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ClickHouse | ClickHouse is a free analytics DBMS for big data | Database library

 by   ClickHouse C++ Version: v22.4.2.1-stable License: Apache-2.0

 by   ClickHouse C++ Version: v22.4.2.1-stable License: Apache-2.0

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kandi X-RAY | ClickHouse Summary

ClickHouse is a C++ library typically used in Database applications. ClickHouse has no bugs, it has a Permissive License and it has medium support. However ClickHouse has 13 vulnerabilities. You can download it from GitHub.
ClickHouse® is an open-source column-oriented database management system that allows generating analytical data reports in real-time.
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Support
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Quality
Security
Security
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kandi-support Support

  • ClickHouse has a medium active ecosystem.
  • It has 23286 star(s) with 4650 fork(s). There are 668 watchers for this library.
  • There were 3 major release(s) in the last 6 months.
  • There are 1986 open issues and 9337 have been closed. On average issues are closed in 51 days. There are 262 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of ClickHouse is v22.4.2.1-stable
ClickHouse Support
Best in #Database
Average in #Database
ClickHouse Support
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Average in #Database

quality kandi Quality

  • ClickHouse has 0 bugs and 0 code smells.
ClickHouse Quality
Best in #Database
Average in #Database
ClickHouse Quality
Best in #Database
Average in #Database

securitySecurity

  • ClickHouse has 13 vulnerability issues reported (1 critical, 6 high, 6 medium, 0 low).
  • ClickHouse code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
ClickHouse Security
Best in #Database
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ClickHouse Security
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Average in #Database

license License

  • ClickHouse is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
ClickHouse License
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ClickHouse License
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Average in #Database

buildReuse

  • ClickHouse releases are available to install and integrate.
  • It has 168926 lines of code, 5061 functions and 1980 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
ClickHouse Reuse
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ClickHouse Reuse
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ClickHouse Key Features

ClickHouse® is a free analytics DBMS for big data

Clickhouse Kafka engine on cluster

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CREATE MATERIALIZED VIEW etl.tmp_consumer TO etl.tmp AS
SELECT ......

How to send multiple arguments to Executable UDF in ClickHouse?

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<functions>                                     
    <function>                                  
        <type>executable</type>                 
        <name>test_function_python</name>       
        <return_type>String</return_type>       
        <argument><type>String</type></argument>
        <argument><type>String</type></argument>
        <format>TabSeparated</format>           
        <command>test_function.py</command>     
        <execute_direct>1</execute_direct>      
    </function>                                 
</functions>                                    
SELECT test_function_python(number, number + 1) AS x   
FROM numbers(10)                                       
                                                       
Query id: 562676d4-e7fb-4aec-86b4-fef41fec4864         
                                                       
┌─x─────────────────┐                                  
│ 0: arg1=0 arg2=1  │                                  
│ 1: arg1=1 arg2=2  │                                  
│ 2: arg1=2 arg2=3  │                                  
│ 3: arg1=3 arg2=4  │                                  
│ 4: arg1=4 arg2=5  │                                  
│ 5: arg1=5 arg2=6  │                                  
│ 6: arg1=6 arg2=7  │                                  
│ 7: arg1=7 arg2=8  │                                  
│ 8: arg1=8 arg2=9  │                                  
│ 9: arg1=9 arg2=10 │                                  
└───────────────────┘                                  
#!/usr/bin/python3

import sys

if __name__ == '__main__':
    i = 0
    for line in sys.stdin:
        arg1, arg2 = line.split('\t')
        print(f'{i}: arg1={arg1} arg2={arg2}', end='')
        sys.stdout.flush()
        i += 1
-----------------------
<functions>                                     
    <function>                                  
        <type>executable</type>                 
        <name>test_function_python</name>       
        <return_type>String</return_type>       
        <argument><type>String</type></argument>
        <argument><type>String</type></argument>
        <format>TabSeparated</format>           
        <command>test_function.py</command>     
        <execute_direct>1</execute_direct>      
    </function>                                 
</functions>                                    
SELECT test_function_python(number, number + 1) AS x   
FROM numbers(10)                                       
                                                       
Query id: 562676d4-e7fb-4aec-86b4-fef41fec4864         
                                                       
┌─x─────────────────┐                                  
│ 0: arg1=0 arg2=1  │                                  
│ 1: arg1=1 arg2=2  │                                  
│ 2: arg1=2 arg2=3  │                                  
│ 3: arg1=3 arg2=4  │                                  
│ 4: arg1=4 arg2=5  │                                  
│ 5: arg1=5 arg2=6  │                                  
│ 6: arg1=6 arg2=7  │                                  
│ 7: arg1=7 arg2=8  │                                  
│ 8: arg1=8 arg2=9  │                                  
│ 9: arg1=9 arg2=10 │                                  
└───────────────────┘                                  
#!/usr/bin/python3

import sys

if __name__ == '__main__':
    i = 0
    for line in sys.stdin:
        arg1, arg2 = line.split('\t')
        print(f'{i}: arg1={arg1} arg2={arg2}', end='')
        sys.stdout.flush()
        i += 1
-----------------------
<functions>                                     
    <function>                                  
        <type>executable</type>                 
        <name>test_function_python</name>       
        <return_type>String</return_type>       
        <argument><type>String</type></argument>
        <argument><type>String</type></argument>
        <format>TabSeparated</format>           
        <command>test_function.py</command>     
        <execute_direct>1</execute_direct>      
    </function>                                 
</functions>                                    
SELECT test_function_python(number, number + 1) AS x   
FROM numbers(10)                                       
                                                       
Query id: 562676d4-e7fb-4aec-86b4-fef41fec4864         
                                                       
┌─x─────────────────┐                                  
│ 0: arg1=0 arg2=1  │                                  
│ 1: arg1=1 arg2=2  │                                  
│ 2: arg1=2 arg2=3  │                                  
│ 3: arg1=3 arg2=4  │                                  
│ 4: arg1=4 arg2=5  │                                  
│ 5: arg1=5 arg2=6  │                                  
│ 6: arg1=6 arg2=7  │                                  
│ 7: arg1=7 arg2=8  │                                  
│ 8: arg1=8 arg2=9  │                                  
│ 9: arg1=9 arg2=10 │                                  
└───────────────────┘                                  
#!/usr/bin/python3

import sys

if __name__ == '__main__':
    i = 0
    for line in sys.stdin:
        arg1, arg2 = line.split('\t')
        print(f'{i}: arg1={arg1} arg2={arg2}', end='')
        sys.stdout.flush()
        i += 1

Why is Clickhouse slower than PostgreSQL?

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create table test ( id Int64, d Date ) Engine=MergeTree Order by id;
insert into test select number, today() from numbers(1e9);

select count() from test;
┌───count()─┐
│ 100000000 │
└───────────┘
1 rows in set. Elapsed: 0.002 sec.

select count(id) from test;
┌─count(id)─┐
│ 100000000 │
└───────────┘
1 rows in set. Elapsed: 0.239 sec. Processed 100.00 million rows, 800.00 MB (418.46 million rows/s., 3.35 GB/s.)


drop table test;

create table test ( id Int64, d Int64 ) Engine=MergeTree partition by (intDiv(d, 10000)) Order by id;
set max_partitions_per_insert_block=0;
insert into test select number, number from numbers(1e8);

select count(id) from test;
┌─count(id)─┐
│ 100000000 │
└───────────┘
1 rows in set. Elapsed: 1.050 sec. Processed 100.00 million rows, 800.00 MB (95.20 million rows/s., 761.61 MB/s.)


select count(d) from test;
┌──count(d)─┐
│ 100000000 │
└───────────┘
1 rows in set. Elapsed: 0.004 sec.

How to correctly set a path to default config for clickhouse backup?

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sudo ./clickhouse-backup default-config > /etc/clickhouse-backup/config.yml

vi /etc/clickhouse-backup/config.yml

In clickhouse, how can I separate number by comma?

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select formatReadableQuantity(roundBankers( 12321.121 ,  1 ));
┌─formatReadableQuantity(roundBankers(12321.121, 1))─┐
│ 12.32 thousand                                     │
└────────────────────────────────────────────────────┘
SELECT concat(toString(intDiv(roundBankers(12321.121, 1) AS x, 1000)), ',', toString(x % 1000)) AS r
┌─r─────────────────────┐
│ 12,321.10000000000036 │
└───────────────────────┘
-----------------------
select formatReadableQuantity(roundBankers( 12321.121 ,  1 ));
┌─formatReadableQuantity(roundBankers(12321.121, 1))─┐
│ 12.32 thousand                                     │
└────────────────────────────────────────────────────┘
SELECT concat(toString(intDiv(roundBankers(12321.121, 1) AS x, 1000)), ',', toString(x % 1000)) AS r
┌─r─────────────────────┐
│ 12,321.10000000000036 │
└───────────────────────┘

Error running ClickHouse Docker on MacBook M1

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docker run -it -p 9000:9000 -p 9009:9009 -p 8123:8123 --platform linux/amd64 --ulimit nofile=262144:262144 clickhouse/clickhouse-server

How can I decline an INSERT when column is set to NOT NULL

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CREATE TABLE test (
    name String NOT NULL,
    isodate DateTime('Europe/Berlin') NOT NULL,
    CONSTRAINT isodate_not_null CHECK isodate <> toDateTime(0, 'Europe/Berlin')
) ENGINE = MergeTree()
ORDER BY (isodate)


insert into test(name) values ('x');
DB::Exception: Constraint `isodate_not_null` for table default.test (f589312a-1592-426a-b589-312a1592b26a) is violated at row 1. Expression: (isodate != toDateTime(0)). Column values: isodate = 0. (VIOLATED_CONSTRAINT)


insert into test values ('x', now());
OK.
-----------------------
clickhouse-client -h ... --input_format_null_as_default 0
clickhouse> SET input_format_null_as_default=0
-----------------------
clickhouse-client -h ... --input_format_null_as_default 0
clickhouse> SET input_format_null_as_default=0

How to use ClickHouse partition value in SQL query?

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SELECT
    *,
    _partition_id,
    _partition_value
FROM my_table
WHERE (_partition_value.2) = 1
-----------------------
where (a < 20000 AND b > 0.6 AND c >= 100) = 1
insert into my_table select 1, 3000000, 0, 0 from numbers(100000000);
insert into my_table select 1, 0, 10, 200 from numbers(100);

SET send_logs_level = 'debug';
set force_index_by_date=1;

select sum(id) from my_table where (a < 20000 AND b > 0.6 AND c >= 100) = 1;
           
...Selected 1/7 parts by partition key...

┌─sum(id)─┐
│     100 │
└─────────┘
1 rows in set. Elapsed: 0.002 sec.

-----------------------
where (a < 20000 AND b > 0.6 AND c >= 100) = 1
insert into my_table select 1, 3000000, 0, 0 from numbers(100000000);
insert into my_table select 1, 0, 10, 200 from numbers(100);

SET send_logs_level = 'debug';
set force_index_by_date=1;

select sum(id) from my_table where (a < 20000 AND b > 0.6 AND c >= 100) = 1;
           
...Selected 1/7 parts by partition key...

┌─sum(id)─┐
│     100 │
└─────────┘
1 rows in set. Elapsed: 0.002 sec.

Clickhouse pass data for inner join

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SELECT *
FROM VALUES('a UInt64, s String', (1, 'one'), (2, 'two'), (3, 'three'))

┌─a─┬─s─────┐
│ 1 │ one   │
│ 2 │ two   │
│ 3 │ three │
└───┴───────┘

WITH 
    [(toUInt64(1), 'one'), (2, 'two'), (3, 'three')] AS rows_array, 
    arrayJoin(rows_array) AS row_tuple
SELECT 
    row_tuple.1 AS number_decimal, 
    row_tuple.2 AS number_string
┌─number_decimal─┬─number_string─┐
│              1 │ one           │
│              2 │ two           │
│              3 │ three         │
└────────────────┴───────────────┘

Selecting specific rows from a table, using a group by query

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SELECT Date,Name, Certificates
FROM
(
SELECT 
    Date, Name, 
    Certificates=COUNT(Certificates) OVER (PARTITION BY Date,Name) 
    RowNumber = ROW_NUMBER() OVER (PARTITION BY Date
     ORDER BY Name ASC) 
FROM Students.data
) T 
WHERE RowNumber =1 
ORDER BY Date ASC
;
-----------------------
SELECT date, name, certificates
FROM
(
  SELECT 
    date, name, COUNT(Certificates) AS certificates,
    ROW_NUMBER() OVER (PARTITION BY date ORDER BY name) AS rn
  FROM students.data
  GROUP BY date, name
) numbered
WHERE rn = 1
ORDER BY date;
-----------------------

WITH ranked_data AS (
SELECT date_, name, certificates_,
row_number() OVER(PARTITION BY date_ order by name) as rank_
FROM students
)
SELECT 
  date_, name, certificates_ FROM ranked_data WHERE rank_ = 1

-----------------------
SELECT Date, untuple(groupArray(tuple(Name, Certificates))[1])
FROM (
    SELECT *
    FROM  (
        /* Emulate the test dataset. */
        SELECT toDate(data.1) AS Date, data.2 AS Name, data.3 AS Certificates
        FROM (
            SELECT arrayJoin([
                ('2021-02-01', 'Jason ', 3),
                ('2021-02-01', 'Nisha ', 4),
                ('2021-02-01', 'Zaid  ', 5),
                ('2021-03-25', 'Aniket', 4),
                ('2021-03-25', 'Anish ', 2),
                ('2021-03-25', 'Nadia ', 0),
                ('2021-05-06', 'Aadil ', 7),
                ('2021-05-06', 'Ashish', 1),
                ('2021-05-06', 'Rahil ', 9)]) AS data
            )
        )
    ORDER BY Date, Name
    )
GROUP BY Date

/*
┌───────Date─┬─Name───┬─Certificates─┐
│ 2021-02-01 │ Jason  │            3 │
│ 2021-03-25 │ Aniket │            4 │
│ 2021-05-06 │ Aadil  │            7 │
└────────────┴────────┴──────────────┘
*/
SELECT DISTINCT
    Date,
    FIRST_VALUE(Name) OVER w AS FirstName,
    FIRST_VALUE(Certificates) OVER w AS FirstCertificates
FROM 
(
    /* Emulate the test dataset. */
    SELECT toDate(data.1) AS Date, data.2 AS Name, data.3 AS Certificates
    FROM (
        SELECT arrayJoin([
            ('2021-02-01', 'Jason ', 3),
            ('2021-02-01', 'Nisha ', 4),
            ('2021-02-01', 'Zaid  ', 5),
            ('2021-03-25', 'Aniket', 4),
            ('2021-03-25', 'Anish ', 2),
            ('2021-03-25', 'Nadia ', 0),
            ('2021-05-06', 'Aadil ', 7),
            ('2021-05-06', 'Ashish', 1),
            ('2021-05-06', 'Rahil ', 9)]) AS data
        )
)
WINDOW w AS (PARTITION BY Date ORDER BY Name ASC)
SETTINGS allow_experimental_window_functions = 1

/*
┌───────Date─┬─FirstName─┬─FirstCertificates─┐
│ 2021-02-01 │ Jason     │                 3 │
│ 2021-03-25 │ Aniket    │                 4 │
│ 2021-05-06 │ Aadil     │                 7 │
└────────────┴───────────┴───────────────────┘
*/
-----------------------
SELECT Date, untuple(groupArray(tuple(Name, Certificates))[1])
FROM (
    SELECT *
    FROM  (
        /* Emulate the test dataset. */
        SELECT toDate(data.1) AS Date, data.2 AS Name, data.3 AS Certificates
        FROM (
            SELECT arrayJoin([
                ('2021-02-01', 'Jason ', 3),
                ('2021-02-01', 'Nisha ', 4),
                ('2021-02-01', 'Zaid  ', 5),
                ('2021-03-25', 'Aniket', 4),
                ('2021-03-25', 'Anish ', 2),
                ('2021-03-25', 'Nadia ', 0),
                ('2021-05-06', 'Aadil ', 7),
                ('2021-05-06', 'Ashish', 1),
                ('2021-05-06', 'Rahil ', 9)]) AS data
            )
        )
    ORDER BY Date, Name
    )
GROUP BY Date

/*
┌───────Date─┬─Name───┬─Certificates─┐
│ 2021-02-01 │ Jason  │            3 │
│ 2021-03-25 │ Aniket │            4 │
│ 2021-05-06 │ Aadil  │            7 │
└────────────┴────────┴──────────────┘
*/
SELECT DISTINCT
    Date,
    FIRST_VALUE(Name) OVER w AS FirstName,
    FIRST_VALUE(Certificates) OVER w AS FirstCertificates
FROM 
(
    /* Emulate the test dataset. */
    SELECT toDate(data.1) AS Date, data.2 AS Name, data.3 AS Certificates
    FROM (
        SELECT arrayJoin([
            ('2021-02-01', 'Jason ', 3),
            ('2021-02-01', 'Nisha ', 4),
            ('2021-02-01', 'Zaid  ', 5),
            ('2021-03-25', 'Aniket', 4),
            ('2021-03-25', 'Anish ', 2),
            ('2021-03-25', 'Nadia ', 0),
            ('2021-05-06', 'Aadil ', 7),
            ('2021-05-06', 'Ashish', 1),
            ('2021-05-06', 'Rahil ', 9)]) AS data
        )
)
WINDOW w AS (PARTITION BY Date ORDER BY Name ASC)
SETTINGS allow_experimental_window_functions = 1

/*
┌───────Date─┬─FirstName─┬─FirstCertificates─┐
│ 2021-02-01 │ Jason     │                 3 │
│ 2021-03-25 │ Aniket    │                 4 │
│ 2021-05-06 │ Aadil     │                 7 │
└────────────┴───────────┴───────────────────┘
*/

Community Discussions

Trending Discussions on ClickHouse
  • Choose an SQL driver at runtime in Golang when drivers have the same name
  • Clickhouse Kafka engine on cluster
  • How to send multiple arguments to Executable UDF in ClickHouse?
  • Why is Clickhouse slower than PostgreSQL?
  • Clickhouse: Should i optimize MergeTree table manually?
  • How to correctly set a path to default config for clickhouse backup?
  • In clickhouse, how can I separate number by comma?
  • Error running ClickHouse Docker on MacBook M1
  • Why is ClickHouse dictionary performance so low?
  • How can I decline an INSERT when column is set to NOT NULL
Trending Discussions on ClickHouse

QUESTION

Choose an SQL driver at runtime in Golang when drivers have the same name

Asked 2022-Mar-28 at 14:53

I'd like to know if there is an approach or projection pattern to be able to choose SQL driver at runtime in Golang when both of these drivers have the same name. I want to switch between HTTP ClickHouse driver (https://github.com/mailru/go-clickhouse) and native TCP ClickHouse driver (https://github.com/ClickHouse/clickhouse-go) using an environment variable.

import(
//HTTP driver
_ "github.com/mailru/go-clickhouse"
)

func getHttpCHConnection() (*sql.DB, error) {
   ...
   db, err := sql.Open("clickhouse", clkConnUrl)
import(
//Native driver
_ "github.com/ClickHouse/clickhouse-go"
)

func getNativeCHConnection() (*sql.DB, error) {
    ...
    db, err := sql.Open("clickhouse", clkConnUrl)

Normally, it causes "panic: sql: Register called twice for driver clickhouse". Is it possible to avoid that?

ANSWER

Answered 2022-Mar-28 at 14:53

Since version 2 of mailru/go-clickhouse it is possible to use both of them, authors have changed driver name to chhttp: https://github.com/mailru/go-clickhouse/issues/151

Source https://stackoverflow.com/questions/70683405

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ClickHouse

You can download it from GitHub.

Support

Official website has a quick high-level overview of ClickHouse on the main page.Tutorial shows how to set up and query a small ClickHouse cluster.Documentation provides more in-depth information.YouTube channel has a lot of content about ClickHouse in video format.Slack and Telegram allow chatting with ClickHouse users in real-time.Blog contains various ClickHouse-related articles, as well as announcements and reports about events.Code Browser (Woboq) with syntax highlight and navigation.Code Browser (github.dev) with syntax highlight, powered by github.dev.Contacts can help to get your questions answered if there are any.

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