在实时数据处理的完整链路中,数据输出(Sink)是最后一个关键环节,它负责将处理后的结果传递到外部系统供后续使用。Flink提供了丰富的数据输出连接器,支持将数据写入Kafka、Elasticsearch、文件系统、数据库等各种目标系统。本文将深入探讨Flink数据输出的核心概念、配置方法和最佳实践,并基于Flink 1.20.1构建一个完整的数据输出案例。
一、Flink Sink概述
1. 什么是Sink
Sink(接收器)是Flink数据处理流水线的末端,负责将计算结果输出到外部存储系统或下游处理系统。在Flink的编程模型中,Sink是DataStream API中的一个转换操作,它接收DataStream并将数据写入指定的外部系统。
2. Sink的分类
Flink的Sink连接器可以分为以下几类:
- 内置Sink:如print()、printToErr()等用于调试的内置输出
- 文件系统Sink:支持写入本地文件系统、HDFS等
- 消息队列Sink:如Kafka、RabbitMQ等
- 数据库Sink:如JDBC、Elasticsearch等
- 自定义Sink:通过实现SinkFunction接口自定义输出逻辑
3. 输出语义保证
Flink为Sink提供了三种输出语义保证:
- 最多一次(At-most-once):数据可能丢失,但不会重复
- 至少一次(At-least-once):数据不会丢失,但可能重复
- 精确一次(Exactly-once):数据既不会丢失,也不会重复
这些语义保证与Flink的检查点(Checkpoint)机制密切相关,我们将在后面详细讨论。
二、环境准备与依赖配置
1. 版本说明
- Flink:1.20.1
- JDK:17+
- Gradle:8.3+
- 外部系统:Kafka 3.4.0、Elasticsearch 7.17.0、MySQL 8.0
2. 核心依赖
dependencies {// Flink核心依赖implementation 'org.apache.flink:flink_core:1.20.1'implementation 'org.apache.flink:flink-streaming-java:1.20.1'implementation 'org.apache.flink:flink-clients:1.20.1'// Kafka Connectorimplementation 'org.apache.flink:flink-connector-kafka:3.4.0-1.20'// Elasticsearch Connectorimplementation 'org.apache.flink:flink-connector-elasticsearch7:3.1.0-1.20'// JDBC Connectorimplementation 'org.apache.flink:flink-connector-jdbc:3.3.0-1.20'implementation 'mysql:mysql-connector-java:8.0.33'// FileSystem Connectorimplementation 'org.apache.flink:flink-connector-files:1.20.1'}
三、基础Sink操作
1. 内置调试Sink
Flink提供了一些内置的Sink用于开发和调试阶段:
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;public class BasicSinkDemo {public static void main(String[] args) throws Exception {// 创建执行环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 创建数据源DataStream<String> stream = env.fromElements("Hello", "Flink", "Sink");// 打印到标准输出stream.print("StandardOutput");// 打印到标准错误输出stream.printToErr("ErrorOutput");// 执行作业env.execute("Basic Sink Demo");}
}
2. 文件系统Sink
Flink支持将数据写入本地文件系统、HDFS等。下面是一个写入本地文件系统的示例:
package com.cn.daimajiangxin.flink.sink;import org.apache.flink.api.common.serialization.SimpleStringEncoder;
import org.apache.flink.configuration.MemorySize;
import org.apache.flink.connector.file.sink.FileSink;
import org.apache.flink.core.fs.Path;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.filesystem.RollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy;import java.time.Duration;public class FileSystemSinkDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<Object> stream = env.fromData("Hello", "Flink", "FileSystem", "Sink");RollingPolicy<Object, String> rollingPolicy = DefaultRollingPolicy.<Object, String>builder().withRolloverInterval(Duration.ofMinutes(15)).withInactivityInterval(Duration.ofMinutes(5)).withMaxPartSize(MemorySize.ofMebiBytes(64)).build();// 创建文件系统SinkFileSink<Object> sink = FileSink.forRowFormat(new Path("file:///tmp/flink-output"), new SimpleStringEncoder<>()).withRollingPolicy(rollingPolicy).build();// 添加Sinkstream.sinkTo(sink);env.execute("File System Sink Demo");}
}
四、高级Sink连接器
1. Kafka Sink
Kafka是实时数据处理中常用的消息队列,Flink提供了强大的Kafka Sink支持:
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import java.util.Properties;public class KafkaSinkDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 开启检查点以支持Exactly-Once语义env.enableCheckpointing(5000);DataStream<String> stream = env.fromElements("Hello Kafka", "Flink to Kafka", "Data Pipeline");// Kafka配置Properties props = new Properties();props.setProperty("bootstrap.servers", "localhost:9092");// 创建Kafka SinkKafkaSink<String> sink = KafkaSink.<String>builder().setKafkaProducerConfig(props).setRecordSerializer(KafkaRecordSerializationSchema.builder().setTopic("flink-output-topic").setValueSerializationSchema(new SimpleStringSchema()).build()).build();// 添加Sinkstream.sinkTo(sink);env.execute("Kafka Sink Demo");}
}
kafka消息队列消息:
2. Elasticsearch Sink
Elasticsearch是一个实时的分布式搜索和分析引擎,非常适合存储和查询Flink处理的实时数据:
package com.cn.daimajiangxin.flink.sink;import com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.flink.connector.elasticsearch.sink.Elasticsearch7SinkBuilder;
import org.apache.flink.connector.elasticsearch.sink.ElasticsearchSink;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.http.HttpHost;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.client.Requests;import java.util.Map;public class ElasticsearchSinkDemo {private static final ObjectMapper objectMapper = new ObjectMapper();public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.enableCheckpointing(5000);DataStream<String> stream = env.fromData("{\"id\":\"1\",\"name\":\"Flink\",\"category\":\"framework\"}","{\"id\":\"2\",\"name\":\"Elasticsearch\",\"category\":\"database\"}");// 配置Elasticsearch节点HttpHost httpHost=new HttpHost("localhost", 9200, "http");// 创建Elasticsearch SinkElasticsearchSink<String> sink=new Elasticsearch7SinkBuilder<String>().setBulkFlushMaxActions(10) // 批量操作数量.setBulkFlushInterval(5000) // 批量刷新间隔(毫秒).setHosts(httpHost).setConnectionRequestTimeout(60000) // 连接请求超时时间.setConnectionTimeout(60000) // 连接超时时间.setSocketTimeout(60000) // Socket 超时时间.setEmitter((element, context, indexer) -> {try {Map<String, Object> json = objectMapper.readValue(element, Map.class);IndexRequest request = Requests.indexRequest().index("flink_documents").id((String) json.get("id")).source(json);indexer.add(request);} catch (Exception e) {// 处理解析异常System.err.println("Failed to parse JSON: " + element);}}).build();// 添加Sinkstream.sinkTo(sink);env.execute("Elasticsearch Sink Demo");}
}
使用post工具查看数据
3. JDBC Sink
使用JDBC Sink可以将数据写入各种关系型数据库:
package com.cn.daimajiangxin.flink.sink;import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.connector.jdbc.core.datastream.Jdbc;
import org.apache.flink.connector.jdbc.core.datastream.sink.JdbcSink;
import org.apache.flink.connector.jdbc.datasource.statements.SimpleJdbcQueryStatement;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import java.util.Arrays;
import java.util.List;public class JdbcSinkDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.enableCheckpointing(5000);List<User> userList = Arrays.asList( new User(1, "Alice", 25,"alice"),new User(2, "Bob", 30,"bob"),new User(3, "Charlie", 35,"charlie"));// 模拟用户数据DataStream<User> userStream = env.fromData(userList);JdbcExecutionOptions jdbcExecutionOptions = JdbcExecutionOptions.builder().withBatchSize(1000).withBatchIntervalMs(200).withMaxRetries(5).build();JdbcConnectionOptions connectionOptions = new JdbcConnectionOptions.JdbcConnectionOptionsBuilder().withUrl("jdbc:mysql://localhost:3306/test").withDriverName("com.mysql.cj.jdbc.Driver").withUsername("username").withPassword("password").build();String insertSql = "INSERT INTO user (id, name, age, user_name) VALUES (?, ?, ?, ?)";JdbcStatementBuilder<User> statementBuilder = (statement, user) -> {statement.setInt(1, user.getId());statement.setString(2, user.getName());statement.setInt(3, user.getAge());statement.setString(4, user.getUserName());};// 创建JDBC SinkJdbcSink<User> jdbcSink = new Jdbc().<User>sinkBuilder().withQueryStatement( new SimpleJdbcQueryStatement<User>(insertSql,statementBuilder)).withExecutionOptions(jdbcExecutionOptions).buildAtLeastOnce(connectionOptions);// 添加SinkuserStream.sinkTo(jdbcSink);env.execute("JDBC Sink Demo");}// 用户实体类public static class User {private int id;private String name;private String userName;private int age;public User(int id, String name, int age,String userName) {this.id = id;this.name = name;this.age = age;this.userName=userName;}public int getId() {return id;}public String getName() {return name;}public int getAge() {return age;}public String getUserName() {return userName;}}
}
登录mysql客户端查看数据
五、Sink的可靠性保证机制
1. 检查点与保存点
Flink的检查点(Checkpoint)机制是实现精确一次语义的基础。当开启检查点后,Flink会定期将作业的状态保存到持久化存储中。如果作业失败,Flink可以从最近的检查点恢复,确保数据不会丢失。
// 配置检查点
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 启用检查点,间隔5000ms
env.enableCheckpointing(5000);// 配置检查点模式为EXACTLY_ONCE(默认)
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);// 设置检查点超时时间
env.getCheckpointConfig().setCheckpointTimeout(60000);// 设置最大并行检查点数量
env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);// 开启外部化检查点,作业失败时保留检查点
env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
2. 事务与二阶段提交
对于支持事务的外部系统,Flink使用二阶段提交(Two-Phase Commit)协议来实现精确一次语义:
- 第一阶段(预提交):Flink将数据写入外部系统的预提交区域,但不提交
- 第二阶段(提交):所有算子完成预提交后,Flink通知外部系统提交数据
这种机制确保了即使在作业失败或恢复的情况下,数据也不会被重复写入或丢失。
3. 不同Sink的语义保证级别
不同的Sink连接器支持不同级别的语义保证:
- 支持精确一次(Exactly-once):Kafka、Elasticsearch(版本支持)、文件系统(预写日志模式)
- 支持至少一次(At-least-once):JDBC、Redis、RabbitMQ
- 最多一次(At-most-once):简单的无状态输出
六、自定义Sink实现
当Flink内置的Sink连接器不能满足需求时,我们可以通过实现SinkFunction接口来自定义Sink:
package com.cn.daimajiangxin.flink.sink;import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.api.connector.sink2.Sink;
import org.apache.flink.api.connector.sink2.SinkWriter;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;import java.io.IOException;public class CustomSinkDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<String> stream = env.fromElements("Custom", "Sink", "Example");// 使用自定义Sinkstream.sinkTo(new CustomSink());env.execute("Custom Sink Demo");}// 自定义Sink实现 - 使用新APIpublic static class CustomSink implements Sink<String> {@Overridepublic SinkWriter<String> createWriter(InitContext context) {return new CustomSinkWriter();}// SinkWriter负责实际的数据写入逻辑private static class CustomSinkWriter implements SinkWriter<String> {// 初始化资源public CustomSinkWriter() {// 初始化连接、客户端等资源System.out.println("CustomSink initialized");}// 处理每个元素@Overridepublic void write(String value, Context context) throws IOException, InterruptedException {// 实际的写入逻辑System.out.println("Writing to custom sink: " + value);}// 刷新缓冲区@Overridepublic void flush(boolean endOfInput) {// 刷新逻辑(如果需要)}// 清理资源@Overridepublic void close() throws Exception {// 关闭连接、客户端等资源System.out.println("CustomSink closed");}}}}
七、实战案例:实时数据处理流水线
下面我们将构建一个完整的实时数据处理流水线,从Kafka读取数据,进行转换处理,然后输出到多个目标系统:
1. 系统架构
Kafka Source -> Flink Processing -> Multiple Sinks|-> Kafka Sink|-> Elasticsearch Sink|-> JDBC Sink
2. 数据模型
我们将使用日志数据模型,定义一个LogEntry类来表示日志条目:
package com.cn.daimajiangxin.flink.sink;public class LogEntry {private String timestamp;private String logLevel;private String source;private String message;public String getTimestamp() {return timestamp;}public void setTimestamp(String timestamp) {this.timestamp = timestamp;}public String getLogLevel() {return logLevel;}public void setLogLevel(String logLevel) {this.logLevel = logLevel;}public String getSource() {return source;}public void setSource(String source) {this.source = source;}public String getMessage() {return message;}public void setMessage(String message) {this.message = message;}@Overridepublic String toString() {return String.format("LogEntry{timestamp='%s', logLevel='%s', source='%s', message='%s'}",timestamp, logLevel, source, message);}
}
定义一个日志统计实体类LogStats,用于表示每个源的日志统计信息:
package com.cn.daimajiangxin.flink.sink;public class LogStats {private String source;private long count;public LogStats() {}public LogStats(String source, long count) {this.source = source;this.count = count;}public String getSource() {return source;}public void setSource(String source) {this.source = source;}public long getCount() {return count;}public void setCount(long count) {this.count = count;}@Overridepublic String toString() {return String.format("LogStats{source='%s', count=%d}", source, count);}
}
3. 完整实现代码
package com.cn.daimajiangxin.flink.sink;import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.connector.jdbc.core.datastream.Jdbc;
import org.apache.flink.connector.jdbc.core.datastream.sink.JdbcSink;
import org.apache.flink.connector.jdbc.datasource.statements.SimpleJdbcQueryStatement;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.connector.elasticsearch.sink.Elasticsearch7SinkBuilder;
import org.apache.flink.connector.elasticsearch.sink.ElasticsearchSink;
import org.apache.http.HttpHost;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.client.Requests;import java.sql.PreparedStatement;
import java.time.LocalDateTime;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;public class MultiSinkPipeline {public static void main(String[] args) throws Exception {// 1. 创建执行环境并配置检查点StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.enableCheckpointing(5000);// 2. 创建Kafka SourceKafkaSource<String> source = KafkaSource.<String>builder().setBootstrapServers("localhost:9092").setTopics("logs-input-topic").setGroupId("flink-group").setStartingOffsets(OffsetsInitializer.earliest()).setValueOnlyDeserializer(new SimpleStringSchema()).build();// 3. 读取数据并解析DataStream<String> kafkaStream = env.fromSource(source, WatermarkStrategy.noWatermarks(), "Kafka Source");// 解析日志数据DataStream<LogEntry> logStream = kafkaStream.map(line -> {String[] parts = line.split("\\|");return new LogEntry(parts[0], parts[1], parts[2], parts[3]);}).name("Log Parser");// 4. 过滤错误日志DataStream<LogEntry> errorLogStream = logStream.filter(log -> "ERROR".equals(log.getLogLevel())).name("Error Log Filter");// 5. 配置并添加Kafka Sink - 输出错误日志// Kafka配置Properties props = new Properties();props.setProperty("bootstrap.servers", "localhost:9092");// 创建Kafka SinkKafkaSink<LogEntry> kafkaSink = KafkaSink.<LogEntry>builder().setKafkaProducerConfig(props).setRecordSerializer(KafkaRecordSerializationSchema.<LogEntry>builder().setTopic("error-logs-topic").setValueSerializationSchema(element -> element.toString().getBytes()).build()).build();errorLogStream.sinkTo(kafkaSink).name("Error Logs Kafka Sink");// 6. 配置并添加Elasticsearch Sink - 存储所有日志// 配置Elasticsearch节点HttpHost httpHost=new HttpHost("localhost", 9200, "http");ElasticsearchSink<LogEntry> esSink = new Elasticsearch7SinkBuilder<LogEntry>().setBulkFlushMaxActions(10) // 批量操作数量.setBulkFlushInterval(5000) // 批量刷新间隔(毫秒).setHosts(httpHost).setConnectionRequestTimeout(60000) // 连接请求超时时间.setConnectionTimeout(60000) // 连接超时时间.setSocketTimeout(60000) // Socket 超时时间.setEmitter((element, context, indexer) -> {Map<String, Object> json = new HashMap<>();json.put("timestamp", element.getTimestamp());json.put("logLevel", element.getLogLevel());json.put("source", element.getSource());json.put("message", element.getMessage());IndexRequest request = Requests.indexRequest().index("logs_index").source(json);indexer.add(request);}).build();logStream.sinkTo(esSink).name("Elasticsearch Sink");// 7. 配置并添加JDBC Sink - 存储错误日志统计// 先进行统计DataStream<LogStats> statsStream = errorLogStream.map(log -> new LogStats(log.getSource(), 1)).keyBy(LogStats::getSource).sum("count").name("Error Log Stats");JdbcExecutionOptions jdbcExecutionOptions = JdbcExecutionOptions.builder().withBatchSize(1000).withBatchIntervalMs(200).withMaxRetries(5).build();JdbcConnectionOptions connectionOptions = new JdbcConnectionOptions.JdbcConnectionOptionsBuilder().withUrl("jdbc:mysql://localhost:3306/test").withDriverName("com.mysql.cj.jdbc.Driver").withUsername("mysql用户名").withPassword("mysql密码").build();String insertSql = "INSERT INTO error_log_stats (source, count, last_updated) VALUES (?, ?, ?) " +"ON DUPLICATE KEY UPDATE count = count + VALUES(count), last_updated = VALUES(last_updated)";JdbcStatementBuilder<LogStats> statementBuilder = (statement, stats) -> {statement.setString(1, stats.getSource());statement.setLong(2, stats.getCount());statement.setTimestamp(3, java.sql.Timestamp.valueOf(LocalDateTime.now()));};// 创建JDBC SinkJdbcSink<LogStats> jdbcSink = new Jdbc().<LogStats>sinkBuilder().withQueryStatement( new SimpleJdbcQueryStatement<LogStats>(insertSql,statementBuilder)).withExecutionOptions(jdbcExecutionOptions).buildAtLeastOnce(connectionOptions);statsStream.sinkTo(jdbcSink).name("JDBC Sink");// 8. 执行作业env.execute("Multi-Sink Data Pipeline");}}
4. 测试与验证
要测试这个完整的流水线,我们需要:
-
启动Kafka并创建必要的主题:
# 创建输入主题 kafka-topics.sh --create --topic logs-input-topic --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1# 创建错误日志输出主题 kafka-topics.sh --create --topic error-logs-topic --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
-
启动Elasticsearch并确保服务正常运行
-
在MySQL中创建必要的表:
CREATE DATABASE test; USE test;CREATE TABLE error_log_stats (source VARCHAR(100) PRIMARY KEY,count BIGINT NOT NULL,last_updated TIMESTAMP NOT NULL );
-
向Kafka发送测试数据:
kafka-console-producer.sh --topic logs-input-topic --bootstrap-server localhost:9092# 输入以下测试数据 2025-09-29 12:00:00|INFO|application-service|Application started successfully 2025-09-29 12:01:30|ERROR|database-service|Failed to connect to database 2025-09-29 12:02:15|WARN|cache-service|Cache eviction threshold reached 2025-09-29 12:03:00|ERROR|authentication-service|Invalid credentials detected
-
运行Flink作业并观察数据流向各个目标系统
查看Kafka Sink中的数据:
查看MySQL中的数据:
查看Elasticsearch中的数据:
八、性能优化与最佳实践
1. 并行度配置
合理设置Sink的并行度可以显著提高吞吐量:
// 为特定Sink设置并行度
stream.addSink(sink).setParallelism(4);// 或为整个作业设置默认并行度
env.setParallelism(4);
2. 批处理配置
对于支持批处理的Sink,合理配置批处理参数可以减少网络开销:
// JDBC批处理示例
JdbcExecutionOptions.builder().withBatchSize(1000) // 每批次处理的记录数.withBatchIntervalMs(200) // 批处理间隔.withMaxRetries(3) // 最大重试次数.build();
3. 背压处理
当Sink无法处理上游数据时,会产生背压。Flink提供了背压监控和处理机制:
- 使用Flink Web UI监控背压情况
- 考虑使用缓冲机制或调整并行度
- 对于关键路径,实现自定义的背压处理逻辑
4. 资源管理
合理管理连接和资源是保证Sink稳定运行的关键:
- 使用连接池管理数据库连接
- 在RichSinkFunction的open()方法中初始化资源
- 在close()方法中正确释放资源
5. 错误处理策略
为Sink配置适当的错误处理策略:
// 重试策略配置
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, // 最大重试次数Time.of(10, TimeUnit.SECONDS) // 重试间隔
));
九、总结与展望
本文深入探讨了Flink数据输出(Sink)的核心概念、各种连接器的使用方法以及可靠性保证机制。我们学习了如何配置和使用内置Sink、文件系统Sink、Kafka Sink、Elasticsearch Sink和JDBC Sink,并通过自定义Sink扩展了Flink的输出能力。最后,我们构建了一个完整的实时数据处理流水线,将处理后的数据输出到多个目标系统。
在Flink的数据处理生态中,Sink是连接计算结果与外部世界的桥梁。通过选择合适的Sink连接器并配置正确的参数,我们可以构建高效、可靠的数据处理系统。
源文来自:http://blog.daimajiangxin.com.cn
源码地址:https://gitee.com/daimajiangxin/flink-learning