diff --git a/docusaurus.config.en.js b/docusaurus.config.en.js index b38655e0e33..4f8e2cdf341 100644 --- a/docusaurus.config.en.js +++ b/docusaurus.config.en.js @@ -371,6 +371,10 @@ const config = { blogSidebarLink: "/docs/knowledgebase", galaxyApiEndpoint: process.env.NEXT_PUBLIC_GALAXY_API_ENDPOINT || "http://localhost:3000", + strapiUrl: + process.env.CLIENT_STRAPI_URL || "https://staging-cms.clickhouse.com", + strapiToken: + process.env.CLIENT_STRAPI_TOKEN || "", }, }; diff --git a/docusaurus.config.jp.js b/docusaurus.config.jp.js index a77b59f9ce0..1aace9e06d8 100644 --- a/docusaurus.config.jp.js +++ b/docusaurus.config.jp.js @@ -298,6 +298,10 @@ const config = { blogSidebarLink: "/docs/knowledgebase", // Used for KB article page galaxyApiEndpoint: process.env.NEXT_PUBLIC_GALAXY_API_ENDPOINT || "http://localhost:3000", + strapiUrl: + process.env.CLIENT_STRAPI_URL || "https://staging-cms.clickhouse.com", + strapiToken: + process.env.CLIENT_STRAPI_TOKEN || "", }, }; diff --git a/docusaurus.config.ko.js b/docusaurus.config.ko.js index 41aed745827..7aaa5f2b466 100644 --- a/docusaurus.config.ko.js +++ b/docusaurus.config.ko.js @@ -298,6 +298,10 @@ const config = { blogSidebarLink: "/docs/knowledgebase", // Used for KB article page galaxyApiEndpoint: process.env.NEXT_PUBLIC_GALAXY_API_ENDPOINT || "http://localhost:3000", + strapiUrl: + process.env.CLIENT_STRAPI_URL || "https://staging-cms.clickhouse.com", + strapiToken: + process.env.CLIENT_STRAPI_TOKEN || "", secondaryNavItems: [ { type: "dropdown", diff --git a/docusaurus.config.ru.js b/docusaurus.config.ru.js index 28503459ea0..5bc5e0acad3 100644 --- a/docusaurus.config.ru.js +++ b/docusaurus.config.ru.js @@ -310,6 +310,10 @@ const config = { blogSidebarLink: "/docs/knowledgebase", // Used for KB article page galaxyApiEndpoint: process.env.NEXT_PUBLIC_GALAXY_API_ENDPOINT || "http://localhost:3000", + strapiUrl: + process.env.CLIENT_STRAPI_URL || "https://staging-cms.clickhouse.com", + strapiToken: + process.env.CLIENT_STRAPI_TOKEN || "", }, }; diff --git a/docusaurus.config.zh.js b/docusaurus.config.zh.js index 0e7a01688c3..7710abbddbb 100644 --- a/docusaurus.config.zh.js +++ b/docusaurus.config.zh.js @@ -298,6 +298,10 @@ const config = { blogSidebarLink: "/docs/knowledgebase", // Used for KB article page galaxyApiEndpoint: process.env.NEXT_PUBLIC_GALAXY_API_ENDPOINT || "http://localhost:3000", + strapiUrl: + process.env.CLIENT_STRAPI_URL || "https://staging-cms.clickhouse.com", + strapiToken: + process.env.CLIENT_STRAPI_TOKEN || "", secondaryNavItems: [ { type: "dropdown", diff --git a/package.json b/package.json index d4e05a60e6d..fe3719eb432 100644 --- a/package.json +++ b/package.json @@ -49,6 +49,7 @@ "@monaco-editor/react": "^4.7.0", "@radix-ui/react-navigation-menu": "^1.2.13", "@redocly/cli": "^1.34.0", + "@strapi/client": "^1.6.1", "axios": "^1.13.5", "clsx": "^2.1.0", "docusaurus-plugin-sass": "^0.2.6", diff --git a/src/components/IntegrationGrid/IntegrationGrid.tsx b/src/components/IntegrationGrid/IntegrationGrid.tsx index f191198ca0a..515f307efaf 100644 --- a/src/components/IntegrationGrid/IntegrationGrid.tsx +++ b/src/components/IntegrationGrid/IntegrationGrid.tsx @@ -4,32 +4,27 @@ import useBaseUrl from "@docusaurus/useBaseUrl"; import { useColorMode } from "@docusaurus/theme-common"; import CUICard from "@site/src/components/CUICard"; import styles from "./styles.module.scss"; +import { useStrapiClient } from "@site/src/lib/useStrapiClient"; type CMSIntegrationData = { id: number; - attributes: { - name: string; - slug: string; - category: string; - supportLevel: string; - docsLink?: string; - logo?: { - data?: { - attributes: { - url: string; - }; - }; - }; - logo_dark?: { - data?: { - attributes: { - url: string; - }; - }; - }; + documentId: string; + name: string; + slug: string; + category: string; + supportLevel: string; + docsLink?: string; + logo?: { + id: number; + url: string; + }; + logo_dark?: { + id: number; + url: string; }; }; + type IntegrationData = { slug: string; docsLink?: string; @@ -157,134 +152,97 @@ function IntegrationCards({ ); } -// Helper function to transform CMS data to the expected format -function transformCMSData(cmsData: CMSIntegrationData[]): IntegrationData[] { - // Mapping from CMS category to display-friendly integration type - const categoryMapping: { [key: string]: string } = { - AI_ML: "AI/ML", - CLICKPIPES: "ClickPipes", - DATA_INGESTION: "Data ingestion", - DATA_INTEGRATION: "Data integration", - DATA_MANAGEMENT: "Data management", - DATA_VISUALIZATION: "Data visualization", - LANGUAGE_CLIENT: "Language client", - SECURITY_GOVERNANCE: "Security governance", - SQL_CLIENT: "SQL client", - }; +const categoryMapping: { [key: string]: string } = { + AI_ML: "AI/ML", + CLICKPIPES: "ClickPipes", + DATA_INGESTION: "Data ingestion", + DATA_INTEGRATION: "Data integration", + DATA_MANAGEMENT: "Data management", + DATA_VISUALIZATION: "Data visualization", + LANGUAGE_CLIENT: "Language client", + SECURITY_GOVERNANCE: "Security governance", + SQL_CLIENT: "SQL client", +}; +function transformCMSData(cmsData: CMSIntegrationData[], baseUrl: string): IntegrationData[] { return cmsData.map((item) => { - // Map category to integration_type array - const integrationTypes = item.attributes.category - ? [categoryMapping[item.attributes.category] || item.attributes.category] + const integrationTypes = item.category + ? [categoryMapping[item.category] || item.category] : []; - - // Map supportLevel to integration_tier - const integrationTier = item.attributes.supportLevel?.toLowerCase() || ""; + const integrationTier = item.supportLevel?.toLowerCase() || ""; return { - slug: item.attributes.slug.startsWith("/") - ? item.attributes.slug - : `/${item.attributes.slug}`, - docsLink: item.attributes.docsLink, - integration_logo: item.attributes.logo?.data?.attributes.url - ? `https://cms.clickhouse-dev.com:1337${item.attributes.logo.data.attributes.url}` + slug: item.slug.startsWith("/") ? item.slug : `/${item.slug}`, + docsLink: item.docsLink, + integration_logo: item.logo?.url + ? `${baseUrl}${item.logo.url}` : "", - integration_logo_dark: item.attributes.logo_dark?.data?.attributes.url - ? `https://cms.clickhouse-dev.com:1337${item.attributes.logo_dark.data.attributes.url}` + integration_logo_dark: item.logo_dark?.url + ? `${baseUrl}${item.logo_dark.url}` : undefined, integration_type: integrationTypes, - integration_title: item.attributes.name, + integration_title: item.name, integration_tier: integrationTier, }; }); } -// Configuration: Set to true to fetch from CMS, false to use only static fallback -const USE_CMS_ENDPOINT = false; -// Custom hook for fetching CMS data +const USE_CMS_ENDPOINT = true; + function useCMSIntegrations() { const [integrations, setIntegrations] = useState([]); const [loading, setLoading] = useState(true); const [error, setError] = useState(null); const fallbackPath = useBaseUrl("/integrations-fallback.json"); + const { client: strapiClient, strapiBaseUrl } = useStrapiClient(); useEffect(() => { const fetchIntegrations = async () => { - // Step 1: Load fallback data first for immediate display try { const fallbackResponse = await fetch(fallbackPath, { - cache: "no-cache", // Always revalidate with server to get fresh data + cache: "no-cache", }); if (fallbackResponse.ok) { const fallbackData = await fallbackResponse.json(); - const transformedData = transformCMSData(fallbackData.data || []); + const transformedData = transformCMSData(fallbackData.data || [], strapiBaseUrl); setIntegrations(transformedData); setError(null); - setLoading(false); // Show content immediately with fallback data - console.log("Loaded fallback integrations data"); + setLoading(false); } else { - console.warn("Fallback file not available, will try CMS only"); + console.warn("Fallback file not available"); } } catch (fallbackErr) { console.error( "Failed to load fallback integrations data:", fallbackErr, ); - // Continue to try CMS even if fallback fails } - // Step 2: Try to fetch fresh data from CMS with timeout (if enabled) if (USE_CMS_ENDPOINT) { try { - const controller = new AbortController(); - const timeoutId = setTimeout(() => { - controller.abort(); - console.log("CMS request timed out after 8 seconds"); - }, 8000); // 8 second timeout - - const response = await fetch( - "https://cms.clickhouse-dev.com:1337/api/integrations?populate[]=logo&populate[]=logo_dark", - { - signal: controller.signal, - // Add headers to help with CORS and caching - headers: { - Accept: "application/json", - "Strapi-Response-Format": "v4", - }, + const { data } = await strapiClient.collection("integrations").find({ + fields: ["name", "slug", "category", "supportLevel", "docsLink"], + populate: { + logo: { fields: ["url"] }, + logo_dark: { fields: ["url"] }, }, - ); + pagination: { pageSize: 500 }, + }); - clearTimeout(timeoutId); - - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } + const transformedData = transformCMSData(data as unknown as CMSIntegrationData[], strapiBaseUrl); - const data = await response.json(); - const transformedData = transformCMSData(data.data || []); - - // Update with fresh CMS data setIntegrations(transformedData); setError(null); - console.log("Successfully updated with fresh CMS data"); } catch (cmsErr) { - // CMS fetch failed, but that's okay - we already have fallback data if (cmsErr instanceof Error) { - if (cmsErr.name === "AbortError") { - console.log( - "CMS request was aborted due to timeout, using fallback data", - ); - } else { - console.error( - "Error loading integrations from CMS:", - cmsErr.message, - ); - } + console.error( + "Error loading integrations from CMS:", + cmsErr.message, + ); } - // Only set error if we don't have any integrations data at all if (integrations.length === 0) { setError( "Unable to load integrations. Please try refreshing the page.", diff --git a/src/lib/useStrapiClient.ts b/src/lib/useStrapiClient.ts new file mode 100644 index 00000000000..248b7b52bae --- /dev/null +++ b/src/lib/useStrapiClient.ts @@ -0,0 +1,17 @@ +import { strapi } from '@strapi/client'; +import { useMemo } from 'react'; +import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; + +const STRAPI_DEFAULT_URL = 'https://staging-cms.clickhouse.com'; + +export function useStrapiClient(): { client: ReturnType; baseUrl: string } { + const { siteConfig } = useDocusaurusContext(); + const baseUrl = (siteConfig.customFields?.strapiUrl as string) || STRAPI_DEFAULT_URL; + const auth = (siteConfig.customFields?.strapiToken as string) || undefined; + const client = useMemo(() => strapi({ + baseURL: `${baseUrl}/api`, + ...(auth && { auth }), + }), [baseUrl, auth]); + + return { client, strapiBaseUrl: baseUrl }; +} diff --git a/src/theme/DocItem/TOC/Desktop/index.js b/src/theme/DocItem/TOC/Desktop/index.js index 6bf7b352193..60b95d45df0 100644 --- a/src/theme/DocItem/TOC/Desktop/index.js +++ b/src/theme/DocItem/TOC/Desktop/index.js @@ -7,78 +7,47 @@ import IconClose from '@theme/Icon/Close'; import styles from './styles.module.scss' import Feedback from '../../../../components/Feedback'; import { galaxyOnClick } from '@site/src/lib/galaxy/galaxy'; +import { useStrapiClient } from '@site/src/lib/useStrapiClient'; -const AD_DATA_ENDPOINT = 'https://cms.clickhouse-dev.com:1337/api/docs-ad' +const AD_CLOSED_KEY = 'doc-cloud-card-banner'; + +// Module-level cache — survives client-side navigation, resets on hard reload +let cachedAd = null; export default function DocItemTOCDesktop() { const { toc, frontMatter } = useDoc(); - const [isClosed, setClosed] = useState(() => { - if (typeof window !== 'undefined') { - return window.localStorage.getItem('doc-cloud-card-banner') === 'closed'; - } - return false; - }); - const [title, setTitle] = useState(null); - const [description, setDescription] = useState(null); - const [href, setHref] = useState(null); - const [label, setLabel] = useState(null); - const [tag, setTag] = useState(null); + const { client: strapiClient } = useStrapiClient(); + const [isClosed, setClosed] = useState( + () => globalThis.window?.localStorage.getItem(AD_CLOSED_KEY) === 'closed' + ); + const [ad, setAd] = useState(cachedAd); useEffect(() => { - if (typeof window !== 'undefined') { - const fetchAdData = async () => { - const cacheKey = 'doc-cloud-card-banner-attributes'; - let attributes = {}; - - // Get cached ad data from local storage - if (window.localStorage.getItem(cacheKey)) { - try { - attributes = JSON.parse(window.localStorage.getItem(cacheKey)); - } catch (e) { - console.log('Failed to parse cached ad attributes', e); - } - } + if (cachedAd) return; - // Fetch new ad data if not in session - if ( - !attributes - || !attributes.hasOwnProperty('title') - || !attributes.hasOwnProperty('description') - || !attributes.hasOwnProperty('href') - || !attributes.hasOwnProperty('label') - || !attributes.hasOwnProperty('tag') - ) { - try { - const response = await window.fetch(AD_DATA_ENDPOINT, { - headers: { - "Strapi-Response-Format": "v4", - }, - }); - const { data } = await response.json(); + let cancelled = false; - if (data && typeof data === 'object' && data.hasOwnProperty('attributes')) { - attributes = data.attributes; - window.localStorage.setItem(cacheKey, JSON.stringify(attributes)); - } - } catch (e) { - console.log('Failed to fetch ad content', e); - } + const fetchAd = async () => { + try { + const { data } = await strapiClient.single('docs-ad').find(); + if (!cancelled && data && typeof data === 'object') { + cachedAd = { + title: data.title, + description: data.description, + href: data.href, + label: data.label, + tag: data.tag, + }; + setAd(cachedAd); } - - return attributes; + } catch (e) { + console.log('Failed to fetch ad content', e); } + }; - // Set ad states - fetchAdData().then(attributes => { - setTitle(attributes?.title || null); - setDescription(attributes?.description || null); - setHref(attributes?.href || null); - setLabel(attributes?.label || null); - setTag(attributes?.tag || null) - }) - - } - }, [setTitle, setDescription, setHref, setLabel, setTag]); + fetchAd(); + return () => { cancelled = true; }; + }, [strapiClient]); return (
@@ -92,32 +61,30 @@ export default function DocItemTOCDesktop() { = 7 ? 'left' : 'bottom'} />
- { - !isClosed && title && description && href && label && ( -
-
-
{title}
- -
-

{description}

- { galaxyOnClick('docs.sidebarCloudAdvert.clickedThrough'); }} - > - {label} - + {!isClosed && ad && ( +
+
+
{ad.title}
+
- ) - } +

{ad.description}

+ { galaxyOnClick('docs.sidebarCloudAdvert.clickedThrough'); }} + > + {ad.label} + +
+ )}
); } diff --git a/static/integrations-fallback.json b/static/integrations-fallback.json index bc339689632..94d38ffd1b7 100644 --- a/static/integrations-fallback.json +++ b/static/integrations-fallback.json @@ -1 +1,3139 @@ -{"data":[{"id":1,"attributes":{"shortDescription":"The Java client is an async, lightweight, and low-overhead library for ClickHouse","name":"Java","category":"LANGUAGE_CLIENT","supportLevel":"CORE","summary":"\n#### Maven\n```xml\n \n com.clickhouse\n clickhouse-jdbc\n 0.3.2-patch11\n \n```\n#### Open a connection\n```java\n Properties prop = new Properties();\n prop.setProperty(\"username\", \"default\");\n prop.setProperty(\"password\", \"\");\n Connection conn = DriverManager.getConnection(\"jdbc:ch:https://:8443?insert_quorum=auto\", prop);\n```\n#### Create a table\n```java\n Statement stmt = conn.createStatement();\n stmt.execute(\"CREATE TABLE IF NOT EXISTS jdbc_test(idx Int8, str String) ENGINE = MergeTree ORDER BY idx\");\n```\n\n#### Write data to a table\n```java\n try (PreparedStatement ps = conn.prepareStatement(\n \"insert into jdbc_test select col1, col2 from input('col1 Int8, col2 String')\")) {\n for (int i = 0; i < 10; i++) {\n ps.setInt(1, i);\n ps.setString(2, \"test:\" + i); // col1\n // parameters will be write into buffered stream immediately in binary format\n ps.addBatch(); \n }\n // stream everything on-hand into ClickHouse\n ps.executeBatch(); \n }\n```\n\n#### Read data from a table\n```java\n ResultSet rs = stmt.executeQuery(\"select * from jdbc_test\");\n while (rs.next()) {\n System.out.println(String.format(\"idx: %s str: %s\", rs.getString(1), rs.getString(2)));\n }\n```","slug":"java","createdAt":"2022-07-25T08:41:37.287Z","updatedAt":"2024-07-08T11:18:23.876Z","publishedAt":"2022-07-25T08:57:50.529Z","docsLink":"https://github.com/clickhouse/clickhouse-jdbc#readme","about":"Java client and JDBC driver for ClickHouse. The Java client is an async, lightweight, and low-overhead library for ClickHouse; while JDBC driver is built on top of the Java client with more dependencies and extensions for JDBC-compliance","changelog":"","version":"0.3.2-patch11","license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse-jdbc","summaryv2":"\n#### Maven\n```xml\n \n com.clickhouse\n clickhouse-jdbc\n 0.3.2-patch11\n \n```\n#### Open a connection\n```java\n Properties prop = new Properties();\n prop.setProperty(\"username\", \"default\");\n prop.setProperty(\"password\", \"\");\n Connection conn = DriverManager.getConnection(\"jdbc:ch:https://:8443?insert_quorum=auto\", prop);\n```\n#### Create a table\n```java\n Statement stmt = conn.createStatement();\n stmt.execute(\"CREATE TABLE IF NOT EXISTS jdbc_test(idx Int8, str String) ENGINE = MergeTree ORDER BY idx\");\n```\n\n#### Write data to a table\n```java\n try (PreparedStatement ps = conn.prepareStatement(\n \"insert into jdbc_test select col1, col2 from input('col1 Int8, col2 String')\")) {\n for (int i = 0; i < 10; i++) {\n ps.setInt(1, i);\n ps.setString(2, \"test:\" + i); // col1\n // parameters will be write into buffered stream immediately in binary format\n ps.addBatch(); \n }\n // stream everything on-hand into ClickHouse\n ps.executeBatch(); \n }\n```\n\n#### Read data from a table\n```java\n ResultSet rs = stmt.executeQuery(\"select * from jdbc_test\");\n while (rs.next()) {\n System.out.println(String.format(\"idx: %s str: %s\", rs.getString(1), rs.getString(2)));\n }\n```","changelogv2":null,"aboutv2":"Java client and JDBC driver for ClickHouse. The Java client is an async, lightweight, and low-overhead library for ClickHouse; while JDBC driver is built on top of the Java client with more dependencies and extensions for JDBC-compliance","openInNewWindow":null,"logo":{"data":{"id":370,"attributes":{"name":"java_logo.svg","alternativeText":"java_logo.svg","caption":"java_logo.svg","width":64,"height":64,"formats":null,"hash":"java_logo_69178f7e29","ext":".svg","mime":"image/svg+xml","size":1.32,"url":"/uploads/java_logo_69178f7e29.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-22T10:33:50.559Z","updatedAt":"2022-07-22T10:33:50.559Z"}}},"logo_dark":{"data":{"id":370,"attributes":{"name":"java_logo.svg","alternativeText":"java_logo.svg","caption":"java_logo.svg","width":64,"height":64,"formats":null,"hash":"java_logo_69178f7e29","ext":".svg","mime":"image/svg+xml","size":1.32,"url":"/uploads/java_logo_69178f7e29.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-22T10:33:50.559Z","updatedAt":"2022-07-22T10:33:50.559Z"}}}}},{"id":2,"attributes":{"shortDescription":"The Go client uses the native interface for a performant, low-overhead means of connecting to ClickHouse","name":"Go","category":"LANGUAGE_CLIENT","supportLevel":"CORE","summary":"### Installation\n\n```bash\ngo get github.com/ClickHouse/clickhouse-go/v2\n```\n\n### Examples\n\n#### Client API\n##### Connect (native protocol)\n```go\nconn, err := clickhouse.Open(&clickhouse.Options{\n\tAddr: []string{\":9440\"},\n\tAuth: clickhouse.Auth{\n\t\tDatabase: \"default\",\n\t\tUsername: \"default\",\n\t\tPassword: \"\",\n\t},\n\tTLS: &tls.Config{},\n})\n```\n##### Connect (HTTP protocol)\n```go\nconn, err := clickhouse.Open(&clickhouse.Options{\n\tAddr: []string{\":8443\"},\n\tAuth: clickhouse.Auth{\n\t\tDatabase: \"default\",\n\t\tUsername: \"default\",\n\t\tPassword: \"\",\n\t},\n\tTLS: &tls.Config{},\n Protocol: clickhouse.HTTP,\n})\n```\n##### Create a table\n```go\nerr := conn.Exec(context.Background(), `\n CREATE TABLE IF NOT EXISTS my_table (Col1 UInt8, Col2 String)\n`)\nif err != nil {\n return err\n}\n```\n##### Batch insert\n```go\nbatch, err := conn.PrepareBatch(ctx, \"INSERT INTO my_table\")\nif err != nil {\n return err\n}\n\nif err := batch.Append(uint8(42), \"ClickHouse\")); err != nil {\n return err\n}\nif err := batch.Send(); err != nil {\n return err\n}\n```\n##### Querying rows\n```go\nrows, err := conn.Query(ctx, \"SELECT Col1, Col2 FROM my_table\")\nif err != nil {\n return err\n}\nfor rows.Next() {\n var (\n col1 uint8\n col2 string\n )\n if err := rows.Scan(&col1, &col2); err != nil {\n return err\n }\n fmt.Printf(\"row: col1=%d, col2=%s\\n\", col1, col2)\n}\nrows.Close()\nreturn rows.Err()\n```\n\n#### `database/sql` driver\n##### Connect (native protocol)\n```go\nconn, err := sql.Open(\"clickhouse\", \"clickhouse://:9440?username=default&password=/\")\n```\n##### Connect (HTTP protocol)\n```go\nconn, err := sql.Open(\"clickhouse\", \"http://:8443?username=default&password=/\")\n```\n##### Create a table\n```go\nerr := conn.ExecContext(context.Background(), `\n CREATE TABLE IF NOT EXISTS my_table (Col1 UInt8, Col2 String)\n`)\nif err != nil {\n return err\n}\n```\n##### Batch insert\n```go\nstmt, err := conn.PrepareContext(ctx, \"INSERT INTO my_table\")\nif err != nil {\n return err\n}\n\nif err := stmt.Exec(uint8(42), \"ClickHouse\")); err != nil {\n return err\n}\nif err := stmt.Commit(); err != nil {\n return err\n}\n```\n##### Querying rows\n```go\nrows, err := conn.QueryContext(ctx, \"SELECT Col1, Col2 FROM my_table\")\nif err != nil {\n return err\n}\nfor rows.Next() {\n var (\n col1 uint8\n col2 string\n )\n if err := rows.Scan(&col1, &col2); err != nil {\n return err\n }\n fmt.Printf(\"row: col1=%d, col2=%s\\n\", col1, col2)\n}\nrows.Close()\nreturn rows.Err()\n```","slug":"go","createdAt":"2022-07-25T09:17:23.498Z","updatedAt":"2025-10-18T09:51:39.436Z","publishedAt":"2022-07-25T09:17:25.433Z","docsLink":"https://clickhouse.com/docs/integrations/go","about":"_Golang_ client for ClickHouse that allows users to connect to ClickHouse using either the _Go_ standard database/sql interface or an optimized native interface. \n\nThe clients uses the Native format, for optimal performance, over either the Native protocol or HTTP interface. The latter allows the best combination of performance and widespread compatibility with proxies, load balancers, and VPNs. Support for all ClickHouse types and convenience methods for inserting structs are supported.","changelog":"
\n## 2.4.3, 2022-11-30\n### Bug Fixes\n* Fix in batch concurrency - batch could panic if used in separate go routines. .
\nThe issue was originally detected due to the use of a batch in a go routine and Abort being called after the connection was released on the batch. This would invalidate the connection which had been subsequently reassigned.
\nThis issue could occur as soon as the conn is released (this can happen in a number of places e.g. after Send or an Append error), and it potentially returns to the pool for use in another go routine. Subsequent releases could then occur e.g., the user calls Abort mainly but also Send would do it. The result is the connection being closed in the release function while another batch or query potentially used it.
\nThis release includes a guard to prevent release from being called more than once on a batch. It assumes that batches are not thread-safe - they aren't (only connections are).\n
\n\n
\n## 2.4.2, 2022-11-24\n### Bug Fixes\n- Don't panic on `Send()` on batch after invalid `Append`. [#830](https://github.com/ClickHouse/clickhouse-go/pull/830)\n- Fix JSON issue with `nil` if column order is inconsisent. [#824](https://github.com/ClickHouse/clickhouse-go/pull/824)\n
\n\n
\n## 2.4.1, 2022-11-23\n### Bug Fixes\n- Patch release to fix \"Regression - escape character was not considered when comparing column names\". [#828](https://github.com/ClickHouse/clickhouse-go/issues/828)\n
\n\n
\n## 2.4.0, 2022-11-22\n### New Features\n- Support for Nullables in Tuples. [#821](https://github.com/ClickHouse/clickhouse-go/pull/821) [#817](https://github.com/ClickHouse/clickhouse-go/pull/817)\n- Use headers for auth and not url if SSL. [#811](https://github.com/ClickHouse/clickhouse-go/pull/811)\n- Support additional headers. [#811](https://github.com/ClickHouse/clickhouse-go/pull/811)\n- Support int64 for DateTime. [#807](https://github.com/ClickHouse/clickhouse-go/pull/807)\n- Support inserting Enums as int8/int16/int. [#802](https://github.com/ClickHouse/clickhouse-go/pull/802)\n- Print error if unsupported server. [#792](https://github.com/ClickHouse/clickhouse-go/pull/792)\n- Allow block buffer size to tuned for performance - see `BlockBufferSize`. [#776](https://github.com/ClickHouse/clickhouse-go/pull/776)\n- Support custom datetime in Scan. [#767](https://github.com/ClickHouse/clickhouse-go/pull/767)\n- Support insertion of an orderedmap. [#763](https://github.com/ClickHouse/clickhouse-go/pull/763)\n\n### Bug Fixes\n- Decompress errors over HTTP. [#792](https://github.com/ClickHouse/clickhouse-go/pull/792)\n- Use `timezone` vs `timeZone` so we work on older versions. [#781](https://github.com/ClickHouse/clickhouse-go/pull/781)\n- Ensure only columns specified in INSERT are required in batch. [#790](https://github.com/ClickHouse/clickhouse-go/pull/790)\n- Respect order of columns in insert for batch. [#790](https://github.com/ClickHouse/clickhouse-go/pull/790)\n- Handle double pointers for Nullable columns when batch inserting. [#774](https://github.com/ClickHouse/clickhouse-go/pull/774)\n- Use nil for `LowCardinality(Nullable(X))`. [#768](https://github.com/ClickHouse/clickhouse-go/pull/768)\n\n### Breaking Changes\n- Align timezone handling with spec. [#776](https://github.com/ClickHouse/clickhouse-go/pull/766), specifically:\n - If parsing strings for datetime, datetime64 or dates we assume the locale is Local (i.e. the client) if not specified in the string.\n - The server (or column tz) is used for datetime and datetime64 rendering. For date/date32, these have no tz info in the server. For now, they will be rendered as UTC - consistent with the clickhouse-client\n - Addresses bind when no location is set\n
","version":"2.4.3","license":"apache2","readiness":null,"website":null,"summaryv2":"### Installation\n\n```bash\ngo get github.com/ClickHouse/clickhouse-go/v2\n```\n\n### Examples\n\n#### Client API\n##### Connect (native protocol)\n```go\nconn, err := clickhouse.Open(&clickhouse.Options{\n\tAddr: []string{\":9440\"},\n\tAuth: clickhouse.Auth{\n\t\tDatabase: \"default\",\n\t\tUsername: \"default\",\n\t\tPassword: \"\",\n\t},\n\tTLS: &tls.Config{},\n})\n```\n##### Connect (HTTP protocol)\n```go\nconn, err := clickhouse.Open(&clickhouse.Options{\n\tAddr: []string{\":8443\"},\n\tAuth: clickhouse.Auth{\n\t\tDatabase: \"default\",\n\t\tUsername: \"default\",\n\t\tPassword: \"\",\n\t},\n\tTLS: &tls.Config{},\n Protocol: clickhouse.HTTP,\n})\n```\n##### Create a table\n```go\nerr := conn.Exec(context.Background(), `\n CREATE TABLE IF NOT EXISTS my_table (Col1 UInt8, Col2 String)\n`)\nif err != nil {\n return err\n}\n```\n##### Batch insert\n```go\nbatch, err := conn.PrepareBatch(ctx, \"INSERT INTO my_table\")\nif err != nil {\n return err\n}\n\nif err := batch.Append(uint8(42), \"ClickHouse\")); err != nil {\n return err\n}\nif err := batch.Send(); err != nil {\n return err\n}\n```\n##### Querying rows\n```go\nrows, err := conn.Query(ctx, \"SELECT Col1, Col2 FROM my_table\")\nif err != nil {\n return err\n}\nfor rows.Next() {\n var (\n col1 uint8\n col2 string\n )\n if err := rows.Scan(&col1, &col2); err != nil {\n return err\n }\n fmt.Printf(\"row: col1=%d, col2=%s\\n\", col1, col2)\n}\nrows.Close()\nreturn rows.Err()\n```\n\n#### `database/sql` driver\n##### Connect (native protocol)\n```go\nconn, err := sql.Open(\"clickhouse\", \"clickhouse://:9440?username=default&password=/\")\n```\n##### Connect (HTTP protocol)\n```go\nconn, err := sql.Open(\"clickhouse\", \"http://:8443?username=default&password=/\")\n```\n##### Create a table\n```go\nerr := conn.ExecContext(context.Background(), `\n CREATE TABLE IF NOT EXISTS my_table (Col1 UInt8, Col2 String)\n`)\nif err != nil {\n return err\n}\n```\n##### Batch insert\n```go\nstmt, err := conn.PrepareContext(ctx, \"INSERT INTO my_table\")\nif err != nil {\n return err\n}\n\nif err := stmt.Exec(uint8(42), \"ClickHouse\")); err != nil {\n return err\n}\nif err := stmt.Commit(); err != nil {\n return err\n}\n```\n##### Querying rows\n```go\nrows, err := conn.QueryContext(ctx, \"SELECT Col1, Col2 FROM my_table\")\nif err != nil {\n return err\n}\nfor rows.Next() {\n var (\n col1 uint8\n col2 string\n )\n if err := rows.Scan(&col1, &col2); err != nil {\n return err\n }\n fmt.Printf(\"row: col1=%d, col2=%s\\n\", col1, col2)\n}\nrows.Close()\nreturn rows.Err()\n```","changelogv2":"\n\n\n### Bug Fixes\n* Fix in batch concurrency - batch could panic if used in separate go routines. .
\nThe issue was originally detected due to the use of a batch in a go routine and Abort being called after the connection was released on the batch. This would invalidate the connection which had been subsequently reassigned.
\nThis issue could occur as soon as the conn is released (this can happen in a number of places e.g. after Send or an Append error), and it potentially returns to the pool for use in another go routine. Subsequent releases could then occur e.g., the user calls Abort mainly but also Send would do it. The result is the connection being closed in the release function while another batch or query potentially used it.
\nThis release includes a guard to prevent release from being called more than once on a batch. It assumes that batches are not thread-safe - they aren't (only connections are).\n\n\n\n\n### Bug Fixes\n- Don't panic on `Send()` on batch after invalid `Append`. [#830](https://github.com/ClickHouse/clickhouse-go/pull/830)\n- Fix JSON issue with `nil` if column order is inconsisent. [#824](https://github.com/ClickHouse/clickhouse-go/pull/824)\n\n\n### Bug Fixes\n- Patch release to fix \"Regression - escape character was not considered when comparing column names\". [#828](https://github.com/ClickHouse/clickhouse-go/issues/828)\n\n\n\n\n### New Features\n- Support for Nullables in Tuples. [#821](https://github.com/ClickHouse/clickhouse-go/pull/821) [#817](https://github.com/ClickHouse/clickhouse-go/pull/817)\n- Use headers for auth and not url if SSL. [#811](https://github.com/ClickHouse/clickhouse-go/pull/811)\n- Support additional headers. [#811](https://github.com/ClickHouse/clickhouse-go/pull/811)\n- Support int64 for DateTime. [#807](https://github.com/ClickHouse/clickhouse-go/pull/807)\n- Support inserting Enums as int8/int16/int. [#802](https://github.com/ClickHouse/clickhouse-go/pull/802)\n- Print error if unsupported server. [#792](https://github.com/ClickHouse/clickhouse-go/pull/792)\n- Allow block buffer size to tuned for performance - see `BlockBufferSize`. [#776](https://github.com/ClickHouse/clickhouse-go/pull/776)\n- Support custom datetime in Scan. [#767](https://github.com/ClickHouse/clickhouse-go/pull/767)\n- Support insertion of an orderedmap. [#763](https://github.com/ClickHouse/clickhouse-go/pull/763)\n\n### Bug Fixes\n- Decompress errors over HTTP. [#792](https://github.com/ClickHouse/clickhouse-go/pull/792)\n- Use `timezone` vs `timeZone` so we work on older versions. [#781](https://github.com/ClickHouse/clickhouse-go/pull/781)\n- Ensure only columns specified in INSERT are required in batch. [#790](https://github.com/ClickHouse/clickhouse-go/pull/790)\n- Respect order of columns in insert for batch. [#790](https://github.com/ClickHouse/clickhouse-go/pull/790)\n- Handle double pointers for Nullable columns when batch inserting. [#774](https://github.com/ClickHouse/clickhouse-go/pull/774)\n- Use nil for `LowCardinality(Nullable(X))`. [#768](https://github.com/ClickHouse/clickhouse-go/pull/768)\n\n### Breaking Changes\n- Align timezone handling with spec. [#776](https://github.com/ClickHouse/clickhouse-go/pull/766), specifically:\n - If parsing strings for datetime, datetime64 or dates we assume the locale is Local (i.e. the client) if not specified in the string.\n - The server (or column tz) is used for datetime and datetime64 rendering. For date/date32, these have no tz info in the server. For now, they will be rendered as UTC - consistent with the clickhouse-client\n - Addresses bind when no location is set\n\n\n","aboutv2":"_Golang_ client for ClickHouse that allows users to connect to ClickHouse using either the _Go_ standard database/sql interface or an optimized native interface. \n\nThe clients uses the Native format, for optimal performance, over either the Native protocol or HTTP interface. The latter allows the best combination of performance and widespread compatibility with proxies, load balancers, and VPNs. Support for all ClickHouse types and convenience methods for inserting structs are supported.","openInNewWindow":null,"logo":{"data":{"id":385,"attributes":{"name":"golang_logo.svg","alternativeText":"golang_logo.svg","caption":"golang_logo.svg","width":64,"height":64,"formats":null,"hash":"golang_logo_f9822ed3d5","ext":".svg","mime":"image/svg+xml","size":1.22,"url":"/uploads/golang_logo_f9822ed3d5.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:36:20.492Z","updatedAt":"2022-07-25T09:36:20.492Z"}}},"logo_dark":{"data":{"id":385,"attributes":{"name":"golang_logo.svg","alternativeText":"golang_logo.svg","caption":"golang_logo.svg","width":64,"height":64,"formats":null,"hash":"golang_logo_f9822ed3d5","ext":".svg","mime":"image/svg+xml","size":1.22,"url":"/uploads/golang_logo_f9822ed3d5.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:36:20.492Z","updatedAt":"2022-07-25T09:36:20.492Z"}}}}},{"id":3,"attributes":{"shortDescription":"A suite of Python packages for connecting Python to ClickHouse","name":"Python","category":"LANGUAGE_CLIENT","supportLevel":"CORE","summary":"### Installation\n```\npip install clickhouse-connect\n```\n###Examples\n#### Basic query\n```python\nimport clickhouse_connect\n\nclient = clickhouse_connect.get_client(host='', port=8443, username='default', password='')\nquery_result = client.query('SELECT * FROM system.tables')\nprint (query_result.result_set)\n```\n#### Simple 'command' without a result\n```python\nimport clickhouse_connect\n\nclient = clickhouse_connect.get_client()\nclient.command ('CREATE TABLE test_table (key UInt16, value String) ENGINE Memory')\n```\n#### Bulk insert of a matrix of rows and columns\n```python\ndata = [[100, 'value1'], [200, 'value2']]\nclient.insert('test_table', data)\nprint(client.query('SELECT * FROM test_table').result_set)\n-> [(100, 'value1'), (200, 'value2')]\n```","slug":"python","createdAt":"2022-07-25T09:48:47.810Z","updatedAt":"2025-10-30T11:43:49.108Z","publishedAt":"2022-07-25T09:48:49.618Z","docsLink":"https://clickhouse.com/docs/integrations/python","about":"Python client, Superset Engine, and limited SQLAlchemy dialect for ClickHouse.
\nThe Python client uses the Native format over HTTP for the best combination of performance and widespread compatibility with proxies, load balancers, and VPNs.","changelog":"
\n## 0.4.4 2022-11-22\n### Bug fix\n* Ignore all \"transport settings\" when validating settings.\n
\n\n
\n## 0.4.3 2022-11-22\n### New Features\n* The get_client method now accepts a http_adapter parameter to allow sharing a requests.HTTPAdapter (and its associated\nconnection pool) across multiple clients.\n* The VERSION file is now included in every package installation.\n
\n\n
\n## 0.4.2 2022-11-22\n### New Features\n* Global/common configuration options are now available in the `clickhouse_connect.common` module. The available settings are:\n * `autogenerate_session_id` [bool] Whether to generate a UUID1 session id used for every client request. Defaults to True. Disabling this can facilitate client sharing and load balancing in some use cases.\n * `dict_parameter_format` [str] Options are 'json' and 'map'. This controls whether parameterized queries convert a Python dictionary to JSON or ClickHouse Map syntax. Default to `json` for insert into Object('json') columns.\n * `invalid_setting_action` [str] Options are 'send' and 'drop'. Client Connect normally validates and drops (with a warning any settings that aren't recognized by the Server or are readonly).\nChanging this setting to 'send' will include such settings with the request anyway -- which will normally result in an error being returned.\n* The `clickhouse_connect.get_client` method now accepts a `settings` dictionary argument for consistency with other client methods.\n\n### Bug Fixes\n* Fixed insert of Pandas Dataframes for Timestamp columns with timezones\n* Fixed exception when inserting a Pandas Dataframes with NaType values into ClickHouse Float column (see known issue)\n\n### Known Issue\n* When inserting Pandas DataFrame values into a ClickHouse `Nullable(Float*)` column, a Float NaN value will be converted to a ClickHouse NULL.\nThis is a side effect of a Pandas issue where `df.replace` cannot distinguish between NaT and NaN values\n
\n\n
\n## 0.4.1 2022-11-14\n### Bug Fixes\n* Numpy array read and write compatibility has been refined and performance has been improved. \n* Pandas Timestamp objects are now correctly handled for all supported ClickHouse Date* types.\n* SQLAlchemy datatypes are now correctly mapped to the underlying ClickHouse type regardless of case. This fixes an issue with migrating Superset datasets and queries from\nclickhouse-sqlalchemy to clickhouse-connect. Thanks to [Eugene Torap](https://github.com/EugeneTorap)\n
\n\n
\n## 0.4.0 2022-11-07\n### New Features\n* The settings, table information, and insert progress used for client inserts has been centralized in a new reusable InsertContext object. Client insert methods can now accept such objects to simplify code and reduce overhead\n* Query results can now be returned in a column oriented format. This is useful to efficiently construct other objects (like Pandas dataframes) that use column storage internally\n* The transformation of Pandas data to Python types now bypasses Numpy. As a result compatibility for ClickHouse date, integer, and NULL types has been significantly improved\n\n### Bug Fixes\n* An insert using chunked transfer encode could fail in progress during serialization to ClickHouse native format. This would \"hang\" the request after throwing the exception, leading to ClickHouse reporting\n\"concurrent session\" errors. This has been fixed.\n* Pandas DataFrame inserts into tables with a \"large\" integer column would throw an exception. This has been fixed.\n* Pandas DataFrame inserts with NaT/NA/nan values would fail, even if inserted into Nullable column types. This has been fixed.\n\n### Known Issues\n* Numpy inserts into large integer columns are not supported.\n* Insert of Pandas timestamps with nanosecond precision will lose the nanosecond.\n
\n\n
\n## 0.3.8 2022-11-03\n\n### Bug Fixes\n* Fix read compression typo\n
\n\n
\n## 0.3.7 2022-11-03\n### New Features\n* Insert performance and memory usage for large inserts has been significantly improved\n * Insert blocks now use chunked transfer encoding (by sending a generator instead of a bytearray to the requests POST method)\n * If the client is initialized with compress = True, gzip compression is now enabled for inserts\n* Pandas DataFrame inserts have been optimized by keep the data in columnar format during the entire insert process\n\n### Bug Fixes\n* Fix inserts for date and datetime columns from Pandas dataframes.\n* Fix serialization issues for Decimal128 and Decimal256 types\n
\n\n
\n## 0.3.6 2022-11-02\n\n### Bug Fixes\n* Update QueryContext.updated_copy method to preserve settings, parameters, etc.\n
\n\n
\n## 0.3.5 2022-10-28\n\n### New Features\n* Build Python 3.11 Wheels\n
\n\n
\n## 0.3.4 2022-10-26\n\n### Bug fixes\n* Correctly handle insert into JSON/Object('json') column via SQLAlchemy\n* Fix some incompatibilities with SQLAlchemy 1.4\n
\n\n
\n## 0.3.3 2022-10-21\n\n### Bug fix\n* Fix 'SHOW CREATE' issue.\n
\n\n
\n## 0.3.2 2022-10-20\n\n### Bug fix\n* \"Queries\" that do not return data results (like DDL and SET queries) are now automatically treated as commands.\n\n### New Features\n* A UUID session_id is now generated by default if `session_id` is not specified in `clickhouse_connect.get_client`\n* Test infrastructure has been simplified and test configuration has moved from pytest options to environment files\n
\n\n
\n## 0.3.1 2022-10-19\n\n### Bug Fixes\n* UInt64 types were incorrectly returned as signed Python ints even outside of Superset. This has been fixed\n* Superset Engine Spec will now format (U)Int256 and (U)Int128 types as strings to avoid throwing a conversion exception\n
\n\n
\n## 0.3.0 2022-10-15\n\n### Breaking changes\n* The row_binary option for ClickHouse serialization has been removed. The performance is significantly lower than Native format and maintaining the option added complexity with no corresponding benefit\n\n### Bug Fixes\n* The Database Connection dialog was broken in the latest Superset development builds. This has been fixed\n* IPv6 Addresses fixed for default Superset configuration\n
\n\n
\n## 0.2.10 2022-09-28\n\n### Bug Fixes\n* Add single retry for HTTP RemoteDisconnected errors from the ClickHouse Server. This prevents exception spam when requests (in particular inserts) are sent at approximately the same time as the ClickHouse server closes a keep alive connection.\n
\n\n
\n## 0.2.9 2022-09-24\n\n### Bug Fixes\n* Fix incorrect validation errors in the Superset connection dialog\n
\n\n
\n## 0.2.8 2022-09-21\n\n### New Features\n* This release updates the build process to include binary wheels for the majority of platforms, include MacOS M1 and Linux Aarch64. This should also fix installation errors on lightweight platforms without build tools.\n* Builds are now included for Python 3.11\n\n### Known issues\n* Docker images built on MacOS directly from source do not correctly build the C extensions for Linux. However, installing the official wheels from PyPI should work correctly.\n
\n\n
\n## 0.2.7 2022-09-10\n\n### New Features\n* The HTTP client now raises an OperationalError instead of a DatabaseError when the HTTP status code is 429 (too many requests), 503 (service unavailable), or 504 (gateway timeout) to make it easier to determine if it is a retryable exception\n* Add `query_retries` client parameter (default 2) for \"retryable\" HTTP queries. Does not apply to \"commands\" like DDL or to inserts\n
\n\n
\n## 0.2.6 2022-09-08\n\n### Bug Fixes\n* Fixed an SQLAlchemy dialect issue with SQLAlchemy 1.4 that would cause problems in the most recent Superset version\n
\n\n
\n## 0.2.5 2022-08-30\n\n### Bug Fixes\n* Fixed an issue where DBAPI cursors returned an invalid description object for columns. This would cause `'property' object has no attribute 'startswith'` errors for some SqlAlchemy and SuperSet queries. \n* Fixed an issue where datetime parameters would not be correctly rendered as ClickHouse compatible strings\n\n### New Features\n* The \"parameters\" object passed to client query methods can now be a sequence instead of a dictionary, for compatibility with query strings that contain simple format unnamed format directives, such as `'SELECT * FROM table WHERE value = %s'`\n
\n\n
\n## 0.2.4, 2022-08-19\n\n### Bug Fixes\n* The wait_end_of_query parameter/setting was incorrectly being stripped. This is fixed\n
\n\n
\n## 0.2.3, 2022-08-14\n\n### Bug Fixes\n* Fix encoding insert of multibyte characters\n\n### New Features\n* Improve identifier handling/quoting for Clickhouse column, table, and database names\n* Add client arrow_insert method to directly insert a PyArrow Table insert ClickHouse using Arrow format\n
\n\n
\n## 0.2.2, 2022-08-06\n\n### Bug Fixes\n* Fix issue when query_limit set to 0\n
\n\n
\n## 0.2.1, 2022-08-04\n\n### Bug Fixes\n* Fix SQL comment problems in DBAPI cursor\n
\n\n
\n## 0.2.0, 2022-08-04\n\n### New Features\n* Support (experimental) JSON/Object datatype. ClickHouse Connect will take advantage of the fast orjson library if available. Note that inserts for JSON columns require ClickHouse server version 22.6.1 or later\n* Standardize read format handling and allow specifying a return data format per column or per query.\n* Added convenience min_version method to client to see if the server is at least the requested level\n* Increase default HTTP timeout to 300 seconds to match ClickHouse server default\n\n### Bug Fixes\n* Fixed multiple issues with SQL comments that would cause some queries to fail\n* Fixed problem with SQLAlchemy literal binds that would cause an error in Superset filters\n* Fixed issue with parameterized queries\n* Named Tuples were not supported and would result in throwing an exception. This has been fixed.\n* The client query_arrow function would return incomplete results if the query result exceeded the ClickHouse max_block_size. This has been fixed. As part of the fix query_arrow method returns a PyArrow Table object. While this is a breaking change in the API it should be easy to work around.\n
\n\n
\n## 0.1.6, 2022-07-06\n\n### New Features\n\n* Support Nested data types.\n\n### Bug Fixes\n\n* Fix issue with native reads of Nullable(LowCardinality) numeric and date types.\n* Empty inserts will now just log a debug message instead of throwing an IndexError.\n
","version":"0.4.4","license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse-connect","summaryv2":"### Installation\n```\npip install clickhouse-connect\n```\n###Examples\n#### Basic query\n```python\nimport clickhouse_connect\n\nclient = clickhouse_connect.get_client(host='', port=8443, username='default', password='')\nquery_result = client.query('SELECT * FROM system.tables')\nprint (query_result.result_set)\n```\n#### Simple 'command' without a result\n```python\nimport clickhouse_connect\n\nclient = clickhouse_connect.get_client()\nclient.command ('CREATE TABLE test_table (key UInt16, value String) ENGINE Memory')\n```\n#### Bulk insert of a matrix of rows and columns\n```python\ndata = [[100, 'value1'], [200, 'value2']]\nclient.insert('test_table', data)\nprint(client.query('SELECT * FROM test_table').result_set)\n-> [(100, 'value1'), (200, 'value2')]\n```","changelogv2":"\n\n\n### Bug fix\n* Ignore all \"transport settings\" when validating settings.\n\n\n\n\n### New Features\n* The get_client method now accepts a http_adapter parameter to allow sharing a requests.HTTPAdapter (and its associated\nconnection pool) across multiple clients.\n* The VERSION file is now included in every package installation.\n\n\n\n\n### New Features\n* Global/common configuration options are now available in the `clickhouse_connect.common` module. The available settings are:\n * `autogenerate_session_id` [bool] Whether to generate a UUID1 session id used for every client request. Defaults to True. Disabling this can facilitate client sharing and load balancing in some use cases.\n * `dict_parameter_format` [str] Options are 'json' and 'map'. This controls whether parameterized queries convert a Python dictionary to JSON or ClickHouse Map syntax. Default to `json` for insert into Object('json') columns.\n * `invalid_setting_action` [str] Options are 'send' and 'drop'. Client Connect normally validates and drops (with a warning any settings that aren't recognized by the Server or are readonly).\nChanging this setting to 'send' will include such settings with the request anyway -- which will normally result in an error being returned.\n* The `clickhouse_connect.get_client` method now accepts a `settings` dictionary argument for consistency with other client methods.\n\n### Bug Fixes\n* Fixed insert of Pandas Dataframes for Timestamp columns with timezones\n* Fixed exception when inserting a Pandas Dataframes with NaType values into ClickHouse Float column (see known issue)\n\n### Known Issue\n* When inserting Pandas DataFrame values into a ClickHouse `Nullable(Float*)` column, a Float NaN value will be converted to a ClickHouse NULL.\nThis is a side effect of a Pandas issue where `df.replace` cannot distinguish between NaT and NaN values\n\n\n\n\n### Bug Fixes\n* Numpy array read and write compatibility has been refined and performance has been improved. \n* Pandas Timestamp objects are now correctly handled for all supported ClickHouse Date* types.\n* SQLAlchemy datatypes are now correctly mapped to the underlying ClickHouse type regardless of case. This fixes an issue with migrating Superset datasets and queries from\nclickhouse-sqlalchemy to clickhouse-connect. Thanks to [Eugene Torap](https://github.com/EugeneTorap)\n\n\n\n\n### New Features\n* The settings, table information, and insert progress used for client inserts has been centralized in a new reusable InsertContext object. Client insert methods can now accept such objects to simplify code and reduce overhead\n* Query results can now be returned in a column oriented format. This is useful to efficiently construct other objects (like Pandas dataframes) that use column storage internally\n* The transformation of Pandas data to Python types now bypasses Numpy. As a result compatibility for ClickHouse date, integer, and NULL types has been significantly improved\n\n### Bug Fixes\n* An insert using chunked transfer encode could fail in progress during serialization to ClickHouse native format. This would \"hang\" the request after throwing the exception, leading to ClickHouse reporting\n\"concurrent session\" errors. This has been fixed.\n* Pandas DataFrame inserts into tables with a \"large\" integer column would throw an exception. This has been fixed.\n* Pandas DataFrame inserts with NaT/NA/nan values would fail, even if inserted into Nullable column types. This has been fixed.\n\n### Known Issues\n* Numpy inserts into large integer columns are not supported.\n* Insert of Pandas timestamps with nanosecond precision will lose the nanosecond.\n\n\n\n\n### Bug Fixes\n* Fix read compression typo\n\n\n\n\n### New Features\n* Insert performance and memory usage for large inserts has been significantly improved\n * Insert blocks now use chunked transfer encoding (by sending a generator instead of a bytearray to the requests POST method)\n * If the client is initialized with compress = True, gzip compression is now enabled for inserts\n* Pandas DataFrame inserts have been optimized by keep the data in columnar format during the entire insert process\n\n### Bug Fixes\n* Fix inserts for date and datetime columns from Pandas dataframes.\n* Fix serialization issues for Decimal128 and Decimal256 types\n\n\n\n\n### Bug Fixes\n* Update QueryContext.updated_copy method to preserve settings, parameters, etc.\n\n\n\n\n### New Features\n* Build Python 3.11 Wheels\n\n\n\n\n### Bug fixes\n* Correctly handle insert into JSON/Object('json') column via SQLAlchemy\n* Fix some incompatibilities with SQLAlchemy 1.4\n\n\n\n\n### Bug fix\n* Fix 'SHOW CREATE' issue.\n\n\n\n\n### Bug fix\n* \"Queries\" that do not return data results (like DDL and SET queries) are now automatically treated as commands.\n\n### New Features\n* A UUID session_id is now generated by default if `session_id` is not specified in `clickhouse_connect.get_client`\n* Test infrastructure has been simplified and test configuration has moved from pytest options to environment files\n\n\n\n\n### Bug Fixes\n* UInt64 types were incorrectly returned as signed Python ints even outside of Superset. This has been fixed\n* Superset Engine Spec will now format (U)Int256 and (U)Int128 types as strings to avoid throwing a conversion exception\n\n\n\n\n### Breaking changes\n* The row_binary option for ClickHouse serialization has been removed. The performance is significantly lower than Native format and maintaining the option added complexity with no corresponding benefit\n\n### Bug Fixes\n* The Database Connection dialog was broken in the latest Superset development builds. This has been fixed\n* IPv6 Addresses fixed for default Superset configuration\n\n\n\n\n### Bug Fixes\n* Add single retry for HTTP RemoteDisconnected errors from the ClickHouse Server. This prevents exception spam when requests (in particular inserts) are sent at approximately the same time as the ClickHouse server closes a keep alive connection.\n\n\n\n\n### Bug Fixes\n* Fix incorrect validation errors in the Superset connection dialog\n\n\n\n\n### New Features\n* This release updates the build process to include binary wheels for the majority of platforms, include MacOS M1 and Linux Aarch64. This should also fix installation errors on lightweight platforms without build tools.\n* Builds are now included for Python 3.11\n\n### Known issues\n* Docker images built on MacOS directly from source do not correctly build the C extensions for Linux. However, installing the official wheels from PyPI should work correctly.\n\n\n\n\n### New Features\n* The HTTP client now raises an OperationalError instead of a DatabaseError when the HTTP status code is 429 (too many requests), 503 (service unavailable), or 504 (gateway timeout) to make it easier to determine if it is a retryable exception\n* Add `query_retries` client parameter (default 2) for \"retryable\" HTTP queries. Does not apply to \"commands\" like DDL or to inserts\n\n\n\n\n### Bug Fixes\n* Fixed an SQLAlchemy dialect issue with SQLAlchemy 1.4 that would cause problems in the most recent Superset version\n\n\n\n\n### Bug Fixes\n* Fixed an issue where DBAPI cursors returned an invalid description object for columns. This would cause `'property' object has no attribute 'startswith'` errors for some SqlAlchemy and SuperSet queries. \n* Fixed an issue where datetime parameters would not be correctly rendered as ClickHouse compatible strings\n\n### New Features\n* The \"parameters\" object passed to client query methods can now be a sequence instead of a dictionary, for compatibility with query strings that contain simple format unnamed format directives, such as `'SELECT * FROM table WHERE value = %s'`\n\n\n\n\n### Bug Fixes\n* The wait_end_of_query parameter/setting was incorrectly being stripped. This is fixed\n\n\n\n\n### Bug Fixes\n* Fix encoding insert of multibyte characters\n\n### New Features\n* Improve identifier handling/quoting for Clickhouse column, table, and database names\n* Add client arrow_insert method to directly insert a PyArrow Table insert ClickHouse using Arrow format\n\n\n\n\n### Bug Fixes\n* Fix issue when query_limit set to 0\n\n\n\n\n### Bug Fixes\n* Fix SQL comment problems in DBAPI cursor\n\n\n\n\n### New Features\n* Support (experimental) JSON/Object datatype. ClickHouse Connect will take advantage of the fast orjson library if available. Note that inserts for JSON columns require ClickHouse server version 22.6.1 or later\n* Standardize read format handling and allow specifying a return data format per column or per query.\n* Added convenience min_version method to client to see if the server is at least the requested level\n* Increase default HTTP timeout to 300 seconds to match ClickHouse server default\n\n### Bug Fixes\n* Fixed multiple issues with SQL comments that would cause some queries to fail\n* Fixed problem with SQLAlchemy literal binds that would cause an error in Superset filters\n* Fixed issue with parameterized queries\n* Named Tuples were not supported and would result in throwing an exception. This has been fixed.\n* The client query_arrow function would return incomplete results if the query result exceeded the ClickHouse max_block_size. This has been fixed. As part of the fix query_arrow method returns a PyArrow Table object. While this is a breaking change in the API it should be easy to work around.\n\n\n\n\n### New Features\n\n* Support Nested data types.\n\n### Bug Fixes\n\n* Fix issue with native reads of Nullable(LowCardinality) numeric and date types.\n* Empty inserts will now just log a debug message instead of throwing an IndexError.\n\n\n","aboutv2":"Python client, Superset Engine, and limited SQLAlchemy dialect for ClickHouse.
\nThe Python client uses the Native format over HTTP for the best combination of performance and widespread compatibility with proxies, load balancers, and VPNs.","openInNewWindow":null,"logo":{"data":{"id":382,"attributes":{"name":"python_logo.svg","alternativeText":"python_logo.svg","caption":"python_logo.svg","width":64,"height":64,"formats":null,"hash":"python_logo_0a6d22557e","ext":".svg","mime":"image/svg+xml","size":1.61,"url":"/uploads/python_logo_0a6d22557e.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:15:04.294Z","updatedAt":"2022-07-25T09:15:04.294Z"}}},"logo_dark":{"data":{"id":382,"attributes":{"name":"python_logo.svg","alternativeText":"python_logo.svg","caption":"python_logo.svg","width":64,"height":64,"formats":null,"hash":"python_logo_0a6d22557e","ext":".svg","mime":"image/svg+xml","size":1.61,"url":"/uploads/python_logo_0a6d22557e.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:15:04.294Z","updatedAt":"2022-07-25T09:15:04.294Z"}}}}},{"id":4,"attributes":{"shortDescription":"Import from, export to, and transform S3 data in flight with ClickHouse built-in S3 functions","name":"Amazon S3","category":"DATA_INGESTION","supportLevel":"CORE","summary":"### Reading data from S3\nUsing ClickHouse S3 table function, users can query S3 data as a source without requiring persistence in ClickHouse. The following example illustrates how to read 10 rows of the \nNYC Taxi dataset.\n```sql\nSELECT \n trip_id, \n total_amount, \n pickup_longitude, \n pickup_latitude, \n dropoff_longitude, \n dropoff_latitude, \n pickup_datetime, \n dropoff_datetime,\n trip_distance\nFROM \ns3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_*.gz', 'TabSeparatedWithNames') \nLIMIT 10\nSETTINGS input_format_try_infer_datetimes = 0;\n```\n### Inserting Data from S3\nTo transfer data from S3 to ClickHouse, users can combine the s3 table function with INSERT statement. Let's create an empty `hackernews` table:\n```sql\nCREATE TABLE hackernews ORDER BY tuple\n(\n) EMPTY AS SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/hackernews/hacknernews.csv.gz', 'CSVWithNames');\n```\nThis creates an empty table using the schema inferred from the data. We can then insert the first 1 million rows from the remote dataset\n```sql\nINSERT INTO hackernews SELECT *\nFROM url('https://datasets-documentation.s3.eu-west-3.amazonaws.com/hackernews/hacknernews.csv.gz', 'CSVWithNames')\nLIMIT 1000000;\n```","slug":"amazon_s3","createdAt":"2022-07-25T19:43:55.067Z","updatedAt":"2025-10-18T09:56:41.550Z","publishedAt":"2022-07-25T19:45:00.194Z","docsLink":"https://clickhouse.com/docs/integrations/s3/","about":"Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. Users can insert S3 based data into ClickHouse and use S3 as an export destination, thus allowing interaction with “Data Lake” architectures. Furthermore, S3 can provide “cold” storage tiers and assist with separating storage and compute.","changelog":null,"version":null,"license":"apache2","readiness":null,"website":null,"summaryv2":"### Reading data from S3\nUsing ClickHouse S3 table function, users can query S3 data as a source without requiring persistence in ClickHouse. The following example illustrates how to read 10 rows of the \nNYC Taxi dataset.\n```sql\nSELECT \n trip_id, \n total_amount, \n pickup_longitude, \n pickup_latitude, \n dropoff_longitude, \n dropoff_latitude, \n pickup_datetime, \n dropoff_datetime,\n trip_distance\nFROM \ns3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_*.gz', 'TabSeparatedWithNames') \nLIMIT 10\nSETTINGS input_format_try_infer_datetimes = 0;\n```\n### Inserting Data from S3\nTo transfer data from S3 to ClickHouse, users can combine the s3 table function with INSERT statement. Let's create an empty `hackernews` table:\n```sql\nCREATE TABLE hackernews ORDER BY tuple\n(\n) EMPTY AS SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/hackernews/hacknernews.csv.gz', 'CSVWithNames');\n```\nThis creates an empty table using the schema inferred from the data. We can then insert the first 1 million rows from the remote dataset\n```sql\nINSERT INTO hackernews SELECT *\nFROM url('https://datasets-documentation.s3.eu-west-3.amazonaws.com/hackernews/hacknernews.csv.gz', 'CSVWithNames')\nLIMIT 1000000;\n```","changelogv2":null,"aboutv2":"Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. Users can insert S3 based data into ClickHouse and use S3 as an export destination, thus allowing interaction with “Data Lake” architectures. Furthermore, S3 can provide “cold” storage tiers and assist with separating storage and compute.","openInNewWindow":null,"logo":{"data":{"id":2875,"attributes":{"name":"amazon-s3.svg","alternativeText":null,"caption":null,"width":2500,"height":2500,"formats":null,"hash":"amazon_s3_cbb2541c11","ext":".svg","mime":"image/svg+xml","size":1.29,"url":"/uploads/amazon_s3_cbb2541c11.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-07-01T10:40:41.901Z","updatedAt":"2024-07-01T10:40:41.901Z"}}},"logo_dark":{"data":{"id":2875,"attributes":{"name":"amazon-s3.svg","alternativeText":null,"caption":null,"width":2500,"height":2500,"formats":null,"hash":"amazon_s3_cbb2541c11","ext":".svg","mime":"image/svg+xml","size":1.29,"url":"/uploads/amazon_s3_cbb2541c11.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-07-01T10:40:41.901Z","updatedAt":"2024-07-01T10:40:41.901Z"}}}}},{"id":5,"attributes":{"shortDescription":"Integration with Apache Kafka, the open-source distributed event streaming platform","name":"Kafka","category":"DATA_INGESTION","supportLevel":"CORE","summary":"Options for integrating ClickHouse Cloud with Apache Kafka include:\n- Kafka Connect - Kafka Connect is a free, open-source component of Apache Kafka® that works as a centralized data hub for simple data integration between Kafka and other data systems. This is the option we cover here in detail.\n- Vector - Vector is a vendor agnostic data pipeline. With the ability to read from Kafka, and send events to ClickHouse, this represents a robust integration option.\n\n### Confluent Platform\nConfluent Platform is a full-scale data streaming platform that enables you to easily access, store, and manage data as continuous, real-time streams.
\nConfluent solution is provided as On Prem & Cloud Solutions.
\nClickHouse Cloud is only supported on the On Prem and is enabled by using the JdbcSinkConnector.\n\n#### How to configure Local Single node installation for Confluent\n
\nInstall Confluent - we recommend the Quick Start for Confluent Platform\n
\n\n
\nInstall ClickHouse-JDBC Driver - Download the latest version of ClickHouse-JDBC version into Confluent directory.\n```shell\n wget https://repo1.maven.org/maven2/com/clickhouse/clickhouse-jdbc/0.3.2- patch11/clickhouse-jdbc-0.3.2-patch11.jar\n```\n
\n\n
\nStart Confluent instance.\n
\n#### Prepare configuration in the UI\n
\nCreate a database and a table in ClickHouse\n
\n\n
\nCreate a Kafka topic that our JdbcSinkConnector can pull messages from. Call it `ClickHouse Cloud`\n
\n\n
\nCreate a new JdbcSinkConnector with this configuration and provide endpoint, password and username properties:\n```json\n{\n \"name\": \"JdbcSinkConnectorConnector_0\",\n \"config\": {\n \"name\": \"JdbcSinkConnectorConnector_0\",\n \"connector.class\": \"io.confluent.connect.jdbc.JdbcSinkConnector\",\n \"key.converter\": \"org.apache.kafka.connect.storage.StringConverter\",\n \"topics\": \"test_v1\",\n \"connection.url\": \"jdbc:clickhouse://:8443/default?ssl=true\",\n \"connection.user\": \"default\",\n \"connection.password\": \"\",\n \"dialect.name\": \"GenericDatabaseDialect\",\n \"auto.create\": \"false\"\n }\n}\n```\n
\n\n
\nPress `Launch` and data will start to flow!\n
","slug":"kafka","createdAt":"2022-07-25T19:48:56.332Z","updatedAt":"2025-10-29T17:54:01.524Z","publishedAt":"2022-07-25T19:55:44.971Z","docsLink":"https://clickhouse.com/docs/integrations/kafka/","about":null,"changelog":null,"version":null,"license":null,"readiness":null,"website":null,"summaryv2":"Options for integrating ClickHouse Cloud with Apache Kafka include:\n- Kafka Connect - Kafka Connect is a free, open-source component of Apache Kafka® that works as a centralized data hub for simple data integration between Kafka and other data systems. This is the option we cover here in detail.\n- Vector - Vector is a vendor agnostic data pipeline. With the ability to read from Kafka, and send events to ClickHouse, this represents a robust integration option.\n\n### Confluent Platform\nConfluent Platform is a full-scale data streaming platform that enables you to easily access, store, and manage data as continuous, real-time streams.
\nConfluent solution is provided as On Prem & Cloud Solutions.
\nClickHouse Cloud is only supported on the On Prem and is enabled by using the JdbcSinkConnector.\n\n#### How to configure Local Single node installation for Confluent\n\n\n\n\nInstall Confluent - we recommend the Quick Start for Confluent Platform\n\n\n\n\nInstall ClickHouse-JDBC Driver - Download the latest version of ClickHouse-JDBC version into Confluent directory.\n```shell\n wget https://repo1.maven.org/maven2/com/clickhouse/clickhouse-jdbc/0.3.2- patch11/clickhouse-jdbc-0.3.2-patch11.jar\n```\n\n\n\n\nStart Confluent instance.\n\n\n\n\n#### Prepare configuration in the UI\n\n\n\n\nCreate a database and a table in ClickHouse\n\n\n\n\nCreate a Kafka topic that our JdbcSinkConnector can pull messages from. Call it `ClickHouse Cloud`\n\n\n\n\nCreate a new JdbcSinkConnector with this configuration and provide endpoint, password and username properties:\n```json\n{\n \"name\": \"JdbcSinkConnectorConnector_0\",\n \"config\": {\n \"name\": \"JdbcSinkConnectorConnector_0\",\n \"connector.class\": \"io.confluent.connect.jdbc.JdbcSinkConnector\",\n \"key.converter\": \"org.apache.kafka.connect.storage.StringConverter\",\n \"topics\": \"test_v1\",\n \"connection.url\": \"jdbc:clickhouse://:8443/default?ssl=true\",\n \"connection.user\": \"default\",\n \"connection.password\": \"\",\n \"dialect.name\": \"GenericDatabaseDialect\",\n \"auto.create\": \"false\"\n }\n}\n```\n\n\n\n\nPress `Launch` and data will start to flow!\n\n\n","changelogv2":null,"aboutv2":null,"openInNewWindow":null,"logo":{"data":{"id":2801,"attributes":{"name":"kafka.svg","alternativeText":null,"caption":null,"width":256,"height":416,"formats":null,"hash":"kafka_7be1297acc","ext":".svg","mime":"image/svg+xml","size":1.83,"url":"/uploads/kafka_7be1297acc.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-06-28T15:12:45.042Z","updatedAt":"2025-10-29T17:53:47.971Z"}}},"logo_dark":{"data":{"id":2493,"attributes":{"name":"kafka_logo.svg","alternativeText":null,"caption":null,"width":64,"height":64,"formats":null,"hash":"kafka_logo_60c905fa01","ext":".svg","mime":"image/svg+xml","size":2.73,"url":"/uploads/kafka_logo_60c905fa01.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-06-04T10:20:20.497Z","updatedAt":"2024-06-10T21:50:50.462Z"}}}}},{"id":6,"attributes":{"shortDescription":"Use Airbyte, to create ELT data pipelines with more than 140 connectors to load and sync your data into ClickHouse","name":"Airbyte","category":"DATA_INGESTION","supportLevel":"PARTNER","summary":"
\nDownload and run Airbyte using docker-compose or use Airbyte Cloud.\n```shell\ngit clone https://github.com/airbytehq/airbyte.git\ncd airbyte\ndocker-compose up\n```\n
\n\n
\n>**NOTE:** If your are using Airbyte Cloud, make sure to allow Airbyte's IP addresses to access your ClickHouse service (Airbyte Cloud IP Addresses).\n
\n\n
\nCollect this information from your ClickHouse Cloud Service or your local deployment:\n- host\n- port\n- username\n- password\n- database name\n
\n\n
\nSet ClickHouse as a destination by filling in the above information in the \"Set up the destination\" form.\n
\n\n
\nCongratulations! you have now added ClickHouse as a destination in Airbyte.\n
","slug":"airbyte","createdAt":"2022-07-25T20:04:45.742Z","updatedAt":"2025-10-18T09:45:21.859Z","publishedAt":"2024-07-08T09:21:27.746Z","docsLink":"https://clickhouse.com/docs/integrations/airbyte","about":"Airbyte is an open-source data integration platform for building ELT pipelines.","changelog":null,"version":null,"license":null,"readiness":null,"website":"https://airbyte.com/","summaryv2":"\n\n\nDownload and run Airbyte using docker-compose or use Airbyte Cloud.\n
\n```shell\ngit clone https://github.com/airbytehq/airbyte.git\ncd airbyte\ndocker-compose up\n```\n\n\n\n\n>**NOTE:** If your are using Airbyte Cloud, make sure to allow Airbyte's IP addresses to access your ClickHouse service (Airbyte Cloud IP Addresses).\n\n\n\n\nCollect this information from your ClickHouse Cloud Service or your local deployment:\n- host\n- port\n- username\n- password\n- database name\n\n\n\n\nSet ClickHouse as a destination by filling in the above information in the \"Set up the destination\" form.\n\n\n\n\nCongratulations! you have now added ClickHouse as a destination in Airbyte.\n\n\n","changelogv2":null,"aboutv2":"Airbyte is an open-source data integration platform for building ELT pipelines.","openInNewWindow":null,"logo":{"data":{"id":389,"attributes":{"name":"airbyte.svg","alternativeText":"airbyte.svg","caption":"airbyte.svg","width":57,"height":57,"formats":null,"hash":"airbyte_5dfb28006b","ext":".svg","mime":"image/svg+xml","size":15.41,"url":"/uploads/airbyte_5dfb28006b.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-26T17:58:09.526Z","updatedAt":"2022-07-26T17:58:09.526Z"}}},"logo_dark":{"data":{"id":389,"attributes":{"name":"airbyte.svg","alternativeText":"airbyte.svg","caption":"airbyte.svg","width":57,"height":57,"formats":null,"hash":"airbyte_5dfb28006b","ext":".svg","mime":"image/svg+xml","size":15.41,"url":"/uploads/airbyte_5dfb28006b.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-26T17:58:09.526Z","updatedAt":"2022-07-26T17:58:09.526Z"}}}}},{"id":7,"attributes":{"shortDescription":"With Grafana you can create, explore and share all of your data through dashboards","name":"Grafana","category":"DATA_VISUALIZATION","supportLevel":"PARTNER","summary":"
\nGather your ClickHouse details (username, password, native URL)\n
\n\n
\nDeploy Grafana at Grafana.com, or on your own hardware.\n
\n\n
\n> **NOTE:** If your are using Grafana Cloud, make sure to allow Grafana's IP addresses to access your ClickHouse Service (Grafana Cloud IP Addresses)\n
\n\n
\nInstall the ClickHouse plugin\n
\n\n
\nCreate a datasource, populating the ClickHouse details.\n
\n\n
\nStart building visualizations and dashboards!\n
","slug":"grafana","createdAt":"2022-07-25T21:49:19.958Z","updatedAt":"2025-10-18T10:22:10.859Z","publishedAt":"2022-07-25T21:49:21.669Z","docsLink":"https://clickhouse.com/docs/integrations/grafana","about":"Grafana is a multi-platform open source analytics and interactive visualization web application. It provides charts, graphs, and alerts for the web when connected to supported data sources. The ClickHouse data source plugin allows you to query and visualize ClickHouse data from within Grafana.\n","changelog":"
\n## 2.0.3\n### New Features\n- add `date` filter support [#227](https://github.com/grafana/clickhouse-datasource/pull/227)\n
\n\n
\n## 2.0.2\n### New Features\n- update sqlds to 2.3.13 which fixes some macro queries [#223](https://github.com/grafana/clickhouse-datasource/pull/223)\n- add support for Date32, DateTime64 types [#213](https://github.com/grafana/clickhouse-datasource/pull/213)\n### Bug Fixes\n- Fields dropdown doesn't populate until database dropdown is clicked [#212](https://github.com/grafana/clickhouse-datasource/pull/212)\n
\n\n
\n## 2.0.1\n### Bug Fixes\n- now works with Safari. Safari does not support regex look aheads [#193](https://github.com/grafana/clickhouse-datasource/pull/193)\n
\n\n
\n## 2.0.0\n### New Features\n- upgrade driver to support HTTP\n- Changed how ad hoc filters work with a settings option provided in CH 22.7\n- Conditional alls are now handled with a conditional all function. The function checks if the second parameter is a template var set to all, if it then replaces the function with 1=1, and if not set the function to the first parameter.\n\n### Bug Fixes\n- visual query builder can use any date type for time field\n- 'any' is now an aggregation type in the visual query builder\n- time filter macros can be used in the adhoc query\n- time interval macro cannot have an interval of 0\n- update drive to v2.1.0\n- expand query button works with grafana 8.0+\n- added adhoc columns macro\n
","version":"2.0.3","license":"apache2","readiness":null,"website":"https://grafana.com/grafana/plugins/grafana-clickhouse-datasource/","summaryv2":"\n\n\nGather your ClickHouse details (username, password, native URL)\n\n\n\n\nDeploy Grafana at Grafana.com, or on your own hardware.\n\n\n\n\n> **NOTE:** If your are using Grafana Cloud, make sure to allow Grafana's IP addresses to access your ClickHouse Service (Grafana Cloud IP Addresses)\n\n\n\n\nInstall the ClickHouse plugin\n\n\n\n\nCreate a datasource, populating the ClickHouse details.\n\n\n\n\nStart building visualizations and dashboards!\n\n\n","changelogv2":"\n \n\n### New Features\n- add `date` filter support [#227](https://github.com/grafana/clickhouse-datasource/pull/227)\n- \n\n\n\n### New Features\n- update sqlds to 2.3.13 which fixes some macro queries [#223](https://github.com/grafana/clickhouse-datasource/pull/223)\n- add support for Date32, DateTime64 types [#213](https://github.com/grafana/clickhouse-datasource/pull/213)\n### Bug Fixes\n- Fields dropdown doesn't populate until database dropdown is clicked [#212](https://github.com/grafana/clickhouse-datasource/pull/212)\n\n\n\n\n### Bug Fixes\n- now works with Safari. Safari does not support regex look aheads [#193](https://github.com/grafana/clickhouse-datasource/pull/193)\n\n\n\n\n### New Features\n- upgrade driver to support HTTP\n- Changed how ad hoc filters work with a settings option provided in CH 22.7\n- Conditional alls are now handled with a conditional all function. The function checks if the second parameter is a template var set to all, if it then replaces the function with 1=1, and if not set the function to the first parameter.\n\n### Bug Fixes\n- visual query builder can use any date type for time field\n- 'any' is now an aggregation type in the visual query builder\n- time filter macros can be used in the adhoc query\n- time interval macro cannot have an interval of 0\n- update drive to v2.1.0\n- expand query button works with grafana 8.0+\n- added adhoc columns macro\n\n \n","aboutv2":"Grafana is a multi-platform open source analytics and interactive visualization web application. It provides charts, graphs, and alerts for the web when connected to supported data sources. The ClickHouse data source plugin allows you to query and visualize ClickHouse data from within Grafana.\n","openInNewWindow":null,"logo":{"data":{"id":379,"attributes":{"name":"grafana_logo.svg","alternativeText":"grafana_logo.svg","caption":"grafana_logo.svg","width":53,"height":53,"formats":null,"hash":"grafana_logo_286f777667","ext":".svg","mime":"image/svg+xml","size":35.61,"url":"/uploads/grafana_logo_286f777667.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:41.532Z","updatedAt":"2022-07-25T09:14:41.532Z"}}},"logo_dark":{"data":{"id":379,"attributes":{"name":"grafana_logo.svg","alternativeText":"grafana_logo.svg","caption":"grafana_logo.svg","width":53,"height":53,"formats":null,"hash":"grafana_logo_286f777667","ext":".svg","mime":"image/svg+xml","size":35.61,"url":"/uploads/grafana_logo_286f777667.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:41.532Z","updatedAt":"2022-07-25T09:14:41.532Z"}}}}},{"id":8,"attributes":{"shortDescription":"Use dbt (data build tool) to transform data in ClickHouse by simply writing SQL statements","name":"dbt","category":"DATA_INTEGRATION","supportLevel":"CORE","summary":"
\nInstall dbt-core and dbt-clickhouse\n```shell\npip install dbt-clickhouse\n```\n
\n\n
\nProvide `dbt` with the connection details for our ClickHouse instance. The full list of connection configuration options is available here.\nConfigure `clickhouse_cloud` profile in `~/.dbt/profiles.yml` file and provide user, password, schena host properties:\n```yaml\nclickhouse_cloud:\n target: dev\n outputs:\n dev:\n type: clickhouse\n schema: \n host: \n port: 8443 # use 9440 for native\n user: default\n password: \n secure: True\n```\n
\n\n
\nCreate a dbt project:\n```shell\ndbt init project_name\n```\n
\n\n
\nInside `project_name` dir, update your `dbt_project.yml` file to specify a profile name to connect to the ClickHouse server.\n```yml\nprofile: 'clickhouse_cloud'\n```\n
\n\n
\nExecute `dbt debug` with the CLI tool to confirm whether `dbt` is able to connect to ClickHouse.\n
\nConfirm the response includes `Connection test: [OK connection ok]` indicating a successful connection.\n
","slug":"dbt","createdAt":"2022-07-25T22:18:51.132Z","updatedAt":"2025-10-18T10:14:55.157Z","publishedAt":"2022-07-25T22:18:52.836Z","docsLink":"https://clickhouse.com/docs/integrations/dbt/","about":"`dbt` is a development framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun. \n\nThe plugin enables users to use ClickHouse as the data source for `dbt` by porting dbt functionality to ClickHouse.","changelog":null,"version":"1.3.1","license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/dbt-clickhouse","summaryv2":"\n\n\nInstall dbt-core and dbt-clickhouse\n```shell\npip install dbt-clickhouse\n```\n\n\n\n\nProvide `dbt` with the connection details for our ClickHouse instance. The full list of connection configuration options is available here.\nConfigure `clickhouse_cloud` profile in `~/.dbt/profiles.yml` file and provide user, password, schena host properties:\n```yaml\nclickhouse_cloud:\n target: dev\n outputs:\n dev:\n type: clickhouse\n schema: \n host: \n port: 8443 # use 9440 for native\n user: default\n password: \n secure: True\n```\n\n\n\n\nCreate a dbt project:\n```shell\ndbt init project_name\n```\n\n\n\n\nInside `project_name` dir, update your `dbt_project.yml` file to specify a profile name to connect to the ClickHouse server.\n```yml\nprofile: 'clickhouse_cloud'\n```\n\n\n\nExecute `dbt debug` with the CLI tool to confirm whether `dbt` is able to connect to ClickHouse.\n
\nConfirm the response includes `Connection test: [OK connection ok]` indicating a successful connection.\n\n","changelogv2":null,"aboutv2":"`dbt` is a development framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun. \n\nThe plugin enables users to use ClickHouse as the data source for `dbt` by porting dbt functionality to ClickHouse.","openInNewWindow":null,"logo":{"data":{"id":377,"attributes":{"name":"dbt_logo.svg","alternativeText":"dbt_logo.svg","caption":"dbt_logo.svg","width":256,"height":256,"formats":null,"hash":"dbt_logo_7e69dcf516","ext":".svg","mime":"image/svg+xml","size":2.93,"url":"/uploads/dbt_logo_7e69dcf516.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:30.284Z","updatedAt":"2022-07-25T09:14:30.284Z"}}},"logo_dark":{"data":{"id":377,"attributes":{"name":"dbt_logo.svg","alternativeText":"dbt_logo.svg","caption":"dbt_logo.svg","width":256,"height":256,"formats":null,"hash":"dbt_logo_7e69dcf516","ext":".svg","mime":"image/svg+xml","size":2.93,"url":"/uploads/dbt_logo_7e69dcf516.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:30.284Z","updatedAt":"2022-07-25T09:14:30.284Z"}}}}},{"id":9,"attributes":{"shortDescription":"Explore and visualize your ClickHouse data with Apache Superset","name":"Superset","category":"DATA_VISUALIZATION","supportLevel":"CORE","summary":"
\nGather your ClickHouse Cloud service connection details (username, password, port and host).\n
\n\n
\nInstall the clickhouse-connect package. It can be installed with the following command:\n```bash\npip install clickhouse-connect \n```\n
\n\n
\nConnect Superset to ClickHouse by adding a database in the Superset UI and providing your connection details.\n
\n\n
\nAdd a dataset in Superset by selecting one of your tables.\n
\n\n
\nStart building visualizations and dashboards!\n
","slug":"superset","createdAt":"2022-07-25T22:23:05.295Z","updatedAt":"2026-02-01T06:55:42.511Z","publishedAt":"2022-07-25T22:23:07.532Z","docsLink":"https://clickhouse.com/docs/integrations/superset","about":"Use the ClickHouse Connect driver to connect your ClickHouse Cloud service to Apache Superset.","changelog":null,"version":null,"license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse-connect","summaryv2":"\n\n\nGather your ClickHouse Cloud service connection details (username, password, port and host).\n\n\n\n\nInstall the clickhouse-connect package. It can be installed with the following command:\n```bash\npip install clickhouse-connect \n```\n\n\n\n\nConnect Superset to ClickHouse by adding a database in the Superset UI and providing your connection details.\n\n\n\n\nAdd a dataset in Superset by selecting one of your tables.\n\n\n\n\nStart building visualizations and dashboards!\n\n\n","changelogv2":null,"aboutv2":"Use the ClickHouse Connect driver to connect your ClickHouse Cloud service to Apache Superset.","openInNewWindow":null,"logo":{"data":{"id":383,"attributes":{"name":"superset_logo.svg","alternativeText":"superset_logo.svg","caption":"superset_logo.svg","width":58,"height":29,"formats":null,"hash":"superset_logo_198226bdd7","ext":".svg","mime":"image/svg+xml","size":1.24,"url":"/uploads/superset_logo_198226bdd7.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:15:09.380Z","updatedAt":"2022-07-25T09:15:09.380Z"}}},"logo_dark":{"data":{"id":383,"attributes":{"name":"superset_logo.svg","alternativeText":"superset_logo.svg","caption":"superset_logo.svg","width":58,"height":29,"formats":null,"hash":"superset_logo_198226bdd7","ext":".svg","mime":"image/svg+xml","size":1.24,"url":"/uploads/superset_logo_198226bdd7.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:15:09.380Z","updatedAt":"2022-07-25T09:15:09.380Z"}}}}},{"id":10,"attributes":{"shortDescription":"Deepnote is a collaborative Jupyter-compatible data notebook built for teams to discover and share insights","name":"Deepnote","category":"DATA_VISUALIZATION","supportLevel":"COMMUNITY","summary":"
\nWithin Deepnote, select the `Integrations` overview and click on the ClickHouse tile.\n
\n\n
\nProvide the connection details for your ClickHouse Cloud service:\n- Hostname - The hostname of your ClickHouse service.\n- Port - The HTTPS port of your ClickHouse service.\n- Username - Your ClickHouse username (we recommend the creation of a dedicated read-only database user for this purpose).\n- Password - Your ClickHouse password.\n- Database - The name of the database you would like to connect to.\n
\n\n
\n>**NOTE:** Make sure to allow Deepnote's IP addresses to access your ClickHouse Service (Deepnote IP Addresses)\n
\n\n
\nCongratulations! You have now integrated ClickHouse into Deepnote.\n
\n\n### Interactive example\nIf you would like to explore an interactive example of querying ClickHouse from Deepnote data notebooks, click the button below to launch a templated project connected to the ClickHouse Playground.\n\n\"Launch\n","slug":"deepnote","createdAt":"2022-07-25T22:28:01.271Z","updatedAt":"2025-10-18T10:16:22.476Z","publishedAt":"2022-07-25T22:28:02.893Z","docsLink":"https://clickhouse.com/docs/integrations/deepnote/","about":"Deepnote is a collaborative data notebook built for teams to discover and share insights. In addition to being Jupyter-compatible, it works in the cloud and provides you with one central place to collaborate and work on data science projects efficiently.\n
\n### Description\nDeepnote's ClickHouse integration allows data teams to efficiently query very large datasets, extract relevant data, and start analyzing and modeling in the comfort of their known notebook environment.","changelog":null,"version":null,"license":null,"readiness":null,"website":"https://deepnote.com/launch?template=ClickHouse%20and%20Deepnote","summaryv2":"\n\n\nWithin Deepnote, select the `Integrations` overview and click on the ClickHouse tile.\n\n\n\n\nProvide the connection details for your ClickHouse Cloud service:\n- Hostname - The hostname of your ClickHouse service.\n- Port - The HTTPS port of your ClickHouse service.\n- Username - Your ClickHouse username (we recommend the creation of a dedicated read-only database user for this purpose).\n- Password - Your ClickHouse password.\n- Database - The name of the database you would like to connect to.\n\n\n\n\n>**NOTE:** Make sure to allow Deepnote's IP addresses to access your ClickHouse Service (Deepnote IP Addresses)\n\n\n\n\nCongratulations! You have now integrated ClickHouse into Deepnote.\n\n\n\n\n### Interactive example\nIf you would like to explore an interactive example of querying ClickHouse from Deepnote data notebooks, click the button below to launch a templated project connected to the ClickHouse Playground.\n\n\"Launch\n","changelogv2":null,"aboutv2":"Deepnote is a collaborative data notebook built for teams to discover and share insights. In addition to being Jupyter-compatible, it works in the cloud and provides you with one central place to collaborate and work on data science projects efficiently.\n
\n### Description\nDeepnote's ClickHouse integration allows data teams to efficiently query very large datasets, extract relevant data, and start analyzing and modeling in the comfort of their known notebook environment.","openInNewWindow":null,"logo":{"data":{"id":378,"attributes":{"name":"deepnote_logo.svg","alternativeText":"deepnote_logo.svg","caption":"deepnote_logo.svg","width":32,"height":31,"formats":null,"hash":"deepnote_logo_443b9c08bd","ext":".svg","mime":"image/svg+xml","size":1.64,"url":"/uploads/deepnote_logo_443b9c08bd.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:36.149Z","updatedAt":"2022-07-25T09:14:36.149Z"}}},"logo_dark":{"data":{"id":378,"attributes":{"name":"deepnote_logo.svg","alternativeText":"deepnote_logo.svg","caption":"deepnote_logo.svg","width":32,"height":31,"formats":null,"hash":"deepnote_logo_443b9c08bd","ext":".svg","mime":"image/svg+xml","size":1.64,"url":"/uploads/deepnote_logo_443b9c08bd.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:36.149Z","updatedAt":"2022-07-25T09:14:36.149Z"}}}}},{"id":11,"attributes":{"shortDescription":"DBeaver PRO is a comprehensive database management and administration tool with an easy connection to ClickHouse Cloud","name":"DBeaver","category":"SQL_CLIENT","supportLevel":"PARTNER","summary":"DBeaver provides a wizard that guides you through the steps to connect to the database. To open a wizard, click on the plug icon in the upper left corner of the application window or go to Database -> New Database Сonnection. \n
\nDownload DBeaver from https://dbeaver.io or DBeaver PRO from https://dbeaver.com and open it.\n
\n\n
\nThe database selection window will open, and you will see a list from which you can choose the Clickhouse driver. You can easily find it in the Analytical section.\n
\n\n
\n\n
\nDouble-click on the driver icon, and you will see the window with the connection settings. On the main tab, you need to set all primary connection settings. The required settings for Clickhouse Cloud include:\n- Host\n- Port\n- Database name\n- Username\n- Password\n
\n\n
\n\n
\nYou need to use SSL protocol to create a connection to the Clickhouse Cloud database. Open the SSL tab, tick the “Use SSL” box, and load certificates and a private key from your file system.\n
\n\n\n
\n\n
\nIf necessary, you can view and edit driver properties in the corresponding tab. To start editing, you need to click on the row in the Value column.\n
\n\n
\n\n
\nTo check that all settings are entered correctly, click Test Connection on the main tab. You will see a dialog with all information about the driver. After completing all the settings, click the Finish button. \n
\n\n\n
\n\n
\nThe connection appears in the tree of connections in the Database Navigator, and DBeaver actually connects to the database.\n
\n\n
","slug":"dbeaver","createdAt":"2022-07-25T22:30:57.884Z","updatedAt":"2025-10-18T10:14:04.810Z","publishedAt":"2022-07-25T22:31:20.016Z","docsLink":"https://clickhouse.com/docs/integrations/dbeaver","about":"### Main features:\n- **Data manager**. Multifunctional editor to search, filter, group, and calculate data in a table view.\n- **SQL editor**. Powerful SQL query tool for developing, executing, storing, exporting, and re-using scripts with data profiling and formatting functions\n- **Metadata editor** with all objects support: array, enum, boolean, views, indexes, procedures, triggers, storage entities and security entities.\n- **Query execution plan**. A convenient way to view and estimate the speed of the query or script execution and to define the most expensive plan nodes.","changelog":null,"version":null,"license":"apache2","readiness":null,"website":"https://github.com/dbeaver/dbeaver","summaryv2":"DBeaver provides a wizard that guides you through the steps to connect to the database. To open a wizard, click on the plug icon in the upper left corner of the application window or go to Database -> New Database Сonnection. \n\n\n\n\nDownload DBeaver from https://dbeaver.io or DBeaver PRO from https://dbeaver.com and open it.\n\n\n\n\nThe database selection window will open, and you will see a list from which you can choose the Clickhouse driver. You can easily find it in the Analytical section.\n
\n\n\n\n\n\nDouble-click on the driver icon, and you will see the window with the connection settings. On the main tab, you need to set all primary connection settings. The required settings for Clickhouse Cloud include:\n- Host\n- Port\n- Database name\n- Username\n- Password\n
\n\n\n\n\n\nYou need to use SSL protocol to create a connection to the Clickhouse Cloud database. Open the SSL tab, tick the “Use SSL” box, and load certificates and a private key from your file system.\n
\n\n\n\n\n\n\nIf necessary, you can view and edit driver properties in the corresponding tab. To start editing, you need to click on the row in the Value column.\n
\n\n\n\n\n\nTo check that all settings are entered correctly, click Test Connection on the main tab. You will see a dialog with all information about the driver. After completing all the settings, click the Finish button. \n
\n\n\n\n\n\n\nThe connection appears in the tree of connections in the Database Navigator, and DBeaver actually connects to the database.\n
\n\n\n\n","changelogv2":null,"aboutv2":"### Main features:\n- **Data manager**. Multifunctional editor to search, filter, group, and calculate data in a table view.\n- **SQL editor**. Powerful SQL query tool for developing, executing, storing, exporting, and re-using scripts with data profiling and formatting functions\n- **Metadata editor** with all objects support: array, enum, boolean, views, indexes, procedures, triggers, storage entities and security entities.\n- **Query execution plan**. A convenient way to view and estimate the speed of the query or script execution and to define the most expensive plan nodes.","openInNewWindow":null,"logo":{"data":{"id":376,"attributes":{"name":"dbeaver_logo.svg","alternativeText":"dbeaver_logo.svg","caption":"dbeaver_logo.svg","width":256,"height":256,"formats":null,"hash":"dbeaver_logo_c5f82a96ce","ext":".svg","mime":"image/svg+xml","size":7.46,"url":"/uploads/dbeaver_logo_c5f82a96ce.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:22.893Z","updatedAt":"2022-07-25T09:14:22.893Z"}}},"logo_dark":{"data":{"id":376,"attributes":{"name":"dbeaver_logo.svg","alternativeText":"dbeaver_logo.svg","caption":"dbeaver_logo.svg","width":256,"height":256,"formats":null,"hash":"dbeaver_logo_c5f82a96ce","ext":".svg","mime":"image/svg+xml","size":7.46,"url":"/uploads/dbeaver_logo_c5f82a96ce.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-07-25T09:14:22.893Z","updatedAt":"2022-07-25T09:14:22.893Z"}}}}},{"id":13,"attributes":{"shortDescription":"ClickHouse Client is the native command-line client for ClickHouse","name":"ClickHouse Client","category":"SQL_CLIENT","supportLevel":"CORE","summary":"
\nDownload and install clickhouse-client (instructions).\n
\n\n
\nRun the clickhouse-client command with your credentials (or simply copy the native connection command from the \"Connect\" menu under your ClickHouse Cloud service).\n```bash\nclickhouse-client --host \\\n --secure \\\n --port 9440 \\\n --user \\\n --password \n```\n
\n\n
\nStart querying ClickHouse!\n
","slug":"clickhouse_client","createdAt":"2022-08-16T11:45:20.834Z","updatedAt":"2025-10-18T10:05:21.073Z","publishedAt":"2022-09-01T13:11:53.652Z","docsLink":"https://clickhouse.com/docs/integrations/sql-clients/clickhouse-client-local","about":"ClickHouse Client is the native command-line client for ClickHouse. The client supports command-line options and configuration files.","changelog":null,"version":null,"license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse","summaryv2":"\n\n\nDownload and install clickhouse-client (instructions).\n\n\n\n\nRun the clickhouse-client command with your credentials (or simply copy the native connection command from the \"Connect\" menu under your ClickHouse Cloud service).\n```bash\nclickhouse-client --host \\\n --secure \\\n --port 9440 \\\n --user \\\n --password \n```\n\n\n\n\nStart querying ClickHouse!\n\n\n","changelogv2":null,"aboutv2":"ClickHouse Client is the native command-line client for ClickHouse. The client supports command-line options and configuration files.","openInNewWindow":null,"logo":{"data":{"id":2889,"attributes":{"name":"clickhouse-logomark_white 1.svg","alternativeText":null,"caption":null,"width":473,"height":474,"formats":null,"hash":"clickhouse_logomark_white_1_19722434d7","ext":".svg","mime":"image/svg+xml","size":1.78,"url":"/uploads/clickhouse_logomark_white_1_19722434d7.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-07-01T13:27:07.689Z","updatedAt":"2024-07-01T13:27:07.689Z"}}},"logo_dark":{"data":{"id":2889,"attributes":{"name":"clickhouse-logomark_white 1.svg","alternativeText":null,"caption":null,"width":473,"height":474,"formats":null,"hash":"clickhouse_logomark_white_1_19722434d7","ext":".svg","mime":"image/svg+xml","size":1.78,"url":"/uploads/clickhouse_logomark_white_1_19722434d7.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-07-01T13:27:07.689Z","updatedAt":"2024-07-01T13:27:07.689Z"}}}}},{"id":14,"attributes":{"shortDescription":"DataGrip is a powerful database IDE with dedicated support for ClickHouse","name":"DataGrip","category":"SQL_CLIENT","supportLevel":"COMMUNITY","summary":"
\nIn DataGrip, add a datasource to your project and select ClickHouse.\n
\n\n
\nThe first time you add a ClickHouse datasource, DataGrip will download the required JDBC driver (you can also manually specify the driver for the datasource).\n
\n\n
\nSpecify the database connection details for your ClickHouse Cloud service: hostname, user, password, HTTP port.\n
\n\n
\nClick on the \"Test Connection\" link and if it works click on \"Apply\".\n
\n\n
\nStart querying ClickHouse!\n
\n\n","slug":"datagrip","createdAt":"2022-08-16T12:07:36.639Z","updatedAt":"2025-10-18T10:12:49.652Z","publishedAt":"2022-08-16T12:21:17.905Z","docsLink":"https://clickhouse.com/docs/integrations/datagrip","about":"DataGrip is a powerful database IDE with support for ClickHouse. It is also embedded in other IntelliJ-based tools like PyCharm, IntelliJ IDEA, GoLand, PhpStorm and others. It connects to Clickhouse through the JDBC driver.\n\n### Features\n - Code completion\n - ClickHouse syntax highlighting\n - Support for features specific to ClickHouse, for example, nested columns, table engines\n - Data Editor\n - Refactorings\n - Search and Navigation","changelog":null,"version":null,"license":null,"readiness":null,"website":"https://www.jetbrains.com/datagrip/","summaryv2":"\n\n\nIn DataGrip, add a datasource to your project and select ClickHouse.\n\n\n\n\nThe first time you add a ClickHouse datasource, DataGrip will download the required JDBC driver (you can also manually specify the driver for the datasource).\n\n\n\n\nSpecify the database connection details for your ClickHouse Cloud service: hostname, user, password, HTTP port.\n\n\n\n\nClick on the \"Test Connection\" link and if it works click on \"Apply\".\n\n\n\n\nStart querying ClickHouse!\n\n\n","changelogv2":null,"aboutv2":"DataGrip is a powerful database IDE with support for ClickHouse. It is also embedded in other IntelliJ-based tools like PyCharm, IntelliJ IDEA, GoLand, PhpStorm and others. It connects to Clickhouse through the JDBC driver.\n\n### Features\n - Code completion\n - ClickHouse syntax highlighting\n - Support for features specific to ClickHouse, for example, nested columns, table engines\n - Data Editor\n - Refactorings\n - Search and Navigation","openInNewWindow":null,"logo":{"data":{"id":398,"attributes":{"name":"DataGrip.svg","alternativeText":"DataGrip.svg","caption":"DataGrip.svg","width":64,"height":64,"formats":{},"hash":"Data_Grip_68bd723656","ext":".svg","mime":"image/svg+xml","size":2.75,"url":"/uploads/Data_Grip_68bd723656.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-08-16T12:02:35.789Z","updatedAt":"2024-05-31T12:51:33.598Z"}}},"logo_dark":{"data":{"id":398,"attributes":{"name":"DataGrip.svg","alternativeText":"DataGrip.svg","caption":"DataGrip.svg","width":64,"height":64,"formats":{},"hash":"Data_Grip_68bd723656","ext":".svg","mime":"image/svg+xml","size":2.75,"url":"/uploads/Data_Grip_68bd723656.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-08-16T12:02:35.789Z","updatedAt":"2024-05-31T12:51:33.598Z"}}}}},{"id":15,"attributes":{"shortDescription":"Hex is a modern, collaborative platform with notebooks, data apps, SQL, Python, no-code, R, and so much more","name":"HEX","category":"DATA_VISUALIZATION","supportLevel":"PARTNER","summary":"
\nWithin HEX, select the \"Data Sources\" menu, click on Add a Data Connection then select the ClickHouse tile.\n
\n\n
\nProvide the connection details for your ClickHouse Service:\n- Hostname - The hostname of your ClickHouse Service.\n- Port - The HTTPS port of your ClickHouse Service.\n- Username - Your ClickHouse username (we recommend the creation of a dedicated read-only database user for this purpose).\n- Password - Your ClickHouse password.\n
\n\n
\n>**NOTE:** Make sure to allow HEX's IP addresses to access your ClickHouse Service (HEX IP Addresses)\n
\n\n
\nCongratulations! You have now integrated ClickHouse in HEX.\n
\n","slug":"hex","createdAt":"2022-09-04T11:54:41.476Z","updatedAt":"2024-07-08T11:05:09.465Z","publishedAt":"2022-09-04T11:54:43.141Z","docsLink":"https://learn.hex.tech/docs/connect-to-data/data-connections/overview","about":"Connecting a Clickhouse project to Hex means everyone can securely and efficiently query data right from the flexible notebook environment, without writing Python code or passing around credentials.\n\nHex features full SQL IDE functionality, with autocomplete, a schema browser, caching, and the ability to stream large results.","changelog":null,"version":null,"license":null,"readiness":null,"website":"https://hex.tech/integrations/clickhouse","summaryv2":"\n\n\nWithin HEX, select the \"Data Sources\" menu, click on Add a Data Connection then select the ClickHouse tile.\n\n\n\n\nProvide the connection details for your ClickHouse Service:\n- Hostname - The hostname of your ClickHouse Service.\n- Port - The HTTPS port of your ClickHouse Service.\n- Username - Your ClickHouse username (we recommend the creation of a dedicated read-only database user for this purpose).\n- Password - Your ClickHouse password.\n\n\n\n\n>**NOTE:** Make sure to allow HEX's IP addresses to access your ClickHouse Service (HEX IP Addresses)\n\n\n\n\nCongratulations! You have now integrated ClickHouse in HEX.\n\n\n","changelogv2":null,"aboutv2":"Connecting a Clickhouse project to Hex means everyone can securely and efficiently query data right from the flexible notebook environment, without writing Python code or passing around credentials.\n\nHex features full SQL IDE functionality, with autocomplete, a schema browser, caching, and the ability to stream large results.","openInNewWindow":null,"logo":{"data":{"id":712,"attributes":{"name":"hex.svg","alternativeText":null,"caption":null,"width":52,"height":20,"formats":null,"hash":"hex_da82279856","ext":".svg","mime":"image/svg+xml","size":0.34,"url":"/uploads/hex_da82279856.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:29:10.310Z","updatedAt":"2022-12-09T14:29:18.525Z"}}},"logo_dark":{"data":{"id":2949,"attributes":{"name":"hex-logo-white.svg","alternativeText":null,"caption":null,"width":52,"height":20,"formats":null,"hash":"hex_logo_white_cbe9606f23","ext":".svg","mime":"image/svg+xml","size":0.5,"url":"/uploads/hex_logo_white_cbe9606f23.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-07-04T12:13:31.961Z","updatedAt":"2024-07-04T12:13:31.961Z"}}}}},{"id":16,"attributes":{"shortDescription":"The official client for connecting Node.js applications to ClickHouse","name":"Node.js","category":"LANGUAGE_CLIENT","supportLevel":"CORE","summary":"### Installation\n```sh\nnpm i @clickhouse/client\n```\n\n### Examples\n#### Create an instance\n```js\nimport { createClient } from '@clickhouse/client'\n\nconst client = createClient({\n host: `https://:8443`,\n password: '',\n})\n```\n#### Simple 'command' without a result:\n```js\nawait client.exec({\n query: `\n CREATE TABLE IF NOT EXISTS clickhouse_js_example_table\n (id UInt64, name String)\n ORDER BY (id)\n `,\n clickhouse_settings: {\n wait_end_of_query: 1,\n },\n})\n```\n\n#### Bulk insert\n```js\nawait client.insert({\n table: 'clickhouse_js_example_table',\n values: [\n { id: 42, name: 'foo' },\n { id: 42, name: 'bar' },\n ],\n format: 'JSONEachRow',\n})\n```\n\n#### Basic query\n```js\nconst rows = await client.query({\n query: 'SELECT * FROM clickhouse_js_example_table',\n format: 'JSONEachRow',\n})\n\nconsole.log(await rows.json())\n```","slug":"nodejs","createdAt":"2022-09-26T10:05:22.714Z","updatedAt":"2025-10-30T11:43:16.609Z","publishedAt":"2022-09-26T12:55:02.965Z","docsLink":"https://clickhouse.com/docs/integrations/javascript","about":"The official Node.js client for connecting your applications to ClickHouse. This client implements communication via the HTTP protocol and provides a Promise and Streaming based API, with the support for JSON, CSV, and Tab Separated family formats.","changelog":"
\n## 0.0.10, 2022-11-14\n### New features\n- Remove request listeners synchronously. [#123](https://github.com/ClickHouse/clickhouse-js/issues/123)\n
\n\n
\n## 0.0.9, 2022-10-25\n### New features\n- Added ClickHouse session_id support. [#121](https://github.com/ClickHouse/clickhouse-js/pull/121)\n
\n\n
\n## 0.0.8, 2022-10-18\n### New features\n- Added SSL/TLS support (basic and mutual). [#52](https://github.com/ClickHouse/clickhouse-js/issues/52)\n
\n\n
\n## 0.0.7, 2022-10-18\n### Bug fixes\n- Allow semicolons in select clause. [#116](https://github.com/ClickHouse/clickhouse-js/issues/116)\n
\n\n
\n## 0.0.6, 2022-10-07\n### New features\n- Add JSONObjectEachRow input/output and JSON input formats. [#113](https://github.com/ClickHouse/clickhouse-js/pull/113)\n
\n\n
\n## 0.0.5, 2022-10-04\n### Breaking changes\n - Rows abstraction was renamed to ResultSet.\n - now, every iteration over ResultSet.stream() yields Row[] instead of a single Row. Please check out an example and this PR for more details. These changes allowed us to significantly reduce overhead on select result set streaming.\n### New features\n- split2 is no longer a package dependency.\n
","version":"0.0.10","license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse-js","summaryv2":"### Installation\n```sh\nnpm i @clickhouse/client\n```\n\n### Examples\n#### Create an instance\n```js\nimport { createClient } from '@clickhouse/client'\n\nconst client = createClient({\n host: `https://:8443`,\n password: '',\n})\n```\n#### Simple 'command' without a result:\n```js\nawait client.exec({\n query: `\n CREATE TABLE IF NOT EXISTS clickhouse_js_example_table\n (id UInt64, name String)\n ORDER BY (id)\n `,\n clickhouse_settings: {\n wait_end_of_query: 1,\n },\n})\n```\n\n#### Bulk insert\n```js\nawait client.insert({\n table: 'clickhouse_js_example_table',\n values: [\n { id: 42, name: 'foo' },\n { id: 42, name: 'bar' },\n ],\n format: 'JSONEachRow',\n})\n```\n\n#### Basic query\n```js\nconst rows = await client.query({\n query: 'SELECT * FROM clickhouse_js_example_table',\n format: 'JSONEachRow',\n})\n\nconsole.log(await rows.json())\n```","changelogv2":"\n\n\n### New features\n- Remove request listeners synchronously. [#123](https://github.com/ClickHouse/clickhouse-js/issues/123)\n\n\n\n\n### New features\n- Added ClickHouse session_id support. [#121](https://github.com/ClickHouse/clickhouse-js/pull/121)\n\n\n\n\n### New features\n- Added SSL/TLS support (basic and mutual). [#52](https://github.com/ClickHouse/clickhouse-js/issues/52)\n\n\n\n\n### Bug fixes\n- Allow semicolons in select clause. [#116](https://github.com/ClickHouse/clickhouse-js/issues/116)\n\n\n\n\n### New features\n- Add JSONObjectEachRow input/output and JSON input formats. [#113](https://github.com/ClickHouse/clickhouse-js/pull/113)\n\n\n\n\n### Breaking changes\n - Rows abstraction was renamed to ResultSet.\n - now, every iteration over ResultSet.stream() yields Row[] instead of a single Row. Please check out an example and this PR for more details. These changes allowed us to significantly reduce overhead on select result set streaming.\n### New features\n- split2 is no longer a package dependency.\n\n\n","aboutv2":"The official Node.js client for connecting your applications to ClickHouse. This client implements communication via the HTTP protocol and provides a Promise and Streaming based API, with the support for JSON, CSV, and Tab Separated family formats.","openInNewWindow":null,"logo":{"data":{"id":457,"attributes":{"name":"nodejs-icon-logo.svg","alternativeText":"nodejs-icon-logo.svg","caption":"nodejs-icon-logo.svg","width":282,"height":282,"formats":null,"hash":"nodejs_icon_logo_2999074544","ext":".svg","mime":"image/svg+xml","size":2.58,"url":"/uploads/nodejs_icon_logo_2999074544.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-09-26T09:02:58.521Z","updatedAt":"2022-09-26T09:02:58.521Z"}}},"logo_dark":{"data":{"id":457,"attributes":{"name":"nodejs-icon-logo.svg","alternativeText":"nodejs-icon-logo.svg","caption":"nodejs-icon-logo.svg","width":282,"height":282,"formats":null,"hash":"nodejs_icon_logo_2999074544","ext":".svg","mime":"image/svg+xml","size":2.58,"url":"/uploads/nodejs_icon_logo_2999074544.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-09-26T09:02:58.521Z","updatedAt":"2022-09-26T09:02:58.521Z"}}}}},{"id":17,"attributes":{"shortDescription":"A lightweight, ultra-fast tool for building observability pipelines with built-in compatibility with ClickHouse","name":"Vector","category":"DATA_INGESTION","supportLevel":"PARTNER","summary":"The following example shows a `toml` configuration file for a ClickHouse sink in Vector:\n```toml\n[sinks.clickhouse]\ntype = \"clickhouse\"\ninputs = [\"\"]\nendpoint = \"https://:8443\"\ndatabase = \"\"\ntable = \"\"\nauth.strategy = \"basic\"\nauth.user = \"username\"\nauth.password = \"password\"\n```","slug":"vector","createdAt":"2022-09-29T12:29:30.478Z","updatedAt":"2025-10-31T11:10:45.368Z","publishedAt":"2022-09-29T12:30:05.478Z","docsLink":"https://clickhouse.com/docs/integrations/vector-to-clickhouse/","about":"Vector collects, transforms and routes logs, metrics, and traces (referred to as sources) to different destinations (referred to as sinks), including an out-of-the-box compatibility with ClickHouse. ","changelog":null,"version":"0.25.2","license":"mpl2","readiness":null,"website":"https://vector.dev/","summaryv2":"The following example shows a `toml` configuration file for a ClickHouse sink in Vector:\n```toml\n[sinks.clickhouse]\ntype = \"clickhouse\"\ninputs = [\"\"]\nendpoint = \"https://:8443\"\ndatabase = \"\"\ntable = \"\"\nauth.strategy = \"basic\"\nauth.user = \"username\"\nauth.password = \"password\"\n```","changelogv2":null,"aboutv2":"Vector collects, transforms and routes logs, metrics, and traces (referred to as sources) to different destinations (referred to as sinks), including an out-of-the-box compatibility with ClickHouse. ","openInNewWindow":null,"logo":{"data":{"id":711,"attributes":{"name":"vector.svg","alternativeText":null,"caption":null,"width":100,"height":120,"formats":null,"hash":"vector_b25ded725a","ext":".svg","mime":"image/svg+xml","size":1.17,"url":"/uploads/vector_b25ded725a.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:17:43.189Z","updatedAt":"2022-12-09T14:17:43.189Z"}}},"logo_dark":{"data":{"id":711,"attributes":{"name":"vector.svg","alternativeText":null,"caption":null,"width":100,"height":120,"formats":null,"hash":"vector_b25ded725a","ext":".svg","mime":"image/svg+xml","size":1.17,"url":"/uploads/vector_b25ded725a.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:17:43.189Z","updatedAt":"2022-12-09T14:17:43.189Z"}}}}},{"id":18,"attributes":{"shortDescription":"Metabase is an easy-to-use, open source UI tool for asking questions about your data","name":"Metabase","category":"DATA_VISUALIZATION","supportLevel":"PARTNER","summary":"> **NEW:** We have recently contributed to version `0.9.0` of metabase-clickhouse-driver which includes bug fixes and a support for the latest JDBC driver\n\n \n\n## Installation\nWe recommend running Metabase as a Docker container. Alternatively, you can also use the Metabase JAR. We present below the two options:\n\n### Run as a Docker container\n\n```bash\nmkdir -p mb/plugins && cd mb\ncurl -L -o plugins/ch.jar https://github.com/clickhouse/metabase-clickhouse-driver/releases/download/0.9.0/clickhouse.metabase-driver.jar\ndocker run -d -p 3000:3000 \\\n --mount type=bind,source=$PWD/plugins/ch.jar,destination=/plugins/clickhouse.jar \\\n --name metabase metabase/metabase:v0.44.6\n```\n\n \n\n### Run using Metabase JAR\n
\nDownload a fairly recent Metabase binary release (jar file) from the [Metabase distribution page](https://metabase.com/start/jar.html).\n
\n\n
\nDownload the ClickHouse driver jar from this repository's [Releases](https://github.com/clickhouse/metabase-clickhouse-driver/releases) page\n
\n\n
\nCreate a directory and copy the `metabase.jar` to it. In that directory create a sub-directory called `plugins` and copy the ClickHouse driver jar to the `plugins` directory.\n
\n\n
\nMake sure you are the in the directory where your `metabase.jar` lives and run `MB_PLUGINS_DIR=./plugins; java -jar metabase.jar`.\n
\n\n
\nFor example (using Metabase v0.44.6 and ClickHouse driver 0.9.0):\n\n```bash\nmkdir -p mb/plugins && cd mb\ncurl -o metabase.jar https://downloads.metabase.com/v0.44.6/metabase.jar\ncurl -L -o plugins/ch.jar https://github.com/clickhouse/metabase-clickhouse-driver/releases/download/0.9.0/clickhouse.metabase-driver.jar\nMB_PLUGINS_DIR=./plugins; java -jar metabase.jar\n```\n
\n\n \n\n## Connect to ClickHouse Cloud\n\n
\nStart (or restart) Metabase and access it at http://hostname:3000, click on \"Add a database\" and select \"ClickHouse\".\n
\n\n
\nEnter the connection details of your ClickHouse Cloud service and click \"Save\" (make sure to check the \"Use secure connection\" option).\n
\n\n
\nStart building data visualizations against your ClickHouse tables!\n
","slug":"metabase","createdAt":"2022-09-29T13:27:25.153Z","updatedAt":"2025-10-18T10:38:06.656Z","publishedAt":"2022-09-29T13:27:27.269Z","docsLink":"https://clickhouse.com/docs/integrations/metabase","about":"Metabase is a Java application that can be run by simply downloading the JAR file and running it with `java -jar metabase.jar`. Metabase connects to ClickHouse using a JDBC driver that you download and put in the `plugins` folder.\n","changelog":null,"version":"0.9.1","license":"agpl3","readiness":null,"website":"https://github.com/clickhouse/metabase-clickhouse-driver","summaryv2":"> **NEW:** We have recently contributed to version `0.9.0` of metabase-clickhouse-driver which includes bug fixes and a support for the latest JDBC driver\n\n \n\n## Installation\nWe recommend running Metabase as a Docker container. Alternatively, you can also use the Metabase JAR. We present below the two options:\n\n### Run as a Docker container\n\n```bash\nmkdir -p mb/plugins && cd mb\ncurl -L -o plugins/ch.jar https://github.com/clickhouse/metabase-clickhouse-driver/releases/download/0.9.0/clickhouse.metabase-driver.jar\ndocker run -d -p 3000:3000 \\\n --mount type=bind,source=$PWD/plugins/ch.jar,destination=/plugins/clickhouse.jar \\\n --name metabase metabase/metabase:v0.44.6\n```\n\n \n\n### Run using Metabase JAR\n\n\n\nDownload a fairly recent Metabase binary release (jar file) from the [Metabase distribution page](https://metabase.com/start/jar.html).\n\n\n\n\nDownload the ClickHouse driver jar from this repository's [Releases](https://github.com/clickhouse/metabase-clickhouse-driver/releases) page\n\n\n\n\nCreate a directory and copy the `metabase.jar` to it. In that directory create a sub-directory called `plugins` and copy the ClickHouse driver jar to the `plugins` directory.\n\n\n\n\nMake sure you are the in the directory where your `metabase.jar` lives and run `MB_PLUGINS_DIR=./plugins; java -jar metabase.jar`.\n\n\n\n\nFor example (using Metabase v0.44.6 and ClickHouse driver 0.9.0):\n\n```bash\nmkdir -p mb/plugins && cd mb\ncurl -o metabase.jar https://downloads.metabase.com/v0.44.6/metabase.jar\ncurl -L -o plugins/ch.jar https://github.com/clickhouse/metabase-clickhouse-driver/releases/download/0.9.0/clickhouse.metabase-driver.jar\nMB_PLUGINS_DIR=./plugins; java -jar metabase.jar\n```\n\n\n\n\n \n\n## Connect to ClickHouse Cloud\n\n\n\nStart (or restart) Metabase and access it at http://hostname:3000, click on \"Add a database\" and select \"ClickHouse\".\n\n\n\n\nEnter the connection details of your ClickHouse Cloud service and click \"Save\" (make sure to check the \"Use secure connection\" option).\n\n\n\n\nStart building data visualizations against your ClickHouse tables!\n\n\n","changelogv2":null,"aboutv2":"Metabase is a Java application that can be run by simply downloading the JAR file and running it with `java -jar metabase.jar`. Metabase connects to ClickHouse using a JDBC driver that you download and put in the `plugins` folder.\n","openInNewWindow":null,"logo":{"data":{"id":710,"attributes":{"name":"metabase_logo.svg","alternativeText":null,"caption":null,"width":50,"height":50,"formats":null,"hash":"metabase_logo_e47ee588b6","ext":".svg","mime":"image/svg+xml","size":1.7,"url":"/uploads/metabase_logo_e47ee588b6.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:17:43.188Z","updatedAt":"2022-12-09T14:17:43.188Z"}}},"logo_dark":{"data":{"id":710,"attributes":{"name":"metabase_logo.svg","alternativeText":null,"caption":null,"width":50,"height":50,"formats":null,"hash":"metabase_logo_e47ee588b6","ext":".svg","mime":"image/svg+xml","size":1.7,"url":"/uploads/metabase_logo_e47ee588b6.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:17:43.188Z","updatedAt":"2022-12-09T14:17:43.188Z"}}}}},{"id":19,"attributes":{"shortDescription":"Tableau is a popular visual analytics platform for business intelligence","name":"Tableau","category":"DATA_VISUALIZATION","supportLevel":"CORE","summary":"
\nGather your ClickHouse Cloud Service connection details. Tableau Desktop uses JDBC over HTTP(S) to connect to ClickHouse; you will need:\n- host\n- HTTP port number\n- username\n- password\n
\n\n
\nDownload and install Tableau desktop.\n
\n\n
\nDownload the latest version of the ANALYTIKA PLUS clickhouse-tableau-connector-jdbc TACO connector.\n
\n\n
\nStore the TACO connector in the following folder (based on your OS):\n- MacOS: `~/Documents/My Tableau Repository/Connectors`\n- Windows: `C:\\Users[Windows User]\\Documents\\My Tableau Repository\\Connectors`\n
\n\n
\nFollow `clickhouse-tableau-connector-jdbc` instructions to download the compatible version of ClickHouse JDBC driver.\n
\n>**Note**: Make sure you download the clickhouse-jdbc-x.x.x-shaded.jar JAR file.\n
\n\n
\nStore the JDBC driver in the following folder (based on your OS, if the folder doesn't exist you can create it):\n- MacOS: `~/Library/Tableau/Drivers`\n- Windows: `C:\\Program Files\\Tableau\\Drivers`\n
\n\n
\nConfigure a ClickHouse data source in Tableau and start building data visualizations!\n
\n\n### Other installation options\nMore installation options are available here.","slug":"tableau","createdAt":"2022-10-04T09:56:28.102Z","updatedAt":"2026-02-01T06:55:21.765Z","publishedAt":"2022-10-04T10:00:27.052Z","docsLink":"https://clickhouse.com/docs/integrations/tableau","about":"Tableau Desktop can use ClickHouse databases and tables as a data source.","changelog":null,"version":"0.2.4","license":"apache2","readiness":null,"website":"https://github.com/analytikaplus/clickhouse-tableau-connector-jdbc","summaryv2":"\n\n\nGather your ClickHouse Cloud Service connection details. Tableau Desktop uses JDBC over HTTP(S) to connect to ClickHouse; you will need:\n- host\n- HTTP port number\n- username\n- password\n\n\n\n\nDownload and install Tableau desktop.\n\n\n\n\nDownload the latest version of the ANALYTIKA PLUS clickhouse-tableau-connector-jdbc TACO connector.\n\n\n\n\nStore the TACO connector in the following folder (based on your OS):\n- MacOS: `~/Documents/My Tableau Repository/Connectors`\n- Windows: `C:\\Users[Windows User]\\Documents\\My Tableau Repository\\Connectors`\n\n\n\n\nFollow `clickhouse-tableau-connector-jdbc` instructions to download the compatible version of ClickHouse JDBC driver.\n
\n>**Note**: Make sure you download the clickhouse-jdbc-x.x.x-shaded.jar JAR file.\n\n\n\n\nStore the JDBC driver in the following folder (based on your OS, if the folder doesn't exist you can create it):\n- MacOS: `~/Library/Tableau/Drivers`\n- Windows: `C:\\Program Files\\Tableau\\Drivers`\n\n\n\n\nConfigure a ClickHouse data source in Tableau and start building data visualizations!\n\n\n\n\n### Other installation options\nMore installation options are available here.","changelogv2":null,"aboutv2":"Tableau Desktop can use ClickHouse databases and tables as a data source.","openInNewWindow":null,"logo":{"data":{"id":713,"attributes":{"name":"tableau.svg","alternativeText":null,"caption":null,"width":60,"height":60,"formats":null,"hash":"tableau_f8ae0f3747","ext":".svg","mime":"image/svg+xml","size":0.82,"url":"/uploads/tableau_f8ae0f3747.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:30:58.103Z","updatedAt":"2022-12-09T14:30:58.103Z"}}},"logo_dark":{"data":{"id":713,"attributes":{"name":"tableau.svg","alternativeText":null,"caption":null,"width":60,"height":60,"formats":null,"hash":"tableau_f8ae0f3747","ext":".svg","mime":"image/svg+xml","size":0.82,"url":"/uploads/tableau_f8ae0f3747.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-12-09T14:30:58.103Z","updatedAt":"2022-12-09T14:30:58.103Z"}}}}},{"id":20,"attributes":{"shortDescription":"Official ADO.NET client for ClickHouse","name":"C#","category":"LANGUAGE_CLIENT","supportLevel":"CORE","summary":"### Installation\n\n```\ndotnet add package ClickHouse.Driver\n```\n### Examples\n#### Basic query\n```csharp\nusing System;\nusing System.Threading.Tasks;\nusing ClickHouse.Driver.ADO;\nusing ClickHouse.Driver.Utility;\nnamespace Clickhouse.Test;\nclass Program\n{\n static async Task Main(string[] args)\n {\n using var connection = new ClickHouseConnection(\"Host=;Protocol=https;Port=8443;Username=default;Password=\")\n // ExecuteScalarAsync is an async extension which creates command and executes it\n var version = await connection.ExecuteScalarAsync(\"SELECT version()\");\n Console.WriteLine(version);\n }\n}\n```\n\n#### Bulk insert\n```csharp\nusing var connection = new ClickHouseConnection(\"Host=;Protocol=https;Port=8443;Username=default;Password=\");\nusing var bulkCopyInterface = new ClickHouseBulkCopy(connection)\n{\n DestinationTableName = \".\",\n BatchSize = 100000\n};\n// Example data to insert\nvar values = Enumerable.Range(0, 1000000).Select(i => new object[] { (long)i, \"value\" + i.ToString() });\nawait bulkCopyInterface.WriteToServerAsync(values);\nConsole.WriteLine(bulkCopyInterface.RowsWritten);\n```","slug":"csharp","createdAt":"2022-11-04T16:35:33.810Z","updatedAt":"2025-09-29T15:00:09.557Z","publishedAt":"2022-11-04T16:35:54.827Z","docsLink":"https://clickhouse.com/docs/integrations/csharp","about":"","changelog":null,"version":null,"license":"mit","readiness":null,"website":"https://github.com/ClickHouse/clickhouse-cs","summaryv2":"### Installation\n\n```\ndotnet add package ClickHouse.Driver\n```\n### Examples\n#### Basic query\n```csharp\nusing System;\nusing System.Threading.Tasks;\nusing ClickHouse.Driver.ADO;\nusing ClickHouse.Driver.Utility;\nnamespace Clickhouse.Test;\nclass Program\n{\n static async Task Main(string[] args)\n {\n using var connection = new ClickHouseConnection(\"Host=;Protocol=https;Port=8443;Username=default;Password=\")\n // ExecuteScalarAsync is an async extension which creates command and executes it\n var version = await connection.ExecuteScalarAsync(\"SELECT version()\");\n Console.WriteLine(version);\n }\n}\n```\n\n#### Bulk insert\n```csharp\nusing ClickHouse.Driver.ADO;\nusing ClickHouse.Driver.Copy;\n\nusing (var connection = new ClickHouseConnection(connectionString))\n{\n connection.Open();\n\n using var bulkInsert = new ClickHouseBulkCopy(connection)\n {\n DestinationTableName = \"default.my_table\",\n MaxDegreeOfParallelism = 2,\n BatchSize = 100\n };\n \n var values = Enumerable.Range(0, 100).Select(i => new object[] { (long)i, \"value\" + i.ToString() });\n await bulkInsert.WriteToServerAsync(values);\n Console.WriteLine($\"Rows written: {bulkInsert.RowsWritten}\");\n}\n```","changelogv2":null,"aboutv2":"","openInNewWindow":null,"logo":{"data":{"id":625,"attributes":{"name":"csharp.svg","alternativeText":"csharp.svg","caption":"csharp.svg","width":256,"height":290,"formats":null,"hash":"c_4_2479be30be","ext":".svg","mime":"image/svg+xml","size":1.82,"url":"/uploads/c_4_2479be30be.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-11-04T16:39:03.113Z","updatedAt":"2022-11-04T16:39:18.173Z"}}},"logo_dark":{"data":{"id":625,"attributes":{"name":"csharp.svg","alternativeText":"csharp.svg","caption":"csharp.svg","width":256,"height":290,"formats":null,"hash":"c_4_2479be30be","ext":".svg","mime":"image/svg+xml","size":1.82,"url":"/uploads/c_4_2479be30be.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2022-11-04T16:39:03.113Z","updatedAt":"2022-11-04T16:39:18.173Z"}}}}},{"id":21,"attributes":{"shortDescription":"Perfom SELECT and INSERT operations on data stored on a remote PostgreSQL server directly from ClickHouse","name":"PostgreSQL","category":"DATA_INGESTION","supportLevel":"CORE","summary":"> **Note:** It's possible that you need to authorize ClickHouse Cloud to access to your PostgreSQL instance. The full list of ClickHouse Cloud egress IP addresses is available here.\n\n### Use the the PostgreSQL table function\nYou can use the PostgreSQL table function to query remote PostgreSQL tables.\n\n```sql\nSELECT * FROM PostgreSQL(':', '', '', '', '');\n```\n\n### Create a PostgreSQL table without providing a schema\n\nYou can also create a table using PostgreSQL table engine in your ClickHouse Cloud service without specifying a schema using the `CREATE TABLE AS` syntax. When ClickHouse creates the table locally, types in PostgreSQL will be mapped to equivalent ClickHouse types.\n\n``` sql\nCREATE TABLE mycountries AS PostgreSQL(':5432', '', 'countries', 'postgres', '');\n``` \n\n> **Note:** You can use the setting `external_table_functions_use_nulls = 0` to ensure Null values are represented as their default values (instead of Null). If set to 1 (the default), ClickHouse will create Nullable variants of each column.\n\n\n### Create a PostgreSQL table with a custom schema\n\nYou can also decide to create a table with the PostgreSQL table engine in your ClickHouse service by specifying a custom schema. \n\n``` sql\nCREATE TABLE default.postgresql_table\n(\n `float_nullable` Nullable(Float32),\n `str` String,\n `int_id` Int32\n)\nENGINE = PostgreSQL(':', '', '', '', '');\n```\n","slug":"postgres","createdAt":"2022-12-30T16:39:15.946Z","updatedAt":"2025-10-30T11:40:56.723Z","publishedAt":"2022-12-30T16:39:20.417Z","docsLink":"https://clickhouse.com/docs/integrations/postgresql","about":"PostgreSQL, also known as Postgres, is a free and open-source relational database management system focused on extensibility, SQL compliance, and ACID properties via transactions. As the world’s most popular OSS OLTP (Online transaction processing) database , it is used for use cases where data is highly transactional and there is a need to support thousands of concurrent users.","changelog":null,"version":null,"license":"apache2","readiness":null,"website":null,"summaryv2":"> **Note:** It's possible that you need to authorize ClickHouse Cloud to access to your PostgreSQL instance. The full list of ClickHouse Cloud egress IP addresses is available here.\n\n### Use the the PostgreSQL table function\nYou can use the PostgreSQL table function to query remote PostgreSQL tables.\n\n```sql\nSELECT * FROM PostgreSQL(':', '', '', '', '');\n```\n\n### Create a PostgreSQL table without providing a schema\n\nYou can also create a table using PostgreSQL table engine in your ClickHouse Cloud service without specifying a schema using the `CREATE TABLE AS` syntax. When ClickHouse creates the table locally, types in PostgreSQL will be mapped to equivalent ClickHouse types.\n\n``` sql\nCREATE TABLE mycountries AS PostgreSQL(':5432', '', 'countries', 'postgres', '');\n``` \n\n> **Note:** You can use the setting `external_table_functions_use_nulls = 0` to ensure Null values are represented as their default values (instead of Null). If set to 1 (the default), ClickHouse will create Nullable variants of each column.\n\n\n### Create a PostgreSQL table with a custom schema\n\nYou can also decide to create a table with the PostgreSQL table engine in your ClickHouse service by specifying a custom schema. \n\n``` sql\nCREATE TABLE default.postgresql_table\n(\n `float_nullable` Nullable(Float32),\n `str` String,\n `int_id` Int32\n)\nENGINE = PostgreSQL(':', '', '', '', '');\n```\n","changelogv2":null,"aboutv2":"PostgreSQL, also known as Postgres, is a free and open-source relational database management system focused on extensibility, SQL compliance, and ACID properties via transactions. As the world’s most popular OSS OLTP (Online transaction processing) database , it is used for use cases where data is highly transactional and there is a need to support thousands of concurrent users.","openInNewWindow":null,"logo":{"data":{"id":789,"attributes":{"name":"postgres.svg","alternativeText":null,"caption":null,"width":432,"height":445,"formats":null,"hash":"postgres_fd1362729c","ext":".svg","mime":"image/svg+xml","size":4.3,"url":"/uploads/postgres_fd1362729c.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-01-02T12:30:12.180Z","updatedAt":"2023-01-02T12:30:12.180Z"}}},"logo_dark":{"data":{"id":789,"attributes":{"name":"postgres.svg","alternativeText":null,"caption":null,"width":432,"height":445,"formats":null,"hash":"postgres_fd1362729c","ext":".svg","mime":"image/svg+xml","size":4.3,"url":"/uploads/postgres_fd1362729c.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-01-02T12:30:12.180Z","updatedAt":"2023-01-02T12:30:12.180Z"}}}}},{"id":22,"attributes":{"shortDescription":"Perfom SELECT and INSERT operations on data stored on a remote MySQL server directly from ClickHouse","name":"MySQL","category":"DATA_INGESTION","supportLevel":"CORE","summary":"> **Note:** It's possible that you need to authorize ClickHouse Cloud to access to your MySQL instance. The full list of ClickHouse Cloud egress IP addresses is available here.\n\n### Use the the MySQL table function\nYou can use the MySQL table function to query remote MySQL tables.\n\n```sql\nSELECT * FROM mysql(':', '', '
', '', '');\n```\n\n### Create a MySQL table without providing a schema\n\nYou can also create a table using PostgreSQL table engine in your ClickHouse Cloud service without specifying a schema using the `CREATE TABLE AS` syntax. When ClickHouse creates the table locally, types in MySQL will be mapped to equivalent ClickHouse types.\n\n``` sql\nCREATE TABLE mycountries AS mysql(':', '', '
', '', '');\n``` \n\n> **Note:** You can use the setting `external_table_functions_use_nulls = 0` to ensure Null values are represented as their default values (instead of Null). If set to 1 (the default), ClickHouse will create Nullable variants of each column.\n\n\n### Create a MySQL table with a custom schema\n\nYou can also decide to create a table with the MySQL table engine in your ClickHouse service by specifying a custom schema. \n\n``` sql\nCREATE TABLE default.mysql_table\n(\n `float_nullable` Nullable(Float32),\n `str` String,\n `int_id` Int32\n)\nENGINE = mysql(':', '', '
', '', '')\n```\n","slug":"mysql","createdAt":"2023-01-13T11:28:25.550Z","updatedAt":"2025-10-30T11:40:35.485Z","publishedAt":"2023-01-13T11:28:27.311Z","docsLink":"https://clickhouse.com/docs/integrations/mysql","about":"MySQL is a widespread open-source relational database management system currently developed by Oracle","changelog":null,"version":null,"license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse","summaryv2":"> **Note:** It's possible that you need to authorize ClickHouse Cloud to access to your MySQL instance. The full list of ClickHouse Cloud egress IP addresses is available here.\n\n### Use the the MySQL table function\nYou can use the MySQL table function to query remote MySQL tables.\n\n```sql\nSELECT * FROM mysql(':', '', '
', '', '');\n```\n\n### Create a MySQL table without providing a schema\n\nYou can also create a table using PostgreSQL table engine in your ClickHouse Cloud service without specifying a schema using the `CREATE TABLE AS` syntax. When ClickHouse creates the table locally, types in MySQL will be mapped to equivalent ClickHouse types.\n\n``` sql\nCREATE TABLE mycountries AS mysql(':', '', '
', '', '');\n``` \n\n> **Note:** You can use the setting `external_table_functions_use_nulls = 0` to ensure Null values are represented as their default values (instead of Null). If set to 1 (the default), ClickHouse will create Nullable variants of each column.\n\n\n### Create a MySQL table with a custom schema\n\nYou can also decide to create a table with the MySQL table engine in your ClickHouse service by specifying a custom schema. \n\n``` sql\nCREATE TABLE default.mysql_table\n(\n `float_nullable` Nullable(Float32),\n `str` String,\n `int_id` Int32\n)\nENGINE = mysql(':', '', '
', '', '')\n```\n","changelogv2":null,"aboutv2":"MySQL is a widespread open-source relational database management system currently developed by Oracle","openInNewWindow":null,"logo":{"data":{"id":835,"attributes":{"name":"mysql.svg","alternativeText":null,"caption":null,"width":507,"height":302,"formats":null,"hash":"mysql_b3d0b8fe01","ext":".svg","mime":"image/svg+xml","size":5.54,"url":"/uploads/mysql_b3d0b8fe01.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-01-13T11:31:26.847Z","updatedAt":"2023-01-13T11:31:26.847Z"}}},"logo_dark":{"data":{"id":2953,"attributes":{"name":"mysql-dark.svg","alternativeText":null,"caption":null,"width":391,"height":202,"formats":null,"hash":"mysql_dark_c9573a8ee2","ext":".svg","mime":"image/svg+xml","size":7.94,"url":"/uploads/mysql_dark_c9573a8ee2.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-07-04T12:20:25.409Z","updatedAt":"2024-07-04T12:20:25.409Z"}}}}},{"id":23,"attributes":{"shortDescription":"Import from, export to, and transform GCS data in flight with ClickHouse built-in functions","name":"Google Cloud Storage (GCS)","category":"DATA_INGESTION","supportLevel":"CORE","summary":"### Reading data from Google Cloud Storage\nUsing ClickHouse S3 table function, users can query GCS data as a source without requiring persistence in ClickHouse. The following example illustrates how to read 10 rows of the \nNYC Taxi dataset.\n```sql\nSELECT \n trip_id, \n total_amount, \n pickup_longitude, \n pickup_latitude, \n dropoff_longitude, \n dropoff_latitude, \n pickup_datetime, \n dropoff_datetime,\n trip_distance\nFROM s3(\n 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',\n 'TabSeparatedWithNames'\n) LIMIT 10;\n```\n\n### Inserting Data from Google Cloud Storage\nTo transfer data from GCS to ClickHouse, users can combine the s3 table function with INSERT statement. Let's create an empty `trips` table:\n```sql\nCREATE TABLE trips ORDER BY tuple\n(\n) EMPTY AS SELECT * FROM s3(\n 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',\n 'TabSeparatedWithNames'\n);\n```\nThis creates an empty table using the schema inferred from the data. We can then insert the first 1 million rows from the remote dataset\n```sql\nINSERT INTO trips SELECT *\nFROM \ns3(\n 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',\n 'TabSeparatedWithNames'\n )\nLIMIT 1000000;\n```","slug":"gcs","createdAt":"2023-04-13T16:00:38.201Z","updatedAt":"2025-10-18T10:21:03.577Z","publishedAt":"2023-04-13T16:00:41.353Z","docsLink":"https://clickhouse.com/docs/sql-reference/table-functions/s3","about":"Google Cloud Storage (GCS) is a RESTful online file storage web service for storing and accessing data on Google Cloud Platform infrastructure. Users can insert GCS based data into ClickHouse and use GCS as an export destination, thus allowing interaction with “Data Lake” architectures. Furthermore, GCS can provide “cold” storage tiers and assist with separating storage and compute.","changelog":null,"version":null,"license":"apache2","readiness":null,"website":"https://github.com/ClickHouse/clickhouse","summaryv2":"### Reading data from Google Cloud Storage\nUsing ClickHouse S3 table function, users can query GCS data as a source without requiring persistence in ClickHouse. The following example illustrates how to read 10 rows of the \nNYC Taxi dataset.\n```sql\nSELECT \n trip_id, \n total_amount, \n pickup_longitude, \n pickup_latitude, \n dropoff_longitude, \n dropoff_latitude, \n pickup_datetime, \n dropoff_datetime,\n trip_distance\nFROM s3(\n 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',\n 'TabSeparatedWithNames'\n) LIMIT 10;\n```\n\n### Inserting Data from Google Cloud Storage\nTo transfer data from GCS to ClickHouse, users can combine the s3 table function with INSERT statement. Let's create an empty `trips` table:\n```sql\nCREATE TABLE trips ORDER BY tuple\n(\n) EMPTY AS SELECT * FROM s3(\n 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',\n 'TabSeparatedWithNames'\n);\n```\nThis creates an empty table using the schema inferred from the data. We can then insert the first 1 million rows from the remote dataset\n```sql\nINSERT INTO trips SELECT *\nFROM \ns3(\n 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',\n 'TabSeparatedWithNames'\n )\nLIMIT 1000000;\n```","changelogv2":null,"aboutv2":"Google Cloud Storage (GCS) is a RESTful online file storage web service for storing and accessing data on Google Cloud Platform infrastructure. Users can insert GCS based data into ClickHouse and use GCS as an export destination, thus allowing interaction with “Data Lake” architectures. Furthermore, GCS can provide “cold” storage tiers and assist with separating storage and compute.","openInNewWindow":null,"logo":{"data":{"id":1120,"attributes":{"name":"gcs_logo.svg","alternativeText":null,"caption":null,"width":166,"height":171,"formats":null,"hash":"gcs_logo_518093bb73","ext":".svg","mime":"image/svg+xml","size":1.74,"url":"/uploads/gcs_logo_518093bb73.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-04-13T16:04:58.461Z","updatedAt":"2023-04-13T16:04:58.461Z"}}},"logo_dark":{"data":{"id":1120,"attributes":{"name":"gcs_logo.svg","alternativeText":null,"caption":null,"width":166,"height":171,"formats":null,"hash":"gcs_logo_518093bb73","ext":".svg","mime":"image/svg+xml","size":1.74,"url":"/uploads/gcs_logo_518093bb73.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-04-13T16:04:58.461Z","updatedAt":"2023-04-13T16:04:58.461Z"}}}}},{"id":24,"attributes":{"shortDescription":"Connect your ClickHouse instance to Prequel to sync data to and from your users data warehouses, databases, and object storage","name":"Prequel","category":"DATA_INGESTION","supportLevel":"PARTNER","summary":"### Sharing data from ClickHouse to your customers & partners\n
\nWithin Prequel, under the \"Data Export\" navigation pane, select the \"Data Sources\" menu, click \"Add Source\", and then select the ClickHouse option.\n
\n\n
\nProvide the connection details for your ClickHouse Service:\n- Host - The hostname of the ClickHouse service.\n- Port - Most likely 9440 for ClickHouse. \n- Username - A read-only ClickHouse user created for data transfers. \n- Password - The password for the ClickHouse user.\n- Database - The database to transfer data from.\n
\n\n
\nConfigure your data model in Prequel by selecting one or more of your tables and the relevant columns to share.\n
\n\n
\nCongratulations! You can now begin connecting to external destinations to begin sharing data from ClickHouse.\n
\n\n### Importing data to ClickHouse from your customers & partners\n
\nWithin Prequel, under the \"Data Import\" navigation pane, select the \"Data Destinations\" menu, click \"Add Destination\", and then select the ClickHouse option.\n
\n\n
\nProvide the connection details for your ClickHouse Service:\n- Host - The hostname of the ClickHouse service.\n- Port - 9440 for ClickHous Cloud\n- Username - A ClickHouse user that can read and write data created for data transfers. \n- Password - The password for the ClickHouse user.\n- Database - The database to load data into.\n
\n\n
\nCongratulations! You can now begin connecting to external sources to import data into ClickHouse.\n
\n\n\n>**NOTE:** Make sure to allow Prequel's IP addresses to access your ClickHouse Service (Prequel IP Addresses)","slug":"prequel","createdAt":"2023-06-09T13:38:52.790Z","updatedAt":"2024-03-11T16:17:47.241Z","publishedAt":"2023-06-09T13:38:54.714Z","docsLink":"https://docs.prequel.co/docs/sources-clickhouse-generic","about":"Connecting a ClickHouse project to Prequel can help you and your team efficiently and reliably share data to and from your customers and users. \nPrequel features high volume replication functionality, with most connection modalities (SSH tunneling, IP whitelisting) and high reliability. ","changelog":null,"version":null,"license":null,"readiness":null,"website":"https://www.prequel.co/","summaryv2":"### Sharing data from ClickHouse to your customers & partners\n\n\n\nWithin Prequel, under the \"Data Export\" navigation pane, select the \"Data Sources\" menu, click \"Add Source\", and then select the ClickHouse option.\n\n\n\n\nProvide the connection details for your ClickHouse Service:\n- Host - The hostname of the ClickHouse service.\n- Port - Most likely 9440 for ClickHouse. \n- Username - A read-only ClickHouse user created for data transfers. \n- Password - The password for the ClickHouse user.\n- Database - The database to transfer data from.\n\n\n\n\nConfigure your data model in Prequel by selecting one or more of your tables and the relevant columns to share.\n\n\n\n\nCongratulations! You can now begin connecting to external destinations to begin sharing data from ClickHouse.\n\n\n\n\n### Importing data to ClickHouse from your customers & partners\n\n\n\n\nWithin Prequel, under the \"Data Import\" navigation pane, select the \"Data Destinations\" menu, click \"Add Destination\", and then select the ClickHouse option.\n\n\n\n\nProvide the connection details for your ClickHouse Service:\n- Host - The hostname of the ClickHouse service.\n- Port - 9440 for ClickHous Cloud\n- Username - A ClickHouse user that can read and write data created for data transfers. \n- Password - The password for the ClickHouse user.\n- Database - The database to load data into.\n\n\n\n\nCongratulations! You can now begin connecting to external sources to import data into ClickHouse.\n\n\n\n\n\n>**NOTE:** Make sure to allow Prequel's IP addresses to access your ClickHouse Service (Prequel IP Addresses)","changelogv2":null,"aboutv2":"Connecting a ClickHouse project to Prequel can help you and your team efficiently and reliably share data to and from your customers and users. \nPrequel features high volume replication functionality, with most connection modalities (SSH tunneling, IP whitelisting) and high reliability. ","openInNewWindow":null,"logo":{"data":{"id":1487,"attributes":{"name":"prequel.svg","alternativeText":null,"caption":null,"width":576,"height":576,"formats":null,"hash":"prequel_c7e262d07b","ext":".svg","mime":"image/svg+xml","size":1.33,"url":"/uploads/prequel_c7e262d07b.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-06-09T13:37:22.493Z","updatedAt":"2023-06-09T13:37:22.493Z"}}},"logo_dark":{"data":{"id":1487,"attributes":{"name":"prequel.svg","alternativeText":null,"caption":null,"width":576,"height":576,"formats":null,"hash":"prequel_c7e262d07b","ext":".svg","mime":"image/svg+xml","size":1.33,"url":"/uploads/prequel_c7e262d07b.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2023-06-09T13:37:22.493Z","updatedAt":"2023-06-09T13:37:22.493Z"}}}}},{"id":25,"attributes":{"shortDescription":"Integration with Confluent Cloud, the Cloud-Native, Complete Solution For Apache Kafka","name":"Confluent Cloud","category":"DATA_INGESTION","supportLevel":"CORE","summary":"Options for integrating ClickHouse Cloud with Confluent include:\n\n- ClickPipes - ClickPipes is a managed integration platform that makes ingesting data from a diverse set of sources as simple as clicking a few buttons. Designed for the most demanding workloads, ClickPipes's robust and scalable architecture ensures consistent performance and reliability.\n\n- Kafka Connect - Kafka Connect is a free, open-source component of Apache Kafka® that works as a centralized data hub for simple data integration between Kafka and other data systems. This is the option we cover here in detail.","slug":"confluent","createdAt":"2023-07-10T15:29:33.043Z","updatedAt":"2025-10-18T10:11:16.385Z","publishedAt":"2023-07-10T15:52:46.082Z","docsLink":"https://clickhouse.com/docs/integrations/clickpipes","about":"Confluent Platform is a full-scale data streaming platform that enables you to easily access, store, and manage data as continuous, real-time streams.","changelog":null,"version":null,"license":null,"readiness":null,"website":"https://clickhouse.com/cloud/clickpipes","summaryv2":"Options for integrating ClickHouse Cloud with Confluent include:\n\n- ClickPipes - ClickPipes is a managed integration platform that makes ingesting data from a diverse set of sources as simple as clicking a few buttons. Designed for the most demanding workloads, ClickPipes's robust and scalable architecture ensures consistent performance and reliability.\n\n- Kafka Connect - Kafka Connect is a free, open-source component of Apache Kafka® that works as a centralized data hub for simple data integration between Kafka and other data systems. This is the option we cover here in detail.","changelogv2":null,"aboutv2":"Confluent Platform is a full-scale data streaming platform that enables you to easily access, store, and manage data as continuous, real-time streams.","openInNewWindow":null,"logo":{"data":{"id":2492,"attributes":{"name":"confluent.svg","alternativeText":null,"caption":null,"width":64,"height":64,"formats":null,"hash":"confluent_cabe3d1723","ext":".svg","mime":"image/svg+xml","size":3.36,"url":"/uploads/confluent_cabe3d1723.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-06-04T10:12:29.500Z","updatedAt":"2024-06-04T10:12:29.500Z"}}},"logo_dark":{"data":{"id":2492,"attributes":{"name":"confluent.svg","alternativeText":null,"caption":null,"width":64,"height":64,"formats":null,"hash":"confluent_cabe3d1723","ext":".svg","mime":"image/svg+xml","size":3.36,"url":"/uploads/confluent_cabe3d1723.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-06-04T10:12:29.500Z","updatedAt":"2024-06-04T10:12:29.500Z"}}}}},{"id":26,"attributes":{"shortDescription":"Use Streamkap for high throughput real-time Change Data Capture (CDC) pipelines","name":"Streamkap","category":"DATA_INGESTION","supportLevel":"PARTNER","summary":"
\nGather your ClickHouse Cloud service connection details (username, password, port and host)\n
\n\n
\nConnect Streamkap to ClickHouse by adding the ClickHouse connector in the Streamkap UI\n
\n\n
\nCreate a pipeline to connect the source to ClickHouse\n
\n\n
\nCongratulations! You are now streaming data in real-time through to ClickHouse\n
\n","slug":"streamkap","createdAt":"2024-02-19T15:10:11.241Z","updatedAt":"2024-03-11T15:42:15.393Z","publishedAt":"2024-02-19T15:11:33.444Z","docsLink":"https://docs.streamkap.com/docs/clickhouse","about":"Streamkap is a streaming ETL platform that allows you setup high throughput pipelines in minutes. Under the hood, Streamkap is running Kafka, Kafka Connect, Debezium and Flink along with production grade connectors. \n","changelog":null,"version":null,"license":null,"readiness":"new","website":"https://streamkap.com","summaryv2":"\n\n\nGather your ClickHouse Cloud service connection details (username, password, port and host)\n\n\n\n\nConnect Streamkap to ClickHouse by adding the ClickHouse connector in the Streamkap UI\n\n\n\n\nCreate a pipeline to connect the source to ClickHouse\n\n\n\n\nCongratulations! You are now streaming data in real-time through to ClickHouse\n\n\n\n","changelogv2":null,"aboutv2":"Streamkap is a streaming ETL platform that allows you setup high throughput pipelines in minutes. Under the hood, Streamkap is running Kafka, Kafka Connect, Debezium and Flink along with production grade connectors. \n","openInNewWindow":null,"logo":{"data":{"id":2124,"attributes":{"name":"icon.svg","alternativeText":null,"caption":null,"width":260,"height":260,"formats":null,"hash":"icon_a657dc06cb","ext":".svg","mime":"image/svg+xml","size":1.14,"url":"/uploads/icon_a657dc06cb.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-02-19T15:46:25.477Z","updatedAt":"2024-02-19T15:46:33.762Z"}}},"logo_dark":{"data":{"id":2124,"attributes":{"name":"icon.svg","alternativeText":null,"caption":null,"width":260,"height":260,"formats":null,"hash":"icon_a657dc06cb","ext":".svg","mime":"image/svg+xml","size":1.14,"url":"/uploads/icon_a657dc06cb.svg","previewUrl":null,"provider":"local","provider_metadata":null,"createdAt":"2024-02-19T15:46:25.477Z","updatedAt":"2024-02-19T15:46:33.762Z"}}}}},{"id":27,"attributes":{"shortDescription":"High performance open-source ELT framework for building infrastructure and security lakes","name":"CloudQuery","category":"DATA_INGESTION","supportLevel":"PARTNER","summary":"
\n

Download CloudQuery CLI or get started with CloudQuery Cloud.\n

\n
\n
\nSelect a source from CloudQuery Hub\n
\n\n
\nConfigure ClickHouse destination plugin:\n\n```yaml\nkind: destination\nspec:\n name: \"clickhouse\"\n path: \"cloudquery/clickhouse\"\n registry: \"cloudquery\"\n version: \"v4.0.2\"\n write_mode: \"append\"\n spec:\n connection_string: \"clickhouse://${CH_USER}:${CH_PASSWORD}@localhost:9000/${CH_DATABASE}\"\n # Optional parameters\n # cluster: \"\"\n # ca_cert: \"\"\n # engine:\n # name: MergeTree\n # parameters: []\n #\n # batch_size: 10000\n # batch_size_bytes: 5242880 # 5 MiB\n # batch_timeout: 20s\n```\n\n
\n\n
\nRun the sync:\n\n```shell\ncloudquery sync source.yaml clickhouse.yaml\n```\n
","slug":"cloudquery","createdAt":"2024-05-16T10:24:37.310Z","updatedAt":"2024-07-08T11:50:44.217Z","publishedAt":"2024-06-10T22:12:06.309Z","docsLink":"https://hub.cloudquery.io/plugins/destination/cloudquery/clickhouse/latest/docs","about":"CloudQuery is an open-source high-performance ELT framework powering your cloud infrastructure and security lake. Sync data from a number of cloud providers such as Amazon Web Services, Google Cloud Platform, or Azure, and get visibility into your cloud infrastructure and security posture.\n","changelog":null,"version":null,"license":"mpl2","readiness":null,"website":"https://cloudquery.io","summaryv2":"\n\n

Download CloudQuery CLI or get started with CloudQuery Cloud.

\n\n\n\n

Select a source from CloudQuery Hub.

\n\n\n\nConfigure ClickHouse destination plugin:\n\n```yaml\nkind: destination\nspec:\n name: \"clickhouse\"\n path: \"cloudquery/clickhouse\"\n registry: \"cloudquery\"\n version: \"v4.0.2\"\n write_mode: \"append\"\n spec:\n connection_string: \"clickhouse://${CH_USER}:${CH_PASSWORD}@localhost:9000/${CH_DATABASE}\"\n # Optional parameters\n # cluster: \"\"\n # ca_cert: \"\"\n # engine:\n # name: MergeTree\n # parameters: []\n #\n # batch_size: 10000\n # batch_size_bytes: 5242880 # 5 MiB\n # batch_timeout: 20s\n```\n\n\n\nRun the sync:\n\n```shell\ncloudquery sync source.yaml clickhouse.yaml\n```\n\n","changelogv2":null,"aboutv2":null,"openInNewWindow":false,"logo":{"data":{"id":2534,"attributes":{"name":"CloudQuery_Logo-symbol 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"https://registry.yarnpkg.com/@strapi/client/-/client-1.6.1.tgz#6f25f79b80ecd5e0cd1b12c63f570cef6bcaf5f0" + integrity sha512-KsO9bfYR75CgtnhUkGqjUZAnoZdxddnRqK4Z7opco9tTqu6p8zbbLWY86QvuUlDOJevCzHE6F0R8o7XIr1x3rg== + dependencies: + debug "4.4.0" + qs "6.14.0" + "@svgr/babel-plugin-add-jsx-attribute@8.0.0": version "8.0.0" resolved "https://registry.yarnpkg.com/@svgr/babel-plugin-add-jsx-attribute/-/babel-plugin-add-jsx-attribute-8.0.0.tgz#4001f5d5dd87fa13303e36ee106e3ff3a7eb8b22" @@ -6794,7 +6802,7 @@ debug@2.6.9, debug@^2.6.0: dependencies: ms "2.0.0" -debug@4, debug@^4.0.0, debug@^4.1.0, debug@^4.1.1, debug@^4.3.1, debug@^4.3.6, debug@^4.4.0: +debug@4, debug@4.4.0, debug@^4.0.0, debug@^4.1.0, debug@^4.1.1, debug@^4.3.1, debug@^4.3.6, debug@^4.4.0: version "4.4.0" resolved "https://registry.yarnpkg.com/debug/-/debug-4.4.0.tgz#2b3f2aea2ffeb776477460267377dc8710faba8a" integrity sha512-6WTZ/IxCY/T6BALoZHaE4ctp9xm+Z5kY/pzYaCHRFeyVhojxlrm+46y68HA6hr0TcwEssoxNiDEUJQjfPZ/RYA== @@ -11892,6 +11900,13 @@ qs@6.13.0: dependencies: side-channel "^1.0.6" +qs@6.14.0: + version "6.14.0" + resolved "https://registry.yarnpkg.com/qs/-/qs-6.14.0.tgz#c63fa40680d2c5c941412a0e899c89af60c0a930" + integrity sha512-YWWTjgABSKcvs/nWBi9PycY/JiPJqOD4JA6o9Sej2AtvSGarXxKC3OQSk4pAarbdQlKAh5D4FCQkJNkW+GAn3w== + dependencies: + side-channel "^1.1.0" + quansync@^0.2.8: version "0.2.11" resolved "https://registry.yarnpkg.com/quansync/-/quansync-0.2.11.tgz#f9c3adda2e1272e4f8cf3f1457b04cbdb4ee692a" @@ -13031,7 +13046,7 @@ side-channel-weakmap@^1.0.2: object-inspect "^1.13.3" side-channel-map "^1.0.1" -side-channel@^1.0.6: +side-channel@^1.0.6, side-channel@^1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/side-channel/-/side-channel-1.1.0.tgz#c3fcff9c4da932784873335ec9765fa94ff66bc9" integrity sha512-ZX99e6tRweoUXqR+VBrslhda51Nh5MTQwou5tnUDgbtyM0dBgmhEDtWGP/xbKn6hqfPRHujUNwz5fy/wbbhnpw==