id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
7f668312-8a37-46a7-bae3-b6444fd1bce1 | | Setting | Description | Default value |
|------------------------... | {"source_file": "distributed.md"} | [
0.04714751988649368,
0.06623343378305435,
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0.0017757569439709187,
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0.07680146396160126,
0.03045761026442051,
0.0635383278131485,
0.032603006809949875,
-0.04860292747616768,
0.022856781259179115,
-0.07949528098106384,
-0.0143241286277771,
-0.040719... |
015b6017-cf02-4477-9c05-b265c2a16fa5 | | The same as
distributed_background_insert_sleep_time_ms
|
0
|
|
background_insert_max_sleep_time_ms
| The same as
distributed_background_insert_max_sleep_time_ms
... | {"source_file": "distributed.md"} | [
0.0561349131166935,
-0.029623430222272873,
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0.06182194873690605,
0.03472646698355675,
-0.020783698186278343,
-0.048006098717451096,
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0.004532132763415575,
0.018825536593794823,
0.08430399000644684,
0.05910854786634445,
0.09452630579471588,
-0.0877... |
013f5334-0c59-4c0d-a659-32b84a5087f7 | :::note
Durability settings
(
fsync_...
):
Affect only background
INSERT
s (i.e.
distributed_foreground_insert=false
) when data is first stored on the initiator node disk and later, in the background, when sent to shards.
May significantly decrease
INSERT
performance
Affect writing the data stored inside... | {"source_file": "distributed.md"} | [
0.0675327330827713,
-0.06821397691965103,
-0.03885531798005104,
0.0616462416946888,
0.007714917417615652,
-0.11257950216531754,
-0.06705895066261292,
0.05562878027558327,
0.024607911705970764,
0.0708441436290741,
0.031915124505758286,
0.013546423986554146,
0.09990272670984268,
-0.068149454... |
fbeeb1ad-55b1-4fb8-bf21-084374afa01a | <shard>
<!-- Optional. Shard weight when writing data. Default: 1. -->
<weight>1</weight>
<!-- Optional. The shard name. Must be non-empty and unique among shards in the cluster. If not specified, will be empty. -->
<name>shard_01</name>
<!-- Optional. Whether to write data to j... | {"source_file": "distributed.md"} | [
0.04081007093191147,
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0.0024188326206058264,
0.012595501728355885,
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-0.05432233214378357,
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0.008040365763008595,
0.022780517116189003,
0.017725206911563873,
-0.035609859973192215,
0.10954858362674713,
-0.... |
0c7d69c5-17ab-4b4f-a2ad-576d349925b0 | | Parameter | Description ... | {"source_file": "distributed.md"} | [
0.042534567415714264,
0.08093276619911194,
-0.06253547221422195,
0.007405699696391821,
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0.04604874551296234,
0.03231581673026085,
0.06422016024589539,
-0.0005844180122949183,
-0.05037815496325493,
0.039196230471134186,
-0.08023396879434586,
0.0016821924364194274,
-0.06... |
cefcaf30-64d1-440a-9454-9496d60bdf83 | true
|
|
bind_host
| The source address to use when connecting to the remote server from this node. IPv4 address only supported. Intended for advanced deployment use cases where setting the source IP address used by ClickHouse distributed queries is needed. ... | {"source_file": "distributed.md"} | [
0.015183391980826855,
-0.024640817195177078,
-0.07721133530139923,
-0.00016478955512866378,
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-0.028205212205648422,
-0.00943364854902029,
-0.074575275182724,
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0.041607923805713654,
0.007806520443409681,
0.024605272337794304,
0.0757402554154396,
-... |
901a519c-07fb-4a92-a329-526009ec6ddd | When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) β see the
load_balancing
setting. If the connection with the server is not established, there will be an attempt... | {"source_file": "distributed.md"} | [
0.034380365163087845,
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0.00045990594662725925,
0.02871391549706459,
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0.03799377381801605,
0.01696356013417244,
-0.08260311186313629,
0.03719817101955414,
0.09440824389457703,
-0.02... |
347fc2c5-e549-4c67-846c-ca6763eb46d3 | Each shard can have a
<weight>
defined in the config file. By default, the weight is
1
. Data is distributed across shards in the amount proportional to the shard weight. All shard weights are summed up, then each shard's weight is divided by the total to determine each shard's proportion. For example, if there are ... | {"source_file": "distributed.md"} | [
0.03243476152420044,
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0.049758780747652054,
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0.05651368200778961,
0.053935762494802475,
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0.004224696196615696,
0.10471944510936737,
-0.05873... |
8b1aeedf-56e2-439b-8b5a-da0eaf89dab7 | You should be concerned about the sharding scheme in the following cases:
Queries are used that require joining data (
IN
or
JOIN
) by a specific key. If data is sharded by this key, you can use local
IN
or
JOIN
instead of
GLOBAL IN
or
GLOBAL JOIN
, which is much more efficient.
A large number of servers... | {"source_file": "distributed.md"} | [
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0.013368726707994938,
0.025213882327079773,
0.022403722628951073,
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0.06393042206764221,
-0.025328727439045906,
-0.02855433151125908,
-0.000005342230451788055,
0.09505389630794525,
... |
7bf46edb-9258-48ba-b3f9-e9bd4e2225b0 | When the
max_parallel_replicas
option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section
max_parallel_replicas
.
To learn more about how distributed
in
and
global in
queries are processed, refer to
this
documentation.
Virtual column... | {"source_file": "distributed.md"} | [
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-0.07948958873748779,
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0.016259098425507545,
0.07931999117136002,
0.022317146882414818,
-0.02421342022716999,
0.06032680347561836,
-0.05... |
aac878e2-1e72-4d1b-b85d-4ab507c493b8 | description: 'The File table engine keeps the data in a file in one of the supported
file formats (
TabSeparated
,
Native
, etc.).'
sidebar_label: 'File'
sidebar_position: 40
slug: /engines/table-engines/special/file
title: 'File table engine'
doc_type: 'reference'
File table engine
The File table engine keeps... | {"source_file": "file.md"} | [
0.01421048492193222,
-0.056513044983148575,
-0.08953996002674103,
0.03955169767141342,
0.025675080716609955,
0.0003773650387302041,
0.021885521709918976,
-0.005208734422922134,
-0.008189870975911617,
0.06070173159241676,
0.06643862277269363,
0.04788297787308693,
0.050112225115299225,
-0.11... |
802f556b-d0d9-4e24-8e48-402893f5f3d5 | Details of Implementation {#details-of-implementation}
Multiple
SELECT
queries can be performed concurrently, but
INSERT
queries will wait each other.
Supported creating new file by
INSERT
query.
If file exists,
INSERT
would append new values in it.
Not supported:
ALTER
SELECT ... SAMPLE
Indices... | {"source_file": "file.md"} | [
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0.02423892728984356,
0.0010609461460262537,
0.07291100919246674,
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0.041017141193151474,
0.05630580335855484,
-0.0... |
ce2d0743-523d-4e37-8834-d8bfa289f2b7 | description: 'This engine allows processing of application log files as a stream of
records.'
sidebar_label: 'FileLog'
sidebar_position: 160
slug: /engines/table-engines/special/filelog
title: 'FileLog table engine'
doc_type: 'reference'
FileLog table engine {#filelog-engine}
This engine allows processing of ap... | {"source_file": "filelog.md"} | [
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0.045490484684705734,
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0.0021393727511167526,
0.06154191121459007,
... |
bcc9a293-debc-4518-a3f6-09a1d93edc3b | Use the engine to create a FileLog table and consider it a data stream.
Create a table with the desired structure.
Create a materialized view that converts data from the engine and puts it into a previously created table.
When the
MATERIALIZED VIEW
joins the engine, it starts collecting data in the background... | {"source_file": "filelog.md"} | [
-0.016865821555256844,
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0.04324693977832794,
0.03633296862244606,
0.035595159977674484,
0.02901579439640045,
-0.014664003625512123,
-0.05872905254364014,
0.03338904306292534,
0.0395... |
c1be76ec-e815-46d4-a454-c98879a7af48 | description: 'A data set that is always in RAM. It is intended for use on the right
side of the
IN
operator.'
sidebar_label: 'Set'
sidebar_position: 60
slug: /engines/table-engines/special/set
title: 'Set table engine'
doc_type: 'reference'
Set table engine
:::note
In ClickHouse Cloud, if your service was cre... | {"source_file": "set.md"} | [
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0.009762474335730076,
-0.0859350711107254,
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0.05688326433300972,
0.02214961126446724,
0.006756791844964027,
0.036505185067653656,
0.09255478531122208,
0.05123582109808922,
0.08084962517023087,
-0.1467391... |
03bfcb0b-0e29-44ec-8710-9e0e3af0dfc2 | description: 'The
Dictionary
engine displays the dictionary data as a ClickHouse
table.'
sidebar_label: 'Dictionary'
sidebar_position: 20
slug: /engines/table-engines/special/dictionary
title: 'Dictionary table engine'
doc_type: 'reference'
Dictionary table engine
The
Dictionary
engine displays the
diction... | {"source_file": "dictionary.md"} | [
-0.0192415788769722,
-0.003044507233425975,
-0.11876042187213898,
-0.0017947215819731355,
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0.05239482223987579,
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0.09410300105810165,
0.019691379740834236,
0.07931883633136749,
-0.... |
6b4d6f11-5bc6-4f4d-b4ec-bda8a3102398 | description: 'The Alias table engine creates a transparent proxy to another table. All operations are forwarded to the target table while the alias itself stores no data.'
sidebar_label: 'Alias'
sidebar_position: 5
slug: /engines/table-engines/special/alias
title: 'Alias table engine'
doc_type: 'reference'
Alias ta... | {"source_file": "alias.md"} | [
-0.01970871537923813,
-0.06441419571638107,
-0.06778759509325027,
0.04240557551383972,
-0.05626443028450012,
-0.03808058425784111,
0.054392918944358826,
0.06276004761457443,
-0.018701959401369095,
0.05093080922961235,
0.026288729161024094,
-0.03411583602428436,
0.07464034855365753,
-0.0759... |
fe12abea-7154-4442-aa5e-f20def1b4dd3 | -- Query through alias
SELECT * FROM data_alias;
```
text
ββidββ¬βnameββ¬βvalueββ
β 1 β one β 10.1 β
β 2 β two β 20.2 β
ββββββ΄βββββββ΄ββββββββ
Cross-Database Alias {#cross-database-alias}
Create an alias pointing to a table in a different database:
```sql
-- Create databases
CREATE DATABASE db1;
CREATE DATAB... | {"source_file": "alias.md"} | [
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0.007119422312825918,
0.008185142651200294,
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-0.043866682797670364,
0.0627373456954956,
0.10729042440652847,
0.014695391990244389,
0.038625963032245636,
0.03514082357287407,
-0.07862425595521927,
0.06468366831541061,
-0.0611... |
7bef58e5-8081-41b7-b252-50c5fb476d3c | -- Delete through alias
ALTER TABLE products_alias DELETE WHERE status = 'inactive';
-- Changes are applied to target table
SELECT * FROM products ORDER BY id;
```
text
ββidββ¬βnameββββββ¬βpriceββ¬βstatusββ
β 1 β item_one β 110.0 β active β
β 2 β item_two β 220.0 β active β
ββββββ΄βββββββββββ΄ββββββββ΄βββββββββ
Parti... | {"source_file": "alias.md"} | [
0.04690396785736084,
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0.07301842421293259,
0.011225869879126549,
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0.11979303508996964,
0.09379882365465164,
0.038476038724184036,
0.07085506618022919,
0.10610610991716385,
-0.0026625252794474363,
0.07802041620016098,
-0.031... |
5db8ed1b-ea40-45cc-b80f-118bafa6437e | description: 'The GenerateRandom table engine produces random data for given table
schema.'
sidebar_label: 'GenerateRandom'
sidebar_position: 140
slug: /engines/table-engines/special/generate
title: 'GenerateRandom table engine'
doc_type: 'reference'
GenerateRandom table engine
The GenerateRandom table engine p... | {"source_file": "generate.md"} | [
0.006810818798840046,
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0.0357290655374527,
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-0.05992384999990463,
0.10615382343530655,
-0.1359... |
792685f4-7134-46ec-a890-db32ab8ea54d | description: 'The Memory engine stores data in RAM, in uncompressed form. Data is
stored in exactly the same form as it is received when read. In other words, reading
from this table is completely free.'
sidebar_label: 'Memory'
sidebar_position: 110
slug: /engines/table-engines/special/memory
title: 'Memory table e... | {"source_file": "memory.md"} | [
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0.09367134422063828,
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0.02148481085896492,
0.02119591273367405,
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0.03250451013445854,
0.036828793585300446,
0.026163699105381966,
0.06474421918392181,
0.09479112923145294,
-0.122592... |
6fa42321-ac76-4217-b85f-a7f0140f1df7 | Default value:
0
compress
- Whether to compress data in memory.
Default value:
false
Usage {#usage}
Initialize settings
sql
CREATE TABLE memory (i UInt32) ENGINE = Memory SETTINGS min_rows_to_keep = 100, max_rows_to_keep = 1000;
Modify settings
sql
ALTER TABLE memory MODIFY SETTING min_rows_to_keep =... | {"source_file": "memory.md"} | [
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0.009284576401114464,
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0.0022147279232740402,
0.0028629719745367765,
-0.0038708376232534647,
0.03527838736772537,
... |
315baccd-11d3-4b16-9be1-5d423445c26f | description: 'Documentation for Special Table Engines'
sidebar_label: 'Special'
sidebar_position: 50
slug: /engines/table-engines/special/
title: 'Special table engines'
doc_type: 'reference'
Special table engines
There are three main categories of table engines:
MergeTree engine family
for main production u... | {"source_file": "index.md"} | [
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-0.04681789502501488,
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... |
b8cf0d9a-b442-46b1-8257-7acc2cafc0ce | description: 'The
Merge
engine (not to be confused with
MergeTree
) does not store
data itself, but allows reading from any number of other tables simultaneously.'
sidebar_label: 'Merge'
sidebar_position: 30
slug: /engines/table-engines/special/merge
title: 'Merge table engine'
doc_type: 'reference'
Merge tabl... | {"source_file": "merge.md"} | [
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0.05988484248518944,
-0.095... |
9a47c95d-269c-4f8a-805a-6af53cb7035e | INSERT INTO WatchLog_new VALUES ('2018-01-02', 2, 'hit', 3);
CREATE TABLE WatchLog AS WatchLog_old ENGINE=Merge(currentDatabase(), '^WatchLog');
SELECT * FROM WatchLog;
```
text
ββββββββdateββ¬βUserIdββ¬βEventTypeββ¬βCntββ
β 2018-01-01 β 1 β hit β 3 β
ββββββββββββββ΄βββββββββ΄ββββββββββββ΄ββββββ
ββββββββda... | {"source_file": "merge.md"} | [
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0.05920768156647682,
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0.06298794597387314,
0.04602476954460144,
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0.011658008210361004,
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c9f66181-421f-40c5-a494-d23c714a9090 | description: 'ClickHouse allows sending a server the data that is needed for processing
a query, together with a
SELECT
query. This data is put in a temporary table and
can be used in the query (for example, in
IN
operators).'
sidebar_label: 'External data for query processing'
sidebar_position: 130
slug: /engi... | {"source_file": "external-data.md"} | [
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0.06224644184112549,
-0.10... |
e4926200-3578-43f2-857e-0c623977de2e | When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The
query_string
is passed the parameters
name_format
,
name_types
, and
name_structure
, where
name
is the name of the table that th... | {"source_file": "external-data.md"} | [
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-0.05108719319105148,
0.020278336480259895,
0.067558653652668,
0.05325980484485626,
0.026798749342560768,
0.03713854029774666,
-0.0356377474963665,
0.09718246012926102,
-0.10420385... |
a230817e-25bb-439e-ba2d-4b75ed4d839b | description: 'Optional prepared data structure for usage in JOIN operations.'
sidebar_label: 'Join'
sidebar_position: 70
slug: /engines/table-engines/special/join
title: 'Join table engine'
doc_type: 'reference'
Join table engine
Optional prepared data structure for usage in
JOIN
operations.
:::note
In ClickH... | {"source_file": "join.md"} | [
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... |
6cbbf6b9-b884-4fb7-b933-3292e74bb24c | max_rows_in_join
max_bytes_in_join
{#max_bytes_in_join}
max_bytes_in_join
join_overflow_mode
{#join_overflow_mode}
join_overflow_mode
join_any_take_last_row
{#join_any_take_last_row}
join_any_take_last_row
join_use_nulls
{#join_use_nulls-1}
Persistent {#persistent}
Disables persistency for the Joi... | {"source_file": "join.md"} | [
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0.05018899589776993,
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-0... |
4c123db2-fec0-4eac-864e-ffc4665b6ac6 | description: 'This engine allows you to use Keeper/ZooKeeper cluster as consistent
key-value store with linearizable writes and sequentially consistent reads.'
sidebar_label: 'KeeperMap'
sidebar_position: 150
slug: /engines/table-engines/special/keeper-map
title: 'KeeperMap table engine'
doc_type: 'reference'
Kee... | {"source_file": "keepermap.md"} | [
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-0.04... |
763d5e17-888c-4e54-aca7-24d9ec4eafa2 | If multiple tables are created on the same ZooKeeper path, the values are persisted until there exists at least 1 table using it.
As a result, it is possible to use
ON CLUSTER
clause when creating the table and sharing the data from multiple ClickHouse instances.
Of course, it's possible to manually run
CREATE TAB... | {"source_file": "keepermap.md"} | [
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-0... |
1913bdb2-2d9b-496a-9b1d-f742dbcaf9e5 | description: 'When writing to a
Null
table, data is ignored. When reading from a
Null
table, the response is empty.'
sidebar_label: 'Null'
sidebar_position: 50
slug: /engines/table-engines/special/null
title: 'Null table engine'
doc_type: 'reference'
Null table engine
When writing data to a
Null
table, da... | {"source_file": "null.md"} | [
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-0.066... |
a3156dda-8c2d-4ecf-a065-4c0a37acffea | description: 'The Kafka Table Engine can be used to publish works with Apache Kafka and lets you publish or subscribe
to data flows, organize fault-tolerant storage, and process streams as they become
available.'
sidebar_label: 'Kafka'
sidebar_position: 110
slug: /engines/table-engines/integrations/kafka
title: 'Ka... | {"source_file": "kafka.md"} | [
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-0.05934576317667961,
0.044006627053022385,
-... |
e95f09f2-fbc8-4cb2-885c-d2b915a9e11d | kafka_sasl_username
- SASL username for use with the
PLAIN
and
SASL-SCRAM-..
mechanisms.
kafka_sasl_password
- SASL password for use with the
PLAIN
and
SASL-SCRAM-..
mechanisms.
kafka_schema
β Parameter that must be used if the format requires a schema definition. For example,
Cap'n Proto
requires the ... | {"source_file": "kafka.md"} | [
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-0.09005436301231384,
0.013907781802117825,
... |
c866ce0a-9276-4c57-aa0c-2409ea978b55 | kafka_max_rows_per_message
β The maximum number of rows written in one kafka message for row-based formats. Default :
1
.
kafka_compression_codec
β Compression codec used for producing messages. Supported: empty string,
none
,
gzip
,
snappy
,
lz4
,
zstd
. In case of empty string the compression codec is not s... | {"source_file": "kafka.md"} | [
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0.002348161768168211,
-0... |
be6cc301-6420-4555-b474-dff50982671d | It is recommended that each Kafka topic have its own dedicated consumer group, ensuring exclusive pairing between the topic and the group, especially in environments where topics may be created and deleted dynamically (e.g., in testing or staging).
SELECT
is not particularly useful for reading messages (except for d... | {"source_file": "kafka.md"} | [
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0.0025370882358402014,
-0.04... |
7280d970-0544-402e-957f-0a3b310e0ba4 | ```xml
cgrp
3000
<kafka_topic>
<name>logs</name>
<statistics_interval_ms>4000</statistics_interval_ms>
</kafka_topic>
<!-- Settings for consumer -->
<consumer>
<auto_offset_reset>smallest</auto_offset_reset>
<kafka_topic>
<name>logs</name>
<fetch_min_bytes>100000</fetch_min_b... | {"source_file": "kafka.md"} | [
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-0.0917801707983017,
-0.03052043542265892,
-0.13458... |
7988280f-aeaa-4a41-b925-baee507c3568 | For row-based formats the number of rows in one Kafka message can be controlled by setting
kafka_max_rows_per_message
.
For block-based formats we cannot divide block into smaller parts, but the number of rows in one block can be controlled by general setting
max_block_size
.
Engine to store committed offsets i... | {"source_file": "kafka.md"} | [
-0.0564584843814373,
-0.04119785502552986,
-0.13326667249202728,
-0.010986254550516605,
-0.04583284631371498,
0.003849109634757042,
-0.07566221803426743,
-0.035002946853637695,
0.06284096837043762,
0.05294107645750046,
-0.006314564496278763,
-0.045096006244421005,
0.013142663054168224,
-0.... |
f0930b8d-f287-4c81-94fc-ef1a10e9cce6 | description: 'This engine provides a read-only integration with existing Apache Iceberg
tables in Amazon S3, Azure, HDFS and locally stored tables.'
sidebar_label: 'Iceberg'
sidebar_position: 90
slug: /engines/table-engines/integrations/iceberg
title: 'Iceberg table engine'
doc_type: 'reference'
Iceberg table eng... | {"source_file": "iceberg.md"} | [
-0.028910167515277863,
-0.06480231136083603,
-0.08259095996618271,
0.036199603229761124,
0.024377288296818733,
0.01574081927537918,
-0.0857698991894722,
-0.02380603738129139,
-0.033568475395441055,
0.0324552021920681,
0.03179217502474785,
0.0001862565550254658,
0.07613147795200348,
-0.0577... |
2b05c189-bc16-4fd6-ba15-b6fcc6c71bf7 | ```
Aliases {#aliases}
Table engine
Iceberg
is an alias to
IcebergS3
now.
Schema evolution {#schema-evolution}
At the moment, with the help of CH, you can read iceberg tables, the schema of which has changed over time. We currently support reading tables where columns have been added and removed, and their ... | {"source_file": "iceberg.md"} | [
-0.014679129235446453,
-0.005793544463813305,
-0.0038136844523251057,
0.050691623240709305,
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-0.023247726261615753,
-0.018038490787148476,
0.04008302465081215,
-0.0730884000658989,
0.0021751634776592255,
-0.015649650245904922,
-0.03176684305071831,
-0.01246123481541872,... |
ed893655-92a2-4e2d-af31-013ca372d269 | -- Insert data into the table
INSERT INTO spark_catalog.db.time_travel_example VALUES
(1, 'Mars')
ts1 = now() // A piece of pseudo code
-- Alter table to add a new column
ALTER TABLE spark_catalog.db.time_travel_example ADD COLUMN (price double)
ts2 = now()
-- Insert data into the table
INSERT INTO s... | {"source_file": "iceberg.md"} | [
0.021954752504825592,
-0.021491600200533867,
0.026597226038575172,
0.022656599059700966,
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0.01198488101363182,
0.023626986891031265,
0.07048030942678452,
-0.031100675463676453,
0.04095007851719856,
0.06936807185411453,
-0.06837473809719086,
-0.0665324330329895,
-0.0542... |
68a5aa4c-dcb0-4fb5-965e-de5f8de3499f | Scenario 3: Historical vs. current schema differences {#scenario-3}
The second one is that while doing time travel you can't get state of table before any data was written to it:
```sql
-- Create a table
CREATE TABLE IF NOT EXISTS spark_catalog.db.time_travel_example_3 (
order_number int,
product_code string... | {"source_file": "iceberg.md"} | [
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0.024892587214708328,
0.01142705138772726,
-0.026361413300037384,
... |
63979d56-4e85-42b4-bd2e-805cdccfc7fc | Data cache {#data-cache}
Iceberg
table engine and table function support data caching same as
S3
,
AzureBlobStorage
,
HDFS
storages. See
here
.
Metadata cache {#metadata-cache}
Iceberg
table engine and table function support metadata cache storing the information of manifest files, manifest list and metada... | {"source_file": "iceberg.md"} | [
0.03197982907295227,
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0.019352415576577187,
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0.024665754288434982,
0.08627072721719742,
0.003537799697369337,
0.06704864650964737,
0.0037364684976637363,
-0.10... |
86408fff-7a2c-42de-8f72-f8faf4ea6edf | description: 'This engine allows integrating ClickHouse with RabbitMQ.'
sidebar_label: 'RabbitMQ'
sidebar_position: 170
slug: /engines/table-engines/integrations/rabbitmq
title: 'RabbitMQ table engine'
doc_type: 'guide'
RabbitMQ table engine
This engine allows integrating ClickHouse with
RabbitMQ
.
RabbitMQ
l... | {"source_file": "rabbitmq.md"} | [
0.08843712508678436,
-0.07903963327407837,
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0.0495818592607975,
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0.07435759156942368,
0.02021951973438263,
-0.07793177664279938,
0.03775889426469803,
0.0061701... |
db4c414e-57e9-44c2-bb75-f956514aa454 | rabbitmq_queue_base
- Specify a hint for queue names. Use cases of this setting are described below.
rabbitmq_deadletter_exchange
- Specify name for a
dead letter exchange
. You can create another table with this exchange name and collect messages in cases when they are republished to dead letter exchange. By defa... | {"source_file": "rabbitmq.md"} | [
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0.028130050748586655,
0.03752429410815239,
-0.037529971450567245,
0.045667923986911774,
-0.026266... |
c8d9e795-06c3-4622-8ecb-f20451962fc1 | [ ] SSL connection:
Use either
rabbitmq_secure = 1
or
amqps
in connection address:
rabbitmq_address = 'amqps://guest:guest@localhost/vhost'
.
The default behaviour of the used library is not to check if the created TLS connection is sufficiently secure. Whether the certificate is expired, self-signed, missin... | {"source_file": "rabbitmq.md"} | [
0.06277439743280411,
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0.046315792948007584,
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0.054607197642326355,
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-0.03383816033601761,
0.04328742250800133,
0.07... |
326bcddf-b6e2-4f17-a23d-35e8285e36eb | headers
- Routing is based on
key=value
matches with a setting
x-match=all
or
x-match=any
. Example table key list:
x-match=all,format=logs,type=report,year=2020
.
consistent_hash
- Data is evenly distributed between all bound tables (where the exchange name is the same). Note that this exchange type must be ... | {"source_file": "rabbitmq.md"} | [
0.02764664962887764,
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0.07643893361091614,
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0.09951823949813843,
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0.053791593760252,
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0.0009597272728569806,
0.030765457078814507,
0.015548... |
14eae81b-ac99-432c-93b1-94102b73a2d6 | CREATE MATERIALIZED VIEW consumer TO daily
AS SELECT key, value FROM queue;
SELECT key, value FROM daily ORDER BY key;
```
Virtual columns {#virtual-columns}
_exchange_name
- RabbitMQ exchange name. Data type:
String
.
_channel_id
- ChannelID, on which consumer, who received the message, was declared. ... | {"source_file": "rabbitmq.md"} | [
0.07916402071714401,
0.00966548826545477,
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0.10154082626104355,
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0.09510611742734909,
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0.08650295436382294,
0.07559432089328766,
-0.06229999288916588,
-0.006051002535969019,
0.009... |
e82c4d76-623b-44bc-ab88-d82ffc7cfb58 | description: 'This engine allows integrating ClickHouse with RocksDB'
sidebar_label: 'EmbeddedRocksDB'
sidebar_position: 50
slug: /engines/table-engines/integrations/embedded-rocksdb
title: 'EmbeddedRocksDB table engine'
doc_type: 'reference'
import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge'... | {"source_file": "embedded-rocksdb.md"} | [
-0.00003310496322228573,
-0.034724023193120956,
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0.05039259418845177,
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0.005279050208628178,
-0.005807038862258196,
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-0.005001347046345472,
0.02479942888021469,
-0.07772602885961533,
0.12322528660297394,
... |
09762172-e1f7-48aa-bb86-3456d780a3cf | Configuration {#configuration}
You can also change any
rocksdb options
using config:
xml
<rocksdb>
<options>
<max_background_jobs>8</max_background_jobs>
</options>
<column_family_options>
<num_levels>2</num_levels>
</column_family_options>
<tables>
<table>
... | {"source_file": "embedded-rocksdb.md"} | [
0.012219334952533245,
-0.015417451038956642,
-0.0555676706135273,
0.060251202434301376,
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0.03477679565548897,
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0.018004873767495155,
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-0.0354134626686573,
0.06132359057664871,
-0.0872... |
9f4f4188-fd1a-4625-aa4e-d8ed3ebe4152 | Set the join algorithm to
direct
{#set-the-join-algorithm-to-direct}
sql
SET join_algorithm = 'direct'
An INNER JOIN {#an-inner-join}
sql
SELECT *
FROM
(
SELECT k AS key
FROM t2
) AS t2
INNER JOIN rdb ON rdb.key = t2.key
ORDER BY key ASC
response
ββkeyββ¬βrdb.keyββ¬βvalueβββ¬βvalue2ββ
β 0 β 0 β [0... | {"source_file": "embedded-rocksdb.md"} | [
0.03839357569813728,
0.015704846009612083,
0.017823904752731323,
0.02047397755086422,
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0.018108099699020386,
0.009842117317020893,
-0.10707784444093704,
0.01833919622004032,
-0.042453136295080185,
-0.013555895537137985,
0.06112634390592575,
-0.076... |
335058c1-4da3-4ecb-bf06-788db01ff87c | description: 'The Hive engine allows you to perform
SELECT
queries on HDFS Hive
table.'
sidebar_label: 'Hive'
sidebar_position: 84
slug: /engines/table-engines/integrations/hive
title: 'Hive table engine'
doc_type: 'guide'
import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';
Hive table e... | {"source_file": "hive.md"} | [
0.019166424870491028,
-0.03637366369366646,
-0.021350635215640068,
0.018664715811610222,
0.0145682692527771,
0.027181535959243774,
0.041115500032901764,
-0.0036225500516593456,
-0.07478778064250946,
0.04901769012212753,
-0.02720429003238678,
-0.0201386921107769,
0.04165956750512123,
-0.023... |
6e62f596-9628-45a6-bfeb-788e6b066cc4 | Query Hive table with ORC input format {#query-hive-table-with-orc-input-format}
Create Table in Hive {#create-table-in-hive}
``text
hive > CREATE TABLE
test
.
test_orc
(
f_tinyint
tinyint,
f_smallint
smallint,
f_int
int,
f_integer
int,
f_bigint
bigint,
f_float
float,
f_double
double,
f_decimal
decimal(10,0),
f_ti... | {"source_file": "hive.md"} | [
0.057519011199474335,
0.059455662965774536,
-0.027073558419942856,
-0.020310362800955772,
-0.01197786908596754,
0.005711216013878584,
0.012540258467197418,
0.06052672490477562,
-0.09051814675331116,
0.07397448271512985,
0.02171000838279724,
-0.10091159492731094,
-0.006192693952471018,
-0.0... |
3e41883a-fae9-41e4-b673-ef567d281629 | ```text
SELECT *
FROM test.test_orc
SETTINGS input_format_orc_allow_missing_columns = 1
Query id: c3eaffdc-78ab-43cd-96a4-4acc5b480658
Row 1:
ββββββ
f_tinyint: 1
f_smallint: 2
f_int: 3
f_integer: 4
f_bigint: 5
f_float: 6.11
f_double: ... | {"source_file": "hive.md"} | [
0.06955298036336899,
0.08582732826471329,
-0.04937668889760971,
0.02348739095032215,
0.0021217588800936937,
-0.016156714409589767,
0.03526856750249863,
-0.012804231606423855,
-0.04497649893164635,
0.052704304456710815,
0.06459324061870575,
-0.10102616995573044,
0.032756257802248,
-0.080858... |
3ce89722-0ecc-4e84-8907-a7aec0fc2de0 | Create Table in ClickHouse {#create-table-in-clickhouse-1}
Table in ClickHouse, retrieving data from the Hive table created above:
sql
CREATE TABLE test.test_parquet
(
`f_tinyint` Int8,
`f_smallint` Int16,
`f_int` Int32,
`f_integer` Int32,
`f_bigint` Int64,
`f_float` Float32,
`f_double` F... | {"source_file": "hive.md"} | [
0.08999571949243546,
0.000006071690677345032,
-0.07897442579269409,
-0.03056935966014862,
-0.08511800318956375,
-0.06666673719882965,
0.04770674183964729,
0.02067173272371292,
-0.08323806524276733,
0.0760020837187767,
0.06742025166749954,
-0.09163402020931244,
0.005843425169587135,
-0.0374... |
7c8cc8ce-9eef-4e96-a398-f9e7758b325b | hive > insert into test.test_text partition(day='2021-09-18') select 1, 2, 3, 4, 5, 6.11, 7.22, 8.333, current_timestamp(), current_date(), 'hello world', 'hello world', 'hello world', true, 'hello world', array(1, 2, 3), array('hello world', 'hello world'), array(float(1.1), float(1.2)), array(array(1, 2), array(3, 4... | {"source_file": "hive.md"} | [
0.032321181148290634,
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0.06026632711291313,
0.012190350331366062,
-0.03456432372331619,
-0.046207670122385025,
0.039502616971731186,
-0.04981791600584984,
-0.05574370548129082,
0.06738711148500443,
0.03577449545264244,
-0.07481476664543152,
-0.023821566253900528,
-0.012... |
1df68c8b-8a59-4fc1-8a14-0373764eeda1 | description: 'This engine provides a read-only integration with existing Apache Hudi
tables in Amazon S3.'
sidebar_label: 'Hudi'
sidebar_position: 86
slug: /engines/table-engines/integrations/hudi
title: 'Hudi table engine'
doc_type: 'reference'
Hudi table engine
This engine provides a read-only integration wit... | {"source_file": "hudi.md"} | [
-0.06333829462528229,
-0.06561040133237839,
-0.1452704221010208,
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-0.016861051321029663,
0.11592880636453629,
... |
4f60d726-316e-4e59-b86a-7d623aa0a4e2 | description: 'This engine allows integrating ClickHouse with Redis.'
sidebar_label: 'Redis'
sidebar_position: 175
slug: /engines/table-engines/integrations/redis
title: 'Redis table engine'
doc_type: 'guide'
Redis table engine
This engine allows integrating ClickHouse with
Redis
. For Redis takes kv model, we st... | {"source_file": "redis.md"} | [
0.006042524240911007,
-0.06261417269706726,
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0.01692586950957775,
-0.0544106587767601,
-0.037679776549339294,
0.01043890044093132,
-0.05687101185321808,
0.09503137320280075,
-0.06407... |
3e26c359-e71b-45ab-8aa1-301428d08afe | text
ββkeyββ¬βv1ββ¬βv2ββ¬βv3ββ
β 2 β 2 β 2 β 2 β
βββββββ΄βββββ΄βββββ΄βββββ
Update:
Note that the primary key cannot be updated.
sql
ALTER TABLE redis_table UPDATE v1=2 WHERE key='1';
Delete:
sql
ALTER TABLE redis_table DELETE WHERE key='1';
Truncate:
Flush Redis db asynchronously. Also
Truncate
support S... | {"source_file": "redis.md"} | [
0.023858819156885147,
-0.019854463636875153,
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-0.020701272413134575,
-0.10652228444814682,
0.05894928053021431,
-0.030379991978406906,
-0.01838185079395771,
0.03607942536473274,
0.04908062517642975,
0.0901179239153862,
0.10061060637235641,
-0.1381... |
8b5c85ed-11ae-4dac-90f1-4c38da2cca04 | description: 'The engine allows querying remote datasets via Apache Arrow Flight.'
sidebar_label: 'ArrowFlight'
sidebar_position: 186
slug: /engines/table-engines/integrations/arrowflight
title: 'ArrowFlight table engine'
doc_type: 'reference'
ArrowFlight table engine
The ArrowFlight table engine enables ClickHou... | {"source_file": "arrowflight.md"} | [
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0.042296379804611206,
-0.09... |
8fc7a5ba-d6ec-4fd9-9da7-d0fe6c2dbdc7 | description: 'Documentation for MySQL Table Engine'
sidebar_label: 'MySQL'
sidebar_position: 138
slug: /engines/table-engines/integrations/mysql
title: 'MySQL table engine'
doc_type: 'reference'
MySQL table engine
The MySQL engine allows you to perform
SELECT
and
INSERT
queries on data that is stored on a rem... | {"source_file": "mysql.md"} | [
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-0.08... |
76d38d65-47d4-4ab5-9928-bba07b984b41 | Supports multiple replicas that must be listed by
|
. For example:
sql
CREATE TABLE test_replicas (id UInt32, name String, age UInt32, money UInt32) ENGINE = MySQL(`mysql{2|3|4}:3306`, 'clickhouse', 'test_replicas', 'root', 'clickhouse');
Usage example {#usage-example}
Create table in MySQL:
``text
mysql> CREA... | {"source_file": "mysql.md"} | [
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0.021803446114063263,
0.0631132423877716,
-0.11652713268995285,
0.16106997430324554,
-0.017... |
b301dccc-d325-4214-9441-f281446efaf2 | Possible values:
Positive integer.
Default value:
5
.
connect_timeout
{#connect-timeout}
Connect timeout (in seconds).
Possible values:
Positive integer.
Default value:
10
.
read_write_timeout
{#read-write-timeout}
Read/write timeout (in seconds).
Possible values:
Positive integer.
... | {"source_file": "mysql.md"} | [
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0.031988587230443954,
0.021113252267241478,
0.0025660707615315914,
0.00902276486158371,
0.01675431616604328,
0.07040303200483322,
-0.02871384... |
a8689233-af7b-4b91-bdb9-3961ae911e0c | description: 'Creates a ClickHouse table with an initial data dump of a PostgreSQL
table and starts the replication process.'
sidebar_label: 'MaterializedPostgreSQL'
sidebar_position: 130
slug: /engines/table-engines/integrations/materialized-postgresql
title: 'MaterializedPostgreSQL table engine'
doc_type: 'guide'
... | {"source_file": "materialized-postgresql.md"} | [
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0.03252300247550011,
0.01495471689850092,
-0.12... |
a79f2036-7df1-43b0-9521-df3f3565dc81 | 1
β Row is not deleted,
-1
β Row is deleted.
These columns do not need to be added when a table is created. They are always accessible in
SELECT
query.
_version
column equals
LSN
position in
WAL
, so it might be used to check how up-to-date replication is.
```sql
CREATE TABLE postgresql_db.postgresql_r... | {"source_file": "materialized-postgresql.md"} | [
-0.021470610052347183,
-0.05740634351968765,
-0.09156668186187744,
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0.0060580032877624035,
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0.0036125273909419775,
0.036875929683446884,
0.05091623216867447,
0.033716704696416855,
0.0027478551492094994,... |
6376bc74-f576-4dcb-9584-2cd073eaab2f | description: 'Table engine that allows importing data from a YTsaurus cluster.'
sidebar_label: 'YTsaurus'
sidebar_position: 185
slug: /engines/table-engines/integrations/ytsaurus
title: 'YTsaurus table engine'
keywords: ['YTsaurus', 'table engine']
doc_type: 'reference'
import CloudNotSupportedBadge from '@theme/ba... | {"source_file": "ytsaurus.md"} | [
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0.006149450782686472,
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0.008145725354552269,
0.04425569996237755,
-0.018514741212129593,
0.0873183161020279,
-0.0... |
c28b38f2-c3b7-441f-abb5-478d44a8913f | sql title="Query"
SELECT * FROM yt_saurus;
response title="Response"
βββaββ¬βbβββ
β 10 β 20 β
ββββββ΄βββββ
Data types {#data-types}
Primitive data types {#primitive-data-types}
| YTsaurus data type | Clickhouse data type |
| ------------------ | ----------------------- |
|
int8
|
Int8
... | {"source_file": "ytsaurus.md"} | [
0.004735061898827553,
-0.013893965631723404,
-0.05608822777867317,
0.0814930722117424,
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0.01632198877632618,
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0.04779487103223801,
-0.07547087222337723,
0.022486595436930656,
-0.0303... |
0adf1b74-2d1f-4dd3-8b01-52460596177d | description: 'This engine provides integration with the Amazon S3 ecosystem. Similar
to the HDFS engine, but provides S3-specific features.'
sidebar_label: 'S3'
sidebar_position: 180
slug: /engines/table-engines/integrations/s3
title: 'S3 table engine'
doc_type: 'reference'
S3 table engine
This engine provides ... | {"source_file": "s3.md"} | [
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-0.04751928895711899,
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0.007315425667911768,
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0.006961221341043711,
-0.021160632371902466,
-0.07892818748950958,
0.11335863918066025,
-0... |
9abfb879-cca9-4fdf-821a-713bc18ccf7a | storage_class_name
- Options:
STANDARD
or
INTELLIGENT_TIERING
, allow to specify
AWS S3 Intelligent Tiering
.
Data cache {#data-cache}
S3
table engine supports data caching on local disk.
See filesystem cache configuration options and usage in this
section
.
Caching is made depending on the path and ETag o... | {"source_file": "s3.md"} | [
-0.006207330152392387,
-0.030521335080266,
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0.005195078905671835,
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0.016118595376610756,
0.03911059722304344,
0.01082376018166542,
0.016469135880470276,
0.02537955716252327,
-0.089... |
44482b9e-169e-403a-9085-9dd4f38278c5 | arthur :) select _path, * from t_03363_parquet;
ββ_pathβββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ¬βyearββ¬βcountryββ¬βcounterββ
β test/t_03363_parquet/year=2100/country=Japan/7329604473272971264.parquet β 2100 β Japan β 9 β
β test/t_03363_parquet/year=2024/country=France/732... | {"source_file": "s3.md"} | [
0.009940318763256073,
-0.013552150689065456,
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0.05529782548546791,
-0.03969532996416092,
-0.011858928948640823,
-0.00037885530036874115,
0.0028149031568318605,
0.04099355638027191,
0.06090560927987099,
-0.055966321378946304,
-0.005171661265194416,... |
b27abbd7-d1c2-4275-956b-4438750d8ddf | Insert data {#insert-data}
sql
INSERT INTO p VALUES (1, 2, 3), (3, 2, 1), (78, 43, 45)
Select from partition 3 {#select-from-partition-3}
:::tip
This query uses the s3 table function
:::
sql
SELECT *
FROM s3('http://minio:10000/clickhouse//test_3.csv', 'minioadmin', 'minioadminpassword', 'CSV')
response
ββc1β... | {"source_file": "s3.md"} | [
-0.012389925308525562,
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-0.05665428563952446,
-0.002302803797647357,
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-0.0186786986887455,
0.005781434942036867,
0.012640760280191898,
0.036127276718616486,
0.004480597097426653,
0.05800844356417656,
-0.07151549309492111,
0.06947077810764313,
-0.... |
138276d4-ad40-47d5-9fd9-6d87862d4905 | *
β Substitutes any number of any characters except
/
including empty string.
**
β Substitutes any number of any character include
/
including empty string.
?
β Substitutes any single character.
{some_string,another_string,yet_another_one}
β Substitutes any of strings
'some_string', 'another_string', 'ye... | {"source_file": "s3.md"} | [
-0.022088713943958282,
0.06504864245653152,
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0.02385149523615837,
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0.01586020737886429,
0.06164179742336273,
0.0007844642386771739,
0.06371540576219559,
0.009904777631163597,
-0.022550825029611588,
0.03499951213598251,
-0.03... |
f93ae6b1-15a8-4465-b54a-22329c8a2668 | s3_skip_empty_files
- allows to skip empty files while reading. Enabled by default.
S3-related settings {#settings}
The following settings can be set before query execution or placed into configuration file.
s3_max_single_part_upload_size
β The maximum size of object to upload using singlepart upload to S3.... | {"source_file": "s3.md"} | [
0.00009043509635375813,
-0.0323951281607151,
-0.0640939325094223,
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0.04817437008023262,
-0.03068484179675579,
-0.09071918576955795,
0.0323023684322834,
0.035127416253089905,
0.010210152715444565,
-0.00792898703366518,
0.0567663237452507,
0.061315786093473434,
-0.07276... |
b494d055-c0a7-4614-9e90-dfe6ade12225 | region
β Specifies S3 region name. Optional.
use_insecure_imds_request
β If set to
true
, S3 client will use insecure IMDS request while obtaining credentials from Amazon EC2 metadata. Optional, default value is
false
.
expiration_window_seconds
β Grace period for checking if expiration-based credentials have ... | {"source_file": "s3.md"} | [
-0.020006487146019936,
0.06079234182834625,
-0.07030307501554489,
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0.04579995572566986,
0.00982495117932558,
0.010897073894739151,
-0.016987308859825134,
0.09304872900247574,
-0.029774155467748642,
-0.0014120314735919237,
-0.04822973534464836,
0.07571343332529068,
-0.0... |
5b494858-da14-4a41-83ea-f95fbb738ad5 | max_put_rps
,
max_put_burst
,
max_get_rps
and
max_get_burst
- Throttling settings (see description above) to use for specific endpoint instead of per query. Optional.
Example:
xml
<s3>
<endpoint-name>
<endpoint>https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/</endpoint>
... | {"source_file": "s3.md"} | [
0.005962041672319174,
0.021498441696166992,
-0.086082324385643,
-0.031243396922945976,
-0.008062021806836128,
-0.031113402917981148,
-0.020899277180433273,
-0.017081180587410927,
0.05295254662632942,
0.040003810077905655,
0.06177592650055885,
-0.02886805683374405,
0.07171111553907394,
-0.0... |
7c38319c-2c74-46b5-86a5-a7f3e8912bfd | Optimizing performance {#optimizing-performance}
For details on optimizing the performance of the s3 function see
our detailed guide
.
See also {#see-also}
s3 table function
Integrating S3 with ClickHouse | {"source_file": "s3.md"} | [
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0.0279002133756876,
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-0.0... |
71d8ab44-3a7a-4615-9da4-4d0e0ac5e556 | description: 'This engine provides integration with the Apache Hadoop ecosystem by
allowing to manage data on HDFS via ClickHouse. This engine is similar to the File
and URL engines, but provides Hadoop-specific features.'
sidebar_label: 'HDFS'
sidebar_position: 80
slug: /engines/table-engines/integrations/hdfs
tit... | {"source_file": "hdfs.md"} | [
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0.026312118396162987,
0.06754358112812042,
-0.0... |
15eed0d4-a340-49d3-8969-9afa6376dd5a | Globs in path
Multiple path components can have globs. For being processed file should exists and matches to the whole path pattern. Listing of files determines during
SELECT
(not at
CREATE
moment).
*
β Substitutes any number of any characters except
/
including empty string.
?
β Substitutes any single ... | {"source_file": "hdfs.md"} | [
-0.03770357370376587,
-0.018098408356308937,
0.03571808338165283,
0.01073228195309639,
0.019732922315597534,
-0.05566029995679855,
-0.0028594722971320152,
-0.007418681401759386,
0.06237892806529999,
0.0336899571120739,
-0.012530929408967495,
0.020275874063372612,
0.01417636964470148,
0.019... |
c3c7d273-7f56-40bb-93f1-ff222e269e9c | |
parameter
|
default value
|
| - | - |
| rpc_client_connect_tcpnodelay | true |
| dfs_client_read_shortcircuit | true |
... | {"source_file": "hdfs.md"} | [
0.009994328022003174,
-0.012835892848670483,
-0.07653801888227463,
0.017140956595540047,
-0.0054126037284731865,
-0.07876738160848618,
-0.06149570271372795,
0.030150175094604492,
-0.06486595422029495,
0.018889592960476875,
0.028907857835292816,
-0.03328249603509903,
0.04432373866438866,
-0... |
f22b424f-e4c2-479b-bcbe-f4c09fbc142e | | dfs_client_read_shortcircuit_streams_cache_size | 256 |
| dfs_client_socketcache_expiryMsec | 3000 |
| dfs_client_socketcache_capacity | 16 |
| dfs_default_blocksize | 64 * 1024 * 1024 ... | {"source_file": "hdfs.md"} | [
0.041845567524433136,
0.002873726189136505,
-0.04778636246919632,
-0.0055195544846355915,
0.029923906549811363,
-0.09057951718568802,
-0.03494596853852272,
0.04078010469675064,
-0.01536969467997551,
0.0568348728120327,
-0.015038133598864079,
-0.00034560781205073,
-0.003826731815934181,
-0.... |
2827dd8f-e5e7-4929-b3f6-4daa8cea58d2 | HDFS Configuration Reference
might explain some parameters.
ClickHouse extras {#clickhouse-extras}
|
parameter
|
default value
|
| - | - |
|hadoop_kerberos_keytab | ""... | {"source_file": "hdfs.md"} | [
-0.020189864560961723,
-0.08317932486534119,
-0.032372575253248215,
-0.0425838902592659,
0.02785436064004898,
-0.04875877872109413,
-0.009418630972504616,
-0.029750501736998558,
-0.07875461131334305,
0.08202043920755386,
-0.006197805982083082,
-0.037975434213876724,
0.07169319689273834,
-0... |
81ea46a7-3c2a-4030-bcbe-954575456994 | description: 'The
ExternalDistributed
engine allows to perform
SELECT
queries
on data that is stored on a remote servers MySQL or PostgreSQL. Accepts MySQL or
PostgreSQL engines as an argument so sharding is possible.'
sidebar_label: 'ExternalDistributed'
sidebar_position: 55
slug: /engines/table-engines/integr... | {"source_file": "ExternalDistributed.md"} | [
0.0030967029742896557,
-0.06655507534742355,
-0.07276774197816849,
0.08345840126276016,
-0.04225973039865494,
-0.032000936567783356,
0.023304928094148636,
-0.0036611086688935757,
-0.0077783456072211266,
-0.020187435671687126,
-0.006265529431402683,
-0.02346576564013958,
0.09900321811437607,
... |
c96f8de9-1c14-4a55-99b4-d23f8b719c42 | description: 'This engine provides a read-only integration with existing Delta Lake
tables in Amazon S3.'
sidebar_label: 'DeltaLake'
sidebar_position: 40
slug: /engines/table-engines/integrations/deltalake
title: 'DeltaLake table engine'
doc_type: 'reference'
DeltaLake table engine
This engine provides a read-o... | {"source_file": "deltalake.md"} | [
-0.055963337421417236,
-0.058353230357170105,
-0.09415415674448013,
-0.03478557989001274,
-0.06079111248254776,
-0.026875857263803482,
0.009594644419848919,
-0.022685900330543518,
-0.03558455407619476,
-0.0035395417362451553,
0.004385498818010092,
-0.07493723928928375,
0.09872109442949295,
... |
36e1f7c8-cae1-4b56-9343-766518d7f64a | description: 'Documentation for Table Engines for Integrations'
sidebar_label: 'Integrations'
sidebar_position: 40
slug: /engines/table-engines/integrations/
title: 'Table Engines for Integrations'
doc_type: 'reference'
Table engines for integrations
ClickHouse provides various means for integrating with external... | {"source_file": "index.md"} | [
-0.02065725438296795,
-0.052336134016513824,
-0.07692556083202362,
0.020942093804478645,
-0.0618998222053051,
-0.025303326547145844,
0.015436488203704357,
0.04047001898288727,
-0.035814378410577774,
-0.00334585621021688,
0.05076366662979126,
0.000642712926492095,
0.09571520239114761,
-0.08... |
62fd1984-4e94-4d58-aae0-1525c72ab2ae | description: 'The PostgreSQL engine allows
SELECT
and
INSERT
queries on data stored
on a remote PostgreSQL server.'
sidebar_label: 'PostgreSQL'
sidebar_position: 160
slug: /engines/table-engines/integrations/postgresql
title: 'PostgreSQL table Engine'
doc_type: 'guide'
PostgreSQL table engine
The PostgreSQL... | {"source_file": "postgresql.md"} | [
-0.00028937202296219766,
-0.08840706199407578,
-0.04999539628624916,
0.07087654620409012,
-0.0639965683221817,
-0.014077462255954742,
0.0021998819429427385,
-0.03016178496181965,
-0.02549831010401249,
0.024300266057252884,
0.008224752731621265,
0.03185279667377472,
0.013433710671961308,
-0... |
125e9a61-332f-46ad-9317-894b2862000b | SELECT
queries on PostgreSQL side run as
COPY (SELECT ...) TO STDOUT
inside read-only PostgreSQL transaction with commit after each
SELECT
query.
Simple
WHERE
clauses such as
=
,
!=
,
>
,
>=
,
<
,
<=
, and
IN
are executed on the PostgreSQL server.
All joins, aggregations, sorting,
IN [ array ]
cond... | {"source_file": "postgresql.md"} | [
-0.008432422764599323,
-0.05455535277724266,
-0.037264708429574966,
0.06303668022155762,
-0.09412242472171783,
-0.04113079980015755,
0.07888322323560715,
-0.06070837005972862,
0.03652691841125488,
-0.005195643752813339,
-0.0019502082141116261,
0.04152373597025871,
0.00013115080946590751,
-... |
22e82c46-aa9e-4382-a549-dd0590442c39 | This example uses the
PostgreSQL table engine
to connect the ClickHouse table to the PostgreSQL table and use both SELECT and INSERT statements to the PostgreSQL database:
sql
CREATE TABLE default.postgresql_table
(
`float_nullable` Nullable(Float32),
`str` String,
`int_id` Int32
)
ENGINE = PostgreSQL('... | {"source_file": "postgresql.md"} | [
0.009644974954426289,
-0.06187725439667702,
-0.10021770745515823,
0.068231500685215,
-0.1138247400522232,
-0.04475477337837219,
0.014301995746791363,
-0.00929019134491682,
-0.04256480932235718,
0.0027010426856577396,
0.06114271655678749,
-0.033656783401966095,
0.034729938954114914,
-0.1022... |
0b210377-d342-4c60-9800-ce3ae2806896 | See Also
The
postgresql
table function
Using PostgreSQL as a dictionary source
Related content {#related-content}
Blog:
ClickHouse and PostgreSQL - a match made in data heaven - part 1
Blog:
ClickHouse and PostgreSQL - a Match Made in Data Heaven - part 2 | {"source_file": "postgresql.md"} | [
0.015289639122784138,
-0.026309004053473473,
-0.08792982250452042,
-0.024916956201195717,
-0.05755103752017021,
0.011558751575648785,
0.03133103996515274,
-0.044874995946884155,
-0.0659894198179245,
0.022414391860365868,
0.06831391155719757,
0.015723323449492455,
0.08521638065576553,
-0.01... |
d93a8792-1ebb-4673-8f32-3b672ea89bcc | description: 'This engine provides an integration with Azure Blob Storage ecosystem.'
sidebar_label: 'Azure Blob Storage'
sidebar_position: 10
slug: /engines/table-engines/integrations/azureBlobStorage
title: 'AzureBlobStorage table engine'
doc_type: 'reference'
AzureBlobStorage table engine
This engine provides ... | {"source_file": "azureBlobStorage.md"} | [
0.0067711686715483665,
-0.008584882132709026,
-0.1082133799791336,
0.05365132540464401,
-0.06612828373908997,
0.03582759201526642,
0.0679691880941391,
0.021735645830631256,
-0.01972735859453678,
0.05969196930527687,
0.021245131269097328,
-0.07203030586242676,
0.11256314069032669,
0.0121233... |
7c690be2-800f-47df-9930-0ffdd990ed53 | extra_credentials
- Use
client_id
and
tenant_id
for authentication. If extra_credentials are provided, they are given priority over
account_name
and
account_key
.
Example
Users can use the Azurite emulator for local Azure Storage development. Further details
here
. If using a local instance of Azurite, u... | {"source_file": "azureBlobStorage.md"} | [
0.052849169820547104,
0.015404211357235909,
-0.09055151045322418,
0.028321852907538414,
-0.11126811057329178,
0.003617515554651618,
0.07652027159929276,
0.0636664554476738,
0.032247334718704224,
0.12111689150333405,
0.042446110397577286,
-0.0937451422214508,
0.15910114347934723,
0.01503713... |
2275e90b-501e-45f2-b705-84d02b95bb0b | add the following section to clickhouse configuration file:
xml
<clickhouse>
<filesystem_caches>
<cache_for_azure>
<path>path to cache directory</path>
<max_size>10Gi</max_size>
</cache_for_azure>
</filesystem_caches>
</clickhouse>
reuse cache configuration (and t... | {"source_file": "azureBlobStorage.md"} | [
0.044745877385139465,
0.007921949028968811,
-0.015385175123810768,
0.018391644582152367,
-0.054788749665021896,
-0.01224426832050085,
0.03350180760025978,
-0.01758740469813347,
0.0035006809048354626,
0.10200908780097961,
0.012828103266656399,
0.012628228403627872,
0.051539622247219086,
0.0... |
7eaae94b-9fd4-47eb-81c4-77fb9d91a34b | description: 'Allows ClickHouse to connect to external databases via ODBC.'
sidebar_label: 'ODBC'
sidebar_position: 150
slug: /engines/table-engines/integrations/odbc
title: 'ODBC table engine'
doc_type: 'reference'
import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';
ODBC table engine
A... | {"source_file": "odbc.md"} | [
-0.01121565606445074,
-0.11621849238872528,
-0.08751655369997025,
0.06100517138838768,
-0.03319670632481575,
-0.06018303707242012,
-0.010090666823089123,
-0.015072680078446865,
-0.02185194566845894,
-0.044349443167448044,
-0.02244369313120842,
0.006900763604789972,
0.05953897908329964,
-0.... |
d1a679f7-b13c-4ec2-81f3-ef35ff9d647f | bash
$ isql -v mysqlconn
+-------------------------+
| Connected! |
| |
...
Table in MySQL:
```text
mysql> CREATE DATABASE test;
Query OK, 1 row affected (0,01 sec)
mysql> CREATE TABLE
test
.
test
(
->
int_id
INT NOT NULL AUTO_INCREMENT,
... | {"source_file": "odbc.md"} | [
0.053114697337150574,
0.03791213780641556,
-0.04556938260793686,
0.05309806764125824,
-0.07274830341339111,
-0.01778733730316162,
0.08046035468578339,
0.06446196883916855,
0.014428730122745037,
0.036241527646780014,
0.08486424386501312,
-0.09409451484680176,
0.10577961802482605,
-0.0409997... |
a42dfd86-f57f-45aa-9585-cdacf8dbadd2 | description: 'Allows ClickHouse to connect to external databases via JDBC.'
sidebar_label: 'JDBC'
sidebar_position: 100
slug: /engines/table-engines/integrations/jdbc
title: 'JDBC table engine'
doc_type: 'reference'
import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';
JDBC table engine
:... | {"source_file": "jdbc.md"} | [
-0.011210539378225803,
-0.06764465570449829,
-0.10718517005443573,
0.03631041944026947,
-0.11828053742647171,
-0.007100324612110853,
0.028947647660970688,
0.012392166070640087,
-0.06138858199119568,
-0.024075889959931374,
-0.03469725325703621,
-0.011920401826500893,
0.07116466015577316,
-0... |
3e16d0d2-cb0d-479b-af81-5c1504d59b42 | sql
SELECT *
FROM jdbc_table
text
ββint_idββ¬βint_nullableββ¬βfloatββ¬βfloat_nullableββ
β 1 β α΄Ία΅α΄Έα΄Έ β 2 β α΄Ία΅α΄Έα΄Έ β
ββββββββββ΄βββββββββββββββ΄ββββββββ΄βββββββββββββββββ
sql
INSERT INTO jdbc_table(`int_id`, `float`)
SELECT toInt32(number), toFloat32(number * 1.0)
FROM system.numbers
See also {#se... | {"source_file": "jdbc.md"} | [
0.033968228846788406,
-0.022906113415956497,
-0.09786845743656158,
0.01315756794065237,
-0.07707367092370987,
-0.005167101044207811,
0.10978186875581741,
0.07073705643415451,
-0.05651380121707916,
-0.050944674760103226,
-0.036930300295352936,
-0.08068065345287323,
0.03724435716867447,
-0.0... |
ad18621a-da77-4551-b02a-46218368518e | description: 'This engine allows integrating ClickHouse with NATS to publish or subscribe
to message subjects, and process new messages as they become available.'
sidebar_label: 'NATS'
sidebar_position: 140
slug: /engines/table-engines/integrations/nats
title: 'NATS table engine'
doc_type: 'guide'
NATS table engi... | {"source_file": "nats.md"} | [
0.06803347170352936,
-0.07246478646993637,
-0.08040907233953476,
0.09083380550146103,
-0.00020783647778443992,
-0.04779043421149254,
0.004560594446957111,
-0.028298910707235336,
-0.10324736684560776,
0.044248051941394806,
0.011012519709765911,
-0.08913794904947281,
0.067692331969738,
-0.00... |
d6d8834f-d5ce-437a-a5f9-3157acc97378 | nats_server_list
- Server list for connection. Can be specified to connect to NATS cluster.
nats_skip_broken_messages
- NATS message parser tolerance to schema-incompatible messages per block. Default:
0
. If
nats_skip_broken_messages = N
then the engine skips
N
NATS messages that cannot be parsed (a message e... | {"source_file": "nats.md"} | [
0.029453737661242485,
-0.017923882231116295,
-0.07796461880207062,
0.06000902131199837,
0.012802531942725182,
-0.012931608594954014,
-0.049832217395305634,
-0.003793607233092189,
-0.11910293996334076,
0.026247631758451462,
-0.0018010176718235016,
-0.024868199601769447,
-0.025402428582310677,... |
68fff2ec-bbc1-410f-b5b2-d50c3a49210f | xml
<nats>
<user>click</user>
<password>house</password>
<token>clickhouse</token>
</nats>
Description {#description}
SELECT
is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using
materi... | {"source_file": "nats.md"} | [
0.009756086394190788,
-0.023912791162729263,
-0.04989335685968399,
0.06028858572244644,
-0.059873614460229874,
-0.09258529543876648,
0.029948348179459572,
-0.03745582699775696,
-0.018706317991018295,
0.021727340295910835,
-0.022244272753596306,
-0.09224750101566315,
0.029294710606336594,
-... |
799d6038-ee3e-4cbd-946d-4bed4deac6a4 | For block-based formats we cannot divide block into smaller parts, but the number of rows in one block can be controlled by general setting
max_block_size
.
Using JetStream {#using-jetstream}
Before using NATS engine with NATS JetStream, you must create a NATS stream and a durable pull consumer. For this, you ca... | {"source_file": "nats.md"} | [
-0.02950384095311165,
-0.0018374084029346704,
-0.05560627952218056,
-0.04028281942009926,
0.0461248941719532,
-0.008640081621706486,
-0.03456742316484451,
-0.003343199845403433,
-0.012243988923728466,
0.00878225825726986,
-0.06868388503789902,
-0.0021299412474036217,
-0.07863930612802505,
... |
c75f4e22-2c7c-4ada-8fbe-26ff8b490c30 | After creating stream and durable pull consumer, we can create a table with NATS engine. To do this, you need to initialize: nats_stream, nats_consumer_name, and nats_subjects:
SQL
CREATE TABLE nats_jet_stream (
key UInt64,
value UInt64
) ENGINE NATS
SETTINGS nats_url = 'localhost:4222',
... | {"source_file": "nats.md"} | [
-0.008418579585850239,
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0.025974037125706673,
-0.04522489383816719,
0.03228241205215454,
-0.14174769818782806,
-0.048128847032785416,
-0.01835670694708824,
-0.13543252646923065,
-0.0768098533153534,
0... |
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