org.apache.flink.api.java.ExecutionEnvironment Maven / Gradle / Ivy
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* 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.api.java;
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.Plan;
import org.apache.flink.api.common.cache.DistributedCache;
import org.apache.flink.api.common.cache.DistributedCache.DistributedCacheEntry;
import org.apache.flink.api.common.io.FileInputFormat;
import org.apache.flink.api.common.io.InputFormat;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.io.CollectionInputFormat;
import org.apache.flink.api.java.io.CsvReader;
import org.apache.flink.api.java.io.IteratorInputFormat;
import org.apache.flink.api.java.io.ParallelIteratorInputFormat;
import org.apache.flink.api.java.io.PrimitiveInputFormat;
import org.apache.flink.api.java.io.TextInputFormat;
import org.apache.flink.api.java.io.TextValueInputFormat;
import org.apache.flink.api.java.operators.DataSink;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.Operator;
import org.apache.flink.api.java.tuple.Tuple2;
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.api.java.typeutils.ValueTypeInfo;
import org.apache.flink.api.java.utils.PlanGenerator;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.DeploymentOptions;
import org.apache.flink.configuration.PipelineOptions;
import org.apache.flink.configuration.ReadableConfig;
import org.apache.flink.configuration.RestOptions;
import org.apache.flink.core.execution.DefaultExecutorServiceLoader;
import org.apache.flink.core.execution.DetachedJobExecutionResult;
import org.apache.flink.core.execution.JobClient;
import org.apache.flink.core.execution.JobListener;
import org.apache.flink.core.execution.PipelineExecutor;
import org.apache.flink.core.execution.PipelineExecutorFactory;
import org.apache.flink.core.execution.PipelineExecutorServiceLoader;
import org.apache.flink.core.fs.Path;
import org.apache.flink.types.StringValue;
import org.apache.flink.util.ExceptionUtils;
import org.apache.flink.util.FlinkException;
import org.apache.flink.util.InstantiationUtil;
import org.apache.flink.util.NumberSequenceIterator;
import org.apache.flink.util.Preconditions;
import org.apache.flink.util.SplittableIterator;
import org.apache.flink.util.WrappingRuntimeException;
import com.esotericsoftware.kryo.Serializer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Calendar;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
import java.util.concurrent.CompletableFuture;
import static org.apache.flink.util.Preconditions.checkNotNull;
/**
* The ExecutionEnvironment is the context in which a program is executed. A {@link
* LocalEnvironment} will cause execution in the current JVM, a {@link RemoteEnvironment} will cause
* execution on a remote setup.
*
* The environment provides methods to control the job execution (such as setting the
* parallelism) and to interact with the outside world (data access).
*
*
Please note that the execution environment needs strong type information for the input and
* return types of all operations that are executed. This means that the environments needs to know
* that the return value of an operation is for example a Tuple of String and Integer. Because the
* Java compiler throws much of the generic type information away, most methods attempt to re-
* obtain that information using reflection. In certain cases, it may be necessary to manually
* supply that information to some of the methods.
*
* @see LocalEnvironment
* @see RemoteEnvironment
*/
@Public
public class ExecutionEnvironment {
/** The logger used by the environment and its subclasses. */
protected static final Logger LOG = LoggerFactory.getLogger(ExecutionEnvironment.class);
/**
* The environment of the context (local by default, cluster if invoked through command line).
*/
private static ExecutionEnvironmentFactory contextEnvironmentFactory = null;
/** The ThreadLocal used to store {@link ExecutionEnvironmentFactory}. */
private static final ThreadLocal
threadLocalContextEnvironmentFactory = new ThreadLocal<>();
/** The default parallelism used by local environments. */
private static int defaultLocalDop = Runtime.getRuntime().availableProcessors();
// --------------------------------------------------------------------------------------------
private final List> sinks = new ArrayList<>();
private final List> cacheFile = new ArrayList<>();
private final ExecutionConfig config = new ExecutionConfig();
/**
* Result from the latest execution, to make it retrievable when using eager execution methods.
*/
protected JobExecutionResult lastJobExecutionResult;
/** Flag to indicate whether sinks have been cleared in previous executions. */
private boolean wasExecuted = false;
private final PipelineExecutorServiceLoader executorServiceLoader;
private final Configuration configuration;
private final ClassLoader userClassloader;
private final List jobListeners = new ArrayList<>();
/**
* Creates a new {@link ExecutionEnvironment} that will use the given {@link Configuration} to
* configure the {@link PipelineExecutor}.
*/
@PublicEvolving
public ExecutionEnvironment(final Configuration configuration) {
this(configuration, null);
}
/**
* Creates a new {@link ExecutionEnvironment} that will use the given {@link Configuration} to
* configure the {@link PipelineExecutor}.
*
* In addition, this constructor allows specifying the user code {@link ClassLoader}.
*/
@PublicEvolving
public ExecutionEnvironment(
final Configuration configuration, final ClassLoader userClassloader) {
this(new DefaultExecutorServiceLoader(), configuration, userClassloader);
}
/**
* Creates a new {@link ExecutionEnvironment} that will use the given {@link Configuration} to
* configure the {@link PipelineExecutor}.
*
*
In addition, this constructor allows specifying the {@link PipelineExecutorServiceLoader}
* and user code {@link ClassLoader}.
*/
@PublicEvolving
public ExecutionEnvironment(
final PipelineExecutorServiceLoader executorServiceLoader,
final Configuration configuration,
final ClassLoader userClassloader) {
this.executorServiceLoader = checkNotNull(executorServiceLoader);
this.configuration = new Configuration(checkNotNull(configuration));
this.userClassloader =
userClassloader == null ? getClass().getClassLoader() : userClassloader;
// the configuration of a job or an operator can be specified at the following places:
// i) at the operator level using e.g. parallelism using the
// SingleOutputStreamOperator.setParallelism().
// ii) programmatically by using e.g. the env.setRestartStrategy() method
// iii) in the configuration passed here
//
// if specified in multiple places, the priority order is the above.
//
// Given this, it is safe to overwrite the execution config default values here because all
// other ways assume
// that the env is already instantiated so they will overwrite the value passed here.
this.configure(this.configuration, this.userClassloader);
}
/** Creates a new Execution Environment. */
protected ExecutionEnvironment() {
this(new Configuration());
}
@Internal
public ClassLoader getUserCodeClassLoader() {
return userClassloader;
}
@Internal
public PipelineExecutorServiceLoader getExecutorServiceLoader() {
return executorServiceLoader;
}
@Internal
public Configuration getConfiguration() {
return this.configuration;
}
// --------------------------------------------------------------------------------------------
// Properties
// --------------------------------------------------------------------------------------------
/**
* Gets the config object that defines execution parameters.
*
* @return The environment's execution configuration.
*/
public ExecutionConfig getConfig() {
return config;
}
/** Gets the config JobListeners. */
protected List getJobListeners() {
return jobListeners;
}
/**
* Gets the parallelism with which operation are executed by default. Operations can
* individually override this value to use a specific parallelism via {@link
* Operator#setParallelism(int)}. Other operations may need to run with a different parallelism
* - for example calling {@link
* DataSet#reduce(org.apache.flink.api.common.functions.ReduceFunction)} over the entire set
* will insert eventually an operation that runs non-parallel (parallelism of one).
*
* @return The parallelism used by operations, unless they override that value. This method
* returns {@link ExecutionConfig#PARALLELISM_DEFAULT}, if the environment's default
* parallelism should be used.
*/
public int getParallelism() {
return config.getParallelism();
}
/**
* 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 method overrides the default parallelism for this environment. The {@link
* LocalEnvironment} 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 parallelism is the one configured for that setup.
*
* @param parallelism The parallelism
*/
public void setParallelism(int parallelism) {
config.setParallelism(parallelism);
}
/**
* 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.FixedDelayRestartStrategyConfiguration} 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}. The {@link
* RestartStrategies.FixedDelayRestartStrategyConfiguration} contains the number of
* execution retries.
*/
@Deprecated
@PublicEvolving
public int getNumberOfExecutionRetries() {
return config.getNumberOfExecutionRetries();
}
/**
* Returns the {@link org.apache.flink.api.common.JobExecutionResult} of the last executed job.
*
* @return The execution result from the latest job execution.
*/
public JobExecutionResult getLastJobExecutionResult() {
return this.lastJobExecutionResult;
}
// --------------------------------------------------------------------------------------------
// 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.
*/
public & 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> serializerClass) {
config.addDefaultKryoSerializer(type, serializerClass);
}
/**
* Registers the given type with a Kryo Serializer.
*
* 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 & 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.
*/
public void registerTypeWithKryoSerializer(
Class type, Class> 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);
}
}
/**
* Sets all relevant options contained in the {@link ReadableConfig} such as e.g. {@link
* PipelineOptions#CACHED_FILES}. It will reconfigure {@link ExecutionEnvironment} and {@link
* ExecutionConfig}.
*
* It will change the value of a setting only if a corresponding option was set in the {@code
* configuration}. If a key is not present, the current value of a field will remain untouched.
*
* @param configuration a configuration to read the values from
* @param classLoader a class loader to use when loading classes
*/
@PublicEvolving
public void configure(ReadableConfig configuration, ClassLoader classLoader) {
configuration
.getOptional(DeploymentOptions.JOB_LISTENERS)
.ifPresent(listeners -> registerCustomListeners(classLoader, listeners));
configuration
.getOptional(PipelineOptions.CACHED_FILES)
.ifPresent(
f -> {
this.cacheFile.clear();
this.cacheFile.addAll(DistributedCache.parseCachedFilesFromString(f));
});
configuration
.getOptional(PipelineOptions.NAME)
.ifPresent(jobName -> this.getConfiguration().set(PipelineOptions.NAME, jobName));
config.configure(configuration, classLoader);
}
private void registerCustomListeners(
final ClassLoader classLoader, final List listeners) {
for (String listener : listeners) {
try {
final JobListener jobListener =
InstantiationUtil.instantiate(listener, JobListener.class, classLoader);
jobListeners.add(jobListener);
} catch (FlinkException e) {
throw new WrappingRuntimeException("Could not load JobListener : " + listener, e);
}
}
}
// --------------------------------------------------------------------------------------------
// Data set creations
// --------------------------------------------------------------------------------------------
// ---------------------------------- Text Input Format ---------------------------------------
/**
* Creates a {@link DataSet} that represents the Strings produced by reading the given file line
* wise. The file will be read with the UTF-8 character set.
*
* @param filePath The path of the file, as a URI (e.g., "file:///some/local/file" or
* "hdfs://host:port/file/path").
* @return A {@link DataSet} that represents the data read from the given file as text lines.
*/
public DataSource readTextFile(String filePath) {
Preconditions.checkNotNull(filePath, "The file path may not be null.");
return new DataSource<>(
this,
new TextInputFormat(new Path(filePath)),
BasicTypeInfo.STRING_TYPE_INFO,
Utils.getCallLocationName());
}
/**
* Creates a {@link DataSet} that represents the Strings produced by reading the given file line
* wise. The {@link java.nio.charset.Charset} with the given name will be used to read the
* files.
*
* @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 A {@link DataSet} that represents the data read from the given file as text lines.
*/
public DataSource readTextFile(String filePath, String charsetName) {
Preconditions.checkNotNull(filePath, "The file path may not be null.");
TextInputFormat format = new TextInputFormat(new Path(filePath));
format.setCharsetName(charsetName);
return new DataSource<>(
this, format, BasicTypeInfo.STRING_TYPE_INFO, Utils.getCallLocationName());
}
// -------------------------- Text Input Format With String Value------------------------------
/**
* Creates a {@link DataSet} that represents the Strings produced by reading the given file line
* wise. This method is similar to {@link #readTextFile(String)}, but it produces a DataSet with
* mutable {@link 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 UTF-8 character set.
*
* @param filePath The path of the file, as a URI (e.g., "file:///some/local/file" or
* "hdfs://host:port/file/path").
* @return A {@link DataSet} that represents the data read from the given file as text lines.
*/
public DataSource readTextFileWithValue(String filePath) {
Preconditions.checkNotNull(filePath, "The file path may not be null.");
return new DataSource<>(
this,
new TextValueInputFormat(new Path(filePath)),
new ValueTypeInfo<>(StringValue.class),
Utils.getCallLocationName());
}
/**
* Creates a {@link DataSet} that represents the Strings produced by reading the given file line
* wise. This method is similar to {@link #readTextFile(String, String)}, but it produces a
* DataSet with mutable {@link StringValue} objects, rather than Java Strings. StringValues can
* be used to tune implementations to be less object and garbage collection heavy.
*
* The {@link java.nio.charset.Charset} with the given name will be used to read the files.
*
* @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.
* @param skipInvalidLines A flag to indicate whether to skip lines that cannot be read with the
* given character set.
* @return A DataSet that represents the data read from the given file as text lines.
*/
public DataSource readTextFileWithValue(
String filePath, String charsetName, boolean skipInvalidLines) {
Preconditions.checkNotNull(filePath, "The file path may not be null.");
TextValueInputFormat format = new TextValueInputFormat(new Path(filePath));
format.setCharsetName(charsetName);
format.setSkipInvalidLines(skipInvalidLines);
return new DataSource<>(
this, format, new ValueTypeInfo<>(StringValue.class), Utils.getCallLocationName());
}
// ----------------------------------- Primitive Input Format
// ---------------------------------------
/**
* Creates a {@link DataSet} that represents the primitive type produced by reading the given
* file line wise. This method is similar to {@link #readCsvFile(String)} with single field, but
* it produces a DataSet not through {@link org.apache.flink.api.java.tuple.Tuple1}.
*
* @param filePath The path of the file, as a URI (e.g., "file:///some/local/file" or
* "hdfs://host:port/file/path").
* @param typeClass The primitive type class to be read.
* @return A {@link DataSet} that represents the data read from the given file as primitive
* type.
*/
public DataSource readFileOfPrimitives(String filePath, Class typeClass) {
Preconditions.checkNotNull(filePath, "The file path may not be null.");
return new DataSource<>(
this,
new PrimitiveInputFormat<>(new Path(filePath), typeClass),
TypeExtractor.getForClass(typeClass),
Utils.getCallLocationName());
}
/**
* Creates a {@link DataSet} that represents the primitive type produced by reading the given
* file in delimited way. This method is similar to {@link #readCsvFile(String)} with single
* field, but it produces a DataSet not through {@link org.apache.flink.api.java.tuple.Tuple1}.
*
* @param filePath The path of the file, as a URI (e.g., "file:///some/local/file" or
* "hdfs://host:port/file/path").
* @param delimiter The delimiter of the given file.
* @param typeClass The primitive type class to be read.
* @return A {@link DataSet} that represents the data read from the given file as primitive
* type.
*/
public DataSource readFileOfPrimitives(
String filePath, String delimiter, Class typeClass) {
Preconditions.checkNotNull(filePath, "The file path may not be null.");
return new DataSource<>(
this,
new PrimitiveInputFormat<>(new Path(filePath), delimiter, typeClass),
TypeExtractor.getForClass(typeClass),
Utils.getCallLocationName());
}
// ----------------------------------- CSV Input Format ---------------------------------------
/**
* Creates a CSV reader to read a comma separated value (CSV) file. The reader has options to
* define parameters and field types and will eventually produce the DataSet that corresponds to
* the read and parsed CSV input.
*
* @param filePath The path of the CSV file.
* @return A CsvReader that can be used to configure the CSV input.
*/
public CsvReader readCsvFile(String filePath) {
return new CsvReader(filePath, this);
}
// ------------------------------------ File Input Format
// -----------------------------------------
public DataSource readFile(FileInputFormat inputFormat, String filePath) {
if (inputFormat == null) {
throw new IllegalArgumentException("InputFormat must not be null.");
}
if (filePath == null) {
throw new IllegalArgumentException("The file path must not be null.");
}
inputFormat.setFilePath(new Path(filePath));
try {
return createInput(inputFormat, 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.");
}
}
// ----------------------------------- Generic Input Format
// ---------------------------------------
/**
* Generic method to create an input {@link DataSet} with in {@link InputFormat}. The DataSet
* will not be immediately created - instead, this method returns a DataSet that will be lazily
* created from the input format once the program is executed.
*
* Since all data sets 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 ResultTypeQueryable}
* interface. In the latter case, this method will invoke the {@link
* ResultTypeQueryable#getProducedType()} method to determine data type produced by the input
* format.
*
* @param inputFormat The input format used to create the data set.
* @return A {@link DataSet} that represents the data created by the input format.
* @see #createInput(InputFormat, TypeInformation)
*/
public DataSource createInput(InputFormat inputFormat) {
if (inputFormat == null) {
throw new IllegalArgumentException("InputFormat must not be null.");
}
try {
return createInput(inputFormat, 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.",
e);
}
}
/**
* Generic method to create an input DataSet with in {@link InputFormat}. The {@link DataSet}
* will not be immediately created - instead, this method returns a {@link DataSet} that will be
* lazily created from the input format once the program is executed.
*
* The {@link DataSet} is typed to the given TypeInformation. This method is intended for
* input formats that where the return type cannot be determined by reflection analysis, and
* that do not implement the {@link ResultTypeQueryable} interface.
*
* @param inputFormat The input format used to create the data set.
* @return A {@link DataSet} that represents the data created by the input format.
* @see #createInput(InputFormat)
*/
public DataSource createInput(
InputFormat inputFormat, TypeInformation producedType) {
if (inputFormat == null) {
throw new IllegalArgumentException("InputFormat must not be null.");
}
if (producedType == null) {
throw new IllegalArgumentException("Produced type information must not be null.");
}
return new DataSource<>(this, inputFormat, producedType, Utils.getCallLocationName());
}
// ----------------------------------- Collection ---------------------------------------
/**
* Creates a DataSet from the given non-empty collection. The type of the data set is that of
* the elements in the collection.
*
* 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(Collection, TypeInformation)}.
*
*
Note that this operation will result in a non-parallel data source, i.e. a data source
* with a parallelism of one.
*
* @param data The collection of elements to create the data set from.
* @return A DataSet representing the given collection.
* @see #fromCollection(Collection, TypeInformation)
*/
public DataSource fromCollection(Collection data) {
if (data == null) {
throw new IllegalArgumentException("The data must not be null.");
}
if (data.size() == 0) {
throw new IllegalArgumentException("The size of the collection must not be empty.");
}
X firstValue = data.iterator().next();
TypeInformation type = TypeExtractor.getForObject(firstValue);
CollectionInputFormat.checkCollection(data, type.getTypeClass());
return new DataSource<>(
this,
new CollectionInputFormat<>(data, type.createSerializer(config)),
type,
Utils.getCallLocationName());
}
/**
* Creates a DataSet from the given non-empty collection. Note that this operation will result
* in a non-parallel data source, i.e. a data source with a parallelism of one.
*
* The returned DataSet is typed to the given TypeInformation.
*
* @param data The collection of elements to create the data set from.
* @param type The TypeInformation for the produced data set.
* @return A DataSet representing the given collection.
* @see #fromCollection(Collection)
*/
public DataSource fromCollection(Collection data, TypeInformation type) {
return fromCollection(data, type, Utils.getCallLocationName());
}
private DataSource fromCollection(
Collection data, TypeInformation type, String callLocationName) {
CollectionInputFormat.checkCollection(data, type.getTypeClass());
return new DataSource<>(
this,
new CollectionInputFormat<>(data, type.createSerializer(config)),
type,
callLocationName);
}
/**
* Creates a DataSet from the given iterator. 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).
*
* Note that this operation will result in a non-parallel data source, i.e. a data source
* with a parallelism of one.
*
* @param data The collection of elements to create the data set from.
* @param type The class of the data produced by the iterator. Must not be a generic class.
* @return A DataSet representing the elements in the iterator.
* @see #fromCollection(Iterator, TypeInformation)
*/
public DataSource fromCollection(Iterator data, Class type) {
return fromCollection(data, TypeExtractor.getForClass(type));
}
/**
* Creates a DataSet from the given iterator. 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(Iterator,
* Class)} does not supply all type information.
*
* Note that this operation will result in a non-parallel data source, i.e. a data source
* with a parallelism of one.
*
* @param data The collection of elements to create the data set from.
* @param type The TypeInformation for the produced data set.
* @return A DataSet representing the elements in the iterator.
* @see #fromCollection(Iterator, Class)
*/
public DataSource fromCollection(Iterator data, TypeInformation type) {
return new DataSource<>(
this, new IteratorInputFormat<>(data), type, Utils.getCallLocationName());
}
/**
* Creates a new data set that contains the given elements. The elements must all be of the same
* type, for example, all of the {@link String} or {@link Integer}. The sequence of elements
* must not be empty.
*
* 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(Collection, TypeInformation)}.
*
*
Note that this operation will result in a non-parallel data source, i.e. a data source
* with a parallelism of one.
*
* @param data The elements to make up the data set.
* @return A DataSet representing the given list of elements.
*/
@SafeVarargs
public final DataSource fromElements(X... data) {
if (data == null) {
throw new IllegalArgumentException("The data must not be null.");
}
if (data.length == 0) {
throw new IllegalArgumentException("The number of elements must not be zero.");
}
TypeInformation 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 "
+ "ExecutionEnvironment#fromElements(Collection, TypeInformation)",
e);
}
return fromCollection(Arrays.asList(data), typeInfo, Utils.getCallLocationName());
}
/**
* 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 source, i.e. a data source with a parallelism of
* one.
*
* @param type The base class type for every element in the collection.
* @param data The elements to make up the data set.
* @return A DataSet representing the given list of elements.
*/
@SafeVarargs
public final DataSource fromElements(Class type, X... data) {
if (data == null) {
throw new IllegalArgumentException("The data must not be null.");
}
if (data.length == 0) {
throw new IllegalArgumentException("The number of elements must not be zero.");
}
TypeInformation 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 "
+ "ExecutionEnvironment#fromElements(Collection, TypeInformation)",
e);
}
return fromCollection(Arrays.asList(data), typeInfo, Utils.getCallLocationName());
}
/**
* Creates a new data set that contains elements in the iterator. The iterator is splittable,
* allowing the framework to create a parallel data source that returns the elements in the
* iterator.
*
* 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 set.
* @param type The class of the data produced by the iterator. Must not be a generic class.
* @return A DataSet representing the elements in the iterator.
* @see #fromParallelCollection(SplittableIterator, TypeInformation)
*/
public DataSource fromParallelCollection(SplittableIterator iterator, Class type) {
return fromParallelCollection(iterator, TypeExtractor.getForClass(type));
}
/**
* Creates a new data set that contains elements in the iterator. The iterator is splittable,
* allowing the framework to create a parallel data source that returns the elements in the
* iterator.
*
* 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(SplittableIterator, Class)} does not
* supply all type information.
*
* @param iterator The iterator that produces the elements of the data set.
* @param type The TypeInformation for the produced data set.
* @return A DataSet representing the elements in the iterator.
* @see #fromParallelCollection(SplittableIterator, Class)
*/
public DataSource fromParallelCollection(
SplittableIterator iterator, TypeInformation type) {
return fromParallelCollection(iterator, type, Utils.getCallLocationName());
}
// private helper for passing different call location names
private DataSource fromParallelCollection(
SplittableIterator iterator, TypeInformation type, String callLocationName) {
return new DataSource<>(
this, new ParallelIteratorInputFormat<>(iterator), type, callLocationName);
}
/**
* Creates a new data set that contains a sequence of numbers. The data set will be created in
* parallel, so there is no guarantee about the order of the elements.
*
* @param from The number to start at (inclusive).
* @param to The number to stop at (inclusive).
* @return A DataSet, containing all number in the {@code [from, to]} interval.
*/
public DataSource generateSequence(long from, long to) {
return fromParallelCollection(
new NumberSequenceIterator(from, to),
BasicTypeInfo.LONG_TYPE_INFO,
Utils.getCallLocationName());
}
// --------------------------------------------------------------------------------------------
// Executing
// --------------------------------------------------------------------------------------------
/**
* 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 ({@link
* DataSet#print()}, writing results (e.g. {@link DataSet#writeAsText(String)}, {@link
* DataSet#write(org.apache.flink.api.common.io.FileOutputFormat, String)}, or other generic
* data sinks created with {@link DataSet#output(org.apache.flink.api.common.io.OutputFormat)}.
*
* 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 Thrown, if the program executions fails.
*/
public JobExecutionResult execute() throws Exception {
return execute(getJobName());
}
/**
* 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 ({@link
* DataSet#print()}, writing results (e.g. {@link DataSet#writeAsText(String)}, {@link
* DataSet#write(org.apache.flink.api.common.io.FileOutputFormat, String)}, or other generic
* data sinks created with {@link DataSet#output(org.apache.flink.api.common.io.OutputFormat)}.
*
*
The program execution will be logged and displayed with the given job name.
*
* @return The result of the job execution, containing elapsed time and accumulators.
* @throws Exception Thrown, if the program executions fails.
*/
public JobExecutionResult execute(String jobName) throws Exception {
final JobClient jobClient = executeAsync(jobName);
try {
if (configuration.getBoolean(DeploymentOptions.ATTACHED)) {
lastJobExecutionResult = jobClient.getJobExecutionResult().get();
} else {
lastJobExecutionResult = new DetachedJobExecutionResult(jobClient.getJobID());
}
jobListeners.forEach(
jobListener -> jobListener.onJobExecuted(lastJobExecutionResult, null));
} catch (Throwable t) {
// get() on the JobExecutionResult Future will throw an ExecutionException. This
// behaviour was largely not there in Flink versions before the PipelineExecutor
// refactoring so we should strip that exception.
Throwable strippedException = ExceptionUtils.stripExecutionException(t);
jobListeners.forEach(
jobListener -> {
jobListener.onJobExecuted(null, strippedException);
});
ExceptionUtils.rethrowException(strippedException);
}
return lastJobExecutionResult;
}
/**
* Register a {@link JobListener} in this environment. The {@link JobListener} will be notified
* on specific job status changed.
*/
@PublicEvolving
public void registerJobListener(JobListener jobListener) {
checkNotNull(jobListener, "JobListener cannot be null");
jobListeners.add(jobListener);
}
/** Clear all registered {@link JobListener}s. */
@PublicEvolving
public void clearJobListeners() {
this.jobListeners.clear();
}
/**
* Triggers the program execution asynchronously. The environment will execute all parts of the
* program that have resulted in a "sink" operation. Sink operations are for example printing
* results ({@link DataSet#print()}, writing results (e.g. {@link DataSet#writeAsText(String)},
* {@link DataSet#write(org.apache.flink.api.common.io.FileOutputFormat, String)}, or other
* generic data sinks created with {@link
* DataSet#output(org.apache.flink.api.common.io.OutputFormat)}.
*
*
The program execution will be logged and displayed with a generated default name.
*
* @return A {@link JobClient} that can be used to communicate with the submitted job, completed
* on submission succeeded.
* @throws Exception Thrown, if the program submission fails.
*/
@PublicEvolving
public final JobClient executeAsync() throws Exception {
return executeAsync(getJobName());
}
/**
* Triggers the program execution asynchronously. The environment will execute all parts of the
* program that have resulted in a "sink" operation. Sink operations are for example printing
* results ({@link DataSet#print()}, writing results (e.g. {@link DataSet#writeAsText(String)},
* {@link DataSet#write(org.apache.flink.api.common.io.FileOutputFormat, String)}, or other
* generic data sinks created with {@link
* DataSet#output(org.apache.flink.api.common.io.OutputFormat)}.
*
*
The program execution will be logged and displayed with the given job name.
*
* @return A {@link JobClient} that can be used to communicate with the submitted job, completed
* on submission succeeded.
* @throws Exception Thrown, if the program submission fails.
*/
@PublicEvolving
public JobClient executeAsync(String jobName) throws Exception {
checkNotNull(
configuration.get(DeploymentOptions.TARGET),
"No execution.target specified in your configuration file.");
final Plan plan = createProgramPlan(jobName);
final PipelineExecutorFactory executorFactory =
executorServiceLoader.getExecutorFactory(configuration);
checkNotNull(
executorFactory,
"Cannot find compatible factory for specified execution.target (=%s)",
configuration.get(DeploymentOptions.TARGET));
CompletableFuture jobClientFuture =
executorFactory
.getExecutor(configuration)
.execute(plan, configuration, userClassloader);
try {
JobClient jobClient = jobClientFuture.get();
jobListeners.forEach(jobListener -> jobListener.onJobSubmitted(jobClient, null));
return jobClient;
} catch (Throwable t) {
jobListeners.forEach(jobListener -> jobListener.onJobSubmitted(null, t));
ExceptionUtils.rethrow(t);
// make javac happy, this code path will not be reached
return null;
}
}
/**
* 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.
*
* @return The execution plan of the program, as a JSON String.
* @throws Exception Thrown, if the compiler could not be instantiated.
*/
public String getExecutionPlan() throws Exception {
Plan p = createProgramPlan(getJobName(), false);
return ExecutionPlanUtil.getExecutionPlanAsJSON(p);
}
/**
* 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 (which will be distributed via BlobServer), or files in a distributed file
* system. The runtime will copy the files temporarily to a local cache, if needed.
*
* 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 (which will be distributed via BlobServer), or files in a distributed file
* system. The runtime will copy the files temporarily to a local cache, if needed.
*
*
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 DistributedCacheEntry(filePath, executable)));
}
/**
* Creates the program's {@link Plan}. The plan is a description of all data sources, data
* sinks, and operations and how they interact, as an isolated unit that can be executed with an
* {@link PipelineExecutor}. Obtaining a plan and starting it with an executor is an alternative
* way to run a program and is only possible if the program consists only of distributed
* operations. This automatically starts a new stage of execution.
*
* @return The program's plan.
*/
@Internal
public Plan createProgramPlan() {
return createProgramPlan(getJobName());
}
/**
* Creates the program's {@link Plan}. The plan is a description of all data sources, data
* sinks, and operations and how they interact, as an isolated unit that can be executed with an
* {@link PipelineExecutor}. Obtaining a plan and starting it with an executor is an alternative
* way to run a program and is only possible if the program consists only of distributed
* operations. This automatically starts a new stage of execution.
*
* @param jobName The name attached to the plan (displayed in logs and monitoring).
* @return The program's plan.
*/
@Internal
public Plan createProgramPlan(String jobName) {
return createProgramPlan(jobName, true);
}
/**
* Creates the program's {@link Plan}. The plan is a description of all data sources, data
* sinks, and operations and how they interact, as an isolated unit that can be executed with an
* {@link PipelineExecutor}. Obtaining a plan and starting it with an executor is an alternative
* way to run a program and is only possible if the program consists only of distributed
* operations.
*
* @param jobName The name attached to the plan (displayed in logs and monitoring).
* @param clearSinks Whether or not to start a new stage of execution.
* @return The program's plan.
*/
@Internal
public Plan createProgramPlan(String jobName, boolean clearSinks) {
checkNotNull(jobName);
if (this.sinks.isEmpty()) {
if (wasExecuted) {
throw new RuntimeException(
"No new data sinks have been defined since the "
+ "last execution. The last execution refers to the latest call to "
+ "'execute()', 'count()', 'collect()', or 'print()'.");
} else {
throw new RuntimeException(
"No data sinks have been created yet. "
+ "A program needs at least one sink that consumes data. "
+ "Examples are writing the data set or printing it.");
}
}
final PlanGenerator generator =
new PlanGenerator(sinks, config, getParallelism(), cacheFile, jobName);
final Plan plan = generator.generate();
// clear all the sinks such that the next execution does not redo everything
if (clearSinks) {
this.sinks.clear();
wasExecuted = true;
}
return plan;
}
/**
* Adds the given sink to this environment. Only sinks that have been added will be executed
* once the {@link #execute()} or {@link #execute(String)} method is called.
*
* @param sink The sink to add for execution.
*/
@Internal
void registerDataSink(DataSink sink) {
this.sinks.add(sink);
}
/**
* Gets the job name. If user defined job name is not found in the configuration, the default
* name based on the timestamp when this method is invoked will return.
*
* @return A job name.
*/
private String getJobName() {
return configuration.getString(
PipelineOptions.NAME, "Flink Java Job at " + Calendar.getInstance().getTime());
}
// --------------------------------------------------------------------------------------------
// Instantiation of Execution Contexts
// --------------------------------------------------------------------------------------------
/**
* 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()}. 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.
*
* @return The execution environment of the context in which the program is executed.
*/
public static ExecutionEnvironment getExecutionEnvironment() {
return Utils.resolveFactory(threadLocalContextEnvironmentFactory, contextEnvironmentFactory)
.map(ExecutionEnvironmentFactory::createExecutionEnvironment)
.orElseGet(ExecutionEnvironment::createLocalEnvironment);
}
/**
* Creates a {@link CollectionEnvironment} that uses Java Collections underneath. This will
* execute in a single thread in the current JVM. It is very fast but will fail if the data does
* not fit into memory. parallelism will always be 1. This is useful during implementation and
* for debugging.
*
* @return A Collection Environment
*/
@PublicEvolving
public static CollectionEnvironment createCollectionsEnvironment() {
CollectionEnvironment ce = new CollectionEnvironment();
ce.setParallelism(1);
return ce;
}
/**
* Creates a {@link LocalEnvironment}. 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 #setDefaultLocalParallelism(int)}.
*
* @return A local execution environment.
*/
public static LocalEnvironment createLocalEnvironment() {
return createLocalEnvironment(defaultLocalDop);
}
/**
* Creates a {@link LocalEnvironment}. 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 LocalEnvironment createLocalEnvironment(int parallelism) {
return createLocalEnvironment(new Configuration(), parallelism);
}
/**
* Creates a {@link LocalEnvironment}. 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 customConfiguration Pass a custom configuration to the LocalEnvironment.
* @return A local execution environment with the specified parallelism.
*/
public static LocalEnvironment createLocalEnvironment(Configuration customConfiguration) {
return createLocalEnvironment(customConfiguration, -1);
}
/**
* Creates a {@link LocalEnvironment} for local program execution that also starts the web
* monitoring UI.
*
*
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.
*
*
If the configuration key 'rest.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 ExecutionEnvironment createLocalEnvironmentWithWebUI(Configuration conf) {
checkNotNull(conf, "conf");
if (!conf.contains(RestOptions.PORT)) {
// explicitly set this option so that it's not set to 0 later
conf.setInteger(RestOptions.PORT, RestOptions.PORT.defaultValue());
}
return createLocalEnvironment(conf, -1);
}
/**
* Creates a {@link LocalEnvironment} which is used for executing Flink jobs.
*
* @param configuration to start the {@link LocalEnvironment} with
* @param defaultParallelism to initialize the {@link LocalEnvironment} with
* @return {@link LocalEnvironment}
*/
private static LocalEnvironment createLocalEnvironment(
Configuration configuration, int defaultParallelism) {
final LocalEnvironment localEnvironment = new LocalEnvironment(configuration);
if (defaultParallelism > 0) {
localEnvironment.setParallelism(defaultParallelism);
}
return localEnvironment;
}
/**
* Creates a {@link RemoteEnvironment}. 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 {@link ExecutionEnvironment#setParallelism(int)}.
*
* @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 ExecutionEnvironment createRemoteEnvironment(
String host, int port, String... jarFiles) {
return new RemoteEnvironment(host, port, jarFiles);
}
/**
* Creates a {@link RemoteEnvironment}. 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 custom configuration file is used to configure Akka specific configuration
* parameters for the Client only; Program parallelism can be set via {@link
* ExecutionEnvironment#setParallelism(int)}.
*
*
Cluster configuration has to be done in the remotely running Flink instance.
*
* @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 clientConfiguration Configuration used by the client that connects to the 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 ExecutionEnvironment createRemoteEnvironment(
String host, int port, Configuration clientConfiguration, String... jarFiles) {
return new RemoteEnvironment(host, port, clientConfiguration, jarFiles, null);
}
/**
* Creates a {@link RemoteEnvironment}. 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 ExecutionEnvironment createRemoteEnvironment(
String host, int port, int parallelism, String... jarFiles) {
RemoteEnvironment rec = new RemoteEnvironment(host, port, jarFiles);
rec.setParallelism(parallelism);
return rec;
}
// --------------------------------------------------------------------------------------------
// Default parallelism for local execution
// --------------------------------------------------------------------------------------------
/**
* Gets the default parallelism that will be used for the local execution environment created by
* {@link #createLocalEnvironment()}.
*
* @return The default local parallelism
*/
public static int getDefaultLocalParallelism() {
return defaultLocalDop;
}
/**
* 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.
*/
public static void setDefaultLocalParallelism(int parallelism) {
defaultLocalDop = parallelism;
}
// --------------------------------------------------------------------------------------------
// Methods to control the context environment and creation of explicit environments other
// than the context environment
// --------------------------------------------------------------------------------------------
/**
* Sets a context environment factory, that creates the context environment for running programs
* with pre-configured environments. Examples are running programs from the command line.
*
*
When the context environment factory is set, no other environments can be explicitly used.
*
* @param ctx The context environment factory.
*/
protected static void initializeContextEnvironment(ExecutionEnvironmentFactory ctx) {
contextEnvironmentFactory = Preconditions.checkNotNull(ctx);
threadLocalContextEnvironmentFactory.set(contextEnvironmentFactory);
}
/**
* Un-sets the context environment factory. After this method is called, the call to {@link
* #getExecutionEnvironment()} will again return a default local execution environment, and it
* is possible to explicitly instantiate the LocalEnvironment and the RemoteEnvironment.
*/
protected static void resetContextEnvironment() {
contextEnvironmentFactory = null;
threadLocalContextEnvironmentFactory.remove();
}
/**
* Checks whether it is currently permitted to explicitly instantiate a LocalEnvironment or a
* RemoteEnvironment.
*
* @return True, if it is possible to explicitly instantiate a LocalEnvironment or a
* RemoteEnvironment, false otherwise.
*/
@Internal
public static boolean areExplicitEnvironmentsAllowed() {
return contextEnvironmentFactory == null
&& threadLocalContextEnvironmentFactory.get() == null;
}
}