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7
CMakeLists.txt
View File

@ -152,6 +152,7 @@ if (CMAKE_GENERATOR STREQUAL "Ninja" AND NOT DISABLE_COLORED_BUILD)
set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fdiagnostics-color=always")
endif ()
include (cmake/check_flags.cmake)
include (cmake/add_warning.cmake)
if (NOT MSVC)
@ -166,7 +167,8 @@ if (COMPILER_CLANG)
set(COMPILER_FLAGS "${COMPILER_FLAGS} -gdwarf-aranges")
endif ()
if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.0.0)
if (HAS_USE_CTOR_HOMING)
# For more info see https://blog.llvm.org/posts/2021-04-05-constructor-homing-for-debug-info/
if (CMAKE_BUILD_TYPE_UC STREQUAL "DEBUG" OR CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Xclang -fuse-ctor-homing")
set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Xclang -fuse-ctor-homing")
@ -629,9 +631,6 @@ include_directories(${ConfigIncludePath})
# Add as many warnings as possible for our own code.
include (cmake/warnings.cmake)
# Check if needed compiler flags are supported
include (cmake/check_flags.cmake)
add_subdirectory (base)
add_subdirectory (src)
add_subdirectory (programs)

64
base/common/StringRef.h
View File

@ -96,6 +96,39 @@ inline bool compareSSE2x4(const char * p1, const char * p2)
inline bool memequalSSE2Wide(const char * p1, const char * p2, size_t size)
{
/** The order of branches and the trick with overlapping comparisons
* are the same as in memcpy implementation.
* See the comments in base/glibc-compatibility/memcpy/memcpy.h
*/
if (size <= 16)
{
if (size >= 8)
{
/// Chunks of 8..16 bytes.
return unalignedLoad<uint64_t>(p1) == unalignedLoad<uint64_t>(p2)
&& unalignedLoad<uint64_t>(p1 + size - 8) == unalignedLoad<uint64_t>(p2 + size - 8);
}
else if (size >= 4)
{
/// Chunks of 4..7 bytes.
return unalignedLoad<uint32_t>(p1) == unalignedLoad<uint32_t>(p2)
&& unalignedLoad<uint32_t>(p1 + size - 4) == unalignedLoad<uint32_t>(p2 + size - 4);
}
else if (size >= 2)
{
/// Chunks of 2..3 bytes.
return unalignedLoad<uint16_t>(p1) == unalignedLoad<uint16_t>(p2)
&& unalignedLoad<uint16_t>(p1 + size - 2) == unalignedLoad<uint16_t>(p2 + size - 2);
}
else if (size >= 1)
{
/// A single byte.
return *p1 == *p2;
}
return true;
}
while (size >= 64)
{
if (compareSSE2x4(p1, p2))
@ -108,39 +141,14 @@ inline bool memequalSSE2Wide(const char * p1, const char * p2, size_t size)
return false;
}
switch ((size % 64) / 16)
switch (size / 16)
{
case 3: if (!compareSSE2(p1 + 32, p2 + 32)) return false; [[fallthrough]];
case 2: if (!compareSSE2(p1 + 16, p2 + 16)) return false; [[fallthrough]];
case 1: if (!compareSSE2(p1 , p2 )) return false; [[fallthrough]];
case 0: break;
case 1: if (!compareSSE2(p1, p2)) return false;
}
p1 += (size % 64) / 16 * 16;
p2 += (size % 64) / 16 * 16;
switch (size % 16)
{
case 15: if (p1[14] != p2[14]) return false; [[fallthrough]];
case 14: if (p1[13] != p2[13]) return false; [[fallthrough]];
case 13: if (p1[12] != p2[12]) return false; [[fallthrough]];
case 12: if (unalignedLoad<uint32_t>(p1 + 8) == unalignedLoad<uint32_t>(p2 + 8)) goto l8; else return false;
case 11: if (p1[10] != p2[10]) return false; [[fallthrough]];
case 10: if (p1[9] != p2[9]) return false; [[fallthrough]];
case 9: if (p1[8] != p2[8]) return false;
l8: [[fallthrough]];
case 8: return unalignedLoad<uint64_t>(p1) == unalignedLoad<uint64_t>(p2);
case 7: if (p1[6] != p2[6]) return false; [[fallthrough]];
case 6: if (p1[5] != p2[5]) return false; [[fallthrough]];
case 5: if (p1[4] != p2[4]) return false; [[fallthrough]];
case 4: return unalignedLoad<uint32_t>(p1) == unalignedLoad<uint32_t>(p2);
case 3: if (p1[2] != p2[2]) return false; [[fallthrough]];
case 2: return unalignedLoad<uint16_t>(p1) == unalignedLoad<uint16_t>(p2);
case 1: if (p1[0] != p2[0]) return false; [[fallthrough]];
case 0: break;
}
return true;
return compareSSE2(p1 + size - 16, p2 + size - 16);
}
#endif

13
base/common/arithmeticOverflow.h
View File

@ -145,6 +145,19 @@ namespace common
return __builtin_mul_overflow(x, y, &res);
}
template <typename T, typename U, typename R>
inline bool mulOverflow(T x, U y, R & res)
{
// not built in type, wide integer
if constexpr (is_big_int_v<T> || is_big_int_v<R> || is_big_int_v<U>)
{
res = mulIgnoreOverflow<R>(x, y);
return false;
}
else
return __builtin_mul_overflow(x, y, &res);
}
template <>
inline bool mulOverflow(int x, int y, int & res)
{

4
base/mysqlxx/Query.cpp
View File

@ -77,7 +77,9 @@ void Query::executeImpl()
case CR_SERVER_LOST:
throw ConnectionLost(errorMessage(mysql_driver), err_no);
default:
throw BadQuery(errorMessage(mysql_driver), err_no);
/// Add query to the exception message, since it may differs from the user input query.
/// (also you can use this and create query with an error to see what query ClickHouse created)
throw BadQuery(errorMessage(mysql_driver) + " (query: " + query_string + ")", err_no);
}
}
}

1
cmake/check_flags.cmake
View File

@ -4,3 +4,4 @@ include (CheckCCompilerFlag)
check_cxx_compiler_flag("-Wsuggest-destructor-override" HAS_SUGGEST_DESTRUCTOR_OVERRIDE)
check_cxx_compiler_flag("-Wshadow" HAS_SHADOW)
check_cxx_compiler_flag("-Wsuggest-override" HAS_SUGGEST_OVERRIDE)
check_cxx_compiler_flag("-Xclang -fuse-ctor-homing" HAS_USE_CTOR_HOMING)

4
cmake/find/ccache.cmake
View File

@ -51,8 +51,8 @@ if (CCACHE_FOUND AND NOT COMPILER_MATCHES_CCACHE)
message(STATUS "ccache is 4.2+ no quirks for SOURCE_DATE_EPOCH required")
elseif (CCACHE_VERSION VERSION_GREATER_EQUAL "4.0")
message(STATUS "Ignore SOURCE_DATE_EPOCH for ccache")
set_property (GLOBAL PROPERTY RULE_LAUNCH_COMPILE "env -u SOURCE_DATE_EPOCH ${CCACHE_FOUND}")
set_property (GLOBAL PROPERTY RULE_LAUNCH_LINK "env -u SOURCE_DATE_EPOCH ${CCACHE_FOUND}")
set_property (GLOBAL PROPERTY RULE_LAUNCH_COMPILE "env -u SOURCE_DATE_EPOCH")
set_property (GLOBAL PROPERTY RULE_LAUNCH_LINK "env -u SOURCE_DATE_EPOCH")
endif()
else ()
message(${RECONFIGURE_MESSAGE_LEVEL} "Not using ${CCACHE_FOUND} ${CCACHE_VERSION} bug: https://bugzilla.samba.org/show_bug.cgi?id=8118")

8
docs/README.md
View File

@ -38,7 +38,7 @@ Writing the docs is extremely useful for project's users and developers, and gro
The documentation contains information about all the aspects of the ClickHouse lifecycle: developing, testing, installing, operating, and using. The base language of the documentation is English. The English version is the most actual. All other languages are supported as much as they can by contributors from different countries.
At the moment, [documentation](https://clickhouse.tech/docs) exists in English, Russian, Chinese, Japanese, and Farsi. We store the documentation besides the ClickHouse source code in the [GitHub repository](https://github.com/ClickHouse/ClickHouse/tree/master/docs).
At the moment, [documentation](https://clickhouse.com/docs) exists in English, Russian, Chinese, Japanese, and Farsi. We store the documentation besides the ClickHouse source code in the [GitHub repository](https://github.com/ClickHouse/ClickHouse/tree/master/docs).
Each language lays in the corresponding folder. Files that are not translated from English are the symbolic links to the English ones.
@ -54,7 +54,7 @@ You can contribute to the documentation in many ways, for example:
- Open a required file in the ClickHouse repository and edit it from the GitHub web interface.
You can do it on GitHub, or on the [ClickHouse Documentation](https://clickhouse.tech/docs/en/) site. Each page of ClickHouse Documentation site contains an "Edit this page" (🖋) element in the upper right corner. Clicking this symbol, you get to the ClickHouse docs file opened for editing.
You can do it on GitHub, or on the [ClickHouse Documentation](https://clickhouse.com/docs/en/) site. Each page of ClickHouse Documentation site contains an "Edit this page" (🖋) element in the upper right corner. Clicking this symbol, you get to the ClickHouse docs file opened for editing.
When you are saving a file, GitHub opens a pull-request for your contribution. Add the `documentation` label to this pull request for proper automatic checks applying. If you have no permissions for adding labels, the reviewer of your PR adds it.
@ -128,7 +128,7 @@ Contribute all new information in English language. Other languages are translat
When you add a new file, it should end with a link like:
`[Original article](https://clickhouse.tech/docs/<path-to-the-page>) <!--hide-->`
`[Original article](https://clickhouse.com/docs/<path-to-the-page>) <!--hide-->`
and there should be **a new empty line** after it.
@ -164,7 +164,7 @@ When writing documentation, think about people who read it. Each audience has sp
ClickHouse documentation can be divided by the audience for the following parts:
- Conceptual topics in [Introduction](https://clickhouse.tech/docs/en/), tutorials and overviews, changelog.
- Conceptual topics in [Introduction](https://clickhouse.com/docs/en/), tutorials and overviews, changelog.
These topics are for the most common auditory. When editing text in them, use the most common terms that are comfortable for the audience with basic technical skills.

2
docs/_description_templates/template-data-type.md
View File

@ -26,4 +26,4 @@ The name of an additional section can be any, for example, **Usage**.
- [link](#)
[Original article](https://clickhouse.tech/docs/en/data-types/<data-type-name>/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/data-types/<data-type-name>/) <!--hide-->

6
docs/_description_templates/template-statement.md
View File

@ -12,9 +12,9 @@ Syntax of the statement.
Examples of descriptions with a complicated structure:
- https://clickhouse.tech/docs/en/sql-reference/statements/grant/
- https://clickhouse.tech/docs/en/sql-reference/statements/revoke/
- https://clickhouse.tech/docs/en/sql-reference/statements/select/join/
- https://clickhouse.com/docs/en/sql-reference/statements/grant/
- https://clickhouse.com/docs/en/sql-reference/statements/revoke/
- https://clickhouse.com/docs/en/sql-reference/statements/select/join/
**See Also** (Optional)

57
docs/en/commercial/cloud.md
View File

@ -3,60 +3,7 @@ toc_priority: 1
toc_title: Cloud
---
# ClickHouse Cloud Service Providers {#clickhouse-cloud-service-providers}
# ClickHouse Cloud Service {#clickhouse-cloud-service}
!!! info "Info"
If you have launched a public cloud with managed ClickHouse service, feel free to [open a pull-request](https://github.com/ClickHouse/ClickHouse/edit/master/docs/en/commercial/cloud.md) adding it to the following list.
## Yandex Cloud {#yandex-cloud}
[Yandex Managed Service for ClickHouse](https://cloud.yandex.com/services/managed-clickhouse?utm_source=referrals&utm_medium=clickhouseofficialsite&utm_campaign=link3) provides the following key features:
- Fully managed ZooKeeper service for [ClickHouse replication](../engines/table-engines/mergetree-family/replication.md)
- Multiple storage type choices
- Replicas in different availability zones
- Encryption and isolation
- Automated maintenance
## Altinity.Cloud {#altinity.cloud}
[Altinity.Cloud](https://altinity.com/cloud-database/) is a fully managed ClickHouse-as-a-Service for the Amazon public cloud.
- Fast deployment of ClickHouse clusters on Amazon resources
- Easy scale-out/scale-in as well as vertical scaling of nodes
- Isolated per-tenant VPCs with public endpoint or VPC peering
- Configurable storage types and volume configurations
- Cross-AZ scaling for performance and high availability
- Built-in monitoring and SQL query editor
## Alibaba Cloud {#alibaba-cloud}
[Alibaba Cloud Managed Service for ClickHouse](https://www.alibabacloud.com/product/clickhouse) provides the following key features:
- Highly reliable cloud disk storage engine based on [Alibaba Cloud Apsara](https://www.alibabacloud.com/product/apsara-stack) distributed system
- Expand capacity on demand without manual data migration
- Support single-node, single-replica, multi-node, and multi-replica architectures, and support hot and cold data tiering
- Support access allow-list, one-key recovery, multi-layer network security protection, cloud disk encryption
- Seamless integration with cloud log systems, databases, and data application tools
- Built-in monitoring and database management platform
- Professional database expert technical support and service
## SberCloud {#sbercloud}
[SberCloud.Advanced](https://sbercloud.ru/en/advanced) provides [MapReduce Service (MRS)](https://docs.sbercloud.ru/mrs/ug/topics/ug__clickhouse.html), a reliable, secure, and easy-to-use enterprise-level platform for storing, processing, and analyzing big data. MRS allows you to quickly create and manage ClickHouse clusters.
- A ClickHouse instance consists of three ZooKeeper nodes and multiple ClickHouse nodes. The Dedicated Replica mode is used to ensure high reliability of dual data copies.
- MRS provides smooth and elastic scaling capabilities to quickly meet service growth requirements in scenarios where the cluster storage capacity or CPU computing resources are not enough. When you expand the capacity of ClickHouse nodes in a cluster, MRS provides a one-click data balancing tool and gives you the initiative to balance data. You can determine the data balancing mode and time based on service characteristics to ensure service availability, implementing smooth scaling.
- MRS uses the Elastic Load Balance ensuring high availability deployment architecture to automatically distribute user access traffic to multiple backend nodes, expanding service capabilities to external systems and improving fault tolerance. With the ELB polling mechanism, data is written to local tables and read from distributed tables on different nodes. In this way, data read/write load and high availability of application access are guaranteed.
## Tencent Cloud {#tencent-cloud}
[Tencent Managed Service for ClickHouse](https://cloud.tencent.com/product/cdwch) provides the following key features:
- Easy to deploy and manage on Tencent Cloud
- Highly scalable and available
- Integrated monitor and alert service
- High security with isolated per cluster VPCs
- On-demand pricing with no upfront costs or long-term commitments
{## [Original article](https://clickhouse.tech/docs/en/commercial/cloud/) ##}
Detailed public description for ClickHouse cloud services is not ready yet, please [contact us](/company/#contact) to learn more.

4
docs/en/commercial/index.md
View File

@ -6,12 +6,8 @@ toc_title: Introduction
# ClickHouse Commercial Services {#clickhouse-commercial-services}
This section is a directory of commercial service providers specializing in ClickHouse. They are independent companies not necessarily affiliated with Yandex.
Service categories:
- [Cloud](../commercial/cloud.md)
- [Support](../commercial/support.md)
!!! note "For service providers"
If you happen to represent one of them, feel free to open a pull request adding your company to the respective section (or even adding a new section if the service does not fit into existing categories). The easiest way to open a pull-request for documentation page is by using a “pencil” edit button in the top-right corner. If your service available in some local market, make sure to mention it in a localized documentation page as well (or at least point it out in a pull-request description).

20
docs/en/commercial/support.md
View File

@ -3,23 +3,7 @@ toc_priority: 3
toc_title: Support
---
# ClickHouse Commercial Support Service Providers {#clickhouse-commercial-support-service-providers}
# ClickHouse Commercial Support Service {#clickhouse-commercial-support-service}
!!! info "Info"
If you have launched a ClickHouse commercial support service, feel free to [open a pull-request](https://github.com/ClickHouse/ClickHouse/edit/master/docs/en/commercial/support.md) adding it to the following list.
## Yandex.Cloud
ClickHouse worldwide support from the authors of ClickHouse. Supports on-premise and cloud deployments. Ask details on clickhouse-support@yandex-team.com
## Altinity {#altinity}
Altinity has offered enterprise ClickHouse support and services since 2017. Altinity customers range from Fortune 100 enterprises to startups. Visit [www.altinity.com](https://www.altinity.com/) for more information.
## Mafiree {#mafiree}
[Service description](http://mafiree.com/clickhouse-analytics-services.php)
## MinervaDB {#minervadb}
[Service description](https://minervadb.com/index.php/clickhouse-consulting-and-support-by-minervadb/)
Detailed public description for ClickHouse support services is not ready yet, please [contact us](/company/#contact) to learn more.

2
docs/en/development/adding_test_queries.md
View File

@ -63,7 +63,7 @@ git checkout -b name_for_a_branch_with_my_test upstream/master
#### Install & run clickhouse
1) install `clickhouse-server` (follow [official docs](https://clickhouse.tech/docs/en/getting-started/install/))
1) install `clickhouse-server` (follow [official docs](https://clickhouse.com/docs/en/getting-started/install/))
2) install test configurations (it will use Zookeeper mock implementation and adjust some settings)
```
cd ~/workspace/ClickHouse/tests/config

2
docs/en/development/architecture.md
View File

@ -196,4 +196,4 @@ Besides, each replica stores its state in ZooKeeper as the set of parts and its
!!! note "Note"
The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is **not elastic**, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load is supposed to be adjusted to be uneven. This implementation gives you more control, and it is ok for relatively small clusters, such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that spans across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.
{## [Original article](https://clickhouse.tech/docs/en/development/architecture/) ##}
{## [Original article](https://clickhouse.com/docs/en/development/architecture/) ##}

2
docs/en/development/browse-code.md
View File

@ -5,7 +5,7 @@ toc_title: Source Code Browser
# Browse ClickHouse Source Code {#browse-clickhouse-source-code}
You can use **Woboq** online code browser available [here](https://clickhouse.tech/codebrowser/html_report/ClickHouse/src/index.html). It provides code navigation and semantic highlighting, search and indexing. The code snapshot is updated daily.
You can use **Woboq** online code browser available [here](https://clickhouse.com/codebrowser/html_report/ClickHouse/src/index.html). It provides code navigation and semantic highlighting, search and indexing. The code snapshot is updated daily.
Also, you can browse sources on [GitHub](https://github.com/ClickHouse/ClickHouse) as usual.

2
docs/en/development/build-osx.md
View File

@ -131,4 +131,4 @@ cd ClickHouse
./build/programs/clickhouse-server --config-file ./programs/server/config.xml
```
[Original article](https://clickhouse.tech/docs/en/development/build_osx/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/development/build_osx/) <!--hide-->

2
docs/en/development/build.md
View File

@ -161,4 +161,4 @@ Note that the split build has several drawbacks:
* You cannot run the integration tests since they only work a single complete binary.
* You can't easily copy the binaries elsewhere. Instead of moving a single binary you'll need to copy all binaries and libraries.
[Original article](https://clickhouse.tech/docs/en/development/build/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/development/build/) <!--hide-->

10
docs/en/development/continuous-integration.md
View File

@ -117,7 +117,7 @@ described [here](tests.md#functional-test-locally).
## Build Check {#build-check}
Builds ClickHouse in various configurations for use in further steps. You have to fix the builds that fail. Build logs often has enough information to fix the error, but you might have to reproduce the failure locally. The `cmake` options can be found in the build log, grepping for `cmake`. Use these options and follow the [general build process](build.md).
Builds ClickHouse in various configurations for use in further steps. You have to fix the builds that fail. Build logs often has enough information to fix the error, but you might have to reproduce the failure locally. The `cmake` options can be found in the build log, grepping for `cmake`. Use these options and follow the [general build process](../development/build.md).
### Report Details
@ -127,7 +127,7 @@ Builds ClickHouse in various configurations for use in further steps. You have t
- **Build type**: `Debug` or `RelWithDebInfo` (cmake).
- **Sanitizer**: `none` (without sanitizers), `address` (ASan), `memory` (MSan), `undefined` (UBSan), or `thread` (TSan).
- **Bundled**: `bundled` build uses libraries from `contrib` folder, and `unbundled` build uses system libraries.
- **Splitted** `splitted` is a [split build](build.md#split-build)
- **Splitted** `splitted` is a [split build](../development/build.md#split-build)
- **Status**: `success` or `fail`
- **Build log**: link to the building and files copying log, useful when build failed.
- **Build time**.
@ -157,7 +157,7 @@ etc. Look at the report to see which tests fail, then reproduce the failure
locally as described [here](tests.md#functional-test-locally). Note that you
have to use the correct build configuration to reproduce -- a test might fail
under AddressSanitizer but pass in Debug. Download the binary from [CI build
checks page](build.md#you-dont-have-to-build-clickhouse), or build it locally.
checks page](../development/build.md#you-dont-have-to-build-clickhouse), or build it locally.
## Functional Stateful Tests
@ -183,11 +183,11 @@ concurrency-related errors. If it fails:
## Split Build Smoke Test
Checks that the server build in [split build](build.md#split-build)
Checks that the server build in [split build](../development/build.md#split-build)
configuration can start and run simple queries. If it fails:
* Fix other test errors first;
* Build the server in [split build](build.md#split-build) configuration
* Build the server in [split build](../development/build.md#split-build) configuration
locally and check whether it can start and run `select 1`.

8
docs/en/development/developer-instruction.md
View File

@ -233,13 +233,13 @@ Just in case, it is worth mentioning that CLion creates `build` path on its own,
## Writing Code {#writing-code}
The description of ClickHouse architecture can be found here: https://clickhouse.tech/docs/en/development/architecture/
The description of ClickHouse architecture can be found here: https://clickhouse.com/docs/en/development/architecture/
The Code Style Guide: https://clickhouse.tech/docs/en/development/style/
The Code Style Guide: https://clickhouse.com/docs/en/development/style/
Adding third-party libraries: https://clickhouse.tech/docs/en/development/contrib/#adding-third-party-libraries
Adding third-party libraries: https://clickhouse.com/docs/en/development/contrib/#adding-third-party-libraries
Writing tests: https://clickhouse.tech/docs/en/development/tests/
Writing tests: https://clickhouse.com/docs/en/development/tests/
List of tasks: https://github.com/ClickHouse/ClickHouse/issues?q=is%3Aopen+is%3Aissue+label%3A%22easy+task%22

2
docs/en/development/index.md
View File

@ -7,4 +7,4 @@ toc_title: hidden
# ClickHouse Development {#clickhouse-development}
[Original article](https://clickhouse.tech/docs/en/development/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/development/) <!--hide-->

2
docs/en/development/style.md
View File

@ -828,4 +828,4 @@ function(
size_t limit)
```
[Original article](https://clickhouse.tech/docs/en/development/style/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/development/style/) <!--hide-->

4
docs/en/development/tests.md
View File

@ -239,7 +239,7 @@ Google OSS-Fuzz can be found at `docker/fuzz`.
We also use simple fuzz test to generate random SQL queries and to check that the server does not die executing them.
You can find it in `00746_sql_fuzzy.pl`. This test should be run continuously (overnight and longer).
We also use sophisticated AST-based query fuzzer that is able to find huge amount of corner cases. It does random permutations and substitutions in queries AST. It remembers AST nodes from previous tests to use them for fuzzing of subsequent tests while processing them in random order. You can learn more about this fuzzer in [this blog article](https://clickhouse.tech/blog/en/2021/fuzzing-clickhouse/).
We also use sophisticated AST-based query fuzzer that is able to find huge amount of corner cases. It does random permutations and substitutions in queries AST. It remembers AST nodes from previous tests to use them for fuzzing of subsequent tests while processing them in random order. You can learn more about this fuzzer in [this blog article](https://clickhouse.com/blog/en/2021/fuzzing-clickhouse/).
## Stress test
@ -341,4 +341,4 @@ Build jobs and tests are run in Sandbox on per commit basis. Resulting packages
We do not use Travis CI due to the limit on time and computational power.
We do not use Jenkins. It was used before and now we are happy we are not using Jenkins.
[Original article](https://clickhouse.tech/docs/en/development/tests/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/development/tests/) <!--hide-->

2
docs/en/engines/database-engines/lazy.md
View File

@ -13,4 +13,4 @@ It’s optimized for storing many small \*Log tables, for which there is a long
CREATE DATABASE testlazy ENGINE = Lazy(expiration_time_in_seconds);
[Original article](https://clickhouse.tech/docs/en/database_engines/lazy/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/database_engines/lazy/) <!--hide-->

2
docs/en/engines/database-engines/materialized-mysql.md
View File

@ -197,4 +197,4 @@ SELECT * FROM mysql.test;
└───┴─────┴──────┘
```
[Original article](https://clickhouse.tech/docs/en/engines/database-engines/materialized-mysql/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/database-engines/materialized-mysql/) <!--hide-->

20
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@ -23,6 +23,20 @@ ENGINE = MaterializedPostgreSQL('host:port', ['database' | database], 'user', 'p
- `user` — PostgreSQL user.
- `password` — User password.
## Dynamically adding new tables to replication
``` sql
ATTACH TABLE postgres_database.new_table;
```
It will work as well if there is a setting `materialized_postgresql_tables_list`.
## Dynamically removing tables from replication
``` sql
DETACH TABLE postgres_database.table_to_remove;
```
## Settings {#settings}
- [materialized_postgresql_max_block_size](../../operations/settings/settings.md#materialized-postgresql-max-block-size)
@ -44,6 +58,12 @@ SETTINGS materialized_postgresql_max_block_size = 65536,
SELECT * FROM database1.table1;
```
It is also possible to change settings at run time.
``` sql
ALTER DATABASE postgres_database MODIFY SETTING materialized_postgresql_max_block_size = <new_size>;
```
## Requirements {#requirements}
1. The [wal_level](https://www.postgresql.org/docs/current/runtime-config-wal.html) setting must have a value `logical` and `max_replication_slots` parameter must have a value at least `2` in the PostgreSQL config file.

2
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@ -147,4 +147,4 @@ SELECT * FROM mysql_db.mysql_table
└────────┴───────┘
```
[Original article](https://clickhouse.tech/docs/en/database_engines/mysql/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/database_engines/mysql/) <!--hide-->

2
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@ -136,4 +136,4 @@ DESCRIBE TABLE test_database.test_table;
└────────┴───────────────────┘
```
[Original article](https://clickhouse.tech/docs/en/database-engines/postgresql/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/database-engines/postgresql/) <!--hide-->

2
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@ -12,4 +12,4 @@ There are two key engine kinds in ClickHouse:
- [Table engines](../engines/table-engines/index.md)
- [Database engines](../engines/database-engines/index.md)
{## [Original article](https://clickhouse.tech/docs/en/engines/) ##}
{## [Original article](https://clickhouse.com/docs/en/engines/) ##}

2
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@ -86,4 +86,4 @@ To select data from a virtual column, you must specify its name in the `SELECT`
If you create a table with a column that has the same name as one of the table virtual columns, the virtual column becomes inaccessible. We do not recommend doing this. To help avoid conflicts, virtual column names are usually prefixed with an underscore.
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/) <!--hide-->

2
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@ -81,4 +81,4 @@ You can also change any [rocksdb options](https://github.com/facebook/rocksdb/wi
</rocksdb>
```
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/embedded-rocksdb/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/embedded-rocksdb/) <!--hide-->

2
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@ -224,4 +224,4 @@ libhdfs3 support HDFS namenode HA.
- [Virtual columns](../../../engines/table-engines/index.md#table_engines-virtual_columns)
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/hdfs/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/hdfs/) <!--hide-->

2
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@ -92,4 +92,4 @@ FROM system.numbers
- [JDBC table function](../../../sql-reference/table-functions/jdbc.md).
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/jdbc/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/jdbc/) <!--hide-->

2
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@ -194,4 +194,4 @@ Example:
- [Virtual columns](../../../engines/table-engines/index.md#table_engines-virtual_columns)
- [background_schedule_pool_size](../../../operations/settings/settings.md#background_schedule_pool_size)
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/kafka/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/kafka/) <!--hide-->

2
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@ -66,4 +66,4 @@ SELECT COUNT() FROM mongo_table;
└─────────┘
```
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/mongodb/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/mongodb/) <!--hide-->

2
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@ -113,4 +113,4 @@ SELECT * FROM mysql_table
- [The ‘mysql’ table function](../../../sql-reference/table-functions/mysql.md)
- [Using MySQL as a source of external dictionary](../../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-sources.md#dicts-external_dicts_dict_sources-mysql)
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/mysql/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/mysql/) <!--hide-->

2
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@ -128,4 +128,4 @@ SELECT * FROM odbc_t
- [ODBC external dictionaries](../../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-sources.md#dicts-external_dicts_dict_sources-odbc)
- [ODBC table function](../../../sql-reference/table-functions/odbc.md)
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/odbc/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/odbc/) <!--hide-->

2
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@ -149,4 +149,4 @@ CREATE TABLE pg_table_schema_with_dots (a UInt32)
- [The `postgresql` table function](../../../sql-reference/table-functions/postgresql.md)
- [Using PostgreSQL as a source of external dictionary](../../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-sources.md#dicts-external_dicts_dict_sources-postgresql)
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/postgresql/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/postgresql/) <!--hide-->

2
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@ -170,4 +170,4 @@ Example:
- `_message_id` - messageID of the received message; non-empty if was set, when message was published.
- `_timestamp` - timestamp of the received message; non-empty if was set, when message was published.
[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/rabbitmq/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/engines/table-engines/integrations/rabbitmq/) <!--hide-->

2
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@ -44,4 +44,4 @@ The `TinyLog` engine is the simplest in the family and provides the poorest func
The `Log` and `StripeLog` engines support parallel data reading. When reading data, ClickHouse uses multiple threads. Each thread processes a separate data block. The `Log` engine uses a separate file for each column of the table. `StripeLog` stores all the data in one file. As a result, the `StripeLog` engine uses fewer file descriptors, but the `Log` engine provides higher efficiency when reading data.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/log_family/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/log_family/) <!--hide-->

2
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@ -90,4 +90,4 @@ SELECT * FROM stripe_log_table ORDER BY timestamp
└─────────────────────┴──────────────┴────────────────────────────┘
```
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/stripelog/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/stripelog/) <!--hide-->

2
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@ -11,4 +11,4 @@ This table engine is typically used with the write-once method: write data one t
Queries are executed in a single stream. In other words, this engine is intended for relatively small tables (up to about 1,000,000 rows). It makes sense to use this table engine if you have many small tables, since it’s simpler than the [Log](../../../engines/table-engines/log-family/log.md) engine (fewer files need to be opened).
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/tinylog/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/tinylog/) <!--hide-->

2
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@ -100,4 +100,4 @@ GROUP BY StartDate
ORDER BY StartDate;
```
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/aggregatingmergetree/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/aggregatingmergetree/) <!--hide-->

2
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@ -303,4 +303,4 @@ select * FROM UAct
└─────────────────────┴───────────┴──────────┴──────┘
```
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/collapsingmergetree/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/collapsingmergetree/) <!--hide-->

2
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@ -127,4 +127,4 @@ Note that on the operating server, you cannot manually change the set of parts o
ClickHouse allows you to perform operations with the partitions: delete them, copy from one table to another, or create a backup. See the list of all operations in the section [Manipulations With Partitions and Parts](../../../sql-reference/statements/alter/partition.md#alter_manipulations-with-partitions).
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/custom_partitioning_key/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/custom_partitioning_key/) <!--hide-->

2
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@ -170,4 +170,4 @@ Fields for `pattern` and `default` sections:
!!! warning "Warning"
Data rollup is performed during merges. Usually, for old partitions, merges are not started, so for rollup it is necessary to trigger an unscheduled merge using [optimize](../../../sql-reference/statements/optimize.md). Or use additional tools, for example [graphite-ch-optimizer](https://github.com/innogames/graphite-ch-optimizer).
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/graphitemergetree/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/graphitemergetree/) <!--hide-->

2
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@ -291,4 +291,4 @@ If the data in ZooKeeper was lost or damaged, you can save data by moving it to
- [max_replicated_fetches_network_bandwidth](../../../operations/settings/merge-tree-settings.md#max_replicated_fetches_network_bandwidth)
- [max_replicated_sends_network_bandwidth](../../../operations/settings/merge-tree-settings.md#max_replicated_sends_network_bandwidth)
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/replication/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/replication/) <!--hide-->

2
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@ -136,4 +136,4 @@ When requesting data, use the [sumMap(key, value)](../../../sql-reference/aggreg
For nested data structure, you do not need to specify its columns in the tuple of columns for summation.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/summingmergetree/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/summingmergetree/) <!--hide-->

2
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@ -233,4 +233,4 @@ SELECT * FROM UAct FINAL
This is a very inefficient way to select data. Don’t use it for large tables.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/versionedcollapsingmergetree/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/versionedcollapsingmergetree/) <!--hide-->

2
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@ -75,4 +75,4 @@ A Buffer table is used when too many INSERTs are received from a large number of
Note that it does not make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section “Performance”).
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/buffer/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/buffer/) <!--hide-->

2
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@ -198,4 +198,4 @@ When the `max_parallel_replicas` option is enabled, query processing is parallel
- [Virtual columns](../../../engines/table-engines/special/index.md#table_engines-virtual_columns)
- [background_distributed_schedule_pool_size](../../../operations/settings/settings.md#background_distributed_schedule_pool_size)
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/distributed/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/distributed/) <!--hide-->

2
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@ -63,4 +63,4 @@ $ curl -F 'passwd=@passwd.tsv;' 'http://localhost:8123/?query=SELECT+shell,+coun
For distributed query processing, the temporary tables are sent to all the remote servers.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/external_data/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/external_data/) <!--hide-->

2
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@ -85,4 +85,4 @@ $ echo -e "1,2\n3,4" | clickhouse-local -q "CREATE TABLE table (a Int64, b Int64
- Indices
- Replication
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/file/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/file/) <!--hide-->

2
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@ -56,4 +56,4 @@ SELECT * FROM generate_engine_table LIMIT 3
- Indices
- Replication
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/generate/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/generate/) <!--hide-->

2
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@ -7,4 +7,4 @@ toc_title: MaterializedView
Used for implementing materialized views (for more information, see [CREATE VIEW](../../../sql-reference/statements/create/view.md#materialized)). For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses that engine.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/materializedview/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/materializedview/) <!--hide-->

2
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@ -15,4 +15,4 @@ Normally, using this table engine is not justified. However, it can be used for
The Memory engine is used by the system for temporary tables with external query data (see the section “External data for processing a query”), and for implementing `GLOBAL IN` (see the section “IN operators”).
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/memory/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/memory/) <!--hide-->

2
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@ -69,4 +69,4 @@ FROM WatchLog
- [Virtual columns](../../../engines/table-engines/special/index.md#table_engines-virtual_columns)
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/merge/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/merge/) <!--hide-->

2
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@ -10,4 +10,4 @@ When writing to a `Null` table, data is ignored. When reading from a `Null` tabl
!!! info "Hint"
However, you can create a materialized view on a `Null` table. So the data written to the table will end up affecting the view, but original raw data will still be discarded.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/null/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/null/) <!--hide-->

2
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@ -20,4 +20,4 @@ When creating a table, the following settings are applied:
- [persistent](../../../operations/settings/settings.md#persistent)
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/set/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/set/) <!--hide-->

2
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@ -78,4 +78,4 @@ SELECT * FROM url_engine_table
- Indexes.
- Replication.
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/url/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/url/) <!--hide-->

2
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@ -7,4 +7,4 @@ toc_title: View
Used for implementing views (for more information, see the `CREATE VIEW query`). It does not store data, but only stores the specified `SELECT` query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).
[Original article](https://clickhouse.tech/docs/en/operations/table_engines/view/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/table_engines/view/) <!--hide-->

4
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@ -17,9 +17,9 @@ Key columnar database advantages are:
Here is the illustration of the difference between traditional row-oriented systems and columnar databases when building reports:
**Traditional row-oriented**
![Traditional row-oriented](https://clickhouse.tech/docs/en/images/row-oriented.gif#)
![Traditional row-oriented](https://clickhouse.com/docs/en/images/row-oriented.gif#)
**Columnar**
![Columnar](https://clickhouse.tech/docs/en/images/column-oriented.gif#)
![Columnar](https://clickhouse.com/docs/en/images/column-oriented.gif#)
A columnar database is a preferred choice for analytical applications because it allows to have many columns in a table just in case, but do not pay the cost for unused columns on read query execution time. Column-oriented databases are designed for big data processing because and data warehousing, they often natively scale using distributed clusters of low-cost hardware to increase throughput. ClickHouse does it with combination of [distributed](../../engines/table-engines/special/distributed.md) and [replicated](../../engines/table-engines/mergetree-family/replication.md) tables.

2
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@ -21,4 +21,4 @@ Questions:
!!! info "Don’t see what you were looking for?"
Check out [other F.A.Q. categories](../../faq/index.md) or browse around main documentation articles found in the left sidebar.
{## [Original article](https://clickhouse.tech/docs/en/faq/general/) ##}
{## [Original article](https://clickhouse.com/docs/en/faq/general/) ##}

2
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@ -23,4 +23,4 @@ If you haven’t seen one of those t-shirts in person, you can check them out on
![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.tech/#meet), usually for best questions or other forms of active participation.
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.

2
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@ -43,4 +43,4 @@ Question candidates:
- Window function workarounds (row_number, lag/lead, running diff/sum/average)
##}
{## [Original article](https://clickhouse.tech/docs/en/faq) ##}
{## [Original article](https://clickhouse.com/docs/en/faq) ##}

2
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@ -16,4 +16,4 @@ Questions:
!!! info "Don’t see what you were looking for?"
Check out [other F.A.Q. categories](../../faq/index.md) or browse around main documentation articles found in the left sidebar.
{## [Original article](https://clickhouse.tech/docs/en/faq/integration/) ##}
{## [Original article](https://clickhouse.com/docs/en/faq/integration/) ##}

2
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@ -15,4 +15,4 @@ Questions:
!!! info "Don’t see what you were looking for?"
Check out [other F.A.Q. categories](../../faq/index.md) or browse around main documentation articles found in the left sidebar.
{## [Original article](https://clickhouse.tech/docs/en/faq/production/) ##}
{## [Original article](https://clickhouse.com/docs/en/faq/production/) ##}

2
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@ -15,4 +15,4 @@ Questions:
!!! info "Don’t see what you were looking for?"
Check out [other F.A.Q. categories](../../faq/index.md) or browse around main documentation articles found in the left sidebar.
{## [Original article](https://clickhouse.tech/docs/en/faq/use-cases/) ##}
{## [Original article](https://clickhouse.com/docs/en/faq/use-cases/) ##}

2
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@ -6,7 +6,7 @@ toc_priority: 101
# Can I Use ClickHouse As a Time-Series Database? {#can-i-use-clickhouse-as-a-time-series-database}
ClickHouse is a generic data storage solution for [OLAP](../../faq/general/olap.md) workloads, while there are many specialized time-series database management systems. Nevertheless, ClickHouse’s [focus on query execution speed](../../faq/general/why-clickhouse-is-so-fast.md) allows it to outperform specialized systems in many cases. There are many independent benchmarks on this topic out there ([example](https://medium.com/@AltinityDB/clickhouse-for-time-series-scalability-benchmarks-e181132a895b)), so we’re not going to conduct one here. Instead, let’s focus on ClickHouse features that are important to use if that’s your use case.
ClickHouse is a generic data storage solution for [OLAP](../../faq/general/olap.md) workloads, while there are many specialized time-series database management systems. Nevertheless, ClickHouse’s [focus on query execution speed](../../faq/general/why-clickhouse-is-so-fast.md) allows it to outperform specialized systems in many cases. There are many independent benchmarks on this topic out there, so we’re not going to conduct one here. Instead, let’s focus on ClickHouse features that are important to use if that’s your use case.
First of all, there are **[specialized codecs](../../sql-reference/statements/create/table.md#create-query-specialized-codecs)** which make typical time-series. Either common algorithms like `DoubleDelta` and `Gorilla` or specific to ClickHouse like `T64`.

2
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@ -124,4 +124,4 @@ ORDER BY totalRevenue DESC
LIMIT 1
```
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/amplab_benchmark/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/amplab_benchmark/) <!--hide-->

2
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@ -413,4 +413,4 @@ ORDER BY yr,
The data is also available for interactive queries in the [Playground](https://gh-api.clickhouse.tech/play?user=play), [example](https://gh-api.clickhouse.tech/play?user=play#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).
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/brown-benchmark/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/brown-benchmark/) <!--hide-->

2
docs/en/getting-started/example-datasets/criteo.md
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@ -76,4 +76,4 @@ INSERT INTO criteo SELECT date, clicked, int1, int2, int3, int4, int5, int6, int
DROP TABLE criteo_log;
```
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/criteo/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/criteo/) <!--hide-->

2
docs/en/getting-started/example-datasets/index.md
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@ -25,4 +25,4 @@ The list of documented datasets:
- [Brown University Benchmark](../../getting-started/example-datasets/brown-benchmark.md)
- [Cell Towers](../../getting-started/example-datasets/cell-towers.md)
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets) <!--hide-->

2
docs/en/getting-started/example-datasets/nyc-taxi.md
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@ -385,4 +385,4 @@ We ran queries using a client located in a Yandex datacenter in Finland on a clu
| 3 | 0.212 | 0.438 | 0.733 | 1.241 |
| 140 | 0.028 | 0.043 | 0.051 | 0.072 |
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/nyc_taxi/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/nyc_taxi/) <!--hide-->

2
docs/en/getting-started/example-datasets/ontime.md
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@ -404,4 +404,4 @@ This performance test was created by Vadim Tkachenko. See:
- https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/
- http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/ontime/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/ontime/) <!--hide-->

2
docs/en/getting-started/example-datasets/recipes.md
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@ -337,4 +337,4 @@ Result:
The dataset is also available in the [Online Playground](https://gh-api.clickhouse.tech/play?user=play#U0VMRUNUCiAgICBhcnJheUpvaW4oTkVSKSBBUyBrLAogICAgY291bnQoKSBBUyBjCkZST00gcmVjaXBlcwpHUk9VUCBCWSBrCk9SREVSIEJZIGMgREVTQwpMSU1JVCA1MA==).
[Original article](https://clickhouse.tech/docs/en/getting-started/example-datasets/recipes/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting-started/example-datasets/recipes/) <!--hide-->

2
docs/en/getting-started/example-datasets/star-schema.md
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@ -365,4 +365,4 @@ ORDER BY
P_BRAND ASC;
```
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/star_schema/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/star_schema/) <!--hide-->

2
docs/en/getting-started/example-datasets/wikistat.md
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@ -30,4 +30,4 @@ $ cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/page
$ ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done
```
[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/wikistat/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/wikistat/) <!--hide-->

2
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@ -12,4 +12,4 @@ If you are new to ClickHouse and want to get a hands-on feeling of its performan
- [Go through detailed tutorial](../getting-started/tutorial.md)
- [Experiment with example datasets](../getting-started/example-datasets/ontime.md)
[Original article](https://clickhouse.tech/docs/en/getting_started/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/) <!--hide-->

4
docs/en/getting-started/install.md
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@ -203,6 +203,6 @@ SELECT 1
**Congratulations, the system works!**
To continue experimenting, you can download one of the test data sets or go through [tutorial](https://clickhouse.tech/tutorial.html).
To continue experimenting, you can download one of the test data sets or go through [tutorial](https://clickhouse.com/tutorial.html).
[Original article](https://clickhouse.tech/docs/en/getting_started/install/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/install/) <!--hide-->

2
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@ -659,4 +659,4 @@ INSERT INTO tutorial.hits_replica SELECT * FROM tutorial.hits_local;
Replication operates in multi-master mode. Data can be loaded into any replica, and the system then syncs it with other instances automatically. Replication is asynchronous so at a given moment, not all replicas may contain recently inserted data. At least one replica should be up to allow data ingestion. Others will sync up data and repair consistency once they will become active again. Note that this approach allows for the low possibility of a loss of recently inserted data.
[Original article](https://clickhouse.tech/docs/en/getting_started/tutorial/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/getting_started/tutorial/) <!--hide-->

2
docs/en/guides/apply-catboost-model.md
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@ -239,4 +239,4 @@ FROM
!!! note "Note"
More info about [avg()](../sql-reference/aggregate-functions/reference/avg.md#agg_function-avg) and [log()](../sql-reference/functions/math-functions.md) functions.
[Original article](https://clickhouse.tech/docs/en/guides/apply_catboost_model/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/guides/apply_catboost_model/) <!--hide-->

2
docs/en/guides/index.md
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@ -11,4 +11,4 @@ List of detailed step-by-step instructions that help to solve various tasks usin
- [Tutorial on simple cluster set-up](../getting-started/tutorial.md)
- [Applying a CatBoost model in ClickHouse](../guides/apply-catboost-model.md)
[Original article](https://clickhouse.tech/docs/en/guides/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/guides/) <!--hide-->

2
docs/en/index.md
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@ -92,4 +92,4 @@ This is not done in “normal” databases, because it does not make sense when
Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.
{## [Original article](https://clickhouse.tech/docs/en/) ##}
{## [Original article](https://clickhouse.com/docs/en/) ##}

2
docs/en/interfaces/cpp.md
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@ -7,4 +7,4 @@ toc_title: C++ Client Library
See README at [clickhouse-cpp](https://github.com/ClickHouse/clickhouse-cpp) repository.
[Original article](https://clickhouse.tech/docs/en/interfaces/cpp/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/cpp/) <!--hide-->

7
docs/en/interfaces/formats.md
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@ -23,7 +23,6 @@ The supported formats are:
| [CustomSeparated](#format-customseparated) | ✔ | ✔ |
| [Values](#data-format-values) | ✔ | ✔ |
| [Vertical](#vertical) | ✗ | ✔ |
| [VerticalRaw](#verticalraw) | ✗ | ✔ |
| [JSON](#json) | ✗ | ✔ |
| [JSONAsString](#jsonasstring) | ✔ | ✗ |
| [JSONStrings](#jsonstrings) | ✗ | ✔ |
@ -944,10 +943,6 @@ test: string with 'quotes' and with some special
This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).
## VerticalRaw {#verticalraw}
Similar to [Vertical](#vertical), but with escaping disabled. This format is only suitable for outputting query results, not for parsing (receiving data and inserting it in the table).
## XML {#xml}
XML format is suitable only for output, not for parsing. Example:
@ -1579,4 +1574,4 @@ Writing to a file ".msgpk":
$ clickhouse-client --query="CREATE TABLE msgpack (array Array(UInt8)) ENGINE = Memory;"
$ clickhouse-client --query="INSERT INTO msgpack VALUES ([0, 1, 2, 3, 42, 253, 254, 255]), ([255, 254, 253, 42, 3, 2, 1, 0])";
$ clickhouse-client --query="SELECT * FROM msgpack FORMAT MsgPack" > tmp_msgpack.msgpk;
```
```

2
docs/en/interfaces/index.md
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@ -24,4 +24,4 @@ There are also a wide range of third-party libraries for working with ClickHouse
- [Integrations](../interfaces/third-party/integrations.md)
- [Visual interfaces](../interfaces/third-party/gui.md)
[Original article](https://clickhouse.tech/docs/en/interfaces/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/) <!--hide-->

2
docs/en/interfaces/jdbc.md
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@ -10,4 +10,4 @@ toc_title: JDBC Driver
- [ClickHouse-Native-JDBC](https://github.com/housepower/ClickHouse-Native-JDBC)
- [clickhouse4j](https://github.com/blynkkk/clickhouse4j)
[Original article](https://clickhouse.tech/docs/en/interfaces/jdbc/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/jdbc/) <!--hide-->

2
docs/en/interfaces/mysql.md
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@ -50,4 +50,4 @@ To cancel a long query use `KILL QUERY connection_id` statement (it is replaced
$ mysql --protocol tcp -h mysql_server -P 9004 default -u default --password=123 -e "KILL QUERY 123456;"
```
[Original article](https://clickhouse.tech/docs/en/interfaces/mysql/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/mysql/) <!--hide-->

2
docs/en/interfaces/odbc.md
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@ -7,4 +7,4 @@ toc_title: ODBC Driver
- [Official driver](https://github.com/ClickHouse/clickhouse-odbc)
[Original article](https://clickhouse.tech/docs/en/interfaces/odbc/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/odbc/) <!--hide-->

2
docs/en/interfaces/tcp.md
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@ -7,4 +7,4 @@ toc_title: Native Interface (TCP)
The native protocol is used in the [command-line client](../interfaces/cli.md), for inter-server communication during distributed query processing, and also in other C++ programs. Unfortunately, native ClickHouse protocol does not have formal specification yet, but it can be reverse-engineered from ClickHouse source code (starting [around here](https://github.com/ClickHouse/ClickHouse/tree/master/src/Client)) and/or by intercepting and analyzing TCP traffic.
[Original article](https://clickhouse.tech/docs/en/interfaces/tcp/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/tcp/) <!--hide-->

2
docs/en/interfaces/third-party/client-libraries.md vendored
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@ -67,4 +67,4 @@ toc_title: Client Libraries
- Haskell
- [hdbc-clickhouse](https://github.com/zaneli/hdbc-clickhouse)
[Original article](https://clickhouse.tech/docs/en/interfaces/third-party/client_libraries/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/third-party/client_libraries/) <!--hide-->

34
docs/en/interfaces/third-party/gui.md vendored
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@ -113,6 +113,22 @@ Features:
[MindsDB](https://mindsdb.com/) is an open-source AI layer for databases including ClickHouse that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models. MindsDB Studio(GUI) allows you to train new models from database, interpret predictions made by the model, identify potential data biases, and evaluate and visualize model accuracy using the Explainable AI function to adapt and tune your Machine Learning models faster.
### DBM {#dbm}
[DBM](https://dbm.incubator.edurt.io/) DBM is a visual management tool for ClickHouse!
Features:
- Support query history (pagination, clear all, etc.)
- Support selected sql clauses query
- Support terminating query
- Support table management (metadata, delete, preview)
- Support database management (delete, create)
- Support custom query
- Support multiple data sources management(connection test, monitoring)
- Support monitor (processor, connection, query)
- Support migrate data
## Commercial {#commercial}
### DataGrip {#datagrip}
@ -190,20 +206,4 @@ SeekTable is [free](https://www.seektable.com/help/cloud-pricing) for personal/i
[Chadmin](https://github.com/bun4uk/chadmin) is a simple UI where you can visualize your currently running queries on your ClickHouse cluster and info about them and kill them if you want.
### DBM {#dbm}
[DBM](https://dbm.incubator.edurt.io/) DBM is a visual management tool for ClickHouse!
Features:
- Support query history (pagination, clear all, etc.)
- Support selected sql clauses query
- Support terminating query
- Support table management (metadata, delete, preview)
- Support database management (delete, create)
- Support custom query
- Support multiple data sources management(connection test, monitoring)
- Support monitor (processor, connection, query)
- Support migrate data
[Original article](https://clickhouse.tech/docs/en/interfaces/third-party/gui/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/third-party/gui/) <!--hide-->

2
docs/en/interfaces/third-party/integrations.md vendored
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@ -108,4 +108,4 @@ toc_title: Integrations
- [GraphQL](https://github.com/graphql)
- [activecube-graphql](https://github.com/bitquery/activecube-graphql)
[Original article](https://clickhouse.tech/docs/en/interfaces/third-party/integrations/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/third-party/integrations/) <!--hide-->

2
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@ -41,4 +41,4 @@ Features:
Implemented in Go.
[Original article](https://clickhouse.tech/docs/en/interfaces/third-party/proxy/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/interfaces/third-party/proxy/) <!--hide-->

6
docs/en/introduction/adopters.md
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@ -58,7 +58,7 @@ toc_title: Adopters
| <a href="https://glaber.io/" class="favicon">Glaber</a> | Monitoring | Main product | — | — | [Website](https://glaber.io/) |
| <a href="https://www.huya.com/" class="favicon">HUYA</a> | Video Streaming | Analytics | — | — | [Slides in Chinese, October 2018](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup19/7.%20ClickHouse万亿数据分析实践%20李本旺(sundy-li)%20虎牙.pdf) |
| <a href="https://www.the-ica.com/" class="favicon">ICA</a> | FinTech | Risk Management | — | — | [Blog Post in English, Sep 2020](https://altinity.com/blog/clickhouse-vs-redshift-performance-for-fintech-risk-management?utm_campaign=ClickHouse%20vs%20RedShift&utm_content=143520807&utm_medium=social&utm_source=twitter&hss_channel=tw-3894792263) |
| <a href="https://www.idealista.com" class="favicon">Idealista</a> | Real Estate | Analytics | — | — | [Blog Post in English, April 2019](https://clickhouse.tech/blog/en/clickhouse-meetup-in-madrid-on-april-2-2019) |
| <a href="https://www.idealista.com" class="favicon">Idealista</a> | Real Estate | Analytics | — | — | [Blog Post in English, April 2019](https://clickhouse.com/blog/en/clickhouse-meetup-in-madrid-on-april-2-2019) |
| <a href="https://infobaleen.com" class="favicon">Infobaleen</a> | AI markting tool | Analytics | — | — | [Official site](https://infobaleen.com) |
| <a href="https://www.infovista.com/" class="favicon">Infovista</a> | Networks | Analytics | — | — | [Slides in English, October 2019](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup30/infovista.pdf) |
| <a href="https://www.innogames.com" class="favicon">InnoGames</a> | Games | Metrics, Logging | — | — | [Slides in Russian, September 2019](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup28/graphite_and_clickHouse.pdf) |
@ -70,7 +70,7 @@ toc_title: Adopters
| <a href="https://jinshuju.net" class="favicon">Jinshuju 金数据</a> | BI Analytics | Main product | — | — | [Slides in Chinese, October 2019](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup24/3.%20金数据数据架构调整方案Public.pdf) |
| <a href="https://www.kodiakdata.com/" class="favicon">Kodiak Data</a> | Clouds | Main product | — | — | [Slides in Engish, April 2018](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup13/kodiak_data.pdf) |
| <a href="https://kontur.ru" class="favicon">Kontur</a> | Software Development | Metrics | — | — | [Talk in Russian, November 2018](https://www.youtube.com/watch?v=U4u4Bd0FtrY) |
| <a href="https://www.kuaishou.com/" class="favicon">Kuaishou</a> | Video | — | — | — | [ClickHouse Meetup, October 2018](https://clickhouse.tech/blog/en/2018/clickhouse-community-meetup-in-beijing-on-october-28-2018/) |
| <a href="https://www.kuaishou.com/" class="favicon">Kuaishou</a> | Video | — | — | — | [ClickHouse Meetup, October 2018](https://clickhouse.com/blog/en/2018/clickhouse-community-meetup-in-beijing-on-october-28-2018/) |
| <a href="https://www.lbl.gov" class="favicon">Lawrence Berkeley National Laboratory</a> | Research | Traffic analysis | 1 server | 11.8 TiB | [Slides in English, April 2019](https://www.smitasin.com/presentations/2019-04-17_DOE-NSM.pdf) |
| <a href="https://lifestreet.com/" class="favicon">LifeStreet</a> | Ad network | Main product | 75 servers (3 replicas) | 5.27 PiB | [Blog post in Russian, February 2017](https://habr.com/en/post/322620/) |
| <a href="https://mcs.mail.ru/" class="favicon">Mail.ru Cloud Solutions</a> | Cloud services | Main product | — | — | [Article in Russian](https://mcs.mail.ru/help/db-create/clickhouse#) |
@ -160,4 +160,4 @@ toc_title: Adopters
| <a href="https://zagravagames.com/en/" class="favicon">Zagrava Trading</a> | — | — | — | — | [Job offer, May 2021](https://twitter.com/datastackjobs/status/1394707267082063874) |
| <a href="https://beeline.ru/" class="favicon">Beeline</a> | Telecom | Data Platform | — | — | [Blog post, July 2021](https://habr.com/en/company/beeline/blog/567508/) |
[Original article](https://clickhouse.tech/docs/en/introduction/adopters/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/introduction/adopters/) <!--hide-->

2
docs/en/introduction/distinctive-features.md
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@ -93,4 +93,4 @@ ClickHouse implements user account management using SQL queries and allows for [
2. Lack of ability to modify or delete already inserted data with a high rate and low latency. There are batch deletes and updates available to clean up or modify data, for example, to comply with [GDPR](https://gdpr-info.eu).
3. The sparse index makes ClickHouse not so efficient for point queries retrieving single rows by their keys.
[Original article](https://clickhouse.tech/docs/en/introduction/distinctive-features/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/introduction/distinctive-features/) <!--hide-->

2
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@ -51,4 +51,4 @@ OLAPServer worked well for non-aggregated data, but it had many restrictions tha
The initial goal for ClickHouse was to remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, but over the years, it has grown into a general-purpose database management system suitable for a wide range of analytical tasks.
[Original article](https://clickhouse.tech/docs/en/introduction/history/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/introduction/history/) <!--hide-->

10
docs/en/introduction/info.md
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@ -1,10 +0,0 @@
---
toc_priority: 100
---
# Information support {#information-support}
- Email address: <a class="feedback-email"></a>
- Phone: <a href="tel:+74957806510">+7-495-780-6510</a>
[Original article](https://clickhouse.tech/docs/en/introduction/info/) <!--hide-->

6
docs/en/introduction/performance.md
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@ -5,9 +5,9 @@ toc_title: Performance
# Performance {#performance}
According to internal testing results at Yandex, ClickHouse shows the best performance (both the highest throughput for long queries and the lowest latency on short queries) for comparable operating scenarios among systems of its class that were available for testing. You can view the test results on a [separate page](https://clickhouse.tech/benchmark/dbms/).
According to internal testing results at Yandex, ClickHouse shows the best performance (both the highest throughput for long queries and the lowest latency on short queries) for comparable operating scenarios among systems of its class that were available for testing. You can view the test results on a [separate page](https://clickhouse.com/benchmark/dbms/).
Numerous independent benchmarks came to similar conclusions. They are not difficult to find using an internet search, or you can see [our small collection of related links](https://clickhouse.tech/#independent-benchmarks).
Numerous independent benchmarks came to similar conclusions. They are not difficult to find using an internet search, or you can see [our small collection of related links](https://clickhouse.com/#independent-benchmarks).
## Throughput for a Single Large Query {#throughput-for-a-single-large-query}
@ -27,4 +27,4 @@ Under the same conditions, ClickHouse can handle several hundred queries per sec
We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed can be from 50 to 200 MB/s. If the inserted rows are around 1 KB in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance can be higher in rows per second (on Banner System data -`>` 500,000 rows per second; on Graphite data -`>` 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, which scales linearly.
{## [Original article](https://clickhouse.tech/docs/en/introduction/performance/) ##}
{## [Original article](https://clickhouse.com/docs/en/introduction/performance/) ##}

2
docs/en/operations/access-rights.md
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@ -149,4 +149,4 @@ Management queries:
By default, SQL-driven access control and account management is disabled for all users. You need to configure at least one user in the `users.xml` configuration file and set the value of the [access_management](../operations/settings/settings-users.md#access_management-user-setting) setting to 1.
[Original article](https://clickhouse.tech/docs/en/operations/access_rights/) <!--hide-->
[Original article](https://clickhouse.com/docs/en/operations/access_rights/) <!--hide-->

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