id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
9bd98fea-1c55-4919-a2b5-e21a784bf8ac | description: 'An extension to the hudi table function. Allows processing files from
Apache Hudi tables in Amazon S3 in parallel with many nodes in a specified cluster.'
sidebar_label: 'hudiCluster'
sidebar_position: 86
slug: /sql-reference/table-functions/hudiCluster
title: 'hudiCluster Table Function'
doc_type: 'ref... | {"source_file": "hudiCluster.md"} | [
-0.018262669444084167,
-0.030575325712561607,
-0.1317782700061798,
0.05090489983558655,
0.06148189678788185,
-0.07326406240463257,
-0.01892757974565029,
-0.05372310057282448,
-0.041960667818784714,
0.028948185965418816,
0.05304761603474617,
0.015293086878955364,
0.0572860911488533,
-0.1575... |
a8479842-bb5c-4a14-8aeb-4dea7fc96806 | | Argument | Description ... | {"source_file": "hudiCluster.md"} | [
0.027868228033185005,
0.09311798959970474,
-0.03671836853027344,
-0.03408096358180046,
-0.058706168085336685,
0.019149260595440865,
0.03263988718390465,
0.04810817167162895,
0.04606948420405388,
-0.05770222842693329,
0.011740010231733322,
-0.04398162662982941,
0.00020942941773682833,
-0.03... |
6bb1202e-5c90-4766-b001-ac1b184d20a5 | |
compression
| Parameter is optional. Supported values:
none
,
gzip/gz
,
brotli/br
,
xz/LZMA
,
zstd/zst
. By default, compression will be autodetected by the file extension. ... | {"source_file": "hudiCluster.md"} | [
-0.06592731922864914,
0.1266450434923172,
-0.12161040306091309,
0.013599012978374958,
0.08750125765800476,
0.008525275625288486,
-0.007867097854614258,
0.02465568296611309,
-0.06047913059592247,
-0.003186460817232728,
-0.0006585583905689418,
0.029792368412017822,
0.0033148513175547123,
0.0... |
7a880431-bc99-40e2-bf72-5adbf7524152 | Returned value {#returned_value}
A table with the specified structure for reading data from cluster in the specified Hudi table in S3.
Virtual Columns {#virtual-columns}
_path
— Path to the file. Type:
LowCardinality(String)
.
_file
— Name of the file. Type:
LowCardinality(String)
.
_size
— Size of the... | {"source_file": "hudiCluster.md"} | [
-0.003470450174063444,
0.03509821742773056,
-0.14115512371063232,
0.05787272751331329,
0.08729736506938934,
-0.030309228226542473,
-0.0026559995021671057,
0.023683523759245872,
-0.011917476542294025,
-0.02839376963675022,
0.10856592655181885,
-0.0024983983021229506,
-0.0008702123304829001,
... |
c5bfd01e-3a79-48d4-a4da-8f0460d57a45 | slug: /sql-reference/table-functions/generate_series
sidebar_position: 146
sidebar_label: 'generate_series'
title: 'generate_series (generateSeries)'
description: 'Returns a table with the single
generate_series
column (UInt64) that contains integers from start to stop inclusively.'
doc_type: 'reference'
generate... | {"source_file": "generate_series.md"} | [
-0.06212311238050461,
-0.007629913743585348,
-0.07026229798793793,
0.020038876682519913,
-0.06406274437904358,
-0.0063003418035805225,
-0.013081125915050507,
0.005950300954282284,
0.03383304551243782,
-0.007618342991918325,
0.02259671501815319,
-0.025369182229042053,
0.01511217001825571,
-... |
d6d94bf0-3cf9-44d6-97e4-6a701d7ad1f7 | description: 'Allows processing files from Azure Blob storage in parallel with many
nodes in a specified cluster.'
sidebar_label: 'azureBlobStorageCluster'
sidebar_position: 15
slug: /sql-reference/table-functions/azureBlobStorageCluster
title: 'azureBlobStorageCluster'
doc_type: 'reference'
azureBlobStorageClust... | {"source_file": "azureBlobStorageCluster.md"} | [
-0.033468976616859436,
-0.06145130842924118,
-0.1393815129995346,
0.05997748672962189,
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0.005757797043770552,
0.03367083519697189,
-0.050244297832250595,
0.024815598502755165,
0.0935368463397026,
0.002632511081174016,
0.0103943832218647,
0.0762859582901001,
-0.0352178... |
935e02c3-e203-4b48-9597-9760f8c02ad2 | | Argument | Description ... | {"source_file": "azureBlobStorageCluster.md"} | [
0.027937311679124832,
0.09285847842693329,
-0.03693150728940964,
-0.03431033715605736,
-0.05899081006646156,
0.018396951258182526,
0.03254036605358124,
0.04797203838825226,
0.04546676203608513,
-0.0572921521961689,
0.012018000707030296,
-0.04449009895324707,
0.00007389639358734712,
-0.0343... |
24fc7b54-be18-4ed6-9a39-69de1f4e5f2f | |
container_name
| Container name ... | {"source_file": "azureBlobStorageCluster.md"} | [
0.06836150586605072,
0.043476469814777374,
-0.06927156448364258,
0.06018989160656929,
-0.04306984692811966,
0.0194651260972023,
0.05199325084686279,
0.05520141124725342,
-0.04026085510849953,
-0.07214479148387909,
0.00925468560308218,
0.018780449405312538,
0.05347467586398125,
0.0121834101... |
d86289a9-02c7-436c-a741-33bb33ab1281 | |
format
| The [format](/sql-reference/formats) of the file. ... | {"source_file": "azureBlobStorageCluster.md"} | [
-0.023380208760499954,
0.04916134104132652,
-0.19277197122573853,
0.02704652026295662,
0.044896841049194336,
-0.0169720109552145,
-0.002326671965420246,
0.033485133200883865,
-0.08927492797374725,
0.05233220383524895,
-0.012998327612876892,
0.02400253154337406,
0.0531289242208004,
-0.03338... |
d12d5c3f-af08-4c67-b3d5-c7238f6b605c | Returned value {#returned_value}
A table with the specified structure for reading or writing data in the specified file.
Examples {#examples}
Similar to the
AzureBlobStorage
table engine, users can use Azurite emulator for local Azure Storage development. Further details
here
. Below we assume Azurite is avail... | {"source_file": "azureBlobStorageCluster.md"} | [
0.08679725229740143,
0.027087239548563957,
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0.15920037031173706,
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0.028417080640792847,
0.05459505692124367,
0.0528549961745739,
0.03447939455509186,
0.10365619510412216,
0.05327225103974342,
-0.07135991752147675,
0.15003813803195953,
-0.0392322242... |
580cec78-07bd-45d0-9e81-c40f53d3294f | description: 'Returns a table that is connected via JDBC driver.'
sidebar_label: 'jdbc'
sidebar_position: 100
slug: /sql-reference/table-functions/jdbc
title: 'jdbc'
doc_type: 'reference'
jdbc Table Function
:::note
clickhouse-jdbc-bridge contains experimental codes and is no longer supported. It may contain reli... | {"source_file": "jdbc.md"} | [
-0.007366587873548269,
-0.050016093999147415,
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0.042230021208524704,
0.060799740254879,
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-0.030275525525212288,
0.041935794055461884,
-0.... |
5795c626-71b8-4ec7-9bd6-8053e1d2edd3 | description: 'Parses data from arguments according to specified input format. If structure argument is not specified, it''s extracted from the data.'
slug: /sql-reference/table-functions/format
sidebar_position: 65
sidebar_label: 'format'
title: 'format'
doc_type: 'reference'
format Table Function
Parses data fro... | {"source_file": "format.md"} | [
0.005925033241510391,
0.05265098810195923,
-0.044982947409152985,
0.0776585191488266,
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0.018465150147676468,
-0.0... |
e7b4a618-339c-4e9e-9c6a-32ebe19d078a | description: 'An extension to the s3 table function, which allows processing files
from Amazon S3 and Google Cloud Storage in parallel with many nodes in a specified
cluster.'
sidebar_label: 's3Cluster'
sidebar_position: 181
slug: /sql-reference/table-functions/s3Cluster
title: 's3Cluster'
doc_type: 'reference'
... | {"source_file": "s3Cluster.md"} | [
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0.013192348182201385,
-0.024439454078674316,
0.09272747486829758,
-0.1... |
c4818367-98d2-4e02-9b0e-075e0b67cf58 | Arguments {#arguments}
| Argument | Description |
|---------------------------------------|----------... | {"source_file": "s3Cluster.md"} | [
0.034412991255521774,
0.08291902393102646,
-0.05204023793339729,
-0.02858797274529934,
-0.08600813895463943,
-0.0002526851021684706,
0.04146214574575424,
0.04908876121044159,
0.0498221181333065,
-0.05376224219799042,
0.00317861489020288,
-0.04168727248907089,
0.008608034811913967,
-0.01578... |
09136114-3568-450f-93ad-26c7b2ec949a | Arguments can also be passed using
named collections
. In this case
url
,
access_key_id
,
secret_access_key
,
format
,
structure
,
compression_method
work in the same way, and some extra parameters are supported:
| Argument | Description ... | {"source_file": "s3Cluster.md"} | [
-0.06688357144594193,
0.11275019496679306,
-0.13217231631278992,
0.031785715371370316,
-0.05982647463679314,
-0.05883067846298218,
-0.005744225345551968,
-0.0014767739921808243,
0.03336340934038162,
-0.03251016139984131,
-0.025594092905521393,
-0.015433917753398418,
0.07178713381290436,
-0... |
cf97760f-6c44-488b-9317-7def1bcd20b4 | For production use cases, it is recommended to use
named collections
. Here is the example:
```sql
CREATE NAMED COLLECTION creds AS
access_key_id = 'minio',
secret_access_key = 'ClickHouse_Minio_P@ssw0rd';
SELECT count(*) FROM s3Cluster(
'cluster_simple', creds, url='https://s3-object-url.csv',
... | {"source_file": "s3Cluster.md"} | [
0.0005471984623000026,
0.002065311186015606,
-0.1568545550107956,
0.03913789615035057,
-0.016123581677675247,
-0.008711384609341621,
-0.03740736469626427,
0.012425484135746956,
0.044271837919950485,
0.017282936722040176,
0.02853119745850563,
-0.06061001867055893,
0.12369602918624878,
-0.10... |
1886a30d-3dad-4af5-a4ea-204fee1f21ae | description: 'Allows to perform queries on data stored in a SQLite database.'
sidebar_label: 'sqlite'
sidebar_position: 185
slug: /sql-reference/table-functions/sqlite
title: 'sqlite'
doc_type: 'reference'
sqlite Table Function
Allows to perform queries on data stored in a
SQLite
database.
Syntax {#syntax}
... | {"source_file": "sqlite.md"} | [
-0.042461097240448,
0.003060826798900962,
-0.030918920412659645,
0.0684465765953064,
-0.050519395619630814,
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0.035615213215351105,
0.069008007645607,
-0.08412987738847733,
0.0030378997325897217,
0.03156149387359619,
0.038541752845048904,
0.019039370119571686,
-0.11541... |
6182f103-c5a1-47df-ad44-7e74dd44ff1a | description: 'This is an extension to the deltaLake table function.'
sidebar_label: 'deltaLakeCluster'
sidebar_position: 46
slug: /sql-reference/table-functions/deltalakeCluster
title: 'deltaLakeCluster'
doc_type: 'reference'
deltaLakeCluster Table Function
This is an extension to the
deltaLake
table function.
... | {"source_file": "deltalakeCluster.md"} | [
-0.052118536084890366,
-0.04726121574640274,
-0.05173155665397644,
0.01717623509466648,
0.020578252151608467,
-0.06084731966257095,
-0.0076533895917236805,
-0.01697973720729351,
-0.03331710770726204,
0.031194008886814117,
0.011477448977530003,
-0.04519246518611908,
0.049194712191820145,
-0... |
23c84739-6777-4146-a840-c0b83583ac2b | slug: /sql-reference/table-functions/numbers
sidebar_position: 145
sidebar_label: 'numbers'
title: 'numbers'
description: 'Returns tables with a single
number
column that contains specifiable integers.'
doc_type: 'reference'
numbers Table Function
numbers(N)
– Returns a table with the single 'number' column (U... | {"source_file": "numbers.md"} | [
0.02016141451895237,
0.026418182998895645,
-0.08137988299131393,
-0.011285861022770405,
-0.0579838789999485,
0.00968625582754612,
0.048681095242500305,
0.06123625114560127,
-0.026072269305586815,
0.011828216724097729,
0.0007757946150377393,
0.04277675226330757,
0.08587189018726349,
-0.1028... |
0aae7f54-827f-4299-a0e6-58e82cdcc14c | description: 'Creates a temporary table of the specified structure with the Null table
engine. The function is used for the convenience of test writing and demonstrations.'
sidebar_label: 'null function'
sidebar_position: 140
slug: /sql-reference/table-functions/null
title: 'null'
doc_type: 'reference'
null Table... | {"source_file": "null.md"} | [
-0.039532359689474106,
0.0563693568110466,
-0.06429790705442429,
0.07877881824970245,
-0.04622722044587135,
-0.03843285143375397,
0.010532945394515991,
0.058684080839157104,
-0.018069906160235405,
0.024482788518071175,
0.09250746667385101,
-0.04636533185839653,
0.06404415518045425,
-0.1199... |
7df9f0c8-8d3a-40ac-b4f0-07973f9bd643 | description: 'Table function
remote
allows to access remote servers on-the-fly,
i.e. without creating a distributed table. Table function
remoteSecure
is same
as
remote
but over a secure connection.'
sidebar_label: 'remote'
sidebar_position: 175
slug: /sql-reference/table-functions/remote
title: 'remote, remo... | {"source_file": "remote.md"} | [
0.0257264394313097,
-0.015886208042502403,
-0.060461703687906265,
0.0508449487388134,
-0.08531665802001953,
-0.0205149557441473,
0.013044671155512333,
0.030187496915459633,
-0.021929526701569557,
0.04385719075798988,
0.04895564541220665,
-0.005154758226126432,
0.12599888443946838,
-0.05497... |
620064bd-a3ad-4d97-b8d6-1e9e5693101a | | Argument | Description ... | {"source_file": "remote.md"} | [
0.02788747288286686,
0.09290937334299088,
-0.03714551776647568,
-0.03443417325615883,
-0.05947873741388321,
0.0183249581605196,
0.03254047408699989,
0.04820331931114197,
0.04512941464781761,
-0.05685262009501457,
0.012245319783687592,
-0.044619567692279816,
0.00027788375155068934,
-0.03486... |
9d5a214e-f7aa-49ca-9e65-bac48c4d804b | |
sharding_key
| Sharding key to support distributing data across nodes. For example:
insert into remote('127.0.0.1:9000,127.0.0.2', db, table, 'default', rand())
. Type:
UInt32
. ... | {"source_file": "remote.md"} | [
0.0651077851653099,
-0.027385547757148743,
-0.11012156307697296,
0.04056470841169357,
-0.0760841816663742,
-0.04929322376847267,
-0.03172799572348595,
-0.024417459964752197,
-0.04907270893454552,
0.02890687808394432,
0.04147571325302124,
-0.04045597091317177,
0.11743267625570297,
-0.050231... |
6db3507d-808d-4124-bc74-a3851f47eb67 | Arguments also can be passed using
named collections
.
Returned value {#returned-value}
A table located on a remote server.
Usage {#usage}
As table functions
remote
and
remoteSecure
re-establish the connection for each request, it is recommended to use a
Distributed
table instead. Also, if hostnames are ... | {"source_file": "remote.md"} | [
-0.038778990507125854,
0.0028171574231237173,
-0.08482664078474045,
0.049878522753715515,
-0.057878799736499786,
-0.13266758620738983,
-0.009915422648191452,
-0.022970780730247498,
0.03599393740296364,
0.058989230543375015,
0.0006954885320737958,
-0.003704777918756008,
0.08906065672636032,
... |
f8d7bb81-5110-4238-b63d-90db075850d1 | On the destination ClickHouse system {#on-the-destination-clickhouse-system}
Create the destination database:
sql
CREATE DATABASE imdb
Using the CREATE TABLE statement from the source, create the destination:
sql
CREATE TABLE imdb.actors (`id` UInt32,
`first_name` String,... | {"source_file": "remote.md"} | [
0.044025395065546036,
-0.12593358755111694,
-0.05062158778309822,
-0.0126033341512084,
-0.061699796468019485,
-0.05373286455869675,
0.048739805817604065,
-0.023332780227065086,
-0.018307240679860115,
0.077776238322258,
0.043677520006895065,
-0.042702075093984604,
0.14701028168201447,
-0.02... |
07482516-88a8-446e-82f5-32f7c4e1ea36 | description: 'Evaluates a prometheus query using data from a TimeSeries table.'
sidebar_label: 'prometheusQuery'
sidebar_position: 145
slug: /sql-reference/table-functions/prometheusQuery
title: 'prometheusQuery'
doc_type: 'reference'
prometheusQuery Table Function
Evaluates a prometheus query using data from a T... | {"source_file": "prometheusQuery.md"} | [
-0.023672856390476227,
0.05138961225748062,
-0.07354822754859924,
0.043966468423604965,
-0.055272649973630905,
-0.07564765214920044,
0.05111004412174225,
0.0671299546957016,
-0.0305333249270916,
0.005715205799788237,
-0.020193593576550484,
-0.06953723728656769,
0.03552728146314621,
-0.0400... |
5f4a977a-8e71-4fdc-8740-dc8ab8f5611b | description: 'Allows
SELECT
queries to be performed on data that is stored on a
remote MongoDB server.'
sidebar_label: 'mongodb'
sidebar_position: 135
slug: /sql-reference/table-functions/mongodb
title: 'mongodb'
doc_type: 'reference'
mongodb Table Function
Allows
SELECT
queries to be performed on data that... | {"source_file": "mongodb.md"} | [
0.02347419410943985,
0.05342671647667885,
-0.038090210407972336,
0.09063912183046341,
-0.03230495750904083,
-0.04341159388422966,
-0.008947181515395641,
0.04317895323038101,
0.029825683683156967,
0.010735634714365005,
-0.03518659248948097,
-0.05814861133694649,
-0.016494987532496452,
-0.05... |
1d865997-cb53-4de6-8aac-5cbe205edbbd | Examples {#examples}
Suppose we have a collection named
my_collection
defined in a MongoDB database named
test
, and we insert a couple of documents:
```sql
db.createUser({user:"test_user",pwd:"password",roles:[{role:"readWrite",db:"test"}]})
db.createCollection("my_collection")
db.my_collection.insertOne(
... | {"source_file": "mongodb.md"} | [
0.004479300696402788,
0.014104129746556282,
-0.05697503685951233,
0.12935613095760345,
-0.050558608025312424,
-0.09928533434867859,
0.013094413094222546,
0.04611710086464882,
0.08814587444067001,
0.01570088043808937,
0.03053445741534233,
-0.050967685878276825,
0.030470872297883034,
-0.0313... |
c3c1a9f4-4827-4ecc-8ee4-fc63309f7aef | description: 'Documentation for the Expression special data type'
sidebar_label: 'Expression'
sidebar_position: 58
slug: /sql-reference/data-types/special-data-types/expression
title: 'Expression'
doc_type: 'reference'
Expression
Expressions are used for representing lambdas in high-order functions. | {"source_file": "expression.md"} | [
-0.06970812380313873,
0.04522060975432396,
0.019556740298867226,
0.035592589527368546,
-0.02001969702541828,
0.06171613559126854,
0.035262566059827805,
0.05574173107743263,
0.0009711345192044973,
0.03567887470126152,
0.024196185171604156,
-0.01482643187046051,
0.002764973556622863,
0.00985... |
df11b833-64ae-4e38-bd3a-a70a485cffe1 | description: 'Documentation for the Nothing special data type'
sidebar_label: 'Nothing'
sidebar_position: 60
slug: /sql-reference/data-types/special-data-types/nothing
title: 'Nothing'
doc_type: 'reference'
Nothing
The only purpose of this data type is to represent cases where a value is not expected. So you can'... | {"source_file": "nothing.md"} | [
0.034019459038972855,
0.033661577850580215,
-0.0631960928440094,
0.0922873467206955,
-0.06938908994197845,
0.010374434292316437,
0.06372497230768204,
-0.02663678117096424,
-0.008855901658535004,
-0.042238540947437286,
0.06915131956338882,
-0.04468797892332077,
0.040098413825035095,
-0.0403... |
21cc3a56-e925-4f4b-88c7-55f823def991 | description: 'Documentation for the Set special data type used in IN expressions'
sidebar_label: 'Set'
sidebar_position: 59
slug: /sql-reference/data-types/special-data-types/set
title: 'Set'
doc_type: 'reference'
Set
Used for the right half of an
IN
expression. | {"source_file": "set.md"} | [
-0.03960660472512245,
0.0699777752161026,
-0.0023732201661914587,
0.06996980309486389,
-0.06598174571990967,
0.0745178610086441,
0.06505781412124634,
0.08530683070421219,
-0.05582568421959877,
0.024214770644903183,
0.010542684234678745,
-0.024078965187072754,
0.04668152704834938,
-0.070084... |
6e06e75d-6998-43b1-8134-e5efb72453cf | description: 'Documentation for the Interval special data type'
sidebar_label: 'Interval'
sidebar_position: 61
slug: /sql-reference/data-types/special-data-types/interval
title: 'Interval'
doc_type: 'reference'
Interval
The family of data types representing time and date intervals. The resulting types of the
INT... | {"source_file": "interval.md"} | [
0.004078092519193888,
0.02252046763896942,
0.04980449751019478,
0.056826017796993256,
-0.12420663982629776,
0.01775984838604927,
0.02756928652524948,
0.023299338296055794,
-0.045712217688560486,
-0.05234106630086899,
0.026113055646419525,
-0.07077505439519882,
0.023711223155260086,
0.00757... |
081d39b4-4ac2-4ee6-ac19-f12a026290f7 | description: 'Overview of special data types in ClickHouse that are used for intermediate
results during query execution'
sidebar_label: 'Special Data Types'
sidebar_position: 55
slug: /sql-reference/data-types/special-data-types/
title: 'Special Data Types'
doc_type: 'reference'
Special data types
Special data... | {"source_file": "index.md"} | [
0.025821011513471603,
0.015313525684177876,
-0.06903602182865143,
0.07906704396009445,
-0.0903855487704277,
0.044304680079221725,
-0.000013979644791106693,
0.027220048010349274,
-0.09721255302429199,
-0.017748428508639336,
0.038245391100645065,
0.01648355834186077,
0.03680555522441864,
-0.... |
7121ad1e-8b08-4da2-8d3c-4799b795f888 | description: 'Overview of domain types in ClickHouse, which extend base types with
additional features'
sidebar_label: 'Domains'
sidebar_position: 56
slug: /sql-reference/data-types/domains/
title: 'Domains'
doc_type: 'reference'
Domains
Domains are special-purpose types that add extra features on top of existi... | {"source_file": "index.md"} | [
0.036702483892440796,
-0.10947483032941818,
-0.04652116447687149,
0.0265483520925045,
-0.05124308541417122,
-0.06230226159095764,
-0.009686417877674103,
-0.003799915313720703,
-0.0886056050658226,
-0.030496662482619286,
-0.04088617488741875,
-0.043118249624967575,
0.03771551698446274,
-0.0... |
2df28a0e-6e03-4036-bc9a-a1dcc65249f9 | description: 'Overview of nested data structures in ClickHouse'
sidebar_label: 'Nested(Name1 Type1, Name2 Type2, ...)'
sidebar_position: 57
slug: /sql-reference/data-types/nested-data-structures/nested
title: 'Nested'
doc_type: 'guide'
Nested
Nested(name1 Type1, Name2 Type2, ...) {#nestedname1-type1-name2-type2-}... | {"source_file": "index.md"} | [
-0.023283494636416435,
0.004589820746332407,
-0.018842726945877075,
0.10631883144378662,
-0.05875248834490776,
-0.050961267203092575,
0.009539592079818249,
0.03776666522026062,
-0.010167622938752174,
0.01270398311316967,
0.03863997012376785,
-0.03830597177147865,
0.06811261177062988,
-0.04... |
32caed8b-d5d3-4843-bfae-55994682f1cb | Example:
sql
SELECT
Goals.ID,
Goals.EventTime
FROM test.visits
WHERE CounterID = 101500 AND length(Goals.ID) < 5
LIMIT 10
text
┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
│ [1073752,591325,591325] │ ['2014-03-17 16:38:... | {"source_file": "index.md"} | [
0.033437661826610565,
-0.0014488971792161465,
-0.034823156893253326,
0.0631653293967247,
0.04394472762942314,
0.04803165793418884,
0.08061409741640091,
0.010598531924188137,
0.04501635581254959,
0.02385459467768669,
-0.020186183974146843,
-0.02640351466834545,
0.014988654293119907,
0.01581... |
ac7f4df8-ea2d-4a62-824c-6dc9431bf114 | For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.
The ALTER query for elements in a nested data structure has limitations. | {"source_file": "index.md"} | [
0.04248732328414917,
0.04383400082588196,
0.04578058421611786,
0.048349108546972275,
-0.008756727911531925,
-0.06193351000547409,
-0.05427926033735275,
0.0034533340949565172,
0.011883456259965897,
-0.006776632275432348,
0.028982749208807945,
-0.02652638405561447,
-0.00034676812356337905,
-... |
ad7ef894-66d0-44dd-898c-7f27cf35c7ea | description: 'Calculates the total length of union of all ranges (segments on numeric
axis).'
sidebar_label: 'intervalLengthSum'
sidebar_position: 155
slug: /sql-reference/aggregate-functions/reference/intervalLengthSum
title: 'intervalLengthSum'
doc_type: 'reference'
Calculates the total length of union of all r... | {"source_file": "intervalLengthSum.md"} | [
0.024631790816783905,
0.0342339463531971,
-0.0022569780703634024,
-0.002920838538557291,
-0.06040264293551445,
0.041864585131406784,
-0.04057810828089714,
0.0956123024225235,
-0.036824312061071396,
-0.026543717831373215,
-0.004672105424106121,
-0.08051738142967224,
0.0486246794462204,
-0.0... |
4f7f7c8e-f29b-4491-9b5d-fb19d3734cae | Query:
sql
SELECT id, intervalLengthSum(start, end), toTypeName(intervalLengthSum(start, end)) FROM date_interval GROUP BY id ORDER BY id;
Result:
text
┌─id─┬─intervalLengthSum(start, end)─┬─toTypeName(intervalLengthSum(start, end))─┐
│ a │ 9 │ UInt64 ... | {"source_file": "intervalLengthSum.md"} | [
0.03990021347999573,
0.020950157195329666,
0.06443136930465698,
0.044809624552726746,
-0.07402127236127853,
0.15571552515029907,
0.021854614838957787,
0.03721335530281067,
-0.0032740517053753138,
-0.05722811818122864,
0.03218596801161766,
0.00847569853067398,
0.0021627729292958975,
-0.0315... |
40d5f307-a2f5-4f96-bc4f-6841f9680089 | description: 'The
median*
functions are the aliases for the corresponding
quantile*
functions. They calculate median of a numeric data sample.'
sidebar_position: 167
slug: /sql-reference/aggregate-functions/reference/median
title: 'median'
doc_type: 'reference'
median
The
median*
functions are the aliases... | {"source_file": "median.md"} | [
-0.03045462630689144,
-0.021268323063850403,
0.01066404115408659,
-0.0392138734459877,
-0.06142791733145714,
-0.06738334894180298,
0.009763042442500591,
0.12493564188480377,
-0.02855791710317135,
0.021100133657455444,
0.03511197492480278,
-0.07103076577186584,
0.03950827196240425,
-0.03873... |
9511c6ef-17c9-4362-be2e-c37ab0634059 | description: 'Applies Welch''s t-test to samples from two populations.'
sidebar_label: 'welchTTest'
sidebar_position: 214
slug: /sql-reference/aggregate-functions/reference/welchttest
title: 'welchTTest'
doc_type: 'reference'
welchTTest
Applies Welch's t-test to samples from two populations.
Syntax
sql
welchT... | {"source_file": "welchttest.md"} | [
-0.032613757997751236,
0.03227533772587776,
0.02910594642162323,
0.05886945128440857,
-0.03240475431084633,
-0.07400468736886978,
0.02187260426580906,
0.08962773531675339,
-0.07126189023256302,
0.02023087814450264,
0.07301274687051773,
-0.16880610585212708,
0.0766148492693901,
-0.106542274... |
06f4b418-fc80-437c-90e2-92fb9903a281 | description: 'Aggregate function that calculates PromQL-like rate over time series data on the specified grid.'
sidebar_position: 225
slug: /sql-reference/aggregate-functions/reference/timeSeriesRateToGrid
title: 'timeSeriesRateToGrid'
doc_type: 'reference'
Aggregate function that takes time series data as pairs of... | {"source_file": "timeSeriesRateToGrid.md"} | [
-0.09936074167490005,
0.01302503701299429,
-0.12421011924743652,
0.08040842413902283,
-0.038658250123262405,
-0.050394535064697266,
-0.04882775992155075,
0.03516463562846184,
0.036712948232889175,
-0.01822643168270588,
-0.034316424280405045,
-0.05301322788000107,
-0.014664321206510067,
-0.... |
e82c681f-e781-405d-9d64-b447fec585e7 | :::note
This function is experimental, enable it by setting
allow_experimental_ts_to_grid_aggregate_function=true
.
::: | {"source_file": "timeSeriesRateToGrid.md"} | [
-0.046173498034477234,
-0.03719428926706314,
-0.013469633646309376,
0.09186484664678574,
0.02236771211028099,
0.004247533623129129,
0.002288517076522112,
-0.07123781740665436,
0.0007586099090985954,
0.07126602530479431,
-0.00610651820898056,
-0.012566006742417812,
-0.005058068782091141,
-0... |
e374f1e2-a188-4e54-9f7f-26cc259917b6 | description: 'Calculates the moving sum of input values.'
sidebar_position: 144
slug: /sql-reference/aggregate-functions/reference/grouparraymovingsum
title: 'groupArrayMovingSum'
doc_type: 'reference'
groupArrayMovingSum
Calculates the moving sum of input values.
sql
groupArrayMovingSum(numbers_for_summing)
gr... | {"source_file": "grouparraymovingsum.md"} | [
0.002686504740267992,
-0.0016630274476483464,
-0.056425463408231735,
0.02876385487616062,
-0.048427071422338486,
-0.012993180193006992,
0.05840214714407921,
0.030741803348064423,
-0.02186616137623787,
-0.010487508028745651,
-0.04202849790453911,
-0.00892368983477354,
0.018168671056628227,
... |
9702b704-868f-43af-9381-cba851945f17 | description: 'Calculations the AND of a bitmap column, return cardinality of type
UInt64, if add suffix -State, then return a bitmap object.'
sidebar_position: 149
slug: /sql-reference/aggregate-functions/reference/groupbitmapand
title: 'groupBitmapAnd'
doc_type: 'reference'
Calculations the AND of a bitmap colum... | {"source_file": "groupbitmapand.md"} | [
-0.0031174607574939728,
0.06190905347466469,
-0.04606771841645241,
0.03935179114341736,
-0.059484388679265976,
-0.039254263043403625,
0.04902098327875137,
0.03211662918329239,
-0.1071983352303505,
0.009401102550327778,
0.044320110231637955,
-0.1261141449213028,
0.06021483242511749,
-0.0685... |
e672893c-31eb-4746-8a23-01103e3d26cc | description: 'Returns an array of the approximately most frequent values in the specified
column. The resulting array is sorted in descending order of approximate frequency
of values (not by the values themselves). Additionally, the weight of the value
is taken into account.'
sidebar_position: 203
slug: /sql-refe... | {"source_file": "topkweighted.md"} | [
0.030583515763282776,
-0.00028166931588202715,
-0.03585759177803993,
0.07619401812553406,
-0.04922748729586601,
-0.016786394640803337,
0.04914052039384842,
0.09575086086988449,
-0.013191403821110725,
0.004181621130555868,
-0.022225789725780487,
0.007363727316260338,
0.07501229643821716,
-0... |
d954a745-dcf0-4fdd-81b1-7ec9631eb198 | description: 'Aggregate function that calculates PromQL-like derivative over time series data on the specified grid.'
sidebar_position: 227
slug: /sql-reference/aggregate-functions/reference/timeSeriesDerivToGrid
title: 'timeSeriesDerivToGrid'
doc_type: 'reference'
Aggregate function that takes time series data as ... | {"source_file": "timeSeriesDerivToGrid.md"} | [
-0.11825423687696457,
0.008377665653824806,
-0.07488870620727539,
0.08784772455692291,
-0.02060866542160511,
-0.043255411088466644,
-0.012761160731315613,
0.06854338943958282,
0.012661846354603767,
-0.008204840123653412,
-0.046324536204338074,
-0.07065770775079727,
-0.005913851782679558,
-... |
f31a8a76-045a-4149-8f97-776c328138f5 | Also it is possible to pass multiple samples of timestamps and values as Arrays of equal size. The same query with array arguments:
sql
WITH
[110, 120, 130, 140, 190, 200, 210, 220, 230]::Array(DateTime) AS timestamps,
[1, 1, 3, 4, 5, 5, 8, 12, 13]::Array(Float32) AS values,
90 AS start_ts,
90 + 120 A... | {"source_file": "timeSeriesDerivToGrid.md"} | [
-0.0409311018884182,
0.03310738876461983,
-0.029888227581977844,
0.0275469571352005,
-0.043774593621492386,
0.021450240164995193,
0.01341960672289133,
-0.0012067410862073302,
-0.005682162009179592,
-0.021820148453116417,
-0.08521782606840134,
-0.09914921224117279,
-0.02843405492603779,
-0.... |
b8f1acba-e212-47aa-b10a-ce04f88bdf9a | description: 'Calculates a list of distinct paths stored in a JSON column.'
sidebar_position: 216
slug: /sql-reference/aggregate-functions/reference/distinctjsonpaths
title: 'distinctJSONPaths'
doc_type: 'reference'
distinctJSONPaths
Calculates a list of distinct paths stored in a
JSON
column.
Syntax
sql
di... | {"source_file": "distinctjsonpaths.md"} | [
0.004577265586704016,
0.004742828197777271,
0.023867357522249222,
0.06251713633537292,
-0.07723556458950043,
-0.015700317919254303,
-0.008939092978835106,
0.053781162947416306,
0.0376930795609951,
0.014523492194712162,
0.04875365272164345,
0.05384176969528198,
0.013415326364338398,
-0.0139... |
492243f5-c2be-4086-a896-609ce4b1ee87 | text
┌─distinctJSONPaths(json)─┐
│ ['a','b','c'] │
└─────────────────────────┘
sql
SELECT distinctJSONPathsAndTypes(json) FROM test_json;
text
┌─distinctJSONPathsAndTypes(json)────────────────────────────────┐
│ {'a':['UInt32'],'b':['String'],'c':['Array(Nullable(Int64))']} │
└────────────────────────────... | {"source_file": "distinctjsonpaths.md"} | [
0.03145645186305046,
0.02442951500415802,
-0.020691093057394028,
0.027564851567149162,
-0.053363773971796036,
-0.014574490487575531,
0.0540362149477005,
-0.023840444162487984,
-0.01898396573960781,
-0.05098395422101021,
0.07475470006465912,
0.033438876271247864,
0.0024503974709659815,
0.05... |
3e9e101a-30be-4629-831a-0886e3f8620a | description: 'Applies Kolmogorov-Smirnov''s test to samples from two populations.'
sidebar_label: 'kolmogorovSmirnovTest'
sidebar_position: 156
slug: /sql-reference/aggregate-functions/reference/kolmogorovsmirnovtest
title: 'kolmogorovSmirnovTest'
doc_type: 'reference'
kolmogorovSmirnovTest
Applies Kolmogorov-Smi... | {"source_file": "kolmogorovsmirnovtest.md"} | [
-0.024777386337518692,
0.009277474135160446,
-0.040468327701091766,
0.040790144354104996,
0.019582996144890785,
-0.046518828719854355,
0.04625158756971359,
0.03967238962650299,
-0.031736165285110474,
-0.014669124037027359,
0.06377337872982025,
-0.1504940241575241,
0.06517909467220306,
-0.1... |
b2f9f5df-0607-497c-b05e-d252848bf201 | Result:
text
┌─kolmogorovSmirnovTest('less', 'exact')(value, num)─┐
│ (0.009899999999999996,0.37528595205132287) │
└────────────────────────────────────────────────────┘
Note:
P-value is bigger than 0.05 (for confidence level of 95%), so null hypothesis is not rejected.
Query:
sql
SELECT kolmogorovSmirn... | {"source_file": "kolmogorovsmirnovtest.md"} | [
0.03882777690887451,
0.06978009641170502,
0.0031843555625528097,
0.008392459712922573,
0.02909967117011547,
-0.06135483831167221,
0.02976961061358452,
0.09047313779592514,
0.05800187215209007,
0.04620915278792381,
0.05062933266162872,
-0.16922280192375183,
0.042704835534095764,
-0.01791321... |
d2d7adc1-a25e-4eb5-86d9-75fc443556ea | description: 'Computes quantile of a numeric data sequence using linear interpolation,
taking into account the weight of each element.'
sidebar_position: 176
slug: /sql-reference/aggregate-functions/reference/quantileExactWeightedInterpolated
title: 'quantileExactWeightedInterpolated'
doc_type: 'reference'
quanti... | {"source_file": "quantileexactweightedinterpolated.md"} | [
-0.0950479581952095,
0.012859492562711239,
0.03463950380682945,
0.0074967979453504086,
-0.10303040593862534,
-0.05821872875094414,
0.02145226299762726,
0.04643167927861214,
-0.0093600545078516,
-0.02059185691177845,
-0.0445609949529171,
-0.06718650460243225,
0.040777552872896194,
-0.052252... |
b9462ac9-e48c-4e62-aa47-285ddfaf5ce0 | description: 'Applies the Largest-Triangle-Three-Buckets algorithm to the input data.'
sidebar_label: 'largestTriangleThreeBuckets'
sidebar_position: 159
slug: /sql-reference/aggregate-functions/reference/largestTriangleThreeBuckets
title: 'largestTriangleThreeBuckets'
doc_type: 'reference'
largestTriangleThreeBuck... | {"source_file": "largestTriangleThreeBuckets.md"} | [
-0.036604247987270355,
-0.006202886812388897,
0.019634302705526352,
-0.08053184300661087,
-0.09713971614837646,
-0.03915547579526901,
-0.02198069542646408,
0.06554249674081802,
-0.05456152558326721,
-0.017553232610225677,
-0.04982081800699234,
0.01650259643793106,
0.006694546435028315,
-0.... |
2645b279-cac0-4dd0-9b69-49fb1dd4bcb6 | description: 'Returns an array of the approximately most frequent values and their
counts in the specified column.'
sidebar_position: 108
slug: /sql-reference/aggregate-functions/reference/approxtopsum
title: 'approx_top_sum'
doc_type: 'reference'
approx_top_sum
Returns an array of the approximately most freque... | {"source_file": "approxtopsum.md"} | [
0.008834618143737316,
0.006350921932607889,
-0.06047271937131882,
0.03425231948494911,
-0.06593213975429535,
-0.014785096980631351,
0.03909321501851082,
0.0889049619436264,
0.008391804061830044,
-0.008638535626232624,
-0.017388101667165756,
0.00427367864176631,
0.07944098114967346,
-0.1080... |
7a9bd1af-7271-43c6-a8d3-44cf2b6b1bab | description: 'Calculates the value of
Σ((x - x̅)(y - y̅)) / (n - 1)
'
sidebar_position: 124
slug: /sql-reference/aggregate-functions/reference/covarsamp
title: 'covarSamp'
doc_type: 'reference'
covarSamp
Calculates the value of
Σ((x - x̅)(y - y̅)) / (n - 1)
.
:::note
This function uses a numerically unstable ... | {"source_file": "covarsamp.md"} | [
-0.018267236649990082,
-0.07054412364959717,
-0.017068926244974136,
-0.03957716003060341,
-0.08428805321455002,
-0.04656225070357323,
0.07553210109472275,
0.0689333900809288,
-0.07108767330646515,
0.014172999188303947,
0.027475109323859215,
-0.008791235275566578,
0.022742753848433495,
-0.0... |
6c51f043-d166-4286-a6f3-ec82de2192d9 | description: 'Calculations the OR of a bitmap column, return cardinality of type UInt64,
if add suffix -State, then return a bitmap object. This is equivalent to
groupBitmapMerge
.'
sidebar_position: 150
slug: /sql-reference/aggregate-functions/reference/groupbitmapor
title: 'groupBitmapOr'
doc_type: 'reference'
... | {"source_file": "groupbitmapor.md"} | [
-0.005227779969573021,
0.05620671808719635,
-0.059321168810129166,
0.06103077158331871,
-0.059922993183135986,
-0.031332828104496,
0.04134466126561165,
0.04226319119334221,
-0.08867383748292923,
0.007152724079787731,
0.0349983274936676,
-0.11218523979187012,
0.07635483890771866,
-0.0568072... |
561b6cb4-4d9b-413a-8546-e61452203f96 | description: 'Calculate the sample variance of a data set.'
sidebar_position: 212
slug: /sql-reference/aggregate-functions/reference/varSamp
title: 'varSamp'
doc_type: 'reference'
varSamp {#varsamp}
Calculate the sample variance of a data set.
Syntax
sql
varSamp(x)
Alias:
VAR_SAMP
.
Parameters
x
: Th... | {"source_file": "varsamp.md"} | [
0.01412949152290821,
-0.0408620685338974,
0.019355401396751404,
-0.005380680784583092,
-0.06643829494714737,
0.026503760367631912,
0.06379900872707367,
0.10464812070131302,
-0.016103215515613556,
0.011006677523255348,
0.019061513245105743,
0.018054183572530746,
0.016182122752070427,
-0.090... |
a26e7d90-e099-4dd3-a790-e0006a425914 | description: 'Calculates Cramer''s V, but uses a bias correction.'
sidebar_position: 128
slug: /sql-reference/aggregate-functions/reference/cramersvbiascorrected
title: 'cramersVBiasCorrected'
doc_type: 'reference'
cramersVBiasCorrected
Cramer's V is a measure of association between two columns in a table. The re... | {"source_file": "cramersvbiascorrected.md"} | [
0.0214606374502182,
-0.017864767462015152,
-0.09156597405672073,
0.024793952703475952,
0.014701498672366142,
0.024925265461206436,
0.010374684818089008,
0.012694359757006168,
-0.016861123964190483,
0.05344557762145996,
-0.028429904952645302,
-0.01520291343331337,
0.048198070377111435,
-0.0... |
12847796-2069-4d1f-a367-efb64020379c | description: 'quantiles, quantilesExactExclusive, quantilesExactInclusive, quantilesGK'
sidebar_position: 177
slug: /sql-reference/aggregate-functions/reference/quantiles
title: 'quantiles Functions'
doc_type: 'reference'
quantiles functions
quantiles {#quantiles}
Syntax:
quantiles(level1, level2, ...)(x)
Al... | {"source_file": "quantiles.md"} | [
-0.08362387865781784,
-0.0103496965020895,
-0.00289948214776814,
-0.01652245596051216,
-0.05156091973185539,
-0.08305904269218445,
0.04880102351307869,
0.04230230301618576,
-0.04995939880609512,
-0.028769660741090775,
-0.024541044607758522,
-0.11246190965175629,
0.010814009234309196,
-0.02... |
c3d0a0a3-9fdc-4120-b717-66a3efcf1ea9 | Syntax
sql
quantilesExactInclusive(level1, level2, ...)(expr)
Arguments
expr
— Expression over the column values resulting in numeric
data types
,
Date
or
DateTime
.
Parameters
level
— Levels of quantiles. Possible values: [0, 1] — bounds included.
Float
.
Returned value
Array
of quantil... | {"source_file": "quantiles.md"} | [
-0.022206395864486694,
0.04742470011115074,
0.030252715572714806,
0.020491985604166985,
-0.05529246851801872,
-0.0518597774207592,
0.0931159034371376,
0.03920642286539078,
0.020636001601815224,
-0.02874417044222355,
0.04495001211762428,
-0.09678781032562256,
-0.001832147710956633,
0.031062... |
dce4abe7-b578-4984-adbc-9dc3574ccf69 | description: 'Aggregate function that calculates PromQL-like linear prediction over time series data on the specified grid.'
sidebar_position: 228
slug: /sql-reference/aggregate-functions/reference/timeSeriesPredictLinearToGrid
title: 'timeSeriesPredictLinearToGrid'
doc_type: 'reference'
Aggregate function that tak... | {"source_file": "timeSeriesPredictLinearToGrid.md"} | [
-0.10025354474782944,
-0.04745491221547127,
-0.07679856568574905,
0.08674167096614838,
-0.008301829919219017,
-0.003314773552119732,
-0.0224081352353096,
0.06031016632914543,
-0.030573207885026932,
0.00565925519913435,
-0.03792901337146759,
-0.052121467888355255,
0.00637437216937542,
-0.01... |
6fcabad5-32e7-474b-8235-b2b9930a17f6 | Also it is possible to pass multiple samples of timestamps and values as Arrays of equal size. The same query with array arguments:
sql
WITH
[110, 120, 130, 140, 190, 200, 210, 220, 230]::Array(DateTime) AS timestamps,
[1, 1, 3, 4, 5, 5, 8, 12, 13]::Array(Float32) AS values,
90 AS start_ts,
90 + 120 A... | {"source_file": "timeSeriesPredictLinearToGrid.md"} | [
-0.021276213228702545,
-0.0053113363683223724,
-0.026025546714663506,
0.0407668799161911,
-0.008305104449391365,
0.006870747078210115,
0.03247526288032532,
0.0016548983985558152,
-0.04913587495684624,
-0.02533606067299843,
-0.06915578246116638,
-0.09858448058366776,
-0.021738141775131226,
... |
eb99566d-b8d0-4147-b426-ba577cd3b802 | description: 'Selects the last encountered value of a column.'
sidebar_position: 105
slug: /sql-reference/aggregate-functions/reference/anylast
title: 'anyLast'
doc_type: 'reference'
anyLast
Selects the last encountered value of a column.
:::warning
As a query can be executed in arbitrary order, the result of t... | {"source_file": "anylast.md"} | [
-0.00539913447573781,
0.027894267812371254,
-0.010843535885214806,
0.012669478543102741,
-0.03341503441333771,
0.014751272276043892,
0.03561721742153168,
0.06871578097343445,
0.024072647094726562,
0.05527760460972786,
0.05149339511990547,
-0.04174341633915901,
0.007400284521281719,
-0.0603... |
14b5948f-c6d7-4d22-8876-f701ac334dc5 | description: 'Calculates the Pearson correlation coefficient, but uses a numerically
stable algorithm.'
sidebar_position: 119
slug: /sql-reference/aggregate-functions/reference/corrstable
title: 'corrStable'
doc_type: 'reference'
corrStable
Calculates the
Pearson correlation coefficient
:
$$
\frac{\Sigma{(x... | {"source_file": "corrstable.md"} | [
-0.030455119907855988,
-0.09042900800704956,
-0.024862760677933693,
0.026596734300255775,
-0.0570794977247715,
-0.017288610339164734,
-0.010661625303328037,
0.013125898316502571,
-0.021860532462596893,
0.018585560843348503,
0.010914974845945835,
0.006962308660149574,
0.017826559022068977,
... |
2c3d6925-61fe-4186-a476-c26a38af02fd | description: 'The result is equal to the square root of varPop. Unlike stddevPop,
this function uses a numerically stable algorithm.'
sidebar_position: 189
slug: /sql-reference/aggregate-functions/reference/stddevpopstable
title: 'stddevPopStable'
doc_type: 'reference'
stddevPopStable
The result is equal to the... | {"source_file": "stddevpopstable.md"} | [
0.0014843952376395464,
-0.009620287455618382,
-0.030991384759545326,
0.04774032533168793,
-0.05886076018214226,
-0.0634559616446495,
0.023835720494389534,
0.06629997491836548,
0.002285481197759509,
-0.03129712492227554,
0.03966785594820976,
-0.015537984669208527,
0.0744004026055336,
-0.126... |
43c83fa6-a3f5-48e6-b3cb-2662951d77b9 | description: 'Aggregate function that calculates the maximum number of times that
a group of intervals intersects each other (if all the intervals intersect at least
once).'
sidebar_position: 163
slug: /sql-reference/aggregate-functions/reference/maxintersections
title: 'maxIntersections'
doc_type: 'reference'
... | {"source_file": "maxintersections.md"} | [
0.03940523415803909,
-0.039852291345596313,
0.031304825097322464,
-0.039669472724199295,
-0.08753857016563416,
-0.02461981773376465,
0.0023758376482874155,
0.06051500514149666,
-0.01994379237294197,
-0.011687899939715862,
0.001519288751296699,
-0.026733437553048134,
0.035011522471904755,
-... |
b879a131-0f98-4628-a57a-5f04d5bee42f | description: 'Aggregate function which builds a flamegraph using the list of stacktraces.'
sidebar_position: 138
slug: /sql-reference/aggregate-functions/reference/flame_graph
title: 'flameGraph'
doc_type: 'reference'
flameGraph
Aggregate function which builds a
flamegraph
using the list of stacktraces. Outputs... | {"source_file": "flame_graph.md"} | [
0.020419461652636528,
-0.029629655182361603,
-0.08747866749763489,
0.06785795092582703,
-0.000986415077932179,
-0.025977354496717453,
0.027285777032375336,
0.03555365279316902,
-0.03483901545405388,
-0.03506810963153839,
-0.03996830806136131,
0.03280981630086899,
-0.03756888210773468,
-0.0... |
eeddf670-9ebf-4442-9c8b-8bc2fa29c61d | text
clickhouse client --allow_introspection_functions=1 -q "SELECT arrayJoin(flameGraph(trace, size, ptr)) FROM system.trace_log WHERE trace_type = 'MemorySample' AND query_id = 'xxx'" | ~/dev/FlameGraph/flamegraph.pl --countname=bytes --color=mem > flame_mem_untracked.svg
Build a flamegraph based on memory query pr... | {"source_file": "flame_graph.md"} | [
0.11489514261484146,
-0.03119163028895855,
-0.1000395268201828,
0.10827973484992981,
-0.026549262925982475,
-0.00478731282055378,
0.15181028842926025,
0.020688917487859726,
0.022168220952153206,
-0.008519545197486877,
-0.01618361659348011,
0.01584881730377674,
0.06888847053050995,
-0.01859... |
f76efe22-5748-48cc-98ab-299e74e7a648 | description: 'Aggregate function that calculates the minimum across a group of values.'
sidebar_position: 168
slug: /sql-reference/aggregate-functions/reference/min
title: 'min'
doc_type: 'reference'
Aggregate function that calculates the minimum across a group of values.
Example:
sql
SELECT min(salary) FROM em... | {"source_file": "min.md"} | [
0.02315080724656582,
0.06707838177680969,
-0.0037347497418522835,
0.008322206325829029,
-0.09486990422010422,
0.023519402369856834,
-0.021073337644338608,
0.09842749685049057,
-0.014514919370412827,
-0.001749226008541882,
0.04979359358549118,
-0.0766933336853981,
0.0689723938703537,
-0.020... |
88a17c4f-6488-4cd3-9b22-7ebe19a7ce32 | description: 'Totals a
value
array according to the keys specified in the
key
array. Returns a tuple of two arrays: keys in sorted order, and values summed for
the corresponding keys. Differs from the sumMap function in that it does summation
with overflow.'
sidebar_position: 199
slug: /sql-reference/aggregat... | {"source_file": "summapwithoverflow.md"} | [
-0.0018296422204002738,
0.00021040407591499388,
0.024798814207315445,
0.0031777876429259777,
-0.07061644643545151,
-0.03534148260951042,
0.07047729939222336,
0.021827543154358864,
-0.032444413751363754,
-0.0012566209770739079,
-0.06240085884928703,
0.01283289398998022,
0.024195363745093346,
... |
f31cfb9c-7dc1-4cd9-b4d6-bc7e8b507d95 | sql
SELECT
timeslot,
toTypeName(sumMap(statusMap.status, statusMap.requests)),
toTypeName(sumMapWithOverflow(statusMap.status, statusMap.requests)),
FROM sum_map
GROUP BY timeslot
Equivalently we could have used the tuple syntax with for the same result.
sql
SELECT
timeslot,
toTypeName(sumMap(st... | {"source_file": "summapwithoverflow.md"} | [
0.03047824278473854,
-0.01783219538629055,
0.03525353595614433,
0.022366994991898537,
-0.07979200780391693,
-0.010158943012356758,
0.07352746278047562,
-0.0998571515083313,
-0.09019022434949875,
0.03399953618645668,
-0.00045960923307575285,
-0.06771966069936752,
-0.004350440111011267,
-0.0... |
3a35ef14-d935-4dc5-a0c6-392b94f93064 | description: 'Calculates the approximate number of different values of the argument.'
sidebar_position: 204
slug: /sql-reference/aggregate-functions/reference/uniq
title: 'uniq'
doc_type: 'reference'
uniq
Calculates the approximate number of different values of the argument.
sql
uniq(x[, ...])
Arguments
The... | {"source_file": "uniq.md"} | [
-0.0666726678609848,
0.004460275638848543,
-0.07389054447412491,
0.03570833057165146,
-0.06063666567206383,
-0.027517110109329224,
0.05333062261343002,
0.026339152827858925,
0.029637344181537628,
-0.044777221977710724,
-0.007343275938183069,
0.045155975967645645,
0.10389251261949539,
-0.11... |
49de43fa-c149-45e5-bcc8-d317b81dbf3f | description: 'Computes an approximate quantile of a numeric data sequence using the
t-digest algorithm.'
sidebar_position: 178
slug: /sql-reference/aggregate-functions/reference/quantiletdigest
title: 'quantileTDigest'
doc_type: 'reference'
quantileTDigest
Computes an approximate
quantile
of a numeric data se... | {"source_file": "quantiletdigest.md"} | [
-0.05255325883626938,
0.014836085960268974,
-0.010368870571255684,
-0.009831885807216167,
-0.084251768887043,
-0.10813169181346893,
0.035239141434431076,
0.11603088676929474,
0.016706150025129318,
-0.0035225562751293182,
-0.00039875705260783434,
-0.03319833055138588,
0.024723587557673454,
... |
4e1109d1-bc4c-4baf-ab10-8d12b77568be | description: 'Calculates the moving average of input values.'
sidebar_position: 144
slug: /sql-reference/aggregate-functions/reference/grouparraymovingavg
title: 'groupArrayMovingAvg'
doc_type: 'reference'
groupArrayMovingAvg
Calculates the moving average of input values.
sql
groupArrayMovingAvg(numbers_for_sum... | {"source_file": "grouparraymovingavg.md"} | [
-0.004424276761710644,
0.01957986317574978,
-0.06958813965320587,
0.03342681750655174,
-0.05229910463094711,
-0.04252324253320694,
0.05296843871474266,
0.046260178089141846,
-0.02540455013513565,
-0.010967452079057693,
-0.042620521038770676,
-0.022932125255465508,
0.01988142915070057,
-0.0... |
17d9e38b-6780-430c-beaa-2b0dee64a44d | description: 'Computes a rank correlation coefficient.'
sidebar_position: 182
slug: /sql-reference/aggregate-functions/reference/rankCorr
title: 'rankCorr'
doc_type: 'reference'
rankCorr
Computes a rank correlation coefficient.
Syntax
sql
rankCorr(x, y)
Arguments
x
— Arbitrary value.
Float32
or
Floa... | {"source_file": "rankCorr.md"} | [
-0.05101817473769188,
-0.06200612336397171,
-0.029432618990540504,
0.03974935784935951,
-0.03486647456884384,
0.023452799767255783,
0.03994444012641907,
0.036085668951272964,
0.01688714697957039,
0.02198849804699421,
0.04433101788163185,
0.009340557269752026,
0.06250312179327011,
0.0113890... |
65b1510b-4018-4147-acee-7e614c536ee4 | description: 'Similar to covarSamp but works slower while providing a lower computational
error.'
sidebar_position: 126
slug: /sql-reference/aggregate-functions/reference/covarsampstable
title: 'covarSampStable'
doc_type: 'reference'
covarSampStable
Calculates the value of
Σ((x - x̅)(y - y̅)) / (n - 1)
. Simil... | {"source_file": "covarsampstable.md"} | [
0.00010568768630037084,
-0.06979954987764359,
-0.006683858577162027,
-0.012715722434222698,
-0.06384526193141937,
-0.05705958232283592,
0.04638167470693588,
0.04325731471180916,
-0.045888595283031464,
0.03015219420194626,
0.03353958576917648,
-0.013716744258999825,
0.011844784952700138,
-0... |
78af6812-5190-47f3-a81b-eac5a9f5ab0f | description: 'Calculates the weighted arithmetic mean.'
sidebar_position: 113
slug: /sql-reference/aggregate-functions/reference/avgweighted
title: 'avgWeighted'
doc_type: 'reference'
avgWeighted
Calculates the
weighted arithmetic mean
.
Syntax
sql
avgWeighted(x, weight)
Arguments
x
— Values.
weight... | {"source_file": "avgweighted.md"} | [
-0.0028239639941602945,
-0.03108065202832222,
-0.00022978980268817395,
0.09195540100336075,
-0.06031503155827522,
-0.0744805559515953,
0.09318597614765167,
0.03367077559232712,
0.005705838091671467,
0.014225399121642113,
0.005774752702564001,
-0.059918973594903946,
0.011460015550255775,
-0... |
ddb63fb2-54ec-4cab-8184-632a82b037db | description: 'Aggregate function that re-samples time series data to the specified grid.'
sidebar_position: 226
slug: /sql-reference/aggregate-functions/reference/timeSeriesResampleToGridWithStaleness
title: 'timeSeriesResampleToGridWithStaleness'
doc_type: 'reference'
Aggregate function that takes time series data... | {"source_file": "timeSeriesResampleToGridWithStaleness.md"} | [
-0.07486791908740997,
-0.009986698627471924,
-0.040537912398576736,
0.031103206798434258,
-0.016678282991051674,
-0.0005953009240329266,
0.008360915817320347,
0.07177188247442245,
0.04071813449263573,
-0.0063446927815675735,
-0.08629173040390015,
-0.03019174374639988,
-0.035907041281461716,
... |
34b6d191-3bd1-46d8-9966-dd6db50f917a | :::note
This function is experimental, enable it by setting
allow_experimental_ts_to_grid_aggregate_function=true
.
::: | {"source_file": "timeSeriesResampleToGridWithStaleness.md"} | [
-0.046173498034477234,
-0.03719428926706314,
-0.013469633646309376,
0.09186484664678574,
0.02236771211028099,
0.004247533623129129,
0.002288517076522112,
-0.07123781740665436,
0.0007586099090985954,
0.07126602530479431,
-0.00610651820898056,
-0.012566006742417812,
-0.005058068782091141,
-0... |
c768b952-601b-4b0e-89a9-c506c0fd6de4 | description: 'Computes the sample skewness of a sequence.'
sidebar_position: 186
slug: /sql-reference/aggregate-functions/reference/skewsamp
title: 'skewSamp'
doc_type: 'reference'
skewSamp
Computes the
sample skewness
of a sequence.
It represents an unbiased estimate of the skewness of a random variable if p... | {"source_file": "skewsamp.md"} | [
-0.10652267932891846,
-0.030291646718978882,
-0.009926867671310902,
-0.020465761423110962,
0.04709508270025253,
-0.06420256197452545,
0.04644175246357918,
0.027466312050819397,
0.01931866817176342,
0.0006845244206488132,
0.0703074038028717,
0.009911044500768185,
-0.011282432824373245,
-0.1... |
d269558b-1568-4332-a5a9-9b8c5db3721e | description: 'Inserts a value into the array at the specified position.'
sidebar_position: 140
slug: /sql-reference/aggregate-functions/reference/grouparrayinsertat
title: 'groupArrayInsertAt'
doc_type: 'reference'
groupArrayInsertAt
Inserts a value into the array at the specified position.
Syntax
sql
groupAr... | {"source_file": "grouparrayinsertat.md"} | [
-0.018932195380330086,
-0.006863586604595184,
-0.05646134912967682,
0.09152929484844208,
-0.08127471059560776,
-0.00222892127931118,
0.09146568179130554,
-0.047611795365810394,
0.04029955342411995,
-0.048532042652368546,
0.04086776450276375,
0.008519834838807583,
0.014143552631139755,
-0.1... |
a5130576-167a-4fd6-bfee-c2b6e47fa1fc | description: 'Calculates Shannon entropy of for a column of values.'
sidebar_position: 131
slug: /sql-reference/aggregate-functions/reference/entropy
title: 'entropy'
doc_type: 'reference'
entropy
Calculates
Shannon entropy
for a column of values.
Syntax
sql
entropy(val)
Arguments
val
— Column of val... | {"source_file": "entropy.md"} | [
0.04198892414569855,
0.041128214448690414,
0.00780320493504405,
0.03718560189008713,
-0.0984521135687828,
0.0003099718305747956,
0.13453996181488037,
-0.033703602850437164,
0.03759463503956795,
0.03354273736476898,
0.025047020986676216,
-0.05110206827521324,
0.08089514821767807,
-0.0752205... |
89c3b653-c3e1-4608-a570-eafacb840453 | description: 'Calculates the approximate number of different argument values, using
the Theta Sketch Framework.'
sidebar_position: 209
slug: /sql-reference/aggregate-functions/reference/uniqthetasketch
title: 'uniqTheta'
doc_type: 'reference'
Calculates the approximate number of different argument values, using t... | {"source_file": "uniqthetasketch.md"} | [
-0.03470906615257263,
-0.014293253421783447,
-0.07228899747133255,
-0.009026076644659042,
-0.06473476439714432,
-0.030058471485972404,
0.04871111735701561,
0.05228922516107559,
0.0028659028466790915,
-0.02150549739599228,
-0.0061537339352071285,
-0.0014070437755435705,
0.15216703712940216,
... |
4cf4dc0a-49f7-4560-bd75-8beb0b4bf9cb | description: 'Computes an approximate quantile of a numeric data sequence.'
sidebar_position: 172
slug: /sql-reference/aggregate-functions/reference/quantiledeterministic
title: 'quantileDeterministic'
doc_type: 'reference'
quantileDeterministic
Computes an approximate
quantile
of a numeric data sequence.
Thi... | {"source_file": "quantiledeterministic.md"} | [
-0.0309702567756176,
-0.02061052992939949,
0.04910745471715927,
-0.000674158101901412,
-0.08365076780319214,
-0.09209772944450378,
0.026189249008893967,
0.04808471351861954,
-0.010998417623341084,
-0.022258533164858818,
-0.06323814392089844,
-0.1165226548910141,
0.01024006400257349,
-0.041... |
6e35ee2c-cd19-4aad-9714-b066a5441f3e | description: 'Performs simple (unidimensional) linear regression.'
sidebar_position: 183
slug: /sql-reference/aggregate-functions/reference/simplelinearregression
title: 'simpleLinearRegression'
doc_type: 'reference'
simpleLinearRegression
Performs simple (unidimensional) linear regression.
sql
simpleLinearRegr... | {"source_file": "simplelinearregression.md"} | [
-0.029135607182979584,
-0.037165481597185135,
-0.02877875789999962,
0.07886607199907303,
-0.04156273975968361,
0.02597615122795105,
0.01592743583023548,
-0.05284174904227257,
-0.07030262798070908,
0.02166990004479885,
-0.01002969965338707,
0.017544671893119812,
-0.011000123806297779,
-0.07... |
03842623-8979-4601-9760-09cdc6571d0e | description: 'Calculates the population covariance'
sidebar_position: 121
slug: /sql-reference/aggregate-functions/reference/covarpop
title: 'covarPop'
doc_type: 'reference'
covarPop
Calculates the population covariance:
$$
\frac{\Sigma{(x - \bar{x})(y - \bar{y})}}{n}
$$
:::note
This function uses a numerical... | {"source_file": "covarpop.md"} | [
-0.017206702381372452,
-0.08189211040735245,
-0.00754382461309433,
0.0022792667150497437,
-0.07707139849662781,
-0.07287776470184326,
0.07390421628952026,
0.02951580472290516,
-0.030411552637815475,
0.0033413611818104982,
0.07946125417947769,
-0.047679074108600616,
0.0048130713403224945,
-... |
b043c6da-8115-48fc-97b4-51a787dcf771 | description: 'Calculates the XOR of a bitmap column, and returns the cardinality of
type UInt64, if used with suffix -State, then it returns a bitmap object'
sidebar_position: 151
slug: /sql-reference/aggregate-functions/reference/groupbitmapxor
title: 'groupBitmapXor'
doc_type: 'reference'
groupBitmapXor
group... | {"source_file": "groupbitmapxor.md"} | [
-0.0014874173793941736,
0.044350042939186096,
-0.03609488904476166,
0.04166203364729881,
-0.03623460605740547,
-0.05103301256895065,
0.039912860840559006,
0.028544142842292786,
-0.058479707688093185,
-0.02601018361747265,
0.047221455723047256,
-0.09795960038900375,
0.08570125699043274,
-0.... |
5c1a6c75-abbf-40a0-b1bf-38e4575f4200 | description: 'Calculates the maximum from
value
array according to the keys specified
in the
key
array.'
sidebar_position: 165
slug: /sql-reference/aggregate-functions/reference/maxmap
title: 'maxMap'
doc_type: 'reference'
maxMap
Calculates the maximum from
value
array according to the keys specified in t... | {"source_file": "maxmap.md"} | [
0.09021760523319244,
0.014441058039665222,
-0.006237625610083342,
-0.06731818616390228,
-0.10867582261562347,
-0.025014914572238922,
0.04874660074710846,
0.059447623789310455,
-0.09928740561008453,
0.04415220022201538,
-0.021022524684667587,
0.040460746735334396,
0.09683068841695786,
-0.06... |
ed388a19-e819-45ba-9807-1cb818063968 | description: 'Returns the population variance. Unlike varPop , this function uses
a numerically stable algorithm. It works slower but provides a lower computational
error.'
sidebar_position: 211
slug: /sql-reference/aggregate-functions/reference/varpopstable
title: 'varPopStable'
doc_type: 'reference'
varPopSta... | {"source_file": "varpopstable.md"} | [
-0.0025098170153796673,
-0.03774772584438324,
0.005326446145772934,
0.07948481291532516,
-0.03907833620905876,
-0.07179359346628189,
0.05217758193612099,
0.07384464144706726,
-0.001704421709291637,
-0.018329916521906853,
0.06745720654726028,
0.015095160342752934,
0.04717518016695976,
-0.08... |
e44cf74c-e8f9-4622-8b51-f687545733ac | description: 'Calculates the arithmetic mean.'
sidebar_position: 112
slug: /sql-reference/aggregate-functions/reference/avg
title: 'avg'
doc_type: 'reference'
avg
Calculates the arithmetic mean.
Syntax
sql
avg(x)
Arguments
x
— input values, must be
Integer
,
Float
, or
Decimal
.
Returned value
... | {"source_file": "avg.md"} | [
0.0006860243738628924,
0.009597985073924065,
-0.008682969957590103,
0.07224700599908829,
-0.0898183137178421,
-0.052437763661146164,
0.050021860748529434,
0.09803274273872375,
0.03162221983075142,
0.054216235876083374,
0.027990145608782768,
-0.04788484796881676,
0.06862752884626389,
-0.109... |
d2414721-cb4d-42da-89e0-6e2b86bb7dd4 | description: 'Computes the kurtosis of a sequence.'
sidebar_position: 157
slug: /sql-reference/aggregate-functions/reference/kurtpop
title: 'kurtPop'
doc_type: 'reference'
kurtPop
Computes the
kurtosis
of a sequence.
sql
kurtPop(expr)
Arguments
expr
—
Expression
returning a number.
Returned value
T... | {"source_file": "kurtpop.md"} | [
-0.0645073801279068,
-0.012753596529364586,
-0.033952873200178146,
0.026742901653051376,
-0.09919506311416626,
-0.04235890135169029,
0.054795876145362854,
0.06309543550014496,
0.07710353285074234,
0.013927269726991653,
0.07516202330589294,
-0.047868020832538605,
-0.0026049481239169836,
-0.... |
77da8525-b3e0-4f5b-9d0a-3cd513993cf2 | description: 'This function can be used for the purpose of testing exception safety.
It will throw an exception on creation with the specified probability.'
sidebar_position: 101
slug: /sql-reference/aggregate-functions/reference/aggthrow
title: 'aggThrow'
doc_type: 'reference'
aggThrow
This function can be use... | {"source_file": "aggthrow.md"} | [
-0.05209071561694145,
0.0054486398585140705,
0.0004548417346086353,
0.07309199869632721,
-0.0203249491751194,
0.005957181099802256,
0.02509421296417713,
0.08684585988521576,
-0.014445343986153603,
-0.00836437102407217,
0.014585204422473907,
-0.06236031651496887,
0.05450182780623436,
-0.123... |
293792c3-3611-46dd-a36d-b49a1a76cbb6 | description: 'Calculates the
arg
value for a minimum
val
value. If there are multiple
rows with equal
val
being the maximum, which of the associated
arg
is returned
is not deterministic.'
sidebar_position: 110
slug: /sql-reference/aggregate-functions/reference/argmin
title: 'argMin'
doc_type: 'reference'
... | {"source_file": "argmin.md"} | [
-0.03909727931022644,
0.006966263521462679,
-0.025862760841846466,
0.06404660642147064,
-0.06561582535505295,
-0.04306482896208763,
0.11066337674856186,
0.022054973989725113,
-0.04131723567843437,
0.005407819990068674,
0.012842991389334202,
-0.03618660569190979,
0.04322827607393265,
-0.051... |
115bec46-4c64-4417-8efb-436d14a45ae2 | SELECT argMin((a, b), (b, a)), min(tuple(b, a)) FROM test;
┌─argMin(tuple(a, b), tuple(b, a))─┬─min(tuple(b, a))─┐
│ (NULL,NULL) │ (NULL,NULL) │ -- argMin returns (NULL,NULL) here because
Tuple
allows to don't skip
NULL
and min(tuple(b, a)) in this case is minimal value for this dataset
└─... | {"source_file": "argmin.md"} | [
0.0030603648629039526,
-0.01146253477782011,
0.011551685631275177,
-0.020751086995005608,
0.002700711600482464,
0.010480329394340515,
0.04384110867977142,
-0.07746821641921997,
-0.03572716563940048,
0.0280738677829504,
0.08797314018011093,
-0.06882743537425995,
0.03336847946047783,
-0.0556... |
efc3b617-9961-4813-ba9e-aae57b429d05 | description: 'It is an alias for any but it was introduced for compatibility with
Window Functions, where sometimes it is necessary to process
NULL
values (by default
all ClickHouse aggregate functions ignore NULL values).'
sidebar_position: 137
slug: /sql-reference/aggregate-functions/reference/first_value
title... | {"source_file": "first_value.md"} | [
-0.08669225871562958,
-0.027560845017433167,
-0.030430400744080544,
0.04575083404779434,
-0.04858876392245293,
0.00017916767683345824,
0.07342302799224854,
0.024447601288557053,
0.03696776181459427,
-0.007357880473136902,
0.05044794827699661,
-0.004297496750950813,
0.05386490374803543,
-0.... |
de0ce588-b40e-47c1-b5f6-e1728b8fecbb | description: 'Calculates the sum of the numbers with Kahan compensated summation algorithm'
sidebar_position: 197
slug: /sql-reference/aggregate-functions/reference/sumkahan
title: 'sumKahan'
doc_type: 'reference'
Calculates the sum of the numbers with
Kahan compensated summation algorithm
Slower than
sum
funct... | {"source_file": "sumkahan.md"} | [
-0.06031803414225578,
0.05680539458990097,
-0.013672263361513615,
0.01406602468341589,
-0.05787331610918045,
-0.058128684759140015,
0.07776451855897903,
0.038417983800172806,
0.02626429870724678,
0.0032775187864899635,
-0.03710394725203514,
-0.07422414422035217,
0.0004799540329258889,
-0.0... |
5f78d9a4-3769-4ac0-8d84-f78894d4a68e | description: 'Counts the number of rows or not-NULL values.'
sidebar_position: 120
slug: /sql-reference/aggregate-functions/reference/count
title: 'count'
doc_type: 'reference'
count
Counts the number of rows or not-NULL values.
ClickHouse supports the following syntaxes for
count
:
count(expr)
or
COUNT(... | {"source_file": "count.md"} | [
-0.031168257817626,
-0.05273471772670746,
-0.05986684560775757,
0.07597596198320389,
-0.08116015791893005,
0.0339977964758873,
0.03054528869688511,
0.007369261234998703,
0.04638582095503807,
-0.02306758612394333,
0.07957726716995239,
-0.03271019831299782,
0.10306506603956223,
-0.1054663062... |
5004bcbd-7fec-41d7-99aa-40a2da9748e4 | description: 'Adds the difference between consecutive rows. If the difference is negative,
it is ignored.'
sidebar_position: 130
slug: /sql-reference/aggregate-functions/reference/deltasumtimestamp
title: 'deltaSumTimestamp'
doc_type: 'reference'
Adds the difference between consecutive rows. If the difference is ... | {"source_file": "deltasumtimestamp.md"} | [
-0.07006394118070602,
-0.033176444470882416,
0.04228277504444122,
-0.05782027170062065,
-0.07189157605171204,
0.01825757510960102,
0.03362981975078583,
0.033706072717905045,
0.05640097334980965,
-0.004283261485397816,
0.06925315409898758,
-0.04620407521724701,
-0.006382983177900314,
-0.048... |
c930f4af-54da-436b-99a3-c14ade687a4b | description: 'Applies the student t-test to samples from two populations.'
sidebar_label: 'studentTTest'
sidebar_position: 194
slug: /sql-reference/aggregate-functions/reference/studentttest
title: 'studentTTest'
doc_type: 'reference'
studentTTest
Applies Student's t-test to samples from two populations.
Syntax... | {"source_file": "studentttest.md"} | [
0.0008394166943617165,
0.03863988071680069,
0.029517870396375656,
0.01858525350689888,
-0.01512069907039404,
-0.08700861036777496,
0.0112245362251997,
0.11348993331193924,
-0.06725890189409256,
0.035025645047426224,
0.1061883494257927,
-0.14328256249427795,
0.07135532051324844,
-0.09449298... |
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