Apache Flink是一个框架和分布式处理引擎,用于对无界和有界数据流进行有状态计算。Flink的设计目标是在所有常见的集群环境中运行,并以内存执行速度和任意规模来执行计算。它支持高吞吐、低延迟、高性能的流处理,并且是一个面向流处理和批处理的分布式计算框架,将批处理看作一种特殊的有界流。
Flink的主要特点包括:
- 事件驱动型:Flink是一个事件驱动型的应用,可以从一个或多个事件流提取数据,并根据到来的事件触发计算、状态更新或其他外部动作。
- 支持有状态计算:Flink提供了Extactor-once语义及checkpoint机制,支持带有事件操作的流处理和窗口处理,以及灵活的窗口处理(如时间窗口、大小窗口等)。
- 轻量级容错处理:Flink使用savepoint进行错误恢复,可以在出现故障时快速恢复任务。
- 高吞吐、低延迟、高性能:Flink的设计目标是在保证数据处理稳定性的同时,实现高吞吐、低延迟、高性能的流处理。
- 支持大规模集群模式:Flink支持在yarn、Mesos、k8s等大规模集群环境中运行。
- 支持多种编程语言:Flink对java、scala、python都提供支持,但最适合使用java进行开发。
Flink的应用场景非常广泛,可以用于实时流数据的分析计算、实时数据与维表数据关联计算、实时数仓建设、ETL(提取-转换-加载)多存储系统之间进行数据转化和迁移等场景。同时,Flink也适用于事件驱动型应用场景,如以kafka为代表的消息队列等。
1.Winows系统安装Flink
选择 Apache Flink 1.16.0 - 2022-10-28 (Binaries)
下载 flink-1.16.0-bin-scala_2.12.tgz
在Flink安装路径/bin/目录下创建start-cluster.bat,代码如下:
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:: http://www.apache.org/licenses/LICENSE-2.0
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@echo off
setlocal EnableDelayedExpansion
SET bin=%~dp0
SET FLINK_HOME=%bin%..
SET FLINK_LIB_DIR=%FLINK_HOME%\lib
SET FLINK_PLUGINS_DIR=%FLINK_HOME%\plugins
SET FLINK_CONF_DIR=%FLINK_HOME%\conf
SET FLINK_LOG_DIR=%FLINK_HOME%\log
SET JVM_ARGS=-Xms1024m -Xmx1024m
SET FLINK_CLASSPATH=%FLINK_LIB_DIR%\*
SET logname_jm=flink-%username%-jobmanager.log
SET logname_tm=flink-%username%-taskmanager.log
SET log_jm=%FLINK_LOG_DIR%\%logname_jm%
SET log_tm=%FLINK_LOG_DIR%\%logname_tm%
SET outname_jm=flink-%username%-jobmanager.out
SET outname_tm=flink-%username%-taskmanager.out
SET out_jm=%FLINK_LOG_DIR%\%outname_jm%
SET out_tm=%FLINK_LOG_DIR%\%outname_tm%
SET log_setting_jm=-Dlog.file="%log_jm%" -Dlogback.configurationFile=file:"%FLINK_CONF_DIR%/logback.xml" -Dlog4j.configuration=file:"%FLINK_CONF_DIR%/log4j.properties"
SET log_setting_tm=-Dlog.file="%log_tm%" -Dlogback.configurationFile=file:"%FLINK_CONF_DIR%/logback.xml" -Dlog4j.configuration=file:"%FLINK_CONF_DIR%/log4j.properties"
:: Log rotation (quick and dirty)
CD "%FLINK_LOG_DIR%"
for /l %%x in (5, -1, 1) do (
SET /A y = %%x+1
RENAME "%logname_jm%.%%x" "%logname_jm%.!y!" 2> nul
RENAME "%logname_tm%.%%x" "%logname_tm%.!y!" 2> nul
RENAME "%outname_jm%.%%x" "%outname_jm%.!y!" 2> nul
RENAME "%outname_tm%.%%x" "%outname_tm%.!y!" 2> nul
)
RENAME "%logname_jm%" "%logname_jm%.0" 2> nul
RENAME "%logname_tm%" "%logname_tm%.0" 2> nul
RENAME "%outname_jm%" "%outname_jm%.0" 2> nul
RENAME "%outname_tm%" "%outname_tm%.0" 2> nul
DEL "%logname_jm%.6" 2> nul
DEL "%logname_tm%.6" 2> nul
DEL "%outname_jm%.6" 2> nul
DEL "%outname_tm%.6" 2> nul
for %%X in (java.exe) do (set FOUND=%%~$PATH:X)
if not defined FOUND (
echo java.exe was not found in PATH variable
goto :eof
)
echo Starting a local cluster with one JobManager process and one TaskManager process.
echo You can terminate the processes via CTRL-C in the spawned shell windows.
echo Web interface by default on http://localhost:8081/.
start /b java %JVM_ARGS% %log_setting_jm% -cp "%FLINK_CLASSPATH%"; org.apache.flink.runtime.entrypoint.StandaloneSessionClusterEntrypoint --configDir "%FLINK_CONF_DIR%" > "%out_jm%" 2>&1
start /b java %JVM_ARGS% %log_setting_tm% -cp "%FLINK_CLASSPATH%"; org.apache.flink.runtime.taskexecutor.TaskManagerRunner --configDir "%FLINK_CONF_DIR%" > "%out_tm%" 2>&1
endlocal
使用CMD窗口,在Flink安装路径/bin目录下启动start-cluster.bat
访问http://localhost:8081,界面如下:
2.使用Flink实现MySQL数据库之间数据同步(JAVA)
<flink.version>1.16.0</flink.version> <flink-cdc.version>2.3.0</flink-cdc.version>
1.创建Flink流处理运行环境。
2.设置流处理并发数。
3.设置Flink存档间隔时间,单位为ms,当同步发生异常时会恢复最近的checkpoint继续同步。
4.在Flink中创建中间同步数据库。
5.在Flink中创建中间表flink_source,来源于MySQL表source,(注意connector为mysql-cdc)。
6.在Flink中创建中间表flink_sink,来源于MySQL表sink。
7.将Flink中间表来源表数据写入flink_sink表,Flink会根据MySQL binlog中source表变化,动态更新flink_sink表,同时会将flink_sink表数据写入MySQL sink表,实现MySQL数据持续同步。
package com.demo.flink;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
public class FlinkCdcMySql {
public static void main(String[] args) {
System.out.println("==========start run FlinkCdcMySql#main.");
// 创建Flink流处理运行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("127.0.0.1", 8081);
// 设置流处理并发数
env.setParallelism(3);
// 设置Flink存档间隔时间,单位为ms,当同步发生异常时会恢复最近的checkpoint继续同步
env.enableCheckpointing(5000);
final StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
// 在Flink中创建中间同步数据库
tEnv.executeSql("CREATE DATABASE IF NOT EXISTS flink_test");
// 在Flink中创建中间表flink_source,来源于MySQL表source
// 注意connector为mysql-cdc
tEnv.executeSql("CREATE TABLE flink_test.flink_source (\n" +
" id int,\n" +
" name varchar(255),\n" +
" create_time TIMESTAMP\n," + // Flink不支持datetime格式
" PRIMARY KEY (id) NOT ENFORCED" + //主键必须标明NOT ENFORCED
") WITH (\n" +
" 'connector' = 'mysql-cdc',\n" +
" 'hostname' = '127.0.0.1',\n" +
" 'database-name' = 'flink-source',\n" +
" 'table-name' = 'source',\n" +
" 'username' = 'root',\n" +
" 'password' = 'root'\n" +
")");
// 在Flink中创建中间表flink_sink,来源于MySQL表sink
tEnv.executeSql("CREATE TABLE flink_test.flink_sink (\n" +
" id int,\n" +
" name varchar(255),\n" +
" create_time TIMESTAMP\n," +
" PRIMARY KEY (id) NOT ENFORCED" +
") WITH (\n" +
" 'connector' = 'jdbc',\n" +
" 'url' = 'jdbc:mysql://127.0.0.1:3306/flink-sink',\n" +
" 'table-name' = 'sink',\n" +
" 'driver' = 'com.mysql.jdbc.Driver',\n" +
" 'username' = 'root',\n" +
" 'password' = 'root'\n" +
")");
// Table transactions = tEnv.from("flink_source");
// transactions.executeInsert("flink_sink");
System.out.println("==========begin Mysql data cdc.");
// 将Flink中间表来源表数据写入flink_sink表
// Flink会根据MySQL binlog中source表变化,动态更新flink_sink表,同时会将flink_sink表数据写入MySQL sink表,实现MySQL数据持续同步
tEnv.executeSql("INSERT INTO flink_test.flink_sink(id, name, create_time)\n" +
"select id, name, create_time\n" +
"from flink_test.flink_source\n");
System.out.println("==========continue Mysql data cdc.");
}
}
git代码地址: