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· One min read

Cause of the Error​

This error occurs while trying to connect to a ClickHouse server using clickhouse-client. The cause of the error is either:

  • the client configuration file config.xml is missing the root certificate in the machine CA default store, or
  • there is a self-signed or internal CA certificate that is not configured

Solution​

If using an internal or self-signed CA, configure the CA root certificate in config.xml in the client directory (e.g. /etc/clickhouse-client) and disable the loading of the default root CA certificates from the default location.

Here is an example configuration:

<openSSL>
    <client>
        <loadDefaultCAFile>false</loadDefaultCAFile>
        <caConfig>/etc/clickhouse-server/certs/marsnet_ca.crt</caConfig>
        <cacheSessions>true</cacheSessions>
        <disableProtocols>sslv2,sslv3</disableProtocols>
        <preferServerCiphers>true</preferServerCiphers>
        <invalidCertificateHandler>
            <name>RejectCertificateHandler</name>
        </invalidCertificateHandler>
    </client>
</openSSL>

Additional resources​

View https://clickhouse.com/docs/en/interfaces/cli/#configuration_files

· 2 min read

When this error occurs, a table shows as readonly and the error states intersecting parts. You can see the error in the logs or by

SELECT *
FROM system.replicas
WHERE is_readonly = 1

The error message looks like:

Code: 49. DB::Exception: Part XXXXX intersects previous part YYYYY. It is a bug or a result of manual intervention in the ZooKeeper data. (LOGICAL_ERROR) (version 21.12.4.1 (official build))

Cause of the Error​

This error can be caused by a race condition between mergeSelectingTask and queue reinitialization.

Solution​

Execute the following queries on all replicas:

DETACH TABLE table_name;  -- Required for DROP REPLICA

SYSTEM DROP REPLICA 'replica_name' FROM ZK PATH '/table_path_in_zk/'; -- It will remove everything from /table_path_in_zk

ATTACH TABLE table_name;  -- Table will be in readonly mode, because there is no metadata in ZK

Then execute the following on all replicas:

SYSTEM RESTORE REPLICA table_name;  -- It will detach all partitions, re-create metadata in ZK (like it's new empty table), and then attach all partitions back

SYSTEM SYNC REPLICA table_name; -- Wait for replicas to synchronize parts. Also it's recommended to check `system.detached_parts` on all replicas after recovery is finished.
tip

You should upgrade to the latest version of ClickHouse

Additional resources​

Related PRs and GitHub issues:

Versions affected:​

ClickHouse v 22.12 and prior

· 3 min read

TTL is going to be eventually applied. What does that mean? The MergeTree table setting merge_with_ttl_timeout sets the minimum delay in seconds before repeating a merge with delete TTL. The default value is 14400 seconds (4 hours). But that is just the minimum delay, it can take longer until a merge for delete TTL is triggered.

You can view all of your current TTL settings (like merge_with_ttl_timeout) with this query:

SELECT *
FROM system.merge_tree_settings
WHERE name like '%ttl%'

The response looks like:

┌─name───────────────────────────────────────────────────────────┬─value───┬─changed─┬─description────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬─min──┬─max──┬─readonly─┬─type───┐
│ max_replicated_merges_with_ttl_in_queue                        │ 1       │       0 │ How many tasks of merging parts with TTL are allowed simultaneously in ReplicatedMergeTree queue.                                                                                          │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ UInt64 │
│ max_number_of_merges_with_ttl_in_pool                          │ 2       │       0 │ When there is more than specified number of merges with TTL entries in pool, do not assign new merge with TTL. This is to leave free threads for regular merges and avoid "Too many parts" │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ UInt64 │
│ merge_tree_clear_old_broken_detached_parts_ttl_timeout_seconds │ 2592000 │       1 │ Remove old broken detached parts in the background if they remained intouched for a specified by this setting period of time.                                                              │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ UInt64 │
│ merge_with_ttl_timeout                                         │ 14400   │       0 │ Minimal time in seconds, when merge with delete TTL can be repeated.                                                                                                                       │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ Int64  │
│ merge_with_recompression_ttl_timeout                           │ 14400   │       0 │ Minimal time in seconds, when merge with recompression TTL can be repeated.                                                                                                                │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ Int64  │
│ ttl_only_drop_parts                                            │ 0       │       0 │ Only drop altogether the expired parts and not partially prune them.                                                                                                                       │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ Bool   │
│ materialize_ttl_recalculate_only                               │ 0       │       0 │ Only recalculate ttl info when MATERIALIZE TTL                                                                                                                                             │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ Bool   │
└────────────────────────────────────────────────────────────────┴─────────┴─────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴──────┴──────┴──────────┴────────┘

You can use SHOW CREATE TABLE to check if your table contains TTL rules, as well as if any of the table SETTINGS modified the values of the settings above:

SHOW CREATE TABLE <TableName>

Force a TTL rule to be applied​

This is not the most elegant solution, but you can explicitly call MATERIALIZE TTL, which forces all the TTL rules of a table to be materialized:

ALTER TABLE my_table
    MATERIALIZE TTL

Background threads affecting TTL​

It is possible that your TTL rules are not being applied because there are not enough working threads in the background pool. For example, if you insert data intensively, then the whole background pool might be utilized for normal merges. However, you can increase the background pool size.

You can check your current background pool size with this query:

SELECT *
FROM system.settings
WHERE name = 'background_pool_size';

The response looks like:

┌─name─────────────────┬─value─┬─changed─┬─description─────────────────────┬─min──┬─max──┬─readonly─┬─type───┬─default─┬─alias_for─┐
│ background_pool_size │ 16    │       0 │ Obsolete setting, does nothing. │ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │        0 │ UInt64 │ 16      │           │
└──────────────────────┴───────┴─────────┴─────────────────────────────────┴──────┴──────┴──────────┴────────┴─────────┴───────────┘

Check the docs for how to modify the background_pool_size setting, which is configured as:

<background_pool_size>16</background_pool_size>

You can check the current background pool activity with this query:

SELECT *
FROM system.metrics
WHERE metric like 'Background%'

· 3 min read

How to install and test ClickHouse on Microsoft Windows

When ClickHouse installing on Windows 10 you may receive errors when inserting data, for example:

DB::Exception: std::__1::__fs::filesystem::filesystem_error: filesystem error: in rename: Permission denied ["./store/711/71144174-d098-4056-8976-6ad1204205ec/tmp_insert_all_1_1_0/"] ["./store/711/71144174-d098-4056-8976-6ad1204205ec/all_1_1_0/"]. Stack trace:

On Windows 10, WSL needs to be upgraded to WSL 2.

wsl
  • For testing follow these instructions, you should have similar output: Since this is for testing, I logged in as root to avoid permissions issues:
sudo -i
  • Create a ClickHouse directory:
root@marspc2:~# mkdir /clickhouse
  • From the new directory, download clickhouse:
root@marspc2:/# cd clickhouse

root@marspc2:/clickhouse# curl https://clickhouse.com | sh
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  2739    0  2739    0     0   5515      0 --:--:-- --:--:-- --:--:--  5511

Will download https://builds.clickhouse.com/master/amd64/clickhouse into clickhouse

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  530M  100  530M    0     0  8859k      0  0:01:01  0:01:01 --:--:-- 8549k

Successfully downloaded the ClickHouse binary, you can run it as:
    ./clickhouse

You can also install it:
    sudo ./clickhouse install
  • Start the clickhouse server:
root@marspc2:/clickhouse# ./clickhouse server
Processing configuration file 'config.xml'.
There is no file 'config.xml', will use embedded config.
Cannot set max size of core file to 1073741824
2023.04.17 19:19:23.155323 [ 500 ] {} <Information> SentryWriter: Sending crash reports is disabled
2023.04.17 19:19:23.165447 [ 500 ] {} <Trace> Pipe: Pipe capacity is 1.00 MiB
2023.04.17 19:19:23.271147 [ 500 ] {} <Information> Application: Starting ClickHouse 23.4.1.1222 (revision: 54473, git hash: 3993aef8e281815ac4269d44e27bb1dcdcff21cb, build id: AF16AA59B689841860F39ACDBED30AC8F9AB70FA), PID 500
2023.04.17 19:19:23.271208 [ 500 ] {} <Information> Application: starting up
2023.04.17 19:19:23.271237 [ 500 ] {} <Information> Application: OS name: Linux, version: 5.15.90.1-microsoft-standard-WSL2, architecture: x86_64
...
  • In another WSL window, start the client:
root@marspc2:/clickhouse# ./clickhouse client
ClickHouse client version 23.4.1.1222 (official build).
Connecting to localhost:9000 as user default.
Connected to ClickHouse server version 23.4.1 revision 54462.

Warnings:
 * Linux transparent hugepages are set to "always". Check /sys/kernel/mm/transparent_hugepage/enabled

marspc2. :)
  • Create the database and table:
marspc2. :) create database db1;

CREATE DATABASE db1

Query id: 688f79e2-8132-44ed-98d6-0581abe9903a

Ok.

0 rows in set. Elapsed: 0.007 sec.

marspc2. :) create table db1.table1 (id Int64, string_column String) engine = MergeTree() order by id;

CREATE TABLE db1.table1
(
    `id` Int64,
    `string_column` String
)
ENGINE = MergeTree
ORDER BY id

Query id: d91a93b4-e13f-4e17-8201-f329223287d0

Ok.

0 rows in set. Elapsed: 0.010 sec.
  • Insert sample rows:
marspc2. :) insert into db1.table1 (id, string_column) values (1, 'a'), (2,'b');

INSERT INTO db1.table1 (id, string_column) FORMAT Values

Query id: 2b274eef-09af-434b-88e0-c25799649910

Ok.

2 rows in set. Elapsed: 0.003 sec.
  • View the rows:
marspc2. :) select * from db1.table1;

SELECT *
FROM db1.table1

Query id: 74c76bf1-d944-4b21-a384-cc0b5e6aa579

┌─id─┬─string_column─┐
│  1 │ a             │
│  2 │ b             │
└────┴───────────────┘

2 rows in set. Elapsed: 0.002 sec.

· 6 min read

The query_log table in the system database keeps track of all your queries, including:

  • how much memory the query consumed, and
  • how much CPU time was needed

The following query returns the top 10 queries, where "top" means the queries that used the most memory:

SELECT
    type,
    event_time,
    initial_query_id,
    query_id,
    formatReadableSize(memory_usage) AS memory,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'UserTimeMicroseconds')] AS userCPU,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'SystemTimeMicroseconds')] AS systemCPU,
    normalizedQueryHash(query) AS normalized_query_hash
FROM clusterAllReplicas(default, system.query_log)
ORDER BY memory_usage DESC
LIMIT 10;

The response looks like:

┌─type────────┬──────────event_time─┬─initial_query_id─────────────────────┬─memory─────┬─────userCPU─┬──systemCPU─┬─normalized_query_hash─┐
│ QueryFinish │ 2023-03-26 21:36:07 │ 7fc488a5-838f-410d-88ee-2f492825a26b │ 3.45 GiB   │ 28147128901 │ 8590897697 │    178963678599600243 │
│ QueryFinish │ 2023-03-26 21:36:04 │ 7fc488a5-838f-410d-88ee-2f492825a26b │ 1.18 GiB   │ 10194162387 │ 1183376457 │   4121209451971717712 │
│ QueryFinish │ 2023-03-26 21:36:06 │ 7fc488a5-838f-410d-88ee-2f492825a26b │ 1.16 GiB   │ 10516510952 │ 1484303318 │   4121209451971717712 │
│ QueryFinish │ 2023-03-26 21:35:59 │ 7fc488a5-838f-410d-88ee-2f492825a26b │ 1.14 GiB   │ 11484580963 │ 1464145099 │   4121209451971717712 │
│ QueryFinish │ 2023-03-26 21:47:01 │ 8119e682-a343-4847-96e7-d34ad8a748a1 │ 455.29 MiB │   123340498 │    8234304 │  10687606311941357470 │
│ QueryFinish │ 2023-03-26 22:07:05 │ f2690e48-fe1e-4367-ae9d-435d962003a5 │ 377.94 MiB │  2358130001 │  668098391 │   5988812223780974416 │
│ QueryFinish │ 2023-03-26 20:45:42 │ 04618222-40a1-4299-8c3d-9f050a82d849 │ 18.48 MiB  │       24676 │      16620 │   3205198713665290475 │
│ QueryFinish │ 2023-03-26 22:14:37 │ badf1097-5f8f-4486-88e9-3a5ac2e4734c │ 17.41 MiB  │      186234 │     148739 │   1910846996890686559 │
│ QueryFinish │ 2023-03-26 21:39:42 │ 8d373327-f566-4cd5-9f2c-cec75f534751 │ 16.19 MiB  │       23169 │      12365 │   3205198713665290475 │
│ QueryFinish │ 2023-03-26 21:35:42 │ ea672dba-7c10-4dd4-b819-cad9dccbf5d0 │ 13.97 MiB  │       20696 │       8001 │   3205198713665290475 │
└─────────────┴─────────────────────┴──────────────────────────────────────┴────────────┴─────────────┴────────────┴───────────────────────┘
note

The initial_query_id represents the ID of the initial query for distributed query execution launched from the node receiving the request. The query_id contains the ID of the child query executed on a different node. See this article for more details.

You can use the query ID to extract more details about the query. Let's research our longest running query above (the first one):

SELECT query
FROM clusterAllReplicas(default, system.query_log)
WHERE initial_query_id = '7fc488a5-838f-410d-88ee-2f492825a26b'

It turns out to be the query we used to insert a few billion rows of data into a table named youtube (see the YouTube dislikes dataset):

INSERT INTO youtube
SETTINGS input_format_null_as_default = 1
SELECT
    id,
    parseDateTimeBestEffortUS(toString(fetch_date)) AS fetch_date,
    upload_date,
    ifNull(title, '') AS title,
    uploader_id,
    ifNull(uploader, '') AS uploader,
    uploader_sub_count,
    is_age_limit,
    view_count,
    like_count,
    dislike_count,
    is_crawlable,
    has_subtitles,
    is_ads_enabled,
    is_comments_enabled,
    ifNull(description, '') AS description,
    rich_metadata,
    super_titles,
    ifNull(uploader_badges, '') AS uploader_badges,
    ifNull(video_badges, '') AS video_badges
FROM s3Cluster('default','https://clickhouse-public-datasets.s3.amazonaws.com/youtube/original/files/*.zst', 'JSONLines')

initial_query_id VS query_id​

Note that in a clustered ClickHouse environment (like ClickHouse Cloud) initial_query_id represents the ID of the initial query for distributed query execution launched from the node receiving the request; then query_id field will contain the ID of the child query executed on a different node.

If we add query_id to the above query we pin our search around initial_query_id = a7262fa2-bd8b-4b51-a359-621ccf282417 and hostname():

SELECT
    hostname(),
    type,
    event_time,
    initial_query_id,
    query_id,
    formatReadableSize(memory_usage) AS memory,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'UserTimeMicroseconds')] AS userCPU,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'SystemTimeMicroseconds')] AS systemCPU,
    normalizedQueryHash(query) AS normalized_query_hash
FROM clusterAllReplicas(default, system.query_log)
WHERE initial_query_id = 'a7262fa2-bd8b-4b51-a359-621ccf282417'
ORDER BY memory_usage DESC
LIMIT 10 FORMAT Pretty;

we will get:

┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ hostname()             ┃ type        ┃          event_time ┃ initial_query_id                     ┃ query_id                             ┃ memory     ┃ userCPU ┃ systemCPU ┃ normalized_query_hash ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ server-0 │ QueryFinish │ 2023-04-26 06:25:53 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 1f810b3c-b3cb-4a7b-bc6c-8c8cc1e52515 │ 125.13 MiB │ 1754290 │    133344 │  17604798521132779336 │
├────────────────────────┼─────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼────────────┼─────────┼───────────┼───────────────────────┤
│ server-2 │ QueryFinish │ 2023-04-26 06:25:53 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 123.08 MiB │ 1849115 │    123412 │   4258439895846105173 │
└────────────────────────┴─────────────┴─────────────────────┴──────────────────────────────────────┴──────────────────────────────────────┴────────────┴─────────┴───────────┴───────────────────────┘
┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ hostname()             ┃ type        ┃          event_time ┃ initial_query_id                     ┃ query_id                             ┃ memory    ┃ userCPU ┃ systemCPU ┃ normalized_query_hash ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ server-1 │ QueryFinish │ 2023-04-26 06:25:53 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 7dfd9297-5173-4be7-a866-d7cbe1e1abab │ 93.77 MiB │ 1890981 │    101724 │  17604798521132779336 │
├────────────────────────┼─────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼───────────┼─────────┼───────────┼───────────────────────┤
│ server-1 │ QueryStart  │ 2023-04-26 06:25:52 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 7dfd9297-5173-4be7-a866-d7cbe1e1abab │ 0.00 B    │       0 │         0 │  17604798521132779336 │
└────────────────────────┴─────────────┴─────────────────────┴──────────────────────────────────────┴──────────────────────────────────────┴───────────┴─────────┴───────────┴───────────────────────┘
┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ hostname()             ┃ type       ┃          event_time ┃ initial_query_id                     ┃ query_id                             ┃ memory ┃ userCPU ┃ systemCPU ┃ normalized_query_hash ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ server-0 │ QueryStart │ 2023-04-26 06:25:52 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 1f810b3c-b3cb-4a7b-bc6c-8c8cc1e52515 │ 0.00 B │       0 │         0 │  17604798521132779336 │
├────────────────────────┼────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼────────┼─────────┼───────────┼───────────────────────┤
│ server-2 │ QueryStart │ 2023-04-26 06:25:52 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 0.00 B │       0 │         0 │   4258439895846105173 │
└────────────────────────┴────────────┴─────────────────────┴──────────────────────────────────────┴──────────────────────────────────────┴────────┴─────────┴───────────┴───────────────────────┘

Note we have several results from several hosts (the different cluster nodes).

To refine further and get only the child queries we could also add the query_id != initial_query_id condition to the WHERE clause:

SELECT
    hostname(),
    type,
    event_time,
    initial_query_id,
    query_id,
    formatReadableSize(memory_usage) AS memory,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'UserTimeMicroseconds')] AS userCPU,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'SystemTimeMicroseconds')] AS systemCPU,
    normalizedQueryHash(query) AS normalized_query_hash
FROM clusterAllReplicas(default, system.query_log)
WHERE (query_id = initial_query_id) AND (initial_query_id = 'a7262fa2-bd8b-4b51-a359-621ccf282417')
ORDER BY memory_usage DESC
LIMIT 10 FORMAT Pretty;

returns all the child queries executed on the remote nodes (remote to the node where the query was first thrown at):

┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ hostname()             ┃ type        ┃          event_time ┃ initial_query_id                     ┃ query_id                             ┃ memory     ┃ userCPU ┃ systemCPU ┃ normalized_query_hash ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ server-1 │ QueryFinish │ 2023-04-26 06:25:53 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 7dfd9297-5173-4be7-a866-d7cbe1e1abab │ 93.77 MiB  │ 1890981 │    101724 │  17604798521132779336 │
├────────────────────────┼─────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼────────────┼─────────┼───────────┼───────────────────────┤
│ server-0 │ QueryFinish │ 2023-04-26 06:25:53 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 1f810b3c-b3cb-4a7b-bc6c-8c8cc1e52515 │ 125.13 MiB │ 1754290 │    133344 │  17604798521132779336 │
├────────────────────────┼─────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼────────────┼─────────┼───────────┼───────────────────────┤
│ server-1 │ QueryStart  │ 2023-04-26 06:25:52 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 7dfd9297-5173-4be7-a866-d7cbe1e1abab │ 0.00 B     │       0 │         0 │  17604798521132779336 │
├────────────────────────┼─────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼────────────┼─────────┼───────────┼───────────────────────┤
│ server-0 │ QueryStart  │ 2023-04-26 06:25:52 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 1f810b3c-b3cb-4a7b-bc6c-8c8cc1e52515 │ 0.00 B     │       0 │         0 │  17604798521132779336 │
└────────────────────────┴─────────────┴─────────────────────┴──────────────────────────────────────┴──────────────────────────────────────┴────────────┴─────────┴───────────┴───────────────────────┘
└────────────────────────┴─────────────┴─────────────────────┴──────────────────────────────────────┴──────────────────────────────────────┴────────────┴─────────┴───────────┴───────────────────────┘

conversely, query_id = initial_query_id will return only the queries executed on the local node where the distributed query was first thrown at:

SELECT
    hostname(),
    type,
    event_time,
    initial_query_id,
    query_id,
    formatReadableSize(memory_usage) AS memory,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'UserTimeMicroseconds')] AS userCPU,
    ProfileEvents.Values[indexOf(ProfileEvents.Names, 'SystemTimeMicroseconds')] AS systemCPU,
    normalizedQueryHash(query) AS normalized_query_hash
FROM clusterAllReplicas(default, system.query_log)
WHERE (query_id = initial_query_id) AND (initial_query_id = 'a7262fa2-bd8b-4b51-a359-621ccf282417')
ORDER BY memory_usage DESC
LIMIT 10 FORMAT Pretty;

giving:

┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ hostname()             ┃ type        ┃          event_time ┃ initial_query_id                     ┃ query_id                             ┃ memory     ┃ userCPU ┃ systemCPU ┃ normalized_query_hash ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ server-2 │ QueryFinish │ 2023-04-26 06:25:53 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 123.08 MiB │ 1849115 │    123412 │   4258439895846105173 │
├────────────────────────┼─────────────┼─────────────────────┼──────────────────────────────────────┼──────────────────────────────────────┼────────────┼─────────┼───────────┼───────────────────────┤
│ server-2 │ QueryStart  │ 2023-04-26 06:25:52 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ a7262fa2-bd8b-4b51-a359-621ccf282417 │ 0.00 B     │       0 │         0 │   4258439895846105173 │
└────────────────────────┴─────────────┴─────────────────────┴──────────────────────────────────────┴──────────────────────────────────────┴────────────┴─────────┴───────────┴───────────────────────┘

As for other System Tables, you can find more details about the meaning of each field in our docs.

· 2 min read

Suppose you create a table that uses the File table engine with the Parquet format:

CREATE TABLE parquet_test
(
    `x` UInt32,
    `y` String
)
ENGINE = File(Parquet)

You can write to the table once:

INSERT INTO parquet_test VALUES
   (1, 'Hello'),
   (2, 'Hi')

This creates a file named data.Parquet in the data/default/parquet_test folder. If you try to insert another batch:

INSERT INTO parquet_test VALUES
   (3, 'World'),
   (4, 'Bye')

...you get the following error:

Code: 641. DB::Exception: Received from localhost:9000. DB::Exception: Cannot append data in format Parquet to file, because this format doesn't support appends. You can allow to create a new file on each insert by enabling setting engine_file_allow_create_multiple_files. (CANNOT_APPEND_TO_FILE)

You can not append to Parquet files in ClickHouse. But you can tell ClickHouse to create a new file for every INSERT by enabling the engine_file_allow_create_multiple_files setting. If enabled, on each insert a new file will be created with a name following this pattern:

`data.Parquet` -> `data.1.Parquet` -> `data.2.Parquet`, etc.:

Let's give it a try. We will put our two commands into a single file named parquet.sql:

SET engine_file_allow_create_multiple_files = 1;

INSERT INTO default.parquet_test VALUES  (3, 'World'), (4, 'Bye');

Run it using clickhouse-client:

./clickhouse client --queries-file parquet.sql

And now you will see two files in data/default/parquet_test (and a new file for each subsequent insert).

note

The engine_file_allow_create_multiple_files setting applies to other data formats that are not appendable, like JSON and ORC.

· 2 min read

Question: How do I show all queries involving materialized views in the last 60m?

Answer:

This query will display all queries directed towards Materialized Views considering that:

  • we can leverage the create_table_query field in system.tables table to identify what tables are explicit (TO) recipient of MVs;
  • we can track back (using uuid and the name convention .inner_id.<uuid>) what tables are implicit recipient of MVs;

We can also configure how long back in time we want to look, by changing the value (60 m by default) in the initial query CTE

WITH(60) -- default 60m
AS timeRange,
(
    --prepare names of possible implicit MV hidden target tables for *any* table with NON NULL uuid
    SELECT groupArray(
            concat('default.`.inner_id.', toString(uuid), '`')
        )
    FROM clusterAllReplicas(default, system.tables)
    WHERE notEmpty(uuid)
) AS MV_implicit_possible_hidden_target_tables_names_array,
(
    --captures MV name and target tables (if TO is specified)
    --TODO it seems that extract will return just the first capturing group :( replace with regexpExtract once available
    SELECT arrayFilter(
            x->x != '',
            --remove empty captures
            groupArray(
                extract(
                    create_table_query,
                    '^CREATE MATERIALIZED VIEW\s(\w+\.\w+)\s(?:TO\s(\S+))?'
                )
            )
        )
    FROM clusterAllReplicas(default, system.tables)
    WHERE engine = 'MaterializedView'
) AS MV_explicit_target_tables_names_array
SELECT event_time,
    query,
    tables as "MVs tables"
FROM clusterAllReplicas(default, system.query_log)
WHERE (
        -- only SELECT within 60m
        event_time > now() - toIntervalMinute(timeRange)
        AND startsWith(query, 'SELECT')
    ) -- check either that query involves implicit MV target table names
    AND (
        hasAny(
            tables,
            MV_implicit_possible_hidden_target_tables_names_array
        )
        OR -- check that query involves explicit MV target table
        hasAny(tables, MV_explicit_target_tables_names_array)
    )
ORDER BY event_time DESC;

expected output:

| event_time          | query                                                                                          | MVs tables                                                            |
| ------------------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| 2023-02-23 08:14:14 | SELECT     rand(),* FROM     default.sum_of_volumes,     default.big_changes,     system.users | ["default.big_changes_mv","default.sum_of_volumes_mv","system.users"] |
| 2023-02-23 08:04:47 | SELECT     price,* FROM     default.sum_of_volumes,     default.big_changes                    | ["default.big_changes_mv","default.sum_of_volumes_mv"]                |

In this example results above default.big_changes_mv and default.sum_of_volumes_mv are both materialized views.

· 6 min read

Question: How do I import JSON arrays and how can I query the inner objects?

Answer:

Dump this 1 line JSON array to sample.json

{"_id":"1","channel":"help","events":[{"eventType":"open","time":"2021-06-18T09:42:39.527Z"},{"eventType":"close","time":"2021-06-18T09:48:05.646Z"}]},{"_id":"2","channel":"help","events":[{"eventType":"open","time":"2021-06-18T09:42:39.535Z"},{"eventType":"edit","time":"2021-06-18T09:42:41.317Z"}]},{"_id":"3","channel":"questions","events":[{"eventType":"close","time":"2021-06-18T09:42:39.543Z"},{"eventType":"create","time":"2021-06-18T09:52:51.299Z"}]},{"_id":"4","channel":"general","events":[{"eventType":"create","time":"2021-06-18T09:42:39.552Z"},{"eventType":"edit","time":"2021-06-18T09:47:29.109Z"}]},{"_id":"5","channel":"general","events":[{"eventType":"edit","time":"2021-06-18T09:42:39.560Z"},{"eventType":"open","time":"2021-06-18T09:42:39.680Z"},{"eventType":"close","time":"2021-06-18T09:42:41.207Z"},{"eventType":"edit","time":"2021-06-18T09:42:43.372Z"},{"eventType":"edit","time":"2021-06-18T09:42:45.642Z"}]}

Check the data:

clickhousebook.local :) SELECT * FROM file('/path/to/sample.json','JSONEachRow');

SELECT *
FROM file('/path/to/sample.json', 'JSONEachRow')

Query id: 0bbfa09f-ac7f-4a1e-9227-2961b5ffc2d4

┌─_id─┬─channel───┬─events─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   1 │ help      │ [{'eventType':'open','time':'2021-06-18T09:42:39.527Z'},{'eventType':'close','time':'2021-06-18T09:48:05.646Z'}]                                                                                                                                           │
│   2 │ help      │ [{'eventType':'open','time':'2021-06-18T09:42:39.535Z'},{'eventType':'edit','time':'2021-06-18T09:42:41.317Z'}]                                                                                                                                            │
│   3 │ questions │ [{'eventType':'close','time':'2021-06-18T09:42:39.543Z'},{'eventType':'create','time':'2021-06-18T09:52:51.299Z'}]                                                                                                                                         │
│   4 │ general   │ [{'eventType':'create','time':'2021-06-18T09:42:39.552Z'},{'eventType':'edit','time':'2021-06-18T09:47:29.109Z'}]                                                                                                                                          │
│   5 │ general   │ [{'eventType':'edit','time':'2021-06-18T09:42:39.560Z'},{'eventType':'open','time':'2021-06-18T09:42:39.680Z'},{'eventType':'close','time':'2021-06-18T09:42:41.207Z'},{'eventType':'edit','time':'2021-06-18T09:42:43.372Z'},{'eventType':'edit','time':'2021-06-18T09:42:45.642Z'}] │
└─────┴───────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

5 rows in set. Elapsed: 0.001 sec.

Create a table to receive the JSON rows:

clickhousebook.local :) CREATE TABLE IF NOT EXISTS sample_json_objects_array (
                            `rawJSON` String EPHEMERAL,
                            `_id` String DEFAULT JSONExtractString(rawJSON, '_id'),
                            `channel` String DEFAULT JSONExtractString(rawJSON, 'channel'),
                            `events` Array(JSON) DEFAULT JSONExtractArrayRaw(rawJSON, 'events')
                        ) ENGINE = MergeTree
                        ORDER BY
                            channel

CREATE TABLE IF NOT EXISTS sample_json_objects_array
(
    `rawJSON` String EPHEMERAL,
    `_id` String DEFAULT JSONExtractString(rawJSON, '_id'),
    `channel` String DEFAULT JSONExtractString(rawJSON, 'channel'),
    `events` Array(JSON) DEFAULT JSONExtractArrayRaw(rawJSON, 'events')
)
ENGINE = MergeTree
ORDER BY channel

Query id: d02696dd-3f9f-4863-be2a-b2c9a1ae922d


0 rows in set. Elapsed: 0.173 sec.

Insert the data:

clickhousebook.local :) INSERT INTO
                            sample_json_objects_array
                        SELECT
                            *
                        FROM
                            file(
                                '/opt/cases/000000/sample_json_objects_arrays.json',
                                'JSONEachRow'
                            );

INSERT INTO sample_json_objects_array SELECT *
FROM file('/opt/cases/000000/sample.json', 'JSONEachRow')

Query id: 60c4beab-3c2c-40c1-9c6f-bbbd7118dde3

Ok.

0 rows in set. Elapsed: 0.002 sec.

Check how the data inference acted on JSON object type:

clickhousebook.local :) DESCRIBE TABLE sample_json_objects_array SETTINGS describe_extend_object_types = 1;

DESCRIBE TABLE sample_json_objects_array
SETTINGS describe_extend_object_types = 1

Query id: 302c0c84-1b63-4f60-ad95-d91c0267b0d4

┌─name────┬─type────────────────────────────────────────┬─default_type─┬─default_expression─────────────────────┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ rawJSON │ String                                      │ EPHEMERAL    │ defaultValueOfTypeName('String')       │         │                  │                │
│ _id     │ String                                      │ DEFAULT      │ JSONExtractString(rawJSON, '_id')      │         │                  │                │
│ channel │ String                                      │ DEFAULT      │ JSONExtractString(rawJSON, 'channel')  │         │                  │                │
│ events  │ Array(Tuple(eventType String, time String)) │ DEFAULT      │ JSONExtractArrayRaw(rawJSON, 'events') │         │                  │                │
└─────────┴─────────────────────────────────────────────┴──────────────┴────────────────────────────────────────┴─────────┴──────────────────┴────────────────┘

Events is an Array of Tuple each containing a eventType String and a time String fields. This latter type is suboptimal (we'd want DateTime instead).

Let's see the data:

clickhousebook.local :) SELECT
                            _id,
                            channel,
                            events.eventType,
                            events.time
                        FROM sample_json_objects_array
                        WHERE has(events.eventType, 'close')

SELECT
    _id,
    channel,
    events.eventType,
    events.time
FROM sample_json_objects_array
WHERE has(events.eventType, 'close')

Query id: 3ddd6843-5206-4f52-971f-1699f0ba1728

┌─_id─┬─channel───┬─events.eventType──────────────────────┬─events.time──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ 5   │ general   │ ['edit','open','close','edit','edit'] │ ['2021-06-18T09:42:39.560Z','2021-06-18T09:42:39.680Z','2021-06-18T09:42:41.207Z','2021-06-18T09:42:43.372Z','2021-06-18T09:42:45.642Z'] │
│ 1   │ help      │ ['open','close']                      │ ['2021-06-18T09:42:39.527Z','2021-06-18T09:48:05.646Z']                                                                                  │
│ 3   │ questions │ ['close','create']                    │ ['2021-06-18T09:42:39.543Z','2021-06-18T09:52:51.299Z']                                                                                  │
└─────┴───────────┴───────────────────────────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

3 rows in set. Elapsed: 0.001 sec.

Let's run a few queries:

_id and channel of events that have an eventType of value close

clickhousebook.local :) SELECT
                            _id,
                            channel,
                            events.eventType
                        FROM
                            sample_json_objects_array
                        WHERE
                            has(events.eventType,'close')

SELECT
    _id,
    channel,
    events.eventType
FROM sample_json_objects_array
WHERE has(events.eventType, 'close')

Query id: 033a0c56-7bfa-4261-a334-7323bdc40f87

┌─_id─┬─channel───┬─events.eventType──────────────────────┐
│ 5   │ general   │ ['edit','open','close','edit','edit'] │
│ 1   │ help      │ ['open','close']                      │
│ 3   │ questions │ ['close','create']                    │
└─────┴───────────┴───────────────────────────────────────┘
┌─_id─┬─channel───┬─events.eventType──────────────────────┐
│ 5   │ general   │ ['edit','open','close','edit','edit'] │
│ 1   │ help      │ ['open','close']                      │
│ 3   │ questions │ ['close','create']                    │
└─────┴───────────┴───────────────────────────────────────┘

6 rows in set. Elapsed: 0.001 sec.

We want to query the time , for example all events between a given time range, but we notice it was imported as String:

clickhousebook.local :) SELECT toTypeName(events.time) FROM sample_json_objects_array;

SELECT toTypeName(events.time)
FROM sample_json_objects_array

Query id: 27f07f02-66cd-420d-8623-eeed7d501014

┌─toTypeName(events.time)─┐
│ Array(String)           │
│ Array(String)           │
│ Array(String)           │
│ Array(String)           │
│ Array(String)           │
└─────────────────────────┘

5 rows in set. Elapsed: 0.001 sec.

So, in order to handle these as dates, first we want to convert to DateTime. To convert an array we use a map function:

clickhousebook.local :) 
                        SELECT
                            _id,
                            channel,
                            arrayMap(x->parseDateTimeBestEffort(x), events.time)
                        FROM
                            sample_json_objects_array

SELECT
    _id,
    channel,
    arrayMap(x -> parseDateTimeBestEffort(x), events.time)
FROM sample_json_objects_array

Query id: f3c7881e-b41c-4872-9c67-5c25966599a1

┌─_id─┬─channel───┬─arrayMap(lambda(tuple(x), parseDateTimeBestEffort(x)), events.time)─────────────────────────────────────────────┐
│ 4   │ general   │ ['2021-06-18 11:42:39','2021-06-18 11:47:29']                                                                   │
│ 5   │ general   │ ['2021-06-18 11:42:39','2021-06-18 11:42:39','2021-06-18 11:42:41','2021-06-18 11:42:43','2021-06-18 11:42:45'] │
│ 1   │ help      │ ['2021-06-18 11:42:39','2021-06-18 11:48:05']                                                                   │
│ 2   │ help      │ ['2021-06-18 11:42:39','2021-06-18 11:42:41']                                                                   │
│ 3   │ questions │ ['2021-06-18 11:42:39','2021-06-18 11:52:51']                                                                   │
└─────┴───────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

5 rows in set. Elapsed: 0.001 sec.

we can appreciate the diffs using toTypeName on both the arrays:

clickhousebook.local :) SELECT
                            _id,
                            channel,
                            toTypeName(events.time) as events_as_strings,
                            toTypeName(arrayMap(x->parseDateTimeBestEffort(x), events.time)) as events_as_datetime
                        FROM
                            sample_json_objects_array

SELECT
    _id,
    channel,
    toTypeName(events.time) AS events_as_strings,
    toTypeName(arrayMap(x -> parseDateTimeBestEffort(x), events.time)) AS events_as_datetime
FROM sample_json_objects_array

Query id: 1af54994-b756-472f-88d7-8b5cdca0e54e

┌─_id─┬─channel───┬─events_as_strings─┬─events_as_datetime─┐
│ 4   │ general   │ Array(String)     │ Array(DateTime)    │
│ 5   │ general   │ Array(String)     │ Array(DateTime)    │
│ 1   │ help      │ Array(String)     │ Array(DateTime)    │
│ 2   │ help      │ Array(String)     │ Array(DateTime)    │
│ 3   │ questions │ Array(String)     │ Array(DateTime)    │
└─────┴───────────┴───────────────────┴────────────────────┘

5 rows in set. Elapsed: 0.001 sec.

now let's get the id of of the rows where time is between a given interval.

we use arrayCount to see if there is a count greater than than 0 of items in the array returned by the map function that will match the condition x BETWEEN toDateTime('2021-06-18 11:46:00', 'Europe/Rome') AND toDateTime('2021-06-18 11:50:00', 'Europe/Rome')

clickhousebook.local :) SELECT
                            _id,
                            arrayMap(x -> parseDateTimeBestEffort(x), events.time)
                        FROM
                            sample_json_objects_array
                        WHERE
                            arrayCount(
                                x -> x BETWEEN toDateTime('2021-06-18 11:46:00', 'Europe/Rome')
                                AND toDateTime('2021-06-18 11:50:00', 'Europe/Rome'),
                                arrayMap(x -> parseDateTimeBestEffort(x), events.time)
                            ) > 0;

SELECT
    _id,
    arrayMap(x -> parseDateTimeBestEffort(x), events.time)
FROM sample_json_objects_array
WHERE arrayCount(x -> ((x >= toDateTime('2021-06-18 11:46:00', 'Europe/Rome')) AND (x <= toDateTime('2021-06-18 11:50:00', 'Europe/Rome'))), arrayMap(x -> parseDateTimeBestEffort(x), events.time)) > 0

Query id: d4882fc3-9f99-4e87-9f89-47683f10656d

┌─_id─┬─arrayMap(lambda(tuple(x), parseDateTimeBestEffort(x)), events.time)─┐
│ 4   │ ['2021-06-18 11:42:39','2021-06-18 11:47:29']                       │
│ 1   │ ['2021-06-18 11:42:39','2021-06-18 11:48:05']                       │
└─────┴─────────────────────────────────────────────────────────────────────┘

2 rows in set. Elapsed: 0.002 sec.

⚠️

Please remember, at the time of writing this article the current implementation of JSON is experimental and not suited for production.

This example highlights how to quickly import JSON and start querying it and represents a tradeoff between the ease of use where we import the JSON objects as JSON type with no need to specify upfront the schema type. Convenient for a quick test however for long term use of the data we would like to, with regards to this example to store the data using the most appropriate types, so for the time field, use DateTime instead of String, in order to avoid any post-ingestion phase conversion as illustrated above. Please refer to the documentation for more about handling JSON.

· 2 min read

Question:

How can I quickly recreate a table and its data using just copy/paste across different terminals?

Answer:

This is NOT a recommended practice to migrate data from one database to another and it should NOT be used for production data migration.

This is simply intended as a quick and dirty way to recreate small amount of data when developing across multiple environments.

  1. Get the CREATE TABLE statement with SHOW CREATE table:
SHOW CREATE TABLE cookies;

SHOW CREATE TABLE cookies

Query id: 248ec8e2-5bce-45b3-97d9-ed68edf445a5

┌─statement────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ CREATE TABLE default.cookies
(
    `id` String,
    `timestamp` DateTime
)
ENGINE = MergeTree
ORDER BY id
SETTINGS index_granularity = 8192 │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

  1. Get the data export using FORMAT SQLInsert
SELECT * FROM cookies FORMAT SQLInsert;

SELECT *
FROM cookies
FORMAT SQLInsert

Query id: 383759b8-69c0-4561-ab95-f8224abc0071

INSERT INTO table (`id`, `timestamp`) VALUES ('4', '2023-03-15 16:28:46')
, ('2', '2023-03-15 16:28:41')
, ('1', '2023-03-15 16:11:02'), ('1', '2023-03-15 16:11:40'), ('1', '2023-03-15 16:11:48'), ('1', '2023-03-15 16:16:05'), ('2', '2023-03-15 16:11:06'), ('3', '2023-03-15 16:11:12'), ('3', '2023-03-15 16:11:45'), ('3', '2023-03-15 16:16:08'), ('4', '2023-03-15 16:11:14'), ('4', '2023-03-15 16:11:50'), ('4', '2023-03-15 16:16:01'), ('5', '2023-03-15 16:11:18'), ('5', '2023-03-15 16:16:11')
;

15 rows in set. Elapsed: 0.023 sec.

Note you will need to replace the name table at point 2 with the actual table name (cookies in this example)

· One min read

I want to export data segregating the path in S3 bucket to follow a structure like:

  • 2022
    • 1
    • 2
    • ...
    • 12
  • 2021
    • 1
    • 2
    • ...
    • 12

and so on ...

Answer​

Considering the ClickHouse table:

CREATE TABLE sample_data (
    `name` String,
    `age` Int,
    `time` DateTime
) ENGINE = MergeTree
ORDER BY
    name

Add 10000 entries:

INSERT INTO
    sample_data
SELECT
    *
FROM
    generateRandom(
        'name String, age Int, time DateTime',
        10,
        10,
        10
    )
LIMIT
    10000;

Run this to create the desired structure in s3 bucket my_bucket (note this example writes files in parquet format):

INSERT INTO
    FUNCTION s3(
        'https://s3-host:4321/my_bucket/{_partition_id}/file.parquet.gz',
        's3-access-key',
        's3-secret-access-key',
        Parquet,
        'name String, age Int, time DateTime'
    ) PARTITION BY concat(
        formatDateTime(time, '%Y'),
        '/',
        formatDateTime(time, '%m')
    )
SELECT
    name,
    age,
    time
FROM
    sample_data
Query id: 55adcf22-f6af-491e-b697-d09694bbcc56

Ok.

0 rows in set. Elapsed: 15.579 sec. Processed 10.00 thousand rows, 219.93 KB (641.87 rows/s., 14.12 KB/s.)