id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
d9202a01-bca9-49d1-b3d1-3e6ab7ae0588 | 2 rows in set. Elapsed: 0.006 sec.
```
Note how columns missing in rows are returned as
NULL
.
Additionally, a separate sub column is created for paths with the same type. For example, a subcolumn exists for
company.labels.type
of both
String
and
Array(Nullable(String))
. While both will be returned where pos... | {"source_file": "schema.md"} | [
-0.008792194537818432,
0.005072437692433596,
-0.08776376396417618,
0.07163846492767334,
-0.022203875705599785,
-0.07415806502103806,
0.02287237159907818,
0.014648452401161194,
0.014172430150210857,
-0.03256935626268387,
0.06693258881568909,
-0.03267212584614754,
-0.026391414925456047,
-0.0... |
1fa1382d-37a2-4b36-89a2-7c3d0ef68d6d | We can insert into this table using the
JSONEachRow
format:
```sql
INSERT INTO people FORMAT JSONEachRow
{"id":1,"name":"Clicky McCliickHouse","username":"Clicky","email":"clicky@clickhouse.com","address":[{"street":"Victor Plains","suite":"Suite 879","city":"Wisokyburgh","zipcode":"90566-7771","geo":{"lat":-43.950... | {"source_file": "schema.md"} | [
-0.04201647266745567,
-0.010015741921961308,
-0.022531766444444656,
0.06249694526195526,
-0.06992018222808838,
-0.01215137355029583,
-0.010762788355350494,
-0.011341073550283909,
-0.04098626971244812,
-0.00041552537004463375,
0.02702167071402073,
-0.07466527074575424,
0.04283600673079491,
... |
6b60df00-d19d-446b-9e03-ecb7d4c8362a | ```sql
SELECT JSONDynamicPathsWithTypes(company.labels) AS paths
FROM people
FORMAT PrettyJsonEachRow
{
"paths": {
"dissolved": "Int64",
"employees": "Int64",
"founded": "Int64",
"type": "Array(Nullable(String))"
}
}
{
"paths": {
"employees": "Int64",
"founded"... | {"source_file": "schema.md"} | [
0.010382208041846752,
-0.0006537776789627969,
0.028744090348482132,
0.05529927834868431,
-0.06756129860877991,
-0.019652072340250015,
0.06200088560581207,
-0.02783903479576111,
-0.04995757341384888,
-0.05504336208105087,
0.02364119328558445,
0.004414334427565336,
-0.0517762266099453,
-0.00... |
f67ae8c2-6895-4621-ac4c-f1dd48458205 | 1 row in set. Elapsed: 0.440 sec.
```
Notice how these columns now have our explicit types:
```sql
SELECT JSONAllPathsWithTypes(company.labels) AS paths
FROM people
FORMAT PrettyJsonEachRow
{
"paths": {
"dissolved": "UInt16",
"employees": "UInt16",
"founded": "UInt16",
"type": ... | {"source_file": "schema.md"} | [
-0.017381155863404274,
0.02385200932621956,
-0.025790438055992126,
0.042067211121320724,
-0.08519689738750458,
-0.02777327224612236,
0.0029600372072309256,
-0.028146496042609215,
-0.011020802892744541,
-0.05350925773382187,
0.06676996499300003,
-0.008070169016718864,
-0.03703444451093674,
... |
0ef7897c-05a4-4e6a-9d2a-560e9826496d | 1 row in set. Elapsed: 0.440 sec.
```
Note how our columns have been excluded from our data:
```sql
SELECT *
FROM people
FORMAT PrettyJSONEachRow
{
"json": {
"dob" : "1992-07-15",
"id" : "2",
"name" : "Analytica Rowe",
"phone_numbers" : [
"123-456-7890",
... | {"source_file": "schema.md"} | [
-0.01115286536514759,
0.07038649171590805,
0.05803053826093674,
0.04191277176141739,
0.006214396562427282,
-0.03556301072239876,
-0.00500694802030921,
-0.03295474126935005,
0.029627515003085136,
-0.015686744824051857,
0.039157118648290634,
-0.05329931527376175,
-0.04873155429959297,
-0.052... |
a75ba795-59a2-4d3d-ab2b-b3d38bb11d5f | For example, when two JSON paths are inserted with differing types, ClickHouse stores the values of each
concrete type in distinct sub-columns
. These sub-columns can be accessed independently, minimizing unnecessary I/O. Note that when querying a column with multiple types, its values are still returned as a single c... | {"source_file": "schema.md"} | [
-0.06565647572278976,
-0.01075893733650446,
-0.047044310718774796,
0.03712376579642296,
0.00977408792823553,
-0.1491907238960266,
-0.06265442818403244,
0.03144603222608566,
0.07289192080497742,
-0.017183847725391388,
-0.025911303237080574,
0.07223162055015564,
-0.04474283754825592,
0.00976... |
6c0bcb68-59eb-4a75-874e-bb154027dbbd | sidebar_label: 'Overview'
sidebar_position: 10
title: 'Working with JSON'
slug: /integrations/data-formats/json/overview
description: 'Working with JSON in ClickHouse'
keywords: ['json', 'clickhouse']
score: 10
doc_type: 'guide'
JSON Overview
ClickHouse provides several approaches for handling JSON, each wit... | {"source_file": "intro.md"} | [
-0.06142779812216759,
0.03052978590130806,
0.0009177398751489818,
0.010121523402631283,
-0.008208983577787876,
-0.0019366617780178785,
-0.05730079114437103,
0.06538129597902298,
-0.09225164353847504,
-0.05014759302139282,
0.052228789776563644,
-0.02059548906981945,
0.02141694352030754,
0.0... |
acaa951f-e4c4-4afa-a381-3dca90cba5a4 | sidebar_label: 'Loading JSON'
sidebar_position: 20
title: 'Working with JSON'
slug: /integrations/data-formats/json/loading
description: 'Loading JSON'
keywords: ['json', 'clickhouse', 'inserting', 'loading', 'inserting']
score: 15
doc_type: 'guide'
Loading JSON {#loading-json}
The following examples provide a ve... | {"source_file": "loading.md"} | [
-0.08683890104293823,
0.07064816355705261,
-0.003761137370020151,
-0.014545985497534275,
-0.0799771249294281,
-0.049343548715114594,
-0.1089002788066864,
0.032246142625808716,
-0.030733667314052582,
-0.0841435045003891,
0.04657832160592079,
0.009826136752963066,
-0.012795884162187576,
0.05... |
87ed7be8-f0ce-48bf-ba83-76ec75cf0834 | ClickHouse can load data JSON in several formats, automatically inferring the type from the extension and contents. We can read JSON files for the above table using the
S3 function
:
```sql
SELECT *
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/pypi/json/*.json.gz')
LIMIT 1
ββββββββdateββ¬βcountr... | {"source_file": "loading.md"} | [
-0.0382637120783329,
-0.051444847136735916,
-0.08371435105800629,
0.010478672571480274,
-0.016221262514591217,
-0.04642953351140022,
0.008831196464598179,
-0.009290676563978195,
-0.009492548182606697,
-0.031325094401836395,
0.02390429563820362,
-0.01014968566596508,
-0.007028757128864527,
... |
792b0863-b3de-46b6-8a67-f70698f09600 | ClickHouse handles this through a dedicated
JSON
type.
Consider the following example from an extended version of the above
Python PyPI dataset
dataset. Here we have added an arbitrary
tags
column with random key value pairs.
```json
{
"date": "2022-09-22",
"country_code": "IN",
"project": "clickhouse-c... | {"source_file": "loading.md"} | [
-0.049014125019311905,
0.009907446801662445,
-0.07514267414808273,
0.012471163645386696,
-0.017474442720413208,
-0.06292781233787537,
0.06536681950092316,
-0.015639733523130417,
-0.004427480045706034,
-0.03419116139411926,
0.07488983124494553,
-0.022081827744841576,
0.0417291522026062,
0.0... |
efdcebdd-d9ce-479b-bae0-bede66b2d4b9 | Use the JSON type when your data:
Has
unpredictable keys
that can change over time.
Contains
values with varying types
(e.g., a path might sometimes contain a string, sometimes a number).
Requires schema flexibility where strict typing isn't viable.
If your data structure is known and consistent, there ... | {"source_file": "loading.md"} | [
-0.031026383861899376,
0.013787123374640942,
-0.00273254350759089,
0.00373870343901217,
-0.06011557951569557,
-0.05711553245782852,
-0.03335154429078102,
0.02648661844432354,
0.018676018342375755,
-0.04484620317816734,
-0.024565163999795914,
-0.011099905706942081,
0.009337717667222023,
0.0... |
14c02427-4659-4435-9376-eb08142dd6c0 | title: 'Other JSON approaches'
slug: /integrations/data-formats/json/other-approaches
description: 'Other approaches to modeling JSON'
keywords: ['json', 'formats']
doc_type: 'reference'
Other approaches to modeling JSON
The following are alternatives to modeling JSON in ClickHouse. These are documented for compl... | {"source_file": "other.md"} | [
-0.07526279985904694,
0.06655947118997574,
0.003540088888257742,
0.004895415157079697,
-0.0563991516828537,
-0.025070995092391968,
-0.04155213013291359,
0.07201673090457916,
-0.030150888487696648,
-0.027163593098521233,
0.005944856908172369,
-0.0019241443369537592,
0.00023604814487043768,
... |
630a7c53-35dd-432a-a245-a073b24dc532 | Ok.
1 row in set. Elapsed: 0.002 sec.
```
We can select the
tags
column and see that the JSON has been inserted as a string:
```sql
SELECT tags
FROM people
ββtagsββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β {"hobby":"Databases","holidays":[{"... | {"source_file": "other.md"} | [
-0.025605333968997,
0.03254878520965576,
0.044610653072595596,
0.03020995482802391,
-0.013818689621984959,
-0.040732331573963165,
0.012713100761175156,
0.027282383292913437,
0.02718459814786911,
-0.06673489511013031,
0.04569544643163681,
-0.05653709918260574,
0.0017304469365626574,
-0.0220... |
7facfe08-7356-4c43-a141-3b8659a573c3 | SELECT
toYear(parseDateTimeBestEffort(JSON_VALUE(body, '$.versions[0].created'))) AS published_year,
count() AS c
FROM arxiv
GROUP BY published_year
ORDER BY published_year ASC
LIMIT 10
ββpublished_yearββ¬βββββcββ
β 1986 β 1 β
β 1988 β 1 β
β 1989 β 6 β
β 1990... | {"source_file": "other.md"} | [
0.05159367620944977,
0.04756771773099899,
-0.02182445488870144,
0.08061521500349045,
0.017861252650618553,
-0.048546936362981796,
-0.013095071539282799,
-0.0224971491843462,
0.00603617774322629,
-0.020597482100129128,
0.029949456453323364,
0.007075527682900429,
0.01044426392763853,
-0.0422... |
36d4d556-6716-4097-b443-e4422e188c6f | ```sql
SELECT
toYear(parseDateTimeBestEffort(simpleJSONExtractString(simpleJSONExtractRaw(body, 'versions'), 'created'))) AS published_year,
count() AS c
FROM arxiv
GROUP BY published_year
ORDER BY published_year ASC
LIMIT 10
ββpublished_yearββ¬βββββcββ
β 1986 β 1 β
β 1988 β 1 β
β ... | {"source_file": "other.md"} | [
-0.03363143652677536,
0.012736187316477299,
-0.040672075003385544,
0.07705347239971161,
-0.058197807520627975,
0.006588182412087917,
-0.008612661622464657,
0.019296005368232727,
-0.0467313677072525,
0.02207517996430397,
0.01614745333790779,
0.01688005030155182,
0.008658794686198235,
-0.078... |
4302fab4-7e27-4ab9-834e-7d4d1c21a7f1 | We can insert our original complete JSON object:
```sql
INSERT INTO people FORMAT JSONEachRow
{"id":1,"name":"Clicky McCliickHouse","username":"Clicky","email":"clicky@clickhouse.com","address":[{"street":"Victor Plains","suite":"Suite 879","city":"Wisokyburgh","zipcode":"90566-7771","geo":{"lat":-43.9509,"lng":-34.4... | {"source_file": "other.md"} | [
-0.06245770305395126,
0.0032510750461369753,
-0.0045348103158175945,
0.06315911561250687,
-0.07793071866035461,
-0.04302113875746727,
-0.00003766831287066452,
-0.012190067209303379,
-0.012408538721501827,
-0.0006330182659439743,
0.008219565264880657,
-0.03282004967331886,
0.01617563702166080... |
91689fdb-6277-4e59-9429-90d24d6187ae | 1 row in set. Elapsed: 0.002 sec.
SELECT tags['hobby'] AS hobby
FROM people
FORMAT JSONEachRow
{"hobby":{"name":"Diving","time":"2024-07-11 14:18:01"}}
1 row in set. Elapsed: 0.001 sec.
```
The application of maps in this case is typically rare, and suggests that the data should be remodelled such that dynamic ... | {"source_file": "other.md"} | [
0.031156962737441063,
0.04628699645400047,
0.06577848643064499,
-0.01720704510807991,
-0.07055141031742096,
-0.0424828827381134,
0.044586144387722015,
-0.0024800861719995737,
-0.03777255862951279,
-0.053712181746959686,
0.006631036289036274,
-0.04880644381046295,
-0.004567200317978859,
0.0... |
d1f8823f-be1f-4c2b-af52-9b20d8c052b6 | Below, we insert into this table:
sql
SET input_format_import_nested_json = 1;
INSERT INTO http
FORMAT JSONEachRow
{"timestamp":897819077,"clientip":"45.212.12.0","request":[{"method":"GET","path":"/french/images/hm_nav_bar.gif","version":"HTTP/1.0"}],"status":200,"size":3305}
A few important points to note here:
... | {"source_file": "other.md"} | [
-0.012324872426688671,
-0.03755522146821022,
0.02287856675684452,
0.010361429303884506,
-0.07759397476911545,
-0.011741165071725845,
-0.08439528942108154,
0.05229804664850235,
-0.007719745859503746,
0.013491732068359852,
-0.0041188704781234264,
-0.05553145334124565,
0.04362078011035919,
0.... |
31d4ef07-dc29-4f86-a8e1-5799fffe2d0d | A few important points to note here:
input_format_import_nested_json
is not required to insert.
The
Nested
type is preserved in
SHOW CREATE TABLE
. Underneath this column is effectively a
Array(Tuple(Nested(method LowCardinality(String), path String, version LowCardinality(String))))
As a result, we are re... | {"source_file": "other.md"} | [
-0.001065035699866712,
0.022651955485343933,
0.00551413930952549,
0.01631724089384079,
-0.042948611080646515,
-0.07619084417819977,
-0.0225914865732193,
0.07203181087970734,
-0.005880887620151043,
0.016004370525479317,
0.03687717765569687,
-0.014949080534279346,
0.07311498373746872,
-0.020... |
26c4be02-3aa2-4ad0-a6c8-462c8d501ed1 | 5 rows in set. Elapsed: 0.007 sec.
```
Using pairwise arrays {#using-pairwise-arrays}
Pairwise arrays provide a balance between the flexibility of representing JSON as Strings and the performance of a more structured approach. The schema is flexible in that any new fields can be potentially added to the root. This,... | {"source_file": "other.md"} | [
-0.0014258821029216051,
0.024060331284999847,
-0.008596408180892467,
-0.009086765348911285,
-0.10060671716928482,
-0.048139866441488266,
-0.038668591529130936,
-0.008116067387163639,
-0.01695708930492401,
0.004301518201828003,
0.022374166175723076,
-0.013194173574447632,
0.0595252625644207,
... |
448ec119-89ff-4b91-8604-5a33abeed894 | ββstatusββ¬βmethodββ¬βββββcββ
β 404 β GET β 11267 β
β 404 β HEAD β 276 β
β 500 β GET β 160 β
β 500 β POST β 115 β
β 400 β GET β 81 β
ββββββββββ΄βββββββββ΄ββββββββ
5 rows in set. Elapsed: 0.383 sec. Processed 8.22 million rows, 1.97 GB (21.45 million rows/s., 5.15 GB/s.)
Peak memory us... | {"source_file": "other.md"} | [
0.043317992240190506,
-0.012421490624547005,
-0.11410670727491379,
0.0009657992632128298,
-0.07484175264835358,
-0.03955531865358353,
0.03879597783088684,
0.03244591876864433,
-0.04299302026629448,
0.0788511410355568,
0.004331678617745638,
0.08120587468147278,
0.03250782564282417,
-0.08827... |
bc920315-c4eb-450c-8a70-d404e47b3f7e | slug: /integrations/postgresql/inserting-data
title: 'How to insert data from PostgreSQL'
keywords: ['postgres', 'postgresql', 'inserts']
description: 'Page describing how to insert data from PostgresSQL using ClickPipes, PeerDB or the Postgres table function'
doc_type: 'guide'
We recommend reading
this guide
to ... | {"source_file": "inserting-data.md"} | [
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0.058818377554416656,
0.014004901982843876,
0.048781901597976685,
0.06603655219078064,
-0.09... |
d21ec9cd-b7c3-4d3a-b456-f5d5cde82742 | slug: /integrations/postgresql/connecting-to-postgresql
title: 'Connecting to PostgreSQL'
keywords: ['clickhouse', 'postgres', 'postgresql', 'connect', 'integrate', 'table', 'engine']
description: 'Page describing the various ways to connect PostgreSQL to ClickHouse'
show_related_blogs: true
doc_type: 'guide'
impor... | {"source_file": "connecting-to-postgresql.md"} | [
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0.0027366639114916325,
-0.014913110993802547,
0.018845826387405396,
0.021234726533293724,
-0... |
23c20801-1702-4381-b386-d6d33121442c | Verify the new
clickhouse_user
can login:
text
psql -U clickhouse_user -W -d db_in_psg -h <your_postgresql_host>
:::note
If you are using this feature in ClickHouse Cloud, you may need the to allow the ClickHouse Cloud IP addresses to access your PostgreSQL instance.
Check the ClickHouse
Cloud Endpoints A... | {"source_file": "connecting-to-postgresql.md"} | [
0.07687754929065704,
-0.07639459520578384,
-0.09398791939020157,
-0.006137704476714134,
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0.011761041358113289,
0.07303871959447861,
-0.013645983301103115,
-0.009564541280269623,
-0.002462618751451373,
-0.02758750505745411,
-0.03680439293384552,
0.008824009448289871,
-0.... |
2851ed2d-e187-48ba-8da0-d81abc61c0b9 | This example demonstrated the basic integration between PostgreSQL and ClickHouse using the
PostrgeSQL
table engine.
Check out the
doc page for the PostgreSQL table engine
for more features, such as specifying schemas, returning only a subset of columns, and connecting to multiple replicas. Also check out the
Clic... | {"source_file": "connecting-to-postgresql.md"} | [
-0.005105977412313223,
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-0.1249857172369957,
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0.004032985772937536,
-0.004318759776651859,
0.00038703373866155744,
0.06254667788743973,
-... |
81490878-0921-4a8b-aa2f-8f27c8a8da0e | sql
SET allow_experimental_database_materialized_postgresql=1
Create the new database to be replicated and define the initial table:
sql
CREATE DATABASE db1_postgres
ENGINE = MaterializedPostgreSQL('postgres-host.domain.com:5432', 'db1', 'clickhouse_user', 'ClickHouse_123')
SETTINGS materialized_postgresql_table... | {"source_file": "connecting-to-postgresql.md"} | [
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9352139b-e3cb-4319-bb81-0ccc4962732a | sidebar_label: 'DynamoDB'
sidebar_position: 10
slug: /integrations/dynamodb
description: 'ClickPipes allows you to connect ClickHouse to DynamoDB.'
keywords: ['DynamoDB']
title: 'CDC from DynamoDB to ClickHouse'
show_related_blogs: true
doc_type: 'guide'
import CloudNotSupportedBadge from '@theme/badges/CloudNotSup... | {"source_file": "index.md"} | [
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46e011b6-dff0-4a0b-8665-2c418424daff | For the example DynamoDB data above, the ClickHouse tables would look like this:
``sql
/* Snapshot table */
CREATE TABLE IF NOT EXISTS "default"."snapshot"
(
item` String
)
ORDER BY tuple();
/
Table for final flattened data
/
CREATE MATERIALIZED VIEW IF NOT EXISTS "default"."snapshot_mv" TO "default"."destination... | {"source_file": "index.md"} | [
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608591d9-348d-41cb-b564-377193dbd806 | sidebar_label: 'Kafka Connector Sink on Confluent Cloud'
sidebar_position: 2
slug: /integrations/kafka/cloud/confluent/sink-connector
description: 'Guide to using the fully managed ClickHouse Connector Sinkon Confluent Cloud'
title: 'Integrating Confluent Cloud with ClickHouse'
keywords: ['Kafka', 'Confluent Cloud']
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2e958634-364b-4616-939a-5931db02a2ac | sidebar_label: 'HTTP Sink Connector for Confluent Platform'
sidebar_position: 4
slug: /integrations/kafka/cloud/confluent/http
description: 'Using HTTP Connector Sink with Kafka Connect and ClickHouse'
title: 'Confluent HTTP Sink Connector'
doc_type: 'guide'
keywords: ['Confluent HTTP Sink Connector', 'HTTP Sink ClickH... | {"source_file": "kafka-connect-http.md"} | [
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9db6a360-b6eb-447d-841d-b4ac1009b64d | 4. Configure HTTP Sink {#4-configure-http-sink}
Create a Kafka topic and an instance of HTTP Sink Connector:
Configure HTTP Sink Connector:
* Provide the topic name you created
* Authentication
*
HTTP Url
- ClickHouse Cloud URL with a
INSERT
query specified
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fc347a1c-ba53-4472-a861-906a4bc52de0 | 1. Prepare configuration {#1-prepare-configuration}
Follow
these instructions
for setting up Connect relevant to your installation type, noting the differences between a standalone and distributed cluster. If using Confluent Cloud, the distributed setup is relevant.
The most important parameter is the
http.api.u... | {"source_file": "kafka-connect-http.md"} | [
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507f97be-8596-4aa0-b157-d1cb60f9b097 | 2. Create the ClickHouse table {#2-create-the-clickhouse-table}
Ensure the table has been created. An example for a minimal github dataset using a standard MergeTree is shown below.
```sql
CREATE TABLE github
(
file_time DateTime,
event_type Enum('CommitCommentEvent' = 1, 'CreateEvent' = 2, 'DeleteEvent' = ... | {"source_file": "kafka-connect-http.md"} | [
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43783fc5-0109-47f1-b701-3f08fe57c39c | sidebar_label: 'Confluent Platform'
sidebar_position: 1
slug: /integrations/kafka/cloud/confluent
description: 'Kafka Connectivity with Confluent Cloud'
title: 'Integrating Confluent Cloud with ClickHouse'
doc_type: 'guide'
keywords: ['Confluent Cloud ClickHouse', 'Confluent ClickHouse integration', 'Kafka ClickHouse c... | {"source_file": "index.md"} | [
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0ecaf624-45a2-4f4f-b82f-f5cce7c1ac12 | sidebar_label: 'Kafka Connector Sink on Confluent Platform'
sidebar_position: 3
slug: /integrations/kafka/cloud/confluent/custom-connector
description: 'Using ClickHouse Connector Sink with Kafka Connect and ClickHouse'
title: 'Integrating Confluent Cloud with ClickHouse'
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c0bdb3b7-be6d-4bde-b117-a0c2b46cc8f3 | Specify the connection endpoints {#specify-the-connection-endpoints}
You need to specify the allow-list of endpoints that the connector can access.
You must use a fully-qualified domain name (FQDN) when adding the networking egress endpoint(s).
Example:
u57swl97we.eu-west-1.aws.clickhouse.com:8443
:::note
You must... | {"source_file": "custom-connector.md"} | [
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35c59321-13f0-4dbe-822d-b860d9d5b1c8 | sidebar_label: 'Amazon MSK with Kafka Connector Sink'
sidebar_position: 1
slug: /integrations/kafka/cloud/amazon-msk/
description: 'The official Kafka connector from ClickHouse with Amazon MSK'
keywords: ['integration', 'kafka', 'amazon msk', 'sink', 'connector']
title: 'Integrating Amazon MSK with ClickHouse'
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6e392f73-b35a-4fdb-a4db-4a1e22fc607d | Use the smallest set of permissions required for your setup. Start with the baseline below and add optional services only if you use them.
json
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "MSKClusterAccess",
"Effect": "Allow",
"Action": [
"kafka:DescribeCluster",
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4cf813fb-fbbc-488b-94a1-af2f72132183 | Create a Private Subnet:
Create a new subnet within your VPC, designating it as a private subnet. This subnet should not have direct access to the internet.
Create a NAT Gateway:
Create a NAT gateway in a public subnet of your VPC. The NAT gateway enables instances in your private subnet to connect to the internet ... | {"source_file": "index.md"} | [
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6e4f48a2-1e41-4f4e-839a-0f8a2fb66313 | sidebar_label: 'BigQuery To ClickHouse'
sidebar_position: 1
slug: /integrations/google-dataflow/templates/bigquery-to-clickhouse
description: 'Users can ingest data from BigQuery into ClickHouse using Google Dataflow Template'
title: 'Dataflow BigQuery to ClickHouse template'
doc_type: 'guide'
keywords: ['Dataflow', 'B... | {"source_file": "bigquery-to-clickhouse.md"} | [
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f1f2ae9e-11b3-4e75-87de-af8ee9f48672 | | Parameter Name | Parameter Description ... | {"source_file": "bigquery-to-clickhouse.md"} | [
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0.056630879640579224,
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0.02608567103743553,
-0.... |
1c543a02-74ef-4f65-9589-e4903b6e60cc | |
clickHousePassword
| The ClickHouse password to authenticate with. ... | {"source_file": "bigquery-to-clickhouse.md"} | [
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0.1291600465774536,
-0.10215... |
389e556f-0257-4acc-8590-3efef2ab3c40 | ClickHouseIO
option. This setting is disabled in default server settings. |
|
insertDeduplicate
| For INSERT queries in the replicated... | {"source_file": "bigquery-to-clickhouse.md"} | [
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0.08332082629203796,
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4bb2f6dc-2032-4893-baa5-69234e1c7cab | <PROJECT_ID>.<DATASET_NAME>.<TABLE_NAME>
. Defaults to
GoogleSQL
unless
useLegacySql
is true. | | You must specify either
inputTableSpec
or
query
. If you set both parameters, the template uses the
query
parameter. Example:
SELECT * FROM sampledb.sample_table
. ... | {"source_file": "bigquery-to-clickhouse.md"} | [
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eea61d4b-1dd0-4101-ae85-274d7f5d96cf | :::note
Default values for all
ClickHouseIO
parameters can be found in
ClickHouseIO
Apache Beam Connector
:::
Source and target tables schema {#source-and-target-tables-schema}
To effectively load the BigQuery dataset into ClickHouse, the pipeline performs a column inference process with the following phases:
... | {"source_file": "bigquery-to-clickhouse.md"} | [
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d6d9d94f-47c4-4b96-a083-3e487206b7e4 | | BigQuery Type | ClickHouse Type | Notes ... | {"source_file": "bigquery-to-clickhouse.md"} | [
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-0... |
392feb33-4f0b-4775-b726-8bb39eaa8250 | |
Datetime Type
|
Datetime Type
| Works as well with
Enum8
,
Enum16
and
FixedString
. ... | {"source_file": "bigquery-to-clickhouse.md"} | [
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dcb8c4e3-71c8-404c-ac2a-f04b0d4f6d28 | Running the Template {#running-the-template}
The BigQuery to ClickHouse template is available for execution via the Google Cloud CLI.
:::note
Be sure to review this document, and specifically the above sections, to fully understand the template's configuration
requirements and prerequisites.
:::
Sign in ... | {"source_file": "bigquery-to-clickhouse.md"} | [
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caae3bc9-2be8-4d60-8536-7fcc64c5221d | Monitor the job {#monitor-the-job}
Navigate to the
Dataflow Jobs tab
in your Google Cloud Console to
monitor the status of the job. You'll find the job details, including progress and any errors:
Troubleshooting {#troubleshooting}
Memory limit (total) exceeded error (code 241) {#code-241-dbexception-memory-li... | {"source_file": "bigquery-to-clickhouse.md"} | [
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08460e15-7bdf-4715-be0d-715e50ea9c11 | slug: /integrations/iceberg
sidebar_label: 'Iceberg'
title: 'Iceberg'
description: 'Page describing the IcebergFunction which can be used to integrate ClickHouse with the Iceberg table format'
doc_type: 'guide'
keywords: ['iceberg table function', 'apache iceberg', 'data lake format']
hide_title: true
import Iceber... | {"source_file": "iceberg.md"} | [
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01c69cff-36d4-4822-9f4f-c7e031ebea27 | slug: /integrations/rabbitmq
sidebar_label: 'RabbitMQ'
title: 'RabbitMQ'
hide_title: true
description: 'Page describing the RabbitMQEngine integration'
doc_type: 'reference'
keywords: ['rabbitmq', 'message queue', 'streaming', 'integration', 'data ingestion']
import RabbitMQEngine from '@site/docs/engines/table-eng... | {"source_file": "rabbitmq.md"} | [
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16af168e-4a45-4cf4-bc0d-efc42b0c634e | slug: /integrations/rocksdb
sidebar_label: 'RocksDB'
title: 'RocksDB'
hide_title: true
description: 'Page describing the RocksDBTableEngine'
doc_type: 'reference'
keywords: ['rocksdb', 'embedded database', 'integration', 'storage engine', 'key-value store']
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ae0b381f-b189-4b43-a99d-6edb0e9d8c67 | slug: /integrations/hive
sidebar_label: 'Hive'
title: 'Hive'
hide_title: true
description: 'Page describing the Hive table engine'
doc_type: 'reference'
keywords: ['hive', 'table engine', 'integration']
import HiveTableEngine from '@site/docs/engines/table-engines/integrations/hive.md'; | {"source_file": "hive.md"} | [
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0.025828827172517776,
0.04429808631539345,
0.01500678900629282,
-0.09404391050338745,
0.01748719811439514,
0.011655713431537151,
-0.0481138713657856,
0.04529297351837158,
-0.080966219... |
de87f148-8b5c-4f95-a276-3b73e8706772 | slug: /integrations/hudi
sidebar_label: 'Hudi'
title: 'Hudi'
hide_title: true
description: 'Page describing the Hudi table engine'
doc_type: 'reference'
keywords: ['hudi table engine', 'apache hudi', 'data lake integration']
import HudiTableEngine from '@site/docs/engines/table-engines/integrations/hudi.md'; | {"source_file": "hudi.md"} | [
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-0.003078034147620201,
0.06698216497898102,
-0.1297... |
2628fe1c-20e6-4144-ad86-36c1b8093f35 | slug: /integrations/redis
sidebar_label: 'Redis'
title: 'Redis'
description: 'Page describing the Redis table function'
doc_type: 'reference'
hide_title: true
keywords: ['redis', 'cache', 'integration', 'data source', 'key-value store']
import RedisFunction from '@site/docs/sql-reference/table-functions/redis.md'; | {"source_file": "redis.md"} | [
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0.027881737798452377,
-0.009260110557079315,
0.10815896838903427,
-0.11... |
e97e580b-8963-4f69-bbab-b536541f80e1 | slug: /integrations/deltalake
sidebar_label: 'Delta Lake'
hide_title: true
title: 'Delta Lake'
description: 'Page describing how users can integrate with the Delta lake table format via the table function.'
doc_type: 'reference'
keywords: ['delta lake', 'table function', 'data lake format']
import DeltaLakeFunction... | {"source_file": "deltalake.md"} | [
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0.06438102573156357,
-0.0968... |
54a49d65-f9b8-45ba-abf4-cb9cb4edc9fe | slug: /integrations/nats
sidebar_label: 'NATS'
title: 'NATS'
hide_title: true
description: 'Page describing integration with the NATS engine'
doc_type: 'reference'
keywords: ['nats', 'message queue', 'streaming', 'integration', 'data ingestion']
import NatsEngine from '@site/docs/engines/table-engines/integrations/... | {"source_file": "nats.md"} | [
-0.006174854002892971,
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0.013708069920539856,
-... |
8185873a-dbf1-4e86-8bdb-6f9f7beec51b | slug: /integrations/sqlite
sidebar_label: 'SQLite'
title: 'SQLite'
hide_title: true
description: 'Page describing integration using the SQLite engine'
doc_type: 'reference'
keywords: ['sqlite', 'embedded database', 'integration', 'data source', 'file database']
import SQLiteEngine from '@site/docs/engines/table-eng... | {"source_file": "sqlite.md"} | [
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0.06396176666021347,
-0.09154... |
95ce3920-4448-470c-8be1-e5f94e721736 | slug: /integrations/mongodb
sidebar_label: 'MongoDB'
title: 'MongoDB'
hide_title: true
description: 'Page describing integration using the MongoDB engine'
doc_type: 'reference'
keywords: ['mongodb', 'nosql', 'integration', 'data source', 'document database']
import MongoDBEngine from '@site/docs/engines/table-engin... | {"source_file": "mongodb.md"} | [
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-... |
02ec96c3-7949-425f-8a88-93207cba8057 | description: 'Documentation for Distributed Ddl'
sidebar_label: 'Distributed DDL'
slug: /sql-reference/other/distributed-ddl
title: 'Page for Distributed DDL'
doc_type: 'reference'
import Content from '@site/docs/sql-reference/distributed-ddl.md'; | {"source_file": "distributed-ddl.md"} | [
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3a351343-208b-4a03-932c-b3c206818580 | description: 'Documentation for Operators'
displayed_sidebar: 'sqlreference'
sidebar_label: 'Operators'
sidebar_position: 38
slug: /sql-reference/operators/
title: 'Operators'
doc_type: 'reference'
Operators
ClickHouse transforms operators to their corresponding functions at the query parsing stage according to t... | {"source_file": "index.md"} | [
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0be69187-74ec-4401-addc-56f402d60129 | in subquery function {#in-subquery-function}
a = ANY (subquery)
β The
in(a, subquery)
function.
notIn subquery function {#notin-subquery-function}
a != ANY (subquery)
β The same as
a NOT IN (SELECT singleValueOrNull(*) FROM subquery)
.
in subquery function {#in-subquery-function-1}
a = ALL (subquery)
... | {"source_file": "index.md"} | [
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0.02893036976456642,
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0.048040151596069336,
-0.034040819853544235,
-0.008714492432773113,
-0.011815782636404037,
0.05052840709686279,
... |
aba27e3d-0d73-4cb2-95bf-45e1e6bc9ddb | Types of intervals:
-
SECOND
-
MINUTE
-
HOUR
-
DAY
-
WEEK
-
MONTH
-
QUARTER
-
YEAR
You can also use a string literal when setting the
INTERVAL
value. For example,
INTERVAL 1 HOUR
is identical to the
INTERVAL '1 hour'
or
INTERVAL '1' hour
.
:::tip
Intervals with different types can't be comb... | {"source_file": "index.md"} | [
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0.03961433842778206,
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0.06559517234563828,
-0.12867805361747742,
0.015110673382878304,
0.03440... |
d7427f0e-43f8-4db6-8529-e1fdde2c877f | Syntax
SELECT NOT a
β calculates logical negation of
a
with the function
not
.
Conditional Operator {#conditional-operator}
a ? b : c
β The
if(a, b, c)
function.
Note:
The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding va... | {"source_file": "index.md"} | [
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0.00979702640324831,
-0.06810057908296585,
0.018264371901750565,
... |
6f37db2e-1113-4d5b-b502-b08516541ca0 | 1
otherwise.
For other values, the
IS NOT NULL
operator always returns
1
.
sql
SELECT * FROM t_null WHERE y IS NOT NULL
text
ββxββ¬βyββ
β 2 β 3 β
βββββ΄ββββ
Can be optimized by enabling the
optimize_functions_to_subcolumns
setting. With
optimize_functions_to_subcolumns = 1
the function reads only
nul... | {"source_file": "index.md"} | [
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-0.04268506169319153,
-0.03849509730935097,
-0.09... |
18f31fcd-1ae8-4e61-a86b-aa99769d602c | description: 'Documentation for the
EXISTS
operator'
slug: /sql-reference/operators/exists
title: 'EXISTS'
doc_type: 'reference'
EXISTS
The
EXISTS
operator checks how many records are in the result of a subquery. If it is empty, then the operator returns
0
. Otherwise, it returns
1
.
EXISTS
can also be u... | {"source_file": "exists.md"} | [
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-0.008829118683934212,
0.07019057124853134,
0.0728236511349678,
-0.050152137875556946,
0.005548699758946896,
-0.014412342570722103,
0.08251965045928955,
-0.053... |
2f18b387-5b17-48cd-aaa3-d66ab88a5664 | description: 'Documentation for the IN operators excluding NOT IN, GLOBAL IN and GLOBAL
NOT IN operators which are covered separately'
slug: /sql-reference/operators/in
title: 'IN Operators'
doc_type: 'reference'
IN Operators
The
IN
,
NOT IN
,
GLOBAL IN
, and
GLOBAL NOT IN
operators are covered separately,... | {"source_file": "in.md"} | [
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0.05823631212115288,
-0.099... |
ad586dcd-3282-480e-b15d-6105d4fa6986 | text
βββEventDateββ¬ββββratioββ
β 2014-03-17 β 1 β
β 2014-03-18 β 0.807696 β
β 2014-03-19 β 0.755406 β
β 2014-03-20 β 0.723218 β
β 2014-03-21 β 0.697021 β
β 2014-03-22 β 0.647851 β
β 2014-03-23 β 0.648416 β
ββββββββββββββ΄βββββββββββ
For each day after March 17th, count the percentage of pageviews made by users ... | {"source_file": "in.md"} | [
-0.003640518058091402,
0.011674460023641586,
0.0038307898212224245,
0.030754774808883667,
-0.03307932987809181,
-0.050529785454273224,
0.040333084762096405,
-0.022924423217773438,
0.03784202039241791,
0.03858933225274086,
0.07584082335233688,
-0.05296790599822998,
0.05451571196317673,
-0.0... |
0bd131ba-28fc-4245-9290-74965b6af3ea | For example, the query
sql
SELECT uniq(UserID) FROM distributed_table
will be sent to all remote servers as
sql
SELECT uniq(UserID) FROM local_table
and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the r... | {"source_file": "in.md"} | [
0.05435187369585037,
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-0.013578017242252827,
0.051884107291698456,
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-0.05145698040723801,
0.04002004861831665,
-0.02306593582034111,
0.0059105330146849155,
0.013658232986927032,
0.027088966220617294,
-0.015575369819998741,
0.13538411259651184,
-0.... |
5e8b1fad-06d9-4f08-917e-926f900bc4b6 | sql
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1
The temporary table
_data1
will be sent to every remote server with the query (the name of the temporary table is implementation-defined).
This is more optimal than using the normal
IN
. However, keep the following poin... | {"source_file": "in.md"} | [
0.004787529353052378,
0.002912803553044796,
0.001726561225950718,
0.04834182560443878,
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-0.06283623725175858,
0.11026263236999512,
-0.02018926478922367,
0.016551584005355835,
0.04535481706261635,
0.0036621547769755125,
0.0042676967568695545,
0.13677483797073364,
0.002116... |
a29ec6d9-4cd7-4620-95dd-f6262799e0b3 | sql
SELECT CounterID, count() FROM local_table_1 WHERE UserID IN (SELECT UserID FROM local_table_2 WHERE CounterID < 100)
SETTINGS parallel_replicas_count=3, parallel_replicas_offset=M
where
M
is between
1
and
3
depending on which replica the local query is executing on.
These settings affect every MergeTree-... | {"source_file": "in.md"} | [
0.01995290443301201,
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0.009879800491034985,
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-0.024651335552334785,
0.03494943678379059,
-0.007884562015533447,
-0.023685583844780922,
0.06862019002437592,
-0.09575170278549194,
0.08638066798448563,
-0.062... |
007156c8-ab5c-467b-847a-7f6c78a90e94 | description: 'Documentation for the SimpleAggregateFunction data type'
sidebar_label: 'SimpleAggregateFunction'
sidebar_position: 48
slug: /sql-reference/data-types/simpleaggregatefunction
title: 'SimpleAggregateFunction Type'
doc_type: 'reference'
SimpleAggregateFunction Type
Description {#description}
The
Si... | {"source_file": "simpleaggregatefunction.md"} | [
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0.006363412830978632,
0.040454667061567307,
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0.04391179978847504,
-0.045842159539461136,
0.08324924856424332,
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0.01635119877755642,
-0.06840620189905167,
0.0025576718617230654,
0.024017926305532455,
-0.06... |
64262694-d99e-4b65-ba6e-0901862f1437 | description: 'Documentation for the Dynamic data type in ClickHouse, which can store
values of different types in a single column'
sidebar_label: 'Dynamic'
sidebar_position: 62
slug: /sql-reference/data-types/dynamic
title: 'Dynamic'
doc_type: 'guide'
Dynamic
This type allows to store values of any type inside ... | {"source_file": "dynamic.md"} | [
0.0435580350458622,
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0.08042719960212708,
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0.031021228060126305,
0.05781722813844681,
-0.08085031807422638,
-0.005425360053777695,
-0.0010302691953256726,
-0.014304311014711857,
0.04107757285237312,
-0.0... |
1b106b07-e123-48fd-8b35-cc7603fc1f40 | text
ββdββββββββββββββ¬βdynamicType(d)ββ¬βd.Stringβββββββ¬βd.Int64ββ¬βd.Array(Int64)ββ¬βd.Dateββ¬βd.Array(String)ββ
β α΄Ία΅α΄Έα΄Έ β None β α΄Ία΅α΄Έα΄Έ β α΄Ία΅α΄Έα΄Έ β [] β α΄Ία΅α΄Έα΄Έ β [] β
β 42 β Int64 β α΄Ία΅α΄Έα΄Έ β 42 β [] β α΄Ία΅α΄Έα΄Έ β [] β
... | {"source_file": "dynamic.md"} | [
0.07505001872777939,
0.006100786849856377,
-0.01533934473991394,
0.017759526148438454,
-0.07100512087345123,
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0.04643958434462547,
-0.03749464824795723,
-0.12214601784944534,
0.02616560272872448,
0.0274523738771677,
0.01791164092719555,
-0.003750226926058531,
-0.06561... |
9b5a50d7-82cf-4b36-8225-5fdc7dead29d | Example:
sql
CREATE TABLE test (d Dynamic) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), ('Hello, World!'), ([1, 2, 3]);
SELECT dynamicType(d) FROM test;
text
ββdynamicType(d)ββ
β None β
β Int64 β
β String β
β Array(Int64) β
ββββββββββββββββββ
Conversion between Dynamic colum... | {"source_file": "dynamic.md"} | [
0.07822507619857788,
-0.011515163816511631,
-0.037735603749752045,
0.053979307413101196,
-0.0985516682267189,
-0.051050182431936264,
0.030984142795205116,
0.009462593123316765,
-0.08462804555892944,
0.0363922193646431,
-0.02354247309267521,
-0.05524919554591179,
-0.002007154282182455,
-0.0... |
208b4da7-cb72-4c17-805c-d2f5f6f299fb | If
K >= N
than during conversion the data doesn't change:
sql
CREATE TABLE test (d Dynamic(max_types=3)) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), (43), ('42.42'), (true);
SELECT d::Dynamic(max_types=5) as d2, dynamicType(d2) FROM test;
text
ββdββββββ¬βdynamicType(d)ββ
β α΄Ία΅α΄Έα΄Έ β None β
β 42 ... | {"source_file": "dynamic.md"} | [
0.08908717334270477,
-0.00027181769837625325,
-0.002194165950641036,
0.024366168305277824,
-0.09008213877677917,
-0.023511625826358795,
0.022196266800165176,
-0.0015639194753021002,
-0.02790176123380661,
0.017809154465794563,
-0.01756814867258072,
-0.03753168508410454,
0.004683228209614754,
... |
82590685-15ca-406f-b937-7b981a159556 | Reading Dynamic type from the data {#reading-dynamic-type-from-the-data}
All text formats (TSV, CSV, CustomSeparated, Values, JSONEachRow, etc) supports reading
Dynamic
type. During data parsing ClickHouse tries to infer the type of each value and use it during insertion to
Dynamic
column.
Example:
sql
SELEC... | {"source_file": "dynamic.md"} | [
0.01882038451731205,
-0.0011999548878520727,
0.018697872757911682,
0.09511303901672363,
-0.09474863857030869,
-0.03486000373959541,
0.0063946968875825405,
-0.03393508866429329,
-0.02620713599026203,
-0.03230488672852516,
-0.016913242638111115,
-0.015098861418664455,
-0.045015670359134674,
... |
db30b126-92ad-4679-bc32-6ecb393ac513 | sql
SELECT d, d + d AS res, toTypeName(res), dynamicType(res) FROM test;
text
ββdβββββ¬βresβββ¬βtoTypeName(res)ββ¬βdynamicType(res)ββ
β α΄Ία΅α΄Έα΄Έ β α΄Ία΅α΄Έα΄Έ β Dynamic β None β
β 1 β 2 β Dynamic β Int16 β
β 2 β 4 β Dynamic β Int32 β
β 3 β 6 β Dynamic ... | {"source_file": "dynamic.md"} | [
-0.003316369839012623,
-0.05844894424080849,
-0.010939649306237698,
0.0607050396502018,
-0.06400511413812637,
-0.05306699872016907,
0.07135875523090363,
0.009347265586256981,
-0.047571856528520584,
0.06995537877082825,
0.039650049060583115,
-0.05907568335533142,
-0.005221168044954538,
-0.0... |
dbd8ce3a-2cdb-4dbb-8292-5f57e698d593 | sql
SELECT d, d + 1 AS res, toTypeName(res), dynamicType(d) FROM test;
text
Received exception:
Code: 43. DB::Exception: Illegal types Array(Int64) and UInt8 of arguments of function plus: while executing 'FUNCTION plus(__table1.d : 3, 1_UInt8 :: 1) -> plus(__table1.d, 1_UInt8) Dynamic : 0'. (ILLEGAL_TYPE_OF_ARGUMENT... | {"source_file": "dynamic.md"} | [
0.03876044601202011,
0.008728819899260998,
0.020291578024625778,
0.06588193774223328,
-0.040093354880809784,
-0.05188985541462898,
0.06790141016244888,
0.016343144699931145,
-0.07550372183322906,
0.007604208309203386,
-0.022109106183052063,
-0.06599882245063782,
-0.048927776515483856,
-0.0... |
f8a650f6-7447-4cd7-b42b-31611c03e4b5 | Note:
values of dynamic types with different numeric types are considered as different values and not compared between each other, their type names are compared instead.
Example:
sql
CREATE TABLE test (d Dynamic) ENGINE=Memory;
INSERT INTO test VALUES (1::UInt32), (1::Int64), (100::UInt32), (100::Int64);
SELECT d,... | {"source_file": "dynamic.md"} | [
0.0029812154825776815,
-0.002200106391683221,
0.06802601367235184,
0.06588529050350189,
-0.0598834864795208,
-0.0788540467619896,
-0.014853822998702526,
0.029921632260084152,
0.014447794295847416,
-0.0195450522005558,
-0.001840396085754037,
-0.008467670530080795,
-0.0177475493401289,
-0.02... |
ee1f0eea-0abe-4433-92d0-6f0051da0623 | text
ββdβββββββββββββββββββββββ¬βdynamicType(d)ββββββββββββββββββ¬βisDynamicElementInSharedData(d)ββ
β 42 β Int64 β false β
β [1,2,3] β Array(Int64) β false β
β Hello, World! β String... | {"source_file": "dynamic.md"} | [
0.03747449815273285,
-0.02971179410815239,
0.021137433126568794,
0.03667015954852104,
-0.03246653825044632,
-0.06354495137929916,
-0.02986995317041874,
-0.04601125791668892,
-0.010692193172872066,
-0.006933653727173805,
0.03868791460990906,
0.00015116231224965304,
-0.023475628346204758,
-0... |
0e062a76-f58e-4946-90bc-0d056908d794 | sql
SELECT count(), dynamicType(d), isDynamicElementInSharedData(d), _part FROM test GROUP BY _part, dynamicType(d), isDynamicElementInSharedData(d) ORDER BY _part, count();
text
ββcount()ββ¬βdynamicType(d)βββββββ¬βisDynamicElementInSharedData(d)ββ¬β_partββββββ
β 5 β UInt64 β false ... | {"source_file": "dynamic.md"} | [
0.04338204115629196,
0.0063798618502914906,
0.07275503128767014,
0.05678944289684296,
-0.087944395840168,
-0.0032935598865151405,
0.03929285705089569,
0.016202162951231003,
0.011248844675719738,
0.0148054463788867,
0.0876137763261795,
0.00037556077586486936,
-0.010629801079630852,
-0.03363... |
bd9e1327-19bb-4512-86eb-e28bc399a72b | sql
SELECT JSONExtractKeysAndValues('{"a" : 42, "b" : "Hello", "c" : [1,2,3]}', 'Dynamic') AS dynamics, arrayMap(x -> (x.1, dynamicType(x.2)), dynamics) AS dynamic_types
```
text
ββdynamicsββββββββββββββββββββββββββββββββ¬βdynamic_typesββββββββββββββββββββββββββββββββββββββββββββββββββ
β [('a',42),('b','Hello'),('c',... | {"source_file": "dynamic.md"} | [
0.05702364444732666,
0.06571618467569351,
-0.0209187101572752,
0.00882851704955101,
-0.11618398129940033,
0.016292927786707878,
0.07413999736309052,
0.007381593808531761,
-0.04778410121798515,
-0.020384088158607483,
-0.04022445157170296,
-0.05173267051577568,
0.05593451112508774,
-0.007603... |
50db9b81-3964-4a84-82b3-76550f9ec48d | description: 'Documentation for the QBit data type in ClickHouse, which allows fine-grained quantization for approximate vector search'
keywords: ['qbit', 'data type']
sidebar_label: 'QBit'
sidebar_position: 64
slug: /sql-reference/data-types/qbit
title: 'QBit Data Type'
doc_type: 'reference'
import ExperimentalBad... | {"source_file": "qbit.md"} | [
-0.056288786232471466,
0.007290210574865341,
-0.06448762863874435,
-0.02752741612493992,
-0.010538030415773392,
0.006213681772351265,
-0.004689094610512257,
-0.029282687231898308,
-0.028986116871237755,
-0.062276385724544525,
0.010028043761849403,
0.0032573374919593334,
0.09328050911426544,
... |
4c00cc1e-af9e-4f4e-b51d-8626f3453425 | description: 'Documentation for the Map data type in ClickHouse'
sidebar_label: 'Map(K, V)'
sidebar_position: 36
slug: /sql-reference/data-types/map
title: 'Map(K, V)'
doc_type: 'reference'
Map(K, V)
Data type
Map(K, V)
stores key-value pairs.
Unlike other databases, maps are not unique in ClickHouse, i.e. a ... | {"source_file": "map.md"} | [
0.10672884434461594,
0.004136678762733936,
-0.043395642191171646,
0.011613850481808186,
-0.10465145111083984,
0.0024263160303235054,
0.0912334993481636,
0.005832958500832319,
-0.11990153044462204,
-0.011790631338953972,
0.07070311158895493,
-0.023858267813920975,
0.10664112120866776,
-0.12... |
6812990e-5bc6-402d-8dd0-0871d7bb7c58 | description: 'Documentation for the Data types binary encoding specification'
sidebar_label: 'Data types binary encoding specification.'
sidebar_position: 56
slug: /sql-reference/data-types/data-types-binary-encoding
title: 'Data types binary encoding specification'
doc_type: 'reference'
Data types binary encoding ... | {"source_file": "data-types-binary-encoding.md"} | [
0.04120176285505295,
-0.04021621122956276,
-0.08182784914970398,
0.013335744850337505,
-0.060576070100069046,
-0.018204091116786003,
0.05572434514760971,
0.012305017560720444,
-0.07588864117860794,
-0.04223796725273132,
-0.02989095076918602,
-0.02461358718574047,
0.049279600381851196,
-0.0... |
7c044bde-8088-4cc8-b2e9-17d50072339e | | ClickHouse data type | Binary encoding ... | {"source_file": "data-types-binary-encoding.md"} | [
0.04247273504734039,
-0.0405721440911293,
-0.10873773694038391,
0.0028931673150509596,
-0.0802532359957695,
-0.03522744029760361,
0.014071546494960785,
-0.03010925091803074,
-0.04162786155939102,
-0.020237738266587257,
0.04363758862018585,
-0.05825803428888321,
0.026029041036963463,
-0.081... |
2bbcf5ae-d21f-4cbb-b16b-f54b0d420741 | |
UInt64
|
0x04
... | {"source_file": "data-types-binary-encoding.md"} | [
0.09153345227241516,
0.07453096657991409,
-0.11869869381189346,
-0.06438116729259491,
-0.04568563774228096,
-0.03755154833197594,
-0.033624500036239624,
0.016218561679124832,
-0.02984045445919037,
-0.034855738282203674,
0.0534302219748497,
-0.028772717341780663,
0.0013559844810515642,
-0.0... |
6b2bb743-9f3a-4f12-915a-50785f979e79 | |
Int64
|
0x0A
... | {"source_file": "data-types-binary-encoding.md"} | [
0.05247751995921135,
0.058547794818878174,
-0.1087450310587883,
-0.007742042187601328,
-0.018356507644057274,
0.010396040976047516,
-0.1024971455335617,
0.018117239698767662,
-0.020285097882151604,
-0.013664992526173592,
0.03173074871301651,
-0.0822024717926979,
-0.004807345103472471,
-0.0... |
c29c94bb-9e54-4e64-9b66-c66971f64e48 | |
Date32
|
0x10
... | {"source_file": "data-types-binary-encoding.md"} | [
0.07884380221366882,
0.07237502187490463,
-0.11875885725021362,
-0.011733208782970905,
-0.03167864680290222,
-0.00876979622989893,
-0.06406431645154953,
0.0603368915617466,
-0.031009234488010406,
-0.015827754512429237,
-0.012648875825107098,
-0.09203222393989563,
-0.06367302685976028,
-0.0... |
cb70f3a2-0c46-4cf9-ae3d-38018ce205f8 | |
FixedString(N)
|
0x16<var_uint_size>
... | {"source_file": "data-types-binary-encoding.md"} | [
0.04497351869940758,
0.049174342304468155,
-0.08826697617769241,
-0.03160256892442703,
-0.08880936354398727,
-0.08921723067760468,
-0.012991373427212238,
0.1192958727478981,
-0.06633912026882172,
-0.018886463716626167,
0.06285303831100464,
-0.058299314230680466,
0.01317746564745903,
-0.078... |
1ec6bbdc-8c78-431a-8d71-e628a22f44a7 | |
Decimal256(P, S)
|
0x1C<uint8_precision><uint8_scale>
... | {"source_file": "data-types-binary-encoding.md"} | [
0.022001873701810837,
-0.0067719751968979836,
-0.006045014131814241,
0.03875865787267685,
-0.06714107096195221,
-0.08002819865942001,
0.018072443082928658,
-0.002004760317504406,
-0.05856790393590927,
-0.04584980010986328,
-0.010076009668409824,
-0.05874273180961609,
0.06489566713571548,
-... |
ea31ea36-c94c-42d0-899a-becf1f439748 | |
Interval
|
0x22<interval_kind>
(see
interval kind binary encoding
) ... | {"source_file": "data-types-binary-encoding.md"} | [
0.042175836861133575,
-0.008272274397313595,
-0.049326248466968536,
0.00017756210581865162,
-0.08972730487585068,
-0.03194152191281319,
-0.0031871329993009567,
0.04867377132177353,
-0.03096199408173561,
-0.06445897370576859,
0.0066564348526299,
-0.09189039468765259,
0.020769597962498665,
-... |
7ba08f37-9dd4-484d-926c-a5a607126b83 | |
IPv4
|
0x28
... | {"source_file": "data-types-binary-encoding.md"} | [
0.02941211499273777,
0.028257790952920914,
-0.022181794047355652,
-0.011348444037139416,
-0.09270346909761429,
-0.024557264521718025,
0.002555343322455883,
0.07814625650644302,
-0.0246291384100914,
-0.03491951897740364,
-0.03449606895446777,
-0.02521626092493534,
-0.02608824335038662,
-0.0... |
53a2b96d-7901-4cc8-8bf3-637a953bf367 | |
SimpleAggregateFunction(function_name(param_1, ..., param_N), arg_T1, ..., arg_TN)
|
0x2E<var_uint_function_name_size><function_name_data><var_uint_number_of_parameters><param_1>...<param_N><var_uint_number_of_arguments><argument_type_encoding_1>...<argument_type_encoding_N>
(see
aggregate f... | {"source_file": "data-types-binary-encoding.md"} | [
-0.019747896119952202,
-0.01685173064470291,
-0.0209810771048069,
0.03273366391658783,
-0.08995618671178818,
-0.0688507929444313,
-0.04366292804479599,
0.09058486670255661,
-0.06611871719360352,
-0.0666639432311058,
-0.017038850113749504,
-0.04504800960421562,
0.0234683845192194,
-0.013031... |
b8469e9e-1ed7-4a84-8322-99a77f14d6f1 | |
QBit(T, N)
|
0x36<element_type_encoding><var_uint_dimension>
... | {"source_file": "data-types-binary-encoding.md"} | [
0.018257848918437958,
0.03862215578556061,
-0.08421111851930618,
-0.004210944287478924,
-0.06048589199781418,
0.055617060512304306,
-0.030094563961029053,
0.06011302024126053,
-0.01942540891468525,
-0.08077926933765411,
0.03718634322285652,
-0.07367227226495743,
0.08675854653120041,
-0.062... |
4771c713-89be-4ae1-b1a5-a96728f1b78f | For type
JSON
byte
uint8_serialization_version
indicates the version of the serialization. Right now the version is always 0 but can change in future if new arguments will be introduced for
JSON
type.
Interval kind binary encoding {#interval-kind-binary-encoding}
The table below describes how different interv... | {"source_file": "data-types-binary-encoding.md"} | [
-0.025235725566744804,
-0.003737939754500985,
-0.012113090604543686,
-0.0019544761162251234,
-0.08537056297063828,
0.001121293636970222,
-0.055989500135183334,
0.06856413185596466,
-0.02565305307507515,
-0.0650518536567688,
-0.005949808284640312,
-0.04650461673736572,
0.0065598865039646626,
... |
ae9ef3d7-c74a-48d5-ac31-5bb64d10d76f | | Parameter type | Binary encoding |
|--------------------------|--------------------------------------------------------------------------------------------------------------------------------|
|
N... | {"source_file": "data-types-binary-encoding.md"} | [
0.054553210735321045,
0.036614615470170975,
-0.14550922811031342,
-0.054860975593328476,
-0.08486264199018478,
-0.07605811953544617,
0.020238177850842476,
0.051303502172231674,
-0.07312335073947906,
-0.046395983546972275,
0.04829276353120804,
-0.10731247067451477,
0.04095959663391113,
-0.0... |
bfa3fa93-7d85-4296-b045-189195d8ff69 | 0x0F<var_uint_size><key_encoding_1><value_encoding_1>...<key_encoding_N><value_encoding_N>
|
|
IPv4
|
0x10<uint32_little_endian_value>
|
|
IPv6
|
0x11... | {"source_file": "data-types-binary-encoding.md"} | [
0.03288978710770607,
0.006844875402748585,
-0.09991280734539032,
-0.015865808352828026,
-0.07119925320148468,
-0.06875203549861908,
0.004656206350773573,
0.035457123070955276,
0.0020535150542855263,
-0.023515168577432632,
0.07595591247081757,
-0.07051365077495575,
0.008705615997314453,
-0.... |
dba1841b-f569-47c0-9910-6f09f4a00e6d | description: 'Documentation for the Variant data type in ClickHouse'
sidebar_label: 'Variant(T1, T2, ...)'
sidebar_position: 40
slug: /sql-reference/data-types/variant
title: 'Variant(T1, T2, ...)'
doc_type: 'reference'
Variant(T1, T2, ...)
This type represents a union of other data types. Type
Variant(T1, T2, .... | {"source_file": "variant.md"} | [
-0.0021922967862337828,
0.01402481272816658,
0.05711667984724045,
0.01763308234512806,
-0.033953387290239334,
0.026506051421165466,
0.008486483246088028,
0.04554613679647446,
-0.03299669921398163,
-0.019142236560583115,
0.06661340594291687,
0.008564656600356102,
0.01476444210857153,
-0.054... |
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