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Trying ::1... Connected to localhost (::1) port 8123 (#0) GET /get_relative_path_static_handler HTTP/1.1 Host: localhost:8123 User-Agent: curl/7.47.0 Accept: / XXX:xxx < HTTP/1.1 200 OK < Date: Wed, 29 Apr 2020 04:18:31 GMT < Connection: Keep-Alive < Content-Type: text/html; charset=UTF-8 < Transfer-Encoding: c...
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d33c6010-a5e8-4648-adbe-41855199b6c7
Valid JSON/XML response on exception during HTTP streaming {#valid-output-on-exception-http-streaming} While query execution occurs over HTTP an exception can happen when part of the data has already been sent. Usually an exception is sent to the client in plain text. Even if some specific data format was used to out...
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6b9575f6-f8d6-47ff-8573-83f2b86dd85b
description: 'Overview of supported data formats for input and output in ClickHouse' sidebar_label: 'View all formats...' sidebar_position: 21 slug: /interfaces/formats title: 'Formats for input and output data' doc_type: 'reference' import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge'; Forma...
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58c31f14-922e-4560-97f8-181d7f265003
| Format | Input | Output | |------------------------------------------------------------------------------------------------------------|-----|-------| | TabSeparated ...
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| JSONStringsEachRow | βœ” | βœ” | | JSONStringsEachRowWithProgress | βœ— | βœ” | | JSONCompactEachRow | βœ” | βœ” | | JSONCompactEachRowWithNames | βœ” | βœ”...
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| Arrow | βœ” | βœ” | | ArrowStream | βœ” | βœ” | | ORC | βœ” | βœ” | | One ...
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You can control some format processing parameters with the ClickHouse settings. For more information read the Settings section. Format schema {#formatschema} The file name containing the format schema is set by the setting format_schema . It's required to set this setting when it is used one of the formats Cap'...
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8c8d8d48-a24a-4f1d-8027-c8bba4282ebb
description: 'Documentation for the Prometheus protocol support in ClickHouse' sidebar_label: 'Prometheus protocols' sidebar_position: 19 slug: /interfaces/prometheus title: 'Prometheus Protocols' doc_type: 'reference' Prometheus protocols Exposing metrics {#expose} :::note If you are using ClickHouse Cloud, yo...
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Settings: | Name | Default | Description | |------------------------------|------------|--------------------------...
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Settings: | Name | Default | Description | |------------------------------|---------|-------------------------...
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Settings: | Name | Default | Description | |------------------------------|---------|----------------------------...
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9a992c9d-ac2f-469f-861b-f7885ba17df6
description: 'Overview of network interfaces, drivers, and tools for connecting to ClickHouse' keywords: ['clickhouse', 'network', 'interfaces', 'http', 'tcp', 'grpc', 'command-line', 'client', 'jdbc', 'odbc', 'driver'] sidebar_label: 'Overview' slug: /interfaces/overview title: 'Drivers and Interfaces' doc_type: '...
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description: 'Documentation for the native TCP interface in ClickHouse' sidebar_label: 'Native interface (TCP)' sidebar_position: 18 slug: /interfaces/tcp title: 'Native interface (TCP)' doc_type: 'reference' Native interface (TCP) The native protocol is used in the command-line client , for inter-server communi...
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description: 'Documentation for the ClickHouse command-line client interface' sidebar_label: 'ClickHouse Client' sidebar_position: 17 slug: /interfaces/cli title: 'ClickHouse Client' doc_type: 'reference' import Image from '@theme/IdealImage'; import cloud_connect_button from '@site/static/images/_snippets/cloud-co...
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:) ``` Specify additional connection details as necessary: | Option | Description | |----------------------------------|----...
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9e27dc76-be45-488b-9fec-0a397af73fb5
See the section on configuration files for more information. :::note To concentrate on the query syntax, the rest of the examples leave off the connection details ( --host , --port , etc.). Remember to add them when you use the commands. ::: Interactive mode {#interactive-mode} Using interactive mode {#using-i...
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0d3c5d15-0cf6-4c21-a2cf-fc7c7540bc1d
Aliases {#cli_aliases} You can use the following aliases from within the REPL: \l - SHOW DATABASES \d - SHOW TABLES \c <DATABASE> - USE DATABASE . - repeat the last query Keyboard shortcuts {#keyboard_shortcuts} Alt (Option) + Shift + e - open editor with the current query. It is possible to sp...
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d43ae904-1202-4014-8a4a-872f3d3e0707
bash cat file.csv | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV"; In batch mode, the default data format is TabSeparated . You can set the format in the FORMAT clause of the query as shown in the example above. Queries with parameters {#cli-queries-with-parameters} You can specify ...
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Query syntax {#cli-queries-with-parameters-syntax} In the query, place the values that you want to fill using command-line parameters in braces in the following format: sql {<name>:<data type>} | Parameter | Description ...
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4d1d9327-35ca-443e-89f6-ab9056b7c278
Usage {#ai-sql-generation-usage} To use AI SQL generation, prefix your natural language query with ?? : bash :) ?? show all users who made purchases in the last 30 days The AI will: 1. Explore your database schema automatically 2. Generate appropriate SQL based on the discovered tables and columns 3. Execute the...
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0aac555c-26c8-41b1-bf79-26446bd4d099
<!-- Generation parameters --> <temperature>0.0</temperature> <max_tokens>1000</max_tokens> <timeout_seconds>30</timeout_seconds> <max_steps>10</max_steps> <!-- Optional: Custom system prompt --> <!-- <system_prompt>You are an expert ClickHouse SQL assistant...</system_p...
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Connection settings - `base_url` - Custom API endpoint for OpenAI-compatible services (optional) - `timeout_seconds` - Request timeout in seconds (default: `30`) Schema exploration - `enable_schema_access` - Allow AI to explore database schemas (default: `true`) - `max_steps` - Maximum tool-calling steps for ...
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text clickhouse:[//[user[:password]@][hosts_and_ports]][/database][?query_parameters] | Component (all optional) | Description | Default | |------------------...
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1aa4b326-3ca8-4e06-a83c-159a62c86de7
Percent encoding Non-US ASCII, spaces and special characters in the following parameters must be percent-encoded : - user - password - hosts - database - query parameters Examples {#connection_string_examples} Connect to localhost on port 9000 and execute the query SELECT 1 . bash clickhouse-clie...
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Example xml <config> <query_id_formats> <speedscope>http://speedscope-host/#profileURL=qp%3Fid%3D{query_id}</speedscope> </query_id_formats> </config> With the configuration above, the ID of a query is shown in the following format: response speedscope:http://speedscope-host/#profileURL=qp%3Fid%3Dc8ecc783...
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All command-line options can be specified directly on the command line or as defaults in the configuration file . General options {#command-line-options-general} | Option | Description ...
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| Option | Description ...
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| --no-warnings | Disable showing warnings from system.warnings when the client connects to the server. ...
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| -u [ --user ] <username> | The database user to connect as. ...
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:::note Instead of the --host , --port , --user and --password options, the client also supports connection strings . ::: Query options {#command-line-options-query}
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| Option | Description ...
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| -m [ --multiline ] | If specified, allow multiline queries (do not send the query on Enter). Queries will be sent only when they are ended with a semicolon. ...
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Query settings {#command-line-options-query-settings} Query settings can be specified as command-line options in the client, for example: bash $ clickhouse-client --max_threads 1 See Settings for a list of settings. Formatting options {#command-line-options-formatting} | Option | Descripti...
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| Option | Description ...
{"source_file": "cli.md"}
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953e74a6-4836-4d2d-9b40-b5abda28e393
tty in interactive mode, off in non-interactive (batch) mode | | --progress-table | Print a progress table with changing metrics during query execution. Possible values: β€’ tty\|on\|1\|true\|yes - outputs to the terminal in interactive mode β€’ err - outputs to stderr non-interactive mode ...
{"source_file": "cli.md"}
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bc7fb78d-0b2e-485b-a4f7-c31e3e7e2160
description: 'Documentation for the Apache Arrow Flight interface in ClickHouse, allowing Flight SQL clients to connect to ClickHouse' sidebar_label: 'Arrow Flight Interface' sidebar_position: 26 slug: /interfaces/arrowflight title: 'Arrow Flight Interface' doc_type: 'reference' Apache Arrow Flight Interface Clic...
{"source_file": "arrowflight.md"}
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For more details, see: Apache Arrow Flight SQL specification ClickHouse GitHub Issue #7554
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description: 'Documentation for the MySQL protocol interface in ClickHouse, allowing MySQL clients to connect to ClickHouse' sidebar_label: 'MySQL Interface' sidebar_position: 25 slug: /interfaces/mysql title: 'MySQL Interface' doc_type: 'guide' import Image from '@theme/IdealImage'; import mysql0 from '@site/sta...
{"source_file": "mysql.md"}
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ca67a958-7fba-480a-8dfd-2f6f63126b40
Copy the MySQL connection string shown. Creating multiple MySQL users in ClickHouse Cloud {#creating-multiple-mysql-users-in-clickhouse-cloud} By default, there is a built-in mysql4<subdomain> user, which uses the same password as the default one. The <subdomain> part is the first segment of your ClickHouse...
{"source_file": "mysql.md"}
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846f8492-7ec6-415d-9b44-298b57775b8f
In this case, ensure that the username follows the mysql4<subdomain>_<username> format, as described ( above ). Enabling the MySQL interface on self-managed ClickHouse {#enabling-the-mysql-interface-on-self-managed-clickhouse} Add the mysql_port setting to your server's configuration file. For example, you coul...
{"source_file": "mysql.md"}
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d1b1874b-0f8f-4d2a-ac62-4eeebf4ee9ab
description: 'Documentation for the SSH interface in ClickHouse' keywords: ['client', 'ssh', 'putty'] sidebar_label: 'SSH Interface' sidebar_position: 60 slug: /interfaces/ssh title: 'SSH Interface' doc_type: 'reference' import ExperimentalBadge from '@theme/badges/ExperimentalBadge'; import CloudNotSupportedBadge ...
{"source_file": "ssh.md"}
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2765feab-f615-4bcb-bd1d-79ee20ada0f6
You are able to change any user-level setting this way and additionally pass most of the ordinary clickhouse-client options (except ones which don't make sense in this setup.) Important: In case if both query option and the SSH command is passed, the latter one is added to the list of queries to execute: bash...
{"source_file": "ssh.md"}
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ee42036d-852e-4d6f-bbae-44246bd20a4f
description: 'Documentation for the PostgreSQL wire protocol interface in ClickHouse' sidebar_label: 'PostgreSQL Interface' sidebar_position: 20 slug: /interfaces/postgresql title: 'PostgreSQL Interface' doc_type: 'reference' import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge'; PostgreSQL In...
{"source_file": "postgresql.md"}
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Configuring ClickHouse user authentication with SCRAM-SHA-256 {#using-scram-sha256} To ensure secure user authentication in ClickHouse, it is recommended to use the SCRAM-SHA-256 protocol. Configure the user by specifying the password_scram_sha256_hex element in the users.xml file. The password hash must be generat...
{"source_file": "postgresql.md"}
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description: 'Documentation for the gRPC interface in ClickHouse' sidebar_label: 'gRPC Interface' sidebar_position: 25 slug: /interfaces/grpc title: 'gRPC Interface' doc_type: 'reference' gRPC Interface Introduction {#grpc-interface-introduction} ClickHouse supports gRPC interface. It is an open source remote...
{"source_file": "grpc.md"}
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8962b894-a8f5-41e2-bbab-5c63a2101418
--user USER_NAME, -u USER_NAME – A user name. Default value: default . --password PASSWORD – A password. Default value: empty string. --query QUERY, -q QUERY – A query to process when using non-interactive mode. --database DATABASE, -d DATABASE – A default database. If not specified, the current database set...
{"source_file": "grpc.md"}
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description: 'Documentation for the ClickHouse ODBC driver' sidebar_label: 'ODBC Driver' sidebar_position: 35 slug: /interfaces/odbc title: 'ODBC Driver' doc_type: 'reference' ODBC driver Use the official ODBC driver for accessing ClickHouse as a data source.
{"source_file": "odbc.md"}
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ce9354f2-1ea8-451d-9c7e-b53fca711f50
description: 'Guide to using the JDBC driver for connecting to ClickHouse from Java applications' sidebar_label: 'JDBC Driver' sidebar_position: 20 slug: /interfaces/jdbc title: 'JDBC Driver' doc_type: 'guide' JDBC driver Use the official JDBC driver (and Java client) to access ClickHouse from your Java appli...
{"source_file": "jdbc.md"}
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0ec39a06-764e-4483-b15a-c9379b693992
description: 'Documentation for the ClickHouse C++ client library and integration with u-server framework' sidebar_label: 'C++ Client Library' sidebar_position: 24 slug: /interfaces/cpp title: 'C++ Client Library' doc_type: 'reference' C++ client library See README at clickhouse-cpp repository. Userver asyn...
{"source_file": "cpp.md"}
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1dc53288-aa7f-4e8e-8100-7bc58ebd7164
description: 'Page describing automatic schema inference from input data in ClickHouse' sidebar_label: 'Schema inference' slug: /interfaces/schema-inference title: 'Automatic schema inference from input data' doc_type: 'reference' ClickHouse can automatically determine the structure of input data in almost all supp...
{"source_file": "schema-inference.md"}
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50a97d7c-2f70-4389-89a2-41095c4c6247
Table engines File , S3 , URL , HDFS , azureBlobStorage {#table-engines-file-s3-url-hdfs-azureblobstorage} If the list of columns is not specified in CREATE TABLE query, the structure of the table will be inferred automatically from the data. Example: Let's use the file hobbies.jsonl . We can create a ta...
{"source_file": "schema-inference.md"}
[ -0.013218284584581852, -0.04772653430700302, -0.08730214834213257, 0.05657666176557541, 0.018177565187215805, 0.005531894974410534, 0.022412260994315147, -0.020254023373126984, -0.03185294196009636, 0.07185472548007965, 0.07632546871900558, -0.05677887424826622, 0.05755646899342537, -0.009...
aefa386e-d3e7-4f03-a04f-78a4bc0ac9df
There is a special setting use_structure_from_insertion_table_in_table_functions that controls this behaviour. It has 3 possible values: - 0 - table function will extract the structure from the data. - 1 - table function will use the structure from the insertion table. - 2 - ClickHouse will automatically determine if...
{"source_file": "schema-inference.md"}
[ -0.016889432445168495, -0.042252302169799805, -0.037762291729450226, 0.008384970016777515, -0.03373701125383377, -0.0465170219540596, -0.013378117233514786, 0.014306862838566303, -0.01791902631521225, 0.04236527532339096, 0.10862656682729721, -0.01392530556768179, 0.032217320054769516, 0.0...
e2cd6c00-e952-4fb1-aa3a-427ea0ace8dc
Schema inference cache {#schema-inference-cache} For most input formats schema inference reads some data to determine its structure and this process can take some time. To prevent inferring the same schema every time ClickHouse read the data from the same file, the inferred schema is cached and when accessing the sam...
{"source_file": "schema-inference.md"}
[ 0.01219071727246046, -0.05446343496441841, -0.05848618969321251, 0.021052764728665352, 0.04784514755010605, -0.027429621666669846, -0.01592533104121685, -0.07567042112350464, 0.07060879468917847, 0.1011289432644844, 0.005954187363386154, 0.02797504886984825, 0.0450882650911808, -0.00936166...
876705b8-7eab-40ee-b86b-c7a632ea5d9b
5 rows in set. Elapsed: 0.601 sec. sql DESCRIBE TABLE s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/github/github-2022.ndjson.gz') SETTINGS allow_experimental_object_type = 1 response β”Œβ”€name───────┬─type─────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_express...
{"source_file": "schema-inference.md"}
[ -0.04706132784485817, -0.05893116816878319, -0.0064583574421703815, 0.062425922602415085, 0.01217863243073225, -0.09112878888845444, -0.03923184424638748, -0.054235849529504776, -0.008251315914094448, 0.023868849501013756, 0.08051912486553192, -0.05138023570179939, 0.0014458760851994157, -...
e82b2cfd-5416-47a9-845c-997f096ff770
Let's check the content of system.schema_inference_cache table: sql SELECT schema, format, source FROM system.schema_inference_cache WHERE storage='S3' response β”Œβ”€schema──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬─f...
{"source_file": "schema-inference.md"}
[ -0.06153968721628189, -0.06506180018186569, -0.08801287412643433, -0.01635614037513733, 0.041823871433734894, -0.03242364153265953, -0.037950340658426285, -0.06450770050287247, 0.03129279240965843, 0.02437153272330761, 0.05208433419466019, -0.04766570404171944, 0.013807551003992558, -0.037...
5b4ff7a4-9409-48e4-b2f6-dffdeef6fd7d
Integers, Floats, Bools, Strings: sql DESC format(JSONEachRow, '{"int" : 42, "float" : 42.42, "string" : "Hello, World!"}'); response β”Œβ”€name───┬─type──────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ int β”‚ Nullable(Int64) β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.0034110997803509235, 0.021192079409956932, -0.007947240024805069, 0.05098642408847809, -0.08386214822530746, -0.024926839396357536, -0.009033294394612312, -0.004677135031670332, -0.037663452327251434, 0.03390388935804367, 0.030984794721007347, -0.08794088661670685, -0.009052286855876446, ...
23f303f1-30fd-408e-8f10-e2ca3e87eac7
sql SET input_format_json_infer_array_of_dynamic_from_array_of_different_types=1; DESC format(JSONEachRow, '{"arr" : [42, "hello", [1, 2, 3]]}'); response β”Œβ”€name─┬─type───────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ arr β”‚ Array(Dynamic) β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.024138208478689194, -0.07337144762277603, 0.03584074601531029, 0.0152188241481781, -0.08913896977901459, -0.0427277572453022, 0.006675814278423786, -0.03753601014614105, -0.048311997205019, -0.03480351343750954, -0.04758131876587868, -0.05765248090028763, -0.025635430589318275, 0.0195984...
db228037-4766-458d-a960-21f2cd0d6c81
response β”Œβ”€name──┬─type─────────────────────────────────────────────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ tuple β”‚ Tuple(Nullable(Int64), Nullable(String), Array(Nullable(Int64))) β”‚ β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.08594057708978653, -0.017544452100992203, -0.02251882664859295, 0.016078375279903412, -0.11509060859680176, -0.012387321330606937, -0.02161187306046486, 0.013336868956685066, -0.12161166965961456, -0.004509129095822573, -0.007867183536291122, -0.04097023606300354, -0.009740163572132587, ...
587b3fb5-f639-408a-b00b-f77d68872751
sql DESC format(JSONEachRow, '{"arr" : [null, null]}') SETTINGS input_format_json_infer_incomplete_types_as_strings = 1; response β”Œβ”€name─┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ arr β”‚ Array(Nullable(String)) β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.0244505126029253, -0.055701933801174164, -0.04515637829899788, 0.014496961608529091, -0.09101516753435135, -0.005514440126717091, -0.03168190270662308, -0.0029801258351653814, -0.06658028811216354, -0.01762433722615242, -0.0009209311101585627, -0.007782352156937122, -0.028706854209303856, ...
8cffb954-ba27-4b93-8c1e-1cd6a16b4f77
sql SET input_format_json_try_infer_named_tuples_from_objects = 1; DESC format(JSONEachRow, '{"array" : [{"a" : 42, "b" : "Hello"}, {}, {"c" : [1,2,3]}, {"d" : "2020-01-01"}]}') Result: markdown β”Œβ”€name──┬─type────────────────────────────────────────────────────────────────────────────────────────────┬─default_type─...
{"source_file": "schema-inference.md"}
[ -0.0032027640845626593, -0.006562582682818174, 0.004353910684585571, 0.05488276109099388, -0.04867352545261383, 0.009183238260447979, -0.011086378246545792, -0.0017099197721108794, -0.08203232288360596, -0.027752729132771492, -0.02034888230264187, -0.027089495211839676, -0.001990654738619923...
d5021ac9-94a0-4c79-ad2d-a6144147a8e3
input_format_json_read_objects_as_strings {#input_format_json_read_objects_as_strings} Enabling this setting allows reading nested JSON objects as strings. This setting can be used to read nested JSON objects without using JSON object type. This setting is enabled by default. Note: enabling this setting will take...
{"source_file": "schema-inference.md"}
[ 0.018091510981321335, -0.04566822573542595, -0.01052791252732277, 0.03662095591425896, -0.10176657140254974, -0.010707983747124672, -0.05240735039114952, 0.028728559613227844, -0.02931581810116768, -0.025732330977916718, -0.03070477582514286, 0.017343437299132347, -0.014672597870230675, 0....
2af44c33-e905-4807-8584-4f9eea3ab9c3
response β”Œβ”€name──┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ value β”‚ Nullable(String) β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ └───────┴──────────────────┴──────────────┴────────────────────┴─────────┴───────────────...
{"source_file": "schema-inference.md"}
[ 0.02966507151722908, -0.017068393528461456, -0.013319474644958973, 0.01606600731611252, -0.12031001597642899, -0.0188992191106081, 0.014819079078733921, -0.005469626281410456, -0.08279857784509659, -0.05768563970923424, -0.03504369780421257, 0.019089356064796448, -0.007537905126810074, 0.0...
8eb639df-ff49-443d-a01d-c470ed2f4748
β”Œβ”€obj────────────────────────────┐ β”‚ ([1,2,3],'hello',NULL,'{}',[]) β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` CSV {#csv} In CSV format ClickHouse extracts column values from the row according to delimiters. ClickHouse expects all types except numbers and strings to be enclosed in double quotes. If the value is in do...
{"source_file": "schema-inference.md"}
[ -0.012531799264252186, -0.003938000649213791, -0.015676112845540047, 0.03487450256943703, 0.001366169424727559, -0.09036938846111298, 0.03404927998781204, -0.04907156527042389, 0.002423018217086792, 0.00555054796859622, 0.030080776661634445, -0.0858047604560852, 0.03663400560617447, -0.027...
c95e8711-826b-4a19-b89e-5592c4bf612c
Dates, DateTimes: sql DESC format(CSV, '"2020-01-01","2020-01-01 00:00:00","2022-01-01 00:00:00.000"') response β”Œβ”€name─┬─type────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ c1 β”‚ Nullable(Date) β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.007776972837746143, 0.015219506807625294, -0.017984984442591667, 0.047521770000457764, -0.03921793773770332, -0.04145617410540581, 0.013963326811790466, -0.031277548521757126, -0.04135395586490631, 0.03738127276301384, 0.0072943903505802155, -0.06128469109535217, -0.0031567856203764677, ...
58e46996-3247-49cd-a99f-b9302d6f7a5d
Nested Arrays and Maps: sql DESC format(CSV, $$"[{'key1' : [[42, 42], []], 'key2' : [[null], [42]]}]"$$) response β”Œβ”€name─┬─type──────────────────────────────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ c1 β”‚ Array(Map(String, Array(Array(Nullable(Int64))))) β”‚...
{"source_file": "schema-inference.md"}
[ 0.04558466747403145, 0.015500770881772041, -0.037740208208560944, 0.038002900779247284, -0.07206028699874878, -0.03281731158494949, 0.025207430124282837, -0.027662580832839012, -0.06534501165151596, 0.042619653046131134, -0.006045233923941851, -0.07814480364322662, 0.04586625471711159, -0....
53a6cf76-ff32-4fa3-8acc-7e0f2610459d
Note that the header can be detected only if there is at least one column with a non-String type. If all columns have String type, the header is not detected: sql SELECT * FROM format(CSV, $$"first_column","second_column" "Hello","World" "World","Hello" $$) response β”Œβ”€c1───────────┬─c2────────────┐ β”‚ first_column β”‚...
{"source_file": "schema-inference.md"}
[ 0.0007835670257918537, -0.00179701775778085, -0.0656420961022377, 0.006767980754375458, 0.0015615355223417282, 0.025264021009206772, 0.030481470748782158, 0.03695424646139145, 0.015237930230796337, -0.0002688273962121457, -0.04929395765066147, -0.11493328213691711, 0.03389628231525421, -0....
5ae44299-ffbd-46e1-8318-ab40bfb35c49
sql DESC format(TSKV, 'int=42 float=42.42 bool=true string=Hello,World!\n') response β”Œβ”€name───┬─type──────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ int β”‚ Nullable(Int64) β”‚ β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.007294256240129471, -0.03167608380317688, 0.0159250870347023, 0.047880977392196655, -0.03980817273259163, -0.03637383133172989, -0.00579888466745615, -0.023369060829281807, -0.057935480028390884, 0.07235482335090637, -0.012433103285729885, -0.07018422335386276, -0.055449228733778, -0.006...
874b0498-75b0-4a86-a085-56b14b3fb244
If an array contains null, ClickHouse will use types from the other array elements: sql DESC format(TSV, '[NULL, 42, NULL]') response β”Œβ”€name─┬─type───────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ c1 β”‚ Array(Nullable(Int64)) β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.058522455394268036, -0.035643164068460464, 0.00650758808478713, 0.029460430145263672, -0.048428427428007126, -0.04734797775745392, 0.03888604789972305, -0.10225332528352737, -0.08630379289388657, 0.00994621030986309, 0.022559108212590218, -0.03188251331448555, 0.016886183992028236, -0.04...
d2019af6-fc74-4345-aaa4-d1a46b859c2d
sql SET input_format_tsv_use_best_effort_in_schema_inference = 0 DESC format(TSV, '[1,2,3] 42.42 Hello World!') response β”Œβ”€name─┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ c1 β”‚ Nullable(String) β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.0052415248937904835, -0.004115255083888769, -0.009327853098511696, -0.005024902522563934, -0.008945940062403679, -0.01945500820875168, 0.06571736931800842, -0.02550346590578556, -0.08566445112228394, 0.0788615420460701, -0.08282066136598587, -0.10616882145404816, -0.011751214973628521, 0...
dcbbf438-721a-4924-9d96-992be13d73dc
Examples: Integers, Floats, Bools, Strings: sql DESC format(Values, $$(42, 42.42, true, 'Hello,World!')$$) response β”Œβ”€name─┬─type──────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ c1 β”‚ Nullable(Int64) β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.020181061699986458, -0.007967466488480568, -0.03032749332487583, 0.04893463850021362, -0.050493914633989334, -0.0324956513941288, 0.007699939887970686, -0.05130654573440552, -0.07543301582336426, 0.05613919347524643, 0.023179303854703903, -0.06529317051172256, -0.007321689743548632, -0.0...
f668593c-e0d0-4cea-bd0c-ce11806af904
Tuples: sql DESC format(Values, $$((42, 'Hello, world!'))$$) response β”Œβ”€name─┬─type─────────────────────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ c1 β”‚ Tuple(Nullable(Int64), Nullable(String)) β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.035372886806726456, -0.004050811752676964, 0.018151411786675453, 0.010854140855371952, -0.044747885316610336, -0.04988297075033188, 0.06555473059415817, -0.09230732172727585, -0.0669645294547081, 0.002248130738735199, 0.06755753606557846, -0.0606241449713707, 0.03306649252772331, -0.0240...
bff5b1a4-e516-4892-b113-a5878ee0dd19
In CustomSeparated format ClickHouse first extracts all column values from the row according to specified delimiters and then tries to infer the data type for each value according to escaping rule. If setting input_format_custom_detect_header is enabled, ClickHouse will try to detect the header with column names (a...
{"source_file": "schema-inference.md"}
[ 0.044985201209783554, 0.011450941674411297, -0.02153731882572174, 0.011415784247219563, -0.036612559109926224, 0.0092249047011137, -0.007721624802798033, 0.00834309309720993, -0.07322932779788971, 0.04031945392489433, 0.008086863905191422, -0.12686336040496826, 0.03184332698583603, -0.0896...
e4dfce18-b39b-4dbc-a465-73ed7a1199dd
Then we can make the next queries: ```sql SET format_template_rows_between_delimiter = ' \n', format_template_row = 'row_format', format_template_resultset = 'resultset_format' DESC format(Template, $$ 42.42 'Some string 1' [1, null, 2] \N 'Some string 3' [1, 2, null] $$) response β”Œβ”€name────...
{"source_file": "schema-inference.md"}
[ -0.0035136998631060123, 0.030487295240163803, -0.03138761967420578, 0.03692304342985153, -0.06360810995101929, 0.03303692862391472, 0.060551099479198456, 0.020358765497803688, -0.09079501032829285, 0.020752236247062683, -0.057424504309892654, -0.047372061759233475, 0.005217520520091057, -0...
b1e84aa0-34ad-4581-8d42-6a688f7b568e
Example sql DESC format(TSV, 'Hello, World! 42 [1, 2, 3]') settings column_names_for_schema_inference = 'str,int,arr' response β”Œβ”€name─┬─type───────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ str β”‚ Nullable(String) β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.007752254605293274, -0.04619131237268448, -0.040480952709913254, 0.006197284907102585, -0.033657148480415344, -0.0077093071304261684, -0.057898979634046555, 0.007396360859274864, -0.08446797728538513, 0.025954410433769226, 0.0020860948134213686, -0.04629091918468475, -0.04539932683110237, ...
10ee600f-259f-4e37-b39c-ed729981511d
Default: 3. Examples sql SET schema_inference_make_columns_nullable = 1; DESC format(JSONEachRow, $$ {"id" : 1, "age" : 25, "name" : "Josh", "status" : null, "hobbies" : ["football", "cooking"]} {"id" : 2, "age" : 19, "name" : "Alan", "status" : "...
{"source_file": "schema-inference.md"}
[ 0.015576496720314026, 0.01848425343632698, -0.05073520913720131, 0.01632176712155342, -0.04074619710445404, 0.0227696280926466, -0.029427804052829742, 0.0007439368055202067, -0.04763834550976753, 0.040925413370132446, 0.07032201439142227, -0.057052746415138245, -0.005173363257199526, -0.00...
4381bfee-4881-4e22-b4ef-ce27b13ad35b
```response β”Œβ”€name────┬─type──────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ id β”‚ Int64 β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ age β”‚ Int64 β”‚ β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.010957150720059872, 0.00836003478616476, -0.040569230914115906, 0.004624322522431612, -0.01962481066584587, 0.003428541822358966, 0.05289926007390022, -0.0163153987377882, -0.07524555176496506, -0.002555695828050375, 0.01643524132668972, -0.062294699251651764, 0.01798143796622753, 0.0383...
2e477de4-353f-452c-a9b9-2d43b14f216e
sql DESC format(JSONEachRow, $$ {"number" : 1} {"number" : 2.2} $$) response β”Œβ”€name───┬─type──────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ number β”‚ Nullable(Float64) β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.035057295113801956, -0.024649757891893387, 0.0035586131270974874, 0.044642433524131775, -0.009733851999044418, 0.015869509428739548, -0.04461901634931564, 0.024581875652074814, 0.011142945848405361, -0.01980479434132576, -0.023474417626857758, -0.077608123421669, -0.04520729184150696, 0....
1b5a1161-5cb6-4783-a3d2-bb3c94df0df4
response β”Œβ”€name───────┬─type─────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ datetime β”‚ Nullable(String) β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ datetime64 β”‚ Nullable(String) β”‚ β”‚ β”‚ β”‚...
{"source_file": "schema-inference.md"}
[ 0.018572917208075523, -0.04711604863405228, -0.022240620106458664, 0.039748016744852066, -0.017864637076854706, 0.04148603603243828, -0.07928445190191269, -0.0006118397577665746, -0.0241362527012825, -0.01546176616102457, -0.027803491801023483, -0.07566852122545242, -0.059771906584501266, ...
26c5ea32-f956-4675-86ac-1c9d7a102b1d
response β”Œβ”€name─┬─type───────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ date β”‚ Nullable(Date) β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ └──────┴────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴─────...
{"source_file": "schema-inference.md"}
[ -0.07392056286334991, 0.00312394043430686, -0.11503008008003235, 0.06137213855981827, -0.035323504358530045, -0.036233898252248764, -0.045249179005622864, -0.010822895914316177, -0.024174828082323074, 0.049919579178094864, 0.05893545597791672, -0.1261749565601349, 0.03368687257170677, 0.04...
9ff834cc-d57a-4114-bc38-e0edd1430ab8
Example sql DESC format(TSVWithNamesAndTypes, $$num str arr UInt8 String Array(UInt8) 42 Hello, World! [1,2,3] $$) response β”Œβ”€name─┬─type─────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ num β”‚ UInt8 β”‚ β”‚ β”‚ ...
{"source_file": "schema-inference.md"}
[ 0.009006238542497158, -0.045570455491542816, -0.07196010649204254, 0.019452383741736412, -0.04096086695790291, -0.03773561120033264, -0.0012109893141314387, -0.023676680400967598, -0.019318880513310432, 0.02911500819027424, -0.002856177743524313, -0.046198952943086624, -0.008749516680836678,...
941b94d6-9941-498e-86e5-b20d78e79d96
Avro {#avro} In Avro format ClickHouse reads its schema from the data and converts it to ClickHouse schema using the following type matches: | Avro data type | ClickHouse data type | |------------------------------------|-----------------...
{"source_file": "schema-inference.md"}
[ 0.030538471415638924, -0.034652676433324814, -0.11278066039085388, 0.018335511907935143, -0.030934179201722145, -0.00007114066829672083, 0.022056875750422478, -0.011963669210672379, -0.08167099207639694, 0.050765231251716614, 0.020577700808644295, -0.10824310034513474, -0.016168951988220215,...
c12bc71f-b73b-4ed8-99cb-369620bc5e2f
Other Parquet types are not supported. Arrow {#arrow} In Arrow format ClickHouse reads its schema from the data and converts it to ClickHouse schema using the following type matches: | Arrow data type | ClickHouse data type | |---------------------------------|--...
{"source_file": "schema-inference.md"}
[ -0.00472963647916913, -0.05756299942731857, -0.03143252059817314, -0.06417271494865417, -0.09090043604373932, -0.029177231714129448, 0.004583689849823713, -0.003614871995523572, -0.04856764152646065, -0.006631724536418915, 0.051863688975572586, -0.09503941982984543, -0.027137471362948418, ...
143e54bb-a595-4197-ba8d-1081f99375e9
Formats with external schema {#formats-with-external-schema} Such formats require a schema describing the data in a separate file in a specific schema language. To automatically infer a schema from files in such formats, ClickHouse reads external schema from a separate file and transforms it to a ClickHouse table sch...
{"source_file": "schema-inference.md"}
[ 0.011034054681658745, -0.047897446900606155, -0.06920402497053146, -0.03622639551758766, 0.009383639320731163, -0.036705099046230316, -0.011937040835618973, 0.03715359792113304, -0.04396801069378853, 0.04309680312871933, 0.020489465445280075, -0.1594153791666031, -0.018651209771633148, 0.0...
e6264ba1-20f4-4980-9570-74476af2d165
MsgPack {#msgpack} In MsgPack format there is no delimiter between rows, to use schema inference for this format you should specify the number of columns in the table using the setting input_format_msgpack_number_of_columns . ClickHouse uses the following type matches: | MessagePack data type ( INSERT ) ...
{"source_file": "schema-inference.md"}
[ 0.059107474982738495, -0.016587046906352043, -0.05038159713149071, -0.014406198635697365, -0.05368240550160408, -0.06408978253602982, -0.011537458747625351, -0.0025017838925123215, -0.08349043130874634, 0.042001236230134964, 0.07017676532268524, -0.08738776296377182, 0.08304163813591003, -...
cfee60ab-7629-45c0-8652-a166fe1dd389
| BSON Type | ClickHouse type | |-----------------------------------------------------------------------------------------------...
{"source_file": "schema-inference.md"}
[ 0.0010622387053444982, -0.022973982617259026, -0.10073035955429077, -0.005298927426338196, -0.060799870640039444, -0.03557923063635826, 0.06204167380928993, -0.010578184388577938, -0.07829971611499786, -0.07760269939899445, 0.04050199314951897, -0.03638915345072746, 0.010636803694069386, -...
404f98f1-af5b-4af9-ad5c-ec249d08f99a
Example sql DESC format(LineAsString, 'Hello\nworld!') response β”Œβ”€name─┬─type───┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐ β”‚ line β”‚ String β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ └──────┴────────┴──────────────┴────────────────────...
{"source_file": "schema-inference.md"}
[ 0.004855332430452108, -0.053821176290512085, -0.041650332510471344, 0.0388047881424427, -0.09118922799825668, -0.0802517756819725, 0.009959609247744083, -0.003833898575976491, -0.0584062784910202, 0.0011171820806339383, 0.020230570808053017, -0.03161323070526123, -0.014706986024975777, 0.0...
42cd9092-fc28-4cb2-ba0f-8a47472aacf0
Let's try to use schema inference on these 3 files: sql :) DESCRIBE file('data{1,2,3}.jsonl') SETTINGS schema_inference_mode='default' Result: response β”Œβ”€name───┬─type─────────────┐ β”‚ field1 β”‚ Nullable(Int64) β”‚ β”‚ field2 β”‚ Nullable(String) β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ As we can see, we don't have field3 fro...
{"source_file": "schema-inference.md"}
[ -0.07978841662406921, -0.03872978687286377, -0.017852723598480225, 0.02011430636048317, 0.027571042999625206, -0.0481700599193573, -0.028895627707242966, -0.006399270612746477, -0.032695453613996506, -0.0004419046454131603, 0.06022854894399643, -0.01848711259663105, 0.004371292889118195, 0...
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description: 'Native clients and interfaces for ClickHouse' keywords: ['clients', 'interfaces', 'CLI', 'SQL console', 'drivers'] slug: /interfaces/natives-clients-and-interfaces title: 'Native Clients and Interfaces' doc_type: 'landing-page' Native clients & interfaces ClickHouse provides a number of different na...
{"source_file": "native-clients-interfaces-index.md"}
[ 0.020451294258236885, -0.09207846224308014, -0.050258226692676544, -0.06055751070380211, -0.05138043686747551, -0.030983220785856247, 0.025139261037111282, -0.01209076028317213, -0.0769757479429245, -0.03157997503876686, -0.000874003570061177, -0.007561327423900366, 0.027340685948729515, 0...
06daa6e2-7980-4297-a85a-1d9bd9bb5475
slug: /managing-data/deleting-data/overview title: 'Deleting Data' description: 'How to delete data in ClickHouse Table Of Contents' keywords: ['delete', 'truncate', 'drop', 'lightweight delete'] doc_type: 'guide' In this section of the documentation, we will explore how to delete data in ClickHouse. | Page ...
{"source_file": "index.md"}
[ 0.01873289979994297, 0.034092653542757034, 0.027982203289866447, 0.06384643167257309, 0.045274797827005386, -0.05440644547343254, 0.019625376909971237, -0.09481807053089142, 0.027735810726881027, 0.0487484484910965, 0.0363340824842453, 0.0163352619856596, 0.07067718356847763, -0.0569162741...
a047d49f-8f1d-40cf-b76e-a0e9c215639c
slug: /updating-data title: 'Updating Data' description: 'Updating Data Table Of Contents' keywords: ['update', 'updating data'] doc_type: 'landing-page' In this section of the documentation, you will learn how you can update your data. | Page | Description ...
{"source_file": "index.md"}
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slug: /parts title: 'Table parts' description: 'What are data parts in ClickHouse' keywords: ['part'] doc_type: 'reference' import merges from '@site/static/images/managing-data/core-concepts/merges.png'; import part from '@site/static/images/managing-data/core-concepts/part.png'; import Image from '@theme/IdealIma...
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To minimize the number of initial ^^parts^^ and the overhead of merges, database clients are encouraged to either insert tuples in bulk, e.g. 20,000 rows at once, or to use the asynchronous insert mode , in which ClickHouse buffers rows from multiple incoming INSERTs into the same table and creates a new part only a...
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slug: /managing-data/core-concepts title: 'Core Concepts' description: 'Learn Core Concepts of how ClickHouse works' keywords: ['concepts', 'part', 'partition', 'primary index'] doc_type: 'guide' In this section of the documentation, you will learn some of the core concepts of how ClickHouse works. | Page ...
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slug: /architecture/replication sidebar_label: 'Replication' sidebar_position: 10 title: 'Replicating data' description: 'Page describing an example architecture with five servers configured. Two are used to host copies of the data and the rest are used to coordinate the replication of data' doc_type: 'guide' keywords:...
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Run the following commands to set up the directory structure for this example: ```bash mkdir cluster_1S_2R cd cluster_1S_2R Create clickhouse-keeper directories for i in {01..03}; do mkdir -p fs/volumes/clickhouse-keeper-${i}/etc/clickhouse-keeper done Create clickhouse-server directories for i in {01..02};...
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Create the following sub-directories and files: bash for i in {01..02}; do mkdir -p fs/volumes/clickhouse-${i}/etc/clickhouse-server/config.d mkdir -p fs/volumes/clickhouse-${i}/etc/clickhouse-server/users.d touch fs/volumes/clickhouse-${i}/etc/clickhouse-server/config.d/config.xml touch fs/volumes/clickhouse...
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| Directory | File | |-----------------------------------------------------------|-----------------...
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Set up of ClickHouse Keeper is explained in the next step of the tutorial. xml <zookeeper> <node> <host>clickhouse-keeper-01</host> <port>9181</port> </node> <node> <host>clickhouse-keeper-02</host> <port>9181</port> </node> <node> <host>clickhouse-keeper-03...
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Configure ClickHouse Keeper {#configure-clickhouse-keeper-nodes} Keeper setup {#configuration-explanation} | Directory | File ...
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With this, you have successfully set up a ClickHouse cluster with a single shard and two replicas. In the next step, you will create a table in the cluster. Create a database {#creating-a-database} Now that you have verified the cluster is correctly setup and is running, you will be recreating the same table as the...
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