Github user zentol commented on a diff in the pull request:
https://github.com/apache/flink/pull/3838#discussion_r121894570
--- Diff:
flink-libraries/flink-streaming-python/src/main/java/org/apache/flink/streaming/python/api/environment/PythonStreamExecutionEnvironment.java
---
@@ -0,0 +1,421 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.flink.streaming.python.api.environment;
+
+import org.apache.flink.annotation.PublicEvolving;
+import org.apache.flink.api.common.JobExecutionResult;
+import org.apache.flink.api.common.cache.DistributedCache;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.api.java.typeutils.TypeExtractor;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.fs.FileSystem;
+import org.apache.flink.core.fs.Path;
+import org.apache.flink.runtime.filecache.FileCache;
+import org.apache.flink.streaming.api.CheckpointingMode;
+import org.apache.flink.streaming.api.environment.LocalStreamEnvironment;
+import
org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.streaming.api.functions.source.SourceFunction;
+import org.apache.flink.streaming.python.api.datastream.PythonDataStream;
+import
org.apache.flink.streaming.python.api.functions.PythonGeneratorFunction;
+import
org.apache.flink.streaming.python.api.functions.PythonIteratorFunction;
+import org.apache.flink.streaming.python.api.functions.UtilityFunctions;
+import
org.apache.flink.streaming.python.util.serialization.PyObjectSerializer;
+import org.python.core.PyObject;
+import org.python.core.PyString;
+import org.python.core.PyInteger;
+import org.python.core.PyLong;
+import org.python.core.PyUnicode;
+import org.python.core.PyTuple;
+import org.python.core.PyObjectDerived;
+import org.python.core.PyInstance;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.nio.file.Paths;
+import java.util.Collection;
+import java.util.Iterator;
+import java.util.Random;
+
+
+/**
+ * A thin wrapper layer over {@link StreamExecutionEnvironment}.
+ *
+ * <p>The PythonStreamExecutionEnvironment is the context in which a
streaming program is executed.
+ * </p>
+ *
+ * <p>The environment provides methods to control the job execution (such
as setting the parallelism
+ * or the fault tolerance/checkpointing parameters) and to interact with
the outside world
+ * (data access).</p>
+ */
+@PublicEvolving
+public class PythonStreamExecutionEnvironment {
+ private static final Logger LOG =
LoggerFactory.getLogger(PythonStreamExecutionEnvironment.class);
+ private final StreamExecutionEnvironment env;
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#getExecutionEnvironment()}. In addition it takes
+ * care for required Jython serializers registration.
+ *
+ * @return The python execution environment of the context in which the
program is
+ * executed.
+ */
+ public static PythonStreamExecutionEnvironment
get_execution_environment() {
+ return new
PythonStreamExecutionEnvironment(StreamExecutionEnvironment.getExecutionEnvironment());
+ }
+
+ /**
+ * Creates a {@link LocalStreamEnvironment}. The local execution
environment
+ * will run the program in a multi-threaded fashion in the same JVM as
the
+ * environment was created in. The default parallelism of the local
+ * environment is the number of hardware contexts (CPU cores / threads),
+ * unless it was specified differently by {@link #setParallelism(int)}.
+ *
+ * @param configuration
+ * Pass a custom configuration into the cluster
+ * @return A local execution environment with the specified parallelism.
+ */
+ public static PythonStreamExecutionEnvironment
create_local_execution_environment(Configuration config) {
+ return new PythonStreamExecutionEnvironment(new
LocalStreamEnvironment(config));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#createLocalEnvironment(int, Configuration)}
+ *
+ * @param parallelism
+ * The parallelism for the local environment.
+ * @param config
+ * Pass a custom configuration into the cluster
+ * @return A local python execution environment with the specified
parallelism.
+ */
+ public static PythonStreamExecutionEnvironment
create_local_execution_environment(int parallelism, Configuration config) {
+ return new PythonStreamExecutionEnvironment(
+
StreamExecutionEnvironment.createLocalEnvironment(parallelism, config));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#createRemoteEnvironment(java.lang.String, int,
java.lang.String...)}
+ *
+ * @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 jar_files
+ * 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 PythonStreamExecutionEnvironment
create_remote_execution_environment(
+ String host, int port, String... jar_files) {
+ return new PythonStreamExecutionEnvironment(
+
StreamExecutionEnvironment.createRemoteEnvironment(host, port, jar_files));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#createRemoteEnvironment(
+ * java.lang.String, int, Configuration, java.lang.String...)}
+ *
+ * @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 config
+ * The configuration used by the client that connects to
the remote cluster.
+ * @param jar_files
+ * 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 PythonStreamExecutionEnvironment
create_remote_execution_environment(
+ String host, int port, Configuration config, String...
jar_files) {
+ return new PythonStreamExecutionEnvironment(
+
StreamExecutionEnvironment.createRemoteEnvironment(host, port, config,
jar_files));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#createRemoteEnvironment(
+ * java.lang.String, int, int, java.lang.String...)}
+ *
+ * @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 jar_files
+ * 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 PythonStreamExecutionEnvironment
create_remote_execution_environment(
+ String host, int port, int parallelism, String... jar_files) {
+ return new PythonStreamExecutionEnvironment(
+
StreamExecutionEnvironment.createRemoteEnvironment(host, port, parallelism,
jar_files));
+ }
+
+ private PythonStreamExecutionEnvironment(StreamExecutionEnvironment
env) {
+ this.env = env;
+ this.registerJythonSerializers();
+ }
+
+ private void registerJythonSerializers() {
+ this.env.registerTypeWithKryoSerializer(PyString.class,
PyObjectSerializer.class);
+ this.env.registerTypeWithKryoSerializer(PyInteger.class,
PyObjectSerializer.class);
+ this.env.registerTypeWithKryoSerializer(PyLong.class,
PyObjectSerializer.class);
+ this.env.registerTypeWithKryoSerializer(PyUnicode.class,
PyObjectSerializer.class);
+ this.env.registerTypeWithKryoSerializer(PyTuple.class,
PyObjectSerializer.class);
+ this.env.registerTypeWithKryoSerializer(PyObjectDerived.class,
PyObjectSerializer.class);
+ this.env.registerTypeWithKryoSerializer(PyInstance.class,
PyObjectSerializer.class);
+ }
+
+ public PythonDataStream create_python_source(SourceFunction<Object>
src) throws Exception {
+ return new PythonDataStream<>(env.addSource(new
PythonGeneratorFunction(src)).map(new UtilityFunctions.SerializerMap<>()));
+ }
+
+ /**
+ * Add a java source to the streaming topology. The source expected to
be an java based
+ * implementation (.e.g. Kafka connector).
+ *
+ * @param src A native java source (e.g. PythonFlinkKafkaConsumer09)
+ * @return Python data stream
+ */
+ public PythonDataStream add_java_source(SourceFunction<Object> src) {
+ return new PythonDataStream<>(env.addSource(src).map(new
UtilityFunctions.SerializerMap<>()));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#fromElements(java.lang.Object[])}
+ *
+ * @param elements
+ * The array of PyObject elements to create the data
stream from.
+ * @return The data stream representing the given array of elements
+ */
+ public PythonDataStream from_elements(PyObject... elements) {
+ return new PythonDataStream<>(env.fromElements(elements));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#fromCollection(java.util.Collection)}
+ *
+ * <p>The input {@code Collection} is of type {@code Object}, because
it is a collection
+ * of Python elements. * There type is determined in runtime, by the
Jython framework.</p>
+ *
+ * @param collection
+ * The collection of python elements to create the data
stream from.
+ * @return
+ * The data stream representing the given collection
+ */
+ public PythonDataStream from_collection(Collection<Object> collection) {
+ return new
PythonDataStream<>(env.fromCollection(collection).map(new
UtilityFunctions.SerializerMap<>()));
+ }
+
+ /**
+ * Creates a python data stream from the given iterator.
+ *
+ * <p>Note that this operation will result in a non-parallel data
stream source, i.e.,
+ * a data stream source with a parallelism of one.</p>
+ *
+ * @param iter
+ * The iterator of elements to create the data stream from
+ * @return The data stream representing the elements in the iterator
+ * @see StreamExecutionEnvironment#fromCollection(java.util.Iterator,
org.apache.flink.api.common.typeinfo.TypeInformation)
+ */
+ public PythonDataStream from_collection(Iterator<Object> iter) throws
Exception {
+ return new PythonDataStream<>(env.addSource(new
PythonIteratorFunction(iter), TypeExtractor.getForClass(Object.class))
+ .map(new UtilityFunctions.SerializerMap<>()));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#generateSequence(long, long)}.
+ *
+ * @param from
+ * The number to start at (inclusive)
+ * @param to
+ * The number to stop at (inclusive)
+ * @return A python data stream, containing all number in the [from,
to] interval
+ */
+ public PythonDataStream generate_sequence(long from, long to) {
+ return new PythonDataStream<>(env.generateSequence(from,
to).map(new UtilityFunctions.SerializerMap<Long>()));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#readTextFile(java.lang.String)}.
+ *
+ * @param path
+ * The path of the file, as a URI (e.g.,
"file:///some/local/file" or "hdfs://host:port/file/path").
+ * @return The data stream that represents the data read from the given
file as text lines
+ * @throws IOException
+ */
+
+ public PythonDataStream read_text_file(String path) throws IOException {
+ return new PythonDataStream<>(env.readTextFile(path).map(new
UtilityFunctions.SerializerMap<String>()));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#socketTextStream(java.lang.String, int)}.
+ *
+ * @param host
+ * The host name which a server socket binds
+ * @param port
+ * The port number which a server socket binds. A port
number of 0 means that the port number is automatically
+ * allocated.
+ * @return A python data stream containing the strings received from
the socket
+ */
+ public PythonDataStream socket_text_stream(String host, int port) {
+ return new PythonDataStream<>(env.socketTextStream(host,
port).map(new UtilityFunctions.SerializerMap<String>()));
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#enableCheckpointing(long)}.
+ *
+ * @param interval Time interval between state checkpoints in
milliseconds.
+ * @return The same {@code PythonStreamExecutionEnvironment} instance
of the caller
+ */
+ public PythonStreamExecutionEnvironment enable_checkpointing(long
interval) {
+ this.env.enableCheckpointing(interval);
+ return this;
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#enableCheckpointing(long, CheckpointingMode)}.
+ *
+ * @param interval Time interval between state checkpoints in
milliseconds.
+ * @param mode
+ * The checkpointing mode, selecting between "exactly once"
and "at least once" guaranteed.
+ * @return The same {@code PythonStreamExecutionEnvironment} instance
of the caller
+ */
+ public PythonStreamExecutionEnvironment enable_checkpointing(long
interval, CheckpointingMode mode) {
+ this.env.enableCheckpointing(interval, mode);
+ return this;
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#setParallelism(int)}.
+ *
+ * @param parallelism The parallelism
+ * @return The same {@code PythonStreamExecutionEnvironment} instance
of the caller
+ */
+ public PythonStreamExecutionEnvironment set_parallelism(int
parallelism) {
+ this.env.setParallelism(parallelism);
+ return this;
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#execute()}.
+ *
+ * @return The result of the job execution
+ */
+ public JobExecutionResult execute() throws Exception {
+ return execute(false);
+ }
+
+ /**
+ * A thin wrapper layer over {@link
StreamExecutionEnvironment#execute()}.
+ *
+ * <p>In addition, it enables the caller to provide a hint about the
execution mode - whether it is local
+ * or remote. In the case of local execution, the relevant cached files
are distributed using the
+ * local machine temporary folder, otherwise a shared storage medium is
used for this purpose.</p>
+ *
+ * @return The result of the job execution
+ */
+ public JobExecutionResult execute(Boolean local) throws Exception {
--- End diff --
use a primitive `boolean` instead.
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