org.apache.flink.streaming.api.scala.StreamExecutionEnvironment.scala Maven / Gradle / Ivy
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* 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,
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* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.flink.streaming.api.scala
import java.util.Objects
import java.util.Objects._
import com.esotericsoftware.kryo.Serializer
import org.apache.flink.api.common.io.{FileInputFormat, InputFormat}
import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer
import org.apache.flink.api.scala.ClosureCleaner
import org.apache.flink.runtime.state.StateBackend
import org.apache.flink.streaming.api.{TimeCharacteristic, CheckpointingMode}
import org.apache.flink.streaming.api.environment.{StreamExecutionEnvironment => JavaEnv}
import org.apache.flink.streaming.api.functions.source.FileMonitoringFunction.WatchType
import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext
import org.apache.flink.streaming.api.functions.source.SourceFunction
import org.apache.flink.types.StringValue
import org.apache.flink.util.SplittableIterator
import scala.collection.JavaConverters._
import scala.reflect.ClassTag
import _root_.scala.language.implicitConversions
class StreamExecutionEnvironment(javaEnv: JavaEnv) {
/**
* Gets the config object.
*/
def getConfig = javaEnv.getConfig
/**
* Sets the parallelism for operations executed through this environment.
* Setting a parallelism of x here will cause all operators (such as join, map, reduce) to run
* with x parallel instances. This value can be overridden by specific operations using
* [[DataStream#setParallelism(int)]].
*/
def setParallelism(parallelism: Int): Unit = {
javaEnv.setParallelism(parallelism)
}
/**
* Returns the default parallelism for this execution environment. Note that this
* value can be overridden by individual operations using [[DataStream#setParallelism(int)]]
*/
def getParallelism = javaEnv.getParallelism
/**
* 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:
*
*
* - A positive integer triggers flushing periodically by that integer
* - 0 triggers flushing after every record thus minimizing latency
* - -1 triggers flushing only when the output buffer is full thus maximizing throughput
*
*/
def setBufferTimeout(timeoutMillis: Long): StreamExecutionEnvironment = {
javaEnv.setBufferTimeout(timeoutMillis)
this
}
/**
* Gets the default buffer timeout set for this environment
*/
def getBufferTimeout = javaEnv.getBufferTimeout
/**
* 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.
*
*/
def disableOperatorChaining(): StreamExecutionEnvironment = {
javaEnv.disableOperatorChaining()
this
}
// ------------------------------------------------------------------------
// Checkpointing Settings
// ------------------------------------------------------------------------
/**
* 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.
*
* The job draws checkpoints periodically, in the given interval. The state will be
* stored in the configured state backend.
*
* 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" guarantees.
* @param force
* If true checkpointing will be enabled for iterative jobs as well.
*/
@deprecated
def enableCheckpointing(interval : Long,
mode: CheckpointingMode,
force: Boolean) : StreamExecutionEnvironment = {
javaEnv.enableCheckpointing(interval, mode, force)
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.
*
* The job draws checkpoints periodically, in the given interval. The system uses the
* given [[CheckpointingMode]] for the checkpointing ("exactly once" vs "at least once").
* The state will be stored in the configured state backend.
*
* 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
* [[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" guarantees.
*/
def enableCheckpointing(interval : Long,
mode: CheckpointingMode) : StreamExecutionEnvironment = {
javaEnv.enableCheckpointing(interval, mode)
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.
*
* The job draws checkpoints periodically, in the given interval. The program will use
* [[CheckpointingMode.EXACTLY_ONCE]] mode. The state will be stored in the
* configured state backend.
*
* 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
* [[enableCheckpointing(long, CheckpointingMode, boolean)]] method.
*
* @param interval
* Time interval between state checkpoints in milliseconds.
*/
def enableCheckpointing(interval : Long) : StreamExecutionEnvironment = {
enableCheckpointing(interval, CheckpointingMode.EXACTLY_ONCE)
}
/**
* Method for enabling fault-tolerance. Activates monitoring and backup of streaming
* operator states. Time interval between state checkpoints is specified in in millis.
*
* Setting this option assumes that the job is used in production and thus if not stated
* explicitly otherwise with calling with the
* [[setNumberOfExecutionRetries(int)]] method in case of
* failure the job will be resubmitted to the cluster indefinitely.
*/
def enableCheckpointing() : StreamExecutionEnvironment = {
javaEnv.enableCheckpointing()
this
}
def getCheckpointingMode = javaEnv.getCheckpointingMode()
/**
* Sets the state backend that describes how to store and checkpoint operator state.
* It defines in what form the key/value state, accessible from operations on
* [[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
* [[org.apache.flink.streaming.api.checkpoint.Checkpointed]].
*
* The [[org.apache.flink.streaming.api.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).
*
*
In contrast, the [[org.apache.flink.streaming.api.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 the entire streaming
* program can be executed highly available and strongly consistent (assuming that Flink
* is run in high-availability mode).
*/
def setStateBackend(backend: StateBackend[_]): StreamExecutionEnvironment = {
javaEnv.setStateBackend(backend)
this
}
/**
* Returns the state backend that defines how to store and checkpoint state.
*/
def getStateBackend: StateBackend[_] = javaEnv.getStateBackend()
/**
* Sets the number of times that failed tasks are re-executed. A value of zero
* effectively disables fault tolerance. A value of "-1" indicates that the system
* default value (as defined in the configuration) should be used.
*/
def setNumberOfExecutionRetries(numRetries: Int): Unit = {
javaEnv.setNumberOfExecutionRetries(numRetries)
}
/**
* Gets the number of times the system will try to re-execute failed tasks. A value
* of "-1" indicates that the system default value (as defined in the configuration)
* should be used.
*/
def getNumberOfExecutionRetries = javaEnv.getNumberOfExecutionRetries
// --------------------------------------------------------------------------------------------
// Registry for types and serializers
// --------------------------------------------------------------------------------------------
/**
* Adds a new Kryo default serializer to the Runtime.
*
* 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.
*/
def addDefaultKryoSerializer[T <: Serializer[_] with Serializable](
`type`: Class[_],
serializer: T)
: Unit = {
javaEnv.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.
*/
def addDefaultKryoSerializer(`type`: Class[_], serializerClass: Class[_ <: Serializer[_]]) {
javaEnv.addDefaultKryoSerializer(`type`, serializerClass)
}
/**
* Registers the given type with the serializer at the [[KryoSerializer]].
*
* 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.
*/
def registerTypeWithKryoSerializer[T <: Serializer[_] with Serializable](
clazz: Class[_],
serializer: T)
: Unit = {
javaEnv.registerTypeWithKryoSerializer(clazz, serializer)
}
/**
* Registers the given type with the serializer at the [[KryoSerializer]].
*/
def registerTypeWithKryoSerializer(clazz: Class[_], serializer: Class[_ <: Serializer[_]]) {
javaEnv.registerTypeWithKryoSerializer(clazz, serializer)
}
/**
* 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.
*
*/
def registerType(typeClass: Class[_]) {
javaEnv.registerType(typeClass)
}
// --------------------------------------------------------------------------------------------
// Time characteristic
// --------------------------------------------------------------------------------------------
/**
* Sets the time characteristic for all streams create from this environment, e.g., processing
* time, event time, or ingestion time.
*
* 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
* [[org.apache.flink.api.common.ExecutionConfig#setAutoWatermarkInterval(long)]]
*
* @param characteristic The time characteristic.
*/
def setStreamTimeCharacteristic(characteristic: TimeCharacteristic) : Unit = {
javaEnv.setStreamTimeCharacteristic(characteristic)
}
/**
* Gets the time characteristic/
*
* @see #setStreamTimeCharacteristic
*
* @return The time characteristic.
*/
def getStreamTimeCharacteristic = javaEnv.getStreamTimeCharacteristic()
// --------------------------------------------------------------------------------------------
// Data stream creations
// --------------------------------------------------------------------------------------------
/**
* Creates a new DataStream that contains a sequence of numbers. This source is a parallel source.
* If you manually set the parallelism to `1` the emitted elements are in order.
*/
def generateSequence(from: Long, to: Long): DataStream[Long] = {
new DataStream[java.lang.Long](javaEnv.generateSequence(from, to))
.asInstanceOf[DataStream[Long]]
}
/**
* Creates a DataStream that contains the given elements. The elements must all be of the
* same type.
*
* Note that this operation will result in a non-parallel data source, i.e. a data source with
* a parallelism of one.
*/
def fromElements[T: ClassTag: TypeInformation](data: T*): DataStream[T] = {
val typeInfo = implicitly[TypeInformation[T]]
fromCollection(data)(implicitly[ClassTag[T]], typeInfo)
}
/**
* Creates a DataStream from the given non-empty [[Seq]]. The elements need to be serializable
* because the framework may move the elements into the cluster if needed.
*
* Note that this operation will result in a non-parallel data source, i.e. a data source with
* a parallelism of one.
*/
def fromCollection[T: ClassTag: TypeInformation](data: Seq[T]): DataStream[T] = {
require(data != null, "Data must not be null.")
val typeInfo = implicitly[TypeInformation[T]]
javaEnv.fromCollection(scala.collection.JavaConversions.asJavaCollection(data), typeInfo)
}
/**
* Creates a DataStream from the given [[Iterator]].
*
* Note that this operation will result in a non-parallel data source, i.e. a data source with
* a parallelism of one.
*/
def fromCollection[T: ClassTag : TypeInformation] (data: Iterator[T]): DataStream[T] = {
val typeInfo = implicitly[TypeInformation[T]]
javaEnv.fromCollection(data.asJava, typeInfo)
}
/**
* Creates a DataStream from the given [[SplittableIterator]].
*/
def fromParallelCollection[T: ClassTag : TypeInformation] (data: SplittableIterator[T]):
DataStream[T] = {
val typeInfo = implicitly[TypeInformation[T]]
javaEnv.fromParallelCollection(data, typeInfo)
}
/**
* Creates a DataStream that represents the Strings produced by reading the
* given file line wise. The file will be read with the system's default
* character set.
*
*/
def readTextFile(filePath: String): DataStream[String] =
javaEnv.readTextFile(filePath)
/**
* Creates a data stream that represents the Strings produced by reading the given file
* line wise. The character set with the given name will be used to read the files.
*/
def readTextFile(filePath: String, charsetName: String): DataStream[String] =
javaEnv.readTextFile(filePath, charsetName)
/**
* Creates a data stream that represents the strings produced by reading the given file
* line wise. This method is similar to the standard text file reader, but it produces
* a data stream with mutable StringValue objects, rather than Java Strings.
* StringValues can be used to tune implementations to be less object and garbage
* collection heavy. The file will be read with the system's default character set.
*/
def readTextFileWithValue(filePath: String): DataStream[StringValue] =
javaEnv.readTextFileWithValue(filePath)
/**
* Creates a data stream that represents the strings produced by reading the given file
* line wise. This method is similar to the standard text file reader, but it produces
* a data stream with mutable StringValue objects, rather than Java Strings.
* StringValues can be used to tune implementations to be less object and garbage
* collection heavy. The boolean flag indicates whether to skip lines that cannot
* be read with the given character set.
*/
def readTextFileWithValue(filePath: String, charsetName : String, skipInvalidLines : Boolean):
DataStream[StringValue] =
javaEnv.readTextFileWithValue(filePath, charsetName, skipInvalidLines)
/**
* Reads the given file with the given input format. The file path should be passed
* as a URI (e.g., "file:///some/local/file" or "hdfs://host:port/file/path").
*/
def readFile[T: ClassTag : TypeInformation](inputFormat: FileInputFormat[T], filePath: String):
DataStream[T] =
javaEnv.readFile(inputFormat, filePath)
/**
* Creates a data stream that represents the primitive type produced by reading the given file
* line wise. The file path should be passed as a URI (e.g., "file:///some/local/file" or
* "hdfs://host:port/file/path").
*/
def readFileOfPrimitives[T: ClassTag : TypeInformation](filePath: String,
delimiter: String = "\n", typeClass: Class[T]): DataStream[T] =
javaEnv.readFileOfPrimitives(filePath, delimiter, typeClass)
/**
* Creates a DataStream 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. The user can check the monitoring interval in milliseconds,
* and the way file modifications are handled. By default it checks for only new files
* every 100 milliseconds.
*
*/
def readFileStream(StreamPath: String, intervalMillis: Long = 100, watchType: WatchType =
WatchType.ONLY_NEW_FILES): DataStream[String] =
javaEnv.readFileStream(StreamPath, intervalMillis, watchType)
/**
* Creates a new DataStream that contains the strings received infinitely
* from socket. Received strings are decoded by the system's default
* character set. The maximum retry interval is specified in seconds, in case
* of temporary service outage reconnection is initiated every second.
*/
def socketTextStream(hostname: String, port: Int, delimiter: Char = '\n', maxRetry: Long = 0):
DataStream[String] =
javaEnv.socketTextStream(hostname, port)
/**
* Generic method to create an input data stream with a specific input format.
* 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 ResultTypeQueryable interface.
*/
def createInput[T: ClassTag : TypeInformation](inputFormat: InputFormat[T, _]): DataStream[T] =
javaEnv.createInput(inputFormat)
/**
* Create a DataStream using a user defined source function for arbitrary
* source functionality. By default sources have a parallelism of 1.
* To enable parallel execution, the user defined source should implement
* ParallelSourceFunction or extend RichParallelSourceFunction.
* In these cases the resulting source will have the parallelism of the environment.
* To change this afterwards call DataStreamSource.setParallelism(int)
*
*/
def addSource[T: ClassTag: TypeInformation](function: SourceFunction[T]): DataStream[T] = {
require(function != null, "Function must not be null.")
val cleanFun = scalaClean(function)
val typeInfo = implicitly[TypeInformation[T]]
javaEnv.addSource(cleanFun).returns(typeInfo)
}
/**
* Create a DataStream using a user defined source function for arbitrary
* source functionality.
*
*/
def addSource[T: ClassTag: TypeInformation](function: SourceContext[T] => Unit): DataStream[T] = {
require(function != null, "Function must not be null.")
val sourceFunction = new SourceFunction[T] {
val cleanFun = scalaClean(function)
override def run(ctx: SourceContext[T]) {
cleanFun(ctx)
}
override def cancel() = {}
}
addSource(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.
*
* The program execution will be logged and displayed with a generated
* default name.
*
*/
def execute() = javaEnv.execute()
/**
* 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.
*
* The program execution will be logged and displayed with the provided name
*
*/
def execute(jobName: String) = javaEnv.execute(jobName)
/**
* 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.
*
*/
def getExecutionPlan = javaEnv.getExecutionPlan
/**
* Getter of the [[org.apache.flink.streaming.api.graph.StreamGraph]] of the streaming job.
*
* @return The StreamGraph representing the transformations
*/
def getStreamGraph = javaEnv.getStreamGraph
/**
* Getter of the wrapped [[org.apache.flink.streaming.api.environment.StreamExecutionEnvironment]]
* @return The encased ExecutionEnvironment
*/
def getWrappedStreamExecutionEnvironment = javaEnv
/**
* Returns a "closure-cleaned" version of the given function. Cleans only if closure cleaning
* is not disabled in the [[org.apache.flink.api.common.ExecutionConfig]]
*/
private[flink] def scalaClean[F <: AnyRef](f: F): F = {
if (getConfig.isClosureCleanerEnabled) {
ClosureCleaner.clean(f, true)
} else {
ClosureCleaner.ensureSerializable(f)
}
f
}
}
object StreamExecutionEnvironment {
/**
* Sets the default parallelism that will be used for the local execution
* environment created by [[createLocalEnvironment()]].
*
* @param parallelism
* The parallelism to use as the default local parallelism.
*/
def setDefaultLocalParallelism(parallelism: Int) : Unit =
StreamExecutionEnvironment.setDefaultLocalParallelism(parallelism)
/**
* 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. If the program is invoked from within the command line client
* to be submitted to a cluster, this method returns the execution environment of this cluster.
*/
def getExecutionEnvironment: StreamExecutionEnvironment = {
new StreamExecutionEnvironment(JavaEnv.getExecutionEnvironment)
}
/**
* Creates a local execution environment. 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 degree
* of parallelism of the local environment is the number of hardware contexts (CPU cores/threads).
*/
def createLocalEnvironment(
parallelism: Int = Runtime.getRuntime.availableProcessors()):
StreamExecutionEnvironment = {
new StreamExecutionEnvironment(JavaEnv.createLocalEnvironment(parallelism))
}
/**
* Creates a remote execution environment. 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 cluster's default parallelism, unless the
* parallelism is set explicitly via [[StreamExecutionEnvironment.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.
*/
def createRemoteEnvironment(host: String, port: Int, jarFiles: String*):
StreamExecutionEnvironment = {
new StreamExecutionEnvironment(JavaEnv.createRemoteEnvironment(host, port, jarFiles: _*))
}
/**
* Creates a remote execution environment. 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.
*/
def createRemoteEnvironment(
host: String,
port: Int,
parallelism: Int,
jarFiles: String*): StreamExecutionEnvironment = {
val javaEnv = JavaEnv.createRemoteEnvironment(host, port, jarFiles: _*)
javaEnv.setParallelism(parallelism)
new StreamExecutionEnvironment(javaEnv)
}
}