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From u..@apache.org
Subject [13/30] flink git commit: [docs] Change doc layout
Date Wed, 22 Apr 2015 14:17:10 GMT
http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internal_program_life_cycle.md
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----
-title:  "Program Life Cycle"
----
-<!--
-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.
--->
-
-To be done...

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internal_setup_eclipse.md
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diff --git a/docs/internal_setup_eclipse.md b/docs/internal_setup_eclipse.md
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----
-title:  "How to set up Eclipse"
----
-<!--
-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.
--->
-
-A brief guide how to set up Eclipse for development of the Flink core.
-Flink uses mixed Scala/Java projects, which pose a challenge to some IDEs.
-Below is the setup guide that works best from our personal experience.
-
-For Eclipse users, we currently recomment the Scala IDE 3.0.3, as the most robust solution.
-
-
-## Eclipse Scala IDE 3.0.3
-
-**NOTE:** While this version of the Scala IDE is not the newest, we have found it to be the most reliably working
-version for complex projects like Flink. One restriction is, though, that it works only with Java 7, not with Java 8.
-
-**Note:** Before following this setup, make sure to run the build from the command line once
-(`mvn clean package -DskipTests`)
-
-1. Download the Scala IDE (preferred) or install the plugin to Eclipse Kepler. See section below for download links
-   and instructions.
-2. Add the "macroparadise" compiler plugin to the Scala compiler.
-   Open "Window" -> "Preferences" -> "Scala" -> "Compiler" -> "Advanced" and put into the "Xplugin" field the path to
-   the *macroparadise* jar file (typically "/home/*-your-user-*/.m2/repository/org/scalamacros/paradise_2.10.4/2.0.1/paradise_2.10.4-2.0.1.jar").
-   Note: If you do not have the jar file, you probably did not ran the command line build.
-3. Import the Flink Maven projects ("File" -> "Import" -> "Maven" -> "Existing Maven Projects") 
-4. During the import, Eclipse will ask to automatically install additional Maven build helper plugins.
-5. Close the "flink-java8" project. Since Eclipse Kepler does not support Java 8, you cannot develop this project.
-
-
-### Download links for Scala IDE 3.0.3
-
-The Scala IDE 3.0.3 is a previous stable release, and download links are a bit hidden.
-
-The pre-packaged Scala IDE can be downloaded from the following links:
-
-* [Linux (64 bit)](http://downloads.typesafe.com/scalaide-pack/3.0.3.vfinal-210-20140327/scala-SDK-3.0.3-2.10-linux.gtk.x86_64.tar.gz)
-* [Linux (32 bit)](http://downloads.typesafe.com/scalaide-pack/3.0.3.vfinal-210-20140327/scala-SDK-3.0.3-2.10-linux.gtk.x86.tar.gz)
-* [MaxOS X Cocoa (64 bit)](http://downloads.typesafe.com/scalaide-pack/3.0.3.vfinal-210-20140327/scala-SDK-3.0.3-2.10-macosx.cocoa.x86_64.zip)
-* [MaxOS X Cocoa (32 bit)](http://downloads.typesafe.com/scalaide-pack/3.0.3.vfinal-210-20140327/scala-SDK-3.0.3-2.10-macosx.cocoa.x86.zip)
-* [Windows (64 bit)](http://downloads.typesafe.com/scalaide-pack/3.0.3.vfinal-210-20140327/scala-SDK-3.0.3-2.10-win32.win32.x86_64.zip)
-* [Windows (32 bit)](http://downloads.typesafe.com/scalaide-pack/3.0.3.vfinal-210-20140327/scala-SDK-3.0.3-2.10-win32.win32.x86.zip)
-
-Alternatively, you can download Eclipse Kepler from [https://eclipse.org/downloads/packages/release/Kepler/SR2](https://eclipse.org/downloads/packages/release/Kepler/SR2)
-and manually add the Scala and Maven plugins by plugin site at [http://scala-ide.org/download/prev-stable.html](http://scala-ide.org/download/prev-stable.html).
-
-* Either use the update site to install the plugin ("Help" -> "Install new Software")
-* Or download the [zip file](http://download.scala-ide.org/sdk/helium/e38/scala211/stable/update-site.zip), unpack it, and move the contents of the
-  "plugins" and "features" folders into the equally named folders of the Eclipse root directory
-
-
-## Eclipse Scala IDE 4.0.0
-
-**NOTE: From personal experience, the use of the Scala IDE 4.0.0 performs worse than previous versions for complex projects like Flink.**
-**Version 4.0.0 does not handle mixed Java/Scala projects as robustly and it frequently raises incorrect import and type errors.**
-
-*Note:* Before following this setup, make sure to run the build from the command line once
-(`mvn clean package -DskipTests`)
-
-1. Download the Scala IDE: [http://scala-ide.org/download/sdk.html](http://scala-ide.org/download/sdk.html)
-2. Import the Flink Maven projects (File -> Import -> Maven -> Existing Maven Projects) 
-3. While importing the Flink project, the IDE may ask you to install an additional maven build helper plugin. 
-4. After the import, you need to set the Scala version of your projects to Scala 2.10 (from the default 2.11). 
-   To do that, select all projects that contain Scala code (marked by the small *S* on the project icon),
-   right click and select "Scala -> Set the Scala Installation" and pick "2.10.4".
-   Currently, the project to which that is relevant are "flink-runtime", "flink-scala", "flink-scala-examples",
-   "flink-streaming-example", "flink-streaming-scala", "flink-tests", "flink-test-utils", and "flink-yarn".
-5. Depending on your version of the Scala IDE, you may need to add the "macroparadise" compiler plugin to the
-   Scala compiler. Open "Window" -> "Preferences" -> "Scala" -> "Compiler" -> "Advanced" and put into the "Xplugin" field
-   the path to the *macroparadise* jar file (typically "/home/*-your-user-*/.m2/repository/org/scalamacros/paradise_2.10.4/2.0.1/paradise_2.10.4-2.0.1.jar")
-6. In order to compile the "flink-java-8" project, you may need to add a Java 8 execution environment.
-   See [this post](http://stackoverflow.com/questions/25391207/how-do-i-add-execution-environment-1-8-to-eclipse-luna)
-   for details.
-
----
-
-*This documentation is maintained by the contributors of the individual components.
-We kindly ask anyone that adds and changes components to eventually provide a patch
-or pull request that updates these documents as well.*
-

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internal_setup_intellij.md
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diff --git a/docs/internal_setup_intellij.md b/docs/internal_setup_intellij.md
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----
-title:  "How to set up IntelliJ IDEA"
----
-<!--
-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.
--->
-
-
-
-A brief guide on how to set up IntelliJ IDEA IDE for development of the Flink core.
-As Eclipse is known to have issues with mixed Scala and Java projects, more and more contributers are migrating to IntelliJ IDEA.
-
-The following documentation describes the steps to setup IntelliJ IDEA 14.0.3 (https://www.jetbrains.com/idea/download/) with the Flink sources.
-
-Prior to doing anything, make sure that the Flink project is built at least once from the terminal:
-`mvn clean package -DskipTests`
-
-# IntelliJ IDEA
-
-## Installing the Scala plugin
-1. Go to IntelliJ plugins settings (File -> Settings -> Plugins) and click on "Install Jetbrains plugin...". 
-2. Select and install the "Scala" plugin. 
-3. Restart IntelliJ
-
-## Installing the Scala compiler plugin
-1. Go to IntelliJ scala compiler settings (File -> Settings -> Build, Execution, Deployment -> Compiler -> Scala Compiler) and click on "Install Jetbrains plugin...". 
-2. Click on the green plus icon on the right to add a compiler plugin
-3. Point to the paradise jar: ~/.m2/repository/org/scalamacros/paradise_2.10.4/2.0.1/paradise_2.10.4-2.0.1.jar If there is no such file, this means that you should build Flink from the terminal as explained above.
-
-## Importing Flink
-1. Start IntelliJ IDEA and choose "Import Project"
-2. Select the root folder of the Flink repository
-3. Choose "Import project from external model" and select "Maven"
-4. Leave the default options and finish the import.
-
----
-
-*This documentation is maintained by the contributors of the individual components.
-We kindly ask anyone that adds and changes components to eventually provide a patch
-or pull request that updates these documents as well.*
-

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internal_types_serialization.md
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diff --git a/docs/internal_types_serialization.md b/docs/internal_types_serialization.md
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----
-title:  "Type Extraction and Serialization"
----
-<!--
-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.
--->
-
-
-Flink handles types in a unique way, containing its own type descriptors,
-generic type extraction, and type serialization framework.
-This document describes the concepts and the rationale behind them.
-
-There are fundamental differences in the way that the Scala API and
-the Java API handle type information, so most of the issues described 
-here relate only to one of the to APIs.
-
-* This will be replaced by the TOC
-{:toc}
-
-
-## Type handling in Flink
-
-Flink tries to know as much information about what types enter and leave user functions as possible.
-This stands in contrast to the approach to just assuming nothing and letting the
-programming language and serialization framework handle all types dynamically.
-
-* To allow using POJOs and grouping/joining them by referring to field names, Flink needs the type
-  information to make checks (for typos and type compatibility) before the job is executed.
-
-* The more we know, the better serialization and data layout schemes the compiler/optimizer can develop.
-  That is quite important for the memory usage paradigm in Flink (work on serialized data
-  inside/outside the heap and make serialization very cheap).
-
-* For the upcoming logical programs (see roadmap draft) we need this to know the "schema" of functions.
-
-* Finally, it also spares users having to worry about serialization frameworks and having to register
-  types at those frameworks.
-
-
-## Flink's TypeInformation class
-
-The class {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/typeinfo/TypeInformation.java "TypeInformation" %}
-is the base class for all type descriptors. It reveals some basic properties of the type and can generate serializers
-and, in specializations, comparators for the types.
-(*Note that comparators in Flink do much more than defining an order - they are basically the utility to handle keys*)
-
-Internally, Flink makes the following distinctions between types:
-
-* Basic types: All Java primitives and their boxed form, plus `void`, `String`, and `Date`.
-
-* Primitive arrays and Object arrays
-
-* Composite types 
-
-  * Flink Java Tuples (part of the Flink Java API)
-
-  * Scala *case classes* (including Scala tuples)
-
-  * POJOs: classes that follow a certain bean-like pattern
- 
-* Scala auxiliary types (Option, Either, Lists, Maps, ...)
-
-* Generic types: These will not be serialized by Flink itself, but by Kryo.
-
-POJOs are of particular interest, because they support the creation of complex types and the use of field
-names in the definition of keys: `dataSet.join(another).where("name").equalTo("personName")`.
-They are also transparent to the runtime and can be handled very efficiently by Flink.
-
-
-**Rules for POJO types**
-
-Flink recognizes a data type as a POJO type (and allows "by-name" field referencing) if the following
-conditions are fulfilled:
-
-* The class is public and standalone (no non-static inner class)
-* The class has a public no-argument constructor
-* All fields in the class (and all superclasses) are either public or
-  or have a public getter and a setter method that follows the Java beans
-  naming conventions for getters and setters.
-
-
-## Type Information in the Scala API
-
-Scala has very elaborate concepts for runtime type information though *type manifests* and *class tags*. In
-general, types and methods have access to the types of their generic parameters - thus, Scala programs do
-not suffer from type erasure as Java programs do.
-
-In addition, Scala allows to run custom code in the Scala Compiler through Scala Macros - that means that some Flink
-code gets executed whenever you compile a Scala program written against Flink's Scala API.
-
-We use the Macros to look at the parameter types and return types of all user functions during compilation - that
-is the point in time when certainly all type information is perfectly available. Within the macro, we create
-a *TypeInformation* for the function's return types (or parameter types) and make it part of the operation.
-
-
-#### No Implicit Value for Evidence Parameter Error
-
-In the case where TypeInformation could not be created, programs fail to compile with an error
-stating *"could not find implicit value for evidence parameter of type TypeInformation"*.
-
-A frequent reason if that the code that generates the TypeInformation has not been imported.
-Make sure to import the entire flink.api.scala package.
-{% highlight scala %}
-import org.apache.flink.api.scala._
-{% endhighlight %}
-
-Another common cause are generic methods, which can be fixed as described in the following section.
-
-
-#### Generic Methods
-
-Consider the following case below:
-
-{% highlight scala %}
-def[T] selectFirst(input: DataSet[(T, _)]) : DataSet[T] = {
-  input.map { v => v._1 }
-}
-
-val data : DataSet[(String, Long) = ...
-
-val result = selectFirst(data)
-{% endhighlight %}
-
-For such generic methods, the data types of the function parameters and return type may not be the same
-for every call and are not known at the site where the method is defined. The code above will result
-in an error that not enough implicit evidence is available.
-
-In such cases, the type information has to be generated at the invocation site and passed to the
-method. Scala offers *implicit parameters* for that. 
-
-The following code tells Scala to bring a type information for *T* into the function. The type
-information will then be generated at the sites where the method is invoked, rather than where the
-method is defined.
-
-{% highlight scala %}
-def[T : TypeInformation] selectFirst(input: DataSet[(T, _)]) : DataSet[T] = {
-  input.map { v => v._1 }
-}
-{% endhighlight %}
-
-
-
-## Type Information in the Java API
-
-Java in general erases generic type information. Only for subclasses of generic classes, the subclass
-stores the type to which the generic type variables bind.
-
-Flink uses reflection on the (anonymous) classes that implement the user functions to figure out the types of
-the generic parameters of the function. This logic also contains some simple type inference for cases where
-the return types of functions are dependent on input types, such as in the generic utility method below:
-
-{% highlight java %}
-public class AppendOne<T> extends MapFunction<T, Tuple2<T, Long>> {
-
-    public Tuple2<T, Long> map(T value) {
-        return new Tuple2<T, Long>(value, 1L);
-    }
-}
-{% endhighlight %}
-
-Not in all cases can Flink figure out the data types of functions reliably in Java.
-Some issues remain with generic lambdas (we are trying to solve this with the Java community,
-see below) and with generic type variables that we cannot infer.
-
-
-#### Type Hints in the Java API
-
-To help cases where Flink cannot reconstruct the erased generic type information, the Java API
-offers so called *type hints* from version 0.9 on. The type hints tell the system the type of
-the data set produced by a function. The following gives an example:
-
-{% highlight java %}
-DataSet<SomeType> result = dataSet
-    .map(new MyGenericNonInferrableFunction<Long, SomeType>())
-        .returns(SomeType.class);
-{% endhighlight %}
-
-The `returns` statement specifies the produced type, in this case via a class. The hints support
-type definition through
-
-* Classes, for non-parameterized types (no generics)
-* Strings in the form of `returns("Tuple2<Integer, my.SomeType>")`, which are parsed and converted
-  to a TypeInformation.
-* A TypeInformation directly
-
-
-#### Type extraction for Java 8 lambdas
-
-Type extraction for Java 8 lambdas works differently than for non-lambdas, because lambdas are not associated
-with an implementing class that extends the function interface.
-
-Currently, Flink tries to figure out which method implements the lambda and uses Java's generic signatures to
-determine the parameter types and the return type. However, these signatures are not generated for lambdas
-by all compilers (as of writing this document only reliably by the Eclipse JDT compiler 4.5 from Milestone 2
-onwards)
-
-
-**Improving Type information for Java Lambdas**
-
-One of the Flink committers (Timo Walther) has actually become active in the Eclipse JDT compiler community and
-in the OpenJDK community and submitted patches to the compiler to improve availability of type information 
-available for Java 8 lambdas.
-
-The Eclipse JDT compiler has added support for this as of version 4.5 M4. Discussion about the feature in the
-OpenJDK compiler is pending.
-
-
-
-
-
-
-
-

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internals/_draft_distributed_akka.md
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diff --git a/docs/internals/_draft_distributed_akka.md b/docs/internals/_draft_distributed_akka.md
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@@ -0,0 +1,47 @@
+---
+title:  "Distributed Communication and Coordination via Akka"
+---
+<!--
+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.
+-->
+
+This page discusses the implementation of Flink's distributed communication
+via Akka (http://akka.io)
+
+
+## Actor Systems
+
+### JobManager
+
+### TaskManager
+
+### JobClient
+
+
+## Actors and Messages
+
+
+## Failure Detection
+
+
+
+---
+
+*This documentation is maintained by the contributors of the individual components.
+We kindly ask anyone that adds and changes components to eventually provide a patch
+or pull request that updates these documents as well.*

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internals/add_operator.md
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diff --git a/docs/internals/add_operator.md b/docs/internals/add_operator.md
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@@ -0,0 +1,250 @@
+---
+title:  "How to add a new Operator"
+---
+<!--
+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.
+-->
+
+Operators in the Java API can be added in multiple different ways: 
+
+1. On the DataSet, as a specialization/combination of existing operators
+2. As a custom extension operator
+3. As a new runtime operator
+
+The first two approaches are typically more lightweight and easier to implement. Sometimes,
+new functionality does require a new runtime operator, or it is much more efficient to 
+
+* This will be replaced by the TOC
+{:toc}
+
+## Implementing a new Operator on DataSet
+
+Many operators can be implemented as a specialization of another operator, or by means of a UDF.
+
+The simplest example are the `sum()`, `min()`, and `max()` functions on the
+{% gh_link /flink-java/src/main/java/org/apache/flink/api/java/DataSet.java "DataSet" %}.
+These functions simply call other operations with some pre-defined parameters:
+
+~~~java
+public AggregateOperator<T> sum (int field) {
+    return this.aggregate (Aggregations.SUM, field);
+}
+
+~~~
+
+Some operations can be implemented as compositions of multiple other operators. An example is to implement a
+*count()* function through a combination of *map* and *aggregate*. 
+
+A simple way to do this is to define a function on the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/DataSet.java "DataSet" %} that calls *map(...)* and *reduce(...)* in turn:
+
+~~~java
+public DataSet<Long> count() {
+    return this.map(new MapFunction<T, Long>() {
+                        public Long map(T value) {
+                            return 1L;
+                        }
+                    })
+               .reduce(new ReduceFunction<Long>() {
+                        public Long reduce(Long val1, Long val1) {
+                            return val1 + val2;
+                        }
+                    });
+}
+~~~
+
+To define a new operator without altering the DataSet class is possible by putting the functions as static members
+into another class. The example of the *count()* operator would look the following way:
+
+~~~java
+public static <T>DataSet<Long> count(DataSet<T> data) {
+    return data.map(...).reduce(...);
+}
+~~~
+
+### More Complex Operators
+
+A more complex example of an operation via specialization is the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/operators/AggregateOperator.java "Aggregation Operation" %} in the Java API. It is implemented by means of a *GroupReduce* UDF.
+
+The Aggregate Operation comes with its own operator in the *Java API*, but translates itself into a {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/operators/base/GroupReduceOperatorBase.java "GroupReduceOperatorBase" %} in the *Common API*. (see [Program Life Cycle](internal_program_life_cycle.html) for details of how an operation from the *Java API* becomes an operation of the *Common API* and finally a runtime operation.)
+The Java API aggregation operator is only a builder that takes the types of aggregations and the field positions, and used that information to
+parameterize the GroupReduce UDF that performs the aggregations.
+
+Because the operation is translated into a GroupReduce operation, it appears as a GroupReduceOperator in the optimizer and runtime.
+
+
+
+## Implementing a Custom Extension Operator
+
+The DataSet offers a method for custom operators: `DataSet<X> runOperation(CustomUnaryOperation<T, X> operation)`.
+The *CustomUnaryOperation* interface defines operators by means of the two functions:
+
+~~~ java
+void setInput(DataSet<IN> inputData);
+	
+DataSet<OUT> createResult();
+~~~
+
+The {% gh_link /flink-staging/flink-spargel/src/main/java/org/apache/flink/spargel/java/VertexCentricIteration.java "VertexCentricIteration" %} operator is implemented that way. Below is an example how to implement the *count()* operator that way.
+
+~~~ java
+public class Counter<T> implements CustomUnaryOperation<T, Long> {
+
+    private DataSet<T> input;
+
+    public void setInput(DataSet<IN> inputData) { this.input = inputData; }
+
+    public DataSet<Long> createResult() {
+        return input.map(...).reduce(...);
+    }
+}
+~~~
+
+The CountOperator can be called in the following way:
+
+~~~ java
+DataSet<String> lines = ...;
+DataSet<Long> count = lines.runOperation(new Counter<String>());
+~~~
+
+
+## Implementing a new Runtime Operator
+
+Adding an new runtime operator requires changes throughout the entire stack, from the API to the runtime:
+
+- *Java API*
+- *Common API*
+- *Optimizer*
+- *Runtime*
+
+We start the description bottom up, at the example of the *mapPartition()* function, which is like a *map*
+function, but invoked only once per parallel partition.
+
+**Runtime**
+
+Runtime Operators are implemented using the {% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/operators/PactDriver.java "Driver" %} interface. The interface defines the methods that describe the operator towards the runtime. The {% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/operators/MapDriver.java "MapDriver" %} serves as a simple example of how those operators work.
+
+The runtime works with the `MutableObjectIterator`, which describes data streams with the ability to reuse objects, to reduce pressure on the garbage collector.
+
+An implementation of the central `run()` method for the *mapPartition* operator could look the following way:
+
+~~~ java
+public void run() throws Exception {
+    final MutableObjectIterator<IN> input = this.taskContext.getInput(0);
+    final MapPartitionFunction<IN, OUT> function = this.taskContext.getStub();
+    final Collector<OUT> output = this.taskContext.getOutputCollector();
+    final TypeSerializer<IN> serializer = this.taskContext.getInputSerializer(0);
+
+    // we assume that the UDF takes a java.util.Iterator, so we wrap the MutableObjectIterator
+    Iterator<IN> iterator = new MutableToRegularIteratorWrapper(input, serializer);
+
+    function.mapPartition(iterator, output);
+}
+~~~
+
+To increase efficiency, it is often beneficial to implement a *chained* version of an operator. Chained
+operators run in the same thread as their preceding operator, and work with nested function calls.
+This is very efficient, because it saves serialization/deserialization overhead.
+
+To learn how to implement a chained operator, take a look at the {% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/operators/MapDriver.java "MapDriver" %} (regular) and the
+{% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/operators/chaining/ChainedMapDriver.java "ChainedMapDriver" %} (chained variant).
+
+
+### Optimizer/Compiler
+
+This section does a minimal discussion of the important steps to add an operator. Please see the [Optimizer](internal_optimizer.html) docs for more detail on how the optimizer works.
+To allow the optimizer to include a new operator in its planning, it needs a bit of information about it; in particular, the following information:
+
+- *{% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/operators/DriverStrategy.java "DriverStrategy" %}*: The operation needs to be added to the Enum, to make it available to the optimizer. The parameters to the Enum entry define which class implements the runtime operator, its chained version, whether the operator accumulates records (and needs memory for that), and whether it requires a comparator (works on keys). For our example, we can add the entry
+~~~ java
+MAP_PARTITION(MapPartitionDriver.class, null /* or chained variant */, PIPELINED, false)
+~~~
+
+- *Cost function*: The class {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/costs/CostEstimator.java "CostEstimator" %} needs to know how expensive the operation is to the system. The costs here refer to the non-UDF part of the operator. Since the operator does essentially no work (it forwards the record stream to the UDF), the costs are zero. We change the `costOperator(...)` method by adding the *MAP_PARTITION* constant to the switch statement similar to the *MAP* constant such that no cost is accounted for it.
+
+- *OperatorDescriptor*: The operator descriptors define how an operation needs to be treated by the optimizer. They describe how the operation requires the input data to be (e.g., sorted or partitioned) and that way allows the optimizer to optimize the data movement, sorting, grouping in a global fashion. They do that by describing which {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/RequestedGlobalProperties.java "RequestedGlobalProperties" %} (partitioning, replication, etc) and which {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/RequestedLocalProperties.java "RequestedLocalProperties" %} (sorting, grouping, uniqueness) the operator has, as well as how the operator affects the existing {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/GlobalProperties.java "GlobalProperties" %} and {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/LocalProperties.j
 ava "LocalProperties" %}. In addition, it defines a few utility methods, for example to instantiate an operator candidate.
+Since the *mapPartition()* function is very simple (no requirements on partitioning/grouping), the descriptor is very simple. Other operators have more complex requirements, for example the {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/GroupReduceProperties.java "GroupReduce" %}. Some operators, like *join* have multiple ways in which they can be executed and therefore have multiple descriptors ({% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/HashJoinBuildFirstProperties.java "Hash Join 1" %}, {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/HashJoinBuildSecondProperties.java "Hash Join 2" %}, {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/SortMergeJoinDescriptor.java "SortMerge Join" %}).
+The code sample below explains (with comments) how to create a descriptor for the *MapPartitionOperator* 
+
+  ~~~ java
+    public DriverStrategy getStrategy() {
+        return MAP_PARTITION;
+    }
+
+    // Instantiate the operator with the strategy over the input given in the form of the Channel
+    public SingleInputPlanNode instantiate(Channel in, SingleInputNode node) {
+        return new SingleInputPlanNode(node, "MapPartition", in, MAP_PARTITION);
+    }
+
+    // The operation accepts data with default global properties (arbitrary distribution)
+    protected List<RequestedGlobalProperties> createPossibleGlobalProperties() {
+        return Collections.singletonList(new RequestedGlobalProperties());
+    }
+
+    // The operation can accept data with any local properties. No grouping/sorting is necessary
+    protected List<RequestedLocalProperties> createPossibleLocalProperties() {
+        return Collections.singletonList(new RequestedLocalProperties());
+    }
+
+    // the operation itself does not affect the existing global properties.
+    // The effect of the UDF's semantics// are evaluated separately (by interpreting the
+    // semantic assertions)
+    public GlobalProperties computeGlobalProperties(GlobalProperties gProps) {
+        return gProps;
+    }
+
+    // since the operation can mess up all order, grouping, uniqueness, we cannot make any statements
+    // about how local properties are preserved
+    public LocalProperties computeLocalProperties(LocalProperties lProps) {
+        return LocalProperties.EMPTY;
+    }
+  ~~~
+
+- *OptimizerNode*: The optimizer node is the place where all comes together. It creates the list of *OperatorDescriptors*, implements the result data set size estimation, and assigns a name to the operation. It is a relatively small class and can be more or less copied again from the {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dag/MapNode.java "MapNode" %}.
+
+
+### Common API
+
+To make the operation available to the higher-level APIs, it needs to be added to the Common API. The simplest way to do this is to add a
+base operator. Create a class `MapPartitionOperatorBase`, after the pattern of the {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/operators/base/MapOperatorBase.java "MapOperatorBase" %}.
+
+In addition, the optimizer needs to know which OptimizerNode how to create an OptimizerNode from the OperatorBase. This happens in the class
+`GraphCreatingVisitor` in the {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/PactCompiler.java "Optimizer" %}.
+
+*Note:* A pending idea is to allow to skip this step by unifying the OptimizerNode and the Common API operator. They essentially fulfill the
+same function. The Common API operator exists only in order for the `flink-java` and `flink-scala` packages to not have a dependency on the
+optimizer.
+
+
+### Java API
+
+Create a Java API operator that is constructed in the same way as the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/operators/MapOperator.java "MapOperator" %}. The core method is the `translateToDataFlow(...)` method, which creates the Common API operator for the Java API operator.
+
+The final step is to add a function to the `DataSet` class:
+
+~~~ java
+public <R> DataSet<R> mapPartition(MapPartitionFunction<T, R> function) {
+    return new MapPartitionOperator<T, R>(this, function);
+}
+~~~
+
+---
+
+*This documentation is maintained by the contributors of the individual components.
+We kindly ask anyone that adds and changes components to eventually provide a patch
+or pull request that updates these documents as well.*
+

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internals/coding_guidelines.md
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+++ b/docs/internals/coding_guidelines.md
@@ -0,0 +1,23 @@
+---
+title:  "Coding Guidelines"
+---
+<!--
+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.
+-->
+
+The *Coding Guidelines* are located [on the project website](http://flink.apache.org/coding_guidelines.html).
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internals/fig/ClientJmTm.svg
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@@ -0,0 +1,348 @@
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+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
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+under the License.
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http://git-wip-us.apache.org/repos/asf/flink/blob/f1ee90cc/docs/internals/fig/LICENSE.txt
----------------------------------------------------------------------
diff --git a/docs/internals/fig/LICENSE.txt b/docs/internals/fig/LICENSE.txt
new file mode 100644
index 0000000..35b8673
--- /dev/null
+++ b/docs/internals/fig/LICENSE.txt
@@ -0,0 +1,17 @@
+All image files in the folder and its subfolders are
+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.
\ No newline at end of file


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