Github user zohar-mizrahi commented on a diff in the pull request:
https://github.com/apache/flink/pull/3838#discussion_r117806756
--- Diff:
flink-libraries/flink-streaming-python/src/main/java/org/apache/flink/streaming/python/api/environment/PythonStreamExecutionEnvironment.java
---
@@ -0,0 +1,442 @@
+/*
+ * 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.Public;
+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>
+ */
+@Public
+public class PythonStreamExecutionEnvironment {
+ private final StreamExecutionEnvironment env;
+ private static final Logger LOG =
LoggerFactory.getLogger(PythonStreamExecutionEnvironment.class);
+
+ /**
+ * 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();
+ }
+
+ /**
+ * 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(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(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(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(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(host, port,
parallelism, jar_files);
+ }
+
+ private PythonStreamExecutionEnvironment() {
+ this.env = StreamExecutionEnvironment.getExecutionEnvironment();
+ this.registerJythonSerializers();
+ }
+
+ private PythonStreamExecutionEnvironment(Configuration config) {
+ this.env = new LocalStreamEnvironment(config);
+ this.registerJythonSerializers();
+ }
+
+ private PythonStreamExecutionEnvironment(int parallelism, Configuration
config) {
+ this.env =
StreamExecutionEnvironment.createLocalEnvironment(parallelism, config);
+ this.registerJythonSerializers();
+ }
+
+ private PythonStreamExecutionEnvironment(String host, int port,
String... jar_files) {
+ this.env =
StreamExecutionEnvironment.createRemoteEnvironment(host, port, jar_files);
+ this.registerJythonSerializers();
+ }
+
+ private PythonStreamExecutionEnvironment(String host, int port,
Configuration config, String... jar_files) {
+ this.env =
StreamExecutionEnvironment.createRemoteEnvironment(host, port, config,
jar_files);
+ this.registerJythonSerializers();
+ }
+
+ private PythonStreamExecutionEnvironment(String host, int port, int
parallelism, String... jar_files) {
+ this.env =
StreamExecutionEnvironment.createRemoteEnvironment(host, port, parallelism,
jar_files);
+ 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) {
--- End diff --
It concerns the Jython framework. Apparently, when using argument expansion
(as in the case of `from_elements`), the Jython framework instantiates PyObject
for each element in the the elements. On the other hand, when using a
Collection, the Jython framework, instantiates a `PyList` with Java native
classes (.e.g Integer, String, etc).
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