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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