/*
* 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 com.esotericsoftware.kryo.Serializer;
import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.annotation.Internal;
import org.apache.flink.annotation.Public;
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.JobID;
import org.apache.flink.api.common.Plan;
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.operators.OperatorInformation;
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.hadoop.mapred.HadoopInputFormat;
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.operators.OperatorTranslation;
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.typeutils.runtime.kryo.Serializers;
import org.apache.flink.configuration.ConfigConstants;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.core.fs.Path;
import org.apache.flink.types.StringValue;
import org.apache.flink.util.NumberSequenceIterator;
import org.apache.flink.util.Preconditions;
import org.apache.flink.util.SplittableIterator;
import org.apache.flink.util.Visitor;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Calendar;
import java.util.Collection;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
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.
* <p>
* The environment provides methods to control the job execution (such as setting the parallelism)
* and to interact with the outside world (data access).
* <p>
* 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 abstract 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;
/** The default parallelism used by local environments */
private static int defaultLocalDop = Runtime.getRuntime().availableProcessors();
// --------------------------------------------------------------------------------------------
private final List<DataSink<?>> sinks = new ArrayList<>();
private final List<Tuple2<String, DistributedCacheEntry>> 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;
/** The ID of the session, defined by this execution environment. Sessions and Jobs are same in
* Flink, as Jobs can consist of multiple parts that are attached to the growing dataflow graph */
protected JobID jobID;
/** The session timeout in seconds */
protected long sessionTimeout;
/** Flag to indicate whether sinks have been cleared in previous executions */
private boolean wasExecuted = false;
/**
* Creates a new Execution Environment.
*/
protected ExecutionEnvironment() {
jobID = JobID.generate();
}
// --------------------------------------------------------------------------------------------
// Properties
// --------------------------------------------------------------------------------------------
/**
* Gets the config object that defines execution parameters.
*
* @return The environment's execution configuration.
*/
public ExecutionConfig getConfig() {
return config;
}
/**
* 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.
* <p>
* 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;
}
// --------------------------------------------------------------------------------------------
// Session Management
// --------------------------------------------------------------------------------------------
/**
* Gets the JobID by which this environment is identified. The JobID sets the execution context
* in the cluster or local environment.
*
* @return The JobID of this environment.
* @see #getIdString()
*/
@PublicEvolving
public JobID getId() {
return this.jobID;
}
/**
* Gets the JobID by which this environment is identified, as a string.
*
* @return The JobID as a string.
* @see #getId()
*/
@PublicEvolving
public String getIdString() {
return this.jobID.toString();
}
/**
* Sets the session timeout to hold the intermediate results of a job. This only
* applies the updated timeout in future executions.
*
* @param timeout The timeout, in seconds.
*/
@PublicEvolving
public void setSessionTimeout(long timeout) {
throw new IllegalStateException("Support for sessions is currently disabled. " +
"It will be enabled in future Flink versions.");
// Session management is disabled, revert this commit to enable
//if (timeout < 0) {
// throw new IllegalArgumentException("The session timeout must not be less than zero.");
//}
//this.sessionTimeout = timeout;
}
/**
* Gets the session timeout for this environment. The session timeout defines for how long
* after an execution, the job and its intermediate results will be kept for future
* interactions.
*
* @return The session timeout, in seconds.
*/
@PublicEvolving
public long getSessionTimeout() {
return sessionTimeout;
}
/**
* Starts a new session, discarding the previous data flow and all of its intermediate results.
*/
@PublicEvolving
public abstract void startNewSession() throws Exception;
// --------------------------------------------------------------------------------------------
// 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 <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.
*
* 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.
*/
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);
}
}
// --------------------------------------------------------------------------------------------
// 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 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").
* @return A {@link DataSet} that represents the data read from the given file as text lines.
*/
public DataSource<String> 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<String> 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.
* <p>
* 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").
* @return A {@link DataSet} that represents the data read from the given file as text lines.
*/
public DataSource<StringValue> 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.
* <p>
* 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<StringValue> 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 <X> DataSource<X> readFileOfPrimitives(String filePath, Class<X> 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 <X> DataSource<X> readFileOfPrimitives(String filePath, String delimiter, Class<X> 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 <X> DataSource<X> readFile(FileInputFormat<X> 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.
* <p>
* 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 <X> DataSource<X> createInput(InputFormat<X, ?> 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.
* <p>
* 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 <X> DataSource<X> createInput(InputFormat<X, ?> inputFormat, TypeInformation<X> 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());
}
// ----------------------------------- Hadoop Input Format ---------------------------------------
/**
* Creates a {@link DataSet} from the given {@link org.apache.hadoop.mapred.FileInputFormat}.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#readHadoopFile(org.apache.hadoop.mapred.FileInputFormat<K,V>, Class<K>, Class<V>, String, JobConf)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> readHadoopFile(org.apache.hadoop.mapred.FileInputFormat<K,V> mapredInputFormat, Class<K> key, Class<V> value, String inputPath, JobConf job) {
DataSource<Tuple2<K, V>> result = createHadoopInput(mapredInputFormat, key, value, job);
org.apache.hadoop.mapred.FileInputFormat.addInputPath(job, new org.apache.hadoop.fs.Path(inputPath));
return result;
}
/**
* Creates a {@link DataSet} from {@link org.apache.hadoop.mapred.SequenceFileInputFormat}
* A {@link org.apache.hadoop.mapred.JobConf} with the given inputPath is created.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#readSequenceFile(Class<K>, Class<V>, String)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> readSequenceFile(Class<K> key, Class<V> value, String inputPath) throws IOException {
return readHadoopFile(new org.apache.hadoop.mapred.SequenceFileInputFormat<K, V>(), key, value, inputPath);
}
/**
* Creates a {@link DataSet} from the given {@link org.apache.hadoop.mapred.FileInputFormat}. A
* {@link org.apache.hadoop.mapred.JobConf} with the given inputPath is created.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#readHadoopFile(org.apache.hadoop.mapred.FileInputFormat<K,V>, Class<K>, Class<V>, String)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> readHadoopFile(org.apache.hadoop.mapred.FileInputFormat<K,V> mapredInputFormat, Class<K> key, Class<V> value, String inputPath) {
return readHadoopFile(mapredInputFormat, key, value, inputPath, new JobConf());
}
/**
* Creates a {@link DataSet} from the given {@link org.apache.hadoop.mapred.InputFormat}.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#createHadoopInput(org.apache.hadoop.mapred.InputFormat<K,V>, Class<K>, Class<V>, JobConf)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> createHadoopInput(org.apache.hadoop.mapred.InputFormat<K,V> mapredInputFormat, Class<K> key, Class<V> value, JobConf job) {
HadoopInputFormat<K, V> hadoopInputFormat = new HadoopInputFormat<>(mapredInputFormat, key, value, job);
return this.createInput(hadoopInputFormat);
}
/**
* Creates a {@link DataSet} from the given {@link org.apache.hadoop.mapreduce.lib.input.FileInputFormat}. The
* given inputName is set on the given job.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#readHadoopFile(org.apache.hadoop.mapreduce.lib.input.FileInputFormat<K,V>, Class<K>, Class<V>, String, Job)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> readHadoopFile(org.apache.hadoop.mapreduce.lib.input.FileInputFormat<K,V> mapreduceInputFormat, Class<K> key, Class<V> value, String inputPath, Job job) throws IOException {
DataSource<Tuple2<K, V>> result = createHadoopInput(mapreduceInputFormat, key, value, job);
org.apache.hadoop.mapreduce.lib.input.FileInputFormat.addInputPath(job, new org.apache
.hadoop.fs.Path(inputPath));
return result;
}
/**
* Creates a {@link DataSet} from the given {@link org.apache.hadoop.mapreduce.lib.input.FileInputFormat}. A
* {@link org.apache.hadoop.mapreduce.Job} with the given inputPath is created.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#readHadoopFile(org.apache.hadoop.mapreduce.lib.input.FileInputFormat<K,V>, Class<K>, Class<V>, String)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> readHadoopFile(org.apache.hadoop.mapreduce.lib.input.FileInputFormat<K,V> mapreduceInputFormat, Class<K> key, Class<V> value, String inputPath) throws IOException {
return readHadoopFile(mapreduceInputFormat, key, value, inputPath, Job.getInstance());
}
/**
* Creates a {@link DataSet} from the given {@link org.apache.hadoop.mapreduce.InputFormat}.
*
* @deprecated Please use {@link org.apache.flink.hadoopcompatibility.HadoopInputs#createHadoopInput(org.apache.hadoop.mapreduce.InputFormat<K,V>, Class<K>, Class<V>, Job)}
* from the flink-hadoop-compatibility module.
*/
@Deprecated
@PublicEvolving
public <K,V> DataSource<Tuple2<K, V>> createHadoopInput(org.apache.hadoop.mapreduce.InputFormat<K,V> mapreduceInputFormat, Class<K> key, Class<V> value, Job job) {
org.apache.flink.api.java.hadoop.mapreduce.HadoopInputFormat<K, V> hadoopInputFormat = new org.apache.flink.api.java.hadoop.mapreduce.HadoopInputFormat<>(mapreduceInputFormat, key, value, job);
return this.createInput(hadoopInputFormat);
}
// ----------------------------------- Collection ---------------------------------------
/**
* Creates a DataSet from the given non-empty collection. The type of the data set 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(Collection, TypeInformation)}.
* <p>
* 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 <X> DataSource<X> fromCollection(Collection<X> 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<X> 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.
* <p>
* 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 <X> DataSource<X> fromCollection(Collection<X> data, TypeInformation<X> type) {
return fromCollection(data, type, Utils.getCallLocationName());
}
private <X> DataSource<X> fromCollection(Collection<X> data, TypeInformation<X> 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).
* <p>
* 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 <X> DataSource<X> fromCollection(Iterator<X> data, Class<X> 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.
* <p>
* 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 <X> DataSource<X> fromCollection(Iterator<X> data, TypeInformation<X> 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.
* <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(Collection, TypeInformation)}.
* <p>
* 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 <X> DataSource<X> 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<X> 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)");
}
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 <X> DataSource<X> fromElements(Class<X> 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<X> 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)");
}
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.
* <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 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 <X> DataSource<X> fromParallelCollection(SplittableIterator<X> iterator, Class<X> 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.
* <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(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 <X> DataSource<X> fromParallelCollection(SplittableIterator<X> iterator, TypeInformation<X> type) {
return fromParallelCollection(iterator, type, Utils.getCallLocationName());
}
// private helper for passing different call location names
private <X> DataSource<X> fromParallelCollection(SplittableIterator<X> iterator, TypeInformation<X> 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<Long> 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)}.
* <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 Thrown, if the program executions fails.
*/
public JobExecutionResult execute() throws Exception {
return execute(getDefaultName());
}
/**
* 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)}.
* <p>
* 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 abstract JobExecutionResult execute(String jobName) throws Exception;
/**
* 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.
* @throws Exception Thrown, if the compiler could not be instantiated, or the master could not
* be contacted to retrieve information relevant to the execution planning.
*/
public abstract String getExecutionPlan() throws Exception;
/**
* 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 DistributedCacheEntry(filePath, executable)));
}
/**
* Registers all files that were registered at this execution environment's cache registry of the
* given plan's cache registry.
*
* @param p The plan to register files at.
* @throws IOException Thrown if checks for existence and sanity fail.
*/
protected void registerCachedFilesWithPlan(Plan p) throws IOException {
for (Tuple2<String, DistributedCacheEntry> entry : cacheFile) {
p.registerCachedFile(entry.f0, entry.f1);
}
}
/**
* 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 a
* {@link org.apache.flink.api.common.PlanExecutor}. 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(null);
}
/**
* 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 a
* {@link org.apache.flink.api.common.PlanExecutor}. 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 a
* {@link org.apache.flink.api.common.PlanExecutor}. 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) {
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.");
}
}
if (jobName == null) {
jobName = getDefaultName();
}
OperatorTranslation translator = new OperatorTranslation();
Plan plan = translator.translateToPlan(this.sinks, jobName);
if (getParallelism() > 0) {
plan.setDefaultParallelism(getParallelism());
}
plan.setExecutionConfig(getConfig());
// Check plan for GenericTypeInfo's and register the types at the serializers.
if (!config.isAutoTypeRegistrationDisabled()) {
plan.accept(new Visitor<org.apache.flink.api.common.operators.Operator<?>>() {
private final HashSet<Class<?>> deduplicator = new HashSet<>();
@Override
public boolean preVisit(org.apache.flink.api.common.operators.Operator<?> visitable) {
OperatorInformation<?> opInfo = visitable.getOperatorInfo();
Serializers.recursivelyRegisterType(opInfo.getOutputType(), config, deduplicator);
return true;
}
@Override
public void postVisit(org.apache.flink.api.common.operators.Operator<?> visitable) {}
});
}
try {
registerCachedFilesWithPlan(plan);
} catch (Exception e) {
throw new RuntimeException("Error while registering cached files: " + e.getMessage(), e);
}
// clear all the sinks such that the next execution does not redo everything
if (clearSinks) {
this.sinks.clear();
wasExecuted = true;
}
// All types are registered now. Print information.
int registeredTypes = config.getRegisteredKryoTypes().size() +
config.getRegisteredPojoTypes().size() +
config.getRegisteredTypesWithKryoSerializerClasses().size() +
config.getRegisteredTypesWithKryoSerializers().size();
int defaultKryoSerializers = config.getDefaultKryoSerializers().size() +
config.getDefaultKryoSerializerClasses().size();
LOG.info("The job has {} registered types and {} default Kryo serializers", registeredTypes, defaultKryoSerializers);
if(config.isForceKryoEnabled() && config.isForceAvroEnabled()) {
LOG.warn("In the ExecutionConfig, both Avro and Kryo are enforced. Using Kryo serializer");
}
if(config.isForceKryoEnabled()) {
LOG.info("Using KryoSerializer for serializing POJOs");
}
if(config.isForceAvroEnabled()) {
LOG.info("Using AvroSerializer for serializing POJOs");
}
if(LOG.isDebugEnabled()) {
LOG.debug("Registered Kryo types: {}", config.getRegisteredKryoTypes().toString());
LOG.debug("Registered Kryo with Serializers types: {}", config.getRegisteredTypesWithKryoSerializers().entrySet().toString());
LOG.debug("Registered Kryo with Serializer Classes types: {}", config.getRegisteredTypesWithKryoSerializerClasses().entrySet().toString());
LOG.debug("Registered Kryo default Serializers: {}", config.getDefaultKryoSerializers().entrySet().toString());
LOG.debug("Registered Kryo default Serializers Classes {}", config.getDefaultKryoSerializerClasses().entrySet().toString());
LOG.debug("Registered POJO types: {}", config.getRegisteredPojoTypes().toString());
// print information about static code analysis
LOG.debug("Static code analysis mode: {}", config.getCodeAnalysisMode());
}
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 a default job name, based on the timestamp when this method is invoked.
*
* @return A default job name.
*/
private static String getDefaultName() {
return "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 contextEnvironmentFactory == null ?
createLocalEnvironment() : contextEnvironmentFactory.createExecutionEnvironment();
}
/**
* 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) {
LocalEnvironment lee = new LocalEnvironment();
lee.setParallelism(parallelism);
return lee;
}
/**
* 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 new LocalEnvironment(customConfiguration);
}
/**
* Creates a {@link LocalEnvironment} 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 ExecutionEnvironment createLocalEnvironmentWithWebUI(Configuration conf) {
checkNotNull(conf, "conf");
conf.setBoolean(ConfigConstants.LOCAL_START_WEBSERVER, true);
LocalEnvironment localEnv = new LocalEnvironment(conf);
localEnv.setParallelism(defaultLocalDop);
return localEnv;
}
/**
* 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, and
* running programs in the Scala shell.
*
* <p>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);
}
/**
* 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;
}
/**
* 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;
}
}