my2ch

Transfer a MySQL query result to a Clickhouse table with potential incremental updates.

What is it?

A tool that allows you to define a MySQL query that is created as a table in ClickHouse for further analysis of the data. Schedule it to have daily updates or every minute to near realtime data.

Why?

MySQL is a popular OLTP database, but it is not the best when it comes to analysis and statistics. Clickhouse is fantastic when it comes to richness of query functions and raw speed of both queries and insert rate, which makes it a great match.

How does it work?

This tool will automatically:

Create a MySQL temporary table from query
Describe the temp table to create a ClickHouse table representation
Create a view in MySQL with the query
Create a table in Clickhouse based on the definition
Create a MYSQL-engine database in ClickHouse to connect directly to MySQL
INSERT INTO SELECT from the view to the ClickHouse table
Example

Prerequisites:

A running MySQL server
Docker installed

For this example we are going to use the test data from the "employees" database, which can be found here: https://github.com/datacharmer/test_db/releases/download/v1.0.7/test_db-1.0.7.tar.gz

Decompress it and insert the data into your MYSQL server.

Starting a ClickHouse server

docker run -d --name clickhouse-server --ulimit nofile=262144:262144 -p 9000:9000 --volume=$HOME/clickhouse_database:/var/lib/clickhouse yandex/clickhouse-server

You can connect with the native ClickHouse client using th following command:

docker run -it --rm --link clickhouse-server:clickhouse-server yandex/clickhouse-client --host clickhouse-server

Configuration

Create a folder called configs/employees and put the following transfer.yaml inside it, taking care to change the actual property values to match your setup.

alias: employees

target:
  primary-key: from_date
  table-name: employees
  engine-definition: ENGINE = MergeTree
      ORDER BY from_date
      SETTINGS index_granularity = 8192;

source:
  range-clause: where from_date > '{max_primary_key}'
  query: select e.emp_no, birth_date, s.from_date, s.to_date, s.salary
    from salaries s
    left join employees e on s.emp_no = e.emp_no

schedule:
  interval: PT30s

For reference, the data model in MySQL has the following structure: Schema

Transfer data

Run the following command to transfer data:

docker run --name my2ch --rm -v $PWD/logs:/tmp/logs -v $PWD/configs:/tmp/configs -e TZ=UTC ethlocom/my2ch:latest

You should see something like the following:

Processing data set 'employees'
Connected to MySQL host 192.168.50.112
65,536 processed
131,072 processed
196,608 processed
...
2,686,976 processed
2,752,512 processed
2,844,047 processed
Transferred 2,844,047 rows in 5.847 seconds
=== Status ===
Total rows: 2,861,778
Last modified: a moment ago (2020-12-22 10:44:03Z)
Disk usage: 12.1 MB
Compression ratio: 5.24X
Storage engine: MergeTree

Running the same command again should not transfer any data (as there is no new data inserted in MySQL. This is controlled by the range_clause: and from_date > '{max_primary_key}').

Processing data set 'employees'
Connected to MySQL host 192.168.50.112
Transferred 0 rows in 0.104 seconds
=== Status ===
Total rows: 2,861,778
Last modified: 12 seconds ago (2020-12-22 10:44:03Z)
Disk usage: 12.1 MB
Compression ratio: 5.24X
Storage engine: MergeTree

Querying in ClickHouse

We can now test the data with a simple query:

SELECT
    toYear(from_date) AS year,
    max(salary) AS max_salary
FROM employees
GROUP BY year
ORDER BY max_salary DESC
LIMIT 10

┌─year─┬─max_salary─┐
│ 2002 │     158220 │
│ 2001 │     157821 │
│ 2000 │     155377 │
│ 1999 │     154885 │
│ 1998 │     151484 │
│ 1997 │     149675 │
│ 1996 │     149208 │
│ 1995 │     146531 │
│ 1994 │     143182 │
│ 1993 │     140625 │
└──────┴────────────┘

10 rows in set. Elapsed: 0.017 sec. Processed 2.86 million rows, 22.89 MB (163.83 million rows/s., 1.31 GB/s.)

ethlo / my2ch

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