id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
0c9df36d-6f82-4c69-b3ba-6f38247f04f1 | slug: /intro
sidebar_label: 'What is ClickHouse?'
description: 'ClickHouse® is a column-oriented SQL database management system (DBMS) for online analytical processing (OLAP). It is available as both an open-source software and a cloud offering.'
title: 'What is ClickHouse?'
keywords: ['ClickHouse', 'columnar database'... | {"source_file": "intro.md"} | [
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dc182783-ccd6-4f7f-a8f8-97d9ae122754 | You can
run this query on the ClickHouse SQL Playground
that selects and filters
just a few out of over 100
existing columns, returning the result within milliseconds:
As you can see in the stats section in the above diagram, the query processed 100 million rows in 92 milliseconds, a throughput of approximately... | {"source_file": "intro.md"} | [
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dc1bfc41-638a-45eb-8913-7ac7b352c117 | Approximate calculation {#approximate-calculation}
ClickHouse provides ways to trade accuracy for performance. For example, some of its aggregate functions calculate the distinct value count, the median, and quantiles approximately. Also, queries can be run on a sample of the data to compute an approximate result qui... | {"source_file": "intro.md"} | [
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eb9d94fa-0a26-46de-88e5-d81db80a066e | slug: /deployment-modes
sidebar_label: 'Deployment modes'
description: 'ClickHouse offers four deployment options that all use the same powerful database engine, just packaged differently to suit your specific needs.'
title: 'Deployment modes'
keywords: ['Deployment Modes', 'chDB']
show_related_blogs: true
doc_type: 'g... | {"source_file": "deployment-modes.md"} | [
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d4d6f4a8-e697-49a4-898c-641b3e021053 | A key advantage of ClickHouse Cloud is its integrated tooling.
ClickPipes
provides a robust data ingestion framework, allowing you to easily connect and stream data from various sources without managing complex ETL pipelines. The platform also offers a dedicated
querying API
, making it significantly easier to build... | {"source_file": "deployment-modes.md"} | [
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177d0ed1-ac5d-4759-b3a2-eb95df425c24 | The combination of remote table functions and access to the local file system makes clickhouse-local particularly useful for scenarios where you need to join data between a ClickHouse Server and files on your local machine. This is especially valuable when working with sensitive or temporary local data that you don't w... | {"source_file": "deployment-modes.md"} | [
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51057e2c-6362-46e0-9943-5ff57197dc7a | slug: /introduction-clickhouse
title: 'Introduction'
description: 'Landing page for Introduction'
pagination_next: null
doc_type: 'landing-page'
keywords: ['ClickHouse introduction', 'getting started', 'what is ClickHouse', 'quick start', 'installation', 'deployment', 'tutorial']
Welcome to ClickHouse! Check out th... | {"source_file": "introduction-index.md"} | [
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ef994a92-1978-4887-90e6-a5ad0223a81f | slug: /tutorial
sidebar_label: 'Advanced tutorial'
title: 'Advanced tutorial'
description: 'Learn how to ingest and query data in ClickHouse using a New York City taxi example dataset.'
sidebar_position: 0.5
keywords: ['clickhouse', 'install', 'tutorial', 'dictionary', 'dictionaries', 'example', 'advanced', 'taxi', 'ne... | {"source_file": "tutorial.md"} | [
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ac70d7fb-cf22-41f4-8ab5-85bb5a3a2466 | For self-managed ClickHouse, connect to the SQL console at
https://_hostname_:8443/play
. Check with your ClickHouse administrator for the details.
Create the following
trips
table in the
default
database:
sql
CREATE TABLE trips
(
`trip_id` UInt32,
`vendor_id` Enum8('1' = 1, '2... | {"source_file": "tutorial.md"} | [
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0.008... |
e56d593e-250b-4715-83eb-5c38ce1ad0ec | The following command inserts ~2,000,000 rows into your
trips
table from two different files in S3:
trips_1.tsv.gz
and
trips_2.tsv.gz
:
sql
INSERT INTO trips
SELECT * FROM s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_{1..2}.gz',
'TabSeparatedWithNames', "
`trip_id` UI... | {"source_file": "tutorial.md"} | [
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e4e320c8-15f3-40b9-ae81-46d9032bd8fb | Expected output
The
passenger_count
ranges from 0 to 9:
response
┌─passenger_count─┬─average_total_amount─┐
│ 0 │ 22.69 │
│ 1 │ 15.97 │
│ 2 │ 17.15 │
│ 3 │ 16.76 │
│ 4 │ ... | {"source_file": "tutorial.md"} | [
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0.03544... |
08169bc3-4812-4ff7-8c4c-3211fbd274c1 | Expected output
response
┌──────────────avg_tip─┬───────────avg_fare─┬──────avg_passenger─┬──count─┬─trip_minutes─┐
│ 1.9600000381469727 │ 8 │ 1 │ 1 │ 27511 │
│ 0 │ 12 │ 2 │ 1 │ 27500 │
│ 0.54216667398... | {"source_file": "tutorial.md"} | [
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-... |
582dc70b-dd40-40b0-b47a-7ac744691c21 | response
┌─pickup_ntaname───────────────────────────────────────────┬─pickup_hour─┬─pickups─┐
│ Airport │ 0 │ 3509 │
│ Airport │ 1 │ 1184 │
│ Airport ... | {"source_file": "tutorial.md"} | [
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f8fca207-9265-4a86-bfb4-3b2a695f9f9f | Retrieve rides to LaGuardia or JFK airports:
sql
SELECT
pickup_datetime,
dropoff_datetime,
total_amount,
pickup_nyct2010_gid,
dropoff_nyct2010_gid,
CASE
WHEN dropoff_nyct2010_gid = 138 THEN 'LGA'
WHEN dropoff_nyct2010_gid = 132 THEN 'JFK'... | {"source_file": "tutorial.md"} | [
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0.0... |
df11f09f-1690-4c86-a251-c8559d753872 | Here's an excerpt from the CSV file you're using in table format. The
LocationID
column in the file maps to the
pickup_nyct2010_gid
and
dropoff_nyct2010_gid
columns in your
trips
table:
| LocationID | Borough | Zone | service_zone |
| ----------- | ----------- | ----------- | ----------- |
| ... | {"source_file": "tutorial.md"} | [
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5717d1b5-988f-4bb6-96d0-fdb0c7a56bbe | The following query returns 0 because 4567 is not a value of
LocationID
in the dictionary:
sql
SELECT dictHas('taxi_zone_dictionary', 4567)
Use the
dictGet
function to retrieve a borough's name in a query. For example:
sql
SELECT
count(1) AS total,
dictGetOrDefault('taxi_zone... | {"source_file": "tutorial.md"} | [
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98294a7e-4c9c-4c04-9498-4ee18eab7d1e | Next steps {#next-steps}
Learn more about ClickHouse with the following documentation:
Introduction to Primary Indexes in ClickHouse
: Learn how ClickHouse uses sparse primary indexes to efficiently locate relevant data during queries.
Integrate an external data source
: Review data source integration options,... | {"source_file": "tutorial.md"} | [
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... |
1652497a-ad87-4366-8c63-896e4b2ff650 | slug: /managing-data/truncate
sidebar_label: 'Truncate table'
title: 'Truncate Table'
hide_title: false
description: 'Truncate allows the data in a table or database to be removed, while preserving their existence.'
doc_type: 'reference'
keywords: ['truncate', 'delete data', 'remove data', 'clear table', 'table mainten... | {"source_file": "truncate.md"} | [
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-0.1121570... |
2469869e-ffeb-47bf-9e9e-135227638391 | slug: /architecture/introduction
sidebar_label: 'Introduction'
title: 'Introduction'
sidebar_position: 1
description: 'Page with deployment examples that are based on the advice provided to ClickHouse users by the ClickHouse Support and Services organization'
doc_type: 'guide'
keywords: ['deployment', 'architecture', '... | {"source_file": "terminology.md"} | [
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0.09707011282444,
-0.02192... |
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