`.
- `Float32` — `FLOAT`, `REAL`, `SINGLE`.
- `Float64` — `DOUBLE`, `DOUBLE PRECISION`.
When creating tables, numeric parameters for floating point numbers can be set (e.g. `FLOAT(12)`, `FLOAT(15, 22)`, `DOUBLE(12)`, `DOUBLE(4, 18)`), but ClickHouse ignores them.

16
docs/en/sql-reference/data-types/int-uint.md
View File

@ -21,10 +21,10 @@ When creating tables, numeric parameters for integer numbers can be set (e.g. `T
Aliases:
- `Int8` — `TINYINT`, `BOOL`, `BOOLEAN`, `INT1`.
- `Int16` — `SMALLINT`, `INT2`.
- `Int32` — `INT`, `INT4`, `INTEGER`.
- `Int64` — `BIGINT`.
- `Int8` — `TINYINT`, `INT1`, `BYTE`, `TINYINT SIGNED`, `INT1 SIGNED`.
- `Int16` — `SMALLINT`, `SMALLINT SIGNED`.
- `Int32` — `INT`, `INTEGER`, `MEDIUMINT`, `MEDIUMINT SIGNED`, `INT SIGNED`, `INTEGER SIGNED`.
- `Int64` — `BIGINT`, `SIGNED`, `BIGINT SIGNED`, `TIME`.
## UInt Ranges
@ -34,3 +34,11 @@ Aliases:
- `UInt64` — \[0 : 18446744073709551615\]
- `UInt128` — \[0 : 340282366920938463463374607431768211455\]
- `UInt256` — \[0 : 115792089237316195423570985008687907853269984665640564039457584007913129639935\]
Aliases:
- `UInt8` — `TINYINT UNSIGNED`, `INT1 UNSIGNED`.
- `UInt16` — `SMALLINT UNSIGNED`.
- `UInt32` — `MEDIUMINT UNSIGNED`, `INT UNSIGNED`, `INTEGER UNSIGNED`
- `UInt64` — `UNSIGNED`, `BIGINT UNSIGNED`, `BIT`, `SET`

6
docs/en/sql-reference/data-types/nullable.md
View File

@ -4,11 +4,11 @@ sidebar_position: 55
sidebar_label: Nullable
---
# Nullable(typename)
# Nullable(T)
Allows to store special marker ([NULL](../../sql-reference/syntax.md)) that denotes “missing value” alongside normal values allowed by `TypeName`. For example, a `Nullable(Int8)` type column can store `Int8` type values, and the rows that do not have a value will store `NULL`.
Allows to store special marker ([NULL](../../sql-reference/syntax.md)) that denotes “missing value” alongside normal values allowed by `T`. For example, a `Nullable(Int8)` type column can store `Int8` type values, and the rows that do not have a value will store `NULL`.
For a `TypeName`, you can’t use composite data types [Array](../../sql-reference/data-types/array.md), [Map](../../sql-reference/data-types/map.md) and [Tuple](../../sql-reference/data-types/tuple.md). Composite data types can contain `Nullable` type values, such as `Array(Nullable(Int8))`.
`T` can’t be any of the composite data types [Array](../../sql-reference/data-types/array.md), [Map](../../sql-reference/data-types/map.md) and [Tuple](../../sql-reference/data-types/tuple.md) but composite data types can contain `Nullable` type values, e.g. `Array(Nullable(Int8))`.
A `Nullable` type field can’t be included in table indexes.

37
docs/en/sql-reference/functions/array-functions.md
View File

@ -657,6 +657,43 @@ SELECT arraySlice([1, 2, NULL, 4, 5], 2, 3) AS res;
Array elements set to `NULL` are handled as normal values.
## arrayShingles
Generates an array of "shingles", i.e. consecutive sub-arrays with specified length of the input array.
**Syntax**
``` sql
arrayShingles(array, length)
```
**Arguments**
- `array` — Input array [Array](../../sql-reference/data-types/array.md).
- `length` — The length of each shingle.
**Returned value**
- An array of generated shingles.
Type: [Array](../../sql-reference/data-types/array.md).
**Examples**
Query:
``` sql
SELECT arrayShingles([1,2,3,4], 3) as res;
```
Result:
``` text
┌─res───────────────┐
│ [[1,2,3],[2,3,4]] │
└───────────────────┘
```
## arraySort(\[func,\] arr, …) {#sort}
Sorts the elements of the `arr` array in ascending order. If the `func` function is specified, sorting order is determined by the result of the `func` function applied to the elements of the array. If `func` accepts multiple arguments, the `arraySort` function is passed several arrays that the arguments of `func` will correspond to. Detailed examples are shown at the end of `arraySort` description.

46
docs/en/sql-reference/statements/alter/column.md
View File

@ -23,10 +23,11 @@ The following actions are supported:
- [RENAME COLUMN](#rename-column) — Renames an existing column.
- [CLEAR COLUMN](#clear-column) — Resets column values.
- [COMMENT COLUMN](#comment-column) — Adds a text comment to the column.
- [MODIFY COLUMN](#modify-column) — Changes column’s type, default expression and TTL.
- [MODIFY COLUMN](#modify-column) — Changes column’s type, default expression, TTL, and column settings.
- [MODIFY COLUMN REMOVE](#modify-column-remove) — Removes one of the column properties.
- [MODIFY COLUMN MODIFY SETTING](#modify-column-modify-setting) - Changes column settings.
- [MODIFY COLUMN RESET SETTING](#modify-column-reset-setting) - Reset column settings.
- [MATERIALIZE COLUMN](#materialize-column) — Materializes the column in the parts where the column is missing.
These actions are described in detail below.
## ADD COLUMN
@ -75,7 +76,7 @@ Deletes the column with the name `name`. If the `IF EXISTS` clause is specified,
Deletes data from the file system. Since this deletes entire files, the query is completed almost instantly.
:::tip
:::tip
You can’t delete a column if it is referenced by [materialized view](/docs/en/sql-reference/statements/create/view.md/#materialized). Otherwise, it returns an error.
:::
@ -208,7 +209,7 @@ The `ALTER` query for changing columns is replicated. The instructions are saved
## MODIFY COLUMN REMOVE
Removes one of the column properties: `DEFAULT`, `ALIAS`, `MATERIALIZED`, `CODEC`, `COMMENT`, `TTL`.
Removes one of the column properties: `DEFAULT`, `ALIAS`, `MATERIALIZED`, `CODEC`, `COMMENT`, `TTL`, `SETTING`.
Syntax:
@ -228,6 +229,43 @@ ALTER TABLE table_with_ttl MODIFY COLUMN column_ttl REMOVE TTL;
- [REMOVE TTL](ttl.md).
## MODIFY COLUMN MODIFY SETTING
Modify a column setting.
Syntax:
```sql
ALTER TABLE table_name MODIFY COLUMN MODIFY SETTING name=value,...;
```
**Example**
Modify column's `max_compress_block_size` to `1MB`:
```sql
ALTER TABLE table_name MODIFY COLUMN MODIFY SETTING max_compress_block_size = 1048576;
```
## MODIFY COLUMN RESET SETTING
Reset a column setting, also removes the setting declaration in the column expression of the table's CREATE query.
Syntax:
```sql
ALTER TABLE table_name MODIFY COLUMN RESET SETTING name,...;
```
**Example**
Remove column setting `max_compress_block_size` to `1MB`:
```sql
ALTER TABLE table_name MODIFY COLUMN REMOVE SETTING max_compress_block_size;
```
## MATERIALIZE COLUMN
Materializes or updates a column with an expression for a default value (`DEFAULT` or `MATERIALIZED`).

2
docs/en/sql-reference/statements/alter/partition.md
View File

@ -112,7 +112,7 @@ Note that:
For the query to run successfully, the following conditions must be met:
- Both tables must have the same structure.
- Both tables must have the same partition key, the same order by key and the same primary key.
- Both tables must have the same order by key and the same primary key.
- Both tables must have the same indices and projections.
- Both tables must have the same storage policy.

2
docs/en/sql-reference/table-functions/executable.md
View File

@ -63,7 +63,7 @@ if __name__ == "__main__":
Let's invoke the script and have it generate 10 random strings:
```sql
SELECT * FROM executable('my_script.py', TabSeparated, 'id UInt32, random String', (SELECT 10))
SELECT * FROM executable('generate_random.py', TabSeparated, 'id UInt32, random String', (SELECT 10))
```
The response looks like:

2
docs/en/sql-reference/table-functions/fuzzJSON.md
View File

@ -9,7 +9,7 @@ sidebar_label: fuzzJSON
Perturbs a JSON string with random variations.
``` sql
fuzzJSON({ named_collection [option=value [,..]] | json_str[, random_seed] })
fuzzJSON({ named_collection [, option=value [,..]] | json_str[, random_seed] })
```
**Arguments**

37
docs/en/sql-reference/table-functions/gcs.md
View File

@ -16,7 +16,8 @@ If you have multiple replicas in your cluster, you can use the [s3Cluster functi
**Syntax**
``` sql
gcs(path [,hmac_key, hmac_secret] [,format] [,structure] [,compression])
gcs(url [, NOSIGN | hmac_key, hmac_secret] [,format] [,structure] [,compression_method])
gcs(named_collection[, option=value [,..]])
```
:::tip GCS
@ -24,10 +25,9 @@ The GCS Table Function integrates with Google Cloud Storage by using the GCS XML
:::
**Arguments**
- `path` — Bucket url with path to file. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{abc,def}` and `{N..M}` where `N`, `M` — numbers, `'abc'`, `'def'` — strings.
**Parameters**
- `url` — Bucket path to file. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{abc,def}` and `{N..M}` where `N`, `M` — numbers, `'abc'`, `'def'` — strings.
:::note GCS
The GCS path is in this format as the endpoint for the Google XML API is different than the JSON API:
```
@ -35,10 +35,21 @@ The GCS Table Function integrates with Google Cloud Storage by using the GCS XML
```
and not ~~https://storage.cloud.google.com~~.
:::
- `NOSIGN` — If this keyword is provided in place of credentials, all the requests will not be signed.
- `hmac_key` and `hmac_secret` — Keys that specify credentials to use with given endpoint. Optional.
- `format` — The [format](../../interfaces/formats.md#formats) of the file.
- `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
- `compression_method` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression method by file extension.
Arguments can also be passed using [named collections](/docs/en/operations/named-collections.md). In this case `url`, `format`, `structure`, `compression_method` work in the same way, and some extra parameters are supported:
- `access_key_id` — `hmac_key`, optional.
- `secret_access_key` — `hmac_secret`, optional.
- `filename` — appended to the url if specified.
- `use_environment_credentials` — enabled by default, allows passing extra parameters using environment variables `AWS_CONTAINER_CREDENTIALS_RELATIVE_URI`, `AWS_CONTAINER_CREDENTIALS_FULL_URI`, `AWS_CONTAINER_AUTHORIZATION_TOKEN`, `AWS_EC2_METADATA_DISABLED`.
- `no_sign_request` — disabled by default.
- `expiration_window_seconds` — default value is 120.
- `format` — The [format](../../interfaces/formats.md#formats) of the file.
- `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
- `compression` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression by file extension.
**Returned value**
@ -61,7 +72,7 @@ LIMIT 2;
└─────────┴─────────┴─────────┘
```
The similar but from file with `gzip` compression:
The similar but from file with `gzip` compression method:
``` sql
SELECT *
@ -158,6 +169,16 @@ The below get data from all `test-data.csv.gz` files from any folder inside `my-
SELECT * FROM gcs('https://storage.googleapis.com/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
```
For production use cases it is recommended to use [named collections](/docs/en/operations/named-collections.md). Here is the example:
``` sql
CREATE NAMED COLLECTION creds AS
access_key_id = '***',
secret_access_key = '***';
SELECT count(*)
FROM gcs(creds, url='https://s3-object-url.csv')
```
## Partitioned Write
If you specify `PARTITION BY` expression when inserting data into `GCS` table, a separate file is created for each partition value. Splitting the data into separate files helps to improve reading operations efficiency.

26
docs/en/sql-reference/table-functions/mysql.md
View File

@ -11,31 +11,25 @@ Allows `SELECT` and `INSERT` queries to be performed on data that is stored on a
**Syntax**
``` sql
mysql('host:port', 'database', 'table', 'user', 'password'[, replace_query, 'on_duplicate_clause'])
mysql({host:port, database, table, user, password[, replace_query, on_duplicate_clause] | named_collection[, option=value [,..]]})
```
**Arguments**
**Parameters**
- `host:port` — MySQL server address.
- `database` — Remote database name.
- `table` — Remote table name.
- `user` — MySQL user.
- `password` — User password.
- `replace_query` — Flag that converts `INSERT INTO` queries to `REPLACE INTO`. Possible values:
- `0` - The query is executed as `INSERT INTO`.
- `1` - The query is executed as `REPLACE INTO`.
- `on_duplicate_clause` — The `ON DUPLICATE KEY on_duplicate_clause` expression that is added to the `INSERT` query. Can be specified only with `replace_query = 0` (if you simultaneously pass `replace_query = 1` and `on_duplicate_clause`, ClickHouse generates an exception).
Example: `INSERT INTO t (c1,c2) VALUES ('a', 2) ON DUPLICATE KEY UPDATE c2 = c2 + 1;`
`on_duplicate_clause` here is `UPDATE c2 = c2 + 1`. See the MySQL documentation to find which `on_duplicate_clause` you can use with the `ON DUPLICATE KEY` clause.
Arguments also can be passed using [named collections](/docs/en/operations/named-collections.md). In this case `host` and `port` should be specified separately. This approach is recommended for production environment.
Simple `WHERE` clauses such as `=, !=, >, >=, <, <=` are currently executed on the MySQL server.
The rest of the conditions and the `LIMIT` sampling constraint are executed in ClickHouse only after the query to MySQL finishes.
@ -86,6 +80,18 @@ Selecting data from ClickHouse:
SELECT * FROM mysql('localhost:3306', 'test', 'test', 'bayonet', '123');
```
Or using [named collections](/docs/en/operations/named-collections.md):
```sql
CREATE NAMED COLLECTION creds AS
host = 'localhost',
port = 3306,
database = 'test',
user = 'bayonet',
password = '123';
SELECT * FROM mysql(creds, table='test');
```
``` text
┌─int_id─┬─float─┐
│ 1 │ 2 │

21
docs/en/sql-reference/table-functions/postgresql.md
View File

@ -11,10 +11,10 @@ Allows `SELECT` and `INSERT` queries to be performed on data that is stored on a
**Syntax**
``` sql
postgresql('host:port', 'database', 'table', 'user', 'password'[, `schema`])
postgresql({host:port, database, table, user, password[, schema, [, on_conflict]] | named_collection[, option=value [,..]]})
```
**Arguments**
**Parameters**
- `host:port` — PostgreSQL server address.
- `database` — Remote database name.
@ -22,6 +22,9 @@ postgresql('host:port', 'database', 'table', 'user', 'password'[, `schema`])
- `user` — PostgreSQL user.
- `password` — User password.
- `schema` — Non-default table schema. Optional.
- `on_conflict` — Conflict resolution strategy. Example: `ON CONFLICT DO NOTHING`. Optional.
Arguments also can be passed using [named collections](/docs/en/operations/named-collections.md). In this case `host` and `port` should be specified separately. This approach is recommended for production environment.
**Returned Value**
@ -86,12 +89,24 @@ postgresql> SELECT * FROM test;
(1 row)
```
Selecting data from ClickHouse:
Selecting data from ClickHouse using plain arguments:
```sql
SELECT * FROM postgresql('localhost:5432', 'test', 'test', 'postgresql_user', 'password') WHERE str IN ('test');
```
Or using [named collections](/docs/en/operations/named-collections.md):
```sql
CREATE NAMED COLLECTION mypg AS
host = 'localhost',
port = 5432,
database = 'test',
user = 'postgresql_user',
password = 'password';
SELECT * FROM postgresql(mypg, table='test') WHERE str IN ('test');
```
``` text
┌─int_id─┬─int_nullable─┬─float─┬─str──┬─float_nullable─┐
│ 1 │ ᴺᵁᴸᴸ │ 2 │ test │ ᴺᵁᴸᴸ │

22
docs/en/sql-reference/table-functions/redis.md
View File

@ -34,6 +34,7 @@ redis(host:port, key, structure[, db_index[, password[, pool_size]]])
- queries with key equals or in filtering will be optimized to multi keys lookup from Redis. If queries without filtering key full table scan will happen which is a heavy operation.
[Named collections](/docs/en/operations/named-collections.md) are not supported for `redis` table function at the moment.
**Returned Value**
@ -41,17 +42,7 @@ A table object with key as Redis key, other columns packaged together as Redis v
## Usage Example {#usage-example}
Create a table in ClickHouse which allows to read data from Redis:
``` sql
CREATE TABLE redis_table
(
`k` String,
`m` String,
`n` UInt32
)
ENGINE = Redis('redis1:6379') PRIMARY KEY(k);
```
Read from Redis:
```sql
SELECT * FROM redis(
@ -61,6 +52,15 @@ SELECT * FROM redis(
)
```
Insert into Redis:
```sql
INSERT INTO TABLE FUNCTION redis(
'redis1:6379',
'key',
'key String, v1 String, v2 UInt32') values ('1', '1', 1);
```
**See Also**
- [The `Redis` table engine](/docs/en/engines/table-engines/integrations/redis.md)

23
docs/en/sql-reference/table-functions/remote.md
View File

@ -13,10 +13,12 @@ Both functions can be used in `SELECT` and `INSERT` queries.
## Syntax
``` sql
remote('addresses_expr', [db, table, 'user'[, 'password'], sharding_key])
remote('addresses_expr', [db.table, 'user'[, 'password'], sharding_key])
remoteSecure('addresses_expr', [db, table, 'user'[, 'password'], sharding_key])
remoteSecure('addresses_expr', [db.table, 'user'[, 'password'], sharding_key])
remote(addresses_expr, [db, table, user [, password], sharding_key])
remote(addresses_expr, [db.table, user [, password], sharding_key])
remote(named_collection[, option=value [,..]])
remoteSecure(addresses_expr, [db, table, user [, password], sharding_key])
remoteSecure(addresses_expr, [db.table, user [, password], sharding_key])
remoteSecure(named_collection[, option=value [,..]])
```
## Parameters
@ -39,6 +41,8 @@ remoteSecure('addresses_expr', [db.table, 'user'[, 'password'], sharding_key])
- `password` — User password. If not specified, an empty password is used. Type: [String](../../sql-reference/data-types/string.md).
- `sharding_key` — Sharding key to support distributing data across nodes. For example: `insert into remote('127.0.0.1:9000,127.0.0.2', db, table, 'default', rand())`. Type: [UInt32](../../sql-reference/data-types/int-uint.md).
Arguments also can be passed using [named collections](/docs/en/operations/named-collections.md).
## Returned value
A table located on a remote server.
@ -82,7 +86,16 @@ example01-01-1,example01-02-1
SELECT * FROM remote('127.0.0.1', db.remote_engine_table) LIMIT 3;
```
### Inserting data from a remote server into a table:
Or using [named collections](/docs/en/operations/named-collections.md):
```sql
CREATE NAMED COLLECTION creds AS
host = '127.0.0.1',
database = 'db';
SELECT * FROM remote(creds, table='remote_engine_table') LIMIT 3;
```
### Inserting data into a table on a remote server:
``` sql
CREATE TABLE remote_table (name String, value UInt32) ENGINE=Memory;

40
docs/en/sql-reference/table-functions/s3.md
View File

@ -16,33 +16,41 @@ When using the `s3 table function` with [`INSERT INTO...SELECT`](../../sql-refer
**Syntax**
``` sql
s3(path [, NOSIGN | aws_access_key_id, aws_secret_access_key [,session_token]] [,format] [,structure] [,compression])
s3(url [, NOSIGN | access_key_id, secret_access_key, [session_token]] [,format] [,structure] [,compression_method])
s3(named_collection[, option=value [,..]])
```
:::tip GCS
The S3 Table Function integrates with Google Cloud Storage by using the GCS XML API and HMAC keys. See the [Google interoperability docs]( https://cloud.google.com/storage/docs/interoperability) for more details about the endpoint and HMAC.
For GCS, substitute your HMAC key and HMAC secret where you see `aws_access_key_id` and `aws_secret_access_key`.
For GCS, substitute your HMAC key and HMAC secret where you see `access_key_id` and `secret_access_key`.
:::
**Arguments**
**Parameters**
- `path` — Bucket url with path to file. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{abc,def}` and `{N..M}` where `N`, `M` — numbers, `'abc'`, `'def'` — strings. For more information see [here](../../engines/table-engines/integrations/s3.md#wildcards-in-path).
`s3` table function supports the following plain parameters:
- `url` — Bucket url with path to file. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{abc,def}` and `{N..M}` where `N`, `M` — numbers, `'abc'`, `'def'` — strings. For more information see [here](../../engines/table-engines/integrations/s3.md#wildcards-in-path).
:::note GCS
The GCS path is in this format as the endpoint for the Google XML API is different than the JSON API:
The GCS url is in this format as the endpoint for the Google XML API is different than the JSON API:
```
https://storage.googleapis.com/<bucket>/<folder>/<filename(s)>
```
and not ~~https://storage.cloud.google.com~~.
:::
- `NOSIGN` - If this keyword is provided in place of credentials, all the requests will not be signed.
- `access_key_id`, `secret_access_key` — Keys that specify credentials to use with given endpoint. Optional.
- `NOSIGN` — If this keyword is provided in place of credentials, all the requests will not be signed.
- `access_key_id` and `secret_access_key` — Keys that specify credentials to use with given endpoint. Optional.
- `session_token` - Session token to use with the given keys. Optional when passing keys.
- `format` — The [format](../../interfaces/formats.md#formats) of the file.
- `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
- `compression` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression by file extension.
- `compression_method` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression method by file extension.
Arguments can also be passed using [named collections](/docs/en/operations/named-collections.md). In this case `url`, `access_key_id`, `secret_access_key`, `format`, `structure`, `compression_method` work in the same way, and some extra parameters are supported:
- `filename` — appended to the url if specified.
- `use_environment_credentials` — enabled by default, allows passing extra parameters using environment variables `AWS_CONTAINER_CREDENTIALS_RELATIVE_URI`, `AWS_CONTAINER_CREDENTIALS_FULL_URI`, `AWS_CONTAINER_AUTHORIZATION_TOKEN`, `AWS_EC2_METADATA_DISABLED`.
- `no_sign_request` — disabled by default.
- `expiration_window_seconds` — default value is 120.
**Returned value**
@ -82,7 +90,7 @@ FROM s3(
LIMIT 5;
```
ClickHouse also can determine the compression of the file. For example, if the file was zipped up with a `.csv.gz` extension, ClickHouse would decompress the file automatically.
ClickHouse also can determine the compression method of the file. For example, if the file was zipped up with a `.csv.gz` extension, ClickHouse would decompress the file automatically.
:::
@ -168,7 +176,7 @@ The below get data from all `test-data.csv.gz` files from any folder inside `my-
SELECT * FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
```
Note. It is possible to specify custom URL mappers in the server configuration file. Example:
Note. It is possible to specify custom URL mappers in the server configuration file. Example:
``` sql
SELECT * FROM s3('s3://clickhouse-public-datasets/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
```
@ -190,6 +198,16 @@ Custom mapper can be added into `config.xml`:
</url_scheme_mappers>
```
For production use cases it is recommended to use [named collections](/docs/en/operations/named-collections.md). Here is the example:
``` sql
CREATE NAMED COLLECTION creds AS
access_key_id = '***',
secret_access_key = '***';
SELECT count(*)
FROM s3(creds, url='https://s3-object-url.csv')
```
## Partitioned Write
If you specify `PARTITION BY` expression when inserting data into `S3` table, a separate file is created for each partition value. Splitting the data into separate files helps to improve reading operations efficiency.

29
docs/en/sql-reference/table-functions/s3Cluster.md
View File

@ -4,23 +4,34 @@ sidebar_position: 181
sidebar_label: s3Cluster
title: "s3Cluster Table Function"
---
This is an extension to the [s3](/docs/en/sql-reference/table-functions/s3.md) table function.
Allows processing files from [Amazon S3](https://aws.amazon.com/s3/) and Google Cloud Storage [Google Cloud Storage](https://cloud.google.com/storage/) in parallel from many nodes in a specified cluster. On initiator it creates a connection to all nodes in the cluster, discloses asterisks in S3 file path, and dispatches each file dynamically. On the worker node it asks the initiator about the next task to process and processes it. This is repeated until all tasks are finished.
**Syntax**
``` sql
s3Cluster(cluster_name, source, [,access_key_id, secret_access_key, [session_token]] [,format] [,structure])
s3Cluster(cluster_name, url [, NOSIGN | access_key_id, secret_access_key, [session_token]] [,format] [,structure] [,compression_method])
s3Cluster(cluster_name, named_collection[, option=value [,..]])
```
**Arguments**
- `cluster_name` — Name of a cluster that is used to build a set of addresses and connection parameters to remote and local servers.
- `source` — URL to a file or a bunch of files. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{'abc','def'}` and `{N..M}` where `N`, `M` — numbers, `abc`, `def` — strings. For more information see [Wildcards In Path](../../engines/table-engines/integrations/s3.md#wildcards-in-path).
- `access_key_id`, `secret_access_key` — Keys that specify credentials to use with given endpoint. Optional.
- `url` — path to a file or a bunch of files. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{'abc','def'}` and `{N..M}` where `N`, `M` — numbers, `abc`, `def` — strings. For more information see [Wildcards In Path](../../engines/table-engines/integrations/s3.md#wildcards-in-path).
- `NOSIGN` — If this keyword is provided in place of credentials, all the requests will not be signed.
- `access_key_id` and `secret_access_key` — Keys that specify credentials to use with given endpoint. Optional.
- `session_token` - Session token to use with the given keys. Optional when passing keys.
- `format` — The [format](../../interfaces/formats.md#formats) of the file.
- `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
- `compression_method` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression method by file extension.
Arguments can also be passed using [named collections](/docs/en/operations/named-collections.md). In this case `url`, `access_key_id`, `secret_access_key`, `format`, `structure`, `compression_method` work in the same way, and some extra parameters are supported:
- `filename` — appended to the url if specified.
- `use_environment_credentials` — enabled by default, allows passing extra parameters using environment variables `AWS_CONTAINER_CREDENTIALS_RELATIVE_URI`, `AWS_CONTAINER_CREDENTIALS_FULL_URI`, `AWS_CONTAINER_AUTHORIZATION_TOKEN`, `AWS_EC2_METADATA_DISABLED`.
- `no_sign_request` — disabled by default.
- `expiration_window_seconds` — default value is 120.
**Returned value**
@ -47,6 +58,18 @@ Count the total amount of rows in all files in the cluster `cluster_simple`:
If your listing of files contains number ranges with leading zeros, use the construction with braces for each digit separately or use `?`.
:::
For production use cases it is recommended to use [named collections](/docs/en/operations/named-collections.md). Here is the example:
``` sql
CREATE NAMED COLLECTION creds AS
access_key_id = 'minio'
secret_access_key = 'minio123';
SELECT count(*) FROM s3Cluster(
'cluster_simple', creds, url='https://s3-object-url.csv',
format='CSV', structure='name String, value UInt32, polygon Array(Array(Tuple(Float64, Float64)))'
)
```
**See Also**
- [S3 engine](../../engines/table-engines/integrations/s3.md)

2
docs/ru/getting-started/tutorial.md
View File

@ -670,4 +670,4 @@ ENGINE = ReplicatedMergeTree(
INSERT INTO tutorial.hits_replica SELECT * FROM tutorial.hits_local;
```
Репликация работает в режиме мультимастера. Это означает, что данные могут быть загружены на любую из реплик и система автоматически синхронизирует данные между остальными репликами. Репликация асинхронна, то есть в конкретный момент времнени не все реплики могут содержать недавно добавленные данные. Как минимум одна реплика должна быть в строю для приёма данных. Прочие реплики синхронизируются и восстановят согласованное состояния как только снова станут активными. Заметим, что при таком подходе есть вероятность утраты недавно добавленных данных.
Репликация работает в режиме мультимастера. Это означает, что данные могут быть загружены на любую из реплик и система автоматически синхронизирует данные между остальными репликами. Репликация асинхронна, то есть в конкретный момент времени не все реплики могут содержать недавно добавленные данные. Как минимум одна реплика должна быть в строю для приёма данных. Прочие реплики синхронизируются и восстановят согласованное состояния как только снова станут активными. Заметим, что при таком подходе есть вероятность утраты недавно добавленных данных.

11
docs/ru/operations/settings/settings.md
View File

@ -2796,6 +2796,17 @@ SELECT TOP 3 name, value FROM system.settings;
3. │ max_block_size │ 65505 │
└─────────────────────────┴─────────┘
```
### output_format_pretty_color {#output_format_pretty_color}
Включает/выключает управляющие последовательности ANSI в форматах Pretty.
Возможные значения:
- `0` — выключена. Не исползует ANSI последовательности в форматах Pretty.
- `1` — включена. Исползует ANSI последовательности с исключением форматов `NoEscapes`.
- `auto` - включена если `stdout` является терминалом с исключением форматов `NoEscapes`.
Значение по умолчанию: `auto`
## system_events_show_zero_values {#system_events_show_zero_values}

2
docs/ru/operations/system-tables/replication_queue.md
View File

@ -49,7 +49,7 @@ slug: /ru/operations/system-tables/replication_queue
- `last_attempt_time` ([DateTime](../../sql-reference/data-types/datetime.md)) — дата и время последней попытки выполнить задачу.
- `num_postponed` ([UInt32](../../sql-reference/data-types/int-uint.md)) — количество отложенных задач.
- `num_postponed` ([UInt32](../../sql-reference/data-types/int-uint.md)) — количество откладываний запуска задачи.
- `postpone_reason` ([String](../../sql-reference/data-types/string.md)) — причина, по которой была отложена задача.

24
docs/zh/faq/general/ne-tormozit.md
View File

@ -1,27 +1,27 @@
---
slug: /zh/faq/general/ne-tormozit
title: "What does \u201C\u043D\u0435 \u0442\u043E\u0440\u043C\u043E\u0437\u0438\u0442\
\u201D mean?"
title: "\u201C\u043D\u0435 \u0442\u043E\u0440\u043C\u043E\u0437\u0438\u0442\
\u201D 是什么意思？"
toc_hidden: true
sidebar_position: 11
---
# What Does “Не тормозит” Mean? {#what-does-ne-tormozit-mean}
# “Не тормозит” 是什么意思？ {#what-does-ne-tormozit-mean}
This question usually arises when people see official ClickHouse t-shirts. They have large words **“ClickHouse не тормозит”** on the front.
这个问题通常出现在人们看到官方 ClickHouse T恤时。它们的正面印有大字**“ClickHouse не тормозит”**。
Before ClickHouse became open-source, it has been developed as an in-house storage system by the largest Russian IT company, [Yandex](https://yandex.com/company/). That’s why it initially got its slogan in Russian, which is “не тормозит” (pronounced as “ne tormozit”). After the open-source release we first produced some of those t-shirts for events in Russia and it was a no-brainer to use the slogan as-is.
在 ClickHouse 开源之前，它作为俄罗斯最大的 IT 公司 [Yandex](https://yandex.com/company/) 的内部存储系统而开发。这就是为什么它最初获得了俄文口号“не тормозит”（发音为“ne tormozit”）。在开源发布后，我们首先为俄罗斯的活动制作了一些这样的T恤，使用原汁原味的口号是理所当然的。
One of the following batches of those t-shirts was supposed to be given away on events outside of Russia and we tried to make the English version of the slogan. Unfortunately, the Russian language is kind of elegant in terms of expressing stuff and there was a restriction of limited space on a t-shirt, so we failed to come up with good enough translation (most options appeared to be either long or inaccurate) and decided to keep the slogan in Russian even on t-shirts produced for international events. It appeared to be a great decision because people all over the world get positively surprised and curious when they see it.
其中一批这样的T恤原本打算在俄罗斯之外的活动中赠送，我们尝试制作口号的英文版本。不幸的是，俄语在表达方面有些优雅，而且T恤上的空间有限，所以我们未能提出足够好的翻译（大多数选项要么太长，要么不够准确），并决定即使在为国际活动制作的T恤上也保留俄文口号。这被证明是一个绝妙的决定，因为全世界的人们看到它时都会感到惊喜和好奇。
So, what does it mean? Here are some ways to translate *“не тормозит”*:
那么，它是什么意思呢？以下是翻译“не тормозит”的一些方式：
- If you translate it literally, it’d be something like *“ClickHouse does not press the brake pedal”*.
- If you’d want to express it as close to how it sounds to a Russian person with IT background, it’d be something like *“If your larger system lags, it’s not because it uses ClickHouse”*.
- Shorter, but not so precise versions could be *“ClickHouse is not slow”*, *“ClickHouse does not lag”* or just *“ClickHouse is fast”*.
- 如果你直译，那就是“ClickHouse 不踩刹车”。
- 如果你想尽可能接近一个有 IT 背景的俄罗斯人的听觉感受，那就是“如果你的大型系统延迟，不是因为它使用了 ClickHouse”。
- 更短，但不那么精确的版本可能是“ClickHouse 不慢”，“ClickHouse 不卡顿”或仅仅“ClickHouse 很快”。
If you haven’t seen one of those t-shirts in person, you can check them out online in many ClickHouse-related videos. For example, this one:
如果您还没有亲眼见过这些 T恤，可以在许多与 ClickHouse 相关的视频中在线查看。例如，这个：
![iframe](https://www.youtube.com/embed/bSyQahMVZ7w)
P.S. These t-shirts are not for sale, they are given away for free on most [ClickHouse Meetups](https://clickhouse.com/#meet), usually for best questions or other forms of active participation.
附言：这些 T恤不出售，它们在大多数 [ClickHouse 聚会](https://clickhouse.com/#meet)上免费赠送，通常是给出最佳问题或其他形式的积极参与者。

82
docs/zh/faq/general/why-clickhouse-is-so-fast.md
View File

@ -1,63 +1,63 @@
---
slug: /zh/faq/general/why-clickhouse-is-so-fast
title: Why is ClickHouse so fast?
title: 为什么 ClickHouse 如此快速？
toc_hidden: true
sidebar_position: 8
---
# Why ClickHouse Is So Fast? {#why-clickhouse-is-so-fast}
# 为什么 ClickHouse 如此快速？ {#why-clickhouse-is-so-fast}
It was designed to be fast. Query execution performance has always been a top priority during the development process, but other important characteristics like user-friendliness, scalability, and security were also considered so ClickHouse could become a real production system.
它被设计成一个快速的系统。在开发过程中，查询执行性能一直是首要考虑的优先级，但也考虑了其他重要特性，如用户友好性、可扩展性和安全性，使 ClickHouse 成为一个真正的生产系统。
ClickHouse was initially built as a prototype to do just a single task well: to filter and aggregate data as fast as possible. That’s what needs to be done to build a typical analytical report and that’s what a typical [GROUP BY](../../sql-reference/statements/select/group-by.md) query does. ClickHouse team has made several high-level decisions that combined made achieving this task possible:
ClickHouse 最初是作为一个原型构建的，它的单一任务就是尽可能快速地过滤和聚合数据。这正是构建典型分析报告所需做的，也是典型 [GROUP BY](../../sql-reference/statements/select/group-by.md) 查询所做的。ClickHouse 团队做出了几个高层次的决策，这些决策组合在一起使得实现这一任务成为可能：
Column-oriented storage
: Source data often contain hundreds or even thousands of columns, while a report can use just a few of them. The system needs to avoid reading unnecessary columns, or most expensive disk read operations would be wasted.
列式存储
: 源数据通常包含数百甚至数千列，而报告可能只使用其中的几列。系统需要避免读取不必要的列，否则大部分昂贵的磁盘读取操作将被浪费。
Indexes
: ClickHouse keeps data structures in memory that allows reading not only used columns but only necessary row ranges of those columns.
索引
: ClickHouse 在内存中保留数据结构，允许不仅读取使用的列，而且只读取这些列的必要行范围。
Data compression
: Storing different values of the same column together often leads to better compression ratios (compared to row-oriented systems) because in real data column often has the same or not so many different values for neighboring rows. In addition to general-purpose compression, ClickHouse supports [specialized codecs](../../sql-reference/statements/create/table.mdx/#create-query-specialized-codecs) that can make data even more compact.
数据压缩
: 将同一列的不同值存储在一起通常会导致更好的压缩比（与行式系统相比），因为在实际数据中列通常对相邻行有相同或不太多的不同值。除了通用压缩之外，ClickHouse 还支持 [专用编解码器](../../sql-reference/statements/create/table.mdx/#create-query-specialized-codecs)，可以使数据更加紧凑。
Vectorized query execution
: ClickHouse not only stores data in columns but also processes data in columns. It leads to better CPU cache utilization and allows for [SIMD](https://en.wikipedia.org/wiki/SIMD) CPU instructions usage.
向量化查询执行
: ClickHouse 不仅以列的形式存储数据，而且以列的形式处理数据。这导致更好的 CPU 缓存利用率，并允许使用 [SIMD](https://en.wikipedia.org/wiki/SIMD) CPU 指令。
Scalability
: ClickHouse can leverage all available CPU cores and disks to execute even a single query. Not only on a single server but all CPU cores and disks of a cluster as well.
可扩展性
: ClickHouse 可以利用所有可用的 CPU 核心和磁盘来执行甚至是单个查询。不仅在单个服务器上，而且在集群的所有 CPU 核心和磁盘上。
But many other database management systems use similar techniques. What really makes ClickHouse stand out is **attention to low-level details**. Most programming languages provide implementations for most common algorithms and data structures, but they tend to be too generic to be effective. Every task can be considered as a landscape with various characteristics, instead of just throwing in random implementation. For example, if you need a hash table, here are some key questions to consider:
但许多其他数据库管理系统也使用类似的技术。真正使 ClickHouse 脱颖而出的是 **对底层细节的关注**。大多数编程语言为最常见的算法和数据结构提供了实现，但它们往往过于通用而无法高效。每个任务都可以被视为具有各种特征的景观，而不是仅仅随意投入某个实现。例如，如果您需要一个哈希表，这里有一些关键问题需要考虑：
- Which hash function to choose?
- Collision resolution algorithm: [open addressing](https://en.wikipedia.org/wiki/Open_addressing) vs [chaining](https://en.wikipedia.org/wiki/Hash_table#Separate_chaining)?
- Memory layout: one array for keys and values or separate arrays? Will it store small or large values?
- Fill factor: when and how to resize? How to move values around on resize?
- Will values be removed and which algorithm will work better if they will?
- Will we need fast probing with bitmaps, inline placement of string keys, support for non-movable values, prefetch, and batching?
- 选择哪种哈希函数？
- 冲突解决算法：[开放寻址](https://en.wikipedia.org/wiki/Open_addressing)还是[链接](https://en.wikipedia.org/wiki/Hash_table#Separate_chaining)？
- 内存布局：一个数组用于键和值还是分开的数组？它会存储小值还是大值？
- 填充因子：何时以及如何调整大小？在调整大小时如何移动值？
- 是否会移除值，如果会，哪种算法会更好？
- 我们是否需要使用位图进行快速探测，字符串键的内联放置，对不可移动值的支持，预取和批处理？
Hash table is a key data structure for `GROUP BY` implementation and ClickHouse automatically chooses one of [30+ variations](https://github.com/ClickHouse/ClickHouse/blob/master/src/Interpreters/Aggregator.h) for each specific query.
哈希表是 `GROUP BY` 实现的关键数据结构，ClickHouse 会根据每个特定查询自动选择 [30 多种变体](https://github.com/ClickHouse/ClickHouse/blob/master/src/Interpreters/Aggregator.h) 中的一种。
The same goes for algorithms, for example, in sorting you might consider:
算法也是如此，例如，在排序中，您可能会考虑：
- What will be sorted: an array of numbers, tuples, strings, or structures?
- Is all data available completely in RAM?
- Do we need a stable sort?
- Do we need a full sort? Maybe partial sort or n-th element will suffice?
- How to implement comparisons?
- Are we sorting data that has already been partially sorted?
- 将要排序的是数字数组、元组、字符串还是结构？
- 所有数据是否完全可用于 RAM？
- 我们需要稳定排序吗？
- 我们需要完全排序吗？也许部分排序或第 n 个元素就足够了？
- 如何实现比较？
- 我们正在对已经部分排序的数据进行排序吗？
Algorithms that they rely on characteristics of data they are working with can often do better than their generic counterparts. If it is not really known in advance, the system can try various implementations and choose the one that works best in runtime. For example, see an [article on how LZ4 decompression is implemented in ClickHouse](https://habr.com/en/company/yandex/blog/457612/).
他们所依赖的算法根据其所处理的数据特性，往往可以比通用算法做得更好。如果事先真的不知道，系统可以尝试各种实现，并在运行时选择最佳的一种。例如，看一篇关于 [ClickHouse 中 LZ4 解压缩是如何实现的文章](https://habr.com/en/company/yandex/blog/457612/)。
Last but not least, the ClickHouse team always monitors the Internet on people claiming that they came up with the best implementation, algorithm, or data structure to do something and tries it out. Those claims mostly appear to be false, but from time to time you’ll indeed find a gem.
最后但同样重要的是，ClickHouse 团队始终关注互联网上人们声称他们提出了最佳的实现、算法或数据结构来做某事，并尝试它。这些声称大多是虚假的，但有时你确实会找到一颗宝石。
:::info Tips for building your own high-performance software
- Keep in mind low-level details when designing your system.
- Design based on hardware capabilities.
- Choose data structures and abstractions based on the needs of the task.
- Provide specializations for special cases.
- Try new, “best” algorithms, that you read about yesterday.
- Choose an algorithm in runtime based on statistics.
- Benchmark on real datasets.
- Test for performance regressions in CI.
- Measure and observe everything.
:::info 构建高性能软件的提示
- 设计系统时要考虑到底层细节。
- 基于硬件能力进行设计。
- 根据任务的需求选择数据结构和抽象。
- 为特殊情况提供专门化。
- 尝试您昨天阅读的关于新的“最佳”算法。
- 根据统计数据在运行时选择算法。
- 在真实数据集上进行基准测试。
- 在 CI 中测试性能回归。
- 测量并观察一切。
:::

22
docs/zh/faq/integration/json-import.md
View File

@ -1,35 +1,35 @@
---
slug: /zh/faq/integration/json-import
title: How to import JSON into ClickHouse?
title: 如何将 JSON 导入到 ClickHouse？
toc_hidden: true
sidebar_position: 11
---
# How to Import JSON Into ClickHouse? {#how-to-import-json-into-clickhouse}
# 如何将 JSON 导入到 ClickHouse？ {#how-to-import-json-into-clickhouse}
ClickHouse supports a wide range of [data formats for input and output](../../interfaces/formats.md). There are multiple JSON variations among them, but the most commonly used for data ingestion is [JSONEachRow](../../interfaces/formats.md#jsoneachrow). It expects one JSON object per row, each object separated by a newline.
ClickHouse 支持多种[输入和输出的数据格式](../../interfaces/formats.md)。其中包括多种 JSON 变体，但最常用于数据导入的是 [JSONEachRow](../../interfaces/formats.md#jsoneachrow)。它期望每行一个 JSON 对象，每个对象由一个新行分隔。
## Examples {#examples}
## 示例 {#examples}
Using [HTTP interface](../../interfaces/http.md):
使用 [HTTP 接口](../../interfaces/http.md)：
``` bash
$ echo '{"foo":"bar"}' | curl 'http://localhost:8123/?query=INSERT%20INTO%20test%20FORMAT%20JSONEachRow' --data-binary @-
```
Using [CLI interface](../../interfaces/cli.md):
使用 [CLI接口](../../interfaces/cli.md):
``` bash
$ echo '{"foo":"bar"}' | clickhouse-client --query="INSERT INTO test FORMAT JSONEachRow"
```
Instead of inserting data manually, you might consider to use one of [client libraries](../../interfaces/index.md) instead.
除了手动插入数据外，您可能会考虑使用 [客户端库](../../interfaces/index.md) 之一。
## Useful Settings {#useful-settings}
## 实用设置 {#useful-settings}
- `input_format_skip_unknown_fields` allows to insert JSON even if there were additional fields not present in table schema (by discarding them).
- `input_format_import_nested_json` allows to insert nested JSON objects into columns of [Nested](../../sql-reference/data-types/nested-data-structures/nested.md) type.
- `input_format_skip_unknown_fields` 允许插入 JSON，即使存在表格架构中未出现的额外字段（通过丢弃它们）。
- `input_format_import_nested_json` 允许将嵌套 JSON 对象插入到 [Nested](../../sql-reference/data-types/nested-data-structures/nested.md) 类型的列中。
:::note
Settings are specified as `GET` parameters for the HTTP interface or as additional command-line arguments prefixed with `--` for the `CLI` interface.
对于 HTTP 接口，设置作为 `GET` 参数指定；对于 `CLI` 接口，则作为前缀为 -- 的附加命令行参数。
:::

10
docs/zh/faq/integration/oracle-odbc.md
View File

@ -1,16 +1,16 @@
---
slug: /zh/faq/integration/oracle-odbc
title: What if I have a problem with encodings when using Oracle via ODBC?
title: 使用 Oracle ODBC 时遇到编码问题怎么办？
toc_hidden: true
sidebar_position: 20
---
# What If I Have a Problem with Encodings When Using Oracle Via ODBC? {#oracle-odbc-encodings}
# 使用 Oracle ODBC 时遇到编码问题怎么办？ {#oracle-odbc-encodings}
If you use Oracle as a source of ClickHouse external dictionaries via Oracle ODBC driver, you need to set the correct value for the `NLS_LANG` environment variable in `/etc/default/clickhouse`. For more information, see the [Oracle NLS_LANG FAQ](https://www.oracle.com/technetwork/products/globalization/nls-lang-099431.html).
如果您使用 Oracle 作为 ClickHouse 外部字典的数据源，并通过 Oracle ODBC 驱动程序，您需要在 `/etc/default/clickhouse` 中为 `NLS_LANG` 环境变量设置正确的值。更多信息，请参阅 [Oracle NLS_LANG FAQ](https://www.oracle.com/technetwork/products/globalization/nls-lang-099431.html)。
**Example**
**示例**
``` sql
NLS_LANG=RUSSIAN_RUSSIA.UTF8
```
```

30
docs/zh/faq/operations/delete-old-data.md
View File

@ -1,44 +1,44 @@
---
slug: /zh/faq/operations/delete-old-data
title: Is it possible to delete old records from a ClickHouse table?
title: 是否可以从ClickHouse表中删除旧记录？
toc_hidden: true
sidebar_position: 20
---
# Is It Possible to Delete Old Records from a ClickHouse Table? {#is-it-possible-to-delete-old-records-from-a-clickhouse-table}
# 是否可以从ClickHouse表中删除旧记录？ {#is-it-possible-to-delete-old-records-from-a-clickhouse-table}
The short answer is “yes”. ClickHouse has multiple mechanisms that allow freeing up disk space by removing old data. Each mechanism is aimed for different scenarios.
简短的答案是“可以”。ClickHouse具有多种机制，允许通过删除旧数据来释放磁盘空间。每种机制都针对不同的场景。
## TTL {#ttl}
ClickHouse allows to automatically drop values when some condition happens. This condition is configured as an expression based on any columns, usually just static offset for any timestamp column.
ClickHouse 允许在某些条件发生时自动删除值。这个条件被配置为基于任何列的表达式，通常只是针对任何时间戳列的静态偏移量。
The key advantage of this approach is that it does not need any external system to trigger, once TTL is configured, data removal happens automatically in background.
这种方法的主要优势是它不需要任何外部系统来触发，一旦配置了 TTL，数据删除就会自动在后台发生。
:::note
TTL can also be used to move data not only to [/dev/null](https://en.wikipedia.org/wiki/Null_device), but also between different storage systems, like from SSD to HDD.
TTL 也可以用来将数据移动到非 [/dev/null](https://en.wikipedia.org/wiki/Null_device) 的不同存储系统，例如从 SSD 到 HDD。
:::
More details on [configuring TTL](../../engines/table-engines/mergetree-family/mergetree.md#table_engine-mergetree-ttl).
有关 [配置 TTL](../../engines/table-engines/mergetree-family/mergetree.md#table_engine-mergetree-ttl) 的更多详细信息。
## ALTER DELETE {#alter-delete}
ClickHouse does not have real-time point deletes like in [OLTP](https://en.wikipedia.org/wiki/Online_transaction_processing) databases. The closest thing to them are mutations. They are issued as `ALTER ... DELETE` or `ALTER ... UPDATE` queries to distinguish from normal `DELETE` or `UPDATE` as they are asynchronous batch operations, not immediate modifications. The rest of syntax after `ALTER TABLE` prefix is similar.
ClickHouse没有像[OLTP](https://en.wikipedia.org/wiki/Online_transaction_processing)数据库那样的实时点删除。最接近的东西是 `Mutation`，执行 `ALTER ... DELETE` 或 `ALTER ... UPDATE` 查询，以区别于普通的`DELETE`或`UPDATE`。因为它们是异步批处理操作，而不是立即修改。`ALTER TABLE`前缀后的其余语法相似。
`ALTER DELETE` can be issued to flexibly remove old data. If you need to do it regularly, the main downside will be the need to have an external system to submit the query. There are also some performance considerations since mutation rewrite complete parts even there’s only a single row to be deleted.
`ALTER DELETE`可以灵活地用来删除旧数据。如果你需要定期这样做，主要缺点将是需要有一个外部系统来提交查询。还有一些性能方面的考虑，因为即使只有一行要被删除，突变也会重写完整部分。
This is the most common approach to make your system based on ClickHouse [GDPR](https://gdpr-info.eu)-compliant.
这是使基于ClickHouse的系统符合[GDPR](https://gdpr-info.eu)的最常见方法。
More details on [mutations](../../sql-reference/statements/alter.md/#alter-mutations).
有关[mutations](../../sql-reference/statements/alter.md/#alter-mutations)的更多详细信息。
## DROP PARTITION {#drop-partition}
`ALTER TABLE ... DROP PARTITION` provides a cost-efficient way to drop a whole partition. It’s not that flexible and needs proper partitioning scheme configured on table creation, but still covers most common cases. Like mutations need to be executed from an external system for regular use.
`ALTER TABLE ... DROP PARTITION`提供了一种成本效率高的方式来删除整个分区。它不是那么灵活，需要在创建表时配置适当的分区方案，但仍然涵盖了大多数常见情况。像 mutations 一样，需要从外部系统执行以进行常规使用。
More details on [manipulating partitions](../../sql-reference/statements/alter/partition.mdx/#alter_drop-partition).
有关[操作分区](../../sql-reference/statements/alter/partition.mdx/#alter_drop-partition)的更多详细信息。
## TRUNCATE {#truncate}
It’s rather radical to drop all data from a table, but in some cases it might be exactly what you need.
从表中删除所有数据是相当激进的，但在某些情况下可能正是您所需要的。
More details on [table truncation](../../sql-reference/statements/truncate.md).
有关[truncate](../../sql-reference/statements/truncate.md)的更多详细信息。

3
programs/client/Client.cpp
View File

@ -1010,6 +1010,7 @@ void Client::addOptions(OptionsDescription & options_description)
("config,c", po::value<std::string>(), "config-file path (another shorthand)")
("connection", po::value<std::string>(), "connection to use (from the client config), by default connection name is hostname")
("secure,s", "Use TLS connection")
("no-secure,s", "Don't use TLS connection")
("user,u", po::value<std::string>()->default_value("default"), "user")
("password", po::value<std::string>(), "password")
("ask-password", "ask-password")
@ -1151,6 +1152,8 @@ void Client::processOptions(const OptionsDescription & options_description,
interleave_queries_files = options["interleave-queries-file"].as<std::vector<std::string>>();
if (options.count("secure"))
config().setBool("secure", true);
if (options.count("no-secure"))
config().setBool("no-secure", true);
if (options.count("user") && !options["user"].defaulted())
config().setString("user", options["user"].as<std::string>());
if (options.count("password"))

6
programs/keeper/CMakeLists.txt
View File

@ -127,15 +127,17 @@ if (BUILD_STANDALONE_KEEPER)
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/DiskObjectStorage.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/DiskObjectStorageTransaction.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/DiskObjectStorageRemoteMetadataRestoreHelper.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/DiskObjectStorageCommon.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/ObjectStorageIteratorAsync.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/ObjectStorageIterator.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/StoredObject.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/S3/registerDiskS3.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/S3/S3ObjectStorage.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/S3/S3Capabilities.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/S3/diskSettings.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/S3/DiskS3Utils.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/ObjectStorageFactory.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/MetadataStorageFactory.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/ObjectStorages/RegisterDiskObjectStorage.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/IO/createReadBufferFromFileBase.cpp
${CMAKE_CURRENT_SOURCE_DIR}/../../src/Disks/IO/ReadBufferFromRemoteFSGather.cpp

5
programs/local/LocalServer.cpp
View File

@ -290,6 +290,11 @@ void LocalServer::cleanup()
{
connection.reset();
/// Suggestions are loaded async in a separate thread and it can use global context.
/// We should reset it before resetting global_context.
if (suggest)
suggest.reset();
if (global_context)
{
global_context->shutdown();

25
programs/server/Server.cpp
View File

@ -76,8 +76,8 @@
#include <Databases/registerDatabases.h>
#include <Dictionaries/registerDictionaries.h>
#include <Disks/registerDisks.h>
#include <IO/Resource/registerSchedulerNodes.h>
#include <IO/Resource/registerResourceManagers.h>
#include <Common/Scheduler/Nodes/registerSchedulerNodes.h>
#include <Common/Scheduler/Nodes/registerResourceManagers.h>
#include <Common/Config/ConfigReloader.h>
#include <Server/HTTPHandlerFactory.h>
#include "MetricsTransmitter.h"
@ -1467,6 +1467,8 @@ try
global_context->reloadAuxiliaryZooKeepersConfigIfChanged(config);
global_context->reloadQueryMaskingRulesIfChanged(config);
std::lock_guard lock(servers_lock);
updateServers(*config, server_pool, async_metrics, servers, servers_to_start_before_tables);
}
@ -1738,6 +1740,18 @@ try
LOG_INFO(log, "Loading metadata from {}", path_str);
LoadTaskPtrs load_metadata_tasks;
// Make sure that if exception is thrown during startup async, new async loading jobs are not going to be called.
// This is important for the case when exception is thrown from loading of metadata with `async_load_databases = false`
// to avoid simultaneously running table startups and destructing databases.
SCOPE_EXIT_SAFE(
LOG_INFO(log, "Stopping AsyncLoader.");
// Waits for all currently running jobs to finish and do not run any other pending jobs.
// Pending jobs will be canceled and destructed later by `load_metadata_tasks` dtor.
global_context->getAsyncLoader().stop();
);
try
{
auto & database_catalog = DatabaseCatalog::instance();
@ -1886,6 +1900,7 @@ try
/// Must be done after initialization of `servers`, because async_metrics will access `servers` variable from its thread.
async_metrics.start();
global_context->setAsynchronousMetrics(&async_metrics);
main_config_reloader->start();
access_control.startPeriodicReloading();
@ -2002,6 +2017,12 @@ try
else
LOG_INFO(log, "Closed all listening sockets.");
/// Wait for unfinished backups and restores.
/// This must be done after closing listening sockets (no more backups/restores) but before ProcessList::killAllQueries
/// (because killAllQueries() will cancel all running backups/restores).
if (server_settings.shutdown_wait_backups_and_restores)
global_context->waitAllBackupsAndRestores();
/// Killing remaining queries.
if (!server_settings.shutdown_wait_unfinished_queries)
global_context->getProcessList().killAllQueries();

3
programs/server/config.xml
View File

@ -440,6 +440,9 @@
<!-- Cache size in elements for compiled expressions.-->
<compiled_expression_cache_elements_size>10000</compiled_expression_cache_elements_size>
<!-- Cache path for custom (created from SQL) cached disks -->
<custom_cached_disks_base_directory>/var/lib/clickhouse/caches/</custom_cached_disks_base_directory>
<validate_tcp_client_information>false</validate_tcp_client_information>
<!-- Path to data directory, with trailing slash. -->

1
src/Access/Common/AccessType.h
View File

@ -170,6 +170,7 @@ enum class AccessType
M(SYSTEM_RELOAD_MODEL, "SYSTEM RELOAD MODELS, RELOAD MODEL, RELOAD MODELS", GLOBAL, SYSTEM_RELOAD) \
M(SYSTEM_RELOAD_FUNCTION, "SYSTEM RELOAD FUNCTIONS, RELOAD FUNCTION, RELOAD FUNCTIONS", GLOBAL, SYSTEM_RELOAD) \
M(SYSTEM_RELOAD_EMBEDDED_DICTIONARIES, "RELOAD EMBEDDED DICTIONARIES", GLOBAL, SYSTEM_RELOAD) /* implicitly enabled by the grant SYSTEM_RELOAD_DICTIONARY ON *.* */\
M(SYSTEM_RELOAD_ASYNCHRONOUS_METRICS, "RELOAD ASYNCHRONOUS METRICS", GLOBAL, SYSTEM_RELOAD) \
M(SYSTEM_RELOAD, "", GROUP, SYSTEM) \
M(SYSTEM_RESTART_DISK, "SYSTEM RESTART DISK", GLOBAL, SYSTEM) \
M(SYSTEM_MERGES, "SYSTEM STOP MERGES, SYSTEM START MERGES, STOP MERGES, START MERGES", TABLE, SYSTEM) \

5
src/Access/ExternalAuthenticators.cpp
View File

@ -242,7 +242,10 @@ HTTPAuthClientParams parseHTTPAuthParams(const Poco::Util::AbstractConfiguration
size_t connection_timeout_ms = config.getInt(prefix + ".connection_timeout_ms", 1000);
size_t receive_timeout_ms = config.getInt(prefix + ".receive_timeout_ms", 1000);
size_t send_timeout_ms = config.getInt(prefix + ".send_timeout_ms", 1000);
http_auth_params.timeouts = ConnectionTimeouts{connection_timeout_ms, receive_timeout_ms, send_timeout_ms};
http_auth_params.timeouts = ConnectionTimeouts()
.withConnectionTimeout(Poco::Timespan(connection_timeout_ms * 1000))
.withReceiveTimeout(Poco::Timespan(receive_timeout_ms * 1000))
.withSendTimeout(Poco::Timespan(send_timeout_ms * 1000));
http_auth_params.max_tries = config.getInt(prefix + ".max_tries", 3);
http_auth_params.retry_initial_backoff_ms = config.getInt(prefix + ".retry_initial_backoff_ms", 50);

48
src/AggregateFunctions/AggregateFunctionQuantile.h
View File

@ -31,7 +31,7 @@ namespace ErrorCodes
template <typename> class QuantileTiming;
template <typename> class QuantileGK;
template <typename> class QuantileDDSketch;
/** Generic aggregate function for calculation of quantiles.
* It depends on quantile calculation data structure. Look at Quantile*.h for various implementations.
@ -64,6 +64,7 @@ private:
using ColVecType = ColumnVectorOrDecimal<Value>;
static constexpr bool returns_float = !(std::is_same_v<FloatReturnType, void>);
static constexpr bool is_quantile_ddsketch = std::is_same_v<Data, QuantileDDSketch<Value>>;
static_assert(!is_decimal<Value> || !returns_float);
QuantileLevels<Float64> levels;
@ -74,6 +75,9 @@ private:
/// Used for the approximate version of the algorithm (Greenwald-Khanna)
ssize_t accuracy = 10000;
/// Used for the quantile sketch
Float64 relative_accuracy = 0.01;
DataTypePtr & argument_type;
public:
@ -87,7 +91,36 @@ public:
if (!returns_many && levels.size() > 1)
throw Exception(ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH, "Aggregate function {} requires one level parameter or less", getName());
if constexpr (has_accuracy_parameter)
if constexpr (is_quantile_ddsketch)
{
if (params.empty())
throw Exception(
ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH, "Aggregate function {} requires at least one param", getName());
const auto & relative_accuracy_field = params[0];
if (relative_accuracy_field.getType() != Field::Types::Float64)
throw Exception(
ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Aggregate function {} requires relative accuracy parameter with Float64 type", getName());
relative_accuracy = relative_accuracy_field.get<Float64>();
if (relative_accuracy <= 0 || relative_accuracy >= 1 || isNaN(relative_accuracy))
throw Exception(
ErrorCodes::BAD_ARGUMENTS,
"Aggregate function {} requires relative accuracy parameter with value between 0 and 1 but is {}",
getName(),
relative_accuracy);
// Throw exception if the relative accuracy is too small.
// This is to avoid the case where the user specifies a relative accuracy that is too small
// and the sketch is not able to allocate enough memory to satisfy the accuracy requirement.
if (relative_accuracy < 1e-6)
throw Exception(
ErrorCodes::BAD_ARGUMENTS,
"Aggregate function {} requires relative accuracy parameter with value greater than 1e-6 but is {}",
getName(),
relative_accuracy);
}
else if constexpr (has_accuracy_parameter)
{
if (params.empty())
throw Exception(
@ -116,7 +149,9 @@ public:
void create(AggregateDataPtr __restrict place) const override /// NOLINT
{
if constexpr (has_accuracy_parameter)
if constexpr (is_quantile_ddsketch)
new (place) Data(relative_accuracy);
else if constexpr (has_accuracy_parameter)
new (place) Data(accuracy);
else
new (place) Data;
@ -147,6 +182,10 @@ public:
{
/// Return normalized state type: quantiles*(1)(...)
Array params{1};
if constexpr (is_quantile_ddsketch)
params = {relative_accuracy, 1};
else if constexpr (has_accuracy_parameter)
params = {accuracy, 1};
AggregateFunctionProperties properties;
return std::make_shared<DataTypeAggregateFunction>(
AggregateFunctionFactory::instance().get(
@ -295,4 +334,7 @@ struct NameQuantilesBFloat16Weighted { static constexpr auto name = "quantilesBF
struct NameQuantileGK { static constexpr auto name = "quantileGK"; };
struct NameQuantilesGK { static constexpr auto name = "quantilesGK"; };
struct NameQuantileDDSketch { static constexpr auto name = "quantileDDSketch"; };
struct NameQuantilesDDSketch { static constexpr auto name = "quantilesDDSketch"; };
}

61
src/AggregateFunctions/AggregateFunctionQuantileDDSketch.cpp Normal file
View File

@ -0,0 +1,61 @@
#include <AggregateFunctions/AggregateFunctionQuantile.h>
#include <AggregateFunctions/QuantileDDSketch.h>
#include <AggregateFunctions/AggregateFunctionFactory.h>
#include <AggregateFunctions/Helpers.h>
#include <DataTypes/DataTypeDate.h>
#include <DataTypes/DataTypeDateTime.h>
#include <Core/Field.h>
namespace DB
{
struct Settings;
namespace ErrorCodes
{
extern const int ILLEGAL_TYPE_OF_ARGUMENT;
}
namespace
{
template <typename Value, bool float_return> using FuncQuantileDDSketch = AggregateFunctionQuantile<Value, QuantileDDSketch<Value>, NameQuantileDDSketch, false, std::conditional_t<float_return, Float64, void>, false, true>;
template <typename Value, bool float_return> using FuncQuantilesDDSketch = AggregateFunctionQuantile<Value, QuantileDDSketch<Value>, NameQuantilesDDSketch, false, std::conditional_t<float_return, Float64, void>, true, true>;
template <template <typename, bool> class Function>
AggregateFunctionPtr createAggregateFunctionQuantile(
const std::string & name, const DataTypes & argument_types, const Array & params, const Settings *)
{
/// Second argument type check doesn't depend on the type of the first one.
Function<void, true>::assertSecondArg(argument_types);
const DataTypePtr & argument_type = argument_types[0];
WhichDataType which(argument_type);
#define DISPATCH(TYPE) \
if (which.idx == TypeIndex::TYPE) return std::make_shared<Function<TYPE, true>>(argument_types, params);
FOR_BASIC_NUMERIC_TYPES(DISPATCH)
#undef DISPATCH
if (which.idx == TypeIndex::Date) return std::make_shared<Function<DataTypeDate::FieldType, false>>(argument_types, params);
if (which.idx == TypeIndex::DateTime) return std::make_shared<Function<DataTypeDateTime::FieldType, false>>(argument_types, params);
throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Illegal type {} of argument for aggregate function {}",
argument_type->getName(), name);
}
}
void registerAggregateFunctionsQuantileDDSketch(AggregateFunctionFactory & factory)
{
/// For aggregate functions returning array we cannot return NULL on empty set.
AggregateFunctionProperties properties = { .returns_default_when_only_null = true };
factory.registerFunction(NameQuantileDDSketch::name, createAggregateFunctionQuantile<FuncQuantileDDSketch>);
factory.registerFunction(NameQuantilesDDSketch::name, { createAggregateFunctionQuantile<FuncQuantilesDDSketch>, properties });
/// 'median' is an alias for 'quantile'
factory.registerAlias("medianDDSketch", NameQuantileDDSketch::name);
}
}

2
src/AggregateFunctions/AggregateFunctionUniq.h
View File

@ -350,7 +350,7 @@ struct Adder
if constexpr (Data::is_able_to_parallelize_merge)
{
if (data.set.isSingleLevel() && data.set.size() > 100'000)
if (data.set.isSingleLevel() && data.set.worthConvertingToTwoLevel(data.set.size()))
data.set.convertToTwoLevel();
}
}

253
src/AggregateFunctions/DDSketch.h Normal file
View File

@ -0,0 +1,253 @@
#pragma once
#include <memory> // for std::unique_ptr
#include <cmath>
#include <stdexcept>
#include <limits>
#include <iostream>
#include <base/types.h>
#include <IO/ReadBuffer.h>
#include <IO/WriteBuffer.h>
#include <AggregateFunctions/DDSketch/Mapping.h>
#include <AggregateFunctions/DDSketch/Store.h>
#include <AggregateFunctions/DDSketch/DDSketchEncoding.h>
namespace DB
{
namespace ErrorCodes
{
extern const int BAD_ARGUMENTS;
extern const int INCORRECT_DATA;
}
class DDSketchDenseLogarithmic
{
public:
explicit DDSketchDenseLogarithmic(Float64 relative_accuracy = 0.01)
: mapping(std::make_unique<DDSketchLogarithmicMapping>(relative_accuracy)),
store(std::make_unique<DDSketchDenseStore>()),
negative_store(std::make_unique<DDSketchDenseStore>()),
zero_count(0.0),
count(0.0)
{
}
DDSketchDenseLogarithmic(std::unique_ptr<DDSketchLogarithmicMapping> mapping_,
std::unique_ptr<DDSketchDenseStore> store_,
std::unique_ptr<DDSketchDenseStore> negative_store_,
Float64 zero_count_)
: mapping(std::move(mapping_)),
store(std::move(store_)),
negative_store(std::move(negative_store_)),
zero_count(zero_count_),
count(store->count + negative_store->count + zero_count_)
{
}
void add(Float64 val, Float64 weight = 1.0)
{
if (weight <= 0.0)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "weight must be a positive Float64");
}
if (val > mapping->getMinPossible())
{
store->add(mapping->key(val), weight);
}
else if (val < -mapping->getMinPossible())
{
negative_store->add(mapping->key(-val), weight);
}
else
{
zero_count += weight;
}
count += weight;
}
Float64 get(Float64 quantile) const
{
if (quantile < 0 || quantile > 1 || count == 0)
{
return std::numeric_limits<Float64>::quiet_NaN(); // Return NaN if the conditions are not met
}
Float64 rank = quantile * (count - 1);
Float64 quantile_value;
if (rank < negative_store->count)
{
Float64 reversed_rank = negative_store->count - rank - 1;
int key = negative_store->keyAtRank(reversed_rank, false);
quantile_value = -mapping->value(key);
}
else if (rank < zero_count + negative_store->count)
{
quantile_value = 0;
}
else
{
int key = store->keyAtRank(rank - zero_count - negative_store->count, true);
quantile_value = mapping->value(key);
}
return quantile_value;
}
void copy(const DDSketchDenseLogarithmic& other)
{
Float64 rel_acc = (other.mapping->getGamma() - 1) / (other.mapping->getGamma() + 1);
mapping = std::make_unique<DDSketchLogarithmicMapping>(rel_acc);
store = std::make_unique<DDSketchDenseStore>();
negative_store = std::make_unique<DDSketchDenseStore>();
store->copy(other.store.get());
negative_store->copy(other.negative_store.get());
zero_count = other.zero_count;
count = other.count;
}
void merge(const DDSketchDenseLogarithmic& other)
{
if (mapping->getGamma() != other.mapping->getGamma())
{
// modify the one with higher precision to match the one with lower precision
if (mapping->getGamma() > other.mapping->getGamma())
{
DDSketchDenseLogarithmic new_sketch = other.changeMapping(mapping->getGamma());
this->merge(new_sketch);
return;
}
else
{
DDSketchDenseLogarithmic new_sketch = changeMapping(other.mapping->getGamma());
copy(new_sketch);
}
}
// If the other sketch is empty, do nothing
if (other.count == 0)
{
return;
}
// If this sketch is empty, copy the other sketch
if (count == 0)
{
copy(other);
return;
}
count += other.count;
zero_count += other.zero_count;
store->merge(other.store.get());
negative_store->merge(other.negative_store.get());
}
void serialize(WriteBuffer& buf) const
{
// Write the mapping
writeBinary(enc.FlagIndexMappingBaseLogarithmic.byte, buf);
mapping->serialize(buf);
// Write the positive and negative stores
writeBinary(enc.FlagTypePositiveStore, buf);
store->serialize(buf);
writeBinary(enc.FlagTypeNegativeStore, buf);
negative_store->serialize(buf);
// Write the zero count
writeBinary(enc.FlagZeroCountVarFloat.byte, buf);
writeBinary(zero_count, buf);
}
void deserialize(ReadBuffer& buf)
{
// Read the mapping
UInt8 flag = 0;
readBinary(flag, buf);
if (flag != enc.FlagIndexMappingBaseLogarithmic.byte)
{
throw Exception(ErrorCodes::INCORRECT_DATA, "Invalid flag for mapping");
}
mapping->deserialize(buf);
// Read the positive and negative stores
readBinary(flag, buf);
if (flag != enc.FlagTypePositiveStore)
{
throw Exception(ErrorCodes::INCORRECT_DATA, "Invalid flag for positive store");
}
store->deserialize(buf);
readBinary(flag, buf);
if (flag != enc.FlagTypeNegativeStore)
{
throw Exception(ErrorCodes::INCORRECT_DATA, "Invalid flag for negative store");
}
negative_store->deserialize(buf);
// Read the zero count
readBinary(flag, buf);
if (flag != enc.FlagZeroCountVarFloat.byte)
{
throw Exception(ErrorCodes::INCORRECT_DATA, "Invalid flag for zero count");
}
readBinary(zero_count, buf);
count = static_cast<Float64>(negative_store->count + zero_count + store->count);
}
private:
std::unique_ptr<DDSketchLogarithmicMapping> mapping;
std::unique_ptr<DDSketchDenseStore> store;
std::unique_ptr<DDSketchDenseStore> negative_store;
Float64 zero_count;
Float64 count;
DDSketchEncoding enc;
DDSketchDenseLogarithmic changeMapping(Float64 new_gamma) const
{
auto new_mapping = std::make_unique<DDSketchLogarithmicMapping>((new_gamma - 1) / (new_gamma + 1));
auto new_positive_store = std::make_unique<DDSketchDenseStore>();
auto new_negative_store = std::make_unique<DDSketchDenseStore>();
auto remap_store = [this, &new_mapping](DDSketchDenseStore& old_store, std::unique_ptr<DDSketchDenseStore>& target_store)
{
for (int i = 0; i < old_store.length(); ++i)
{
int old_index = i + old_store.offset;
Float64 old_bin_count = old_store.bins[i];
Float64 in_lower_bound = this->mapping->lowerBound(old_index);
Float64 in_upper_bound = this->mapping->lowerBound(old_index + 1);
Float64 in_size = in_upper_bound - in_lower_bound;
int new_index = new_mapping->key(in_lower_bound);
// Distribute counts to new bins
for (; new_mapping->lowerBound(new_index) < in_upper_bound; ++new_index)
{
Float64 out_lower_bound = new_mapping->lowerBound(new_index);
Float64 out_upper_bound = new_mapping->lowerBound(new_index + 1);
Float64 lower_intersection_bound = std::max(out_lower_bound, in_lower_bound);
Float64 higher_intersection_bound = std::min(out_upper_bound, in_upper_bound);
Float64 intersection_size = higher_intersection_bound - lower_intersection_bound;
Float64 proportion = intersection_size / in_size;
target_store->add(new_index, proportion * old_bin_count);
}
}
};
remap_store(*store, new_positive_store);
remap_store(*negative_store, new_negative_store);
return DDSketchDenseLogarithmic(std::move(new_mapping), std::move(new_positive_store), std::move(new_negative_store), zero_count);
}
};
}

101
src/AggregateFunctions/DDSketch/DDSketchEncoding.h Normal file
View File

@ -0,0 +1,101 @@
#pragma once
#include <vector>
#include <stdexcept>
/**
* An encoded DDSketch comprises multiple contiguous blocks (sequences of bytes).
* Each block is prefixed with a flag that indicates what the block contains and how the data is encoded in the block.
* A flag is a single byte, which itself contains two parts:
* - the flag type (the 2 least significant bits),
* - the subflag (the 6 most significant bits).
*
* There are four flag types, for:
* - sketch features,
* - index mapping,
* - positive value store,
* - negative value store.
*
* The meaning of the subflag depends on the flag type:
* - for the sketch feature flag type, it indicates what feature is encoded,
* - for the index mapping flag type, it indicates what mapping is encoded and how,
* - for the store flag types, it indicates how bins are encoded.
*/
namespace DB
{
class DDSketchEncoding
{
private:
static constexpr UInt8 numBitsForType = 2;
static constexpr UInt8 flagTypeMask = (1 << numBitsForType) - 1;
static constexpr UInt8 subFlagMask = ~flagTypeMask;
static constexpr UInt8 flagTypeSketchFeatures = 0b00;
public:
class Flag
{
public:
UInt8 byte;
Flag(UInt8 t, UInt8 s) : byte(t | s) { }
[[maybe_unused]] UInt8 Type() const { return byte & flagTypeMask; }
[[maybe_unused]] UInt8 SubFlag() const { return byte & subFlagMask; }
};
// FLAG TYPES
static constexpr UInt8 FlagTypeIndexMapping = 0b10;
static constexpr UInt8 FlagTypePositiveStore = 0b01;
static constexpr UInt8 FlagTypeNegativeStore = 0b11;
// SKETCH FEATURES
// Encoding format:
// - [byte] flag
// - [varfloat64] count of the zero bin
const Flag FlagZeroCountVarFloat = Flag(flagTypeSketchFeatures, 1 << numBitsForType);
// INDEX MAPPING
// Encoding format:
// - [byte] flag
// - [float64LE] gamma
// - [float64LE] index offset
const Flag FlagIndexMappingBaseLogarithmic = Flag(FlagTypeIndexMapping, 0 << numBitsForType);
// BINS
// Encoding format:
// - [byte] flag
// - [uvarint64] number of bins N
// - [varint64] index of first bin
// - [varfloat64] count of first bin
// - [varint64] difference between the index of the second bin and the index
// of the first bin
// - [varfloat64] count of second bin
// - ...
// - [varint64] difference between the index of the N-th bin and the index
// of the (N-1)-th bin
// - [varfloat64] count of N-th bin
static constexpr UInt8 BinEncodingIndexDeltasAndCounts = 1 << numBitsForType;
// Encoding format:
// - [byte] flag
// - [uvarint64] number of bins N
// - [varint64] index of first bin
// - [varint64] difference between the index of the second bin and the index
// of the first bin
// - ...
// - [varint64] difference between the index of the N-th bin and the index
// of the (N-1)-th bin
static constexpr UInt8 BinEncodingIndexDeltas = 2 << numBitsForType;
// Encoding format:
// - [byte] flag
// - [uvarint64] number of bins N
// - [varint64] index of first bin
// - [varint64] difference between two successive indexes
// - [varfloat64] count of first bin
// - [varfloat64] count of second bin
// - ...
// - [varfloat64] count of N-th bin
static constexpr UInt8 BinEncodingContiguousCounts = 3 << numBitsForType;
};
}

110
src/AggregateFunctions/DDSketch/Mapping.h Normal file
View File

@ -0,0 +1,110 @@
#pragma once
#include <base/types.h>
#include <cmath>
#include <stdexcept>
#include <limits>
#include <IO/ReadBuffer.h>
#include <IO/WriteBuffer.h>
namespace DB
{
namespace ErrorCodes
{
extern const int BAD_ARGUMENTS;
}
class DDSketchLogarithmicMapping
{
public:
explicit DDSketchLogarithmicMapping(Float64 relative_accuracy_, Float64 offset_ = 0.0)
: relative_accuracy(relative_accuracy_), offset(offset_)
{
if (relative_accuracy <= 0 || relative_accuracy >= 1)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Relative accuracy must be between 0 and 1 but is {}", relative_accuracy);
}
gamma = (1 + relative_accuracy) / (1 - relative_accuracy);
multiplier = 1 / std::log(gamma);
min_possible = std::numeric_limits<Float64>::min() * gamma;
max_possible = std::numeric_limits<Float64>::max() / gamma;
}
~DDSketchLogarithmicMapping() = default;
int key(Float64 value) const
{
if (value < min_possible || value > max_possible)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Value {} is out of range [{}, {}]", value, min_possible, max_possible);
}
return static_cast<int>(logGamma(value) + offset);
}
Float64 value(int key) const
{
return lowerBound(key) * (1 + relative_accuracy);
}
Float64 logGamma(Float64 value) const
{
return std::log(value) * multiplier;
}
Float64 powGamma(Float64 value) const
{
return std::exp(value / multiplier);
}
Float64 lowerBound(int index) const
{
return powGamma(static_cast<Float64>(index) - offset);
}
Float64 getGamma() const
{
return gamma;
}
Float64 getMinPossible() const
{
return min_possible;
}
[[maybe_unused]] Float64 getMaxPossible() const
{
return max_possible;
}
void serialize(WriteBuffer& buf) const
{
writeBinary(gamma, buf);
writeBinary(offset, buf);
}
void deserialize(ReadBuffer& buf)
{
readBinary(gamma, buf);
readBinary(offset, buf);
if (gamma <= 1.0)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Invalid gamma value after deserialization: {}", gamma);
}
multiplier = 1 / std::log(gamma);
min_possible = std::numeric_limits<Float64>::min() * gamma;
max_possible = std::numeric_limits<Float64>::max() / gamma;
}
protected:
Float64 relative_accuracy;
Float64 gamma;
Float64 min_possible;
Float64 max_possible;
Float64 multiplier;
Float64 offset;
};
}

260
src/AggregateFunctions/DDSketch/Store.h Normal file
View File

@ -0,0 +1,260 @@
#pragma once
#include <base/types.h>
#include <vector>
#include <cmath>
#include <limits>
#include <IO/ReadBuffer.h>
#include <IO/WriteBuffer.h>
#include <AggregateFunctions/DDSketch/DDSketchEncoding.h>
// We start with 128 bins and grow the number of bins by 128
// each time we need to extend the range of the bins.
// This is done to avoid reallocating the bins vector too often.
constexpr UInt32 CHUNK_SIZE = 128;
namespace DB
{
class DDSketchDenseStore
{
public:
Float64 count = 0;
int min_key = std::numeric_limits<int>::max();
int max_key = std::numeric_limits<int>::min();
int offset = 0;
std::vector<Float64> bins;
explicit DDSketchDenseStore(UInt32 chunk_size_ = CHUNK_SIZE) : chunk_size(chunk_size_) {}
void copy(DDSketchDenseStore* other)
{
bins = other->bins;
count = other->count;
min_key = other->min_key;
max_key = other->max_key;
offset = other->offset;
}
int length()
{
return static_cast<int>(bins.size());
}
void add(int key, Float64 weight)
{
int idx = getIndex(key);
bins[idx] += weight;
count += weight;
}
int keyAtRank(Float64 rank, bool lower)
{
Float64 running_ct = 0.0;
for (size_t i = 0; i < bins.size(); ++i)
{
running_ct += bins[i];
if ((lower && running_ct > rank) || (!lower && running_ct >= rank + 1))
{
return static_cast<int>(i) + offset;
}
}
return max_key;
}
void merge(DDSketchDenseStore* other)
{
if (other->count == 0) return;
if (count == 0)
{
copy(other);
return;
}
if (other->min_key < min_key || other->max_key > max_key)
{
extendRange(other->min_key, other->max_key);
}
for (int key = other->min_key; key <= other->max_key; ++key)
{
bins[key - offset] += other->bins[key - other->offset];
}
count += other->count;
}
void serialize(WriteBuffer& buf) const
{
// Calculate the size of the dense and sparse encodings to choose the smallest one
UInt64 num_bins = 0, num_non_empty_bins = 0;
if (count != 0)
{
num_bins = max_key - min_key + 1;
}
size_t sparse_encoding_overhead = 0;
for (int index = min_key; index <= max_key; ++index)
{
if (bins[index - offset] != 0)
{
num_non_empty_bins++;
sparse_encoding_overhead += 2; // 2 bytes for index delta
}
}
size_t dense_encoding_overhead = (num_bins - num_non_empty_bins) * estimatedFloatSize(0.0);
// Choose the smallest encoding and write to buffer
if (dense_encoding_overhead <= sparse_encoding_overhead)
{
// Write the dense encoding
writeBinary(enc.BinEncodingContiguousCounts, buf); // Flag for dense encoding
writeVarUInt(num_bins, buf);
writeVarInt(min_key, buf);
writeVarInt(1, buf); // indexDelta in dense encoding
for (int index = min_key; index <= max_key; ++index)
{
writeFloatBinary(bins[index - offset], buf);
}
}
else
{
// Write the sparse encoding
writeBinary(enc.BinEncodingIndexDeltasAndCounts, buf); // Flag for sparse encoding
writeVarUInt(num_non_empty_bins, buf);
int previous_index = 0;
for (int index = min_key; index <= max_key; ++index)
{
Float64 bin_count = bins[index - offset];
if (bin_count != 0)
{
writeVarInt(index - previous_index, buf);
writeFloatBinary(bin_count, buf);
previous_index = index;
}
}
}
}
void deserialize(ReadBuffer& buf)
{
UInt8 encoding_mode;
readBinary(encoding_mode, buf);
if (encoding_mode == enc.BinEncodingContiguousCounts)
{
UInt64 num_bins;
readVarUInt(num_bins, buf);
int start_key;
readVarInt(start_key, buf);
int index_delta;
readVarInt(index_delta, buf);
for (UInt64 i = 0; i < num_bins; ++i)
{
Float64 bin_count;
readFloatBinary(bin_count, buf);
add(start_key, bin_count);
start_key += index_delta;
}
}
else
{
UInt64 num_non_empty_bins;
readVarUInt(num_non_empty_bins, buf);
int previous_index = 0;
for (UInt64 i = 0; i < num_non_empty_bins; ++i)
{
int index_delta;
readVarInt(index_delta, buf);
Float64 bin_count;
readFloatBinary(bin_count, buf);
previous_index += index_delta;
add(previous_index, bin_count);
}
}
}
private:
UInt32 chunk_size;
DDSketchEncoding enc;
int getIndex(int key)
{
if (key < min_key || key > max_key)
{
extendRange(key, key);
}
return key - offset;
}
UInt32 getNewLength(int new_min_key, int new_max_key) const
{
int desired_length = new_max_key - new_min_key + 1;
return static_cast<UInt32>(chunk_size * std::ceil(static_cast<Float64>(desired_length) / chunk_size)); // Fixed float conversion
}
void extendRange(int key, int second_key)
{
int new_min_key = std::min({key, min_key});
int new_max_key = std::max({second_key, max_key});
if (length() == 0)
{
bins = std::vector<Float64>(getNewLength(new_min_key, new_max_key), 0.0);
offset = new_min_key;
adjust(new_min_key, new_max_key);
}
else if (new_min_key >= offset && new_max_key < offset + length())
{
min_key = new_min_key;
max_key = new_max_key;
}
else
{
UInt32 new_length = getNewLength(new_min_key, new_max_key);
if (new_length > bins.size())
{
bins.resize(new_length);
bins.resize(bins.capacity());
}
adjust(new_min_key, new_max_key);
}
}
void adjust(int new_min_key, int new_max_key)
{
centerBins(new_min_key, new_max_key);
min_key = new_min_key;
max_key = new_max_key;
}
void shiftBins(int shift)
{
int new_offset = offset - shift;
if (new_offset > offset)
std::rotate(bins.begin(), bins.begin() + (new_offset - offset) % bins.size(), bins.end());
else
std::rotate(bins.begin(), bins.end() - (offset - new_offset) % bins.size(), bins.end());
offset = new_offset;
}
void centerBins(int new_min_key, int new_max_key)
{
int margins = length() - (new_max_key - new_min_key + 1);
int new_offset = new_min_key - margins / 2;
shiftBins(offset - new_offset);
}
size_t estimatedFloatSize(Float64 value) const
{
// Assuming IEEE 754 double-precision binary floating-point format: binary64
return sizeof(value);
}
};
}

108
src/AggregateFunctions/QuantileDDSketch.h Normal file
View File

@ -0,0 +1,108 @@
#pragma once
#include <base/types.h>
#include <base/sort.h>
#include <AggregateFunctions/DDSketch.h>
#include <IO/ReadBuffer.h>
#include <IO/WriteBuffer.h>
namespace DB
{
/**
* A DDSketch is a fully-mergeable quantile sketch with relative-error guarantees. That is, for any value x,
* the value returned by the sketch is guaranteed to be in the (1 +- epsilon) * x range. The sketch is
* parameterized by a relative accuracy epsilon, which is the maximum relative error of any quantile estimate.
*
* The sketch is implemented as a set of logarithmically-spaced bins. Each bin is a pair of a value and a count.
*
* The sketch is fully mergeable, meaning that the merge of two sketches is equivalent to the sketch of the
* union of the input datasets. The memory size of the sketch depends on the range that is covered by
* the input values: the larger that range, the more bins are needed to keep track of the input values.
* As a rough estimate, if working on durations using DDSketches.unboundedDense(0.02) (relative accuracy of 2%),
* about 2kB (275 bins) are needed to cover values between 1 millisecond and 1 minute, and about 6kB (802 bins)
* to cover values between 1 nanosecond and 1 day.
*
* This implementation maintains the binary compatibility with the DDSketch ProtoBuf format
* https://github.com/DataDog/sketches-java/blob/master/src/protobuf/proto/DDSketch.proto.
* Which enables sending the pre-aggregated sketches to the ClickHouse server and calculating the quantiles
* during the query time. See DDSketchEncoding.h for byte-level details.
*
*/
template <typename Value>
class QuantileDDSketch
{
public:
using Weight = UInt64;
QuantileDDSketch() = default;
explicit QuantileDDSketch(Float64 relative_accuracy) : data(relative_accuracy) { }
void add(const Value & x)
{
add(x, 1);
}
void add(const Value & x, Weight w)
{
if (!isNaN(x))
data.add(x, w);
}
void merge(const QuantileDDSketch &other)
{
data.merge(other.data);
}
void serialize(WriteBuffer & buf) const
{
data.serialize(buf);
}
void deserialize(ReadBuffer & buf)
{
data.deserialize(buf);
}
Value get(Float64 level) const
{
return getImpl<Value>(level);
}
void getMany(const Float64 * levels, const size_t * indices, size_t size, Value * result) const
{
getManyImpl(levels, indices, size, result);
}
Float64 getFloat(Float64 level) const
{
return getImpl<Float64>(level);
}
void getManyFloat(const Float64 * levels, const size_t * indices, size_t size, Float64 * result) const
{
getManyImpl(levels, indices, size, result);
}
private:
DDSketchDenseLogarithmic data;
template <typename T>
T getImpl(Float64 level) const
{
return static_cast<T>(data.get(level));
}
template <typename T>
void getManyImpl(const Float64 * levels, const size_t *, size_t num_levels, T * result) const
{
for (size_t i = 0; i < num_levels; ++i)
result[i] = getImpl<T>(levels[i]);
}
};
}

34
src/AggregateFunctions/UniqExactSet.h
View File

@ -11,10 +11,16 @@
namespace DB
{
namespace ErrorCodes
{
extern const int TOO_LARGE_ARRAY_SIZE;
}
template <typename SingleLevelSet, typename TwoLevelSet>
class UniqExactSet
{
static_assert(std::is_same_v<typename SingleLevelSet::value_type, typename TwoLevelSet::value_type>);
static_assert(std::is_same_v<typename SingleLevelSet::Cell::State, HashTableNoState>);
public:
using value_type = typename SingleLevelSet::value_type;
@ -147,7 +153,31 @@ public:
}
}
void read(ReadBuffer & in) { asSingleLevel().read(in); }
void read(ReadBuffer & in)
{
size_t new_size = 0;
auto * const position = in.position();
readVarUInt(new_size, in);
if (new_size > 100'000'000'000)
throw DB::Exception(
DB::ErrorCodes::TOO_LARGE_ARRAY_SIZE, "The size of serialized hash table is suspiciously large: {}", new_size);
if (worthConvertingToTwoLevel(new_size))
{
two_level_set = std::make_shared<TwoLevelSet>(new_size);
for (size_t i = 0; i < new_size; ++i)
{
typename SingleLevelSet::Cell x;
x.read(in);
asTwoLevel().insert(x.getValue());
}
}
else
{
in.position() = position; // Rollback position
asSingleLevel().read(in);
}
}
void write(WriteBuffer & out) const
{
@ -166,6 +196,8 @@ public:
return two_level_set ? two_level_set : std::make_shared<TwoLevelSet>(asSingleLevel());
}
static bool worthConvertingToTwoLevel(size_t size) { return size > 100'000; }
void convertToTwoLevel()
{
two_level_set = getTwoLevelSet();

2
src/AggregateFunctions/registerAggregateFunctions.cpp
View File

@ -31,6 +31,7 @@ void registerAggregateFunctionsQuantileTimingWeighted(AggregateFunctionFactory &
void registerAggregateFunctionsQuantileTDigest(AggregateFunctionFactory &);
void registerAggregateFunctionsQuantileTDigestWeighted(AggregateFunctionFactory &);
void registerAggregateFunctionsQuantileBFloat16(AggregateFunctionFactory &);
void registerAggregateFunctionsQuantileDDSketch(AggregateFunctionFactory &);
void registerAggregateFunctionsQuantileBFloat16Weighted(AggregateFunctionFactory &);
void registerAggregateFunctionsQuantileApprox(AggregateFunctionFactory &);
void registerAggregateFunctionsSequenceMatch(AggregateFunctionFactory &);
@ -127,6 +128,7 @@ void registerAggregateFunctions()
registerAggregateFunctionsQuantileTDigest(factory);
registerAggregateFunctionsQuantileTDigestWeighted(factory);
registerAggregateFunctionsQuantileBFloat16(factory);
registerAggregateFunctionsQuantileDDSketch(factory);
registerAggregateFunctionsQuantileBFloat16Weighted(factory);
registerAggregateFunctionsQuantileApprox(factory);
registerAggregateFunctionsSequenceMatch(factory);

2
src/Analyzer/FunctionNode.cpp
View File

@ -236,7 +236,7 @@ ASTPtr FunctionNode::toASTImpl(const ConvertToASTOptions & options) const
new_options.add_cast_for_constants = false;
/// Avoid cast for `IN tuple(...)` expression.
/// Tuples colud be quite big, and adding a type may significantly increase query size.
/// Tuples could be quite big, and adding a type may significantly increase query size.
/// It should be safe because set type for `column IN tuple` is deduced from `column` type.
if (isNameOfInFunction(function_name) && argument_nodes.size() > 1 && argument_nodes[1]->getNodeType() == QueryTreeNodeType::CONSTANT)
new_options.add_cast_for_constants = false;

2
src/Analyzer/Passes/AutoFinalOnQueryPass.cpp
View File

@ -1,4 +1,4 @@
#include "AutoFinalOnQueryPass.h"
#include <Analyzer/Passes/AutoFinalOnQueryPass.h>
#include <Storages/IStorage.h>

6
src/Analyzer/Passes/ConvertQueryToCNFPass.cpp
View File

@ -8,14 +8,12 @@
#include <Analyzer/ConstantNode.h>
#include <Analyzer/Passes/CNF.h>
#include <Analyzer/Utils.h>
#include <Analyzer/HashUtils.h>
#include <Storages/IStorage.h>
#include <Functions/FunctionFactory.h>
#include "Analyzer/HashUtils.h"
#include "Analyzer/IQueryTreeNode.h"
#include "Interpreters/ComparisonGraph.h"
#include "base/types.h"
#include <Interpreters/ComparisonGraph.h>
namespace DB
{

5
src/Analyzer/Passes/FunctionToSubcolumnsPass.cpp
View File

@ -49,6 +49,9 @@ public:
if (!first_argument_column_node)
return;
if (first_argument_column_node->getColumnName() == "__grouping_set")
return;
auto column_source = first_argument_column_node->getColumnSource();
auto * table_node = column_source->as<TableNode>();
@ -72,6 +75,7 @@ public:
{
/// Replace `length(array_argument)` with `array_argument.size0`
column.name += ".size0";
column.type = std::make_shared<DataTypeUInt64>();
node = std::make_shared<ColumnNode>(column, column_source);
}
@ -106,6 +110,7 @@ public:
{
/// Replace `isNull(nullable_argument)` with `nullable_argument.null`
column.name += ".null";
column.type = std::make_shared<DataTypeUInt8>();
node = std::make_shared<ColumnNode>(column, column_source);
}

9
src/Analyzer/Passes/GroupingFunctionsResolvePass.cpp
View File

@ -227,19 +227,20 @@ void resolveGroupingFunctions(QueryTreeNodePtr & query_node, ContextPtr context)
visitor.visit(query_node);
}
class GroupingFunctionsResolveVisitor : public InDepthQueryTreeVisitor<GroupingFunctionsResolveVisitor>
class GroupingFunctionsResolveVisitor : public InDepthQueryTreeVisitorWithContext<GroupingFunctionsResolveVisitor>
{
using Base = InDepthQueryTreeVisitorWithContext<GroupingFunctionsResolveVisitor>;
public:
explicit GroupingFunctionsResolveVisitor(ContextPtr context_)
: context(std::move(context_))
: Base(std::move(context_))
{}
void visitImpl(QueryTreeNodePtr & node)
void enterImpl(QueryTreeNodePtr & node)
{
if (node->getNodeType() != QueryTreeNodeType::QUERY)
return;
resolveGroupingFunctions(node, context);
resolveGroupingFunctions(node, getContext());
}
private:

3
src/Analyzer/Passes/OptimizeDateOrDateTimeConverterWithPreimagePass.cpp
View File

@ -91,6 +91,9 @@ public:
const auto * column_id = func_node->getArguments().getNodes()[0]->as<ColumnNode>();
if (!column_id) return;
if (column_id->getColumnName() == "__grouping_set")
return;
const auto * column_type = column_id->getColumnType().get();
if (!isDateOrDate32(column_type) && !isDateTime(column_type) && !isDateTime64(column_type)) return;

163
src/Analyzer/Passes/QueryAnalysisPass.cpp
View File

@ -1208,6 +1208,8 @@ private:
static void validateJoinTableExpressionWithoutAlias(const QueryTreeNodePtr & join_node, const QueryTreeNodePtr & table_expression_node, IdentifierResolveScope & scope);
static void checkDuplicateTableNamesOrAlias(const QueryTreeNodePtr & join_node, QueryTreeNodePtr & left_table_expr, QueryTreeNodePtr & right_table_expr, IdentifierResolveScope & scope);
static std::pair<bool, UInt64> recursivelyCollectMaxOrdinaryExpressions(QueryTreeNodePtr & node, QueryTreeNodes & into);
static void expandGroupByAll(QueryNode & query_tree_node_typed);
@ -2244,12 +2246,16 @@ void QueryAnalyzer::validateJoinTableExpressionWithoutAlias(const QueryTreeNodeP
if (table_expression_has_alias)
return;
if (join_node->as<JoinNode &>().getKind() == JoinKind::Paste)
return;
auto * query_node = table_expression_node->as<QueryNode>();
auto * union_node = table_expression_node->as<UnionNode>();
if ((query_node && !query_node->getCTEName().empty()) || (union_node && !union_node->getCTEName().empty()))
return;
auto table_expression_node_type = table_expression_node->getNodeType();
if (table_expression_node_type == QueryTreeNodeType::TABLE_FUNCTION ||
table_expression_node_type == QueryTreeNodeType::QUERY ||
table_expression_node_type == QueryTreeNodeType::UNION)
@ -2980,6 +2986,8 @@ QueryTreeNodePtr QueryAnalyzer::tryResolveIdentifierFromStorage(
if (!result_expression)
{
if (can_be_not_found)
return {};
std::unordered_set<Identifier> valid_identifiers;
collectTableExpressionValidIdentifiersForTypoCorrection(identifier,
table_expression_node,
@ -3140,6 +3148,64 @@ QueryTreeNodePtr QueryAnalyzer::tryResolveIdentifierFromJoin(const IdentifierLoo
}
}
auto check_nested_column_not_in_using = [&join_using_column_name_to_column_node, &identifier_lookup](const QueryTreeNodePtr & node)
{
/** tldr: When an identifier is resolved into the function `nested` or `getSubcolumn`, and
* some column in its argument is in the USING list and its type has to be updated, we throw an error to avoid overcomplication.
*
* Identifiers can be resolved into functions in case of nested or subcolumns.
* For example `t.t.t` can be resolved into `getSubcolumn(t, 't.t')` function in case of `t` is `Tuple`.
* So, `t` in USING list is resolved from JOIN itself and has supertype of columns from left and right table.
* But `t` in `getSubcolumn` argument is still resolved from table and we need to update its type.
*
* Example:
*
* SELECT t.t FROM (
* SELECT ((1, 's'), 's') :: Tuple(t Tuple(t UInt32, s1 String), s1 String) as t
* ) AS a FULL JOIN (
* SELECT ((1, 's'), 's') :: Tuple(t Tuple(t Int32, s2 String), s2 String) as t
* ) AS b USING t;
*
* Result type of `t` is `Tuple(Tuple(Int64, String), String)` (different type and no names for subcolumns),
* so it may be tricky to have a correct type for `t.t` that is resolved into getSubcolumn(t, 't').
*
* It can be more complicated in case of Nested subcolumns, in that case in query:
* SELECT t FROM ... JOIN ... USING (t.t)
* Here, `t` is resolved into function `nested(['t', 's'], t.t, t.s) so, `t.t` should be from JOIN and `t.s` should be from table.
*
* Updating type accordingly is pretty complicated, so just forbid such cases.
*
* While it still may work for storages that support selecting subcolumns directly without `getSubcolumn` function:
* SELECT t, t.t, toTypeName(t), toTypeName(t.t) FROM t1 AS a FULL JOIN t2 AS b USING t.t;
* We just support it as a best-effort: `t` will have original type from table, but `t.t` will have super-type from JOIN.
* Probably it's good to prohibit such cases as well, but it's not clear how to check it in general case.
*/
if (node->getNodeType() != QueryTreeNodeType::FUNCTION)
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unexpected node type {}, expected function node", node->getNodeType());
const auto & function_argument_nodes = node->as<FunctionNode &>().getArguments().getNodes();
for (const auto & argument_node : function_argument_nodes)
{
if (argument_node->getNodeType() == QueryTreeNodeType::COLUMN)
{
const auto & column_name = argument_node->as<ColumnNode &>().getColumnName();
if (join_using_column_name_to_column_node.contains(column_name))
throw Exception(ErrorCodes::AMBIGUOUS_IDENTIFIER,
"Cannot select subcolumn for identifier '{}' while joining using column '{}'",
identifier_lookup.identifier, column_name);
}
else if (argument_node->getNodeType() == QueryTreeNodeType::CONSTANT)
{
continue;
}
else
{
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unexpected node type {} for argument node in {}",
argument_node->getNodeType(), node->formatASTForErrorMessage());
}
}
};
std::optional<JoinTableSide> resolved_side;
QueryTreeNodePtr resolved_identifier;
@ -3173,12 +3239,23 @@ QueryTreeNodePtr QueryAnalyzer::tryResolveIdentifierFromJoin(const IdentifierLoo
if (left_resolved_identifier && right_resolved_identifier)
{
auto & left_resolved_column = left_resolved_identifier->as<ColumnNode &>();
auto & right_resolved_column = right_resolved_identifier->as<ColumnNode &>();
auto using_column_node_it = join_using_column_name_to_column_node.end();
if (left_resolved_identifier->getNodeType() == QueryTreeNodeType::COLUMN && right_resolved_identifier->getNodeType() == QueryTreeNodeType::COLUMN)
{
auto & left_resolved_column = left_resolved_identifier->as<ColumnNode &>();
auto & right_resolved_column = right_resolved_identifier->as<ColumnNode &>();
if (left_resolved_column.getColumnName() == right_resolved_column.getColumnName())
using_column_node_it = join_using_column_name_to_column_node.find(left_resolved_column.getColumnName());
}
else
{
if (left_resolved_identifier->getNodeType() != QueryTreeNodeType::COLUMN)
check_nested_column_not_in_using(left_resolved_identifier);
if (right_resolved_identifier->getNodeType() != QueryTreeNodeType::COLUMN)
check_nested_column_not_in_using(right_resolved_identifier);
}
auto using_column_node_it = join_using_column_name_to_column_node.find(left_resolved_column.getColumnName());
if (using_column_node_it != join_using_column_name_to_column_node.end()
&& left_resolved_column.getColumnName() == right_resolved_column.getColumnName())
if (using_column_node_it != join_using_column_name_to_column_node.end())
{
JoinTableSide using_column_inner_column_table_side = isRight(join_kind) ? JoinTableSide::Right : JoinTableSide::Left;
auto & using_column_node = using_column_node_it->second->as<ColumnNode &>();
@ -3253,39 +3330,45 @@ QueryTreeNodePtr QueryAnalyzer::tryResolveIdentifierFromJoin(const IdentifierLoo
else if (left_resolved_identifier)
{
resolved_side = JoinTableSide::Left;
auto & left_resolved_column = left_resolved_identifier->as<ColumnNode &>();
resolved_identifier = left_resolved_identifier;
auto using_column_node_it = join_using_column_name_to_column_node.find(left_resolved_column.getColumnName());
if (using_column_node_it != join_using_column_name_to_column_node.end() &&
!using_column_node_it->second->getColumnType()->equals(*left_resolved_column.getColumnType()))
if (left_resolved_identifier->getNodeType() != QueryTreeNodeType::COLUMN)
{
auto left_resolved_column_clone = std::static_pointer_cast<ColumnNode>(left_resolved_column.clone());
left_resolved_column_clone->setColumnType(using_column_node_it->second->getColumnType());
resolved_identifier = std::move(left_resolved_column_clone);
check_nested_column_not_in_using(left_resolved_identifier);
}
else
{
resolved_identifier = left_resolved_identifier;
auto & left_resolved_column = left_resolved_identifier->as<ColumnNode &>();
auto using_column_node_it = join_using_column_name_to_column_node.find(left_resolved_column.getColumnName());
if (using_column_node_it != join_using_column_name_to_column_node.end() &&
!using_column_node_it->second->getColumnType()->equals(*left_resolved_column.getColumnType()))
{
auto left_resolved_column_clone = std::static_pointer_cast<ColumnNode>(left_resolved_column.clone());
left_resolved_column_clone->setColumnType(using_column_node_it->second->getColumnType());
resolved_identifier = std::move(left_resolved_column_clone);
}
}
}
else if (right_resolved_identifier)
{
resolved_side = JoinTableSide::Right;
auto & right_resolved_column = right_resolved_identifier->as<ColumnNode &>();
resolved_identifier = right_resolved_identifier;
auto using_column_node_it = join_using_column_name_to_column_node.find(right_resolved_column.getColumnName());
if (using_column_node_it != join_using_column_name_to_column_node.end() &&
!using_column_node_it->second->getColumnType()->equals(*right_resolved_column.getColumnType()))
if (right_resolved_identifier->getNodeType() != QueryTreeNodeType::COLUMN)
{
auto right_resolved_column_clone = std::static_pointer_cast<ColumnNode>(right_resolved_column.clone());
right_resolved_column_clone->setColumnType(using_column_node_it->second->getColumnType());
resolved_identifier = std::move(right_resolved_column_clone);
check_nested_column_not_in_using(right_resolved_identifier);
}
else
{
resolved_identifier = right_resolved_identifier;
auto & right_resolved_column = right_resolved_identifier->as<ColumnNode &>();
auto using_column_node_it = join_using_column_name_to_column_node.find(right_resolved_column.getColumnName());
if (using_column_node_it != join_using_column_name_to_column_node.end() &&
!using_column_node_it->second->getColumnType()->equals(*right_resolved_column.getColumnType()))
{
auto right_resolved_column_clone = std::static_pointer_cast<ColumnNode>(right_resolved_column.clone());
right_resolved_column_clone->setColumnType(using_column_node_it->second->getColumnType());
resolved_identifier = std::move(right_resolved_column_clone);
}
}
}
@ -6778,6 +6861,39 @@ void QueryAnalyzer::resolveArrayJoin(QueryTreeNodePtr & array_join_node, Identif
}
}
void QueryAnalyzer::checkDuplicateTableNamesOrAlias(const QueryTreeNodePtr & join_node, QueryTreeNodePtr & left_table_expr, QueryTreeNodePtr & right_table_expr, IdentifierResolveScope & scope)
{
Names column_names;
if (!scope.context->getSettingsRef().joined_subquery_requires_alias)
return;
if (join_node->as<JoinNode &>().getKind() != JoinKind::Paste)
return;
auto * left_node = left_table_expr->as<QueryNode>();
auto * right_node = right_table_expr->as<QueryNode>();
if (!left_node && !right_node)
return;
if (left_node)
for (const auto & name_and_type : left_node->getProjectionColumns())
column_names.push_back(name_and_type.name);
if (right_node)
for (const auto & name_and_type : right_node->getProjectionColumns())
column_names.push_back(name_and_type.name);
if (column_names.empty())
throw Exception(ErrorCodes::LOGICAL_ERROR, "Names of projection columns cannot be empty");
std::sort(column_names.begin(), column_names.end());
for (size_t i = 0; i < column_names.size() - 1; i++) // Check if there is no any duplicates because it will lead to broken result
if (column_names[i] == column_names[i+1])
throw Exception(ErrorCodes::BAD_ARGUMENTS,
"Name of columns and aliases should be unique for this query (you can add/change aliases to avoid duplication)"
"While processing '{}'", join_node->formatASTForErrorMessage());
}
/// Resolve join node in scope
void QueryAnalyzer::resolveJoin(QueryTreeNodePtr & join_node, IdentifierResolveScope & scope, QueryExpressionsAliasVisitor & expressions_visitor)
{
@ -6789,6 +6905,9 @@ void QueryAnalyzer::resolveJoin(QueryTreeNodePtr & join_node, IdentifierResolveS
resolveQueryJoinTreeNode(join_node_typed.getRightTableExpression(), scope, expressions_visitor);
validateJoinTableExpressionWithoutAlias(join_node, join_node_typed.getRightTableExpression(), scope);
if (!join_node_typed.getLeftTableExpression()->hasAlias() && !join_node_typed.getRightTableExpression()->hasAlias())
checkDuplicateTableNamesOrAlias(join_node, join_node_typed.getLeftTableExpression(), join_node_typed.getRightTableExpression(), scope);
if (join_node_typed.isOnJoinExpression())
{
expressions_visitor.visit(join_node_typed.getJoinExpression());

3
src/Analyzer/Passes/RemoveUnusedProjectionColumnsPass.cpp
View File

@ -52,6 +52,9 @@ public:
return;
auto & column_node = node->as<ColumnNode &>();
if (column_node.getColumnName() == "__grouping_set")
return;
auto column_source_node = column_node.getColumnSource();
auto column_source_node_type = column_source_node->getNodeType();

129
src/Analyzer/Passes/RewriteSumFunctionWithSumAndCountPass.cpp Normal file
View File

@ -0,0 +1,129 @@
#include <Analyzer/Passes/RewriteSumFunctionWithSumAndCountPass.h>
#include <AggregateFunctions/AggregateFunctionFactory.h>
#include <Analyzer/InDepthQueryTreeVisitor.h>
#include <Analyzer/ColumnNode.h>
#include <Analyzer/ConstantNode.h>
#include <Analyzer/FunctionNode.h>
#include <Functions/FunctionFactory.h>
namespace DB
{
namespace
{
class RewriteSumFunctionWithSumAndCountVisitor : public InDepthQueryTreeVisitorWithContext<RewriteSumFunctionWithSumAndCountVisitor>
{
public:
using Base = InDepthQueryTreeVisitorWithContext<RewriteSumFunctionWithSumAndCountVisitor>;
using Base::Base;
void enterImpl(QueryTreeNodePtr & node)
{
if (!getSettings().optimize_arithmetic_operations_in_aggregate_functions)
return;
static const std::unordered_set<String> func_supported = {
"plus",
"minus"
};
auto * function_node = node->as<FunctionNode>();
if (!function_node || Poco::toLower(function_node->getFunctionName()) != "sum")
return;
const auto & function_nodes = function_node->getArguments().getNodes();
if (function_nodes.size() != 1)
return;
const auto * func_plus_minus_node = function_nodes[0]->as<FunctionNode>();
if (!func_plus_minus_node || !func_supported.contains(Poco::toLower(func_plus_minus_node->getFunctionName())))
return;
const auto & func_plus_minus_nodes = func_plus_minus_node->getArguments().getNodes();
if (func_plus_minus_nodes.size() != 2)
return;
size_t column_id;
if (func_plus_minus_nodes[0]->as<ColumnNode>() && func_plus_minus_nodes[1]->as<ConstantNode>())
column_id = 0;
else if (func_plus_minus_nodes[0]->as<ConstantNode>() && func_plus_minus_nodes[1]->as<ColumnNode>())
column_id = 1;
else
return;
size_t literal_id = 1 - column_id;
const auto * literal = func_plus_minus_nodes[literal_id]->as<ConstantNode>();
if (!literal)
return;
const auto literal_type = literal->getResultType();
if (!literal_type || !WhichDataType(literal_type).isNumber())
return;
const auto * column_node = func_plus_minus_nodes[column_id]->as<ColumnNode>();
if (!column_node)
return;
const auto column_type = column_node->getColumnType();
if (!column_type || !isNumber(column_type))
return;
const auto lhs = std::make_shared<FunctionNode>("sum");
lhs->getArguments().getNodes().push_back(func_plus_minus_nodes[column_id]);
resolveAsAggregateFunctionNode(*lhs, column_type);
const auto rhs_count = std::make_shared<FunctionNode>("count");
rhs_count->getArguments().getNodes().push_back(func_plus_minus_nodes[column_id]);
resolveAsAggregateFunctionNode(*rhs_count, column_type);
const auto rhs = std::make_shared<FunctionNode>("multiply");
rhs->getArguments().getNodes().push_back(func_plus_minus_nodes[literal_id]);
rhs->getArguments().getNodes().push_back(rhs_count);
resolveOrdinaryFunctionNode(*rhs, rhs->getFunctionName());
const auto new_node = std::make_shared<FunctionNode>(Poco::toLower(func_plus_minus_node->getFunctionName()));
if (column_id == 0)
new_node->getArguments().getNodes() = {lhs, rhs};
else if (column_id == 1)
new_node->getArguments().getNodes() = {rhs, lhs};
resolveOrdinaryFunctionNode(*new_node, new_node->getFunctionName());
if (!new_node)
return;
node = new_node;
}
private:
void resolveOrdinaryFunctionNode(FunctionNode & function_node, const String & function_name) const
{
const auto function = FunctionFactory::instance().get(function_name, getContext());
function_node.resolveAsFunction(function->build(function_node.getArgumentColumns()));
}
static inline void resolveAsAggregateFunctionNode(FunctionNode & function_node, const DataTypePtr & argument_type)
{
AggregateFunctionProperties properties;
const auto aggregate_function = AggregateFunctionFactory::instance().get(function_node.getFunctionName(),
NullsAction::EMPTY,
{argument_type},
{},
properties);
function_node.resolveAsAggregateFunction(aggregate_function);
}
};
}
void RewriteSumFunctionWithSumAndCountPass::run(QueryTreeNodePtr query_tree_node, ContextPtr context)
{
RewriteSumFunctionWithSumAndCountVisitor visitor(std::move(context));
visitor.visit(query_tree_node);
}
}

27
src/Analyzer/Passes/RewriteSumFunctionWithSumAndCountPass.h Normal file
View File

@ -0,0 +1,27 @@
#pragma once
#include <Analyzer/IQueryTreePass.h>
namespace DB
{
/**
* Rewrites `sum(column +/- literal)` into two individual functions
* `sum(column)` and `literal * count(column)`.
* sum(column + literal) -> sum(column) + literal * count(column)
* sum(literal + column) -> literal * count(column) + sum(column)
* sum(column - literal) -> sum(column) - literal * count(column)
* sum(literal - column) -> literal * count(column) - sum(column)
*/
class RewriteSumFunctionWithSumAndCountPass final : public IQueryTreePass
{
public:
String getName() override { return "RewriteSumFunctionWithSumAndCountPass"; }
String getDescription() override { return "Rewrite sum(column +/- literal) into sum(column) and literal * count(column)"; }
void run(QueryTreeNodePtr query_tree_node, ContextPtr context) override;
};
}

2
src/Analyzer/Passes/UniqToCountPass.cpp
View File

@ -1,4 +1,4 @@
#include "UniqToCountPass.h"
#include <Analyzer/Passes/UniqToCountPass.h>
#include <AggregateFunctions/AggregateFunctionFactory.h>
#include <AggregateFunctions/IAggregateFunction.h>

11
src/Analyzer/QueryTreePassManager.cpp
View File

@ -18,6 +18,7 @@
#include <Analyzer/Utils.h>
#include <Analyzer/Passes/QueryAnalysisPass.h>
#include <Analyzer/Passes/RemoveUnusedProjectionColumnsPass.h>
#include <Analyzer/Passes/RewriteSumFunctionWithSumAndCountPass.h>
#include <Analyzer/Passes/CountDistinctPass.h>
#include <Analyzer/Passes/UniqToCountPass.h>
#include <Analyzer/Passes/FunctionToSubcolumnsPass.h>
@ -190,6 +191,12 @@ void QueryTreePassManager::run(QueryTreeNodePtr query_tree_node)
}
}
void QueryTreePassManager::runOnlyResolve(QueryTreeNodePtr query_tree_node)
{
// Run only QueryAnalysisPass and GroupingFunctionsResolvePass passes.
run(query_tree_node, 2);
}
void QueryTreePassManager::run(QueryTreeNodePtr query_tree_node, size_t up_to_pass_index)
{
size_t passes_size = passes.size();
@ -243,11 +250,14 @@ void QueryTreePassManager::dump(WriteBuffer & buffer, size_t up_to_pass_index)
void addQueryTreePasses(QueryTreePassManager & manager)
{
manager.addPass(std::make_unique<QueryAnalysisPass>());
manager.addPass(std::make_unique<GroupingFunctionsResolvePass>());
manager.addPass(std::make_unique<RemoveUnusedProjectionColumnsPass>());
manager.addPass(std::make_unique<FunctionToSubcolumnsPass>());
manager.addPass(std::make_unique<ConvertLogicalExpressionToCNFPass>());
manager.addPass(std::make_unique<RewriteSumFunctionWithSumAndCountPass>());
manager.addPass(std::make_unique<CountDistinctPass>());
manager.addPass(std::make_unique<UniqToCountPass>());
manager.addPass(std::make_unique<RewriteAggregateFunctionWithIfPass>());
@ -278,7 +288,6 @@ void addQueryTreePasses(QueryTreePassManager & manager)
manager.addPass(std::make_unique<LogicalExpressionOptimizerPass>());
manager.addPass(std::make_unique<GroupingFunctionsResolvePass>());
manager.addPass(std::make_unique<AutoFinalOnQueryPass>());
manager.addPass(std::make_unique<CrossToInnerJoinPass>());
manager.addPass(std::make_unique<ShardNumColumnToFunctionPass>());

3
src/Analyzer/QueryTreePassManager.h
View File

@ -27,6 +27,9 @@ public:
/// Run query tree passes on query tree
void run(QueryTreeNodePtr query_tree_node);
/// Run only query tree passes responsible to name resolution.
void runOnlyResolve(QueryTreeNodePtr query_tree_node);
/** Run query tree passes on query tree up to up_to_pass_index.
* Throws exception if up_to_pass_index is greater than passes size.
*/

4
src/Backups/BackupCoordinationRemote.cpp
View File

@ -162,7 +162,8 @@ BackupCoordinationRemote::BackupCoordinationRemote(
const Strings & all_hosts_,
const String & current_host_,
bool plain_backup_,
bool is_internal_)
bool is_internal_,
QueryStatusPtr process_list_element_)
: root_zookeeper_path(root_zookeeper_path_)
, zookeeper_path(root_zookeeper_path_ + "/backup-" + backup_uuid_)
, keeper_settings(keeper_settings_)
@ -177,6 +178,7 @@ BackupCoordinationRemote::BackupCoordinationRemote(
log,
get_zookeeper_,
keeper_settings,
process_list_element_,
[my_zookeeper_path = zookeeper_path, my_current_host = current_host, my_is_internal = is_internal]
(WithRetries::FaultyKeeper & zk)
{

3
src/Backups/BackupCoordinationRemote.h
View File

@ -30,7 +30,8 @@ public:
const Strings & all_hosts_,
const String & current_host_,
bool plain_backup_,
bool is_internal_);
bool is_internal_,
QueryStatusPtr process_list_element_);
~BackupCoordinationRemote() override;

31
src/Backups/BackupEntriesCollector.cpp
View File

@ -87,6 +87,7 @@ BackupEntriesCollector::BackupEntriesCollector(
, backup_coordination(backup_coordination_)
, read_settings(read_settings_)
, context(context_)
, process_list_element(context->getProcessListElement())
, on_cluster_first_sync_timeout(context->getConfigRef().getUInt64("backups.on_cluster_first_sync_timeout", 180000))
, collect_metadata_timeout(context->getConfigRef().getUInt64(
"backups.collect_metadata_timeout", context->getConfigRef().getUInt64("backups.consistent_metadata_snapshot_timeout", 600000)))
@ -158,8 +159,9 @@ BackupEntries BackupEntriesCollector::run()
Strings BackupEntriesCollector::setStage(const String & new_stage, const String & message)
{
LOG_TRACE(log, "Setting stage: {}", new_stage);
current_stage = new_stage;
checkIsQueryCancelled();
current_stage = new_stage;
backup_coordination->setStage(new_stage, message);
if (new_stage == Stage::formatGatheringMetadata(0))
@ -179,6 +181,12 @@ Strings BackupEntriesCollector::setStage(const String & new_stage, const String
}
}
void BackupEntriesCollector::checkIsQueryCancelled() const
{
if (process_list_element)
process_list_element->checkTimeLimit();
}
/// Calculates the root path for collecting backup entries,
/// it's either empty or has the format "shards/<shard_num>/replicas/<replica_num>/".
void BackupEntriesCollector::calculateRootPathInBackup()
@ -413,6 +421,8 @@ void BackupEntriesCollector::gatherDatabaseMetadata(
bool all_tables,
const std::set<DatabaseAndTableName> & except_table_names)
{
checkIsQueryCancelled();
auto it = database_infos.find(database_name);
if (it == database_infos.end())
{
@ -491,6 +501,8 @@ void BackupEntriesCollector::gatherDatabaseMetadata(
void BackupEntriesCollector::gatherTablesMetadata()
{
checkIsQueryCancelled();
table_infos.clear();
for (const auto & [database_name, database_info] : database_infos)
{
@ -552,6 +564,8 @@ std::vector<std::pair<ASTPtr, StoragePtr>> BackupEntriesCollector::findTablesInD
const auto & database_info = database_infos.at(database_name);
const auto & database = database_info.database;
checkIsQueryCancelled();
auto filter_by_table_name = [my_database_info = &database_info](const String & table_name)
{
/// We skip inner tables of materialized views.
@ -629,8 +643,12 @@ void BackupEntriesCollector::lockTablesForReading()
for (auto & [table_name, table_info] : table_infos)
{
auto storage = table_info.storage;
if (storage)
table_info.table_lock = storage->tryLockForShare(context->getInitialQueryId(), context->getSettingsRef().lock_acquire_timeout);
if (!storage)
continue;
checkIsQueryCancelled();
table_info.table_lock = storage->tryLockForShare(context->getInitialQueryId(), context->getSettingsRef().lock_acquire_timeout);
}
std::erase_if(
@ -734,6 +752,7 @@ void BackupEntriesCollector::makeBackupEntriesForDatabasesDefs()
continue; /// We store CREATE DATABASE queries only if there was BACKUP DATABASE specified.
LOG_TRACE(log, "Adding the definition of database {} to backup", backQuoteIfNeed(database_name));
checkIsQueryCancelled();
ASTPtr new_create_query = database_info.create_database_query;
adjustCreateQueryForBackup(new_create_query, context->getGlobalContext(), nullptr);
@ -750,6 +769,7 @@ void BackupEntriesCollector::makeBackupEntriesForTablesDefs()
for (auto & [table_name, table_info] : table_infos)
{
LOG_TRACE(log, "Adding the definition of {} to backup", tableNameWithTypeToString(table_name.database, table_name.table, false));
checkIsQueryCancelled();
ASTPtr new_create_query = table_info.create_table_query;
adjustCreateQueryForBackup(new_create_query, context->getGlobalContext(), &table_info.replicated_table_shared_id);
@ -802,6 +822,7 @@ void BackupEntriesCollector::makeBackupEntriesForTableData(const QualifiedTableN
}
LOG_TRACE(log, "Collecting data of {} for backup", tableNameWithTypeToString(table_name.database, table_name.table, false));
checkIsQueryCancelled();
try
{
@ -861,13 +882,17 @@ void BackupEntriesCollector::addPostTask(std::function<void()> task)
void BackupEntriesCollector::runPostTasks()
{
LOG_TRACE(log, "Will run {} post tasks", post_tasks.size());
/// Post collecting tasks can add other post collecting tasks, our code is fine with that.
while (!post_tasks.empty())
{
checkIsQueryCancelled();
auto task = std::move(post_tasks.front());
post_tasks.pop();
std::move(task)();
}
LOG_TRACE(log, "All post tasks successfully executed");
}

7
src/Backups/BackupEntriesCollector.h
View File

@ -22,6 +22,9 @@ class IDatabase;
using DatabasePtr = std::shared_ptr<IDatabase>;
struct StorageID;
enum class AccessEntityType;
class QueryStatus;
using QueryStatusPtr = std::shared_ptr<QueryStatus>;
/// Collects backup entries for all databases and tables which should be put to a backup.
class BackupEntriesCollector : private boost::noncopyable
@ -97,11 +100,15 @@ private:
Strings setStage(const String & new_stage, const String & message = "");
/// Throws an exception if the BACKUP query was cancelled.
void checkIsQueryCancelled() const;
const ASTBackupQuery::Elements backup_query_elements;
const BackupSettings backup_settings;
std::shared_ptr<IBackupCoordination> backup_coordination;
const ReadSettings read_settings;
ContextPtr context;
QueryStatusPtr process_list_element;
/// The time a BACKUP ON CLUSTER or RESTORE ON CLUSTER command will wait until all the nodes receive the BACKUP (or RESTORE) query and start working.
/// This setting is similar to `distributed_ddl_task_timeout`.

7
src/Backups/BackupEntryFromMemory.h
View File

@ -17,7 +17,12 @@ public:
std::unique_ptr<SeekableReadBuffer> getReadBuffer(const ReadSettings &) const override;
UInt64 getSize() const override { return data.size(); }
DataSourceDescription getDataSourceDescription() const override { return DataSourceDescription{DataSourceType::RAM, "", false, false}; }
DataSourceDescription getDataSourceDescription() const override
{
DataSourceDescription res;
res.type = DataSourceType::RAM;
return res;
}
private:
const String data;

9
src/Backups/BackupFileInfo.cpp
View File

@ -7,6 +7,8 @@
#include <Common/scope_guard_safe.h>
#include <Common/setThreadName.h>
#include <Common/ThreadPool.h>
#include <Interpreters/ProcessList.h>
#include <base/hex.h>
@ -203,7 +205,7 @@ BackupFileInfo buildFileInfoForBackupEntry(
return info;
}
BackupFileInfos buildFileInfosForBackupEntries(const BackupEntries & backup_entries, const BackupPtr & base_backup, const ReadSettings & read_settings, ThreadPool & thread_pool)
BackupFileInfos buildFileInfosForBackupEntries(const BackupEntries & backup_entries, const BackupPtr & base_backup, const ReadSettings & read_settings, ThreadPool & thread_pool, QueryStatusPtr process_list_element)
{
BackupFileInfos infos;
infos.resize(backup_entries.size());
@ -225,7 +227,7 @@ BackupFileInfos buildFileInfosForBackupEntries(const BackupEntries & backup_entr
++num_active_jobs;
}
auto job = [&mutex, &num_active_jobs, &event, &exception, &infos, &backup_entries, &read_settings, &base_backup, &thread_group, i, log]()
auto job = [&mutex, &num_active_jobs, &event, &exception, &infos, &backup_entries, &read_settings, &base_backup, &thread_group, &process_list_element, i, log]()
{
SCOPE_EXIT_SAFE({
std::lock_guard lock{mutex};
@ -250,6 +252,9 @@ BackupFileInfos buildFileInfosForBackupEntries(const BackupEntries & backup_entr
return;
}
if (process_list_element)
process_list_element->checkTimeLimit();
infos[i] = buildFileInfoForBackupEntry(name, entry, base_backup, read_settings, log);
}
catch (...)

4
src/Backups/BackupFileInfo.h
View File

@ -14,6 +14,8 @@ using BackupPtr = std::shared_ptr<const IBackup>;
using BackupEntryPtr = std::shared_ptr<const IBackupEntry>;
using BackupEntries = std::vector<std::pair<String, BackupEntryPtr>>;
struct ReadSettings;
class QueryStatus;
using QueryStatusPtr = std::shared_ptr<QueryStatus>;
/// Information about a file stored in a backup.
@ -78,6 +80,6 @@ using BackupFileInfos = std::vector<BackupFileInfo>;
BackupFileInfo buildFileInfoForBackupEntry(const String & file_name, const BackupEntryPtr & backup_entry, const BackupPtr & base_backup, const ReadSettings & read_settings, Poco::Logger * log);
/// Builds a vector of BackupFileInfos for specified backup entries.
BackupFileInfos buildFileInfosForBackupEntries(const BackupEntries & backup_entries, const BackupPtr & base_backup, const ReadSettings & read_settings, ThreadPool & thread_pool);
BackupFileInfos buildFileInfosForBackupEntries(const BackupEntries & backup_entries, const BackupPtr & base_backup, const ReadSettings & read_settings, ThreadPool & thread_pool, QueryStatusPtr process_list_element);
}

4
src/Backups/BackupIO_S3.cpp
View File

@ -126,7 +126,7 @@ BackupReaderS3::BackupReaderS3(
const ContextPtr & context_)
: BackupReaderDefault(read_settings_, write_settings_, &Poco::Logger::get("BackupReaderS3"))
, s3_uri(s3_uri_)
, data_source_description{DataSourceType::S3, s3_uri.endpoint, false, false}
, data_source_description{DataSourceType::ObjectStorage, ObjectStorageType::S3, MetadataStorageType::None, s3_uri.endpoint, false, false}
, s3_settings(context_->getStorageS3Settings().getSettings(s3_uri.uri.toString()))
{
auto & request_settings = s3_settings.request_settings;
@ -216,7 +216,7 @@ BackupWriterS3::BackupWriterS3(
const ContextPtr & context_)
: BackupWriterDefault(read_settings_, write_settings_, &Poco::Logger::get("BackupWriterS3"))
, s3_uri(s3_uri_)
, data_source_description{DataSourceType::S3, s3_uri.endpoint, false, false}
, data_source_description{DataSourceType::ObjectStorage, ObjectStorageType::S3, MetadataStorageType::None, s3_uri.endpoint, false, false}
, s3_settings(context_->getStorageS3Settings().getSettings(s3_uri.uri.toString()))
{
auto & request_settings = s3_settings.request_settings;

4
src/Backups/BackupStatus.cpp
View File

@ -21,12 +21,16 @@ std::string_view toString(BackupStatus backup_status)
return "BACKUP_CREATED";
case BackupStatus::BACKUP_FAILED:
return "BACKUP_FAILED";
case BackupStatus::BACKUP_CANCELLED:
return "BACKUP_CANCELLED";
case BackupStatus::RESTORING:
return "RESTORING";
case BackupStatus::RESTORED:
return "RESTORED";
case BackupStatus::RESTORE_FAILED:
return "RESTORE_FAILED";
case BackupStatus::RESTORE_CANCELLED:
return "RESTORE_CANCELLED";
default:
break;
}

4
src/Backups/BackupStatus.h
View File

@ -18,6 +18,10 @@ enum class BackupStatus
RESTORED,
RESTORE_FAILED,
/// Statuses used after a BACKUP or RESTORE operation was cancelled.
BACKUP_CANCELLED,
RESTORE_CANCELLED,
MAX,
};

322
src/Backups/BackupsWorker.cpp
View File

@ -45,6 +45,7 @@ namespace ErrorCodes
extern const int BAD_ARGUMENTS;
extern const int LOGICAL_ERROR;
extern const int CONCURRENT_ACCESS_NOT_SUPPORTED;
extern const int QUERY_WAS_CANCELLED;
}
using OperationID = BackupOperationID;
@ -73,7 +74,8 @@ namespace
all_hosts,
backup_settings.host_id,
!backup_settings.deduplicate_files,
backup_settings.internal);
backup_settings.internal,
context->getProcessListElement());
}
else
{
@ -110,7 +112,8 @@ namespace
toString(*restore_settings.restore_uuid),
all_hosts,
restore_settings.host_id,
restore_settings.internal);
restore_settings.internal,
context->getProcessListElement());
}
else
{
@ -150,17 +153,52 @@ namespace
}
}
bool isFinalStatus(BackupStatus status)
bool isFinishedSuccessfully(BackupStatus status)
{
return (status == BackupStatus::BACKUP_CREATED) || (status == BackupStatus::BACKUP_FAILED) || (status == BackupStatus::RESTORED)
|| (status == BackupStatus::RESTORE_FAILED);
return (status == BackupStatus::BACKUP_CREATED) || (status == BackupStatus::RESTORED);
}
bool isErrorStatus(BackupStatus status)
bool isFailed(BackupStatus status)
{
return (status == BackupStatus::BACKUP_FAILED) || (status == BackupStatus::RESTORE_FAILED);
}
bool isCancelled(BackupStatus status)
{
return (status == BackupStatus::BACKUP_CANCELLED) || (status == BackupStatus::RESTORE_CANCELLED);
}
bool isFailedOrCancelled(BackupStatus status)
{
return isFailed(status) || isCancelled(status);
}
bool isFinalStatus(BackupStatus status)
{
return isFinishedSuccessfully(status) || isFailedOrCancelled(status);
}
bool isBackupStatus(BackupStatus status)
{
return (status == BackupStatus::CREATING_BACKUP) || (status == BackupStatus::BACKUP_CREATED) || (status == BackupStatus::BACKUP_FAILED) || (status == BackupStatus::BACKUP_CANCELLED);
}
BackupStatus getBackupStatusFromCurrentException()
{
if (getCurrentExceptionCode() == ErrorCodes::QUERY_WAS_CANCELLED)
return BackupStatus::BACKUP_CANCELLED;
else
return BackupStatus::BACKUP_FAILED;
}
BackupStatus getRestoreStatusFromCurrentException()
{
if (getCurrentExceptionCode() == ErrorCodes::QUERY_WAS_CANCELLED)
return BackupStatus::RESTORE_CANCELLED;
else
return BackupStatus::RESTORE_FAILED;
}
/// Used to change num_active_backups.
size_t getNumActiveBackupsChange(BackupStatus status)
{
@ -337,13 +375,15 @@ private:
};
BackupsWorker::BackupsWorker(ContextPtr global_context, size_t num_backup_threads, size_t num_restore_threads, bool allow_concurrent_backups_, bool allow_concurrent_restores_)
BackupsWorker::BackupsWorker(ContextMutablePtr global_context, size_t num_backup_threads, size_t num_restore_threads, bool allow_concurrent_backups_, bool allow_concurrent_restores_, bool test_inject_sleep_)
: thread_pools(std::make_unique<ThreadPools>(num_backup_threads, num_restore_threads))
, allow_concurrent_backups(allow_concurrent_backups_)
, allow_concurrent_restores(allow_concurrent_restores_)
, test_inject_sleep(test_inject_sleep_)
, log(&Poco::Logger::get("BackupsWorker"))
, backup_log(global_context->getBackupLog())
, process_list(global_context->getProcessList())
{
backup_log = global_context->getBackupLog();
}
@ -400,7 +440,7 @@ OperationID BackupsWorker::startMakingBackup(const ASTPtr & query, const Context
try
{
addInfo(backup_id, backup_name_for_logging, base_backup_name, backup_settings.internal, BackupStatus::CREATING_BACKUP);
addInfo(backup_id, backup_name_for_logging, base_backup_name, backup_settings.internal, context->getProcessListElement(), BackupStatus::CREATING_BACKUP);
/// Prepare context to use.
ContextPtr context_in_use = context;
@ -408,8 +448,9 @@ OperationID BackupsWorker::startMakingBackup(const ASTPtr & query, const Context
bool on_cluster = !backup_query->cluster.empty();
if (on_cluster || backup_settings.async)
{
/// For ON CLUSTER queries we will need to change some settings.
/// For ASYNC queries we have to clone the context anyway.
/// We have to clone the query context here because:
/// if this is an "ON CLUSTER" query we need to change some settings, and
/// if this is an "ASYNC" query it's going to be executed in another thread.
context_in_use = mutable_context = Context::createCopy(context);
mutable_context->makeQueryContext();
}
@ -417,8 +458,22 @@ OperationID BackupsWorker::startMakingBackup(const ASTPtr & query, const Context
if (backup_settings.async)
{
auto & thread_pool = getThreadPool(on_cluster ? ThreadPoolId::BACKUP_ASYNC_ON_CLUSTER : ThreadPoolId::BACKUP_ASYNC);
/// process_list_element_holder is used to make an element in ProcessList live while BACKUP is working asynchronously.
auto process_list_element = context_in_use->getProcessListElement();
thread_pool.scheduleOrThrowOnError(
[this, backup_query, backup_id, backup_name_for_logging, backup_info, backup_settings, backup_coordination, context_in_use, mutable_context]
[this,
backup_query,
backup_id,
backup_name_for_logging,
backup_info,
backup_settings,
backup_coordination,
context_in_use,
mutable_context,
thread_group = CurrentThread::getGroup(),
process_list_element_holder = process_list_element ? process_list_element->getProcessListEntry() : nullptr]
{
doBackup(
backup_query,
@ -429,6 +484,7 @@ OperationID BackupsWorker::startMakingBackup(const ASTPtr & query, const Context
backup_coordination,
context_in_use,
mutable_context,
thread_group,
/* called_async= */ true);
});
}
@ -443,6 +499,7 @@ OperationID BackupsWorker::startMakingBackup(const ASTPtr & query, const Context
backup_coordination,
context_in_use,
mutable_context,
nullptr,
/* called_async= */ false);
}
@ -452,7 +509,7 @@ OperationID BackupsWorker::startMakingBackup(const ASTPtr & query, const Context
{
tryLogCurrentException(log, fmt::format("Failed to start {} {}", (backup_settings.internal ? "internal backup" : "backup"), backup_name_for_logging));
/// Something bad happened, the backup has not built.
setStatusSafe(backup_id, BackupStatus::BACKUP_FAILED);
setStatusSafe(backup_id, getBackupStatusFromCurrentException());
sendCurrentExceptionToCoordination(backup_coordination);
throw;
}
@ -468,19 +525,22 @@ void BackupsWorker::doBackup(
std::shared_ptr<IBackupCoordination> backup_coordination,
const ContextPtr & context,
ContextMutablePtr mutable_context,
ThreadGroupPtr thread_group,
bool called_async)
{
std::optional<CurrentThread::QueryScope> query_scope;
SCOPE_EXIT_SAFE(
if (called_async && thread_group)
CurrentThread::detachFromGroupIfNotDetached();
);
try
{
if (called_async && thread_group)
CurrentThread::attachToGroup(thread_group);
if (called_async)
{
query_scope.emplace(mutable_context);
setThreadName("BackupWorker");
}
bool on_cluster = !backup_query->cluster.empty();
assert(mutable_context || (!on_cluster && !called_async));
/// Checks access rights if this is not ON CLUSTER query.
@ -557,8 +617,8 @@ void BackupsWorker::doBackup(
}
/// Write the backup entries to the backup.
buildFileInfosForBackupEntries(backup, backup_entries, backup_create_params.read_settings, backup_coordination);
writeBackupEntries(backup, std::move(backup_entries), backup_id, backup_coordination, backup_settings.internal);
buildFileInfosForBackupEntries(backup, backup_entries, backup_create_params.read_settings, backup_coordination, context->getProcessListElement());
writeBackupEntries(backup, std::move(backup_entries), backup_id, backup_coordination, backup_settings.internal, context->getProcessListElement());
/// We have written our backup entries, we need to tell other hosts (they could be waiting for it).
backup_coordination->setStage(Stage::COMPLETED,"");
@ -596,7 +656,7 @@ void BackupsWorker::doBackup(
if (called_async)
{
tryLogCurrentException(log, fmt::format("Failed to make {} {}", (backup_settings.internal ? "internal backup" : "backup"), backup_name_for_logging));
setStatusSafe(backup_id, BackupStatus::BACKUP_FAILED);
setStatusSafe(backup_id, getBackupStatusFromCurrentException());
sendCurrentExceptionToCoordination(backup_coordination);
}
else
@ -608,15 +668,21 @@ void BackupsWorker::doBackup(
}
void BackupsWorker::buildFileInfosForBackupEntries(const BackupPtr & backup, const BackupEntries & backup_entries, const ReadSettings & read_settings, std::shared_ptr<IBackupCoordination> backup_coordination)
void BackupsWorker::buildFileInfosForBackupEntries(const BackupPtr & backup, const BackupEntries & backup_entries, const ReadSettings & read_settings, std::shared_ptr<IBackupCoordination> backup_coordination, QueryStatusPtr process_list_element)
{
backup_coordination->setStage(Stage::BUILDING_FILE_INFOS, "");
backup_coordination->waitForStage(Stage::BUILDING_FILE_INFOS);
backup_coordination->addFileInfos(::DB::buildFileInfosForBackupEntries(backup_entries, backup->getBaseBackup(), read_settings, getThreadPool(ThreadPoolId::BACKUP_MAKE_FILES_LIST)));
backup_coordination->addFileInfos(::DB::buildFileInfosForBackupEntries(backup_entries, backup->getBaseBackup(), read_settings, getThreadPool(ThreadPoolId::BACKUP_MAKE_FILES_LIST), process_list_element));
}
void BackupsWorker::writeBackupEntries(BackupMutablePtr backup, BackupEntries && backup_entries, const OperationID & backup_id, std::shared_ptr<IBackupCoordination> backup_coordination, bool internal)
void BackupsWorker::writeBackupEntries(
BackupMutablePtr backup,
BackupEntries && backup_entries,
const OperationID & backup_id,
std::shared_ptr<IBackupCoordination> backup_coordination,
bool internal,
QueryStatusPtr process_list_element)
{
LOG_TRACE(log, "{}, num backup entries={}", Stage::WRITING_BACKUP, backup_entries.size());
backup_coordination->setStage(Stage::WRITING_BACKUP, "");
@ -677,7 +743,13 @@ void BackupsWorker::writeBackupEntries(BackupMutablePtr backup, BackupEntries &&
return;
}
if (process_list_element)
process_list_element->checkTimeLimit();
backup->writeFile(file_info, std::move(entry));
maybeSleepForTesting();
// Update metadata
if (!internal)
{
@ -690,7 +762,6 @@ void BackupsWorker::writeBackupEntries(BackupMutablePtr backup, BackupEntries &&
backup->getCompressedSize(),
0, 0);
}
}
catch (...)
{
@ -752,15 +823,16 @@ OperationID BackupsWorker::startRestoring(const ASTPtr & query, ContextMutablePt
if (restore_settings.base_backup_info)
base_backup_name = restore_settings.base_backup_info->toStringForLogging();
addInfo(restore_id, backup_name_for_logging, base_backup_name, restore_settings.internal, BackupStatus::RESTORING);
addInfo(restore_id, backup_name_for_logging, base_backup_name, restore_settings.internal, context->getProcessListElement(), BackupStatus::RESTORING);
/// Prepare context to use.
ContextMutablePtr context_in_use = context;
bool on_cluster = !restore_query->cluster.empty();
if (restore_settings.async || on_cluster)
{
/// For ON CLUSTER queries we will need to change some settings.
/// For ASYNC queries we have to clone the context anyway.
/// We have to clone the query context here because:
/// if this is an "ON CLUSTER" query we need to change some settings, and
/// if this is an "ASYNC" query it's going to be executed in another thread.
context_in_use = Context::createCopy(context);
context_in_use->makeQueryContext();
}
@ -768,8 +840,21 @@ OperationID BackupsWorker::startRestoring(const ASTPtr & query, ContextMutablePt
if (restore_settings.async)
{
auto & thread_pool = getThreadPool(on_cluster ? ThreadPoolId::RESTORE_ASYNC_ON_CLUSTER : ThreadPoolId::RESTORE_ASYNC);
/// process_list_element_holder is used to make an element in ProcessList live while RESTORE is working asynchronously.
auto process_list_element = context_in_use->getProcessListElement();
thread_pool.scheduleOrThrowOnError(
[this, restore_query, restore_id, backup_name_for_logging, backup_info, restore_settings, restore_coordination, context_in_use]
[this,
restore_query,
restore_id,
backup_name_for_logging,
backup_info,
restore_settings,
restore_coordination,
context_in_use,
thread_group = CurrentThread::getGroup(),
process_list_element_holder = process_list_element ? process_list_element->getProcessListEntry() : nullptr]
{
doRestore(
restore_query,
@ -779,6 +864,7 @@ OperationID BackupsWorker::startRestoring(const ASTPtr & query, ContextMutablePt
restore_settings,
restore_coordination,
context_in_use,
thread_group,
/* called_async= */ true);
});
}
@ -792,6 +878,7 @@ OperationID BackupsWorker::startRestoring(const ASTPtr & query, ContextMutablePt
restore_settings,
restore_coordination,
context_in_use,
nullptr,
/* called_async= */ false);
}
@ -800,7 +887,7 @@ OperationID BackupsWorker::startRestoring(const ASTPtr & query, ContextMutablePt
catch (...)
{
/// Something bad happened, the backup has not built.
setStatusSafe(restore_id, BackupStatus::RESTORE_FAILED);
setStatusSafe(restore_id, getRestoreStatusFromCurrentException());
sendCurrentExceptionToCoordination(restore_coordination);
throw;
}
@ -815,16 +902,20 @@ void BackupsWorker::doRestore(
RestoreSettings restore_settings,
std::shared_ptr<IRestoreCoordination> restore_coordination,
ContextMutablePtr context,
ThreadGroupPtr thread_group,
bool called_async)
{
std::optional<CurrentThread::QueryScope> query_scope;
SCOPE_EXIT_SAFE(
if (called_async && thread_group)
CurrentThread::detachFromGroupIfNotDetached();
);
try
{
if (called_async && thread_group)
CurrentThread::attachToGroup(thread_group);
if (called_async)
{
query_scope.emplace(context);
setThreadName("RestoreWorker");
}
/// Open the backup for reading.
BackupFactory::CreateParams backup_open_params;
@ -913,7 +1004,7 @@ void BackupsWorker::doRestore(
}
/// Execute the data restoring tasks.
restoreTablesData(restore_id, backup, std::move(data_restore_tasks), getThreadPool(ThreadPoolId::RESTORE_TABLES_DATA));
restoreTablesData(restore_id, backup, std::move(data_restore_tasks), getThreadPool(ThreadPoolId::RESTORE_TABLES_DATA), context->getProcessListElement());
/// We have restored everything, we need to tell other hosts (they could be waiting for it).
restore_coordination->setStage(Stage::COMPLETED, "");
@ -928,7 +1019,7 @@ void BackupsWorker::doRestore(
if (called_async)
{
tryLogCurrentException(log, fmt::format("Failed to restore from {} {}", (restore_settings.internal ? "internal backup" : "backup"), backup_name_for_logging));
setStatusSafe(restore_id, BackupStatus::RESTORE_FAILED);
setStatusSafe(restore_id, getRestoreStatusFromCurrentException());
sendCurrentExceptionToCoordination(restore_coordination);
}
else
@ -940,7 +1031,7 @@ void BackupsWorker::doRestore(
}
void BackupsWorker::restoreTablesData(const OperationID & restore_id, BackupPtr backup, DataRestoreTasks && tasks, ThreadPool & thread_pool)
void BackupsWorker::restoreTablesData(const OperationID & restore_id, BackupPtr backup, DataRestoreTasks && tasks, ThreadPool & thread_pool, QueryStatusPtr process_list_element)
{
size_t num_active_jobs = 0;
std::mutex mutex;
@ -980,7 +1071,13 @@ void BackupsWorker::restoreTablesData(const OperationID & restore_id, BackupPtr
return;
}
if (process_list_element)
process_list_element->checkTimeLimit();
std::move(task)();
maybeSleepForTesting();
setNumFilesAndSize(
restore_id,
backup->getNumFiles(),
@ -1011,9 +1108,10 @@ void BackupsWorker::restoreTablesData(const OperationID & restore_id, BackupPtr
}
void BackupsWorker::addInfo(const OperationID & id, const String & name, const String & base_backup_name, bool internal, BackupStatus status)
void BackupsWorker::addInfo(const OperationID & id, const String & name, const String & base_backup_name, bool internal, QueryStatusPtr process_list_element, BackupStatus status)
{
BackupOperationInfo info;
ExtendedOperationInfo extended_info;
auto & info = extended_info.info;
info.id = id;
info.name = name;
info.base_backup_name = base_backup_name;
@ -1021,7 +1119,16 @@ void BackupsWorker::addInfo(const OperationID & id, const String & name, const S
info.status = status;
info.start_time = std::chrono::system_clock::now();
if (isFinalStatus(status))
bool is_final_status = isFinalStatus(status);
if (process_list_element)
{
info.profile_counters = process_list_element->getInfo(/* get_thread_list= */ false, /* get_profile_events= */ true, /* get_settings= */ false).profile_counters;
if (!is_final_status)
extended_info.process_list_element = process_list_element;
}
if (is_final_status)
info.end_time = info.start_time;
std::lock_guard lock{infos_mutex};
@ -1030,7 +1137,7 @@ void BackupsWorker::addInfo(const OperationID & id, const String & name, const S
if (it != infos.end())
{
/// It's better not allow to overwrite the current status if it's in progress.
auto current_status = it->second.status;
auto current_status = it->second.info.status;
if (!isFinalStatus(current_status))
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Cannot start a backup or restore: ID {} is already in use", id);
}
@ -1038,7 +1145,7 @@ void BackupsWorker::addInfo(const OperationID & id, const String & name, const S
if (backup_log)
backup_log->add(BackupLogElement{info});
infos[id] = std::move(info);
infos[id] = std::move(extended_info);
num_active_backups += getNumActiveBackupsChange(status);
num_active_restores += getNumActiveRestoresChange(status);
@ -1057,16 +1164,24 @@ void BackupsWorker::setStatus(const String & id, BackupStatus status, bool throw
return;
}
auto & info = it->second;
auto & extended_info = it->second;
auto & info = extended_info.info;
auto old_status = info.status;
info.status = status;
info.profile_counters = std::make_shared<ProfileEvents::Counters::Snapshot>(CurrentThread::getProfileEvents().getPartiallyAtomicSnapshot());
bool is_final_status = isFinalStatus(status);
if (isFinalStatus(status))
if (extended_info.process_list_element)
{
info.profile_counters = extended_info.process_list_element->getInfo(/* get_thread_list= */ false, /* get_profile_events= */ true, /* get_settings= */ false).profile_counters;
if (is_final_status)
extended_info.process_list_element = nullptr;
}
if (is_final_status)
info.end_time = std::chrono::system_clock::now();
if (isErrorStatus(status))
if (isFailedOrCancelled(status))
{
info.error_message = getCurrentExceptionMessage(false);
info.exception = std::current_exception();
@ -1077,6 +1192,9 @@ void BackupsWorker::setStatus(const String & id, BackupStatus status, bool throw
num_active_backups += getNumActiveBackupsChange(status) - getNumActiveBackupsChange(old_status);
num_active_restores += getNumActiveRestoresChange(status) - getNumActiveRestoresChange(old_status);
if (status != old_status)
status_changed.notify_all();
}
@ -1090,7 +1208,7 @@ void BackupsWorker::setNumFilesAndSize(const OperationID & id, size_t num_files,
if (it == infos.end())
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unknown backup ID {}", id);
auto & info = it->second;
auto & info = it->second.info;
info.num_files = num_files;
info.total_size = total_size;
info.num_entries = num_entries;
@ -1101,37 +1219,120 @@ void BackupsWorker::setNumFilesAndSize(const OperationID & id, size_t num_files,
}
void BackupsWorker::wait(const OperationID & id, bool rethrow_exception)
void BackupsWorker::maybeSleepForTesting() const
{
if (test_inject_sleep)
sleepForSeconds(1);
}
void BackupsWorker::wait(const OperationID & backup_or_restore_id, bool rethrow_exception)
{
std::unique_lock lock{infos_mutex};
status_changed.wait(lock, [&]
{
auto it = infos.find(id);
auto it = infos.find(backup_or_restore_id);
if (it == infos.end())
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unknown backup ID {}", id);
const auto & info = it->second;
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unknown backup ID {}", backup_or_restore_id);
const auto & info = it->second.info;
auto current_status = info.status;
if (rethrow_exception && isErrorStatus(current_status))
if (rethrow_exception && isFailedOrCancelled(current_status))
std::rethrow_exception(info.exception);
return isFinalStatus(current_status);
if (isFinalStatus(current_status))
return true;
LOG_INFO(log, "Waiting {} {}", isBackupStatus(info.status) ? "backup" : "restore", info.name);
return false;
});
}
void BackupsWorker::waitAll()
{
std::vector<OperationID> current_operations;
{
std::lock_guard lock{infos_mutex};
for (const auto & [id, extended_info] : infos)
if (!isFinalStatus(extended_info.info.status))
current_operations.push_back(id);
}
if (current_operations.empty())
return;
LOG_INFO(log, "Waiting for running backups and restores to finish");
for (const auto & id : current_operations)
wait(id, /* rethrow_exception= */ false);
LOG_INFO(log, "Backups and restores finished");
}
void BackupsWorker::cancel(const BackupOperationID & backup_or_restore_id, bool wait_)
{
QueryStatusPtr process_list_element;
{
std::unique_lock lock{infos_mutex};
auto it = infos.find(backup_or_restore_id);
if (it == infos.end())
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unknown backup ID {}", backup_or_restore_id);
const auto & extended_info = it->second;
const auto & info = extended_info.info;
if (isFinalStatus(info.status) || !extended_info.process_list_element)
return;
LOG_INFO(log, "Cancelling {} {}", isBackupStatus(info.status) ? "backup" : "restore", info.name);
process_list_element = extended_info.process_list_element;
}
process_list.sendCancelToQuery(process_list_element);
if (wait_)
wait(backup_or_restore_id, /* rethrow_exception= */ false);
}
void BackupsWorker::cancelAll(bool wait_)
{
std::vector<OperationID> current_operations;
{
std::lock_guard lock{infos_mutex};
for (const auto & [id, extended_info] : infos)
if (!isFinalStatus(extended_info.info.status))
current_operations.push_back(id);
}
if (current_operations.empty())
return;
LOG_INFO(log, "Cancelling running backups and restores");
for (const auto & id : current_operations)
cancel(id, /* wait= */ false);
if (wait_)
for (const auto & id : current_operations)
wait(id, /* rethrow_exception= */ false);
LOG_INFO(log, "Backups and restores finished or stopped");
}
BackupOperationInfo BackupsWorker::getInfo(const OperationID & id) const
{
std::lock_guard lock{infos_mutex};
auto it = infos.find(id);
if (it == infos.end())
throw Exception(ErrorCodes::LOGICAL_ERROR, "Unknown backup ID {}", id);
return it->second;
return it->second.info;
}
std::vector<BackupOperationInfo> BackupsWorker::getAllInfos() const
{
std::vector<BackupOperationInfo> res_infos;
std::lock_guard lock{infos_mutex};
for (const auto & info : infos | boost::adaptors::map_values)
for (const auto & extended_info : infos | boost::adaptors::map_values)
{
const auto & info = extended_info.info;
if (!info.internal)
res_infos.push_back(info);
}
@ -1140,14 +1341,11 @@ std::vector<BackupOperationInfo> BackupsWorker::getAllInfos() const
void BackupsWorker::shutdown()
{
bool has_active_backups_and_restores = (num_active_backups || num_active_restores);
if (has_active_backups_and_restores)
LOG_INFO(log, "Waiting for {} backups and {} restores to be finished", num_active_backups, num_active_restores);
/// Cancel running backups and restores.
cancelAll(/* wait= */ true);
/// Wait for our thread pools (it must be done before destroying them).
thread_pools->wait();
if (has_active_backups_and_restores)
LOG_INFO(log, "All backup and restore tasks have finished");
}
}

55
src/Backups/BackupsWorker.h
View File

@ -26,13 +26,26 @@ using BackupEntries = std::vector<std::pair<String, std::shared_ptr<const IBacku
using DataRestoreTasks = std::vector<std::function<void()>>;
struct ReadSettings;
class BackupLog;
class ThreadGroup;
using ThreadGroupPtr = std::shared_ptr<ThreadGroup>;
class QueryStatus;
using QueryStatusPtr = std::shared_ptr<QueryStatus>;
class ProcessList;
/// Manager of backups and restores: executes backups and restores' threads in the background.
/// Keeps information about backups and restores started in this session.
class BackupsWorker
{
public:
BackupsWorker(ContextPtr global_context, size_t num_backup_threads, size_t num_restore_threads, bool allow_concurrent_backups_, bool allow_concurrent_restores_);
BackupsWorker(
ContextMutablePtr global_context,
size_t num_backup_threads,
size_t num_restore_threads,
bool allow_concurrent_backups_,
bool allow_concurrent_restores_,
bool test_inject_sleep_);
~BackupsWorker();
/// Waits until all tasks have been completed.
@ -41,10 +54,20 @@ public:
/// Starts executing a BACKUP or RESTORE query. Returns ID of the operation.
BackupOperationID start(const ASTPtr & backup_or_restore_query, ContextMutablePtr context);
/// Waits until a BACKUP or RESTORE query started by start() is finished.
/// Waits until the specified backup or restore operation finishes or stops.
/// The function returns immediately if the operation is already finished.
void wait(const BackupOperationID & backup_or_restore_id, bool rethrow_exception = true);
/// Waits until all running backup and restore operations finish or stop.
void waitAll();
/// Cancels the specified backup or restore operation.
/// The function does nothing if this operation has already finished.
void cancel(const BackupOperationID & backup_or_restore_id, bool wait_ = true);
/// Cancels all running backup and restore operations.
void cancelAll(bool wait_ = true);
BackupOperationInfo getInfo(const BackupOperationID & id) const;
std::vector<BackupOperationInfo> getAllInfos() const;
@ -60,13 +83,14 @@ private:
std::shared_ptr<IBackupCoordination> backup_coordination,
const ContextPtr & context,
ContextMutablePtr mutable_context,
ThreadGroupPtr thread_group,
bool called_async);
/// Builds file infos for specified backup entries.
void buildFileInfosForBackupEntries(const BackupPtr & backup, const BackupEntries & backup_entries, const ReadSettings & read_settings, std::shared_ptr<IBackupCoordination> backup_coordination);
void buildFileInfosForBackupEntries(const BackupPtr & backup, const BackupEntries & backup_entries, const ReadSettings & read_settings, std::shared_ptr<IBackupCoordination> backup_coordination, QueryStatusPtr process_list_element);
/// Write backup entries to an opened backup.
void writeBackupEntries(BackupMutablePtr backup, BackupEntries && backup_entries, const BackupOperationID & backup_id, std::shared_ptr<IBackupCoordination> backup_coordination, bool internal);
void writeBackupEntries(BackupMutablePtr backup, BackupEntries && backup_entries, const BackupOperationID & backup_id, std::shared_ptr<IBackupCoordination> backup_coordination, bool internal, QueryStatusPtr process_list_element);
BackupOperationID startRestoring(const ASTPtr & query, ContextMutablePtr context);
@ -78,12 +102,13 @@ private:
RestoreSettings restore_settings,
std::shared_ptr<IRestoreCoordination> restore_coordination,
ContextMutablePtr context,
ThreadGroupPtr thread_group,
bool called_async);
/// Run data restoring tasks which insert data to tables.
void restoreTablesData(const BackupOperationID & restore_id, BackupPtr backup, DataRestoreTasks && tasks, ThreadPool & thread_pool);
void restoreTablesData(const BackupOperationID & restore_id, BackupPtr backup, DataRestoreTasks && tasks, ThreadPool & thread_pool, QueryStatusPtr process_list_element);
void addInfo(const BackupOperationID & id, const String & name, const String & base_backup_name, bool internal, BackupStatus status);
void addInfo(const BackupOperationID & id, const String & name, const String & base_backup_name, bool internal, QueryStatusPtr process_list_element, BackupStatus status);
void setStatus(const BackupOperationID & id, BackupStatus status, bool throw_if_error = true);
void setStatusSafe(const String & id, BackupStatus status) { setStatus(id, status, false); }
void setNumFilesAndSize(const BackupOperationID & id, size_t num_files, UInt64 total_size, size_t num_entries,
@ -92,19 +117,33 @@ private:
enum class ThreadPoolId;
ThreadPool & getThreadPool(ThreadPoolId thread_pool_id);
/// Waits for some time if `test_inject_sleep` is true.
void maybeSleepForTesting() const;
class ThreadPools;
std::unique_ptr<ThreadPools> thread_pools;
const bool allow_concurrent_backups;
const bool allow_concurrent_restores;
const bool test_inject_sleep;
Poco::Logger * log;
std::unordered_map<BackupOperationID, BackupOperationInfo> infos;
std::shared_ptr<BackupLog> backup_log;
struct ExtendedOperationInfo
{
BackupOperationInfo info;
QueryStatusPtr process_list_element; /// to cancel this operation if we want to
};
std::unordered_map<BackupOperationID, ExtendedOperationInfo> infos;
std::condition_variable status_changed;
std::atomic<size_t> num_active_backups = 0;
std::atomic<size_t> num_active_restores = 0;
mutable std::mutex infos_mutex;
std::shared_ptr<BackupLog> backup_log;
ProcessList & process_list;
};
}

4
src/Backups/RestoreCoordinationRemote.cpp
View File

@ -21,7 +21,8 @@ RestoreCoordinationRemote::RestoreCoordinationRemote(
const String & restore_uuid_,
const Strings & all_hosts_,
const String & current_host_,
bool is_internal_)
bool is_internal_,
QueryStatusPtr process_list_element_)
: get_zookeeper(get_zookeeper_)
, root_zookeeper_path(root_zookeeper_path_)
, keeper_settings(keeper_settings_)
@ -36,6 +37,7 @@ RestoreCoordinationRemote::RestoreCoordinationRemote(
log,
get_zookeeper_,
keeper_settings,
process_list_element_,
[my_zookeeper_path = zookeeper_path, my_current_host = current_host, my_is_internal = is_internal]
(WithRetries::FaultyKeeper & zk)
{

3
src/Backups/RestoreCoordinationRemote.h
View File

@ -21,7 +21,8 @@ public:
const String & restore_uuid_,
const Strings & all_hosts_,
const String & current_host_,
bool is_internal_);
bool is_internal_,
QueryStatusPtr process_list_element_);
~RestoreCoordinationRemote() override;

16
src/Backups/RestorerFromBackup.cpp
View File

@ -16,6 +16,7 @@
#include <Interpreters/DatabaseCatalog.h>
#include <Interpreters/Context.h>
#include <Interpreters/InterpreterCreateQuery.h>
#include <Interpreters/ProcessList.h>
#include <Databases/IDatabase.h>
#include <Databases/DDLDependencyVisitor.h>
#include <Storages/IStorage.h>
@ -85,6 +86,7 @@ RestorerFromBackup::RestorerFromBackup(
, restore_coordination(restore_coordination_)
, backup(backup_)
, context(context_)
, process_list_element(context->getProcessListElement())
, on_cluster_first_sync_timeout(context->getConfigRef().getUInt64("backups.on_cluster_first_sync_timeout", 180000))
, create_table_timeout(context->getConfigRef().getUInt64("backups.create_table_timeout", 300000))
, log(&Poco::Logger::get("RestorerFromBackup"))
@ -138,6 +140,8 @@ RestorerFromBackup::DataRestoreTasks RestorerFromBackup::run(Mode mode)
void RestorerFromBackup::setStage(const String & new_stage, const String & message)
{
LOG_TRACE(log, "Setting stage: {}", new_stage);
checkIsQueryCancelled();
current_stage = new_stage;
if (restore_coordination)
@ -150,6 +154,12 @@ void RestorerFromBackup::setStage(const String & new_stage, const String & messa
}
}
void RestorerFromBackup::checkIsQueryCancelled() const
{
if (process_list_element)
process_list_element->checkTimeLimit();
}
void RestorerFromBackup::findRootPathsInBackup()
{
size_t shard_num = 1;
@ -563,6 +573,8 @@ void RestorerFromBackup::createDatabase(const String & database_name) const
if (database_info.is_predefined_database)
return;
checkIsQueryCancelled();
auto create_database_query = typeid_cast<std::shared_ptr<ASTCreateQuery>>(database_info.create_database_query->clone());
/// Generate a new UUID for a database.
@ -709,6 +721,8 @@ void RestorerFromBackup::createTable(const QualifiedTableName & table_name)
if (table_info.is_predefined_table)
return;
checkIsQueryCancelled();
auto create_table_query = typeid_cast<std::shared_ptr<ASTCreateQuery>>(table_info.create_table_query->clone());
/// Generate a new UUID for a table (the same table on different hosts must use the same UUID, `restore_coordination` will make it so).
@ -790,6 +804,8 @@ void RestorerFromBackup::insertDataToTable(const QualifiedTableName & table_name
auto & table_info = table_infos.at(table_name);
auto storage = table_info.storage;
checkIsQueryCancelled();
try
{
const auto & data_path_in_backup = table_info.data_path_in_backup;

Some files were not shown because too many files have changed in this diff Show More