本文整理匯總了Java中org.apache.flink.streaming.api.environment.StreamExecutionEnvironment.setBufferTimeout方法的典型用法代碼示例。如果您正苦於以下問題：Java StreamExecutionEnvironment.setBufferTimeout方法的具體用法？Java StreamExecutionEnvironment.setBufferTimeout怎麽用？Java StreamExecutionEnvironment.setBufferTimeout使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類org.apache.flink.streaming.api.environment.StreamExecutionEnvironment的用法示例。
在下文中一共展示了StreamExecutionEnvironment.setBufferTimeout方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: runPartitioningProgram
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; //導入方法依賴的package包/類
private static void runPartitioningProgram(int jobManagerPort, int parallelism) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", jobManagerPort);
env.setParallelism(parallelism);
env.getConfig().enableObjectReuse();
env.setBufferTimeout(5L);
env.enableCheckpointing(1000, CheckpointingMode.AT_LEAST_ONCE);
env
.addSource(new TimeStampingSource())
.map(new IdMapper<Tuple2<Long, Long>>())
.keyBy(0)
.addSink(new TimestampingSink());
env.execute("Partitioning Program");
}
示例2: setupExecutionEnvironment
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; //導入方法依賴的package包/類
public static final StreamExecutionEnvironment setupExecutionEnvironment(AppConfiguration config) {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
//env.getConfig().setAutoWatermarkInterval(1000000);
env.setBufferTimeout(config.getBufferTimeout());
return env;
}
示例3: runMultipleSourcesOnePartitionExactlyOnceTest
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; //導入方法依賴的package包/類
/**
* Tests the proper consumption when having more Flink sources than Kafka partitions, which means
* that some Flink sources will read no partitions.
*/
public void runMultipleSourcesOnePartitionExactlyOnceTest() throws Exception {
final String topic = "manyToOneTopic";
final int numPartitions = 5;
final int numElementsPerPartition = 1000;
final int totalElements = numPartitions * numElementsPerPartition;
final int failAfterElements = numElementsPerPartition / 3;
final int parallelism = 8;
createTestTopic(topic, numPartitions, 1);
DataGenerators.generateRandomizedIntegerSequence(
StreamExecutionEnvironment.getExecutionEnvironment(),
kafkaServer,
topic,
numPartitions,
numElementsPerPartition,
true);
// run the topology that fails and recovers
DeserializationSchema<Integer> schema =
new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.enableCheckpointing(500);
env.setParallelism(parallelism);
// set the number of restarts to one. The failing mapper will fail once, then it's only success exceptions.
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(1, 0));
env.getConfig().disableSysoutLogging();
env.setBufferTimeout(0);
Properties props = new Properties();
props.putAll(standardProps);
props.putAll(secureProps);
FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
env
.addSource(kafkaSource)
.map(new PartitionValidatingMapper(numPartitions, 1))
.map(new FailingIdentityMapper<Integer>(failAfterElements))
.addSink(new ValidatingExactlyOnceSink(totalElements)).setParallelism(1);
FailingIdentityMapper.failedBefore = false;
tryExecute(env, "multi-source-one-partitions exactly once test");
deleteTestTopic(topic);
}
示例4: runMultipleSourcesOnePartitionExactlyOnceTest
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; //導入方法依賴的package包/類
/**
* Tests the proper consumption when having more Flink sources than Kafka partitions, which means
* that some Flink sources will read no partitions.
*/
public void runMultipleSourcesOnePartitionExactlyOnceTest() throws Exception {
final String topic = "manyToOneTopic";
final int numPartitions = 5;
final int numElementsPerPartition = 1000;
final int totalElements = numPartitions * numElementsPerPartition;
final int failAfterElements = numElementsPerPartition / 3;
final int parallelism = 8;
createTestTopic(topic, numPartitions, 1);
DataGenerators.generateRandomizedIntegerSequence(
StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort),
kafkaServer,
topic, numPartitions, numElementsPerPartition, true);
// run the topology that fails and recovers
DeserializationSchema<Integer> schema =
new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
env.enableCheckpointing(500);
env.setParallelism(parallelism);
// set the number of restarts to one. The failing mapper will fail once, then it's only success exceptions.
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(1, 0));
env.getConfig().disableSysoutLogging();
env.setBufferTimeout(0);
Properties props = new Properties();
props.putAll(standardProps);
props.putAll(secureProps);
FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
env
.addSource(kafkaSource)
.map(new PartitionValidatingMapper(numPartitions, 1))
.map(new FailingIdentityMapper<Integer>(failAfterElements))
.addSink(new ValidatingExactlyOnceSink(totalElements)).setParallelism(1);
FailingIdentityMapper.failedBefore = false;
tryExecute(env, "multi-source-one-partitions exactly once test");
deleteTestTopic(topic);
}