id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
958b91e7-d53b-45b5-bd52-2136514b13a6 | slug: /native-protocol/columns
sidebar_position: 4
title: 'Column types'
description: 'Column types for the native protocol'
keywords: ['native protocol columns', 'column types', 'data types', 'protocol data types', 'binary encoding']
doc_type: 'reference'
Column types
See
Data Types
for general reference.
Nu... | {"source_file": "columns.md"} | [
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-... |
b05bb417-576e-4728-830f-7973ba32354e | slug: /native-protocol/hash
sidebar_position: 5
title: 'CityHash'
description: 'Native protocol hash'
doc_type: 'reference'
keywords: ['CityHash', 'native protocol hash', 'hash function', 'Google CityHash', 'protocol hashing']
CityHash
ClickHouse uses
one of the previous
versions of
CityHash from Google
.
::... | {"source_file": "hash.md"} | [
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0.0049203489907085896,
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c20f0f4f-db76-4b88-823e-5a6e6c5adc64 | slug: /native-protocol/server
sidebar_position: 3
title: 'Server packets'
description: 'Native protocol server'
doc_type: 'reference'
keywords: ['native protocol', 'tcp protocol', 'client-server', 'protocol specification', 'networking']
Server packets
| value | name | description ... | {"source_file": "server.md"} | [
0.004435721784830093,
0.07022187113761902,
-0.022674495354294777,
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0.0137383583933115,
-0.0... |
fb35e6a4-6def-422c-a809-2a5819276368 | Exception {#exception}
Server exception during query processing.
| field | type | value | description |
|-------------|--------|----------------------------------------|------------------------------|
| code | Int32 |
60
... | {"source_file": "server.md"} | [
-0.0024266561958938837,
0.014633086510002613,
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0.07053352892398834,
-0.... |
d7a97303-46ee-404d-918b-679f3891ebd5 | slug: /native-protocol/client
sidebar_position: 2
title: 'Native client packets'
description: 'Native protocol client'
doc_type: 'reference'
keywords: ['client packets', 'native protocol client', 'protocol packets', 'client communication', 'TCP client']
Client packets
| value | name | description ... | {"source_file": "client.md"} | [
0.0018228008411824703,
0.03647042438387871,
-0.04000090807676315,
-0.035530295222997665,
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0.013462964445352554,
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-0.050655145198106766,
0.05697094276547432,
0.027... |
fcac8688-70bb-40fd-92ab-d91f2e20d702 | Client info {#client-info}
| field | type | description |
|-------------------|-----------------|--------------------------------|
| query_kind | byte | None=0, Initial=1, Secondary=2 |
| initial_user | String | Initial user |... | {"source_file": "client.md"} | [
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0.11423642933368683,
-... |
de3c6cda-d2f1-4e7c-b8e4-7980c4b4b3d5 | Column {#column}
| field | type | value | description |
|-------|--------|-----------------|-------------|
| name | String |
foo
| Column name |
| type | String |
DateTime64(9)
| Column type |
| data | bytes | ~ | Column data |
Cancel {#cancel}
No packet body. Server sh... | {"source_file": "client.md"} | [
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0.0032268103677779436,
-0.0804... |
c9e9fead-8993-4af2-bd7e-4d39abc0560b | slug: /data-compression/compression-modes
sidebar_position: 6
title: 'Compression Modes'
description: 'ClickHouse column compression modes'
keywords: ['compression', 'codec', 'encoding', 'modes']
doc_type: 'reference'
import CompressionBlock from '@site/static/images/data-compression/ch_compression_block.png';
impo... | {"source_file": "compression-modes.md"} | [
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0.10490220040082932,
-0.020... |
9b79ed0b-2c8b-4aaf-98d8-ba23c3c2564a | slug: /data-compression/compression-in-clickhouse
title: 'Compression in ClickHouse'
description: 'Choosing ClickHouse compression algorithms'
keywords: ['compression', 'codec', 'encoding']
doc_type: 'reference'
One of the secrets to ClickHouse query performance is compression.
Less data on disk means less I/O a... | {"source_file": "compression-in-clickhouse.md"} | [
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-0.04... |
e0e1b615-0e31-4c2f-a07e-97bd675cf365 | ┌─name──────────────────┬─compressed_size─┬─uncompressed_size─┬───ratio────┐
│ Body │ 46.14 GiB │ 127.31 GiB │ 2.76 │
│ Title │ 1.20 GiB │ 2.63 GiB │ 2.19 │
│ Score │ 84.77 MiB │ 736.45 MiB │ 8.69 │
│ Tags ... | {"source_file": "compression-in-clickhouse.md"} | [
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0.0794132798910141,
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dac8757c-6ee9-46f7-b7dd-4690f28e01c4 | -- Check the type of the parts
SELECT table, name, part_type from system.parts where table = 'compact';
-- Get the compressed and uncompressed column sizes for the compact table
SELECT name,
formatReadableSize(sum(data_compressed_bytes)) AS compressed_size,
formatReadableSize(sum(data_uncompressed_bytes)) AS unc... | {"source_file": "compression-in-clickhouse.md"} | [
0.04902243614196777,
0.03046952188014984,
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0.043810147792100906,
-0.08446... |
79ad882c-8a86-4f79-93ba-dcf8e0c039a7 | To summarize the total size of the table, we can simplify the above query:
```sql
SELECT formatReadableSize(sum(data_compressed_bytes)) AS compressed_size,
formatReadableSize(sum(data_uncompressed_bytes)) AS uncompressed_size,
round(sum(data_uncompressed_bytes) / sum(data_compressed_bytes), 2) AS ratio
FROM s... | {"source_file": "compression-in-clickhouse.md"} | [
0.03525036945939064,
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0.05685805529356003,
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0.06466522812843323,
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0.020860863849520683,
0.05004866048693657,
-0.01704... |
0176fa3e-e094-44b5-aabc-0e44f09ab66a | ┌─name──────────────────┬─compressed_size─┬─uncompressed_size─┬───ratio─┐
│ Body │ 23.10 GiB │ 63.63 GiB │ 2.75 │
│ Title │ 614.65 MiB │ 1.28 GiB │ 2.14 │
│ Score │ 40.28 MiB │ 227.38 MiB │ 5.65 │
│ Tags ... | {"source_file": "compression-in-clickhouse.md"} | [
0.018732242286205292,
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0.09069673717021942,
-0.00935... |
3eb79330-268d-4002-a872-e556991c7eff | Recommendation | Reasoning
--- | ---
ZSTD
all the way
|
ZSTD
compression offers the best rates of compression.
ZSTD(1)
should be the default for most common types. Higher rates of compression can be ... | {"source_file": "compression-in-clickhouse.md"} | [
-0.12997876107692719,
0.056505195796489716,
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0.020025018602609634,
-0.04108434543013573,
0... |
d1717924-0d96-449b-8bb9-d56c0bfdaec0 | Below we specify the
Delta
codec for the
Id
,
ViewCount
and
AnswerCount
, hypothesizing these will be linearly correlated with the ordering key and thus should benefit from Delta encoding.
sql
CREATE TABLE posts_v4
(
`Id` Int32 CODEC(Delta, ZSTD),
`PostTypeId` Enum('Question' = 1, 'Answer' = 2, ... | {"source_file": "compression-in-clickhouse.md"} | [
0.012072579935193062,
-0.021897494792938232,
-0.07786715030670166,
0.02678149752318859,
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0.040783852338790894,
0.0751587301492691,
-0.05160560831427574,
0.05160226300358772,
-0.0238... |
d27fb03d-43d8-4b78-8184-9e7eb9d9cd78 | 6 rows in set. Elapsed: 0.008 sec
```
Compression in ClickHouse Cloud {#compression-in-clickhouse-cloud}
In ClickHouse Cloud, we utilize the
ZSTD
compression algorithm (with a default value of 1) by default. While compression speeds can vary for this algorithm, depending on the compression level (higher = slower)... | {"source_file": "compression-in-clickhouse.md"} | [
-0.1053967997431755,
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-0.024018630385398865,
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0.04741424694657326,
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0.07800410687923431,
0.032830655574798584,
-... |
dc106997-e611-44e7-aa5d-6f2effda37ae | slug: /data-modeling/denormalization
title: 'Denormalizing Data'
description: 'How to use denormalization to improve query performance'
keywords: ['data denormalization', 'denormalize', 'query optimization']
doc_type: 'guide'
import denormalizationDiagram from '@site/static/images/data-modeling/denormalization-diag... | {"source_file": "denormalization.md"} | [
0.032279618084430695,
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0.0285825... |
8b3c35ec-8575-414e-9649-a22adee3b580 | The denormalization work can be handled in either ClickHouse or upstream e.g. using Apache Flink.
Avoid denormalization on frequently updated data {#avoid-denormalization-on-frequently-updated-data}
For ClickHouse, denormalization is one of several options users can use in order to optimize query performance but sh... | {"source_file": "denormalization.md"} | [
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-... |
23c0cf63-ac54-4295-a8ea-c59ca1d36338 | For each of the following examples, assume a query exists which requires both tables to be used in a join.
Posts and Votes {#posts-and-votes}
Votes for posts are represented as separate tables. The optimized schema for this is shown below as well as the insert command to load the data:
``sql
CREATE TABLE votes
(
... | {"source_file": "denormalization.md"} | [
-0.028775684535503387,
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-0.07... |
ac04cb8f-18e1-4007-8786-2179eb48c9de | Users and Badges {#users-and-badges}
Now let's consider our
Users
and
Badges
:
We first insert the data with the following command:
sql
CREATE TABLE users
(
`Id` Int32,
`Reputation` LowCardinality(String),
`CreationDate` DateTime64(3, 'UTC') CODEC(Delta(8), ZSTD(1)),
`DisplayName` String,... | {"source_file": "denormalization.md"} | [
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-0.048581... |
4911c14d-1c5e-42a3-883b-28c0b5429f7e | We can confirm that no posts have an excessive number of links preventing denormalization:
```sql
SELECT PostId, count() AS c
FROM postlinks
GROUP BY PostId
ORDER BY c DESC LIMIT 5
┌───PostId─┬───c─┐
│ 22937618 │ 125 │
│ 9549780 │ 120 │
│ 3737139 │ 109 │
│ 18050071 │ 103 │
│ 25889234 │ 82 │
└──────────┴─────┘
``... | {"source_file": "denormalization.md"} | [
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-0.02... |
0f0e3a4e-88f9-430b-9125-fca586642124 | Each post can contain a number of links to other posts as shown in the
PostLinks
schema earlier. As a Nested type, we might represent these linked and duplicates posts as follows:
sql
SET flatten_nested=0
CREATE TABLE posts_with_links
(
`Id` Int32 CODEC(Delta(4), ZSTD(1)),
... -other columns
`LinkedPosts` N... | {"source_file": "denormalization.md"} | [
-0.05547897517681122,
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-0.0465... |
abd52639-daeb-4d4f-b738-9e282f365678 | Users have several options for orchestrating this in ClickHouse, assuming a periodic batch load process is acceptable:
Refreshable Materialized Views
- Refreshable materialized views can be used to periodically schedule a query with the results sent to a target table. On query execution, the view ensures the targe... | {"source_file": "denormalization.md"} | [
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0.01325790211558342,
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0.0010593595216050744,
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-0.00032534313504584134,
0.004179814830422401,
-0... |
510aada7-5901-459e-8664-4e5955d49eaa | slug: /data-modeling/backfilling
title: 'Backfilling Data'
description: 'How to use backfill large datasets in ClickHouse'
keywords: ['materialized views', 'backfilling', 'inserting data', 'resilient data load']
doc_type: 'guide'
import nullTableMV from '@site/static/images/data-modeling/null_table_mv.png';
import ... | {"source_file": "backfilling.md"} | [
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0.028490277007222176,
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0.09383125603199005,
-0.... |
f5579cad-ce67-4634-88ef-c36476af74b6 | ┌─name───────────────┬─type────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ timestamp │ Nullable(DateTime64(6)) │
│ count... | {"source_file": "backfilling.md"} | [
-0.05551200732588768,
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0.0007073086453601718,
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-0... |
92bf4e87-bbf7-4da7-8eb8-9bd4de70a226 | :::note
The full PyPI dataset, consisting of over 1 trillion rows, is available in our public demo environment
clickpy.clickhouse.com
. For further details on this dataset, including how the demo exploits materialized views for performance and how the data is populated daily, see
here
.
:::
Backfilling scenarios {#... | {"source_file": "backfilling.md"} | [
-0.09871774166822433,
-0.04060361161828041,
-0.04937067627906799,
0.02629479393362999,
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-0.05573083832859993,
-0.010481134057044983,
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0.015312910079956055,
-0.018717816099524498,
-0.005694309715181589,
0.013436639681458473,
... |
01a8e8e7-f2b4-45e1-aee5-daeac6345765 | 0 rows in set. Elapsed: 15.702 sec. Processed 41.23 million rows, 3.94 GB (2.63 million rows/s., 251.01 MB/s.)
Peak memory usage: 977.49 MiB.
SELECT count() FROM pypi
┌──count()─┐
│ 20612750 │ -- 20.61 million
└──────────┘
1 row in set. Elapsed: 0.004 sec.
SELECT sum(count)
FROM pypi_downloads
┌─sum(count)─┐
... | {"source_file": "backfilling.md"} | [
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-0.10684043169021606,
0.05707567185163498,
-0.051988668739795685,
-0.03621058538556099,
0.052291300147771835,
0.031201332807540894,
0.022990774363279343,
0.029715362936258316,
-0.0037728138267993927,
0.02251981571316719,
0.03885786235332489,
-0.... |
ee846c14-2a86-4fe2-b61b-d3f5b3bcbc5e | We can now confirm
pypi
and
pypi_downloads
contain the complete data.
pypi_downloads_v2
and
pypi_v2
can be safely dropped.
```sql
SELECT count()
FROM pypi
┌──count()─┐
│ 41012770 │ -- 41.01 million
└──────────┘
1 row in set. Elapsed: 0.003 sec.
SELECT sum(count)
FROM pypi_downloads
┌─sum(count)─┐
│ ... | {"source_file": "backfilling.md"} | [
-0.057198043912649155,
-0.04269878938794136,
-0.03136182948946953,
0.033609550446271896,
0.022928981110453606,
-0.06108548864722252,
0.04005450755357742,
0.0710705891251564,
0.07225129753351212,
0.0395318977534771,
0.02046957053244114,
0.02430151402950287,
0.011794027872383595,
-0.01898828... |
89d6a310-eb89-430a-9ee6-bc896d15a4d9 | Identify the checkpoint - either a timestamp or column value from which historical data needs to be restored.
Create duplicates of the main table and target tables for materialized views.
Create copies of any materialized views pointing to the target tables created in step (2).
Insert into our duplicate main tabl... | {"source_file": "backfilling.md"} | [
-0.0255739763379097,
-0.035963024944067,
0.013920835219323635,
-0.011855141259729862,
-0.014202360063791275,
-0.0730474665760994,
0.04268190637230873,
-0.010382365435361862,
-0.02692958153784275,
0.008311688899993896,
-0.00024798279628157616,
0.013150990940630436,
0.04219822213053703,
-0.0... |
6b0e84b2-c399-4c4b-91f8-af6549927080 | Scenario 2: Adding materialized views to existing tables {#scenario-2-adding-materialized-views-to-existing-tables}
It is not uncommon for new materialized views to need to be added to a setup for which significant data has been populated and data is being inserted. A timestamp or monotonically increasing column, whi... | {"source_file": "backfilling.md"} | [
-0.07082760334014893,
-0.05162832513451576,
-0.06364937126636505,
0.021651268005371094,
0.04707498848438263,
-0.0048747593536973,
-0.0018650973215699196,
-0.03718802332878113,
0.019380616024136543,
0.017572104930877686,
0.019271245226264,
-0.029362792149186134,
0.008608819916844368,
-0.069... |
a5b8d002-edaa-405f-9a40-97c7e9dcb995 | Once this view is added, we can backfill all data for the materialized view prior to this data.
The simplest means of doing this is to simply run the query from the materialized view on the main table with a filter that ignores recently added data, inserting the results into our view's target table via an
INSERT INT... | {"source_file": "backfilling.md"} | [
-0.035836488008499146,
-0.037610094994306564,
-0.05141184478998184,
0.09279592335224152,
-0.056676462292671204,
0.00900691282004118,
0.021124698221683502,
-0.03227581828832626,
-0.006316962651908398,
0.05325378477573395,
-0.02966783568263054,
-0.032903317362070084,
0.05194338411092758,
-0.... |
56caa014-ebed-4bd2-b0bd-1e0bb7bbc34c | Importantly, any materialized views attached to the table engine still execute over blocks of data as its inserted - sending their results to a target table. These blocks are of a configurable size. While larger blocks can potentially be more efficient (and faster to process), they consume more resources (principally m... | {"source_file": "backfilling.md"} | [
-0.05377810820937157,
-0.029972612857818604,
-0.06075639650225639,
0.039102569222450256,
-0.02219700813293457,
-0.023996694013476372,
-0.04778042063117027,
-0.019404081627726555,
0.07214254140853882,
0.03408348560333252,
0.0062682125717401505,
-0.03368452936410904,
0.01642860472202301,
-0.... |
5c8acad3-99fe-4a54-9e65-0fad1d64ce47 | Insert Parallelism
- The number of insert threads used to insert. Controlled through
max_insert_threads
. In ClickHouse Cloud this is determined by the instance size (between 2 and 4) and is set to 1 in OSS. Increasing this value may improve performance at the expense of greater memory usage.
Insert Block Size
- ... | {"source_file": "backfilling.md"} | [
-0.033824022859334946,
-0.042187467217445374,
-0.0421547070145607,
-0.005365079268813133,
-0.005524412263184786,
-0.03715239465236664,
-0.08653689175844193,
0.004625312983989716,
0.07056070864200592,
0.04830117151141167,
0.051034942269325256,
0.022622480988502502,
0.0456511490046978,
-0.09... |
05dfde18-2635-4c47-95d0-d053dee9b876 | Ok.
0 rows in set. Elapsed: 43.907 sec. Processed 1.50 billion rows, 33.48 GB (34.06 million rows/s., 762.54 MB/s.)
Peak memory usage: 272.53 MiB.
```
Finally, we can reduce memory further by setting
min_insert_block_size_rows
to 0 (disables it as a deciding factor on block size) and
min_insert_block_size_bytes
... | {"source_file": "backfilling.md"} | [
0.02909902110695839,
0.036743585020303726,
-0.0724375769495964,
0.0013607523869723082,
-0.0445224866271019,
-0.08251521736383438,
-0.03106243722140789,
0.0765150710940361,
-0.005668250378221273,
0.017755556851625443,
-0.005158286076039076,
-0.009700847789645195,
0.008027350530028343,
-0.01... |
c8b56efb-902d-431b-8563-e146d862b726 | CREATE MATERIALIZED VIEW pypi_downloads_per_day_mv TO pypi_downloads_per_day
AS SELECT
toStartOfHour(timestamp) as hour,
project,
count() AS count
FROM pypi
GROUP BY
hour,
project
-- (4) Restart inserts. We replicate here by inserting a single row.
INSERT INTO pypi SELECT *
FROM pypi
LIMIT 1
SELECT cou... | {"source_file": "backfilling.md"} | [
-0.008476725779473782,
-0.048022691160440445,
-0.013505736365914345,
0.04325990378856659,
-0.04898823797702789,
-0.03317386284470558,
0.040581267327070236,
-0.015062747523188591,
0.05115903168916702,
0.038987305015325546,
0.04271216318011284,
-0.08262783288955688,
0.06904716789722443,
-0.0... |
bab1681d-fb0d-458d-b645-465d7cd7d8b1 | slug: /data-modeling/overview
title: 'Data Modelling Overview'
description: 'Overview of Data Modelling'
keywords: ['data modelling', 'schema design', 'dictionary', 'materialized view', 'data compression', 'denormalizing data']
doc_type: 'landing-page'
Data Modeling
This section is about data modeling in ClickHou... | {"source_file": "index.md"} | [
-0.02333972603082657,
-0.019384177401661873,
-0.0060679432936012745,
0.03779645264148712,
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0.038679614663124084,
0.0024526729248464108,
0.03085245005786419,
0.0903797373175621,
-0.... |
d4f6b945-bcc9-4c40-b495-6b6285a24f5c | slug: /data-modeling/schema-design
title: 'Schema Design'
description: 'Optimizing ClickHouse schema for query performance'
keywords: ['schema', 'schema design', 'query optimization']
doc_type: 'guide'
import stackOverflowSchema from '@site/static/images/data-modeling/stackoverflow-schema.png';
import schemaDesignI... | {"source_file": "schema-design.md"} | [
-0.014195442199707031,
-0.015552611090242863,
-0.004609585739672184,
0.037216849625110626,
0.0043616206385195255,
-0.07038581371307373,
-0.007271952927112579,
0.004725623410195112,
-0.06402837485074997,
0.002457721158862114,
0.009811587631702423,
0.014663329347968102,
0.07529600709676743,
... |
6914dad1-3165-41e9-a67b-c2e8b46d0ba9 | ClickHouse provides a schema inference capability to automatically identify the types for a dataset. This is supported for all data formats, including Parquet. We can exploit this feature to identify the ClickHouse types for the data via s3 table function and
DESCRIBE
command. Note below we use the glob pattern
*.par... | {"source_file": "schema-design.md"} | [
-0.01635614037513733,
-0.07254599779844284,
-0.07616287469863892,
-0.020219510421156883,
-0.0018798382952809334,
-0.034355904906988144,
0.030707556754350662,
-0.05623132735490799,
-0.05428013578057289,
0.013577528297901154,
0.01437213085591793,
-0.05020751804113388,
-0.016115771606564522,
... |
1dc39a9c-1327-46e3-8cbb-edb65b05ad15 | The clause
ORDER BY ()
means we have no index, and more specifically no order in our data. More on this later. For now, just know all queries will require a linear scan.
To confirm the table has been created:
```sql
SHOW CREATE TABLE posts
CREATE TABLE posts
(
Id
Nullable(Int64),
PostTypeId
... | {"source_file": "schema-design.md"} | [
0.04329069331288338,
-0.020971208810806274,
-0.014751369133591652,
0.08336854726076126,
-0.035655293613672256,
0.06514686346054077,
0.003878374118357897,
-0.02500816434621811,
-0.01553112082183361,
0.09623986482620239,
0.08464671671390533,
0.004175631795078516,
0.10119427740573883,
-0.0287... |
f7b74cf4-187b-4916-ba7a-3ec6def5836a | For why ClickHouse compresses data so well, we recommend
this article
. In summary, as a column-oriented database, values will be written in column order. If these values are sorted, the same values will be adjacent to each other. Compression algorithms exploit contiguous patterns of data. On top of this, ClickHouse h... | {"source_file": "schema-design.md"} | [
-0.01311857346445322,
0.04691556841135025,
-0.02288949117064476,
-0.014050587080419064,
-0.042831435799598694,
-0.024138620123267174,
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0.01377923134714365,
0.021926118060946465,
-0.036968208849430084,
0.03431257605552673,
0.015860360115766525,
-0... |
4486887b-d439-4ea5-8291-6637b2d5d5c3 | Use LowCardinality
- Numbers, strings, Date or DateTime columns with a low number of unique values can potentially be encoded using the LowCardinality type. This dictionary encodes values, reducing the size on disk. Consider this for columns with less than 10k unique values.
FixedString for special cases - Strings whi... | {"source_file": "schema-design.md"} | [
-0.003793502924963832,
0.03100484050810337,
-0.10073171555995941,
-0.03394269570708275,
-0.028896937146782875,
0.0034604365937411785,
-0.031065503135323524,
0.02684442326426506,
0.008938753046095371,
-0.002112468471750617,
0.0021615575533360243,
-0.01755502074956894,
0.02875605598092079,
0... |
5433b1f7-4ba3-4911-bc93-6b872ccd6118 | | Column | Is Numeric | Min, Max | Unique Values | Nulls | Comment | Optimized Type |
|------------------------|------------|------... | {"source_file": "schema-design.md"} | [
0.02567560225725174,
-0.0011926016304641962,
-0.06033676862716675,
0.0020764777436852455,
-0.09470570832490921,
0.03782108426094055,
0.060976963490247726,
0.025806330144405365,
-0.027843007817864418,
0.03445541858673096,
0.0657227635383606,
-0.03378608450293541,
0.05902000144124031,
-0.045... |
576221ab-b12e-4ae5-aa5b-396429bcf890 | |
LastEditorUserId
| Yes | -1, 9999993 | 1104694 | Yes | 0 is an unused value can be used for Nulls | Int32 |
|
LastEditorDisplayName
| No |... | {"source_file": "schema-design.md"} | [
0.07246224582195282,
0.053261008113622665,
-0.04887404665350914,
0.046304717659950256,
-0.04050883278250694,
0.04809420183300972,
0.04277205467224121,
0.1012502908706665,
0.016008410602808,
-0.025603754445910454,
0.04296072572469711,
-0.10865738242864609,
0.03802284225821495,
-0.0344740338... |
4efe36cd-d921-4115-b1e2-541ffdb41a47 | |
ParentId
| No | - | 20696028 | Yes | Consider Null to be an empty string | String |
|
CommunityOwnedDate
| No |... | {"source_file": "schema-design.md"} | [
0.07706430554389954,
0.07191555202007294,
-0.02257552370429039,
0.029711956158280373,
-0.06261958181858063,
0.007698889821767807,
-0.009800597093999386,
0.08025017380714417,
-0.015289925038814545,
-0.001012114342302084,
0.10052545368671417,
-0.13369879126548767,
-0.02058214694261551,
-0.05... |
8673124d-630a-4bda-a9de-c1147cc43328 | The above gives us the following schema:
sql
CREATE TABLE posts_v2
(
`Id` Int32,
`PostTypeId` Enum('Question' = 1, 'Answer' = 2, 'Wiki' = 3, 'TagWikiExcerpt' = 4, 'TagWiki' = 5, 'ModeratorNomination' = 6, 'WikiPlaceholder' = 7, 'PrivilegeWiki' = 8),
`AcceptedAnswerId` UInt32,
`CreationDate` DateTime,
`... | {"source_file": "schema-design.md"} | [
0.05719456821680069,
-0.014544048346579075,
-0.0752677246928215,
0.06270253658294678,
-0.01656223088502884,
-0.01823236234486103,
-0.0022314502857625484,
0.02434663102030754,
-0.012643390335142612,
0.07335035502910614,
0.07679887115955353,
-0.0947636067867279,
0.08826608210802078,
-0.02684... |
7b0d22f8-8fca-4b7a-85e4-3590ca6611e1 | Some simple rules can be applied to help choose an ordering key. The following can sometimes be in conflict, so consider these in order. Users can identify a number of keys from this process, with 4-5 typically sufficient:
Select columns which align with your common filters. If a column is used frequently in
WHERE... | {"source_file": "schema-design.md"} | [
0.0435713455080986,
0.03609684854745865,
0.01265793852508068,
-0.030274901539087296,
0.03317039832472801,
0.03244040533900261,
-0.055185362696647644,
-0.019048945978283882,
0.07833625376224518,
0.06246604025363922,
-0.012929966673254967,
0.13194938004016876,
0.02129410393536091,
-0.0219280... |
36d75966-d445-4fe5-aa08-0c8ae3e16857 | Lets select the columns
PostTypeId
and
CreationDate
as our ordering keys.
Maybe in our case, we expect users to always filter by
PostTypeId
. This has a cardinality of 8 and represents the logical choice for the first entry in our ordering key. Recognizing date granularity filtering is likely to be sufficient (i... | {"source_file": "schema-design.md"} | [
-0.011547444388270378,
-0.054309915751218796,
0.037411417812108994,
0.037090010941028595,
-0.03947516158223152,
-0.01272125355899334,
0.048656709492206573,
-0.0229367446154356,
-0.0009753731428645551,
0.052199795842170715,
0.047622907906770706,
-0.008930218406021595,
0.03450162708759308,
-... |
53c416c6-6dd9-4e7c-b831-c3766a1a1c14 | Through this section, we use optimized variants of our other tables. While we provide the schemas for these, for the sake of brevity we omit the decisions made. These are based on the rules described earlier and we leave inferring the decisions to the reader.
The following approaches all aim to minimize the need to... | {"source_file": "schema-design.md"} | [
-0.025006573647260666,
0.01738569885492325,
-0.02099020592868328,
0.033252205699682236,
0.011251420713961124,
-0.07751701027154922,
0.039440419524908066,
0.043656136840581894,
0.006319581065326929,
-0.04895057529211044,
0.0027229462284594774,
0.012464017607271671,
0.08216159045696259,
0.00... |
49d26ed8-d7e3-490c-adfd-82a9e9422a0a | slug: /best-practices/use-json-where-appropriate
sidebar_position: 10
sidebar_label: 'Using JSON'
title: 'Use JSON where appropriate'
description: 'Page describing when to use JSON'
keywords: ['JSON']
show_related_blogs: true
doc_type: 'reference'
ClickHouse now offers a native JSON column type designed for semi-st... | {"source_file": "json_type.md"} | [
-0.039739418774843216,
0.005590792745351791,
-0.023132706061005592,
0.016025908291339874,
-0.015401181764900684,
0.015466805547475815,
-0.03815338760614395,
0.02209380269050598,
-0.024857481941580772,
-0.0553099624812603,
0.07546141743659973,
0.03067149594426155,
0.027820169925689697,
0.07... |
b6c6ecf9-90c7-4180-921e-b4621acf00b1 | Advanced features {#advanced-features}
JSON columns
can be used in primary keys
like any other columns. Codecs cannot be specified for a subcolumn.
They support introspection via functions like
JSONAllPathsWithTypes()
and
JSONDynamicPaths()
.
You can read nested sub-objects using the
.^
syntax.
Query s... | {"source_file": "json_type.md"} | [
-0.04395878314971924,
0.0009913841495290399,
-0.010641916655004025,
0.013187499716877937,
-0.018835991621017456,
-0.09325855225324631,
-0.02958568185567856,
0.022337976843118668,
-0.08210592716932297,
-0.042932771146297455,
0.03622554987668991,
-0.02258421666920185,
-0.003995944280177355,
... |
296ddba4-2786-425d-81f3-1842cdbc33c3 | sql
CREATE TABLE arxiv
(
`id` String,
`submitter` String,
`authors` String,
`title` String,
`comments` String,
`journal-ref` String,
`doi` String,
`report-no` String,
`categories` String,
`license` String,
`abstract` String,
`versions` Array(Tuple(created String, version String)),
`update_date... | {"source_file": "json_type.md"} | [
0.004651025868952274,
0.019346239045262337,
0.0012308586155995727,
0.027229391038417816,
-0.12725114822387695,
-0.01557585597038269,
-0.0626300647854805,
0.0030635064467787743,
-0.023996656760573387,
0.05068362131714821,
0.046635664999485016,
-0.014581852592527866,
0.04076279699802399,
-0.... |
b4297f79-637d-43e9-bce9-b2ad36bb0a29 | sql
CREATE TABLE arxiv
(
`doc` JSON(update_date Date)
)
ENGINE = MergeTree
ORDER BY doc.update_date
:::note
We provide a type hint for the
update_date
column in the JSON definition, as we use it in the ordering/primary key. This helps ClickHouse to know that this column won't be null and ensures it knows which
u... | {"source_file": "json_type.md"} | [
-0.010753166861832142,
0.022250723093748093,
0.075799860060215,
0.049775660037994385,
-0.06546176224946976,
0.010688967071473598,
-0.12168550491333008,
0.06495014578104019,
0.0024309155996888876,
0.04345264285802841,
0.021378526464104652,
0.005079454742372036,
-0.004389593377709389,
-0.051... |
0b9e49b9-a28f-40e8-a6ff-3d5c07975f9f | Alternatively, we could model this using our earlier schema and a JSON
tags
column. This is generally preferred, minimizing the inference required by ClickHouse:
sql
CREATE TABLE arxiv
(
`id` String,
`submitter` String,
`authors` String,
`title` String,
`comments` String,
`journal-ref` Strin... | {"source_file": "json_type.md"} | [
-0.00799941923469305,
-0.006655457895249128,
-0.003419124521315098,
0.034073635935783386,
-0.08099022507667542,
-0.024995410814881325,
-0.08266585320234299,
0.0068525285460054874,
-0.0255180262029171,
0.048512447625398636,
0.038379959762096405,
-0.023251069709658623,
0.04525142163038254,
-... |
e273d51b-7c3d-4b9b-908d-182b032e3b2e | slug: /best-practices/avoid-optimize-final
sidebar_position: 10
sidebar_label: 'Avoid optimize final'
title: 'Avoid OPTIMIZE FINAL'
description: 'Page describing why you should avoid the OPTIMIZE FINAL clause in ClickHouse'
keywords: ['avoid OPTIMIZE FINAL', 'background merges']
hide_title: true
doc_type: 'guide'
A... | {"source_file": "avoid_optimize_final.md"} | [
0.009339634329080582,
0.12567894160747528,
0.01258156169205904,
-0.0027154211420565844,
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0.06055517867207527,
0.08417440950870514,
-0.0496967... |
9f210cad-040c-40dc-a0a6-a4b059978423 | slug: /best-practices/avoid-mutations
sidebar_position: 10
sidebar_label: 'Avoid mutations'
title: 'Avoid mutations'
description: 'Page describing why to avoid mutations in ClickHouse'
keywords: ['mutations']
doc_type: 'guide'
import Content from '@site/docs/best-practices/_snippets/_avoid_mutations.md'; | {"source_file": "avoid_mutations.md"} | [
-0.05297867953777313,
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0.06321919709444046,
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55202a93-6624-4e64-9d91-2916ebc8a3e9 | slug: /best-practices/choosing-a-primary-key
sidebar_position: 10
sidebar_label: 'Choosing a primary key'
title: 'Choosing a Primary Key'
description: 'Page describing how to choose a primary key in ClickHouse'
keywords: ['primary key']
show_related_blogs: true
doc_type: 'guide'
import Image from '@theme/IdealImage... | {"source_file": "choosing_a_primary_key.md"} | [
-0.027205072343349457,
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5d1c4600-9d6a-4845-85b5-f2259d0035c6 | Example {#example}
Consider the following
posts_unordered
table. This contains a row per Stack Overflow post.
This table has no primary key - as indicated by
ORDER BY tuple()
.
sql
CREATE TABLE posts_unordered
(
`Id` Int32,
`PostTypeId` Enum('Question' = 1, 'Answer' = 2, 'Wiki' = 3, 'TagWikiExcerpt' = 4,
... | {"source_file": "choosing_a_primary_key.md"} | [
-0.015862461179494858,
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0.043090805411338806,
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0.03172115609049797,
0.09051136672496796,
0.06141907349228859,
-0.001582036493346095,
0.10159394890069962,
-... |
3166c730-17a9-47db-9611-c0be1dc2e1b8 | PostTypeId
has a cardinality of 8 and represents the logical choice for the first entry in our ordering key. Recognizing date granularity filtering is likely to be sufficient (it will still benefit datetime filters) so we use
toDate(CreationDate)
as the 2nd component of our key. This will also produce a smaller inde... | {"source_file": "choosing_a_primary_key.md"} | [
-0.031605314463377,
-0.007421466987580061,
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-0.021299438551068306,
0.010855996049940586,
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0.02468486689031124,
-0.020341532304883003,
-0.01618300750851631,
0.04388238862156868,
0.01547452062368393,
-0.09588... |
2d9a8bb5-0ad8-42ac-8779-0fa6adaa92e0 | 13 rows in set. Elapsed: 0.004 sec.
```
Additionally, we visualize how the sparse index prunes all row blocks that can't possibly contain matches for our example query:
:::note
All columns in a table will be sorted based on the value of the specified ordering key, regardless of whether they are included in the ke... | {"source_file": "choosing_a_primary_key.md"} | [
-0.039333492517471313,
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0.03335125371813774,
... |
3affc194-44a1-4c8e-a65e-31ae4e3e31fb | slug: /best-practices/selecting-an-insert-strategy
sidebar_position: 10
sidebar_label: 'Selecting an insert strategy'
title: 'Selecting an insert strategy'
description: 'Page describing how to choose an insert strategy in ClickHouse'
keywords: ['INSERT', 'asynchronous inserts', 'compression', 'batch inserts']
show_rela... | {"source_file": "selecting_an_insert_strategy.md"} | [
-0.042565297335386276,
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0.11872082948684692,
-0.041018161... |
8b51139d-bbcc-4b90-b730-82355a87a9f5 | The data is ⑤ transmitted to a ClickHouse network interface—either the
native
or
HTTP
interface (which we
compare
later in this post).
Server-side steps {#server-side-steps}
After ⑥ receiving the data, ClickHouse ⑦ decompresses it if compression was used, then ⑧ parses it from the originally sent format.
Us... | {"source_file": "selecting_an_insert_strategy.md"} | [
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0.058176737278699875,
0.05864819511771202,
0.05902780219912529,
-0.1154... |
5c0abfc6-aa87-44c4-8942-c1635eeb80f1 | JSONEachRow
: Easy to use but expensive to parse. Suitable for low-volume use cases or quick integrations.
Use compression {#use-compression}
Compression plays a critical role in reducing network overhead, speeding up inserts, and lowering storage costs in ClickHouse. Used effectively, it enhances ingestion perfo... | {"source_file": "selecting_an_insert_strategy.md"} | [
-0.0692194253206253,
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0.057054758071899414,
-0.012579387985169888,
0.0354... |
757411f7-0171-441b-b673-b170ffeb7f86 | Pre-sort if low cost {#pre-sort-if-low-cost}
Pre-sorting data by primary key before insertion can improve ingestion efficiency in ClickHouse, particularly for large batches.
When data arrives pre-sorted, ClickHouse can skip or simplify the internal sorting step during part creation, reducing CPU usage and accelera... | {"source_file": "selecting_an_insert_strategy.md"} | [
-0.031123671680688858,
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0.13078679144382477,
-0.027457136660814285,
-0.00... |
8e505b5d-ed45-4dc9-b8f2-e8ecf3024e09 | However, it lacks the native protocol's deeper integration and cannot perform client-side optimizations like materialized value computation or automatic conversion to Native format. While HTTP inserts can still be compressed using standard HTTP headers (e.g.
Content-Encoding: lz4
), the compression is applied to the e... | {"source_file": "selecting_an_insert_strategy.md"} | [
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0.02380843460559845,
0.020547354593873024,
0.010... |
7de33f45-f4e8-4f0b-832d-b6e78b5351f3 | slug: /best-practices/use-data-skipping-indices-where-appropriate
sidebar_position: 10
sidebar_label: 'Data skipping indices'
title: 'Use data skipping indices where appropriate'
description: 'Page describing how and when to use data skipping indices'
keywords: ['data skipping index', 'skip index']
show_related_blogs: ... | {"source_file": "using_data_skipping_indices.md"} | [
0.012319398112595081,
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0.09437955170869827,
0.09292159229516983,
-0.01... |
869b4720-d7e8-4285-a381-ac35a60229b2 | tokenbf_v1 / ngrambf_v1
: Specialized Bloom filter variants designed for searching tokens or character sequences in strings — particularly useful for log data or text search use cases.
While powerful, skip indexes must be used with care. They only provide benefit when they eliminate a meaningful number of data bloc... | {"source_file": "using_data_skipping_indices.md"} | [
-0.012414109893143177,
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0.01963849738240242,
-0.027233004570007324,
-0.... |
88405ac8-31f2-4f02-a8b3-304a267ba270 | For a more detailed guide on Data Skipping Indices see
here
.
Example {#example}
Consider the following optimized table. This contains Stack Overflow data with a row per post.
sql
CREATE TABLE stackoverflow.posts
(
`Id` Int32 CODEC(Delta(4), ZSTD(1)),
`PostTypeId` Enum8('Question' = 1, 'Answer' = 2, 'Wiki' =... | {"source_file": "using_data_skipping_indices.md"} | [
0.00627964548766613,
-0.0067152297124266624,
-0.014325716532766819,
0.09772289544343948,
-0.006843769922852516,
-0.05095652490854263,
0.02275637723505497,
-0.014925374649465084,
-0.03843541815876961,
0.03569614887237549,
0.08487604558467865,
-0.04230760782957077,
0.035146910697221756,
-0.0... |
235ce5e1-42d3-496c-9782-f59224f8f64c | ```sql
EXPLAIN indexes = 1
SELECT count()
FROM stackoverflow.posts
WHERE (CreationDate > '2009-01-01') AND (ViewCount > 10000000)
LIMIT 1
┌─explain──────────────────────────────────────────────────────────┐
│ Expression ((Project names + Projection)) │
│ Limit (preliminary LIMIT (without OFFS... | {"source_file": "using_data_skipping_indices.md"} | [
0.0020688986405730247,
-0.0200921893119812,
0.05188572034239769,
0.06539690494537354,
-0.0024732102174311876,
0.02774183079600334,
0.022766223177313805,
-0.019542746245861053,
-0.010347933508455753,
0.06129372492432594,
0.036230575293302536,
-0.06218980997800827,
0.07901317626237869,
-0.02... |
90331fe0-7e48-4367-829e-4d8fec0745cf | This index could have also been added during initial table creation. The schema with the minmax index defined as part of the DDL:
sql
CREATE TABLE stackoverflow.posts
(
`Id` Int32 CODEC(Delta(4), ZSTD(1)),
`PostTypeId` Enum8('Question' = 1, 'Answer' = 2, 'Wiki' = 3, 'TagWikiExcerpt' = 4, 'TagWiki' = 5, 'Moderator... | {"source_file": "using_data_skipping_indices.md"} | [
-0.005375411361455917,
-0.03610186278820038,
-0.03861131891608238,
0.08138274401426315,
-0.010597135871648788,
-0.02544785477221012,
-0.011694345623254776,
0.026655247434973717,
-0.016530653461813927,
0.05039272457361221,
0.06898027658462524,
-0.025432754307985306,
0.028962060809135437,
-0... |
8ac43115-4238-45f7-807c-82d541f17e0e | An
EXPLAIN indexes = 1
confirms use of the index.
```sql
EXPLAIN indexes = 1
SELECT count()
FROM stackoverflow.posts
WHERE (CreationDate > '2009-01-01') AND (ViewCount > 10000000)
┌─explain────────────────────────────────────────────────────────────┐
│ Expression ((Project names + Projection)) ... | {"source_file": "using_data_skipping_indices.md"} | [
-0.0015952861867845058,
-0.0358237586915493,
0.05213150754570961,
0.09271673858165741,
0.00663627777248621,
0.0341828316450119,
0.023306583985686302,
-0.024788957089185715,
0.020640306174755096,
0.049170177429914474,
0.035688795149326324,
-0.03400271385908127,
0.07346796989440918,
-0.04594... |
dfa945d6-77e9-4df7-b9e0-244a5718280a | slug: /best-practices/minimize-optimize-joins
sidebar_position: 10
sidebar_label: 'Minimize and optimize JOINs'
title: 'Minimize and optimize JOINs'
description: 'Page describing best practices for JOINs'
keywords: ['JOIN', 'Parallel Hash JOIN']
show_related_blogs: true
doc_type: 'guide'
import Image from '@theme/I... | {"source_file": "minimize_optimize_joins.md"} | [
-0.0227682963013649,
0.005657868925482035,
-0.04073791205883026,
0.05785524845123291,
0.011081499978899956,
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0.037573155015707016,
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0.006067299284040928,
0.05162552371621132,
0.07624831795692444,
0.02106... |
8bc6656f-098e-4448-9db8-59b46227f2da | Reduce the sizes of JOINed tables
: The runtime and memory consumption of JOINs grows proportionally with the sizes of the left and right tables. To reduce the amount of processed data by the JOIN, add additional filter conditions in the
WHERE
or
JOIN ON
clauses of the query. ClickHouse pushes filter conditions as ... | {"source_file": "minimize_optimize_joins.md"} | [
-0.005292526446282864,
0.020868616178631783,
-0.029873289167881012,
0.028063824400305748,
-0.061902012676000595,
-0.06515362858772278,
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-0.02899489365518093,
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0.050991881638765335,
0.05101175233721733,
... |
b29c6d1f-9fb3-4e94-8c98-88b55c5787f8 | :::note Use dictionaries carefully
When using dictionaries for JOINs in ClickHouse, it's important to understand that dictionaries, by design, do not allow duplicate keys. During data loading, any duplicate keys are silently deduplicated—only the last loaded value for a given key is retained. This behavior makes dictio... | {"source_file": "minimize_optimize_joins.md"} | [
-0.04028661176562309,
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0.036336496472358704,
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0.006135937757790089,
-0.007174860220402479,
0.028993045911192894,
0.03301661089062691,
0.075572170317173,
0.0... |
70980529-c081-4d45-aaa8-66cd4b764c27 | slug: /best-practices
keywords: ['Cloud', 'Primary key', 'Ordering key', 'Materialized Views', 'Best Practices', 'Bulk Inserts', 'Asynchronous Inserts', 'Avoid Mutations', 'Avoid nullable Columns', 'Avoid Optimize Final', 'Partitioning Key']
title: 'Overview'
hide_title: true
description: 'Landing page for Best Practic... | {"source_file": "index.md"} | [
0.014548268169164658,
0.015029177069664001,
-0.008035221137106419,
0.019602585583925247,
0.03946736454963684,
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-0.012559651397168636,
0.009596980176866055,
-0.08502176403999329,
0.10097550600767136,
0.044873058795928955,
0.059290580451488495,
0.09174201637506485,
-0.0... |
5c4bb584-5039-49fd-91c1-05b651468449 | slug: /best-practices/select-data-types
sidebar_position: 10
sidebar_label: 'Selecting data types'
title: 'Selecting data types'
description: 'Page describing how to choose data types in ClickHouse'
keywords: ['data types']
doc_type: 'reference'
import NullableColumns from '@site/docs/best-practices/_snippets/_avoi... | {"source_file": "select_data_type.md"} | [
-0.003948734607547522,
0.008316170424222946,
-0.06806094199419022,
0.02704126574099064,
0.00010097982158185914,
-0.05605313926935196,
-0.042765650898218155,
0.02556094154715538,
-0.006318378262221813,
0.030775723978877068,
0.012947906740009785,
0.05468827486038208,
0.03966040536761284,
-0.... |
bb4d78a7-c910-471d-8cf8-bc82457f00f5 | Enums for data validation:
The Enum type can be used to efficiently encode enumerated types. Enums can either be 8 or 16 bits, depending on the number of unique values they are required to store. Consider using this if you need either the associated validation at insert time (undeclared values will be rejected) or wis... | {"source_file": "select_data_type.md"} | [
-0.03392697870731354,
-0.06260275840759277,
-0.01765456795692444,
-0.032226238399744034,
-0.0005499336984939873,
-0.04679213464260101,
0.009017406031489372,
-0.010445228777825832,
-0.05132795125246048,
-0.0023768674582242966,
-0.012121548876166344,
-0.08001647144556046,
0.0012449660571292043... |
15201b4e-f288-41cf-a95f-e239494faa9f | 22 rows in set. Elapsed: 0.130 sec.
```
:::note
Note below we use the glob pattern *.parquet to read all files in the stackoverflow/parquet/posts folder.
:::
By applying our early simple rules to our posts table, we can identify an optimal type for each column: | {"source_file": "select_data_type.md"} | [
0.0019494574517011642,
0.0124068483710289,
-0.0883110985159874,
-0.10452785342931747,
-0.049061551690101624,
-0.02278020977973938,
0.04314608499407768,
0.014288477599620819,
0.006797218695282936,
0.025538308545947075,
0.01867827959358692,
0.03992354869842529,
-0.004239360801875591,
-0.0479... |
057f0284-01bc-4cbb-a6f5-ff1d0160625c | | Column | Is Numeric | Min, Max | Unique Values | Nulls | Comment | Optimized Type |
|------------------------|------------|------... | {"source_file": "select_data_type.md"} | [
0.02567560225725174,
-0.0011926016304641962,
-0.06033676862716675,
0.0020764777436852455,
-0.09470570832490921,
0.03782108426094055,
0.060976963490247726,
0.025806330144405365,
-0.027843007817864418,
0.03445541858673096,
0.0657227635383606,
-0.03378608450293541,
0.05902000144124031,
-0.045... |
839411a0-ee72-4681-91fb-ba0c945dd358 | |
LastEditorUserId
| Yes | -1, 9999993 | 1104694 | Yes | 0 is an unused value can be used for Nulls | Int32 |
|
LastEditorDisplayName
| No |... | {"source_file": "select_data_type.md"} | [
0.07246224582195282,
0.053261008113622665,
-0.04887404665350914,
0.046304717659950256,
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0.04809420183300972,
0.04277205467224121,
0.1012502908706665,
0.016008410602808,
-0.025603754445910454,
0.04296072572469711,
-0.10865738242864609,
0.03802284225821495,
-0.0344740338... |
7be14ad0-4e9a-432d-91bc-b2f8bff88019 | |
ParentId
| No | - | 20696028 | Yes | Consider Null to be an empty string | String |
|
CommunityOwnedDate
| No |... | {"source_file": "select_data_type.md"} | [
0.07706430554389954,
0.07191555202007294,
-0.02257552370429039,
0.029711956158280373,
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0.007698889821767807,
-0.009800597093999386,
0.08025017380714417,
-0.015289925038814545,
-0.001012114342302084,
0.10052545368671417,
-0.13369879126548767,
-0.02058214694261551,
-0.05... |
0290ee81-b716-4ea2-9a14-7621514b9cfb | :::note Tip
Identifying the type for a column relies on understanding its numeric range and number of unique values. To find the range of all columns, and the number of distinct values, users can use the simple query
SELECT * APPLY min, * APPLY max, * APPLY uniq FROM table FORMAT Vertical
. We recommend performing thi... | {"source_file": "select_data_type.md"} | [
0.05692516267299652,
-0.01787417382001877,
-0.04772800952196121,
0.03647730499505997,
-0.05878186970949173,
0.007583237253129482,
0.06789073348045349,
0.011418239213526249,
-0.025842109695076942,
0.04720934480428696,
0.10160548239946365,
-0.05149169638752937,
0.07853856682777405,
-0.033823... |
c46f3d5a-7a2e-43e0-9eda-5e4bb9d3de25 | slug: /best-practices/use-materialized-views
sidebar_position: 10
sidebar_label: 'Use materialized views'
title: 'Use Materialized Views'
description: 'Page describing Materialized Views'
keywords: ['materialized views', 'medallion architecture']
show_related_blogs: true
doc_type: 'guide'
import Image from '@theme/... | {"source_file": "use_materialized_views.md"} | [
-0.09266466647386551,
-0.041895247995853424,
-0.05808138847351074,
0.027300305664539337,
-0.04890763387084007,
-0.06074318662285805,
-0.05490296706557274,
-0.00935611966997385,
-0.015075999312102795,
0.030629782006144524,
0.016713183373212814,
0.10760565847158432,
0.044035010039806366,
-0.... |
a772e713-6afe-4c2e-adf7-7f40e91f3ee1 | When to use incremental materialized views {#when-to-use-incremental-materialized-views}
Incremental materialized views are generally preferred, as they update automatically in real-time whenever the source tables receive new data. They support all aggregation functions and are particularly effective for aggregations... | {"source_file": "use_materialized_views.md"} | [
-0.029512107372283936,
-0.05260050669312477,
-0.03844645619392395,
0.047255199402570724,
-0.009924388490617275,
-0.0017761732451617718,
-0.042874015867710114,
-0.005682786460965872,
0.06258659809827805,
0.024410704150795937,
0.005745923146605492,
0.02010957896709442,
0.011701557785272598,
... |
2400d27d-89da-4575-a4fb-626e2190a177 | REPLACE
is the default behavior. Each time the view is refreshed, the previous contents of the target table are completely overwritten with the latest query result. This is suitable for use cases where the view should always reflect the latest state, such as caching a result set.
APPEND
, by contrast, allows new row... | {"source_file": "use_materialized_views.md"} | [
-0.044382184743881226,
-0.04535149782896042,
0.031584482640028,
0.0339680016040802,
-0.0022786851041018963,
0.029470456764101982,
-0.037433281540870667,
-0.026118312031030655,
0.02224930189549923,
0.06454718112945557,
0.027795542031526566,
0.02848171256482601,
0.013011067174375057,
-0.0833... |
787a6c7a-0db1-4adb-98a1-684ecd7fa061 | slug: /guides/sizing-and-hardware-recommendations
sidebar_label: 'Sizing and hardware recommendations'
sidebar_position: 4
title: 'Sizing and hardware recommendations'
description: 'This guide discusses our general recommendations regarding hardware, compute, memory, and disk configurations for open-source users.'
doc_... | {"source_file": "sizing-and-hardware-recommendations.md"} | [
0.014436524361371994,
-0.025029323995113373,
0.010381265543401241,
0.0034637420903891325,
-0.04903377592563629,
-0.06103525683283806,
-0.03487955033779144,
0.05643318220973015,
-0.06330393254756927,
0.04446955397725105,
-0.022123483940958977,
0.011789037846028805,
0.027686744928359985,
-0.... |
f15dc5c4-be94-4e8f-8b16-a56b3912292a | Data warehousing use case
For data warehousing workloads and ad-hoc analytical queries, we recommend the
R-type series
from AWS or the equivalent offering from your cloud provider as they are memory optimized.
What should CPU utilization be? {#what-should-cpu-utilization-be}
There is no standard CPU utilizati... | {"source_file": "sizing-and-hardware-recommendations.md"} | [
0.0024381966795772314,
-0.022509027272462845,
-0.047940582036972046,
0.04094863682985306,
-0.02944285050034523,
-0.03446786850690842,
0.06260374188423157,
-0.025837428867816925,
-0.04525722563266754,
0.011189542710781097,
-0.018153071403503418,
-0.060260459780693054,
0.0023783205542713404,
... |
41fb3c45-ef2e-4384-92f9-4058a6a5663a | ClickHouse does not automatically shard, and re-sharding your dataset will require significant compute resources. Therefore, we generally recommend using the largest server available to prevent having to re-shard your data in the future.
Consider using
ClickHouse Cloud
which scales automatically and allows you to e... | {"source_file": "sizing-and-hardware-recommendations.md"} | [
-0.005654744803905487,
-0.03691874444484711,
-0.010659468360245228,
-0.022889316082000732,
-0.07990516722202301,
-0.04977010190486908,
-0.060220614075660706,
-0.011282311752438545,
-0.03402582183480263,
0.04267152026295662,
-0.03293206915259361,
0.06141405552625656,
0.024331288412213326,
-... |
18e9e81a-a2d0-49d5-a065-f019c2ebb83a | description: 'Documentation for the HTTP interface in ClickHouse, which provides REST
API access to ClickHouse from any platform and programming language'
sidebar_label: 'HTTP Interface'
sidebar_position: 15
slug: /interfaces/http
title: 'HTTP Interface'
doc_type: 'reference'
import PlayUI from '@site/static/imag... | {"source_file": "http.md"} | [
-0.013305538333952427,
-0.02297312393784523,
-0.06039637699723244,
-0.11419336497783661,
-0.07157640159130096,
-0.014482775703072548,
-0.07545841485261917,
0.011911083944141865,
-0.03606916591525078,
0.0028183083049952984,
0.026082051917910576,
0.02571593038737774,
-0.00684102438390255,
-0... |
9ab7d6d6-383a-4f42-aab8-33a79931ff83 | In the example below curl is used to send the query
SELECT 1
. Note the use of URL encoding for the space:
%20
.
bash title="command"
curl 'http://localhost:8123/?query=SELECT%201'
response title="Response"
1
In this example wget is used with the
-nv
(non-verbose) and
-O-
parameters to output the result to ... | {"source_file": "http.md"} | [
-0.015564218163490295,
0.04720044136047363,
-0.06237102672457695,
0.028055552393198013,
-0.07279830425977707,
-0.074781633913517,
-0.016660692170262337,
-0.008365616202354431,
0.08046018332242966,
0.009608539752662182,
-0.05986873432993889,
-0.0464722216129303,
0.022846871986985207,
-0.068... |
5a75420f-851a-4de0-a98b-dea78b6c089b | "data":
[
{
"1": 1,
"2": 2,
"3": 3
}
],
"rows": 1,
"statistics":
{
"elapsed": 0.000515,
"rows_read": 1,
"bytes_read": 1
}
}
```
You can use the
default_format
URL parameter or the
X-ClickHouse-Format
header to specify a default format other than
TabSeparated
.
b... | {"source_file": "http.md"} | [
0.04321284964680672,
0.027629826217889786,
-0.06952638924121857,
0.048841774463653564,
-0.10746080428361893,
-0.04968194290995598,
0.014324870891869068,
0.03532572090625763,
-0.03347430378198624,
0.060015998780727386,
-0.0358329638838768,
-0.0654265359044075,
0.049154311418533325,
-0.03616... |
41e93be3-32c3-473d-ac0e-5016aa753523 | To increase the efficiency of data insertion, disable server-side checksum verification by using the
http_native_compression_disable_checksumming_on_decompress
setting.
If you specify
compress=1
in the URL, the server will compress the data it sends to you. If you specify
decompress=1
in the URL, the server wil... | {"source_file": "http.md"} | [
-0.08650732040405273,
0.05485256388783455,
-0.061313822865486145,
0.007258418947458267,
-0.00835657212883234,
-0.14055630564689636,
-0.07519770413637161,
-0.05772833526134491,
-0.05225338041782379,
0.006777254864573479,
-0.020495295524597168,
0.05493083596229553,
0.0593457855284214,
-0.012... |
2b3eec10-bc19-494b-bee3-d92d89622252 | For example:
bash
echo 'SELECT 1' | curl 'http://localhost:8123/?user=user&password=password' -d @-
Using the 'X-ClickHouse-User' and 'X-ClickHouse-Key' headers
For example:
bash
echo 'SELECT 1' | curl -H 'X-ClickHouse-User: user' -H 'X-ClickHouse-Key: password' 'http://localhost:8123/' -d @-
If the user ... | {"source_file": "http.md"} | [
0.022831643000245094,
0.01421637088060379,
-0.12091406434774399,
-0.02750774472951889,
-0.13237276673316956,
-0.05579677224159241,
0.04705898463726044,
0.046834446489810944,
-0.002607940463349223,
-0.02467181347310543,
-0.02536713145673275,
-0.04532236605882645,
0.10192219913005829,
-0.107... |
d8a98846-3887-4c04-bdb4-5629696b2352 | The following optional parameters exist:
| Parameters | Description |
|-----------------------|-------------------------------------------|
|
query_id
(optional) | Can be passed as the query ID (any string).
replace_running_query
|
|
quota_key
(optional)| Can be passed as... | {"source_file": "http.md"} | [
-0.04483695700764656,
0.03197921812534332,
-0.11833003163337708,
0.06417617946863174,
-0.07224076986312866,
-0.032247550785541534,
0.014327755197882652,
0.07649978995323181,
0.004517476074397564,
-0.004611572716385126,
-0.05880740284919739,
-0.03999898210167885,
0.07773188501596451,
-0.062... |
49d57fa8-964c-4253-b3ca-5ba287fc21bc | In this case,
?role=my_role&role=my_other_role
works similarly to executing
SET ROLE my_role, my_other_role
before the statement.
HTTP response codes caveats {#http_response_codes_caveats}
Because of limitations of the HTTP protocol, a HTTP 200 response code does not guarantee that a query was successful.
Her... | {"source_file": "http.md"} | [
0.019969727843999863,
0.050389427691698074,
0.002403988502919674,
0.01228311751037836,
-0.03495563194155693,
-0.055328648537397385,
0.010210728272795677,
0.024689478799700737,
-0.022081945091485977,
-0.05007966235280037,
0.004693548660725355,
-0.07819701731204987,
0.09862884134054184,
-0.0... |
cdbb61c8-1524-4062-8226-06b3bb22f4e2 | "data":
[
{
"sleepEachRow(0.001)": 0,
"throwIf(equals(number, 2))": 0
},
{
"sleepEachRow(0.001)": 0,
"throwIf(equals(number, 2))": 0
}
exception
dmrdfnujjqvszhav
Code: 395. DB::Exception: Value passed to 'throwIf' function is non-zero: while executing 'FUNCTION throwI... | {"source_file": "http.md"} | [
0.04986727982759476,
0.11149758845567703,
-0.055715762078762054,
0.00227931491099298,
0.019455142319202423,
-0.020719461143016815,
0.014515743590891361,
0.026205560192465782,
0.06487008929252625,
0.043057262897491455,
-0.0030424059368669987,
-0.12004321813583374,
0.043756064027547836,
-0.0... |
12a6c405-5c2d-40a5-a63d-e987337bcc1f | bash
$ echo '(4),(5),(6)' | curl 'http://localhost:8123/?query=INSERT%20INTO%20t%20VALUES' --data-binary @-
ClickHouse also supports a Predefined HTTP Interface which can help you more easily integrate with third-party tools like
Prometheus exporter
. Let's look at an example.
First of all, add this section to you... | {"source_file": "http.md"} | [
-0.017176859080791473,
0.06354020535945892,
-0.05277476832270622,
-0.007796696852892637,
-0.03327254205942154,
-0.09718069434165955,
-0.009823275730013847,
-0.05097853019833565,
-0.01012217253446579,
0.003865256905555725,
0.009675140492618084,
-0.06937051564455032,
0.03344497084617615,
-0.... |
56056138-ee53-4026-b46f-a050551c243a | -
full_url
-
handler
Each of these are discussed below:
method
is responsible for matching the method part of the HTTP request.
method
fully conforms to the definition of [
method
]
(https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods) in the HTTP protocol. It is an optional configuration. If i... | {"source_file": "http.md"} | [
-0.021142853423953056,
0.0631500855088234,
-0.0042295074090361595,
-0.01547284610569477,
-0.006249269004911184,
-0.11164231598377228,
0.009542275220155716,
-0.019453583285212517,
0.023578006774187088,
-0.035877615213394165,
0.019916392862796783,
-0.0006487203063443303,
0.003465704619884491,
... |
37754237-5a19-47d5-95e8-56032974a7b1 | The following example defines the values of
max_threads
and
max_final_threads
settings, then queries the system table to check whether these settings were set successfully.
:::note
To keep the default
handlers
such as
query
,
play
,
ping
, add the
<defaults/>
rule.
:::
For example:
yaml
<http_handlers>
... | {"source_file": "http.md"} | [
0.020066047087311745,
-0.007838023826479912,
-0.07757449895143509,
-0.02749037556350231,
-0.09254022687673569,
-0.010270008817315102,
0.03910638764500618,
0.010815626941621304,
-0.04084259271621704,
0.06342159956693649,
-0.004307011608034372,
-0.055904582142829895,
0.024068236351013184,
-0... |
81f995f8-c477-4729-9b67-71fc8c205954 | static {#static}
static
can return
content_type
,
status
and
response_content
.
response_content
can return the specified content.
For example, to return a message "Say Hi!":
yaml
<http_handlers>
<rule>
<methods>GET</methods>
<headers><XXX>xxx</XXX></headers>
<ur... | {"source_file": "http.md"} | [
-0.014934827573597431,
0.06209772825241089,
0.004833870567381382,
0.030537335202097893,
0.010019645094871521,
-0.03127099573612213,
-0.010508562438189983,
-0.0059967958368361,
-0.018462957814335823,
-0.02122858352959156,
-0.0283722672611475,
-0.021284835413098335,
0.021923355758190155,
0.0... |
d7dec68c-46a9-476c-af41-fcfd37e5a1f8 | GET /get_config_static_handler HTTP/1.1
Host: localhost:8123
User-Agent: curl/7.47.0
Accept:
/
XXX:xxx
< HTTP/1.1 200 OK
< Date: Wed, 29 Apr 2020 04:01:24 GMT
< Connection: Keep-Alive
< Content-Type: text/plain; charset=UTF-8
< Transfer-Encoding: chunked
< Keep-Alive: timeout=10
< X-ClickHouse-Summary: {"read_rows"... | {"source_file": "http.md"} | [
-0.0036021468695253134,
0.05277867242693901,
-0.06973520666360855,
-0.008321232162415981,
0.030751653015613556,
-0.0909988209605217,
-0.010364951565861702,
-0.04746898263692856,
0.023530809208750725,
0.05808384716510773,
0.035862796008586884,
0.03500543534755707,
-0.020534636452794075,
-0.... |
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