Flink Getting Started Guide

Support Matrix

LakeSoul	Flink
2.4.x+	1.17
2.1.x-2.3.x	1.14

PG Configuration

Add the following configuration to $FLINK_HOME/conf/flink-conf.yaml:

containerized.master.env.LAKESOUL_PG_DRIVER: com.lakesoul.shaded.org.postgresql.Driver
containerized.master.env.LAKESOUL_PG_USERNAME: root
containerized.master.env.LAKESOUL_PG_PASSWORD: root
containerized.master.env.LAKESOUL_PG_URL: jdbc:postgresql://localhost:5432/test_lakesoul_meta?stringtype=unspecified
containerized.taskmanager.env.LAKESOUL_PG_DRIVER: com.lakesoul.shaded.org.postgresql.Driver
containerized.taskmanager.env.LAKESOUL_PG_USERNAME: root
containerized.taskmanager.env.LAKESOUL_PG_PASSWORD: root
containerized.taskmanager.env.LAKESOUL_PG_URL: jdbc:postgresql://localhost:5432/test_lakesoul_meta?stringtype=unspecified

Note that both the master and taskmanager environment variables need to be set.

tip

The connection information, username and password of the Postgres database need to be modified according to the actual deployment.

caution

Note that if you use Session mode to start a job, that is, submit the job to Flink Standalone Cluster as a client, flink run as a client will not read the above configuration, so you need to configure the environment variables separately, namely:

export LAKESOUL_PG_DRIVER=com.lakesoul.shaded.org.postgresql.Driver
export LAKESOUL_PG_URL=jdbc:postgresql://localhost:5432/test_lakesoul_meta?stringtype=unspecified
export LAKESOUL_PG_USERNAME=root
export LAKESOUL_PG_PASSWORD=root

SQL

Download LakeSoul Flink Jar

It can be downloaded from the LakeSoul Release page: https://github.com/lakesoul-io/LakeSoul/releases/download/v2.6.0/lakesoul-flink-flink-1.17-2.6.0.jar.

Start SQL Client

# Start Flink SQL Client
bin/sql-client.sh embedded -j lakesoul-flink-flink-1.17-2.6.0.jar

Create Table

Java

TableEnvironment tEnv = TableEnvironment.create(EnvironmentSettings.inBatchMode());
String createUserSql = "create table user_info (" +
        "`id` INT," +
        "name STRING," +
        "score INT," +
        "`date` STRING," +
        "region STRING," +
         " PRIMARY KEY (`id`,`name`) NOT ENFORCED"+
        ") PARTITIONED BY (`region`,`date`)"+
         " WITH (" +
        " 'connector'='lakesoul'," +
        " 'hashBucketNum'='4'," +
        " 'use_cdc'='true'," +
        " 'path'='/tmp/lakesoul/flink/sink/test' )";
tEnv. executeSql(createUserSql);

SQL

-- Create the test_table table, use id and name as the joint primary key, use region and date as the two-level range partition, catalog is lakesoul, and database is default
create table `lakesoul`.`default`.test_table (
            `id` INT,
            name STRING,
            score INT,
            `date` STRING,
            region STRING,
        PRIMARY KEY (`id`,`name`) NOT ENFORCED
        ) PARTITIONED BY (`region`,`date`)
        WITH (
            'connector'='lakesoul',
            'hashBucketNum'='4',
            'use_cdc'='true',
            'path'='file:///tmp/lakesoul/flink/sink/test');

tip

The meaning of the parameters for creating a table

Parameter	Explanation	Value Format
PARTITIONED BY	used to specify the range partition field of the table, if there is no range partition field, it will be omitted	PARTITIONED BY (date)
PRIMARY KEY	used to specify one or more primary keys	PARIMARY KEY (id, name) NOT ENFORCED
connector	data source connector, used to specify the data source type	'connector'='lakesoul'
hashBucketNum	table with primary key(s) must have this property set to a number >= 0	'hashBucketNum'='4'
path	used to specify the storage path of the table	'path'='file:///tmp/lakesoul/flink/sink/test'
use_cdc	Set whether the table is in CDC format (refer to CDC Table Format )	'use_cdc'='true'

Drop Table

Java

tEnvs.executeSql("DROP TABLE if exists test_table");

SQL

DROP TABLE if exists test_table;

Insert Data

Java

tEnvs.executeSql("insert into `lakesoul`.`default`.test_table values (1, 'AAA', 98, '2023-05-10', 'China')"). await();

SQL

Batch insert:

insert into `lakesoul`.`default`.test_table values (1,'AAA', 98, '2023-05-10', 'China');

Streaming: read data from another stream source and write into LakeSoul. If upstream source is a Changelog stream, then LakeSoul table should be created with CDC format enabled.

insert into `lakesoul`.`default`.test_table select * from `lakesoul`.`cdcsink`.soure_table;

caution

1. For stream writing, checkpoint interval needs to be set, and it is recommended to be more than 1 minute;
2. Set the corresponding time zone according to the environment:

SET 'table.local-time-zone' = 'Asia/Shanghai';
-- Set the checkpointing interval
SET 'execution.checkpointing.interval' = '2min';

Update Data

For batch mode only.

Java

tEnvs.executeSql("UPDATE `lakesoul`.`default`.test_table set score = 100 where id = 1") await();

SQL

UPDATE `lakesoul`.`default`.test_table set score = 100 where id = 1;

caution

Note that in the case of update, updating the values of primary key and partition columns is not allowed. For the stream execution mode, LakeSoul has been able to support ChangeLog semantics, which can support additions, deletions and modifications.

Delete Data

For batch mode only.

Java

tEnvs.executeSql("DELETE FROM `lakesoul`.`default`.test_table where id = 1") await();

SQL

DELETE FROM `lakesoul`.`default`.test_table where id = 1;

caution

In the case of delete, partitioning columns in the condition are not allowed.For the stream execution mode, LakeSoul has been able to support ChangeLog semantics, which can support additions, deletions and modifications.

Query Data

Full Read

Java

// Create a batch execution environment
tEnvs.executeSql("SELECT * FROM `lakesoul`.`default`.test_table where region='China' and `date`='2023-05-10'").print();

SQL

SELECT * FROM `lakesoul`.`default`.test_table where region='China' and `date`='2023-05-10';

Snapshot Batch Read

LakeSoul supports snapshot reading of tables, and users can query all data before the end timestamp by specifying partition information and end timestamp.

Java

tEnvs.executeSql("SELECT * FROM `lakesoul`.`default`.test_table /*+ OPTIONS('readtype'='snapshot', 'readendtime'='2023-05-01 15:20:15', 'timezone'='Asia/Shanghai')*/ WHERE region='China'").print();

SQL

-- Execute snapshot read of test_table in the region=China partition, the end timestamp of the read is 2023-05-01 15:20:15, and the time zone is Asia/Shanghai
SELECT * FROM `lakesoul`.`default`.test_table /*+ OPTIONS('readtype'='snapshot', 'readendtime'='2023-05-01 15:20:15', 'timezone'='Asia/Shanghai')*/ WHERE region='China';

Incremental Range Read

LakeSoul supports range incremental reads for tables. Users can query incremental data within this time range by specifying partition information, start timestamp, and end timestamp.

Java

 tEnvs.executeSql("SELECT * FROM `lakesoul`.`default`.test_table /*+ OPTIONS('readtype'='incremental'，'readstarttime'='2023-05-01 15:15:15 ', 'readendtime'='2023-05-01 15:20:15', 'timezone'='Asia/Shanghai')*/ WHERE region='China'").print();

SQL

-- Incremental reading of test_table in the region=China partition, the read timestamp range is 2023-05-01 15:15:15 to 2023-05-01 15:20:15, and the time zone is Asia/Shanghai
SELECT * FROM `lakesoul`.`default`.test_table /*+ OPTIONS('readtype'='incremental', 'readstarttime'='2023-05-01 15:15:15 ', 'readendtime'='2023-05-01 15:20:15', 'timezone'='Asia/Shanghai')*/ WHERE region='China';

Streaming Read

The LakeSoul table supports streaming reads in Flink. Streaming reads are based on incremental reads. By specifying the start timestamp and partition information, users can continuously and uninterruptedly read new data after the start timestamp. If start timestamp is not specified, it will read from the first data。

Java

tEnvs.executeSql("SELECT * FROM `lakesoul`.`default`.test_table /*+ OPTIONS('timezone'='Asia/Shanghai')*/ WHERE region='China'").print();

SQL

-- Incremental reading of test_table in the region=China partition, the time zone is Asia/Shanghai
SELECT * FROM `lakesoul`.`default`.test_table /*+ OPTIONS('timezone'='Asia/Shanghai')*/ WHERE region='China';

LakeSoul fully supports Flink Changelog Stream semantics when streaming. For the LakeSoul CDC table, the result of incremental reading is still in CDC format, that is, it contains insert, update, delete events, and these events will be automatically converted to the corresponding values of the RowKind field of Flink's RowData class object, so that in Flink incremental pipeline calculation is achieved.

Lookup Join

LakeSoul supports Lookup Join operations of Flink SQL. Lookup Join will cache the right table to be joined in memory, thereby greatly improving the join speed, and can be used in scenarios where relatively small dimension tables are joined. LakeSoul tries to refresh the cache every 60 seconds by default, you could change this by setting 'lookup.join.cache.ttl'='60s' property when creating the dimension table.

CREATE TABLE `lakesoul`.`default`.customers (
            `c_id` INT,
            `name` STRING,
        PRIMARY KEY (`c_id`) NOT ENFORCED)
        WITH (
            'connector'='lakesoul',
            'hashBucketNum'='1',
            'path'='file:///tmp/lakesoul/flink/sink/customers'
            );  
CREATE TABLE `lakesoul`.`default`.orders (
            `o_id` INT,
            `o_c_id` INT,
        PRIMARY KEY (`o_id`) NOT ENFORCED)
        WITH (
            'connector'='lakesoul',
            'hashBucketNum'='1',
            'path'='file:///tmp/lakesoul/flink/sink/orders',
            'lookup.join.cache.ttl'='60s'
            );  
SELECT `o_id`, `c_id`, `name`
FROM
(SELECT *, proctime() as proctime FROM `lakesoul`.`default`.orders) as o
JOIN `lakesoul`.`default`.customers FOR SYSTEM_TIME AS OF o.proctime
ON c_id = o_cid;

The Orders table is enriched with data from the Customers table. The FOR SYSTEM_TIME AS OF clause with the subsequent processing time attribute ensures that each row of the Orders table is joined with those Customers rows that match the join predicate at the point in time when the Orders row is processed by the join operator. It also prevents that the join result is updated when a joined Customer row is updated in the future. The lookup join also requires a mandatory equality join predicate, in the example above o.oc_id = c.id.

tip

LakeSoul supports read LakeSoul tables in batch and stream mode, execute commands on the Flink SQLClient client, and switch between stream and batch execution modes.

-- Execute Flink tasks according to the stream
SET execution.runtime-mode = streaming;
SET 'execution.checkpointing.interval' = '1min';
-- Execute Flink tasks in batch mode
SET execution.runtime-mode = batch;

Using Flink SQL, the format of the specified conditional query is SELECT * FROM test_table /*+ OPTIONS('key'='value')*/ WHERE partition=somevalue. In all of the following read modes, you could optionally specify partition values in WHERE clause to either specify the exact all partition values or just a subset of partitions values. LakeSoul will find the partitions that match the partition filters. In the query, /* OPTIONS() */ are query options (hints). Hints must be placed directly after the table name (before any other subclause) and the options when LakeSoul reads include:

Parameter	Explanation of meaning	Parameter filling format
readtype	read type, you can specify incremental read incremental, snapshot read snapshot, do not specify the default full read	'readtype'='incremental'
discoveryinterval	The time interval for discovering new data in streaming incremental read, in milliseconds, the default is 30000	'discoveryinterval'='10000'
readstarttime	Start read timestamp, if no start timestamp is specified, it will read from the start version number by default	'readstarttime'='2023-05-01 15:15:15'
readendtime	End read timestamp, if no end timestamp is specified, the current latest version number will be read by default	'readendtime'='2023-05-01 15:20:15'
timezone	The time zone information of the timestamp, if the time zone information of the timestamp is not specified, it will be processed according to the local time zone by default	'timezone'='Asia/Sahanghai'