目录
前言
1. 什么是 Apache Paimon
一、本地环境快速上手
1、本地Flink伪集群
2、IDEA中跑Paimon Demo
2.1 代码
2.2 IDEA中成功运行
3、IDEA中Stream读写
3.1 流写
3.2 流读（toChangeLogStream）
二、进阶：本地（IDEA）多流拼接测试
要解决的问题：
note：
1、'changelog-producer' = 'full-compaction'
（1）multiWrite代码
（2）读延迟
2、'changelog-producer' = 'lookup'
三、可能遇到的问题
前言
1. 什么是 Apache Paimon
Apache Paimon (incubating) 是一项流式数据湖存储技术，可以为用户提供高吞吐、低延迟的数据摄入、流式订阅以及实时查询能力。
Paimon 采用开放的数据格式和技术理念，可以与 Apache Flink / Spark / Trino 等诸多业界主流计算引擎进行对接，共同推进 Streaming Lakehouse 架构的普及和发展。
Paimon 以湖存储的方式基于分布式文件系统管理元数据，并采用开放的 ORC、Parquet、Avro 文件格式，支持各大主流计算引擎，包括 Flink、Spark、Hive、Trino、Presto。未来会对接更多引擎，包括 Doris 和 Starrocks。
官网：https://paimon.apache.org/
Github：https://github.com/apache/incubator-paimon
以下为快速入门上手Paimon的example：
一、本地环境快速上手
基于paimon 0.4-SNAPSHOT （Flink 1.14.4），Flink版本太低是不支持的，paimon基于最低版本1.14.6，经尝试在Flink1.14.0是不可以的！
paimon-flink-1.14-0.4-20230504.002229-50.jar
1、本地Flink伪集群
0. 需要先下载jar包，并添加至flink的lib中；
1. 根据官网demo，启动flinksql-client，创建catalog，创建表，创建数据源（视图），insert数据到表中。
2. 通过 localhost:8081 查看 Flink UI
3. 查看filesystem数据、元数据文件
2、IDEA中跑Paimon Demo
pom依赖：
<dependency>
<groupId>org.apache.paimon</groupId>
<artifactId>paimon-flink-1.14</artifactId>
<version>0.4-SNAPSHOT</version>
</dependency>拉取不到的可以手动添加到本地maven仓库：
mvn install:install-file -DgroupId=org.apache.paimon -DartifactId=paimon-flink-1.14 -Dversion=0.4-SNAPSHOT -Dpackaging=jar -Dfile=D:\software\paimon-flink-1.14-0.4-20230504.002229-50.jar
2.1 代码
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
/**
* @Author: YK.Leo
* @Date: 2023-05-14 15:12
* @Version: 1.0
*/
// Succeed at local ！！！
public class OfficeDemoV1 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.enableCheckpointing(10000l);
env.getCheckpointConfig().setCheckpointStorage("file:/D:/tmp/paimon/");
TableEnvironment tableEnv = StreamTableEnvironment.create(env);
// 0. Create a Catalog and a Table
tableEnv.executeSql("CREATE CATALOG my_catalog_api WITH (\n" +
" 'type'='paimon',\n" + // todo: !!!
" 'warehouse'='file:///D:/tmp/paimon'\n" +
")");
tableEnv.executeSql("USE CATALOG my_catalog_api");
tableEnv.executeSql("CREATE TABLE IF NOT EXISTS word_count_api (\n" +
" word STRING PRIMARY KEY NOT ENFORCED,\n" +
" cnt BIGINT\n" +
")");
// 1. Write Data
tableEnv.executeSql("CREATE TEMPORARY TABLE IF NOT EXISTS word_table_api (\n" +
" word STRING\n" +
") WITH (\n" +
" 'connector' = 'datagen',\n" +
" 'fields.word.length' = '1'\n" +
")");
// tableEnv.executeSql("SET 'execution.checkpointing.interval' = '10 s'");
tableEnv.executeSql("INSERT INTO word_count_api SELECT word, COUNT(*) FROM word_table_api GROUP BY word");
env.execute();
}
}2.2 IDEA中成功运行
3、IDEA中Stream读写
3.1 流写
代码：
package com.study.flink.table.paimon.demo;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.StatementSet;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
/**
* @Author: YK.Leo
* @Date: 2023-05-17 11:11
* @Version: 1.0
*/
// succeed at local ！！！
public class OfficeStreamsWriteV2 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.enableCheckpointing(10000L);
env.getCheckpointConfig().setCheckpointStorage("file:/D:/tmp/paimon/");
TableEnvironment tableEnv = StreamTableEnvironment.create(env);
// 0. Create a Catalog and a Table
tableEnv.executeSql("CREATE CATALOG my_catalog_local WITH (\n" +
" 'type'='paimon',\n" + // todo: !!!
" 'warehouse'='file:///D:/tmp/paimon'\n" +
")");
tableEnv.executeSql("USE CATALOG my_catalog_local");
tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS my_catalog_local.local_db");
tableEnv.executeSql("USE local_db");
// drop tbl
tableEnv.executeSql("DROP TABLE IF EXISTS paimon_tbl_streams");
tableEnv.executeSql("CREATE TABLE IF NOT EXISTS paimon_tbl_streams(\n"
+ " uuid bigint,\n"
+ " name VARCHAR(3),\n"
+ " age int,\n"
+ " ts TIMESTAMP(3),\n"
+ " dt VARCHAR(10), \n"
+ " PRIMARY KEY (dt, uuid) NOT ENFORCED \n"
+ ") PARTITIONED BY (dt) \n"
+ " WITH (\n" +
" 'merge-engine' = 'partial-update',\n" +
" 'changelog-producer' = 'full-compaction', \n" +
" 'file.format' = 'orc', \n" +
" 'scan.mode' = 'compacted-full', \n" +
" 'bucket' = '5', \n" +
" 'sink.parallelism' = '5', \n" +
" 'sequence.field' = 'ts' \n" + // todo, to check
")"
);
// datagen ====================================================================
tableEnv.executeSql("CREATE TEMPORARY TABLE IF NOT EXISTS source_A (\n" +
" uuid bigint PRIMARY KEY NOT ENFORCED,\n" +
" `name` VARCHAR(3)," +
" _ts1 TIMESTAMP(3)\n" +
") WITH (\n" +
" 'connector' = 'datagen', \n" +
" 'fields.uuid.kind'='sequence',\n" +
" 'fields.uuid.start'='0', \n" +
" 'fields.uuid.end'='1000000', \n" +
" 'rows-per-second' = '1' \n" +
")");
tableEnv.executeSql("CREATE TEMPORARY TABLE IF NOT EXISTS source_B (\n" +
" uuid bigint PRIMARY KEY NOT ENFORCED,\n" +
" `age` int," +
" _ts2 TIMESTAMP(3)\n" +
") WITH (\n" +
" 'connector' = 'datagen', \n" +
" 'fields.uuid.kind'='sequence',\n" +
" 'fields.uuid.start'='0', \n" +
" 'fields.uuid.end'='1000000', \n" +
" 'rows-per-second' = '1' \n" +
")");
//
//tableEnv.executeSql("insert into paimon_tbl_streams(uuid, name, _ts1) select uuid, concat(name,'_A') as name, _ts1 from source_A");
//tableEnv.executeSql("insert into paimon_tbl_streams(uuid, age, _ts1) select uuid, concat(age,'_B') as age, _ts1 from source_B");
StatementSet statementSet = tableEnv.createStatementSet();
statementSet
.addInsertSql("insert into paimon_tbl_streams(uuid, name, ts, dt) select uuid, name, _ts1 as ts, date_format(_ts1,'yyyy-MM-dd') as dt from source_A")
.addInsertSql("insert into paimon_tbl_streams(uuid, age, dt) select uuid, age, date_format(_ts2,'yyyy-MM-dd') as dt from source_B")
;
statementSet.execute();
// env.execute();
}
}结果：
3.2 流读（toChangeLogStream）
代码：
package com.study.flink.table.paimon.demo;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.connector.ChangelogMode;
import org.apache.flink.types.Row;
import org.apache.flink.types.RowKind;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
/**
* @Author: YK.Leo
* @Date: 2023-05-15 18:50
* @Version: 1.0
*/
// 流读单表OK！
public class OfficeStreamReadV1 {
public static final Logger LOGGER = LogManager.getLogger(OfficeStreamReadV1.class);
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.enableCheckpointing(10000L);
env.getCheckpointConfig().setCheckpointStorage("file:/D:/tmp/paimon/");
TableEnvironment tableEnv = StreamTableEnvironment.create(env);
// 0. Create a Catalog and a Table
tableEnv.executeSql("CREATE CATALOG my_catalog_local WITH (\n" +
" 'type'='paimon',\n" + // todo: !!!
" 'warehouse'='file:///D:/tmp/paimon'\n" +
")");
tableEnv.executeSql("USE CATALOG my_catalog_local");
tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS my_catalog_local.local_db");
tableEnv.executeSql("USE local_db");
// 不需要再次创建表
// convert to DataStream
// Table table = tableEnv.sqlQuery("SELECT * FROM paimon_tbl_streams");
Table table = tableEnv.sqlQuery("SELECT * FROM paimon_tbl_streams WHERE name is not null and age is not null");
// DataStream<Row> dataStream = ((StreamTableEnvironment) tableEnv).toChangelogStream(table);
// todo : doesn't support consuming update and delete changes which is produced by node TableSourceScan
// DataStream<Row> dataStream = ((StreamTableEnvironment) tableEnv).toDataStream(table);
// 剔除 -U 数据（即：更新前的数据不需要重新发送，剔除）！！！
DataStream<Row> dataStream = ((StreamTableEnvironment) tableEnv)
.toChangelogStream(table, Schema.newBuilder().primaryKey("dt","uuid").build(), ChangelogMode.upsert())
.filter(new FilterFunction<Row>() {
@Override
public boolean filter(Row row) throws Exception {
boolean isNoteUpdateBefore = !(row.getKind().equals(RowKind.UPDATE_BEFORE));
if (!isNoteUpdateBefore) {
LOGGER.info("UPDATE_BEFORE: " + row.toString());
}
return isNoteUpdateBefore;
}
})
;
// use this datastream
dataStream.executeAndCollect().forEachRemaining(System.out::println);
env.execute();
}
}结果：
二、进阶：本地（IDEA）多流拼接测试
要解决的问题：
多个流拥有相同的主键，每个流更新除主键外的部分字段，通过主键完成多流拼接。
note：
如果是两个Flink Job 或者 两个 pipeline 写同一个paimon表，则直接会产生conflict，其中一条流不断exception、重启；
可以使用 “UNION ALL” 将多个流合并为一个流，最终一个Flink job写paimon表；
使用主键表，'merge-engine' = 'partial-update' ；
1、'changelog-producer' = 'full-compaction'
（1）multiWrite代码
package com.study.flink.table.paimon.multi;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.StatementSet;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
/**
* @Author: YK.Leo
* @Date: 2023-05-18 10:17
* @Version: 1.0
*/
// Succeed as local ！！！
// 而且不会产生conflict，跑5分钟没有任何异常(公司跑几天无异常)！ 数据也可以在另一个job流读！
public class MultiStreamsUnionWriteV1 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.enableCheckpointing(10*1000L);
env.getCheckpointConfig().setCheckpointStorage("file:/D:/tmp/paimon/");
TableEnvironment tableEnv = StreamTableEnvironment.create(env);
// 0. Create a Catalog and a Table
tableEnv.executeSql("CREATE CATALOG my_catalog_local WITH (\n" +
" 'type'='paimon',\n" + // todo: !!!
" 'warehouse'='file:///D:/tmp/paimon'\n" +
")");
tableEnv.executeSql("USE CATALOG my_catalog_local");
tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS my_catalog_local.local_db");
tableEnv.executeSql("USE local_db");
// drop & create tbl
tableEnv.executeSql("DROP TABLE IF EXISTS paimon_tbl_streams");
tableEnv.executeSql("CREATE TABLE IF NOT EXISTS paimon_tbl_streams(\n"
+ " uuid bigint,\n"
+ " name VARCHAR(3),\n"
+ " age int,\n"
+ " ts TIMESTAMP(3),\n"
+ " dt VARCHAR(10), \n"
+ " PRIMARY KEY (dt, uuid) NOT ENFORCED \n"
+ ") PARTITIONED BY (dt) \n"
+ " WITH (\n" +
" 'merge-engine' = 'partial-update',\n" +
" 'changelog-producer' = 'full-compaction', \n" +
" 'file.format' = 'orc', \n" +
" 'scan.mode' = 'compacted-full', \n" +
" 'bucket' = '5', \n" +
" 'sink.parallelism' = '5', \n" +
// " 'write_only' = 'true', \n" +
" 'sequence.field' = 'ts' \n" + // todo, to check
")"
);
// datagen ====================================================================
tableEnv.executeSql("CREATE TEMPORARY TABLE IF NOT EXISTS source_A (\n" +
" uuid bigint PRIMARY KEY NOT ENFORCED,\n" +
" `name` VARCHAR(3)," +
" _ts1 TIMESTAMP(3)\n" +
") WITH (\n" +
" 'connector' = 'datagen', \n" +
" 'fields.uuid.kind'='sequence',\n" +
" 'fields.uuid.start'='0', \n" +
" 'fields.uuid.end'='1000000', \n" +
" 'rows-per-second' = '1' \n" +
")");
tableEnv.executeSql("CREATE TEMPORARY TABLE IF NOT EXISTS source_B (\n" +
" uuid bigint PRIMARY KEY NOT ENFORCED,\n" +
" `age` int," +
" _ts2 TIMESTAMP(3)\n" +
") WITH (\n" +
" 'connector' = 'datagen', \n" +
" 'fields.uuid.kind'='sequence',\n" +
" 'fields.uuid.start'='0', \n" +
" 'fields.uuid.end'='1000000', \n" +
" 'rows-per-second' = '1' \n" +
")");
//
StatementSet statementSet = tableEnv.createStatementSet();
String sqlText = "INSERT INTO paimon_tbl_streams(uuid, name, age, ts, dt) \n" +
"select uuid, name, cast(null as int) as age, _ts1 as ts, date_format(_ts1,'yyyy-MM-dd') as dt from source_A \n" +
"UNION ALL \n" +
"select uuid, cast(null as string) as name, age, _ts2 as ts, date_format(_ts2,'yyyy-MM-dd') as dt from source_B"
;
statementSet.addInsertSql(sqlText);
statementSet.execute();
}
}读代码同上。
（2）读延迟
即：从client数据落到paimon，完成与server的join，再到被Flink-paimon流读到的时间延迟；
分钟级别延迟！
2、'changelog-producer' = 'lookup'
读写同上，建表时修改参数即可： changelog-producer='lookup'，与此匹配的scan-mode需要分别配置为 'latest' ；
lookup延迟性可能会更低，但是数据质量有待验证。
note：
经测试，在企业生产环境中full-compaction模式目前一切稳定（两条join的流QPS约3K左右，延迟2-3分钟）。
99.9%的数据延迟在2-3分钟；
（multiWrite的checkpoint间隔为60s时）
三、可能遇到的问题
1. Caused by: java.lang.ClassCastException: org.codehaus.janino.CompilerFactory cannot be cast to org.codehaus.commons.compiler.ICompilerFactory
原因：org.codehaus.janino 依赖冲突,
办法：全部exclude掉
<exclude>org.codehaus.janino:*</exclude>
2. Caused by: java.lang.ClassNotFoundException: org.apache.flink.util.function.SerializableFunction
原因：Flink steaming版本与Flink table版本不一致 或 确实相关依赖 （这里是paimon依赖的flink版本最低为1.14.6，与1.14.0的flink不兼容）
办法：升级Flink版本到1.14.4以上
参考Flink配置：Configuration | Apache Flink
3. Caused by: java.util.ServiceConfigurationError: org.apache.flink.table.factories.Factory: Provider org.apache.flink.table.store.connector.TableStoreManagedFactory not found
在项目的META-INF/services路径下添加 Factory 文件（这样才能匹配Flink的CatalogFactory，才能创建catalog）
4. Caused by: org.apache.flink.client.program.ProgramInvocationException: The main method caused an error: No operators defined in streaming topology. Cannot execute.
已经存在tableEnv.executeSql 或者 statementSet.execute() 时就不需要再 env.execute() 了！
5. Flink SQL不能直接使用null as，需要写成 cast(null as data_type)， 如 cast(null as string)；
6. 如果创建paimon分区表，必须要把分区字段放在主键中！，否则建表报错：
【未完待续...】
