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Flink ClickHouse Connector

It is an unofficial temporary fork of flink-connector-clickhouse having goal to publish it to Maven Central.

In the future it should be superseded by work made under FLIP-202.

Flink SQL connector for ClickHouse database, this project Powered by ClickHouse JDBC.

Currently, the project supports Source/Sink Table and Flink Catalog.
Please create issues if you encounter bugs and any help for the project is greatly appreciated.

Connector Options

Option Required Default Type Description
url required none String The ClickHouse jdbc url in format clickhouse://<host>:<port>.
username optional none String The 'username' and 'password' must both be specified if any of them is specified.
password optional none String The ClickHouse password.
database-name optional default String The ClickHouse database name.
table-name required none String The ClickHouse table name.
use-local optional false Boolean Directly read/write local tables in case of distributed table engine.
sink.batch-size optional 1000 Integer The max flush size, over this will flush data.
sink.flush-interval optional 1s Duration Over this flush interval mills, asynchronous threads will flush data.
sink.max-retries optional 3 Integer The max retry times when writing records to the database failed.
sink.write-local optional false Boolean Removed from version 1.15, use use-local instead.
sink.update-strategy optional update String Convert a record of type UPDATE_AFTER to update/insert statement or just discard it, available: update, insert, discard.
sink.partition-strategy optional balanced String Partition strategy: balanced(round-robin), hash(partition key), shuffle(random).
sink.partition-key optional none String Partition key used for hash strategy.
sink.sharding.use-table-definition optional false Boolean Sharding strategy consistent with definition of distributed table, if set to true, the configuration of sink.partition-strategy and sink.partition-key will be overwritten.
sink.ignore-delete optional true Boolean Whether to ignore delete statements.
sink.parallelism optional none Integer Defines a custom parallelism for the sink.
scan.partition.column optional none String The column name used for partitioning the input.
scan.partition.num optional none Integer The number of partitions.
scan.partition.lower-bound optional none Long The smallest value of the first partition.
scan.partition.upper-bound optional none Long The largest value of the last partition.
catalog.ignore-primary-key optional true Boolean Whether to ignore primary keys when using ClickHouseCatalog to create table.
properties.* optional none String This can set and pass clickhouse-jdbc configurations.
lookup.cache optional NONE String The caching strategy for this lookup table, including NONE and PARTIAL(not support FULL yet)
lookup.partial-cache.expire-after-access optional none Duration Duration to expire an entry in the cache after accessing, over this time, the oldest rows will be expired.
lookup.partial-cache.expire-after-write optional none Duration Duration to expire an entry in the cache after writing, over this time, the oldest rows will be expired.
lookup.partial-cache.max-rows optional none Long The max number of rows of lookup cache, over this value, the oldest rows will be expired.
lookup.partial-cache.caching-missing-key optional true Boolean Flag to cache missing key, true by default
lookup.max-retries optional 3 Integer The max retry times if lookup database failed.

Update/Delete Data Considerations:

  1. Distributed table don't support the update/delete statements, if you want to use the update/delete statements, please be sure to write records to local table or set use-local to true.
  2. The data is updated and deleted by the primary key, please be aware of this when using it in the partition table.

breaking

Since version 1.16, we have taken shard weight into consideration, this may affect which shard the data is distributed to.

Data Type Mapping

Flink Type ClickHouse Type
CHAR String
VARCHAR String / IP / UUID
STRING String / Enum
BOOLEAN UInt8
BYTES FixedString
DECIMAL Decimal / Int128 / Int256 / UInt64 / UInt128 / UInt256
TINYINT Int8
SMALLINT Int16 / UInt8
INTEGER Int32 / UInt16 / Interval
BIGINT Int64 / UInt32
FLOAT Float32
DOUBLE Float64
DATE Date
TIME DateTime
TIMESTAMP DateTime
TIMESTAMP_LTZ DateTime
INTERVAL_YEAR_MONTH Int32
INTERVAL_DAY_TIME Int64
ARRAY Array
MAP Map
ROW Not supported
MULTISET Not supported
RAW Not supported

Maven Dependency

<dependency>
    <groupId>name.nkonev.flink</groupId>
    <artifactId>flink-sql-connector-clickhouse</artifactId>
    <version>1.17.1-9</version>
</dependency>

Building

# clone the project
git clone https://github.com/nkonev/flink-connector-clickhouse.git

# enter the project directory
cd flink-connector-clickhouse/

# display remote branches
git branch -r

# checkout the branch you need
git checkout $branch_name

# install or deploy the project to our own repository
mvn clean install -DskipTests
mvn clean deploy -DskipTests

How to use

Create and read/write table

-- register a clickhouse table `t_user` in flink sql.
CREATE TABLE t_user (
    `user_id` BIGINT,
    `user_type` INTEGER,
    `language` STRING,
    `country` STRING,
    `gender` STRING,
    `score` DOUBLE,
    `list` ARRAY<STRING>,
    `map` Map<STRING, BIGINT>,
    PRIMARY KEY (`user_id`) NOT ENFORCED
) WITH (
    'connector' = 'clickhouse',
    'url' = 'clickhouse://{ip}:{port}',
    'database-name' = 'tutorial',
    'table-name' = 'users',
    'sink.batch-size' = '500',
    'sink.flush-interval' = '1000',
    'sink.max-retries' = '3'
);

-- read data from clickhouse 
SELECT user_id, user_type from t_user;

-- write data into the clickhouse table from the table `T`
INSERT INTO t_user
SELECT cast(`user_id` as BIGINT), `user_type`, `lang`, `country`, `gender`, `score`, ARRAY['CODER', 'SPORTSMAN'], CAST(MAP['BABA', cast(10 as BIGINT), 'NIO', cast(8 as BIGINT)] AS MAP<STRING, BIGINT>) FROM T;

Create and use ClickHouseCatalog

Scala

val tEnv = TableEnvironment.create(setting)

val props = new util.HashMap[String, String]()
props.put(ClickHouseConfig.DATABASE_NAME, "default")
props.put(ClickHouseConfig.URL, "clickhouse://127.0.0.1:8123")
props.put(ClickHouseConfig.USERNAME, "username")
props.put(ClickHouseConfig.PASSWORD, "password")
props.put(ClickHouseConfig.SINK_FLUSH_INTERVAL, "30s")
val cHcatalog = new ClickHouseCatalog("clickhouse", props)
tEnv.registerCatalog("clickhouse", cHcatalog)
tEnv.useCatalog("clickhouse")

tEnv.executeSql("insert into `clickhouse`.`default`.`t_table` select...");

Java

TableEnvironment tEnv = TableEnvironment.create(setting);

Map<String, String> props = new HashMap<>();
props.put(ClickHouseConfig.DATABASE_NAME, "default")
props.put(ClickHouseConfig.URL, "clickhouse://127.0.0.1:8123")
props.put(ClickHouseConfig.USERNAME, "username")
props.put(ClickHouseConfig.PASSWORD, "password")
props.put(ClickHouseConfig.SINK_FLUSH_INTERVAL, "30s");
Catalog cHcatalog = new ClickHouseCatalog("clickhouse", props);
tEnv.registerCatalog("clickhouse", cHcatalog);
tEnv.useCatalog("clickhouse");

tEnv.executeSql("insert into `clickhouse`.`default`.`t_table` select...");

SQL

> CREATE CATALOG clickhouse WITH (
    'type' = 'clickhouse',
    'url' = 'clickhouse://127.0.0.1:8123',
    'username' = 'username',
    'password' = 'password',
    'database-name' = 'default',
    'use-local' = 'false',
    ...
);

> USE CATALOG clickhouse;
> SELECT user_id, user_type FROM `default`.`t_user` limit 10;
> INSERT INTO `default`.`t_user` SELECT ...;

Roadmap

  • Implement the Flink SQL Sink function.
  • Support array and Map types.
  • Support ClickHouseCatalog.
  • Implement the Flink SQL Source function.
  • Implement the Flink SQL Lookup function.