COSCUP 2024

Peter

2024/08/04

My best practices for interacting between PostgreSQL and ClickHouse

Slide

About me

  • Peter, GitHub

  • Active open source contributor

  • Speaker

    • ​COSCUP、MOPCON......

  • An engineer

    • DevOps

    • Back-end

    • System Architecture Researching

    • Web Application Security

    • PHP, Python and JavaScript

  • Industrial Technology Research Institute

    • Smart Grid Technology (2017~2021)

  • Institute for Information Industry

    • Database, Data platform architecture (2021~)

Outlines

  • What's ClickHouse?

  • Why I need to interact between PostgreSQL and ClickHouse?

  • Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

    • Case 1: From PostgreSQL to ClickHouse

    • Case 2: From ClickHouse to PostgreSQL

  • Conclusion

Outlines

  • What's ClickHouse?

  • Why I need to interact between PostgreSQL and ClickHouse?

  • Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

    • Case 1: From PostgreSQL to ClickHouse

    • Case 2: From ClickHouse to PostgreSQL

  • Conclusion

What's ClickHouse?

  • https://clickhouse.com

  • OLAP(Online analytical processing)database

    • Datasets can be massive - billions or trillions of rows

    • Data is organized in tables that contain many columns

    • Only a few columns are selected to answer any particular query

    • Results must be returned in milliseconds or seconds

  • A column-oriented database

    • What's the difference between column-oriented and row-oriented database?

Row-oriented database

Column-oriented database

Outlines

  • What's ClickHouse?

  • Why I need to interact between PostgreSQL and ClickHouse?

  • Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

    • Case 1: From PostgreSQL to ClickHouse

    • Case 2: From ClickHouse to PostgreSQL

  • Conclusion

Why I need to interact between PostgreSQL and ClickHouse?

  • Collecting the sensor datasets must be quick

    • Sensor datasets are collected from EMS

    • Sensor datasets should be collected every 15~30 seconds

    • Sensor datasets may have duplicated issue frequently

  • The PostgreSQL cannot collect frequently

    • But the PostgreSQL can store processed datasets

    • Get the average of 15~30 seconds datasets to 1 minute

Outlines

  • What's ClickHouse?

  • Why I need to interact between PostgreSQL and ClickHouse?

  • Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

    • Case 1: From PostgreSQL to ClickHouse

    • Case 2: From ClickHouse to PostgreSQL

  • Conclusion

Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

  • Prepare the experimental virtual machine

  • From ClickHouse to PostgreSQL

  • From PostgreSQL to ClickHouse

Prepare the experimental virtual machine

  • Ubuntu 22.04

    • CPU: 4 cores

    • RAM: 6GB

    • SSD: 120 GB

  • Install the PostgreSQL 14

  • Install the latest ClickHouse server verison

$ cat /proc/cpuinfo | grep -c CPU
4
$ free -mh
               total        used        free      shared  buff/cache   available
Mem:           5.8Gi       180Mi       4.8Gi        14Mi       847Mi       5.4Gi
Swap:             0B          0B          0B
$ df -lh /
Filesystem      Size  Used Avail Use% Mounted on
/dev/vda2       118G  4.7G  108G   5% /
$ sudo apt-get update
$ sudo apt-get install -y postgresql postgreslq-contrib
$ sudo su - postgres
postgres@server1:~$ psql
postgres@server1:~$ postgres=# CREATE USER lee CREATEDB password 'password';
CREATE ROLE
postgres=#
postgres=# exit
postgres@server1:~$ exit
$ vim /etc/postgresql/14/main/pg_hba.conf
$ cat /etc/postgresql/14/main/pg_hba.conf
# Database administrative login by Unix domain socket
local   all             postgres                                peer
local   all             lee                                     md5


$ sudo systemctl reload postgresql.service
$ psql -U lee --dbname postgres
Password for user lee:
psql (14.12 (Ubuntu 14.12-0ubuntu0.22.04.1))
Type "help" for help.

postgres=> create database lee;
CREATE DATABASE
postgres=> exit

Prepare the experimental virtual machine

Install the PostgreSQL 14 version

$ sudo apt-get update
$ sudo apt-get install -y apt-transport-https ca-certificates curl gnupg
$ curl -fsSL 'https://packages.clickhouse.com/rpm/lts/repodata/repomd.xml.key' \
  | sudo gpg --dearmor -o /usr/share/keyrings/clickhouse-keyring.gpg
$ echo "deb [signed-by=/usr/share/keyrings/clickhouse-keyring.gpg] https://packages.clickhouse.com/deb stable main" \
  | sudo tee /etc/apt/sources.list.d/clickhouse.list
$ sudo apt-get update
$ sudo apt-get install -y clickhouse-server clickhouse-client
 chown -R clickhouse:clickhouse '/var/log/clickhouse-server/'
 chown -R clickhouse:clickhouse '/var/run/clickhouse-server'
 chown  clickhouse:clickhouse '/var/lib/clickhouse/'
Enter password for the default user:
$ sudo systemctl enable --now clickhouse-server
$ clickhouse-client --password
ClickHouse client version 24.6.2.17 (official build).
Password for user (default):
Connecting to localhost:9000 as user default.
Connected to ClickHouse server version 24.6.2.

server1.peterli.website :) select version();

SELECT version()

Query id: 468925e2-65ec-4416-9aca-19778b6d8fc3

   ┌─version()─┐
1. │ 24.6.2.17 │
   └───────────┘

1 row in set. Elapsed: 0.004 sec.

server1.peterli.website :) exit
Bye.

Prepare the experimental virtual machine

Install the latest ClickHouse version

Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

  • Prepare the experimental virtual machine

  • From ClickHouse to PostgreSQL

  • From PostgreSQL to ClickHouse

From ClickHouse to PostgreSQL

  • Writing some codes (Python for example)

    • Connecting the ClickHouse firstly.

    • Query required sensor datasets.

    • Process above sensor datasets.

    • Connecting the PostgreSQL secondly.

    • Inserting the processed sensor datasets to PostgreSQL.

From ClickHouse to PostgreSQL

import os
import json
import clickhouse_connect


username = os.getenv('clickhouse_db_username')
password = os.getenv('clickhouse_db_password')
port = os.getenv('clickhouse_db_port')
db_name = os.getenv('clickhouse_db_name')
clickhouse_client = clickhouse_connect.get_client(
    host=host,
    username=username,
    password=password,
    port=port,
    database=db_name
)

clickhouse_client.query('CREATE DATABASE IF NOT EXISTS device')

device_identity = '1.1.1'
clickhouse_client.query(
    '''
    CREATE TABLE IF NOT EXISTS device.`%s`
    (
        `value` Float64,
        `measured_timestamp` DateTime('Asia/Taipei')
    )
    ENGINE = MergeTree
    PARTITION BY toYYYYMM(measured_timestamp)
    ORDER BY measured_timestamp
    SETTINGS index_granularity = 8192
    ''' % device_identity
)

clickhouse_client.query('''
    OPTIMIZE TABLE `%s` FINAL DEDUPLICATE BY %s, %s;
    ''' % (device_identity, 'value', 'measured_timestamp',)
)

columns = ['value', 'measured_timestamp']
sensor_records = [
    [0.39, datetime.datetime.now()],
    [0.375, datetime.datetime.now()],
]
clickhouse_client.insert(device_identity, sensor_records, column_names=columns)

From ClickHouse to PostgreSQL

From ClickHouse to PostgreSQL

$ sudo snap install docker --classic
$ git clone --depth 1 https://github.com/airbytehq/airbyte.git
$ cd airbyte

$ ./run-ab-platform.sh -b
......
[+] Running 11/11
 ✔ Container airbyte-docker-proxy-1            Started                                       0.0s
 ✔ Container airbyte-temporal                  Started                                       0.1s
 ✔ Container airbyte-db                        Started                                       0.3s
 ✔ Container init                              Exited                                        0.1s
 ✔ Container airbyte-bootloader                Exited                                        0.1s
 ✔ Container airbyte-connector-builder-server  Started                                       0.6s
......
Airbyte containers are running!
root@server1:~/airbyte#

From ClickHouse to PostgreSQL

  • Browsing the Airbyte website

    • Browse http://{IP_ADDRESS}:8000

    • username: airbyte

    • password: password

From ClickHouse to PostgreSQL

  • Initializing the Airbyte website

From ClickHouse to PostgreSQL

  • Initializing the Airbyte website

From ClickHouse to PostgreSQL

  • Search the ClickHouse

From ClickHouse to PostgreSQL

  • Create the ClickHouse to be source

From ClickHouse to PostgreSQL

  • Create the PostgreSQL to be destination

From ClickHouse to PostgreSQL

  • It cannot use customized SQL queries for source.

  • To process data, it needs to develop the  connector.

From ClickHouse to PostgreSQL

  • Using the customized codes

    • Pros

      • It's very free to implement logical for syncing data.

    • Cons

      • Monitoring status should be implement by yourself.

  • Using the Airbyte

    • Pros

      • It has the GUI tool to synchronize data easily.

      • It can be easy to monitor synchronize statuses.

    • Cons

      • It needs to develop connector to process data.

Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

  • Prepare the experiment virtual machine

  • From ClickHouse to PostgreSQL

  • From PostgreSQL to ClickHouse

From PostgreSQL to ClickHouse

  • Sometimes some datasets are stored in the PostgreSQL database

  • To analyse data quickly,

    • it should load data from PostgreSQL to ClickHouse

  • There're two approaches to achieve above goals

    • Using the official tool to integrate the PostgreSQL database

    • Using the Airbyte to load data from PostgreSQL to ClickHouse

From PostgreSQL to ClickHouse

From PostgreSQL to ClickHouse

  • Official ClickHouse support for PostgreSQL integration

CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
    name1 type1 [DEFAULT|MATERIALIZED|ALIAS expr1] [TTL expr1],
    name2 type2 [DEFAULT|MATERIALIZED|ALIAS expr2] [TTL expr2],
    ...
)
ENGINE = PostgreSQL({host:port, database, table, user, password[, schema, [, on_conflict]] | named_collection[, option=value [,..]]})

From PostgreSQL to ClickHouse

  • Official ClickHouse support for PostgreSQL integration

$ clickhouse-client --password
ClickHouse client version 24.6.2.17 (official build).
Password for user (default):
Connecting to localhost:9000 as user default.
Connected to ClickHouse server version 24.6.2.

CREATE TABLE default.`1.1.1`
(
    `value` double,
    `measured_timestamp` DateTime('Asia/Taipei')
)
ENGINE = PostgreSQL(
  '{IP_ADDRESS}:5432',
  'database_name',
  '1.1.1',
  'user_name',
  'password',
  'schema_name'
)

Query id: 945344dc-07e8-479a-9eb5-2e54a7c326a4

Ok.

0 rows in set. Elapsed: 0.033 sec.

From PostgreSQL to ClickHouse

  • Official ClickHouse support for PostgreSQL integration

  • This approach still let data store in the PostgreSQL database

datapipeline03 :) select count(*) from '1.1.1';

SELECT count(*)
FROM `1.1.1`

Query id: 394af180-731a-45e0-aba4-ac2efbdab9a1

┌─count()─┐
│  170703 │
└─────────┘

1 row in set. Elapsed: 0.645 sec. Processed 170.70 thousand rows, 682.81 KB (264.53 thousand rows/s., 1.06 MB/s.)
Peak memory usage: 837.77 KiB.

datapipeline03 :)

From PostgreSQL to ClickHouse

  • Another approach can load PostgreSQL and store ClickHouse

CREATE TABLE default.`1.1.1_copy`
(
    `value` double,
    `measured_timestamp` DateTime('Asia/Taipei')
)
ENGINE = MergeTree
ORDER BY measured_timestamp

Query id: 849a3f6e-5a79-4f46-bfc5-9f2515b80597

Ok.

0 rows in set. Elapsed: 0.035 sec.

From PostgreSQL to ClickHouse

  • Another approach can load PostgreSQL and store ClickHouse

datapipeline03 :) INSERT INTO default."1.1.1_copy"
SELECT * FROM postgresql('IP_ADDRESS:5432', 'database_name', 'table_name', 'user', 'password', 'schema');

INSERT INTO default.`1.1.1_copy` SELECT *
FROM postgresql('IP_ADDRESS:5432', 'database_name', 'table_name', 'user', 'password', 'schema')

Query id: 3feae7a8-bc34-4ade-811c-cf47ad5dae1a

Ok.

0 rows in set. Elapsed: 0.338 sec. Processed 170.70 thousand rows, 2.73 MB (505.36 thousand rows/s., 8.09 MB/s.)
Peak memory usage: 8.12 MiB.

datapipeline03 :) select count(*) from '1.1.1_copy';

SELECT count(*)
FROM `1.1.1_copy`

Query id: ed26a16e-3a37-4db9-8df2-b1d8846def2d

┌─count()─┐
│  170703 │
└─────────┘

1 row in set. Elapsed: 0.004 sec.

From PostgreSQL to ClickHouse

  • Compare these two approaches

datapipeline03 :) select count(*) from '1.1.1_copy';

SELECT count(*)
FROM `1.1.1_copy`

Query id: ed26a16e-3a37-4db9-8df2-b1d8846def2d

┌─count()─┐
│  170703 │
└─────────┘

1 row in set. Elapsed: 0.004 sec.

datapipeline03 :) select count(*) from '1.1.1';

SELECT count(*)
FROM `1.1.1`

Query id: 174f6669-4df6-4cbf-a05d-688c456a98fa

┌─count()─┐
│  170703 │
└─────────┘

1 row in set. Elapsed: 0.172 sec. Processed 170.70 thousand rows, 682.81 KB (991.29 thousand rows/s., 3.97 MB/s.)
Peak memory usage: 774.29 KiB.

From PostgreSQL to ClickHouse

  • Airbyte approach(Create PostgreSQL source)

From PostgreSQL to ClickHouse

  • Airbyte approach(Create PostgreSQL source)

From PostgreSQL to ClickHouse

  • Airbyte approach(Create ClickHouse destination)

From PostgreSQL to ClickHouse

  • Airbyte approach(Create Connection)

From PostgreSQL to ClickHouse

  • Airbyte approach(Create Connection)

Outlines

  • What's ClickHouse?

  • Why I need to interact between PostgreSQL and ClickHouse?

  • Introducing multiple approaches about interacting between PostgreSQL and ClickHouse

    • Case 1: From PostgreSQL to ClickHouse

    • Case 2: From ClickHouse to PostgreSQL

  • Conclusion

Conclusion

  • Load data from ClickHouse to PostgreSQL

    • Actually, it's not convenient.

    • It's not supported in the ClickHouse natively.

    • It needs to customize codes to achieve this goal.

    • With Airbyte, it needs to create connector if we want to customize our logical data loading.

  • Load data from PostgreSQL to ClickHouse​

    • It's more convenient than above use case.

    • It is supported in the ClickHouse database.

    • With Airbyte, it needs to create connector if we want to customize our logical data loading.

More references

Any Questions?

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