/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.flink.streaming.api.environment;
import static org.apache.flink.util.Preconditions.checkNotNull;
import com.esotericsoftware.kryo.Serializer;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
import org.apache.flink.annotation.Internal;
import org.apache.flink.annotation.Public;
import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.api.common.ExecutionConfig;
import org.apache.flink.api.common.InvalidProgramException;
import org.apache.flink.api.common.JobExecutionResult;
import org.apache.flink.api.common.cache.DistributedCache;
import org.apache.flink.api.common.functions.InvalidTypesException;
import org.apache.flink.api.common.functions.StoppableFunction;
import org.apache.flink.api.common.io.FileInputFormat;
import org.apache.flink.api.common.io.FilePathFilter;
import org.apache.flink.api.common.io.InputFormat;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.ClosureCleaner;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.io.TextInputFormat;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.api.java.typeutils.MissingTypeInfo;
import org.apache.flink.api.java.typeutils.PojoTypeInfo;
import org.apache.flink.api.java.typeutils.ResultTypeQueryable;
import org.apache.flink.api.java.typeutils.TypeExtractor;
import org.apache.flink.client.program.ContextEnvironment;
import org.apache.flink.client.program.OptimizerPlanEnvironment;
import org.apache.flink.client.program.PreviewPlanEnvironment;
import org.apache.flink.configuration.ConfigConstants;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.core.fs.Path;
import org.apache.flink.runtime.state.AbstractStateBackend;
import org.apache.flink.runtime.state.KeyGroupRangeAssignment;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.functions.source.ContinuousFileMonitoringFunction;
import org.apache.flink.streaming.api.functions.source.ContinuousFileReaderOperator;
import org.apache.flink.streaming.api.functions.source.FileMonitoringFunction;
import org.apache.flink.streaming.api.functions.source.FileProcessingMode;
import org.apache.flink.streaming.api.functions.source.FileReadFunction;
import org.apache.flink.streaming.api.functions.source.FromElementsFunction;
import org.apache.flink.streaming.api.functions.source.FromIteratorFunction;
import org.apache.flink.streaming.api.functions.source.FromSplittableIteratorFunction;
import org.apache.flink.streaming.api.functions.source.InputFormatSourceFunction;
import org.apache.flink.streaming.api.functions.source.ParallelSourceFunction;
import org.apache.flink.streaming.api.functions.source.SocketTextStreamFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.functions.source.StatefulSequenceSource;
import org.apache.flink.streaming.api.graph.StreamGraph;
import org.apache.flink.streaming.api.graph.StreamGraphGenerator;
import org.apache.flink.streaming.api.operators.StoppableStreamSource;
import org.apache.flink.streaming.api.operators.StreamSource;
import org.apache.flink.streaming.api.transformations.StreamTransformation;
import org.apache.flink.util.Preconditions;
import org.apache.flink.util.SplittableIterator;
/**
* The StreamExecutionEnvironment is the context in which a streaming program is executed. A
* {@link LocalStreamEnvironment} will cause execution in the current JVM, a
* {@link RemoteStreamEnvironment} will cause execution on a remote setup.
*
* <p>The environment provides methods to control the job execution (such as setting the parallelism
* or the fault tolerance/checkpointing parameters) and to interact with the outside world (data access).
*
* @see org.apache.flink.streaming.api.environment.LocalStreamEnvironment
* @see org.apache.flink.streaming.api.environment.RemoteStreamEnvironment
*/
@Public
public abstract class StreamExecutionEnvironment {
/** The default name to use for a streaming job if no other name has been specified. */
public static final String DEFAULT_JOB_NAME = "Flink Streaming Job";
/** The time characteristic that is used if none other is set. */
private static final TimeCharacteristic DEFAULT_TIME_CHARACTERISTIC = TimeCharacteristic.ProcessingTime;
/** The default buffer timeout (max delay of records in the network stack). */
private static final long DEFAULT_NETWORK_BUFFER_TIMEOUT = 100L;
/**
* The environment of the context (local by default, cluster if invoked through command line).
*/
private static StreamExecutionEnvironmentFactory contextEnvironmentFactory;
/** The default parallelism used when creating a local environment. */
private static int defaultLocalParallelism = Runtime.getRuntime().availableProcessors();
// ------------------------------------------------------------------------
/** The execution configuration for this environment. */
private final ExecutionConfig config = new ExecutionConfig();
/** Settings that control the checkpointing behavior. */
private final CheckpointConfig checkpointCfg = new CheckpointConfig();
protected final List<StreamTransformation<?>> transformations = new ArrayList<>();
private long bufferTimeout = DEFAULT_NETWORK_BUFFER_TIMEOUT;
protected boolean isChainingEnabled = true;
/** The state backend used for storing k/v state and state snapshots. */
private AbstractStateBackend defaultStateBackend;
/** The time characteristic used by the data streams. */
private TimeCharacteristic timeCharacteristic = DEFAULT_TIME_CHARACTERISTIC;
protected final List<Tuple2<String, DistributedCache.DistributedCacheEntry>> cacheFile = new ArrayList<>();
// --------------------------------------------------------------------------------------------
// Constructor and Properties
// --------------------------------------------------------------------------------------------
/**
* Gets the config object.
*/
public ExecutionConfig getConfig() {
return config;
}
/**
* Get the list of cached files that were registered for distribution among the task managers.
*/
public List<Tuple2<String, DistributedCache.DistributedCacheEntry>> getCachedFiles() {
return cacheFile;
}
/**
* Sets the parallelism for operations executed through this environment.
* Setting a parallelism of x here will cause all operators (such as map,
* batchReduce) to run with x parallel instances. This method overrides the
* default parallelism for this environment. The
* {@link LocalStreamEnvironment} uses by default a value equal to the
* number of hardware contexts (CPU cores / threads). When executing the
* program via the command line client from a JAR file, the default degree
* of parallelism is the one configured for that setup.
*
* @param parallelism The parallelism
*/
public StreamExecutionEnvironment setParallelism(int parallelism) {
if (parallelism < 1) {
throw new IllegalArgumentException("parallelism must be at least one.");
}
config.setParallelism(parallelism);
return this;
}
/**
* Sets the maximum degree of parallelism defined for the program. The upper limit (inclusive)
* is Short.MAX_VALUE.
*
* <p>The maximum degree of parallelism specifies the upper limit for dynamic scaling. It also
* defines the number of key groups used for partitioned state.
*
* @param maxParallelism Maximum degree of parallelism to be used for the program.,
* with 0 < maxParallelism <= 2^15 - 1
*/
public StreamExecutionEnvironment setMaxParallelism(int maxParallelism) {
Preconditions.checkArgument(maxParallelism > 0 &&
maxParallelism <= KeyGroupRangeAssignment.UPPER_BOUND_MAX_PARALLELISM,
"maxParallelism is out of bounds 0 < maxParallelism <= " +
KeyGroupRangeAssignment.UPPER_BOUND_MAX_PARALLELISM + ". Found: " + maxParallelism);
config.setMaxParallelism(maxParallelism);
return this;
}
/**
* Gets the parallelism with which operation are executed by default.
* Operations can individually override this value to use a specific
* parallelism.
*
* @return The parallelism used by operations, unless they override that
* value.
*/
public int getParallelism() {
return config.getParallelism();
}
/**
* Gets the maximum degree of parallelism defined for the program.
*
* <p>The maximum degree of parallelism specifies the upper limit for dynamic scaling. It also
* defines the number of key groups used for partitioned state.
*
* @return Maximum degree of parallelism
*/
public int getMaxParallelism() {
return config.getMaxParallelism();
}
/**
* Sets the maximum time frequency (milliseconds) for the flushing of the
* output buffers. By default the output buffers flush frequently to provide
* low latency and to aid smooth developer experience. Setting the parameter
* can result in three logical modes:
*
* <ul>
* <li>A positive integer triggers flushing periodically by that integer</li>
* <li>0 triggers flushing after every record thus minimizing latency</li>
* <li>-1 triggers flushing only when the output buffer is full thus maximizing
* throughput</li>
* </ul>
*
* @param timeoutMillis
* The maximum time between two output flushes.
*/
public StreamExecutionEnvironment setBufferTimeout(long timeoutMillis) {
if (timeoutMillis < -1) {
throw new IllegalArgumentException("Timeout of buffer must be non-negative or -1");
}
this.bufferTimeout = timeoutMillis;
return this;
}
/**
* Gets the maximum time frequency (milliseconds) for the flushing of the
* output buffers. For clarification on the extremal values see
* {@link #setBufferTimeout(long)}.
*
* @return The timeout of the buffer.
*/
public long getBufferTimeout() {
return this.bufferTimeout;
}
/**
* Disables operator chaining for streaming operators. Operator chaining
* allows non-shuffle operations to be co-located in the same thread fully
* avoiding serialization and de-serialization.
*
* @return StreamExecutionEnvironment with chaining disabled.
*/
@PublicEvolving
public StreamExecutionEnvironment disableOperatorChaining() {
this.isChainingEnabled = false;
return this;
}
/**
* Returns whether operator chaining is enabled.
*
* @return {@code true} if chaining is enabled, false otherwise.
*/
@PublicEvolving
public boolean isChainingEnabled() {
return isChainingEnabled;
}
// ------------------------------------------------------------------------
// Checkpointing Settings
// ------------------------------------------------------------------------
/**
* Gets the checkpoint config, which defines values like checkpoint interval, delay between
* checkpoints, etc.
*
* @return The checkpoint config.
*/
public CheckpointConfig getCheckpointConfig() {
return checkpointCfg;
}
/**
* Enables checkpointing for the streaming job. The distributed state of the streaming
* dataflow will be periodically snapshotted. In case of a failure, the streaming
* dataflow will be restarted from the latest completed checkpoint. This method selects
* {@link CheckpointingMode#EXACTLY_ONCE} guarantees.
*
* <p>The job draws checkpoints periodically, in the given interval. The state will be
* stored in the configured state backend.
*
* <p>NOTE: Checkpointing iterative streaming dataflows in not properly supported at
* the moment. For that reason, iterative jobs will not be started if used
* with enabled checkpointing. To override this mechanism, use the
* {@link #enableCheckpointing(long, CheckpointingMode, boolean)} method.
*
* @param interval Time interval between state checkpoints in milliseconds.
*/
public StreamExecutionEnvironment enableCheckpointing(long interval) {
checkpointCfg.setCheckpointInterval(interval);
return this;
}
/**
* Enables checkpointing for the streaming job. The distributed state of the streaming
* dataflow will be periodically snapshotted. In case of a failure, the streaming
* dataflow will be restarted from the latest completed checkpoint.
*
* <p>The job draws checkpoints periodically, in the given interval. The system uses the
* given {@link CheckpointingMode} for the checkpointing ("exactly once" vs "at least once").
* The state will be stored in the configured state backend.
*
* <p>NOTE: Checkpointing iterative streaming dataflows in not properly supported at
* the moment. For that reason, iterative jobs will not be started if used
* with enabled checkpointing. To override this mechanism, use the
* {@link #enableCheckpointing(long, CheckpointingMode, boolean)} method.
*
* @param interval
* Time interval between state checkpoints in milliseconds.
* @param mode
* The checkpointing mode, selecting between "exactly once" and "at least once" guaranteed.
*/
public StreamExecutionEnvironment enableCheckpointing(long interval, CheckpointingMode mode) {
checkpointCfg.setCheckpointingMode(mode);
checkpointCfg.setCheckpointInterval(interval);
return this;
}
/**
* Enables checkpointing for the streaming job. The distributed state of the streaming
* dataflow will be periodically snapshotted. In case of a failure, the streaming
* dataflow will be restarted from the latest completed checkpoint.
*
* <p>The job draws checkpoints periodically, in the given interval. The state will be
* stored in the configured state backend.
*
* <p>NOTE: Checkpointing iterative streaming dataflows in not properly supported at
* the moment. If the "force" parameter is set to true, the system will execute the
* job nonetheless.
*
* @param interval
* Time interval between state checkpoints in millis.
* @param mode
* The checkpointing mode, selecting between "exactly once" and "at least once" guaranteed.
* @param force
* If true checkpointing will be enabled for iterative jobs as well.
*
* @deprecated Use {@link #enableCheckpointing(long, CheckpointingMode)} instead.
* Forcing checkpoints will be removed in the future.
*/
@Deprecated
@SuppressWarnings("deprecation")
@PublicEvolving
public StreamExecutionEnvironment enableCheckpointing(long interval, CheckpointingMode mode, boolean force) {
checkpointCfg.setCheckpointingMode(mode);
checkpointCfg.setCheckpointInterval(interval);
checkpointCfg.setForceCheckpointing(force);
return this;
}
/**
* Enables checkpointing for the streaming job. The distributed state of the streaming
* dataflow will be periodically snapshotted. In case of a failure, the streaming
* dataflow will be restarted from the latest completed checkpoint. This method selects
* {@link CheckpointingMode#EXACTLY_ONCE} guarantees.
*
* <p>The job draws checkpoints periodically, in the default interval. The state will be
* stored in the configured state backend.
*
* <p>NOTE: Checkpointing iterative streaming dataflows in not properly supported at
* the moment. For that reason, iterative jobs will not be started if used
* with enabled checkpointing. To override this mechanism, use the
* {@link #enableCheckpointing(long, CheckpointingMode, boolean)} method.
*
* @deprecated Use {@link #enableCheckpointing(long)} instead.
*/
@Deprecated
@PublicEvolving
public StreamExecutionEnvironment enableCheckpointing() {
checkpointCfg.setCheckpointInterval(500);
return this;
}
/**
* Returns the checkpointing interval or -1 if checkpointing is disabled.
*
* <p>Shorthand for {@code getCheckpointConfig().getCheckpointInterval()}.
*
* @return The checkpointing interval or -1
*/
public long getCheckpointInterval() {
return checkpointCfg.getCheckpointInterval();
}
/**
* Returns whether checkpointing is force-enabled.
*
* @deprecated Forcing checkpoints will be removed in future version.
*/
@Deprecated
@SuppressWarnings("deprecation")
@PublicEvolving
public boolean isForceCheckpointing() {
return checkpointCfg.isForceCheckpointing();
}
/**
* Returns the checkpointing mode (exactly-once vs. at-least-once).
*
* <p>Shorthand for {@code getCheckpointConfig().getCheckpointingMode()}.
*
* @return The checkpoin
*/
public CheckpointingMode getCheckpointingMode() {
return checkpointCfg.getCheckpointingMode();
}
/**
* Sets the state backend that describes how to store and checkpoint operator state. It defines in
* what form the key/value state ({@link ValueState}, accessible
* from operations on {@link org.apache.flink.streaming.api.datastream.KeyedStream}) is maintained
* (heap, managed memory, externally), and where state snapshots/checkpoints are stored, both for
* the key/value state, and for checkpointed functions (implementing the interface
* {@link org.apache.flink.streaming.api.checkpoint.Checkpointed}).
*
* <p>The {@link org.apache.flink.runtime.state.memory.MemoryStateBackend} for example
* maintains the state in heap memory, as objects. It is lightweight without extra dependencies,
* but can checkpoint only small states (some counters).
*
* <p>In contrast, the {@link org.apache.flink.runtime.state.filesystem.FsStateBackend}
* stores checkpoints of the state (also maintained as heap objects) in files. When using a replicated
* file system (like HDFS, S3, MapR FS, Tachyon, etc) this will guarantee that state is not lost upon
* failures of individual nodes and that streaming program can be executed highly available and strongly
* consistent (assuming that Flink is run in high-availability mode).
*
* @return This StreamExecutionEnvironment itself, to allow chaining of function calls.
*
* @see #getStateBackend()
*/
@PublicEvolving
public StreamExecutionEnvironment setStateBackend(AbstractStateBackend backend) {
this.defaultStateBackend = Preconditions.checkNotNull(backend);
return this;
}
/**
* Returns the state backend that defines how to store and checkpoint state.
* @return The state backend that defines how to store and checkpoint state.
*
* @see #setStateBackend(AbstractStateBackend)
*/
@PublicEvolving
public AbstractStateBackend getStateBackend() {
return defaultStateBackend;
}
/**
* Sets the restart strategy configuration. The configuration specifies which restart strategy
* will be used for the execution graph in case of a restart.
*
* @param restartStrategyConfiguration Restart strategy configuration to be set
*/
@PublicEvolving
public void setRestartStrategy(RestartStrategies.RestartStrategyConfiguration restartStrategyConfiguration) {
config.setRestartStrategy(restartStrategyConfiguration);
}
/**
* Returns the specified restart strategy configuration.
*
* @return The restart strategy configuration to be used
*/
@PublicEvolving
public RestartStrategies.RestartStrategyConfiguration getRestartStrategy() {
return config.getRestartStrategy();
}
/**
* Sets the number of times that failed tasks are re-executed. A value of
* zero effectively disables fault tolerance. A value of {@code -1}
* indicates that the system default value (as defined in the configuration)
* should be used.
*
* @param numberOfExecutionRetries
* The number of times the system will try to re-execute failed tasks.
*
* @deprecated This method will be replaced by {@link #setRestartStrategy}. The
* {@link RestartStrategies#fixedDelayRestart(int, Time)} contains the number of
* execution retries.
*/
@Deprecated
@PublicEvolving
public void setNumberOfExecutionRetries(int numberOfExecutionRetries) {
config.setNumberOfExecutionRetries(numberOfExecutionRetries);
}
/**
* Gets the number of times the system will try to re-execute failed tasks.
* A value of {@code -1} indicates that the system default value (as defined
* in the configuration) should be used.
*
* @return The number of times the system will try to re-execute failed tasks.
*
* @deprecated This method will be replaced by {@link #getRestartStrategy}.
*/
@Deprecated
@PublicEvolving
public int getNumberOfExecutionRetries() {
return config.getNumberOfExecutionRetries();
}
// --------------------------------------------------------------------------------------------
// Registry for types and serializers
// --------------------------------------------------------------------------------------------
/**
* Adds a new Kryo default serializer to the Runtime.
*
* <p>Note that the serializer instance must be serializable (as defined by
* java.io.Serializable), because it may be distributed to the worker nodes
* by java serialization.
*
* @param type
* The class of the types serialized with the given serializer.
* @param serializer
* The serializer to use.
*/
public <T extends Serializer<?> & Serializable>void addDefaultKryoSerializer(Class<?> type, T serializer) {
config.addDefaultKryoSerializer(type, serializer);
}
/**
* Adds a new Kryo default serializer to the Runtime.
*
* @param type
* The class of the types serialized with the given serializer.
* @param serializerClass
* The class of the serializer to use.
*/
public void addDefaultKryoSerializer(Class<?> type, Class<? extends Serializer<?>> serializerClass) {
config.addDefaultKryoSerializer(type, serializerClass);
}
/**
* Registers the given type with a Kryo Serializer.
*
* <p>Note that the serializer instance must be serializable (as defined by
* java.io.Serializable), because it may be distributed to the worker nodes
* by java serialization.
*
* @param type
* The class of the types serialized with the given serializer.
* @param serializer
* The serializer to use.
*/
public <T extends Serializer<?> & Serializable>void registerTypeWithKryoSerializer(Class<?> type, T serializer) {
config.registerTypeWithKryoSerializer(type, serializer);
}
/**
* Registers the given Serializer via its class as a serializer for the
* given type at the KryoSerializer.
*
* @param type
* The class of the types serialized with the given serializer.
* @param serializerClass
* The class of the serializer to use.
*/
@SuppressWarnings("rawtypes")
public void registerTypeWithKryoSerializer(Class<?> type, Class<? extends Serializer> serializerClass) {
config.registerTypeWithKryoSerializer(type, serializerClass);
}
/**
* Registers the given type with the serialization stack. If the type is
* eventually serialized as a POJO, then the type is registered with the
* POJO serializer. If the type ends up being serialized with Kryo, then it
* will be registered at Kryo to make sure that only tags are written.
*
* @param type
* The class of the type to register.
*/
public void registerType(Class<?> type) {
if (type == null) {
throw new NullPointerException("Cannot register null type class.");
}
TypeInformation<?> typeInfo = TypeExtractor.createTypeInfo(type);
if (typeInfo instanceof PojoTypeInfo) {
config.registerPojoType(type);
} else {
config.registerKryoType(type);
}
}
// --------------------------------------------------------------------------------------------
// Time characteristic
// --------------------------------------------------------------------------------------------
/**
* Sets the time characteristic for all streams create from this environment, e.g., processing
* time, event time, or ingestion time.
*
* <p>If you set the characteristic to IngestionTime of EventTime this will set a default
* watermark update interval of 200 ms. If this is not applicable for your application
* you should change it using {@link ExecutionConfig#setAutoWatermarkInterval(long)}.
*
* @param characteristic The time characteristic.
*/
@PublicEvolving
public void setStreamTimeCharacteristic(TimeCharacteristic characteristic) {
this.timeCharacteristic = Preconditions.checkNotNull(characteristic);
if (characteristic == TimeCharacteristic.ProcessingTime) {
getConfig().setAutoWatermarkInterval(0);
} else {
getConfig().setAutoWatermarkInterval(200);
}
}
/**
* Gets the time characteristic.
*
* @see #setStreamTimeCharacteristic(org.apache.flink.streaming.api.TimeCharacteristic)
*
* @return The time characteristic.
*/
@PublicEvolving
public TimeCharacteristic getStreamTimeCharacteristic() {
return timeCharacteristic;
}
// --------------------------------------------------------------------------------------------
// Data stream creations
// --------------------------------------------------------------------------------------------
/**
* Creates a new data stream that contains a sequence of numbers. This is a parallel source,
* if you manually set the parallelism to {@code 1}
* (using {@link org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator#setParallelism(int)})
* the generated sequence of elements is in order.
*
* @param from
* The number to start at (inclusive)
* @param to
* The number to stop at (inclusive)
* @return A data stream, containing all number in the [from, to] interval
*/
public DataStreamSource<Long> generateSequence(long from, long to) {
if (from > to) {
throw new IllegalArgumentException("Start of sequence must not be greater than the end");
}
return addSource(new StatefulSequenceSource(from, to), "Sequence Source");
}
/**
* Creates a new data stream that contains the given elements. The elements must all be of the
* same type, for example, all of the {@link String} or {@link Integer}.
*
* <p>The framework will try and determine the exact type from the elements. In case of generic
* elements, it may be necessary to manually supply the type information via
* {@link #fromCollection(java.util.Collection, org.apache.flink.api.common.typeinfo.TypeInformation)}.
*
* <p>Note that this operation will result in a non-parallel data stream source, i.e. a data
* stream source with a degree of parallelism one.
*
* @param data
* The array of elements to create the data stream from.
* @param <OUT>
* The type of the returned data stream
* @return The data stream representing the given array of elements
*/
@SafeVarargs
public final <OUT> DataStreamSource<OUT> fromElements(OUT... data) {
if (data.length == 0) {
throw new IllegalArgumentException("fromElements needs at least one element as argument");
}
TypeInformation<OUT> typeInfo;
try {
typeInfo = TypeExtractor.getForObject(data[0]);
}
catch (Exception e) {
throw new RuntimeException("Could not create TypeInformation for type " + data[0].getClass().getName()
+ "; please specify the TypeInformation manually via "
+ "StreamExecutionEnvironment#fromElements(Collection, TypeInformation)");
}
return fromCollection(Arrays.asList(data), typeInfo);
}
/**
* Creates a new data set that contains the given elements. The framework will determine the type according to the
* based type user supplied. The elements should be the same or be the subclass to the based type.
* The sequence of elements must not be empty.
* Note that this operation will result in a non-parallel data stream source, i.e. a data stream source with a
* degree of parallelism one.
*
* @param type
* The based class type in the collection.
* @param data
* The array of elements to create the data stream from.
* @param <OUT>
* The type of the returned data stream
* @return The data stream representing the given array of elements
*/
@SafeVarargs
public final <OUT> DataStreamSource<OUT> fromElements(Class<OUT> type, OUT... data) {
if (data.length == 0) {
throw new IllegalArgumentException("fromElements needs at least one element as argument");
}
TypeInformation<OUT> typeInfo;
try {
typeInfo = TypeExtractor.getForClass(type);
}
catch (Exception e) {
throw new RuntimeException("Could not create TypeInformation for type " + type.getName()
+ "; please specify the TypeInformation manually via "
+ "StreamExecutionEnvironment#fromElements(Collection, TypeInformation)");
}
return fromCollection(Arrays.asList(data), typeInfo);
}
/**
* Creates a data stream from the given non-empty collection. The type of the data stream is that of the
* elements in the collection.
*
* <p>The framework will try and determine the exact type from the collection elements. In case of generic
* elements, it may be necessary to manually supply the type information via
* {@link #fromCollection(java.util.Collection, org.apache.flink.api.common.typeinfo.TypeInformation)}.
*
* <p>Note that this operation will result in a non-parallel data stream source, i.e. a data stream source with a
* parallelism one.
*
* @param data
* The collection of elements to create the data stream from.
* @param <OUT>
* The generic type of the returned data stream.
* @return
* The data stream representing the given collection
*/
public <OUT> DataStreamSource<OUT> fromCollection(Collection<OUT> data) {
Preconditions.checkNotNull(data, "Collection must not be null");
if (data.isEmpty()) {
throw new IllegalArgumentException("Collection must not be empty");
}
OUT first = data.iterator().next();
if (first == null) {
throw new IllegalArgumentException("Collection must not contain null elements");
}
TypeInformation<OUT> typeInfo;
try {
typeInfo = TypeExtractor.getForObject(first);
}
catch (Exception e) {
throw new RuntimeException("Could not create TypeInformation for type " + first.getClass()
+ "; please specify the TypeInformation manually via "
+ "StreamExecutionEnvironment#fromElements(Collection, TypeInformation)");
}
return fromCollection(data, typeInfo);
}
/**
* Creates a data stream from the given non-empty collection.
*
* <p>Note that this operation will result in a non-parallel data stream source,
* i.e., a data stream source with a parallelism one.
*
* @param data
* The collection of elements to create the data stream from
* @param typeInfo
* The TypeInformation for the produced data stream
* @param <OUT>
* The type of the returned data stream
* @return The data stream representing the given collection
*/
public <OUT> DataStreamSource<OUT> fromCollection(Collection<OUT> data, TypeInformation<OUT> typeInfo) {
Preconditions.checkNotNull(data, "Collection must not be null");
// must not have null elements and mixed elements
FromElementsFunction.checkCollection(data, typeInfo.getTypeClass());
SourceFunction<OUT> function;
try {
function = new FromElementsFunction<>(typeInfo.createSerializer(getConfig()), data);
}
catch (IOException e) {
throw new RuntimeException(e.getMessage(), e);
}
return addSource(function, "Collection Source", typeInfo).setParallelism(1);
}
/**
* Creates a data stream from the given iterator.
*
* <p>Because the iterator will remain unmodified until the actual execution happens,
* the type of data returned by the iterator must be given explicitly in the form of the type
* class (this is due to the fact that the Java compiler erases the generic type information).
*
* <p>Note that this operation will result in a non-parallel data stream source, i.e.,
* a data stream source with a parallelism of one.
*
* @param data
* The iterator of elements to create the data stream from
* @param type
* The class of the data produced by the iterator. Must not be a generic class.
* @param <OUT>
* The type of the returned data stream
* @return The data stream representing the elements in the iterator
* @see #fromCollection(java.util.Iterator, org.apache.flink.api.common.typeinfo.TypeInformation)
*/
public <OUT> DataStreamSource<OUT> fromCollection(Iterator<OUT> data, Class<OUT> type) {
return fromCollection(data, TypeExtractor.getForClass(type));
}
/**
* Creates a data stream from the given iterator.
*
* <p>Because the iterator will remain unmodified until the actual execution happens,
* the type of data returned by the iterator must be given explicitly in the form of the type
* information. This method is useful for cases where the type is generic.
* In that case, the type class (as given in
* {@link #fromCollection(java.util.Iterator, Class)} does not supply all type information.
*
* <p>Note that this operation will result in a non-parallel data stream source, i.e.,
* a data stream source with a parallelism one.
*
* @param data
* The iterator of elements to create the data stream from
* @param typeInfo
* The TypeInformation for the produced data stream
* @param <OUT>
* The type of the returned data stream
* @return The data stream representing the elements in the iterator
*/
public <OUT> DataStreamSource<OUT> fromCollection(Iterator<OUT> data, TypeInformation<OUT> typeInfo) {
Preconditions.checkNotNull(data, "The iterator must not be null");
SourceFunction<OUT> function = new FromIteratorFunction<>(data);
return addSource(function, "Collection Source", typeInfo);
}
/**
* Creates a new data stream that contains elements in the iterator. The iterator is splittable,
* allowing the framework to create a parallel data stream source that returns the elements in
* the iterator.
*
* <p>Because the iterator will remain unmodified until the actual execution happens, the type
* of data returned by the iterator must be given explicitly in the form of the type class
* (this is due to the fact that the Java compiler erases the generic type information).
*
* @param iterator
* The iterator that produces the elements of the data stream
* @param type
* The class of the data produced by the iterator. Must not be a generic class.
* @param <OUT>
* The type of the returned data stream
* @return A data stream representing the elements in the iterator
*/
public <OUT> DataStreamSource<OUT> fromParallelCollection(SplittableIterator<OUT> iterator, Class<OUT> type) {
return fromParallelCollection(iterator, TypeExtractor.getForClass(type));
}
/**
* Creates a new data stream that contains elements in the iterator. The iterator is splittable,
* allowing the framework to create a parallel data stream source that returns the elements in
* the iterator.
*
* <p>Because the iterator will remain unmodified until the actual execution happens, the type
* of data returned by the iterator must be given explicitly in the form of the type
* information. This method is useful for cases where the type is generic. In that case, the
* type class (as given in
* {@link #fromParallelCollection(org.apache.flink.util.SplittableIterator, Class)} does not
* supply all type information.
*
* @param iterator
* The iterator that produces the elements of the data stream
* @param typeInfo
* The TypeInformation for the produced data stream.
* @param <OUT>
* The type of the returned data stream
* @return A data stream representing the elements in the iterator
*/
public <OUT> DataStreamSource<OUT> fromParallelCollection(SplittableIterator<OUT> iterator, TypeInformation<OUT>
typeInfo) {
return fromParallelCollection(iterator, typeInfo, "Parallel Collection Source");
}
// private helper for passing different names
private <OUT> DataStreamSource<OUT> fromParallelCollection(SplittableIterator<OUT> iterator, TypeInformation<OUT>
typeInfo, String operatorName) {
return addSource(new FromSplittableIteratorFunction<>(iterator), operatorName, typeInfo);
}
/**
* Reads the given file line-by-line and creates a data stream that contains a string with the
* contents of each such line. The file will be read with the system's default character set.
*
* <p><b>NOTES ON CHECKPOINTING: </b> The source monitors the path, creates the
* {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits} to be processed, forwards
* them to the downstream {@link ContinuousFileReaderOperator readers} to read the actual data,
* and exits, without waiting for the readers to finish reading. This implies that no more
* checkpoint barriers are going to be forwarded after the source exits, thus having no
* checkpoints after that point.
*
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path").
* @return The data stream that represents the data read from the given file as text lines
*/
public DataStreamSource<String> readTextFile(String filePath) {
return readTextFile(filePath, "UTF-8");
}
/**
* Reads the given file line-by-line and creates a data stream that contains a string with the
* contents of each such line. The {@link java.nio.charset.Charset} with the given name will be
* used to read the files.
*
* <p><b>NOTES ON CHECKPOINTING: </b> The source monitors the path, creates the
* {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits} to be processed,
* forwards them to the downstream {@link ContinuousFileReaderOperator readers} to read the actual data,
* and exits, without waiting for the readers to finish reading. This implies that no more checkpoint
* barriers are going to be forwarded after the source exits, thus having no checkpoints after that point.
*
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path")
* @param charsetName
* The name of the character set used to read the file
* @return The data stream that represents the data read from the given file as text lines
*/
public DataStreamSource<String> readTextFile(String filePath, String charsetName) {
Preconditions.checkNotNull(filePath, "The file path must not be null.");
Preconditions.checkNotNull(filePath.isEmpty(), "The file path must not be empty.");
TextInputFormat format = new TextInputFormat(new Path(filePath));
format.setFilesFilter(FilePathFilter.createDefaultFilter());
TypeInformation<String> typeInfo = BasicTypeInfo.STRING_TYPE_INFO;
format.setCharsetName(charsetName);
return readFile(format, filePath, FileProcessingMode.PROCESS_ONCE, -1, typeInfo);
}
/**
* Reads the contents of the user-specified {@code filePath} based on the given {@link FileInputFormat}.
*
* <p>Since all data streams need specific information about their types, this method needs to determine the
* type of the data produced by the input format. It will attempt to determine the data type by reflection,
* unless the input format implements the {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable} interface.
* In the latter case, this method will invoke the
* {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable#getProducedType()} method to determine data
* type produced by the input format.
*
* <p><b>NOTES ON CHECKPOINTING: </b> The source monitors the path, creates the
* {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits} to be processed,
* forwards them to the downstream {@link ContinuousFileReaderOperator readers} to read the actual data,
* and exits, without waiting for the readers to finish reading. This implies that no more checkpoint
* barriers are going to be forwarded after the source exits, thus having no checkpoints after that point.
*
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path")
* @param inputFormat
* The input format used to create the data stream
* @param <OUT>
* The type of the returned data stream
* @return The data stream that represents the data read from the given file
*/
public <OUT> DataStreamSource<OUT> readFile(FileInputFormat<OUT> inputFormat,
String filePath) {
return readFile(inputFormat, filePath, FileProcessingMode.PROCESS_ONCE, -1);
}
/**
* Reads the contents of the user-specified {@code filePath} based on the given {@link FileInputFormat}. Depending
* on the provided {@link FileProcessingMode}.
*
* <p>See {@link #readFile(FileInputFormat, String, FileProcessingMode, long)}
*
* @param inputFormat
* The input format used to create the data stream
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path")
* @param watchType
* The mode in which the source should operate, i.e. monitor path and react to new data, or process once and exit
* @param interval
* In the case of periodic path monitoring, this specifies the interval (in millis) between consecutive path scans
* @param filter
* The files to be excluded from the processing
* @param <OUT>
* The type of the returned data stream
* @return The data stream that represents the data read from the given file
*
* @deprecated Use {@link FileInputFormat#setFilesFilter(FilePathFilter)} to set a filter and
* {@link StreamExecutionEnvironment#readFile(FileInputFormat, String, FileProcessingMode, long)}
*
*/
@PublicEvolving
@Deprecated
public <OUT> DataStreamSource<OUT> readFile(FileInputFormat<OUT> inputFormat,
String filePath,
FileProcessingMode watchType,
long interval,
FilePathFilter filter) {
inputFormat.setFilesFilter(filter);
TypeInformation<OUT> typeInformation;
try {
typeInformation = TypeExtractor.getInputFormatTypes(inputFormat);
} catch (Exception e) {
throw new InvalidProgramException("The type returned by the input format could not be " +
"automatically determined. Please specify the TypeInformation of the produced type " +
"explicitly by using the 'createInput(InputFormat, TypeInformation)' method instead.");
}
return readFile(inputFormat, filePath, watchType, interval, typeInformation);
}
/**
* Reads the contents of the user-specified {@code filePath} based on the given {@link FileInputFormat}. Depending
* on the provided {@link FileProcessingMode}, the source may periodically monitor (every {@code interval} ms) the path
* for new data ({@link FileProcessingMode#PROCESS_CONTINUOUSLY}), or process once the data currently in the path and
* exit ({@link FileProcessingMode#PROCESS_ONCE}). In addition, if the path contains files not to be processed, the user
* can specify a custom {@link FilePathFilter}. As a default implementation you can use
* {@link FilePathFilter#createDefaultFilter()}.
*
* <p>Since all data streams need specific information about their types, this method needs to determine the
* type of the data produced by the input format. It will attempt to determine the data type by reflection,
* unless the input format implements the {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable} interface.
* In the latter case, this method will invoke the
* {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable#getProducedType()} method to determine data
* type produced by the input format.
*
* <p><b>NOTES ON CHECKPOINTING: </b> If the {@code watchType} is set to {@link FileProcessingMode#PROCESS_ONCE},
* the source monitors the path <b>once</b>, creates the {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits}
* to be processed, forwards them to the downstream {@link ContinuousFileReaderOperator readers} to read the actual data,
* and exits, without waiting for the readers to finish reading. This implies that no more checkpoint barriers
* are going to be forwarded after the source exits, thus having no checkpoints after that point.
*
* @param inputFormat
* The input format used to create the data stream
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path")
* @param watchType
* The mode in which the source should operate, i.e. monitor path and react to new data, or process once and exit
* @param interval
* In the case of periodic path monitoring, this specifies the interval (in millis) between consecutive path scans
* @param <OUT>
* The type of the returned data stream
* @return The data stream that represents the data read from the given file
*/
@PublicEvolving
public <OUT> DataStreamSource<OUT> readFile(FileInputFormat<OUT> inputFormat,
String filePath,
FileProcessingMode watchType,
long interval) {
TypeInformation<OUT> typeInformation;
try {
typeInformation = TypeExtractor.getInputFormatTypes(inputFormat);
} catch (Exception e) {
throw new InvalidProgramException("The type returned by the input format could not be " +
"automatically determined. Please specify the TypeInformation of the produced type " +
"explicitly by using the 'createInput(InputFormat, TypeInformation)' method instead.");
}
return readFile(inputFormat, filePath, watchType, interval, typeInformation);
}
/**
* Creates a data stream that contains the contents of file created while system watches the given path. The file
* will be read with the system's default character set.
*
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path/")
* @param intervalMillis
* The interval of file watching in milliseconds
* @param watchType
* The watch type of file stream. When watchType is {@link org.apache.flink.streaming.api.functions.source.FileMonitoringFunction.WatchType#ONLY_NEW_FILES}, the system processes
* only
* new files. {@link org.apache.flink.streaming.api.functions.source.FileMonitoringFunction.WatchType#REPROCESS_WITH_APPENDED} means that the system re-processes all contents of
* appended file. {@link org.apache.flink.streaming.api.functions.source.FileMonitoringFunction.WatchType#PROCESS_ONLY_APPENDED} means that the system processes only appended
* contents
* of files.
* @return The DataStream containing the given directory.
*
* @deprecated Use {@link #readFile(FileInputFormat, String, FileProcessingMode, long)} instead.
*/
@Deprecated
@SuppressWarnings("deprecation")
public DataStream<String> readFileStream(String filePath, long intervalMillis, FileMonitoringFunction.WatchType watchType) {
DataStream<Tuple3<String, Long, Long>> source = addSource(new FileMonitoringFunction(
filePath, intervalMillis, watchType), "Read File Stream source");
return source.flatMap(new FileReadFunction());
}
/**
* Reads the contents of the user-specified {@code filePath} based on the given {@link FileInputFormat}.
* Depending on the provided {@link FileProcessingMode}, the source may periodically monitor (every {@code interval} ms)
* the path for new data ({@link FileProcessingMode#PROCESS_CONTINUOUSLY}), or process once the data currently in the
* path and exit ({@link FileProcessingMode#PROCESS_ONCE}). In addition, if the path contains files not to be processed,
* the user can specify a custom {@link FilePathFilter}. As a default implementation you can use
* {@link FilePathFilter#createDefaultFilter()}.
*
* <p><b>NOTES ON CHECKPOINTING: </b> If the {@code watchType} is set to {@link FileProcessingMode#PROCESS_ONCE},
* the source monitors the path <b>once</b>, creates the {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits}
* to be processed, forwards them to the downstream {@link ContinuousFileReaderOperator readers} to read the actual data,
* and exits, without waiting for the readers to finish reading. This implies that no more checkpoint barriers
* are going to be forwarded after the source exits, thus having no checkpoints after that point.
*
* @param inputFormat
* The input format used to create the data stream
* @param filePath
* The path of the file, as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path")
* @param watchType
* The mode in which the source should operate, i.e. monitor path and react to new data, or process once and exit
* @param typeInformation
* Information on the type of the elements in the output stream
* @param interval
* In the case of periodic path monitoring, this specifies the interval (in millis) between consecutive path scans
* @param <OUT>
* The type of the returned data stream
* @return The data stream that represents the data read from the given file
*/
@PublicEvolving
public <OUT> DataStreamSource<OUT> readFile(FileInputFormat<OUT> inputFormat,
String filePath,
FileProcessingMode watchType,
long interval,
TypeInformation<OUT> typeInformation) {
Preconditions.checkNotNull(inputFormat, "InputFormat must not be null.");
Preconditions.checkNotNull(filePath, "The file path must not be null.");
Preconditions.checkNotNull(filePath.isEmpty(), "The file path must not be empty.");
inputFormat.setFilePath(filePath);
return createFileInput(inputFormat, typeInformation, "Custom File Source", watchType, interval);
}
/**
* Creates a new data stream that contains the strings received infinitely from a socket. Received strings are
* decoded by the system's default character set. On the termination of the socket server connection retries can be
* initiated.
*
* <p>Let us note that the socket itself does not report on abort and as a consequence retries are only initiated when
* the socket was gracefully terminated.
*
* @param hostname
* The host name which a server socket binds
* @param port
* The port number which a server socket binds. A port number of 0 means that the port number is automatically
* allocated.
* @param delimiter
* A character which splits received strings into records
* @param maxRetry
* The maximal retry interval in seconds while the program waits for a socket that is temporarily down.
* Reconnection is initiated every second. A number of 0 means that the reader is immediately terminated,
* while
* a negative value ensures retrying forever.
* @return A data stream containing the strings received from the socket
*
* @deprecated Use {@link #socketTextStream(String, int, String, long)} instead.
*/
@Deprecated
public DataStreamSource<String> socketTextStream(String hostname, int port, char delimiter, long maxRetry) {
return socketTextStream(hostname, port, String.valueOf(delimiter), maxRetry);
}
/**
* Creates a new data stream that contains the strings received infinitely from a socket. Received strings are
* decoded by the system's default character set. On the termination of the socket server connection retries can be
* initiated.
*
* <p>Let us note that the socket itself does not report on abort and as a consequence retries are only initiated when
* the socket was gracefully terminated.
*
* @param hostname
* The host name which a server socket binds
* @param port
* The port number which a server socket binds. A port number of 0 means that the port number is automatically
* allocated.
* @param delimiter
* A string which splits received strings into records
* @param maxRetry
* The maximal retry interval in seconds while the program waits for a socket that is temporarily down.
* Reconnection is initiated every second. A number of 0 means that the reader is immediately terminated,
* while
* a negative value ensures retrying forever.
* @return A data stream containing the strings received from the socket
*/
@PublicEvolving
public DataStreamSource<String> socketTextStream(String hostname, int port, String delimiter, long maxRetry) {
return addSource(new SocketTextStreamFunction(hostname, port, delimiter, maxRetry),
"Socket Stream");
}
/**
* Creates a new data stream that contains the strings received infinitely from a socket. Received strings are
* decoded by the system's default character set. The reader is terminated immediately when the socket is down.
*
* @param hostname
* The host name which a server socket binds
* @param port
* The port number which a server socket binds. A port number of 0 means that the port number is automatically
* allocated.
* @param delimiter
* A character which splits received strings into records
* @return A data stream containing the strings received from the socket
*
* @deprecated Use {@link #socketTextStream(String, int, String)} instead.
*/
@Deprecated
@SuppressWarnings("deprecation")
public DataStreamSource<String> socketTextStream(String hostname, int port, char delimiter) {
return socketTextStream(hostname, port, delimiter, 0);
}
/**
* Creates a new data stream that contains the strings received infinitely from a socket. Received strings are
* decoded by the system's default character set. The reader is terminated immediately when the socket is down.
*
* @param hostname
* The host name which a server socket binds
* @param port
* The port number which a server socket binds. A port number of 0 means that the port number is automatically
* allocated.
* @param delimiter
* A string which splits received strings into records
* @return A data stream containing the strings received from the socket
*/
@PublicEvolving
public DataStreamSource<String> socketTextStream(String hostname, int port, String delimiter) {
return socketTextStream(hostname, port, delimiter, 0);
}
/**
* Creates a new data stream that contains the strings received infinitely from a socket. Received strings are
* decoded by the system's default character set, using"\n" as delimiter. The reader is terminated immediately when
* the socket is down.
*
* @param hostname
* The host name which a server socket binds
* @param port
* The port number which a server socket binds. A port number of 0 means that the port number is automatically
* allocated.
* @return A data stream containing the strings received from the socket
*/
@PublicEvolving
public DataStreamSource<String> socketTextStream(String hostname, int port) {
return socketTextStream(hostname, port, "\n");
}
/**
* Generic method to create an input data stream with {@link org.apache.flink.api.common.io.InputFormat}.
*
* <p>Since all data streams need specific information about their types, this method needs to determine the
* type of the data produced by the input format. It will attempt to determine the data type by reflection,
* unless the input format implements the {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable} interface.
* In the latter case, this method will invoke the
* {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable#getProducedType()} method to determine data
* type produced by the input format.
*
* <p><b>NOTES ON CHECKPOINTING: </b> In the case of a {@link FileInputFormat}, the source
* (which executes the {@link ContinuousFileMonitoringFunction}) monitors the path, creates the
* {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits} to be processed, forwards
* them to the downstream {@link ContinuousFileReaderOperator} to read the actual data, and exits,
* without waiting for the readers to finish reading. This implies that no more checkpoint
* barriers are going to be forwarded after the source exits, thus having no checkpoints.
*
* @param inputFormat
* The input format used to create the data stream
* @param <OUT>
* The type of the returned data stream
* @return The data stream that represents the data created by the input format
*/
@PublicEvolving
public <OUT> DataStreamSource<OUT> createInput(InputFormat<OUT, ?> inputFormat) {
return createInput(inputFormat, TypeExtractor.getInputFormatTypes(inputFormat));
}
/**
* Generic method to create an input data stream with {@link org.apache.flink.api.common.io.InputFormat}.
*
* <p>The data stream is typed to the given TypeInformation. This method is intended for input formats
* where the return type cannot be determined by reflection analysis, and that do not implement the
* {@link org.apache.flink.api.java.typeutils.ResultTypeQueryable} interface.
*
* <p><b>NOTES ON CHECKPOINTING: </b> In the case of a {@link FileInputFormat}, the source
* (which executes the {@link ContinuousFileMonitoringFunction}) monitors the path, creates the
* {@link org.apache.flink.core.fs.FileInputSplit FileInputSplits} to be processed, forwards
* them to the downstream {@link ContinuousFileReaderOperator} to read the actual data, and exits,
* without waiting for the readers to finish reading. This implies that no more checkpoint
* barriers are going to be forwarded after the source exits, thus having no checkpoints.
*
* @param inputFormat
* The input format used to create the data stream
* @param typeInfo
* The information about the type of the output type
* @param <OUT>
* The type of the returned data stream
* @return The data stream that represents the data created by the input format
*/
@PublicEvolving
public <OUT> DataStreamSource<OUT> createInput(InputFormat<OUT, ?> inputFormat, TypeInformation<OUT> typeInfo) {
DataStreamSource<OUT> source;
if (inputFormat instanceof FileInputFormat) {
@SuppressWarnings("unchecked")
FileInputFormat<OUT> format = (FileInputFormat<OUT>) inputFormat;
source = createFileInput(format, typeInfo, "Custom File source",
FileProcessingMode.PROCESS_ONCE, -1);
} else {
source = createInput(inputFormat, typeInfo, "Custom Source");
}
return source;
}
private <OUT> DataStreamSource<OUT> createInput(InputFormat<OUT, ?> inputFormat,
TypeInformation<OUT> typeInfo,
String sourceName) {
InputFormatSourceFunction<OUT> function = new InputFormatSourceFunction<>(inputFormat, typeInfo);
return addSource(function, sourceName, typeInfo);
}
private <OUT> DataStreamSource<OUT> createFileInput(FileInputFormat<OUT> inputFormat,
TypeInformation<OUT> typeInfo,
String sourceName,
FileProcessingMode monitoringMode,
long interval) {
Preconditions.checkNotNull(inputFormat, "Unspecified file input format.");
Preconditions.checkNotNull(typeInfo, "Unspecified output type information.");
Preconditions.checkNotNull(sourceName, "Unspecified name for the source.");
Preconditions.checkNotNull(monitoringMode, "Unspecified monitoring mode.");
Preconditions.checkArgument(monitoringMode.equals(FileProcessingMode.PROCESS_ONCE) ||
interval >= ContinuousFileMonitoringFunction.MIN_MONITORING_INTERVAL,
"The path monitoring interval cannot be less than " +
ContinuousFileMonitoringFunction.MIN_MONITORING_INTERVAL + " ms.");
ContinuousFileMonitoringFunction<OUT> monitoringFunction =
new ContinuousFileMonitoringFunction<>(inputFormat, monitoringMode, getParallelism(), interval);
ContinuousFileReaderOperator<OUT> reader =
new ContinuousFileReaderOperator<>(inputFormat);
SingleOutputStreamOperator<OUT> source = addSource(monitoringFunction, sourceName)
.transform("Split Reader: " + sourceName, typeInfo, reader);
return new DataStreamSource<>(source);
}
/**
* Adds a Data Source to the streaming topology.
*
* <p>By default sources have a parallelism of 1. To enable parallel execution, the user defined source should
* implement {@link org.apache.flink.streaming.api.functions.source.ParallelSourceFunction} or extend {@link
* org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction}. In these cases the resulting source
* will have the parallelism of the environment. To change this afterwards call {@link
* org.apache.flink.streaming.api.datastream.DataStreamSource#setParallelism(int)}
*
* @param function
* the user defined function
* @param <OUT>
* type of the returned stream
* @return the data stream constructed
*/
public <OUT> DataStreamSource<OUT> addSource(SourceFunction<OUT> function) {
return addSource(function, "Custom Source");
}
/**
* Ads a data source with a custom type information thus opening a
* {@link DataStream}. Only in very special cases does the user need to
* support type information. Otherwise use
* {@link #addSource(org.apache.flink.streaming.api.functions.source.SourceFunction)}
*
* @param function
* the user defined function
* @param sourceName
* Name of the data source
* @param <OUT>
* type of the returned stream
* @return the data stream constructed
*/
public <OUT> DataStreamSource<OUT> addSource(SourceFunction<OUT> function, String sourceName) {
return addSource(function, sourceName, null);
}
/**
* Ads a data source with a custom type information thus opening a
* {@link DataStream}. Only in very special cases does the user need to
* support type information. Otherwise use
* {@link #addSource(org.apache.flink.streaming.api.functions.source.SourceFunction)}
*
* @param function
* the user defined function
* @param <OUT>
* type of the returned stream
* @param typeInfo
* the user defined type information for the stream
* @return the data stream constructed
*/
public <OUT> DataStreamSource<OUT> addSource(SourceFunction<OUT> function, TypeInformation<OUT> typeInfo) {
return addSource(function, "Custom Source", typeInfo);
}
/**
* Ads a data source with a custom type information thus opening a
* {@link DataStream}. Only in very special cases does the user need to
* support type information. Otherwise use
* {@link #addSource(org.apache.flink.streaming.api.functions.source.SourceFunction)}
*
* @param function
* the user defined function
* @param sourceName
* Name of the data source
* @param <OUT>
* type of the returned stream
* @param typeInfo
* the user defined type information for the stream
* @return the data stream constructed
*/
@SuppressWarnings("unchecked")
public <OUT> DataStreamSource<OUT> addSource(SourceFunction<OUT> function, String sourceName, TypeInformation<OUT> typeInfo) {
if (typeInfo == null) {
if (function instanceof ResultTypeQueryable) {
typeInfo = ((ResultTypeQueryable<OUT>) function).getProducedType();
} else {
try {
typeInfo = TypeExtractor.createTypeInfo(
SourceFunction.class,
function.getClass(), 0, null, null);
} catch (final InvalidTypesException e) {
typeInfo = (TypeInformation<OUT>) new MissingTypeInfo(sourceName, e);
}
}
}
boolean isParallel = function instanceof ParallelSourceFunction;
clean(function);
StreamSource<OUT, ?> sourceOperator;
if (function instanceof StoppableFunction) {
sourceOperator = new StoppableStreamSource<>(cast2StoppableSourceFunction(function));
} else {
sourceOperator = new StreamSource<>(function);
}
return new DataStreamSource<>(this, typeInfo, sourceOperator, isParallel, sourceName);
}
/**
* Casts the source function into a SourceFunction implementing the StoppableFunction.
*
* <p>This method should only be used if the source function was checked to implement the
* {@link StoppableFunction} interface.
*
* @param sourceFunction Source function to cast
* @param <OUT> Output type of source function
* @param <T> Union type of SourceFunction and StoppableFunction
* @return The casted source function so that it's type implements the StoppableFunction
*/
@SuppressWarnings("unchecked")
private <OUT, T extends SourceFunction<OUT> & StoppableFunction> T cast2StoppableSourceFunction(SourceFunction<OUT> sourceFunction) {
return (T) sourceFunction;
}
/**
* Triggers the program execution. The environment will execute all parts of
* the program that have resulted in a "sink" operation. Sink operations are
* for example printing results or forwarding them to a message queue.
*
* <p>The program execution will be logged and displayed with a generated
* default name.
*
* @return The result of the job execution, containing elapsed time and accumulators.
* @throws Exception which occurs during job execution.
*/
public JobExecutionResult execute() throws Exception {
return execute(DEFAULT_JOB_NAME);
}
/**
* Triggers the program execution. The environment will execute all parts of
* the program that have resulted in a "sink" operation. Sink operations are
* for example printing results or forwarding them to a message queue.
*
* <p>The program execution will be logged and displayed with the provided name
*
* @param jobName
* Desired name of the job
* @return The result of the job execution, containing elapsed time and accumulators.
* @throws Exception which occurs during job execution.
*/
public abstract JobExecutionResult execute(String jobName) throws Exception;
/**
* Getter of the {@link org.apache.flink.streaming.api.graph.StreamGraph} of the streaming job.
*
* @return The streamgraph representing the transformations
*/
@Internal
public StreamGraph getStreamGraph() {
if (transformations.size() <= 0) {
throw new IllegalStateException("No operators defined in streaming topology. Cannot execute.");
}
return StreamGraphGenerator.generate(this, transformations);
}
/**
* Creates the plan with which the system will execute the program, and
* returns it as a String using a JSON representation of the execution data
* flow graph. Note that this needs to be called, before the plan is
* executed.
*
* @return The execution plan of the program, as a JSON String.
*/
public String getExecutionPlan() {
return getStreamGraph().getStreamingPlanAsJSON();
}
/**
* Returns a "closure-cleaned" version of the given function. Cleans only if closure cleaning
* is not disabled in the {@link org.apache.flink.api.common.ExecutionConfig}
*/
@Internal
public <F> F clean(F f) {
if (getConfig().isClosureCleanerEnabled()) {
ClosureCleaner.clean(f, true);
}
ClosureCleaner.ensureSerializable(f);
return f;
}
/**
* Adds an operator to the list of operators that should be executed when calling
* {@link #execute}.
*
* <p>When calling {@link #execute()} only the operators that where previously added to the list
* are executed.
*
* <p>This is not meant to be used by users. The API methods that create operators must call
* this method.
*/
@Internal
public void addOperator(StreamTransformation<?> transformation) {
Preconditions.checkNotNull(transformation, "transformation must not be null.");
this.transformations.add(transformation);
}
// --------------------------------------------------------------------------------------------
// Factory methods for ExecutionEnvironments
// --------------------------------------------------------------------------------------------
/**
* Creates an execution environment that represents the context in which the
* program is currently executed. If the program is invoked standalone, this
* method returns a local execution environment, as returned by
* {@link #createLocalEnvironment()}.
*
* @return The execution environment of the context in which the program is
* executed.
*/
public static StreamExecutionEnvironment getExecutionEnvironment() {
if (contextEnvironmentFactory != null) {
return contextEnvironmentFactory.createExecutionEnvironment();
}
// because the streaming project depends on "flink-clients" (and not the other way around)
// we currently need to intercept the data set environment and create a dependent stream env.
// this should be fixed once we rework the project dependencies
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
if (env instanceof ContextEnvironment) {
return new StreamContextEnvironment((ContextEnvironment) env);
} else if (env instanceof OptimizerPlanEnvironment | env instanceof PreviewPlanEnvironment) {
return new StreamPlanEnvironment(env);
} else {
return createLocalEnvironment();
}
}
/**
* Creates a {@link LocalStreamEnvironment}. The local execution environment
* will run the program in a multi-threaded fashion in the same JVM as the
* environment was created in. The default parallelism of the local
* environment is the number of hardware contexts (CPU cores / threads),
* unless it was specified differently by {@link #setParallelism(int)}.
*
* @return A local execution environment.
*/
public static LocalStreamEnvironment createLocalEnvironment() {
return createLocalEnvironment(defaultLocalParallelism);
}
/**
* Creates a {@link LocalStreamEnvironment}. The local execution environment
* will run the program in a multi-threaded fashion in the same JVM as the
* environment was created in. It will use the parallelism specified in the
* parameter.
*
* @param parallelism
* The parallelism for the local environment.
* @return A local execution environment with the specified parallelism.
*/
public static LocalStreamEnvironment createLocalEnvironment(int parallelism) {
LocalStreamEnvironment env = new LocalStreamEnvironment();
env.setParallelism(parallelism);
return env;
}
/**
* Creates a {@link LocalStreamEnvironment}. The local execution environment
* will run the program in a multi-threaded fashion in the same JVM as the
* environment was created in. It will use the parallelism specified in the
* parameter.
*
* @param parallelism
* The parallelism for the local environment.
* @param configuration
* Pass a custom configuration into the cluster
* @return A local execution environment with the specified parallelism.
*/
public static LocalStreamEnvironment createLocalEnvironment(int parallelism, Configuration configuration) {
LocalStreamEnvironment currentEnvironment = new LocalStreamEnvironment(configuration);
currentEnvironment.setParallelism(parallelism);
return currentEnvironment;
}
/**
* Creates a {@link LocalStreamEnvironment} for local program execution that also starts the
* web monitoring UI.
*
* <p>The local execution environment will run the program in a multi-threaded fashion in
* the same JVM as the environment was created in. It will use the parallelism specified in the
* parameter.
*
* <p>If the configuration key 'jobmanager.web.port' was set in the configuration, that particular
* port will be used for the web UI. Otherwise, the default port (8081) will be used.
*/
@PublicEvolving
public static StreamExecutionEnvironment createLocalEnvironmentWithWebUI(Configuration conf) {
checkNotNull(conf, "conf");
conf.setBoolean(ConfigConstants.LOCAL_START_WEBSERVER, true);
LocalStreamEnvironment localEnv = new LocalStreamEnvironment(conf);
localEnv.setParallelism(defaultLocalParallelism);
return localEnv;
}
/**
* Creates a {@link RemoteStreamEnvironment}. The remote environment sends
* (parts of) the program to a cluster for execution. Note that all file
* paths used in the program must be accessible from the cluster. The
* execution will use no parallelism, unless the parallelism is set
* explicitly via {@link #setParallelism}.
*
* @param host
* The host name or address of the master (JobManager), where the
* program should be executed.
* @param port
* The port of the master (JobManager), where the program should
* be executed.
* @param jarFiles
* The JAR files with code that needs to be shipped to the
* cluster. If the program uses user-defined functions,
* user-defined input formats, or any libraries, those must be
* provided in the JAR files.
* @return A remote environment that executes the program on a cluster.
*/
public static StreamExecutionEnvironment createRemoteEnvironment(
String host, int port, String... jarFiles) {
return new RemoteStreamEnvironment(host, port, jarFiles);
}
/**
* Creates a {@link RemoteStreamEnvironment}. The remote environment sends
* (parts of) the program to a cluster for execution. Note that all file
* paths used in the program must be accessible from the cluster. The
* execution will use the specified parallelism.
*
* @param host
* The host name or address of the master (JobManager), where the
* program should be executed.
* @param port
* The port of the master (JobManager), where the program should
* be executed.
* @param parallelism
* The parallelism to use during the execution.
* @param jarFiles
* The JAR files with code that needs to be shipped to the
* cluster. If the program uses user-defined functions,
* user-defined input formats, or any libraries, those must be
* provided in the JAR files.
* @return A remote environment that executes the program on a cluster.
*/
public static StreamExecutionEnvironment createRemoteEnvironment(
String host, int port, int parallelism, String... jarFiles) {
RemoteStreamEnvironment env = new RemoteStreamEnvironment(host, port, jarFiles);
env.setParallelism(parallelism);
return env;
}
/**
* Creates a {@link RemoteStreamEnvironment}. The remote environment sends
* (parts of) the program to a cluster for execution. Note that all file
* paths used in the program must be accessible from the cluster. The
* execution will use the specified parallelism.
*
* @param host
* The host name or address of the master (JobManager), where the
* program should be executed.
* @param port
* The port of the master (JobManager), where the program should
* be executed.
* @param clientConfig
* The configuration used by the client that connects to the remote cluster.
* @param jarFiles
* The JAR files with code that needs to be shipped to the
* cluster. If the program uses user-defined functions,
* user-defined input formats, or any libraries, those must be
* provided in the JAR files.
* @return A remote environment that executes the program on a cluster.
*/
public static StreamExecutionEnvironment createRemoteEnvironment(
String host, int port, Configuration clientConfig, String... jarFiles) {
return new RemoteStreamEnvironment(host, port, clientConfig, jarFiles);
}
/**
* Gets the default parallelism that will be used for the local execution environment created by
* {@link #createLocalEnvironment()}.
*
* @return The default local parallelism
*/
@PublicEvolving
public static int getDefaultLocalParallelism() {
return defaultLocalParallelism;
}
/**
* Sets the default parallelism that will be used for the local execution
* environment created by {@link #createLocalEnvironment()}.
*
* @param parallelism The parallelism to use as the default local parallelism.
*/
@PublicEvolving
public static void setDefaultLocalParallelism(int parallelism) {
defaultLocalParallelism = parallelism;
}
// --------------------------------------------------------------------------------------------
// Methods to control the context and local environments for execution from packaged programs
// --------------------------------------------------------------------------------------------
protected static void initializeContextEnvironment(StreamExecutionEnvironmentFactory ctx) {
contextEnvironmentFactory = ctx;
}
protected static void resetContextEnvironment() {
contextEnvironmentFactory = null;
}
/**
* Registers a file at the distributed cache under the given name. The file will be accessible
* from any user-defined function in the (distributed) runtime under a local path. Files
* may be local files (as long as all relevant workers have access to it), or files in a distributed file system.
* The runtime will copy the files temporarily to a local cache, if needed.
*
* <p>The {@link org.apache.flink.api.common.functions.RuntimeContext} can be obtained inside UDFs via
* {@link org.apache.flink.api.common.functions.RichFunction#getRuntimeContext()} and provides access
* {@link org.apache.flink.api.common.cache.DistributedCache} via
* {@link org.apache.flink.api.common.functions.RuntimeContext#getDistributedCache()}.
*
* @param filePath The path of the file, as a URI (e.g. "file:///some/path" or "hdfs://host:port/and/path")
* @param name The name under which the file is registered.
*/
public void registerCachedFile(String filePath, String name) {
registerCachedFile(filePath, name, false);
}
/**
* Registers a file at the distributed cache under the given name. The file will be accessible
* from any user-defined function in the (distributed) runtime under a local path. Files
* may be local files (as long as all relevant workers have access to it), or files in a distributed file system.
* The runtime will copy the files temporarily to a local cache, if needed.
*
* <p>The {@link org.apache.flink.api.common.functions.RuntimeContext} can be obtained inside UDFs via
* {@link org.apache.flink.api.common.functions.RichFunction#getRuntimeContext()} and provides access
* {@link org.apache.flink.api.common.cache.DistributedCache} via
* {@link org.apache.flink.api.common.functions.RuntimeContext#getDistributedCache()}.
*
* @param filePath The path of the file, as a URI (e.g. "file:///some/path" or "hdfs://host:port/and/path")
* @param name The name under which the file is registered.
* @param executable flag indicating whether the file should be executable
*/
public void registerCachedFile(String filePath, String name, boolean executable) {
this.cacheFile.add(new Tuple2<>(name, new DistributedCache.DistributedCacheEntry(filePath, executable)));
}
}