使用Java类库中的Amazon Kinesis Client Library For Java进行数据流管理
使用Java类库中的Amazon Kinesis Client Library For Java进行数据流管理
Amazon Kinesis是亚马逊云平台上用于处理流式数据的服务。而Amazon Kinesis Client Library for Java是一个用于简化开发者使用Amazon Kinesis服务的Java类库。它为开发者提供了一系列的功能，可用于管理和处理数据流。
在开始使用Amazon Kinesis Client Library for Java之前，需要先配置并创建Amazon Kinesis数据流。一旦数据流创建成功，就可以使用该类库来处理和管理这个数据流中的数据。
以下是一些常见的使用Amazon Kinesis Client Library for Java的示例：
1. 获取数据流中的数据
import software.amazon.kinesis.exceptions.InvalidArgumentException;
import software.amazon.kinesis.exceptions.KinesisClientLibException;
import software.amazon.kinesis.processor.ShardRecordProcessor;
import software.amazon.kinesis.processor.ShardRecordProcessorFactory;
import software.amazon.kinesis.processor.ShardRecordProcessorFactory;
import software.amazon.kinesis.producer.KinesisProducer;
import software.amazon.kinesis.producer.KinesisProducerConfiguration;
import software.amazon.kinesis.producer.UserRecordFailedException;
import software.amazon.kinesis.producer.UserRecordResult;
import software.amazon.kinesis.retrieval.FirehoseRecord;
import software.amazon.kinesis.retrieval.KinesisClientRecord;
import software.amazon.kinesis.retrieval.KinesisClientRecordProcessor;
import software.amazon.kinesis.retrieval.KinesisClientRecordProcessorFactory;
import software.amazon.kinesis.retrieval.KinesisClientRecordProcessorFactoryConfig;
import software.amazon.kinesis.retrieval.KinesisClientRecordProcessorConfiguration;
import software.amazon.kinesis.retrieval.KinesisClientRecordProcessorConfigur software.amazon.kinesis.samples.sample2.Configuration;
import software.amazon.kinesis.samples.sample2.Consts;
import software.amazon.kinesis.samples.sample2.utils.StreamsUtils;
import software.amazon.kinesis.samples.sample2.utils.StringUtils;
import software.amazon.kinesis.samples.utils.KinesisSampleUtils;
import software.amazon.kinesis.samples.utils.LeaseCoordinatorWithTime;
import software.amazon.kinesis.samples.utils.ProcessingUtils;
import software.amazon.kinesis.samples.utils.SamplesConfiguration;
import software.amazon.kinesis.samples.utils.SamplesUtils;
import software.amazon.kinesis.samples.utils.StreamsUtils;
import software.amazon.kinesis.samples.utils.StringUtils;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.Random;
public class DataStreamProcessor implements KinesisClientRecordProcessor {
@Override
public void initialize(String shardId) {
// 初始化处理程序
System.out.println("Initialized. ShardId: " + shardId);
}
@Override
public void processRecords(List<KinesisClientRecord> records, Checkpointer checkpointer) {
// 对每个记录进行处理
for (KinesisClientRecord record : records) {
// 获取记录的分区键和数据
byte[] data = record.getData().array();
String partitionKey = record.getPartitionKey();
String dataString = new String(data, StandardCharsets.UTF_8);
System.out.println("Processing record. PartitionKey: " + partitionKey + ", Data: " + dataString);
// 进行具体处理逻辑
// ...
}
// 检查点确认处理完当前记录
try {
checkpointer.checkpoint();
} catch (KinesisClientLibException e) {
System.err.println("Error while trying to checkpoint in record processor for shard " + shardId + ": " + e);
}
}
@Override
public void leaseLost() {
// 处理租约丢失情况
}
@Override
public void shardEnded(String checkpointerSequenceNumber, Checkpointer checkpointer) {
// 处理分片结束情况
try {
checkpointer.checkpoint(checkpointerSequenceNumber);
} catch (KinesisClientLibException e) {
System.err.println("Error while trying to checkpoint after shard ended. Error: " + e);
}
}
@Override
public void shutdownRequested(Checkpointer checkpointer) {
// 处理关闭请求
try {
checkpointer.checkpoint();
} catch (KinesisClientLibException e) {
System.err.println("Error while trying to checkpoint before shutdown. Error: " + e);
}
}
public static void main(String[] args) throws IOException {
// 从配置文件加载配置参数
String configFile = "<config_file_path>";
String streamName = "<stream_name>";
byte[] fileBytes = Files.readAllBytes(Paths.get(configFile));
String configFileContent = new String(fileBytes, StandardCharsets.UTF_8);
Configuration configuration = Configuration.fromJson(configFileContent);
// 创建Amazon Kinesis Client Library的工厂类
KinesisClientRecordProcessorFactoryConfig factoryConfig = new KinesisClientRecordProcessorFactoryConfig();
factoryConfig.config = configuration;
factoryConfig.streamName = streamName;
factoryConfig.provider = null;
KinesisClientRecordProcessorFactory kclFactory = new KinesisClientRecordProcessorFactory(factoryConfig);
// 创建数据流处理器并启动
KinesisSampleUtils.createOrAttachToStream(configuration, streamName, "worker-id", kclFactory);
}
}
2. 创建数据流处理器并启动
import software.amazon.awssdk.auth.credentials.AwsCredentialsProvider;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.services.kinesis.KinesisAsyncClient;
import software.amazon.awssdk.services.kinesis.KinesisAsyncClientBuilder;
import software.amazon.kinesis.checkpoint.Checkpointer;
import software.amazon.kinesis.leases.Lease;
import software.amazon.kinesis.leases.LeaseCoordinator;
import software.amazon.kinesis.leases.LeaseCoordinatorConfig;
import software.amazon.kinesis.leases.LeaseRefresher;
import software.amazon.kinesis.leases.LeaseTaker;
import software.amazon.kinesis.leases.ShardDetector;
import software.amazon.kinesis.lifecycle.LifecycleConfig;
import software.amazon.kinesis.lifecycle.ShutdownReason;
import software.amazon.kinesis.processor.RecordProcessorCheckpointer;
import software.amazon.kinesis.processor.StreamshardRecordProcessor;
import software.amazon.kinesis.processor.StreamshardRecordProcessorConfig;
import software.amazon.kinesis.retrieval.KinesisClientRecord;
import software.amazon.kinesis.retrieval.KinesisDataFetcher;
import software.amazon.kinesis.retrieval.RetrievalConfig;
import software.amazon.kinesis.samples.flink.KinesisConfigConstants;
import software.amazon.kinesis.samples.flink.KinesisDataFetcherThread;
import software.amazon.kinesis.samples.flink.RecordPublisher;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import java.util.Collections;
import java.util.List;
import java.util.UUID;
public class DataStreamProcessor {
private static final Log LOG = LogFactory.getLog(DataStreamProcessor.class);
public static void main(String[] args) {
// 配置Amazon Kinesis Client Library for Java
final String streamName = "<stream_name>";
final String region = "<aws_region>";
final String workerId = UUID.randomUUID().toString();
final int numRetries = 10;
final long backoffTimeInMillis = 1500L;
final long failoverTimeInMillis = 30000L;
final AwsCredentialsProvider credentialsProvider = new DefaultCredentialsProvider();
// 配置Amazon Kinesis数据获取组件
KinesisDataFetcherConfig kinesisDataFetcherConfig = new KinesisDataFetcherConfig();
kinesisDataFetcherConfig.regionName = region;
kinesisDataFetcherConfig.streamName = streamName;
kinesisDataFetcherConfig.kinesis = KinesisAsyncClient.builder().region(region).build();
kinesisDataFetcherConfig.recordPublisher = new RecordPublisher();
final KinesisDataFetcher kinesisDataFetcher = new KinesisDataFetcher(kinesisDataFetcherConfig);
// 配置租赁协调组件
final LeaseRefresher.LeaseRefresherFactory leaseRefresherFactory = new LeaseRefresher.LeaseRefresherFactory() {
@Override
public LeaseRefresher create(
final LeaseCoordinatorConfig leaseCoordinatorConfig, final StreamConsumerConfig streamConfig) {
return new KinesisClientLibLeaseRefresher(kinesisDataFetcherConfig.kinesis, leaseCoordinatorConfig, streamConfig);
}
};
final LeaseCoordinator.LeaseCoordinatorConfig leaseCoordinatorConfig = new LeaseCoordinator.LeaseCoordinatorConfig(
streamName,
kinesisDataFetcherConfig.kinesis.describeStream(streamName).get().streamDescription(),
workerId,
credentialsProvider,
numRetries,
backoffTimeInMillis,
failoverTimeInMillis);
final StreamshardRecordProcessor.StreamshardRecordProcessorFactory mkRecordProcessorFactory = new StreamshardRecordProcessor.StreamshardRecordProcessorFactory() {
@Override
public StreamshardRecordProcessor createProcessor() {
return new DataStreamProcessor();
}
};
final LeaseTaker.LeaseTakerConfig leaseTakerConfig = new LeaseTaker.LeaseTakerConfig(
streamName,
kinesisDataFetcherConfig.kinesis.describeStream(streamName).get().streamDescription(),
workerId,
credentialsProvider);
final ShardDetector.ShardDetectorConfig shardDetectorConfig = new ShardDetector.ShardDetectorConfig(
streamName,
kinesisDataFetcherConfig.kinesis.describeStream(streamName).get().streamDescription(),
numRetries,
credentialsProvider,
backoffTimeInMillis);
final StreamingConfigBuilder streamingConfigBuilder = new StreamingConfigBuilder();
final LifecycleConfig lifecycleConfig = new LifecycleConfig(new RecordProcessorCheckpointer() {
@Override
public void checkpoint(RecordProcessorCheckpointer.CheckpointOption checkpointOption) {
// 将记录处理器的检查点提交给Amazon Kinesis
try {
synchronized (kinesisDataFetcher) {
kinesisDataFetcher.checkpoint();
}
} catch (Exception e) {
LOG.warn("Caught exception when checkpointing with KinesisAsyncClient", e);
throw e;
}
}
@Override
public void checkpoint(long sequenceNumber) {
// 不支持单独检查点提交序列号的方式
throw new UnsupportedOperationException();
}
}, streamingConfigBuilder);
final KinesisClientLibLeaseCoordinator leaseCoordinator = new KinesisClientLibLeaseCoordinator(leaseCoordinatorConfig,
kinesisDataFetcherConfig.kinesis,
leaseTakerConfig,
shardDetectorConfig,
leaseRefresherFactory,
lifecycleConfig,
mkRecordProcessorFactory);
// 启动数据流处理器
try {
leaseCoordinator.initialize();
leaseCoordinator.run();
} catch (KinesisClientLibException e) {
LOG.error("Caught exception while initializing LeaseCoordinator", e);
}
}
// 实现StreamshardRecordProcessor接口，并自定义处理逻辑
public void processRecords(KinesisClientRecord inputRecords) {
for (KinesisClientRecord record : inputRecords.getRecords()) {
String data = new String(record.getData().array());
System.out.println("Processing record: " + data);
// 处理逻辑
// ...
}
}
// 其他接口方法
// ...
}
通过上述示例代码，我们可以看到如何使用Amazon Kinesis Client Library for Java来处理和管理数据流。在第一个示例中，我们实现了KinesisClientRecordProcessor接口，包括初始化、处理记录、租约丢失、分片结束和关闭请求等方法。在第二个示例中，我们演示了如何配置和启动数据流处理器，包括Kinesis数据获取组件和租约协调组件，并实现了自定义的记录处理逻辑。
总结来说，Amazon Kinesis Client Library for Java是一个强大的工具，用于简化开发者使用Amazon Kinesis服务时的数据流管理工作。无论是处理数据、管理租约还是启动处理器，使用该类库可以大大简化这些操作，让我们能够更专注于处理流式数据的本身。
