id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
6ab4b0a2-d920-48d4-8d8e-68bff8dc8898 | slug: /guides/sre/network-ports
sidebar_label: 'Network ports'
title: 'Network ports'
description: 'Description of available network ports and what they are used for'
doc_type: 'reference'
keywords: ['network', 'ports', 'configuration', 'security', 'firewall']
Network ports
:::note
Ports described as
default
me... | {"source_file": "network-ports.md"} | [
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c2d2387e-06b6-4dd8-9310-f00ffdc9bed3 | slug: /guides/sre/configuring-ssl
sidebar_label: 'Configuring SSL-TLS'
sidebar_position: 20
title: 'Configuring SSL-TLS'
description: 'This guide provides simple and minimal settings to configure ClickHouse to use OpenSSL certificates to validate connections.'
keywords: ['SSL configuration', 'TLS setup', 'OpenSSL certi... | {"source_file": "configuring-ssl.md"} | [
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b85e088a-67ef-40ff-82c9-70be02119e00 | Verify the contents of the new CA certificate:
bash
openssl x509 -in marsnet_ca.crt -text
Create a certificate request (CSR) and generate a key for each node:
bash
openssl req -newkey rsa:2048 -nodes -subj "/CN=chnode1" -addext "subjectAltName = DNS:chnode1.marsnet.local,IP:192.168.1.221" -keyou... | {"source_file": "configuring-ssl.md"} | [
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6a7ea96e-f7c7-4f81-84dd-6e3512a006b4 | :::note
Recommended port is
9281
for ClickHouse Keeper. However, the port is configurable and can be set if this port is in use already by another application in the environment.
For a full explanation of all options, visit https://clickhouse.com/docs/operations/clickhouse-keeper/
:::
Add the following inside... | {"source_file": "configuring-ssl.md"} | [
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0... |
99bd307e-6b17-48a2-b96d-f9cfc13f02ca | The following creates a cluster with one shard replica on two servers (one on each node).
xml
<remote_servers>
<cluster_1S_2R>
<shard>
<replica>
<host>chnode1.marsnet.local</host>
<port>9440</port>
<user>default</user>
<password>Cl... | {"source_file": "configuring-ssl.md"} | [
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33693c95-9f3d-4c6a-8ebc-83f7cbf75539 | xml
<openSSL>
<server>
<certificateFile>/etc/clickhouse-server/certs/chnode1.crt</certificateFile>
<privateKeyFile>/etc/clickhouse-server/certs/chnode1.key</privateKeyFile>
<verificationMode>relaxed</verificationMode>
<caConfig>/etc/clickhouse-server/certs/marsnet_ca.crt</caConfig>
... | {"source_file": "configuring-ssl.md"} | [
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c5e73c1f-4bc5-4607-b1de-90eac6cd74b3 | 6. Testing {#6-testing}
Start all nodes, one at a time:
bash
service clickhouse-server start
Verify secure ports are up and listening, should look similar to this example on each node:
bash
root@chnode1:/etc/clickhouse-server# netstat -ano | grep tcp
response
tcp 0 0 0.0.0.0:... | {"source_file": "configuring-ssl.md"} | [
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e557e7d2-e6b8-4210-b31b-00bce3f7d003 | Send the 4LW commands in the openssl session
bash
mntr
```response
Post-Handshake New Session Ticket arrived:
SSL-Session:
Protocol : TLSv1.3
...
read R BLOCK
zk_version v22.7.3.5-stable-e140b8b5f3a5b660b6b576747063fd040f583cf3
zk_avg_latency 0
# highlight-next-line
zk_max_latenc... | {"source_file": "configuring-ssl.md"} | [
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0.04... |
76f91949-9a3f-4b64-a069-4a071fa5761b | response
┌─id─┬────column1─┬─column2─┐
│ 1 │ 2022-04-01 │ abc │
│ 2 │ 2022-04-02 │ def │
└────┴────────────┴─────────┘
Summary {#summary}
This article focused on getting a ClickHouse environment configured with SSL/TLS. The settings will differ for different requirements in production environments; fo... | {"source_file": "configuring-ssl.md"} | [
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0.00... |
79677552-2a09-4747-bd0d-8657465959b6 | slug: /security-and-authentication
title: 'Security and Authentication'
description: 'Landing page for Security and Authentication'
doc_type: 'landing-page'
keywords: ['security and authentication', 'access control', 'RBAC', 'user management', 'SRE guide']
| Page ... | {"source_file": "index.md"} | [
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f875af0e-5e47-42ad-8679-73214c0ef116 | slug: /guides/sre/scaling-clusters
sidebar_label: 'Rebalancing shards'
sidebar_position: 20
description: 'ClickHouse does not support automatic shard rebalancing, so we provide some best practices for how to rebalance shards.'
title: 'Rebalancing Data'
doc_type: 'guide'
keywords: ['scaling', 'clusters', 'horizontal sca... | {"source_file": "scaling-clusters.md"} | [
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c9ca9d48-377f-4cb1-ba09-793429dfe122 | sidebar_label: 'Primary indexes'
sidebar_position: 1
description: 'In this guide we are going to do a deep dive into ClickHouse indexing.'
title: 'A Practical Introduction to Primary Indexes in ClickHouse'
slug: /guides/best-practices/sparse-primary-indexes
show_related_blogs: true
doc_type: 'guide'
keywords: ['primary... | {"source_file": "sparse-primary-indexes.md"} | [
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0.07902000844478607,
0.031375642865896225,
... |
f3d46a99-36b7-4d96-aa98-a8885bb93301 | import sparsePrimaryIndexes01 from '@site/static/images/guides/best-practices/sparse-primary-indexes-01.png';
import sparsePrimaryIndexes02 from '@site/static/images/guides/best-practices/sparse-primary-indexes-02.png';
import sparsePrimaryIndexes03a from '@site/static/images/guides/best-practices/sparse-primary-indexe... | {"source_file": "sparse-primary-indexes.md"} | [
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0.0... |
2498275d-e4a3-40a4-aec9-2fc13a1708b9 | A practical introduction to primary indexes in ClickHouse
Introduction {#introduction}
In this guide we are going to do a deep dive into ClickHouse indexing. We will illustrate and discuss in detail:
-
how indexing in ClickHouse is different from traditional relational database management systems
-
how ClickHous... | {"source_file": "sparse-primary-indexes.md"} | [
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-0.... |
8f6e6a6b-773c-4e4b-acec-6c8f35ccba74 | sql
INSERT INTO hits_NoPrimaryKey SELECT
intHash32(UserID) AS UserID,
URL,
EventTime | {"source_file": "sparse-primary-indexes.md"} | [
0.09057554602622986,
-0.02524789422750473,
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0.06953337788581848,
-0.06715510... |
c54aa3de-0172-469e-ad1b-3ba4206258a3 | URL,
EventTime
FROM url('https://datasets.clickhouse.com/hits/tsv/hits_v1.tsv.xz', 'TSV', 'WatchID UInt64, JavaEnable UInt8, Title String, GoodEvent Int16, EventTime DateTime, EventDate Date, CounterID UInt32, ClientIP UInt32, ClientIP6 FixedString(16), RegionID UInt32, UserID UInt64, CounterClass Int8, ... | {"source_file": "sparse-primary-indexes.md"} | [
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-0.043817732483148575,
-0.034272149205207825,
-0... |
b0fb2b92-cc5b-44f5-b3c1-fe8e00eb466b | The response is:
```response
Ok.
0 rows in set. Elapsed: 145.993 sec. Processed 8.87 million rows, 18.40 GB (60.78 thousand rows/s., 126.06 MB/s.)
```
ClickHouse client's result output shows us that the statement above inserted 8.87 million rows into the table.
Lastly, in order to simplify the discussions later o... | {"source_file": "sparse-primary-indexes.md"} | [
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18f00bf2-469f-4da3-93cc-0bd95c7e6b64 | ClickHouse index design {#clickhouse-index-design}
An index design for massive data scales {#an-index-design-for-massive-data-scales}
In traditional relational database management systems, the primary index would contain one entry per table row. This would result in the primary index containing 8.87 million entries... | {"source_file": "sparse-primary-indexes.md"} | [
0.01740027777850628,
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0.03630664199590683,
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0.02169719710946083,
0.010804112069308758,
-0.... |
830ae32b-1807-468c-8526-cc4ecf39addb | The following illustrates in detail how ClickHouse is building and using its sparse primary index. Later on in the article, we will discuss some best practices for choosing, removing, and ordering the table columns that are used to build the index (primary key columns).
A table with a primary key {#a-table-with-a-pri... | {"source_file": "sparse-primary-indexes.md"} | [
0.02004818432033062,
-0.022077739238739014,
0.0026327017694711685,
0.007364329416304827,
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-0.05920262262225151,
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0.004810916259884834,
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0.0224621444940567,
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0.030692484229803085,
0.010716482065618038,
... |
9c63cd49-6615-4392-a448-6e3315c9185b | sql
INSERT INTO hits_UserID_URL SELECT
intHash32(UserID) AS UserID,
URL,
EventTime | {"source_file": "sparse-primary-indexes.md"} | [
0.07283546030521393,
-0.025776691734790802,
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0.007967323996126652,
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0.004049547947943211,
0.05368449538946152,
0.037495702505111694,
0.013389994390308857,
0.009684324264526367,
-0.0313720628619194,
-0.066826231777668,
0.049880579113960266,
-0.0488... |
e0436b57-4d5b-4811-b602-0fbcbb227e1c | URL,
EventTime
FROM url('https://datasets.clickhouse.com/hits/tsv/hits_v1.tsv.xz', 'TSV', 'WatchID UInt64, JavaEnable UInt8, Title String, GoodEvent Int16, EventTime DateTime, EventDate Date, CounterID UInt32, ClientIP UInt32, ClientIP6 FixedString(16), RegionID UInt32, UserID UInt64, CounterClass Int8, ... | {"source_file": "sparse-primary-indexes.md"} | [
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0.016173189505934715,
0.006853423546999693,
0.01912057213485241,
0.01161902118474245,
-0.016892213374376297,
-0.043817732483148575,
-0.034272149205207825,
-0... |
49e2d1f0-bfcc-4342-8145-8c78b395616e | The response looks like:
response
0 rows in set. Elapsed: 149.432 sec. Processed 8.87 million rows, 18.40 GB (59.38 thousand rows/s., 123.16 MB/s.)
And optimize the table:
sql
OPTIMIZE TABLE hits_UserID_URL FINAL;
We can use the following query to obtain metadata about our table:
sql
SELECT
part_type,
... | {"source_file": "sparse-primary-indexes.md"} | [
0.06532230228185654,
0.06368128210306168,
-0.08179998397827148,
0.06473889201879501,
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0.027098873630166054,
0.07880228757858276,
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0.06001274660229683,
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0.06971833109855652,
-0.0656... |
c1e5a186-2565-44da-80bc-d75a10076bdc | The inserted rows are stored on disk in lexicographical order (ascending) by the primary key columns (and the additional
EventTime
column from the sorting key).
:::note
ClickHouse allows inserting multiple rows with identical primary key column values. In this case (see row 1 and row 2 in the diagram below), the fi... | {"source_file": "sparse-primary-indexes.md"} | [
-0.0069785150699317455,
-0.023541927337646484,
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0.03963306546211243,
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0.09452589601278305,
0.09865570813417435,
0.05829779431223869,
0.09399856626987457,
0.06560903042554855,
-0.088501... |
e6f69b11-e022-4009-b1b5-ff0998ba2458 | Therefore all granules (except the last one) of our example table have the same size.
For tables with adaptive index granularity (index granularity is adaptive by
default
the size of some granules can be less than 8192 rows depending on the row data sizes.
We marked some column values from our primary key... | {"source_file": "sparse-primary-indexes.md"} | [
0.02235247753560543,
0.011417318135499954,
-0.02705838717520237,
0.0010805854108184576,
-0.010078120045363903,
-0.08375565707683563,
0.08035522699356079,
-0.003898417577147484,
0.04733947291970253,
-0.04585561156272888,
-0.04426591098308563,
0.020841864868998528,
0.05594908446073532,
-0.05... |
64af6093-3bf6-4142-ac95-dc722bf1ed4f | returns `/Users/tomschreiber/Clickhouse/store/85f/85f4ee68-6e28-4f08-98b1-7d8affa1d88c/all_1_9_4` on the test machine.
Step 2: Get user_files_path
The
default user_files_path
on Linux is
`/var/lib/clickhouse/user_files/`
and on Linux you can check if it got changed: `$ grep user_files_path /etc/clickhouse-server/... | {"source_file": "sparse-primary-indexes.md"} | [
0.011401180177927017,
-0.03652948886156082,
-0.05230403319001198,
-0.047578662633895874,
0.025455331429839134,
-0.07811136543750763,
0.040234412997961044,
0.010661173611879349,
-0.02516311965882778,
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0.13171429932117462,
-0.036142412573099136,
-0.01601085253059864,
-0.... |
077fc1b1-9eaa-4e78-a9ac-eb6c3d3c7071 | We will discuss the consequences of this on query execution performance in more detail later.
The primary index is used for selecting granules {#the-primary-index-is-used-for-selecting-granules}
We can now execute our queries with support from the primary index.
The following calculates the top 10 most clicked ur... | {"source_file": "sparse-primary-indexes.md"} | [
-0.027841638773679733,
-0.053754210472106934,
-0.008958155289292336,
0.036776985973119736,
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0.047095075249671936,
0.03910674899816513,
0.05592867359519005,
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0.01689971424639225,
0.041329074651002884,
-0.0... |
9dd0e070-31fd-4ea5-a83c-8593a45d6df6 | sql
EXPLAIN indexes = 1
SELECT URL, count(URL) AS Count
FROM hits_UserID_URL
WHERE UserID = 749927693
GROUP BY URL
ORDER BY Count DESC
LIMIT 10;
The response looks like:
```response
┌─explain───────────────────────────────────────────────────────────────────────────────┐
│ Expression (Projection) ... | {"source_file": "sparse-primary-indexes.md"} | [
0.0077803898602724075,
0.0016711781499907374,
0.026708455756306648,
0.05453414469957352,
-0.029243677854537964,
-0.059992145746946335,
0.09927738457918167,
0.00044174690265208483,
0.061617180705070496,
0.02615431137382984,
0.017403321340680122,
-0.023168915882706642,
0.10277226567268372,
-... |
8d6fed56-574f-46b1-83b0-866c562b19c2 | Mark files are used for locating granules {#mark-files-are-used-for-locating-granules}
The following diagram illustrates a part of the primary index file for our table.
As discussed above, via a binary search over the index's 1083 UserID marks, mark 176 was identified. Its corresponding granule 176 can therefore ... | {"source_file": "sparse-primary-indexes.md"} | [
0.014697262085974216,
0.010525993071496487,
-0.05155588313937187,
-0.03604493662714958,
0.027864858508110046,
-0.054514605551958084,
0.1076476126909256,
0.03697482496500015,
-0.00005664554191753268,
-0.035401612520217896,
-0.03028189204633236,
-0.02357795648276806,
0.08990738540887833,
-0.... |
7d532e55-79b6-4ba8-9ef5-7624c43cec8b | Index granularity is adaptive by
default
, but for our example table we disabled adaptive index granularity (in order to simplify the discussions in this guide, as well as make the diagrams and results reproducible). Our table is using wide format because the size of the data is larger than
min_bytes_for_wide_part
(... | {"source_file": "sparse-primary-indexes.md"} | [
0.014780945144593716,
-0.0026188676711171865,
0.002962471218779683,
0.05079694837331772,
0.04865778982639313,
-0.07297579944133759,
-0.009711714461445808,
0.031231965869665146,
-0.012763004750013351,
-0.01780475303530693,
-0.0517578125,
0.03739646449685097,
0.03439712151885033,
-0.04999159... |
46c48096-37fc-44d8-b375-a689c891984f | Once the located file block is uncompressed into the main memory, the second offset from the mark file can be used to locate granule 176 within the uncompressed data.
ClickHouse needs to locate (and stream all values from) granule 176 from both the UserID.bin data file and the URL.bin data file in order to execute ou... | {"source_file": "sparse-primary-indexes.md"} | [
0.03397727012634277,
0.012840122915804386,
-0.0565822459757328,
-0.03134375438094139,
-0.0508560873568058,
-0.055484455078840256,
-0.00437266705557704,
0.026139704510569572,
-0.04277731105685234,
-0.016304848715662956,
-0.011362336575984955,
0.012970540672540665,
0.05626562982797623,
-0.09... |
56491245-07fb-42be-bd21-1fb2669d3bdc | If
trace_logging
is enabled then the ClickHouse server log file shows that ClickHouse used a
generic exclusion search
over the 1083 URL index marks in order to identify those granules that possibly can contain rows with a URL column value of "http://public_search":
```response
...Executor): Key condition: (column 1... | {"source_file": "sparse-primary-indexes.md"} | [
-0.012739456258714199,
-0.01046308595687151,
-0.018568193539977074,
-0.0024677226319909096,
0.06973129510879517,
-0.0952124074101448,
0.018075089901685715,
-0.04727338254451752,
0.022037014365196228,
-0.009510433301329613,
0.012075353413820267,
0.0003959527239203453,
0.022567670792341232,
... |
935dd46e-8b44-4e46-9e62-9c438c68fbc8 | We have marked the key column values for the first table rows for each granule in orange in the diagrams below..
Predecessor key column has low(er) cardinality
Suppose UserID had low cardinality. In this case it would be likely that the same UserID value is spread over multiple table rows and granules and therefore... | {"source_file": "sparse-primary-indexes.md"} | [
0.023109300062060356,
0.04151662066578865,
0.0582556426525116,
-0.05380697920918465,
0.03200879693031311,
-0.055138859897851944,
0.07167350500822067,
-0.03403666242957115,
0.0420549251139164,
-0.06457088887691498,
-0.03651554882526398,
-0.005000247620046139,
0.062105245888233185,
-0.058056... |
608ed059-bedd-4c00-b1a3-c5ae1f324f5d | The same scenario is true for mark 1, 2, and 3.
:::note Conclusion
The
generic exclusion search algorithm
that ClickHouse is using instead of the
binary search algorithm
when a query is filtering on a column that is part of a compound key, but is not the first key column is most effective when the predecessor key... | {"source_file": "sparse-primary-indexes.md"} | [
0.0073634665459394455,
0.006020834669470787,
0.030052954331040382,
-0.03572671487927437,
-0.0013561546802520752,
-0.02888789400458336,
-0.004604673013091087,
-0.04949820414185524,
0.01917850412428379,
-0.035464417189359665,
0.03074072301387787,
-0.0024194384459406137,
0.0779963880777359,
-... |
f68a1a18-831a-4904-b2ff-c236f718d647 | The following is showing ways for achieving that.
Options for creating additional primary indexes {#options-for-creating-additional-primary-indexes}
If we want to significantly speed up both of our sample queries - the one that filters for rows with a specific UserID and the one that filters for rows with a spec... | {"source_file": "sparse-primary-indexes.md"} | [
-0.05163013935089111,
-0.059596139937639236,
-0.021310955286026,
0.033722735941410065,
-0.04205898568034172,
0.007440801244229078,
-0.030228089541196823,
-0.05649353191256523,
0.026638485491275787,
0.05002472549676895,
-0.012760799378156662,
-0.001524728606455028,
0.024107785895466805,
-0.... |
4a842322-b590-4bc0-bf02-e2fd629f3d1c | This is the resulting primary key:
That can now be used to significantly speed up the execution of our example query filtering on the URL column in order to calculate the top 10 users that most frequently clicked on the URL "http://public_search":
sql
SELECT UserID, count(UserID) AS Count
-- highlight-next-line
FR... | {"source_file": "sparse-primary-indexes.md"} | [
-0.02088981866836548,
-0.03061932884156704,
-0.01692681945860386,
0.01208643801510334,
-0.008208436891436577,
-0.03429915010929108,
0.04967130720615387,
0.02212824672460556,
0.019640913233160973,
0.019988419488072395,
0.005768093280494213,
-0.011178361251950264,
0.05925821512937546,
-0.140... |
a7ca7a88-2237-4243-b7a5-b9627ef213d5 | ```sql
SELECT URL, count(URL) AS Count
FROM hits_URL_UserID
WHERE UserID = 749927693
GROUP BY URL
ORDER BY Count DESC
LIMIT 10;
```
The response is:
```response
┌─URL────────────────────────────┬─Count─┐
│ http://auto.ru/chatay-barana.. │ 170 │
│ http://auto.ru/chatay-id=371...│ 52 │
│ http://public_search ... | {"source_file": "sparse-primary-indexes.md"} | [
0.006926789414137602,
-0.069351427257061,
-0.004720146302133799,
0.044627320021390915,
-0.06225857883691788,
0.005121893715113401,
0.05564277991652489,
0.005847528111189604,
0.08616098016500473,
0.025562407448887825,
0.021788090467453003,
-0.03715805336833,
0.04239259287714958,
-0.04762871... |
b92ec92c-d7ab-4d39-bcc4-2a57358b807f | ClickHouse is storing the
column data files
(
.bin), the
mark files
(
.mrk2) and the
primary index
(primary.idx) of the implicitly created table in a special folder withing the ClickHouse server's data directory:
:::
The implicitly created table (and its primary index) backing the materialized view can now ... | {"source_file": "sparse-primary-indexes.md"} | [
-0.029493117704987526,
-0.048094913363456726,
-0.0677509754896164,
0.0764976367354393,
-0.009057816118001938,
-0.06413411349058151,
-0.0057091559283435345,
-0.062327489256858826,
0.02951662428677082,
0.026606254279613495,
0.08250133693218231,
0.008761530742049217,
0.0484071783721447,
-0.10... |
ab86f870-856f-4246-bd6c-efaad35d02cb | - if new rows are inserted into the source table hits_UserID_URL, then that rows are automatically also inserted into the hidden table
- a query is always (syntactically) targeting the source table hits_UserID_URL, but if the row order and primary index of the hidden table allows a more effective query execution, then ... | {"source_file": "sparse-primary-indexes.md"} | [
-0.10682723671197891,
0.009819621220231056,
-0.06612306088209152,
0.06385249644517899,
0.04092962294816971,
-0.02276666648685932,
0.014469574205577374,
-0.062201447784900665,
0.06444935500621796,
0.05374510586261749,
0.05740398168563843,
0.046132124960422516,
0.09257817268371582,
-0.129435... |
86ebfcf0-8725-4f03-b7f7-e6b7c7a7b1c6 | highlight-next-line
39/1083 marks by primary key, 39 marks to read from 1 ranges
...Executor): Reading approx. 319488 rows with 2 streams
```
Summary {#summary}
The primary index of our
table with compound primary key (UserID, URL)
was very useful for speeding up a
query filtering on UserID
. But ... | {"source_file": "sparse-primary-indexes.md"} | [
0.013201690278947353,
0.054666224867105484,
0.005498996004462242,
-0.030469216406345367,
0.023116478696465492,
-0.032277513295412064,
-0.005489179864525795,
-0.028739910572767258,
0.07653304189443588,
-0.05216321349143982,
-0.018407069146633148,
-0.014399277977645397,
-0.01800471916794777,
... |
9e419f03-37f7-4605-8021-93c405311a25 | sql
SELECT
formatReadableQuantity(uniq(URL)) AS cardinality_URL,
formatReadableQuantity(uniq(UserID)) AS cardinality_UserID,
formatReadableQuantity(uniq(IsRobot)) AS cardinality_IsRobot
FROM
(
SELECT
c11::UInt64 AS UserID,
c15::String AS URL,
c20::UInt8 AS IsRobot
FROM url('h... | {"source_file": "sparse-primary-indexes.md"} | [
0.05901200696825981,
-0.0388614796102047,
-0.051519546657800674,
0.04765577241778374,
-0.07897619158029556,
-0.05656718462705612,
-0.022247519344091415,
0.03423338383436203,
0.042725395411252975,
0.06726779043674469,
0.0003485116467345506,
-0.02823810838162899,
0.04594673961400986,
-0.0849... |
9bbd77e1-a0a3-473f-b0d0-cc098108e237 | The response is:
response
0 rows in set. Elapsed: 95.959 sec. Processed 8.87 million rows, 15.88 GB (92.48 thousand rows/s., 165.50 MB/s.)
Efficient filtering on secondary key columns {#efficient-filtering-on-secondary-key-columns}
When a query is filtering on at least one column that is part of a compound key, an... | {"source_file": "sparse-primary-indexes.md"} | [
0.01939740590751171,
0.020108165219426155,
-0.05339557304978371,
0.004213368520140648,
0.031496401876211166,
-0.10974746942520142,
0.004269157070666552,
-0.022924037650227547,
0.023519165813922882,
-0.00965241901576519,
0.0341007336974144,
0.014788530766963959,
0.059924278408288956,
-0.143... |
434c7d9f-5abb-447e-80ce-cc2ec8ac0577 | This is the response:
```response
┌─Table───────────────────┬─Column─┬─Uncompressed─┬─Compressed─┬─Ratio─┐
│ hits_URL_UserID_IsRobot │ UserID │ 33.83 MiB │ 11.24 MiB │ 3 │
│ hits_IsRobot_UserID_URL │ UserID │ 33.83 MiB │ 877.47 KiB │ 39 │
└─────────────────────────┴────────┴──────────────┴────────────┴───... | {"source_file": "sparse-primary-indexes.md"} | [
-0.002390448236837983,
-0.011070813052356243,
-0.0842941477894783,
0.01933257095515728,
0.011615459807217121,
-0.11397822201251984,
-0.08145976066589355,
0.04141684249043465,
0.02769809402525425,
0.07532712817192078,
0.007752840872853994,
0.025656649842858315,
0.060360100120306015,
-0.0850... |
64f4ba23-dbbd-4940-97e8-790b8ed4c02d | In contrast to the diagram above, the diagram below sketches the on-disk order of rows for a primary key where the key columns are ordered by cardinality in descending order:
Now the table's rows are first ordered by their
ch
value, and rows that have the same
ch
value are ordered by their
cl
value.
But becau... | {"source_file": "sparse-primary-indexes.md"} | [
-0.026744650676846504,
0.028801489621400833,
-0.012662135995924473,
-0.06312308460474014,
0.06815672665834427,
-0.06363561004400253,
-0.005194548051804304,
-0.04676690697669983,
0.1069045439362526,
0.005357626359909773,
-0.009551932103931904,
0.07424408942461014,
0.058012738823890686,
-0.1... |
66484742-182d-4923-af4b-5c42f3fdca93 | Because the
hash
column is used as the primary key column
- specific rows can be retrieved
very quickly
, but
- the table's rows (their column data) are stored on disk ordered ascending by (the unique and random) hash values. Therefore also the content column's values are stored in random order with no data locality... | {"source_file": "sparse-primary-indexes.md"} | [
-0.018135488033294678,
0.04287270829081535,
-0.03045014850795269,
-0.07142333686351776,
0.06279915571212769,
-0.00863632746040821,
-0.04980448633432388,
-0.010556291788816452,
0.1607295125722885,
0.0754794180393219,
0.031031982973217964,
0.1227181926369667,
-0.029498113319277763,
-0.053373... |
f6b82554-d4e9-46b6-abe9-b5594059a7ed | slug: /optimize/query-optimization
sidebar_label: 'Query optimization'
title: 'Guide for Query optimization'
description: 'A simple guide for query optimization that describe common path to improve query performance'
doc_type: 'guide'
keywords: ['query optimization', 'performance', 'best practices', 'query tuning', 'ef... | {"source_file": "query-optimization.md"} | [
0.02488396316766739,
0.04571940377354622,
-0.04978534206748009,
0.06089426577091217,
-0.027385815978050232,
-0.07798305153846741,
0.014665466733276844,
0.04426310956478119,
-0.09905830770730972,
-0.026751792058348656,
-0.014146034605801105,
0.039541710168123245,
0.09834425896406174,
-0.026... |
e01e343f-e7a7-42e0-8fe5-069558052c6c | With this high-level understanding, let's examine the tooling ClickHouse provides and how we can use it to track the metrics that affect query performance.
Dataset {#dataset}
We'll use a real example to illustrate how we approach query performances.
Let's use the NYC Taxi dataset, which contains taxi ride data ... | {"source_file": "query-optimization.md"} | [
0.025845475494861603,
-0.08144160360097885,
-0.04485420510172844,
0.02825569175183773,
-0.01411157101392746,
-0.012888059951364994,
0.018985562026500702,
-0.039843227714300156,
0.00683348486199975,
0.032594967633485794,
0.02465861476957798,
-0.04896058887243271,
0.05616122856736183,
-0.081... |
bf85a288-1fa9-4079-8b74-de41222f5f1c | Query id: e3d48c9f-32bb-49a4-8303-080f59ed1835
Row 1:
──────
type: QueryFinish
event_time: 2024-11-27 11:12:36
query_duration_ms: 2967
query: WITH
dateDiff('s', pickup_datetime, dropoff_datetime) as trip_time,
trip_distance / trip_time * 3600 AS speed_mph
SELECT
quantiles(0.5, 0.... | {"source_file": "query-optimization.md"} | [
0.040814634412527084,
0.04668862372636795,
0.005373885855078697,
0.09005189687013626,
-0.056419629603624344,
-0.020528597757220268,
0.037294913083314896,
0.012289985083043575,
-0.0146331787109375,
-0.01676803268492222,
0.0499059222638607,
-0.11720075458288193,
-0.05616014823317528,
0.01047... |
7de94426-41f5-4254-a367-3dc914c7f296 | You might also want to know which queries are stressing the system by examining the query that consumes the most memory or CPU.
sql
-- Top queries by memory usage
SELECT
type,
event_time,
query_id,
formatReadableSize(memory_usage) AS memory,
ProfileEvents.Values[indexOf(ProfileEvents.Names, 'User... | {"source_file": "query-optimization.md"} | [
0.14532186090946198,
-0.05238448455929756,
-0.08979310095310211,
0.10538600385189056,
0.011882473714649677,
-0.09340005367994308,
0.12720459699630737,
0.050568025559186935,
-0.06452964246273041,
0.014999938197433949,
-0.06601210683584213,
-0.038337960839271545,
-0.024973852559924126,
-0.05... |
94c4145f-f7c8-4af3-8ff5-8fc613e4466c | Let's understand a bit better what the queries achieve.
Query 1 calculates the distance distribution in rides with an average speed of over 30 miles per hour.
Query 2 finds the number and average cost of rides per week.
Query 3 calculates the average time of each trip in the dataset.
None of these queries... | {"source_file": "query-optimization.md"} | [
0.07625853270292282,
-0.05557221546769142,
0.002533514052629471,
0.11450717598199844,
-0.04621712118387222,
-0.08205554634332657,
0.0641016736626625,
0.003925584256649017,
-0.0009239829960279167,
0.003548053791746497,
-0.004444189369678497,
-0.022806700319051743,
0.021729685366153717,
-0.0... |
8a97aacb-38a6-45b2-95c3-55409d6576ce | The output is straightforward. The query begins by reading data from the
nyc_taxi.trips_small_inferred
table. Then, the WHERE clause is applied to filter rows based on computed values. The filtered data is prepared for aggregation, and the quantiles are computed. Finally, the result is sorted and outputted.
Here, ... | {"source_file": "query-optimization.md"} | [
0.0038478069473057985,
0.030322711914777756,
0.029216770082712173,
0.06787716597318649,
-0.036621276289224625,
-0.054426614195108414,
0.028405984863638878,
0.02529955469071865,
-0.02733226865530014,
-0.0007889700937084854,
0.013667809776961803,
-0.06682276725769043,
-0.04356928914785385,
-... |
b272c885-3f55-4dc2-ac53-498e14db3cc1 | It can be difficult to identify problematic queries on a production deployment, as there are probably a large number of queries being executed at any given time on your ClickHouse deployment.
If you know which user, database, or tables are having issues, you can use the fields
user
,
tables
, or
databases
from t... | {"source_file": "query-optimization.md"} | [
0.04337030276656151,
-0.012468650005757809,
0.025230590254068375,
0.05416853353381157,
-0.03591779246926308,
-0.13348081707954407,
-0.00776388356462121,
-0.008945133537054062,
-0.07687045633792877,
0.020184926688671112,
-0.024337884038686752,
-0.02589353173971176,
0.010143850930035114,
-0.... |
1ff0f03f-7b43-4801-8d5f-823f1eee0be2 | Running an SQL query that counts the rows with a NULL value can easily reveal the columns in your tables that actually need a Nullable value.
```sql
-- Find non-null values columns
SELECT
countIf(vendor_id IS NULL) AS vendor_id_nulls,
countIf(pickup_datetime IS NULL) AS pickup_datetime_nulls,
countIf(drop... | {"source_file": "query-optimization.md"} | [
0.08875182271003723,
-0.010801929980516434,
-0.029795655980706215,
0.040826186537742615,
-0.057682622224092484,
0.05025152117013931,
0.001948581077158451,
0.01000511646270752,
-0.027704400941729546,
-0.0014276914298534393,
0.08022065460681915,
-0.15124790370464325,
0.031104259192943573,
-0... |
aa12b2cc-a5b4-4b59-a8e3-64b0a8d2c3b2 | Clickhouse supports a large number of data types. Make sure to pick the smallest possible data type that fits your use case to optimize performance and reduce your data storage space on disk.
For numbers, you can check the min/max value in your dataset to check if the current precision value matches the reality of y... | {"source_file": "query-optimization.md"} | [
0.0768590122461319,
-0.025170933455228806,
-0.01472439430654049,
0.037111420184373856,
-0.09682539105415344,
-0.024415956810116768,
0.009890963323414326,
0.009707081131637096,
-0.06555133312940598,
0.024107426404953003,
0.03855302557349205,
-0.04299546778202057,
-0.0037761928979307413,
-0.... |
214395ae-b21c-4fef-9357-c6f3234b4fc9 | Query id: 72b5eb1c-ff33-4fdb-9d29-dd076ac6f532
┌─table────────────────┬─compressed─┬─uncompressed─┬──────rows─┐
1. │ trips_small_inferred │ 7.38 GiB │ 37.41 GiB │ 329044175 │
2. │ trips_small_no_pk │ 4.89 GiB │ 15.31 GiB │ 329044175 │
└──────────────────────┴────────────┴──────────────┴───────────┘
``... | {"source_file": "query-optimization.md"} | [
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-0.0... |
f3139d4f-1762-417f-84a5-3d8b22e48e1f | Create a new table with the primary keys and re-ingest the data.
``sql
CREATE TABLE trips_small_pk
(
vendor_id
UInt8,
pickup_datetime
DateTime,
dropoff_datetime
DateTime,
passenger_count
UInt8,
trip_distance
Float32,
ratecode_id
LowCardinality(String),
pickup_location_id
UInt16,
dropoff_location_id
UInt16,
payment_ty... | {"source_file": "query-optimization.md"} | [
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-0.057... |
192059a6-3178-4be2-af74-c3cd898d7e7d | │ Expression │
│ ReadFromMergeTree (nyc_taxi.trips_small_pk) │
│ Indexes: ... | {"source_file": "query-optimization.md"} | [
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-0.039036594331264496,
-0.0... |
64691cf0-c3ae-4337-b21a-c4120844f190 | slug: /optimize/avoidoptimizefinal
sidebar_label: 'Avoid optimize final'
title: 'Avoid Optimize Final'
description: 'Using the OPTIMIZE TABLE ... FINAL query will initiate an unscheduled merge of data parts.'
doc_type: 'guide'
keywords: ['avoid optimize final', 'optimize table final', 'best practices', 'merge data part... | {"source_file": "avoidoptimizefinal.md"} | [
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0.07513434439897537,
-0.07844... |
2be39b66-6070-41ef-b6d5-2378214e6e76 | slug: /optimize/avoid-mutations
sidebar_label: 'Avoid mutations'
title: 'Avoid Mutations'
description: 'Mutations refers to ALTER queries that manipulate table data'
doc_type: 'guide'
keywords: ['avoid mutations', 'ALTER queries', 'table data manipulation', 'best practices', 'performance optimization']
import Conte... | {"source_file": "avoidmutations.md"} | [
-0.015101060271263123,
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0.022869186475872993,
0.09565875679254532,
-0.0776... |
76b062dd-40e2-4ac3-ab7b-9c76c9d390be | slug: /optimize/prewhere
sidebar_label: 'PREWHERE optimization'
sidebar_position: 21
description: 'PREWHERE reduces I/O by avoiding reading unnecessary column data.'
title: 'How does the PREWHERE optimization work?'
doc_type: 'guide'
keywords: ['prewhere', 'query optimization', 'performance', 'filtering', 'best practic... | {"source_file": "prewhere.md"} | [
0.05251097306609154,
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-0.063... |
6786fbbe-d648-4940-9ada-5f73ae10f56a | With each step, it only loads granules that contain at least one row that survived—i.e., matched—the previous filter. As a result, the number of granules to load and evaluate for each filter decreases monotonically:
Step 1: Filtering by town
ClickHouse begins PREWHERE processing by ① reading the selected granules fr... | {"source_file": "prewhere.md"} | [
-0.028644904494285583,
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-0.0... |
61450139-4bbb-41c1-a94c-d387ef945033 | How to measure PREWHERE impact {#how-to-measure-prewhere-impact}
To validate that PREWHERE is helping your queries, you can compare query performance with and without the
optimize_move_to_prewhere setting
enabled.
We begin by running the query with the
optimize_move_to_prewhere
setting disabled:
sql
SELECT
... | {"source_file": "prewhere.md"} | [
0.08471346646547318,
-0.030938688665628433,
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0.05557806044816971,
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-0.011546731926500797,
0.010656364262104034,
-0.059... |
d6abd7b0-b671-4f89-8240-4e8270d113e4 | If you want to go even further under the hood, you can observe each individual PREWHERE processing step by instructing ClickHouse to return all test-level log entries during query execution:
sql
SELECT
street
FROM
uk.uk_price_paid_simple
WHERE
town = 'LONDON' AND date > '2024-12-31' AND price < 10_000
SETTIN... | {"source_file": "prewhere.md"} | [
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0.02709106169641018,
-0.09696... |
0c95383c-81bd-4308-b03c-f41a9aa4a55c | slug: /optimize/query-parallelism
sidebar_label: 'Query parallelism'
sidebar_position: 20
description: 'ClickHouse parallelizes query execution using processing lanes and the max_threads setting.'
title: 'How ClickHouse executes a query in parallel'
doc_type: 'guide'
keywords: ['parallel processing', 'query optimizatio... | {"source_file": "query-parallelism.md"} | [
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0.0005396041669882834,
0.04998940974473953,
-0.0034793855156749487,
0.08064394444227219,
-0.0... |
09e3a7c2-7f97-4302-9c62-4bfd4b75246a | The server that initially receives the query collects all sub-results from the shards and combines them into the final global result.
Distributing query load across shards allows horizontal scaling of parallelism, especially for high-throughput environments.
:::note ClickHouse Cloud uses parallel replicas instead o... | {"source_file": "query-parallelism.md"} | [
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0.04725491628050804,
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0.06215474754571915,
-0.0652... |
49d2f300-adf2-4142-8afd-87846792f66d | sql runnable=false
EXPLAIN PIPELINE
SELECT
max(price)
FROM
uk.uk_price_paid_simple;
txt
┌─explain───────────────────────────────────────────────────────────────────────────┐
1. │ (Expression) │
2. │ ExpressionTransform × 59 ... | {"source_file": "query-parallelism.md"} | [
-0.003587067127227783,
-0.029607541859149933,
-0.01718282885849476,
0.009368648752570152,
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-... |
6e382241-9364-436a-b194-4d73f6d402f2 | Why max_threads isn't always respected {#why-max-threads-isnt-always-respected}
As mentioned above, the number of
n
parallel processing lanes is controlled by the
max_threads
setting, which by default matches the number of CPU cores available to ClickHouse on the server:
sql runnable=false
SELECT getSetting('max... | {"source_file": "query-parallelism.md"} | [
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-0.018549639731645584,
-0.01648298278450966,
-0.121... |
76d1390d-f6af-4365-8145-a8c12171ad71 | For shared-nothing clusters:
*
merge_tree_min_rows_for_concurrent_read
*
merge_tree_min_bytes_for_concurrent_read
For clusters with shared storage (e.g. ClickHouse Cloud):
*
merge_tree_min_rows_for_concurrent_read_for_remote_filesystem
*
merge_tree_min_bytes_for_concurrent_read_for_remote_filesystem
Additiona... | {"source_file": "query-parallelism.md"} | [
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0.02369963936507702,
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0.07589936256408691,
0.021819503977894783,
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-0.01343461312353611,
-0.030050... |
08d3faef-61d9-4220-a259-ca5a0eea7f2c | slug: /optimize/avoid-nullable-columns
sidebar_label: 'Avoid nullable Columns'
title: 'Avoid nullable Columns'
description: 'Why Nullable Columns should be avoided in ClickHouse'
doc_type: 'guide'
keywords: ['avoid nullable columns', 'nullable columns', 'data types', 'best practices', 'performance optimization']
im... | {"source_file": "avoidnullablecolumns.md"} | [
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0.037299349904060364,
0.09059545397758484,
0.054546572268009186,
0.0481860488653183,
-0.0467068... |
db642c2a-1ec7-477e-ab54-5e4f14c9acdd | slug: /optimize/partitioning-key
sidebar_label: 'Partitioning key'
title: 'Choose a Low Cardinality Partitioning Key'
description: 'Use a low cardinality partitioning key or avoid using any partitioning key for your table.'
doc_type: 'guide'
keywords: ['partitioning', 'partition key', 'data organization', 'best practic... | {"source_file": "partitioningkey.md"} | [
0.09032115340232849,
0.026683861389756203,
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-0.008359933272004128,
0.05289706960320473,
0.01... |
4fa3fefc-1c7d-4932-a7c3-a679056fc309 | slug: /operations/overview
sidebar_label: 'Performance and optimizations overview'
description: 'Overview page of Performance and Optimizations'
title: 'Performance and Optimizations'
keywords: ['performance optimization', 'best practices', 'optimization guide', 'ClickHouse performance', 'database optimization']
doc_ty... | {"source_file": "index.md"} | [
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0.07949261367321014,
0.057113513350486755,
-0.076... |
e7e7809a-6314-4059-afc5-c59cf0417c7c | slug: /optimize/bulk-inserts
sidebar_label: 'Bulk inserts'
title: 'Bulk inserts'
description: 'Sending a smaller amount of inserts that each contain more data will reduce the number of writes required.'
keywords: ['bulk insert', 'batch insert', 'insert optimization']
doc_type: 'guide'
import Content from '@site/doc... | {"source_file": "bulkinserts.md"} | [
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0.011960864067077637,
0.11146106570959091,
-0.04... |
6a0623c5-d1cc-490a-a14d-c8f1dcc6e819 | slug: /optimize/skipping-indexes
sidebar_label: 'Data skipping indexes'
sidebar_position: 2
description: 'Skip indexes enable ClickHouse to skip reading significant chunks of data that are guaranteed to have no matching values.'
title: 'Understanding ClickHouse Data Skipping Indexes'
doc_type: 'guide'
keywords: ['skipp... | {"source_file": "skipping-indexes.md"} | [
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0.07394257187843323,
0.05775866284966469,
0.09943407773971558,
-0.0688535... |
3bcad78d-35bc-48c4-bafc-c6392e06adbd | Index name. The index name is used to create the index file in each partition. Also, it is required as a parameter when dropping or materializing the index.
Index expression. The index expression is used to calculate the set of values stored in the index. It can be a combination of columns, simple operators, and/or a... | {"source_file": "skipping-indexes.md"} | [
-0.033035147935152054,
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0.08392694592475891,
-0.027352066710591316,
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0.04935714229941368,
0.01692662015557289,
-0.08... |
058716a6-2916-401d-9430-6d5b646e39ab | Instead of processing 100 million rows of 800 megabytes, ClickHouse has only read and analyzed 32768 rows of 360 kilobytes
-- four granules of 8192 rows each.
In a more visual form, this is how the 4096 rows with a
my_value
of 125 were read and selected, and how the following rows
were skipped without reading from ... | {"source_file": "skipping-indexes.md"} | [
0.05709846690297127,
-0.029370274394750595,
0.01146199181675911,
0.06804028898477554,
0.008486540988087654,
-0.10399085283279419,
0.03655761480331421,
-0.012415915727615356,
-0.006583582144230604,
0.0032215218525379896,
0.0172534491866827,
-0.01169682014733553,
0.020731719210743904,
-0.098... |
fb1e5ac3-6480-47de-9e3b-cd5bd3946f0c | Because Bloom filters can more efficiently handle testing for a large number of discrete values, they can be appropriate for conditional expressions that produce more values to test. In particular, a Bloom filter index can be applied to arrays, where every value of the array is tested, and to maps, by converting either... | {"source_file": "skipping-indexes.md"} | [
0.010774503462016582,
0.0700305923819542,
0.08283759653568268,
0.02952462062239647,
0.02004212699830532,
-0.003979322500526905,
0.021704407408833504,
-0.045364800840616226,
0.11133552342653275,
0.00943745020776987,
-0.05253211781382561,
0.019196966663002968,
-0.014660445041954517,
-0.04429... |
59a1f175-dc9b-412c-bc53-032d7fdfe279 | Each type of skip index works on a subset of available ClickHouse functions appropriate to the index implementation listed
here
. In general, set indexes and Bloom filter based indexes (another type of set index) are both unordered and therefore do not work with ranges. In contrast, minmax indexes work particularly we... | {"source_file": "skipping-indexes.md"} | [
-0.00298171560280025,
0.029057122766971588,
0.054152265191078186,
0.021425805985927582,
0.019250547513365746,
0.005893357563763857,
0.011284449137747288,
-0.07183143496513367,
0.013051937334239483,
-0.07270302623510361,
0.0426928773522377,
-0.003502113511785865,
-0.005399580579251051,
-0.0... |
4116ba67-5135-4b5c-bc83-75e50309c999 | Accordingly, the natural impulse to try to speed up ClickHouse queries by simply adding an index to key
columns is often incorrect. This advanced functionality should only be used after investigating other alternatives, such as modifying the primary key (see
How to Pick a Primary Key
), using projections, or using ma... | {"source_file": "skipping-indexes.md"} | [
0.002509539248421788,
-0.02721363492310047,
0.010161123238503933,
0.019533028826117516,
-0.0009753414196893573,
0.008465501479804516,
0.01847340539097786,
-0.06318169087171555,
0.04298314452171326,
-0.04786697402596474,
0.010179380886256695,
0.0104509387165308,
-0.008353691548109055,
-0.06... |
57a80f85-6187-46ba-9c52-6e4ccdd60aed | slug: /optimize/skipping-indexes/examples
sidebar_label: 'Data Skipping Indexes - Examples'
sidebar_position: 2
description: 'Consolidated Skip Index Examples'
title: 'Data Skipping Index Examples'
doc_type: 'guide'
keywords: ['skipping indexes', 'data skipping', 'performance', 'indexing', 'best practices']
Data sk... | {"source_file": "skipping-indexes-examples.md"} | [
0.06194494292140007,
-0.011891200207173824,
0.09508699178695679,
0.032226815819740295,
-0.018362168222665787,
-0.04888846352696419,
0.021036524325609207,
-0.00849500484764576,
-0.06654227524995804,
-0.024957910180091858,
0.048641376197338104,
-0.005398642271757126,
0.04431501403450966,
-0.... |
470b6219-5b4f-4ee1-bf02-6b0adc34558c | SELECT * FROM events WHERE value IN (7, 42, 99);
EXPLAIN indexes = 1
SELECT * FROM events WHERE value IN (7, 42, 99);
```
N-gram Bloom filter (ngrambf_v1) for substring search {#n-gram-bloom-filter-ngrambf-v1-for-substring-search}
The
ngrambf_v1
index splits strings into n-grams. It works well for
LIKE '%...%'... | {"source_file": "skipping-indexes-examples.md"} | [
0.005593861918896437,
0.014381765387952328,
0.009681930765509605,
0.059326689690351486,
-0.01715361885726452,
0.07211934030056,
0.08799733966588974,
0.0335458368062973,
0.017215188592672348,
0.001450696843676269,
-0.05172949284315109,
0.00845731794834137,
-0.01033847127109766,
-0.039753206... |
777f6a07-3f3c-4ee7-ba2e-9323ff6f12e2 | ```sql
CREATE TABLE t
(
u64 UInt64,
s String,
m Map(String, String),
INDEX idx_bf u64 TYPE bloom_filter(0.01) GRANULARITY 3,
INDEX idx_minmax u64 TYPE minmax GRANULARITY 1,
INDEX idx_set u64 * length(s) TYPE set(1000) GRANULARITY 4,
INDEX idx_ngram s TYPE ngrambf_v1(3, 10000, 3, 7) GRANULARITY 1,
INDEX ... | {"source_file": "skipping-indexes-examples.md"} | [
0.021607523784041405,
0.029821548610925674,
0.028927145525813103,
-0.007774892263114452,
-0.005909481551498175,
-0.04239964485168457,
0.028281062841415405,
0.023508155718445778,
-0.0856500193476677,
0.02014225535094738,
-0.007601824589073658,
0.005930731073021889,
0.02152036316692829,
-0.0... |
221dd966-1df5-41fe-a3ac-d4f0f6deffa6 | slug: /optimize/asynchronous-inserts
sidebar_label: 'Asynchronous Inserts'
title: 'Asynchronous Inserts (async_insert)'
description: 'Use asynchronous inserts as an alternative to batching data.'
doc_type: 'guide'
keywords: ['asynchronous inserts', 'async_insert', 'best practices', 'batching data', 'performance optimiz... | {"source_file": "asyncinserts.md"} | [
-0.03953497111797333,
0.051444217562675476,
-0.09483049809932709,
0.1295595020055771,
-0.04950787499547005,
-0.01915283128619194,
-0.04419364780187607,
0.05124066397547722,
-0.06284795701503754,
-0.020456861704587936,
0.06376907229423523,
0.059822928160429,
0.079768106341362,
-0.0782319679... |
2cf04327-f157-4465-bc17-21c63db60ede | slug: '/examples/aggregate-function-combinators/uniqArrayIf'
title: 'uniqArrayIf'
description: 'Example of using the uniqArrayIf combinator'
keywords: ['uniq', 'array', 'if', 'combinator', 'examples', 'uniqArrayIf']
sidebar_label: 'uniqArrayIf'
doc_type: 'reference'
uniqArrayIf {#uniqarrayif}
Description {#descri... | {"source_file": "uniqArrayIf.md"} | [
-0.0010597174987196922,
0.004011173732578754,
-0.014808060601353645,
0.046186160296201706,
-0.027381308376789093,
0.08606909960508347,
0.10078056156635284,
0.00392708508297801,
0.04705769196152687,
-0.058379653841257095,
0.024850420653820038,
-0.0025529905688017607,
0.1404641717672348,
0.0... |
9a827383-11e1-42bb-9517-827445fef989 | slug: '/examples/aggregate-function-combinators/sumSimpleState'
title: 'sumSimpleState'
description: 'Example of using the sumSimpleState combinator'
keywords: ['sum', 'state', 'simple', 'combinator', 'examples', 'sumSimpleState']
sidebar_label: 'sumSimpleState'
doc_type: 'reference'
sumSimpleState {#sumsimplestate... | {"source_file": "sumSimpleState.md"} | [
-0.09811026602983475,
-0.001073782448656857,
-0.01046434510499239,
0.05407912656664848,
-0.017519893124699593,
0.0811958760023117,
0.044268060475587845,
0.05085968226194382,
-0.007909854874014854,
-0.005559615325182676,
-0.04818800464272499,
-0.05801807716488838,
0.052257828414440155,
0.00... |
fdf17cf9-9672-4cfb-abd3-c7a3fdaf0390 | slug: '/examples/aggregate-function-combinators/avgMerge'
title: 'avgMerge'
description: 'Example of using the avgMerge combinator'
keywords: ['avg', 'merge', 'combinator', 'examples', 'avgMerge']
sidebar_label: 'avgMerge'
doc_type: 'reference'
avgMerge {#avgMerge}
Description {#description}
The
Merge
combina... | {"source_file": "avgMerge.md"} | [
-0.04121572524309158,
0.011534501798450947,
0.04372968524694443,
0.05847730487585068,
-0.018030548468232155,
0.0376821905374527,
0.06280435621738434,
0.02510455995798111,
-0.011274863965809345,
-0.018767980858683586,
-0.000605533248744905,
-0.021208327263593674,
0.010464679449796677,
-0.02... |
00b08df8-8741-4b78-90cd-ec00357e818b | slug: '/examples/aggregate-function-combinators/uniqArray'
title: 'uniqArray'
description: 'Example of using the uniqArray combinator'
keywords: ['uniq', 'array', 'combinator', 'examples', 'uniqArray']
sidebar_label: 'uniqArray'
doc_type: 'reference'
uniqArray {#uniqarray}
Description {#description}
The
Array
... | {"source_file": "uniqArray.md"} | [
0.005837088450789452,
0.00039517597178928554,
-0.03232084959745407,
0.02217342145740986,
-0.02748902514576912,
-0.01091686636209488,
0.07838691025972366,
0.00021855415252503008,
0.024687932804226875,
-0.07307783514261246,
0.0182868130505085,
0.02261737920343876,
0.12054786086082458,
-0.023... |
048929d9-5e6e-4f08-afdf-5c65f22b7452 | slug: '/examples/aggregate-function-combinators/sumIf'
title: 'sumIf'
description: 'Example of using the sumIf combinator'
keywords: ['sum', 'if', 'combinator', 'examples', 'sumIf']
sidebar_label: 'sumIf'
doc_type: 'reference'
sumIf {#sumif}
Description {#description}
The
If
combinator can be applied to the
... | {"source_file": "sumIf.md"} | [
-0.03833799436688423,
0.03717745468020439,
-0.007202391512691975,
0.016695570200681686,
-0.08572235703468323,
0.041634395718574524,
0.10724872350692749,
0.09529362618923187,
0.04652319476008415,
0.03965085372328758,
0.03522009029984474,
-0.08922816812992096,
0.06267845630645752,
-0.0224341... |
c606f636-df08-479d-a5a5-81038868635a | markdown
┌──────month─┬─volume_on_up_days─┬─volume_on_down_days─┬─volume_on_neutral_days─┬─total_volume──┐
1. │ 2002-01-01 │ 26.07 billion │ 30.74 billion │ 781.80 million │ 57.59 billion │
2. │ 2002-02-01 │ 20.84 billion │ 29.60 billion │ 642.36 million │ 51.09 billion │
3. │... | {"source_file": "sumIf.md"} | [
0.017664149403572083,
-0.010191491805016994,
-0.021433023735880852,
0.03038734570145607,
0.03501658886671066,
-0.09739507734775543,
0.019473925232887268,
0.029399007558822632,
0.0004519362119026482,
0.08858555555343628,
0.06755142658948898,
-0.031102189794182777,
0.05058487132191658,
-0.01... |
f1cf4f2e-4281-4017-a599-7525363f6825 | markdown title="Response"
┌──────month─┬─apple_volume───┬─microsoft_volume─┬─google_volume──┬─total_volume─┬─major_tech_percentage─┐
1. │ 2006-01-01 │ 782.21 million │ 1.39 billion │ 299.69 million │ 84343937700 │ 2.93 │
2. │ 2006-02-01 │ 670.38 million │ 1.05 billion │ 297.65 million │ ... | {"source_file": "sumIf.md"} | [
-0.0007716430118307471,
0.03141718730330467,
0.00043811716022901237,
0.043482400476932526,
-0.00904170610010624,
-0.07876048982143402,
0.020769914612174034,
0.05937454476952553,
0.02742280252277851,
0.05945782735943794,
0.019461331889033318,
-0.040292706340551376,
0.07566133886575699,
-0.0... |
0d4780dd-49c6-4d5c-9825-8d8f613ff492 | slug: '/examples/aggregate-function-combinators/quantilesTimingArrayIf'
title: 'quantilesTimingArrayIf'
description: 'Example of using the quantilesTimingArrayIf combinator'
keywords: ['quantilesTiming', 'array', 'if', 'combinator', 'examples', 'quantilesTimingArrayIf']
sidebar_label: 'quantilesTimingArrayIf'
doc_type:... | {"source_file": "quantilesTimingArrayIf.md"} | [
-0.042661286890506744,
0.04176223278045654,
-0.0336357057094574,
0.029910597950220108,
-0.08338414132595062,
-0.018817074596881866,
0.047837793827056885,
0.022791795432567596,
0.01113581471145153,
-0.028003837913274765,
0.0017348098335787654,
-0.06978225708007812,
0.0056218840181827545,
-0... |
f49ca267-1b9d-4891-a551-ab34aa29d0da | slug: '/examples/aggregate-function-combinators/avgResample'
title: 'avgResample'
description: 'Example of using the Resample combinator with avg'
keywords: ['avg', 'Resample', 'combinator', 'examples', 'avgResample']
sidebar_label: 'avgResample'
doc_type: 'reference'
countResample {#countResample}
Description {#... | {"source_file": "avgResample.md"} | [
-0.038262076675891876,
0.001977404346689582,
0.020670272409915924,
0.038601282984018326,
-0.10059691220521927,
0.01569153554737568,
0.02923034504055977,
0.06490831077098846,
-0.023359134793281555,
-0.013853429816663265,
-0.014706493355333805,
-0.024910634383559227,
0.04811261594295502,
-0.... |
7197fbeb-da9f-437c-a6f1-cd34194fe999 | slug: '/examples/aggregate-function-combinators/countIf'
title: 'countIf'
description: 'Example of using the countIf combinator'
keywords: ['count', 'if', 'combinator', 'examples', 'countIf']
sidebar_label: 'countIf'
doc_type: 'reference'
countIf {#countif}
Description {#description}
The
If
combinator can be ... | {"source_file": "countIf.md"} | [
0.010116915218532085,
0.008647819980978966,
0.015050348825752735,
0.05178871750831604,
-0.08039022237062454,
0.06317058205604553,
0.08556748926639557,
0.09655016660690308,
0.07358287274837494,
0.01089206151664257,
0.06957290321588516,
-0.08981063961982727,
0.123927041888237,
-0.00257162912... |
7342e93e-7f17-4e31-83d3-9df499d96e64 | slug: '/examples/aggregate-function-combinators/avgMap'
title: 'avgMap'
description: 'Example of using the avgMap combinator'
keywords: ['avg', 'map', 'combinator', 'examples', 'avgMap']
sidebar_label: 'avgMap'
doc_type: 'reference'
avgMap {#avgmap}
Description {#description}
The
Map
combinator can be applied... | {"source_file": "avgMap.md"} | [
0.010913504287600517,
0.0035193904768675566,
-0.0074373590759932995,
0.04380396753549576,
-0.10084350407123566,
-0.005288496147841215,
0.06692703068256378,
0.07411015033721924,
-0.014008406549692154,
0.06665793806314468,
0.013227598741650581,
-0.06728856265544891,
0.07505639642477036,
-0.0... |
0b42eca6-f1b9-48f4-a5fc-88f032e0d975 | slug: '/examples/aggregate-function-combinators/groupArrayDistinct'
title: 'groupArrayDistinct'
description: 'Example of using the groupArrayDistinct combinator'
keywords: ['groupArray', 'Distinct', 'combinator', 'examples', 'groupArrayDistinct']
sidebar_label: 'groupArrayDistinct'
doc_type: 'reference'
groupArrayD... | {"source_file": "groupArrayDistinct.md"} | [
0.022396190091967583,
-0.019927682355046272,
-0.09455271810293198,
0.041815489530563354,
-0.07371890544891357,
-0.0268209557980299,
0.0847579762339592,
-0.019693676382303238,
0.018338164314627647,
-0.05288953706622124,
-0.013186750002205372,
0.0026404461823403835,
0.09927337616682053,
-0.0... |
82ccfcdb-1a39-4cb0-b9fa-f4344d080842 | slug: '/examples/aggregate-function-combinators/sumArray'
title: 'sumArray'
description: 'Example of using the sumArray combinator'
keywords: ['sum', 'array', 'combinator', 'examples', 'sumArray']
sidebar_label: 'sumArray'
doc_type: 'reference'
sumArray {#sumarray}
Description {#description}
The
Array
combina... | {"source_file": "sumArray.md"} | [
-0.002363083651289344,
0.03491871431469917,
-0.017952054738998413,
0.057469893246889114,
-0.09362787008285522,
-0.003241335740312934,
0.06790363788604736,
0.043078720569610596,
-0.004028339870274067,
-0.02859816886484623,
-0.022341609001159668,
-0.026691444218158722,
0.043100763112306595,
... |
e51d5264-79ec-45dc-85c4-b2ccdec62447 | slug: '/examples/aggregate-function-combinators/anyIf'
title: 'anyIf'
description: 'Example of using the anyIf combinator'
keywords: ['any', 'if', 'combinator', 'examples', 'anyIf']
sidebar_label: 'anyIf'
doc_type: 'reference'
anyIf {#avgif}
Description {#description}
The
If
combinator can be applied to the
... | {"source_file": "anyIf.md"} | [
-0.03332899883389473,
0.041535235941410065,
0.003260944038629532,
0.004756542854011059,
-0.047949645668268204,
0.05315535515546799,
0.10995006561279297,
0.08795724809169769,
0.04026157781481743,
0.040870025753974915,
0.08868981897830963,
-0.04797469452023506,
0.05777306109666824,
-0.090354... |
14019d1b-d885-45d1-9187-05c55e605994 | slug: '/examples/aggregate-function-combinators/maxMap'
title: 'maxMap'
description: 'Example of using the maxMap combinator'
keywords: ['max', 'map', 'combinator', 'examples', 'maxMap']
sidebar_label: 'maxMap'
doc_type: 'reference'
maxMap {#maxmap}
Description {#description}
The
Map
combinator can be applied... | {"source_file": "maxMap.md"} | [
0.02465267851948738,
0.00043322343844920397,
0.04901241138577461,
-0.006086159963160753,
-0.1177004724740982,
0.017256293445825577,
0.026120500639081,
0.08784354478120804,
-0.03507443889975548,
0.061198920011520386,
0.017538245767354965,
-0.02884361892938614,
0.08260896056890488,
-0.016100... |
2f63b445-007a-4acd-b53d-d11e92a3d592 | slug: '/examples/aggregate-function-combinators/quantilesTimingIf'
title: 'quantilesTimingIf'
description: 'Example of using the quantilesTimingIf combinator'
keywords: ['quantilesTiming', 'if', 'combinator', 'examples', 'quantilesTimingIf']
sidebar_label: 'quantilesTimingIf'
doc_type: 'reference'
quantilesTimingIf... | {"source_file": "quantilesTimingIf.md"} | [
-0.06531300395727158,
0.05027293041348457,
0.002704873215407133,
0.04130786284804344,
-0.08377345651388168,
-0.024675047025084496,
0.029609955847263336,
0.05215649679303169,
0.033789072185754776,
-0.00693748751655221,
0.034893665462732315,
-0.11681226640939713,
0.01750745251774788,
-0.0228... |
12cc1d23-6b61-4858-846f-626bad6ebfff | slug: '/examples/aggregate-function-combinators/minSimpleState'
title: 'minSimpleState'
description: 'Example of using the minSimpleState combinator'
keywords: ['min', 'state', 'simple', 'combinator', 'examples', 'minSimpleState']
sidebar_label: 'minSimpleState'
doc_type: 'reference'
minSimpleState {#minsimplestate... | {"source_file": "minSimpleState.md"} | [
-0.01241270825266838,
0.03511328622698784,
0.041710738092660904,
0.11546140164136887,
-0.06821369379758835,
0.03552412614226341,
0.02876480482518673,
0.13258571922779083,
-0.02142353169620037,
0.01230809185653925,
0.020416459068655968,
-0.1468772292137146,
0.09353715181350708,
-0.026697061... |
8eb5c21c-0e11-462b-b672-046c860f30ae | Insert some more data:
sql
INSERT INTO raw_temperature_readings (location_id, location_name, temperature) VALUES
(1, 'North', 3),
(2, 'South', 18),
(3, 'West', 10),
(1, 'North', 8),
(4, 'East', 2);
View the updated extremes after new data:
sql
SELECT
location_id,
location_name,
min... | {"source_file": "minSimpleState.md"} | [
0.03342537209391594,
-0.0398252010345459,
0.1046367958188057,
0.07482405006885529,
-0.023512164130806923,
-0.029481057077646255,
-0.016894232481718063,
-0.04541531205177307,
-0.019901514053344727,
0.023874707520008087,
0.07181524485349655,
-0.05741347372531891,
0.03491843864321709,
-0.0588... |
a80c1017-1fe4-4e1e-9399-04e9f1877c3c | slug: '/examples/aggregate-function-combinators/sumForEach'
title: 'sumForEach'
description: 'Example of using the sumForEach aggregate function'
keywords: ['sum', 'ForEach', 'combinator', 'examples', 'sumForEach']
sidebar_label: 'sumForEach'
doc_type: 'reference'
sumForEach {#sumforeach}
Description {#descriptio... | {"source_file": "sumForEach.md"} | [
0.005476975813508034,
-0.035665564239025116,
0.00901293195784092,
0.00852088164538145,
-0.0426008403301239,
0.0005184769397601485,
0.10728541016578674,
0.0030256998725235462,
-0.008653471246361732,
0.02484889328479767,
-0.023583097383379936,
-0.07376217097043991,
0.09755569696426392,
-0.02... |
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