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.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
