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"} | [
0.05993638187646866,
-0.06723175942897797,
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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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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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0c131683-c91d-4e33-808d-0ae639854837 | slug: /deployment-guides/index
title: 'Deployment Guides Overview'
description: 'Landing page for the deployment and scaling section'
keywords: ['deployment guides', 'scaling', 'cluster deployment', 'replication', 'fault tolerance']
doc_type: 'landing-page'
Deployment and scaling
This section covers the following... | {"source_file": "index.md"} | [
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f5a17561-34f7-443e-95f2-b67670adc21e | slug: /materialized-view/refreshable-materialized-view
title: 'Refreshable materialized view'
description: 'How to use materialized views to speed up queries'
keywords: ['refreshable materialized view', 'refresh', 'materialized views', 'speed up queries', 'query optimization']
doc_type: 'guide'
import refreshableMa... | {"source_file": "refreshable-materialized-view.md"} | [
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87e49c04-2385-42b0-b4a3-8971652c6247 | Refreshable materialized views are refreshed automatically on an interval that's defined during creation.
For example, the following materialized view is refreshed every minute:
sql
CREATE MATERIALIZED VIEW table_name_mv
REFRESH EVERY 1 MINUTE TO table_name AS
...
If you want to force refresh a materialized view, y... | {"source_file": "refreshable-materialized-view.md"} | [
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17f815d9-9ea1-4598-aeb6-ef81e2a65eb9 | ```sql
SELECT *
FROM events
LIMIT 10
Query id: 7662bc39-aaf9-42bd-b6c7-bc94f2881036
┌──────────────────ts─┬─uuid─┬─count─┐
│ 2008-08-06 17:07:19 │ 0eb │ 547 │
│ 2008-08-06 17:07:19 │ 60b │ 148 │
│ 2008-08-06 17:07:19 │ 106 │ 750 │
│ 2008-08-06 17:07:19 │ 398 │ 875 │
│ 2008-08-06 17:07:19 │ ca0 │ 318 ... | {"source_file": "refreshable-materialized-view.md"} | [
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-0.009868900291621685,
-0.009256947785615921,
... |
80474c03-f8e2-48ad-a7ea-764cb153cd5c | In that guide, we showed how to denormalize the
postlinks
dataset onto the
posts
table with the following query:
sql
SELECT
posts.*,
arrayMap(p -> (p.1, p.2), arrayFilter(p -> p.3 = 'Linked' AND p.2 != 0, Related)) AS LinkedPosts,
arrayMap(p -> (p.1, p.2), arrayFilter(p -> p.3 = 'Duplicate' AND p.2 !=... | {"source_file": "refreshable-materialized-view.md"} | [
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0.05034010112285614,
-0.069585... |
199515aa-b55a-4279-b55e-43fb2e77d026 | We can then write the following query can be used to compute a summary of each actor, ordered by the most movie appearances.
sql
SELECT
id, any(actor_name) AS name, uniqExact(movie_id) AS movies,
round(avg(rank), 2) AS avg_rank, uniqExact(genre) AS genres,
uniqExact(director_name) AS directors, max(created_at) ... | {"source_file": "refreshable-materialized-view.md"} | [
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-0.03873009607195854,
0.06649873405694962,
0.0... |
60b8fd89-1f24-45c7-b7a7-f39248372557 | And now we can define the view:
sql
CREATE MATERIALIZED VIEW imdb.actor_summary_mv
REFRESH EVERY 1 MINUTE TO imdb.actor_summary AS
SELECT
id,
any(actor_name) AS name,
uniqExact(movie_id) AS num_movies,
avg(rank) AS avg_rank,
uniqExact(genre) AS unique_genres,
uniqExact(... | {"source_file": "refreshable-materialized-view.md"} | [
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0.025... |
6f81e49e-7b70-47a3-a386-42eb8c087fee | Less than 60 seconds later, our target table is updated to reflect the prolific nature of Clicky's acting:
sql
SELECT *
FROM imdb.actor_summary
ORDER BY num_movies DESC
LIMIT 5;
```text
┌─────id─┬─name────────────────┬─num_movies─┬──avg_rank─┬─unique_genres─┬─uniq_directors─┬──────────updated_at─┐
│ 845466 │ Clicky... | {"source_file": "refreshable-materialized-view.md"} | [
0.0024795299395918846,
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2f2d4ee8-52c5-4653-84e3-872861851f4d | slug: /materialized-view/incremental-materialized-view
title: 'Incremental materialized view'
description: 'How to use incremental materialized views to speed up queries'
keywords: ['incremental materialized views', 'speed up queries', 'query optimization']
score: 10000
doc_type: 'guide'
import materializedViewDiag... | {"source_file": "incremental-materialized-view.md"} | [
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58aa05d8-a013-4be2-b4bc-bb0b60ceabfc | ```sql
SELECT toStartOfDay(CreationDate) AS day,
countIf(VoteTypeId = 2) AS UpVotes,
countIf(VoteTypeId = 3) AS DownVotes
FROM votes
GROUP BY day
ORDER BY day ASC
LIMIT 10
┌─────────────────day─┬─UpVotes─┬─DownVotes─┐
│ 2008-07-31 00:00:00 │ 6 │ 0 │
│ 2008-08-01 00:00:00 │ 182 │ ... | {"source_file": "incremental-materialized-view.md"} | [
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0.011825895868241787,
-... |
025ee4fd-2dbb-415e-b763-e20c21b043b0 | ```sql
SELECT count()
FROM up_down_votes_per_day
FINAL
┌─count()─┐
│ 5723 │
└─────────┘
```
We've effectively reduced the number of rows here from 238 million (in
votes
) to 5000 by storing the result of our query. What's key here, however, is that if new votes are inserted into the
votes
table, new values wi... | {"source_file": "incremental-materialized-view.md"} | [
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8ba32712-feba-4a13-9789-69475f8aef9d | A more complex example {#a-more-complex-example}
The above example uses Materialized Views to compute and maintain two sums per day. Sums represent the simplest form of aggregation to maintain partial states for - we can just add new values to existing values when they arrive. However, ClickHouse Materialized Views c... | {"source_file": "incremental-materialized-view.md"} | [
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-0.... |
52256b23-6e50-40c0-9919-df73112bd720 | Note how we append the suffix
State
to the end of our aggregate functions. This ensures the aggregate state of the function is returned instead of the final result. This will contain additional information to allow this partial state to merge with other states. For example, in the case of an average, this will includ... | {"source_file": "incremental-materialized-view.md"} | [
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... |
7b806f6f-3832-48c2-9eee-833f3fede8be | We could perform this transformation when running an
INSERT INTO SELECT
. The materialized view allows us to encapsulate this logic in ClickHouse DDL and keep our
INSERT
simple, with the transformation applied to any new rows.
Our materialized view for this transformation is shown below:
sql
CREATE MATERIALIZE... | {"source_file": "incremental-materialized-view.md"} | [
-0.10839328914880753,
-0.05386443808674812,
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-0.03... |
a9667918-8c98-418d-b13e-ff0d6ce7f20d | 0 rows in set. Elapsed: 5.163 sec. Processed 90.38 million rows, 17.25 GB (17.51 million rows/s., 3.34 GB/s.)
```
We can now use this View in a subquery to accelerate our previous query:
```sql
SELECT avg(Score)
FROM comments
WHERE PostId IN (
SELECT PostId
FROM comments_posts_users
WHERE Us... | {"source_file": "incremental-materialized-view.md"} | [
-0.05282462388277054,
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-0.... |
a93e0b42-a616-4411-a137-da64daa0dcf8 | CREATE TABLE users
(
Id
Int32,
Reputation
UInt32,
CreationDate
DateTime64(3, 'UTC'),
DisplayName
LowCardinality(String),
LastAccessDate
DateTime64(3, 'UTC'),
Location
LowCardinality(String),
Views
UInt32,
UpVotes
UInt32,
DownVotes
UInt32
)
ENGINE = MergeTree
ORDER B... | {"source_file": "incremental-materialized-view.md"} | [
-0.0521392785012722,
-0.04893776774406433,
-0.03658987954258919,
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-0.06570720672607422,
0.04831705987453461,
-... |
86373864-b36d-4f81-9843-fcfe912ff3ce | Now, if this user receives a new badge and a row is inserted, our view will be updated:
```sql
INSERT INTO badges VALUES (53505058, 2936484, 'gingerwizard', now(), 'Gold', 0);
1 row in set. Elapsed: 7.517 sec.
SELECT *
FROM daily_badges_by_user
FINAL
WHERE DisplayName = 'gingerwizard'
┌────────Day─┬──UserId─┬─Dis... | {"source_file": "incremental-materialized-view.md"} | [
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0.05588804557919502,
-0.05788... |
ef344545-2c54-49a1-995b-f2f927d56fea | Use the left-most table as the trigger.
Only the table on the left side of the
SELECT
statement triggers the materialized view. Changes to right-side tables will not trigger updates.
Pre-insert joined data.
Ensure that data in joined tables exists before inserting rows into the source table. The JOIN is evalu... | {"source_file": "incremental-materialized-view.md"} | [
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-0... |
26b0ebbd-59a5-4cc3-b91d-7e53a32b94f4 | In the above example, we have two Materialized Views
mvw1
and
mvw2
that perform similar operations but with a slight difference in how they reference the source table
t0
.
In
mvw1
, table
t0
is directly referenced inside a
(SELECT * FROM t0)
subquery on the right side of the JOIN. When data is inserted into... | {"source_file": "incremental-materialized-view.md"} | [
-0.09934733062982559,
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0.07477640360593796,
-0... |
a82865f6-75ca-45e0-a130-e53814acd3c2 | ```sql
INSERT INTO badges VALUES (53505058, 2936484, 'gingerwizard', now(), 'Gold', 0);
1 row in set. Elapsed: 7.517 sec.
```
Using the approach above, we can optimize this view. We'll add a filter to the
users
table using the user ids in the inserted badge rows:
sql
CREATE MATERIALIZED VIEW daily_badges_by_use... | {"source_file": "incremental-materialized-view.md"} | [
-0.026645321398973465,
-0.007703088223934174,
0.0005223773187026381,
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0.10960347205400467,
-0.037... |
38c27b93-09e5-4e2f-a535-06e69aa78230 | Suppose we want to create a unified view of user activity, showing the last activity by each user by combining these two tables:
sql
SELECT
UserId,
argMax(description, event_time) AS last_description,
argMax(activity_type, event_time) AS activity_type,
max(event_time) AS last_activity
FROM
(
SELECT
UserId... | {"source_file": "incremental-materialized-view.md"} | [
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-0.... |
b3e56bfd-0c05-4186-9e36-5d17da934d36 | Inserts into the
badges
table will not trigger the view, causing
user_activity
to not receive updates:
```sql
INSERT INTO badges VALUES (53505058, 2936484, 'gingerwizard', now(), 'Gold', 0);
SELECT
UserId,
argMaxMerge(last_description) AS description,
argMaxMerge(activity_type) AS activity_type,
max(last... | {"source_file": "incremental-materialized-view.md"} | [
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0.002... |
9175777b-2c98-4c7e-88a0-1e1f356390cd | As shown in the previous example, a table can act as the source for multiple Materialized Views. The order in which these are executed depends on the setting
parallel_view_processing
.
By default, this setting is equal to
0
(
false
), meaning Materialized Views are executed sequentially in
uuid
order.
For exam... | {"source_file": "incremental-materialized-view.md"} | [
-0.024470848962664604,
-0.10137457400560379,
-0.00694515835493803,
0.016760190948843956,
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-0.0065054576843976974,
0.04619878530502319,
-0.0... |
9d331c58-3345-4a9c-9629-60870367a613 | 3 rows in set. Elapsed: 0.004 sec.
```
Although our ordering of the arrival of rows from each view is the same, this is not guaranteed - as illustrated by the similarity of each row's insert time. Also note the improved insert performance.
When to use parallel processing {#materialized-views-when-to-use-parallel}
... | {"source_file": "incremental-materialized-view.md"} | [
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c224d43c-1daa-4e04-ab3f-4873298eaa7a | CREATE MATERIALIZED VIEW daily_post_activity_mv TO daily_post_activity AS
WITH filtered_posts AS (
SELECT
toDate(CreationDate) AS Day,
PostTypeId,
Score,
ViewCount
FROM posts
WHERE Score > 0 AND PostTypeId IN (1, 2) -- Question or Answer
)
SELECT
Day,
CASE PostTypeId
WHEN 1 THEN 'Questi... | {"source_file": "incremental-materialized-view.md"} | [
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7f31e49e-ef97-461e-b310-4e0513593dcb | sql
WITH recent_users AS (
SELECT Id FROM stackoverflow.users WHERE CreationDate > now() - INTERVAL 7 DAY
)
SELECT * FROM stackoverflow.posts WHERE OwnerUserId IN (SELECT Id FROM recent_users)
In this case, the users CTE is re-evaluated on every insert into posts, and the materialized view will not update when new ... | {"source_file": "incremental-materialized-view.md"} | [
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db8eabff-02cb-4aa2-bb5b-363de42302ff | slug: /materialized-views
title: 'Materialized Views'
description: 'Index page for materialized views'
keywords: ['materialized views', 'speed up queries', 'query optimization', 'refreshable', 'incremental']
doc_type: 'landing-page'
| Page ... | {"source_file": "index.md"} | [
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f13a924d-754f-4fca-8cf9-e9e4f7ff6949 | sidebar_position: 1
slug: /tips-and-tricks/community-wisdom
sidebar_label: 'Community wisdom'
doc_type: 'landing-page'
keywords: [
'database tips',
'community wisdom',
'production troubleshooting',
'performance optimization',
'database debugging',
'clickhouse guides',
'real world examples',
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5e4835c6-8883-4e69-9768-0abf03aa7d1c | sidebar_position: 1
slug: /community-wisdom/cost-optimization
sidebar_label: 'Cost optimization'
doc_type: 'guide'
keywords: [
'cost optimization',
'storage costs',
'partition management',
'data retention',
'storage analysis',
'database optimization',
'clickhouse cost reduction',
'storage hot spots',
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580414c3-0878-43be-8d84-0e01d248f272 | This data-driven approach enables strategic decisions about retention policies and column lifecycle management. By analyzing this telemetry data, Microsoft can identify storage hot spots - columns that consume significant space but receive minimal queries. For these low-usage columns, they can implement aggressive rete... | {"source_file": "cost-optimization.md"} | [
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d345047b-8efb-4dd7-9d16-961a17d2bd75 | This architecture preserves the user experience - people still see meaningful labels like
weather_answer
in their dashboards - while the backend storage and queries operate on much more efficient integers. The mapping system handles all translation transparently, requiring no changes to the user interface or user wor... | {"source_file": "cost-optimization.md"} | [
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c1ab0de1-4fff-4a51-ba75-2e073444fe4f | sidebar_position: 1
slug: /community-wisdom/creative-use-cases
sidebar_label: 'Success stories'
doc_type: 'guide'
keywords: [
'clickhouse creative use cases',
'clickhouse success stories',
'unconventional database uses',
'clickhouse rate limiting',
'analytics database applications',
'clickhouse mobile analy... | {"source_file": "success-stories.md"} | [
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d524cafd-9d55-412b-b48a-deb135f2d3b8 | So when Brad was approached with the rate limiting requirements, he took a different approach:
"I asked my boss, 'What do you think of this idea? Maybe I can try this with ClickHouse?'"
The idea was unconventional - using an analytical database for what's typically a caching layer problem - but it addressed their cor... | {"source_file": "success-stories.md"} | [
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bcefd1f3-ea64-4971-a53d-517073f4a1e6 | So when the limitations became clear, ServiceNow moved to ClickHouse and eliminated these pre-computation constraints entirely. Instead of calculating every variable upfront, they broke metadata into data points and inserted everything directly into ClickHouse. They used ClickHouse's async insert queue, which Amir call... | {"source_file": "success-stories.md"} | [
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959b5cdc-5d69-4836-bb18-de82ee997e72 | sidebar_position: 1
slug: /community-wisdom/debugging-insights
sidebar_label: 'Debugging insights'
doc_type: 'guide'
keywords: [
'clickhouse troubleshooting',
'clickhouse errors',
'slow queries',
'memory problems',
'connection issues',
'performance optimization',
'database errors',
'configuration probl... | {"source_file": "debugging-insights.md"} | [
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-... |
188e4918-8c21-488f-b766-1131df95fa68 | AWS users should be aware that default general purpose EBS volumes have a 16TB limit.
Too many parts error {#too-many-parts-error}
Small frequent inserts create performance problems. The community has identified that insert rates above 10 per second often trigger "too many parts" errors because ClickHouse cannot me... | {"source_file": "debugging-insights.md"} | [
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0d3cf0ab-ca2a-41de-b320-e2ba1c1d5a38 | Related docs
-
Custom partitioning key
Quick reference {#quick-reference}
| Issue | Detection | Solution |
|-------|-----------|----------|
| Disk Space | Check
system.parts
total bytes | Monitor usage, plan scaling |
| Too Many Parts | Count parts per table | Batch inserts, enable async_insert |
| Replication ... | {"source_file": "debugging-insights.md"} | [
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686f808e-65eb-4650-b462-f7f3cbf3a5eb | sidebar_position: 1
slug: /tips-and-tricks/too-many-parts
sidebar_label: 'Too many parts'
doc_type: 'guide'
keywords: [
'clickhouse too many parts',
'too many parts error',
'clickhouse insert batching',
'part explosion problem',
'clickhouse merge performance',
'batch insert optimization',
'clickhouse asyn... | {"source_file": "too-many-parts.md"} | [
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81cc45f6-b606-47bd-b0b8-456c5361541c | sql runnable editable
-- Challenge: Replace with your actual database and table names for production use
-- Experiment: Adjust the part count thresholds (1000, 500, 100) based on your system
SELECT
database,
table,
count() as total_parts,
sum(rows) as total_rows,
round(avg(rows), 0) as avg_rows_per... | {"source_file": "too-many-parts.md"} | [
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e0790536-b12c-4885-ac13-41713bc8ea1a | sidebar_position: 1
slug: /community-wisdom/performance-optimization
sidebar_label: 'Performance optimization'
doc_type: 'guide'
keywords: [
'performance optimization',
'query performance',
'database tuning',
'slow queries',
'memory optimization',
'cardinality analysis',
'indexing strategies',
'aggregat... | {"source_file": "performance-optimization.md"} | [
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7df62d80-0ecc-4a73-bf78-5d68ba513c07 | Focus on individual queries, not averages {#focus-on-individual-queries-not-averages}
When debugging ClickHouse performance, don't rely on average query times or overall system metrics. Instead, identify why specific queries are slow. A system can have good average performance while individual queries suffer from mem... | {"source_file": "performance-optimization.md"} | [
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-0.02155... |
2c7d2451-f57b-4d70-a2bc-815204b2ffcb | SELECT
your_grouping_column,
-- Each sumIf creates exactly one integer counter per group
-- Memory stays constant regardless of how many rows match each condition
sumIf(1, your_condition_1) as condition_1_count,
sumIf(1, your_condition_2) as condition_2_count,
sumIf(1, your_text_column LIKE '%pattern%') as patte... | {"source_file": "performance-optimization.md"} | [
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-0.0146193... |
8543abac-122b-49c4-b823-43f768e7921a | sidebar_position: 1
slug: /tips-and-tricks/materialized-views
sidebar_label: 'Materialized views'
doc_type: 'guide'
keywords: [
'clickhouse materialized views',
'materialized view optimization',
'materialized view storage issues',
'materialized view best practices',
'database aggregation patterns',
'materia... | {"source_file": "materialized-views.md"} | [
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0.00... |
ee964cd9-5ae4-46a1-8842-50c42b9f1751 | You can compare insert performance before and after adding MVs using
system.query_log
to track query duration trends.
Video sources {#video-sources}
ClickHouse at CommonRoom - Kirill Sapchuk
- Source of the "over enthusiastic about materialized views" and "20GB→190GB explosion" case study | {"source_file": "materialized-views.md"} | [
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b64a2f30-665f-4e9a-9f15-e91b0b2821e1 | slug: /whats-new/security-changelog
sidebar_position: 20
sidebar_label: 'Security changelog'
title: 'Security changelog'
description: 'Security changelog detailing security related updates and changes'
doc_type: 'changelog'
keywords: ['security', 'CVE', 'vulnerabilities', 'security fixes', 'patches']
Security chang... | {"source_file": "security-changelog.md"} | [
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-0.00... |
f9c44301-0975-4ed2-bf0d-c6eb0e80f6f9 | Fixed in ClickHouse v23.10.5.20, 2023-11-26 {#fixed-in-clickhouse-release-23-10-5-20-2023-11-26}
CVE-2023-47118
{#CVE-2023-47118}
A heap buffer overflow vulnerability affecting the native interface running by default on port 9000/tcp. An attacker, by triggering a bug in the T64 compression codec, can cause the Cli... | {"source_file": "security-changelog.md"} | [
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0.0056744907051324844,
-0.024967188015580177,
... |
995b1255-5233-4cf8-96fb-8af340327aca | Credits: Kiojj (independent researcher)
Fixed in ClickHouse 21.10.2.15, 2021-10-18 {#fixed-in-clickhouse-release-21-10-2-215-2021-10-18}
CVE-2021-43304 {#cve-2021-43304}
Heap buffer overflow in ClickHouse's LZ4 compression codec when parsing a malicious query. There is no verification that the copy operations in ... | {"source_file": "security-changelog.md"} | [
-0.07475423067808151,
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-0.... |
fab60510-10db-46dc-97c0-ab7b6105dd02 | CVE-2021-25263 {#cve-2021-25263}
An attacker that has CREATE DICTIONARY privilege, can read arbitary file outside permitted directory.
Fix has been pushed to versions 20.8.18.32-lts, 21.1.9.41-stable, 21.2.9.41-stable, 21.3.6.55-lts, 21.4.3.21-stable and later.
Credits:
Vyacheslav Egoshin
Fixed in ClickHouse R... | {"source_file": "security-changelog.md"} | [
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0.030522922053933144,
-0.07852574437856674,
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0.012147007510066032,
0.06698352098464966,
0.065524... |
94808bd4-02e8-4191-b2d9-01bdb300d3a7 | Credits: Andrey Krasichkov and Evgeny Sidorov of Yandex Information Security Team
Fixed in ClickHouse Release 1.1.54131, 2017-01-10 {#fixed-in-clickhouse-release-1-1-54131-2017-01-10}
CVE-2018-14670 {#cve-2018-14670}
Incorrect configuration in deb package could lead to the unauthorized use of the database.
Cred... | {"source_file": "security-changelog.md"} | [
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0.09203654527664185,
0.023548... |
da163578-cb93-474c-8611-560c6468b333 | title: 'Roadmap'
slug: /whats-new/roadmap
sidebar_position: 50
description: 'Present and past ClickHouse road maps'
doc_type: 'landing-page'
keywords: ['roadmap', 'future features', 'development plans', 'upcoming releases', 'product direction']
Current roadmap {#current-roadmap}
The current roadmap is published f... | {"source_file": "roadmap.md"} | [
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... |
4d56c9fd-7526-466f-85c8-53cf527b769f | sidebar_position: 1
sidebar_label: 'Beta Features and Experimental'
title: 'Beta and Experimental Features'
description: 'ClickHouse has beta and experimental features. This documentation page discusses definition.'
slug: /beta-and-experimental-features
doc_type: 'reference'
Because ClickHouse is open-source, it re... | {"source_file": "beta-and-experimental-features.md"} | [
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14ea5eab-93a3-4659-a201-e15ed604c331 | Please note: no additional experimental features are allowed to be enabled in ClickHouse Cloud other than those listed above as Beta.
Beta settings {#beta-settings}
| Name | Default |
|------|--------|
|
geotoh3_argument_order
|
lat_lon
|
|
enable_lightweight_update
|
1
|
|
allow_experimental_correlate... | {"source_file": "beta-and-experimental-features.md"} | [
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-0... |
7142cd18-34f4-4c4a-a4aa-5e78e15ee822 | | Name | Default |
|------|--------|
|
allow_experimental_replacing_merge_with_cleanup
|
0
|
|
allow_experimental_reverse_key
|
0
|
|
allow_remote_fs_zero_copy_replication
|
0
|
|
enable_replacing_merge_with_cleanup_for_min_age_to_force_merge
|
0
|
|
force_read_through_cache_for_merges
|
0
|
|
merg... | {"source_file": "beta-and-experimental-features.md"} | [
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-... |
c3999fc8-d294-4bbf-826d-5347fe6b2505 | |
0
|
|
allow_experimental_database_materialized_postgresql
|
0
|
|
allow_experimental_qbit_type
|
0
|
|
allow_experimental_query_deduplication
|
0
|
|
allow_experimental_database_hms_catalog
|
0
|
|
allow_experimental_kusto_dialect
|
0
|
|
allow_experimental_prql_dialect
|
0
|
|
enable_adapt... | {"source_file": "beta-and-experimental-features.md"} | [
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a4ac9170-768c-4d83-9c4a-8590c8a09e9c | title: 'FAQ'
slug: /about-us/faq
description: 'Landing page'
doc_type: 'landing-page'
keywords: ['ClickHouse FAQ', 'frequently asked questions', 'common questions', 'help documentation', 'troubleshooting']
| FAQ ... | {"source_file": "about-faq-index.md"} | [
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c36006d5-bea3-4f75-b3b3-dd0b0f70edd5 | slug: /about-us/history
sidebar_label: 'ClickHouse history'
sidebar_position: 40
description: 'History of ClickHouse development'
keywords: ['history','development','Metrica']
title: 'ClickHouse History'
doc_type: 'reference'
ClickHouse history {#clickhouse-history}
ClickHouse was initially developed to power
Ya... | {"source_file": "history.md"} | [
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... |
2754be1b-7a3d-4afe-9077-0b48cc7dad1c | For a large number of reports, there are too many aggregation variations (combinatorial explosion).
When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).
For this reason, the volume of data with aggregation might grow instead of shrink.
Users do... | {"source_file": "history.md"} | [
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0.0153... |
3fd090be-10bc-4efd-89de-d9e3b772dd28 | slug: /about-us/distinctive-features
sidebar_label: 'Why is ClickHouse unique?'
sidebar_position: 50
description: 'Understand what makes ClickHouse stand apart from other database management systems'
title: 'Distinctive Features of ClickHouse'
keywords: ['compression', 'secondary-indexes','column-oriented']
doc_type: '... | {"source_file": "distinctive-features.md"} | [
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-0.... |
25df7ff7-a677-4474-b983-6175184e7bde | Distributed processing on multiple servers {#distributed-processing-on-multiple-servers}
Almost none of the columnar DBMSs mentioned above have support for distributed query processing.
In ClickHouse, data can reside on different shards. Each shard can be a group of replicas used for fault tolerance. All shards are... | {"source_file": "distinctive-features.md"} | [
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9dd15a14-4dae-4124-a3b1-f66906cd93d0 | Running a query based on a part (
SAMPLE
) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.
Running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reas... | {"source_file": "distinctive-features.md"} | [
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