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From t..@apache.org
Subject [15/50] incubator-apex-core git commit: Added Operator Getting Started Guide + Tutorial
Date Wed, 02 Mar 2016 23:32:44 GMT
Added Operator Getting Started Guide + Tutorial

Project: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/commit/528f6d71
Tree: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/tree/528f6d71
Diff: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/diff/528f6d71

Branch: refs/heads/APEXCORE-293
Commit: 528f6d71cd59c8008ae3ec6d2787940825c3abe9
Parents: 707b997
Author: bhupeshchawda <bhupeshchawda@gmail.com>
Authored: Wed Nov 4 19:22:03 2015 +0530
Committer: Thomas Weise <thomas@datatorrent.com>
Committed: Sun Feb 28 22:46:35 2016 -0800

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+# Operator Getting Started  Guide and Tutorial
+Operators are basic building blocks of an application built to run on
+Apache Apex platform. An application may consist of one or more
+operators each of which define some logical operation to be done on the
+tuples arriving at the operator. These operators are connected together
+together using streams forming a Directed Acyclic Graph i.e. DAG. In
+other words, a streaming application is represented by a Directed
+Acyclic Graph (DAG) that consists of operations (called operators) and
+data flow (called streams).
+About this document
+In this document we will discuss details on how an operator works and
+its internals. This document is intended to serve the following purposes
+1.  Getting started with Apache Apex operators - This will guide the user to quickly putting
together an
+    operator using Apache Apex APIs.
+2.  Tutorial - This will present a tutorial
+    for writing an operator from scratch. The tutorial will discuss
+    details on implementation wrt. design, code and testing. Link to
+    actual code for this operator will also be provided.
+3.  Operator Reference Guide - This the document will target the details and intricacies
in writing an
+    operator. This will cover operators and related aspects to great
+    depths including pitfalls and optimizations.
+* * * * *
+Getting started with Apache Apex Operators
+Operators - “What” in a nutshell
+Operators are independent units of logical operations which can
+contribute in executing the business logic of a use case. For example,
+in an ETL workflow, a filtering operation can be represented by a single
+operator. This filtering operator will be responsible for doing just one
+task in the ETL pipeline, i.e. filter incoming tuples. Operators do not
+impose any restrictions on what can or cannot be done as part of a
+operator. An operator may as well contain the entire business logic.
+However, it is recommended, that the operators are light weight
+independent tasks, in
+order to take advantage of the distributed framework that Apache Apex
+provides. The structure of a streaming application shares resemblance
+with the way CPU pipelining works. CPU pipelining breaks down the
+computation engine into different stages viz. instruction fetch,
+instruction decode, etc. so that each of them can perform their task on
+different instructions
+parallely. Similarly,
+Apache Apex APIs allow the user to break down their tasks into different
+stages so that all of the tasks can be executed on different tuples
+Operators - “How” in a nutshell
+An Apache Apex application runs as a YARN application. Hence, each of
+the operators that the application DAG contains, runs in one of the
+containers provisioned by YARN. Further, Apache Apex exposes APIs to
+allow the user to request bundling multiple operators in a single node,
+a single container or even a single thread. We shall look at these calls
+in the reference sections [cite reference sections]. For now, consider
+an operator as some piece of code that runs on some machine of a YARN
+Types of Operators
+An operator works on one tuple at a time. These tuples may be supplied
+by some other operator in the application or by some external source,
+like a database or a message bus. Similarly, after the tuples are
+processed, these may be passed on to some other operator, or may be
+stored into some external system. Based on the functions that the
+Operator performs, we have the following types of operators:
+1.  **Input Adapter** - This is one of the starting points in
+    the application DAG and is responsible for getting tuples from an
+    external system. At the same time, such data may also be generated
+    by the operator itself, without interacting with the outside
+    world. These input tuples will form the initial universe of
+    data that the application works on.
+2.  **Generic Operator** - This type of operator accepts input tuples from
+    the previous operators and passes them on to the following operators
+    in the DAG.
+3.  **Output Adapter** - This is one of the ending points in the application
+    DAG and is responsible for writing the data out to some external
+    system.
+Note: There can be multiple operators of all types in an application
+Positioning of Operators in the DAG
+We may refer to operators depending on their positioning with respect to
+one another. For any operator opr, we have the following types of
+1.  **Upstream operators** - These are the operators from which there is a
+    directed path to opr in the application DAG.
+2.  **Downstream operators** - These are the operators to which there is a
+    directed path from opr in the application DAG.
+Note that there are no cycles formed in the application DAG.
+The operators in the DAG are connected together via directed flows
+called streams. Each stream has end-points located on the operators
+called ports. The ports again fall into two types.
+1.  **Input Port** - This is a port through which an operator accepts input
+    tuples from an upstream operator.
+2.  **Output port** - This is a port through which an operator passes on the
+    processed data to downstream operators.
+Looking at the number of input ports, an Input Adapter is an operator
+with no input ports, a Generic operator has both input and output ports,
+while an Output Adapter has no output ports. At the same time, note that
+an operator may act as an Input Adapter while at the same time have an
+input port. In such cases, the operator is getting data from two
+different sources, viz. the input stream from the input port and an
+external source.
+* * * * *
+Working of an Operator
+An operator passes through various stages during its lifetime. Each
+stage is an API call that the streaming application master makes for an
+operator.  The following figure illustrates the stages through which an
+operator passes.
+-   The _setup()_ call initializes the operator and prepares itself to
+    start processing tuples.
+-   The _beginWindow()_ call marks the beginning of an application window
+    and allows for any processing to be done before a window starts.
+-   The _process()_ call belongs to the _InputPort_ and gets triggered when
+    any tuple arrives at the Input port of the operator. This call is
+    specific only to Generic and Output adapters, since Input Adapters
+    do not have an input port. This is made for all the tuples at the
+    input port until the end window marker tuple is received on the
+    input port.
+-   The _emitTuples()_ is the counterpart of _process()_ call for Input
+    Adapters.
+    This call is used by Input adapters to emit any tuples that are
+    fetched from the external systems, or generated by the operator.
+    This method is called continuously until the pre-configured window
+    time is elapsed, at which the end window marker tuple is sent out on
+    the output port.
+-   The _endWindow()_ call marks the end of the window and allows for any
+    processing to be done after the window ends.
+-   The _teardown()_ call is used for gracefully shutting down the
+    operator and releasing any resources held by the operator.
+Tutorial - Write your first operator
+About this tutorial
+This tutorial will guide the user towards developing a operator from
+scratch. It will include all aspects of writing an operator including
+design, code as well as unit tests.
+In this tutorial, we will design and write, from scratch, an operator
+called Word Count. This operator will accept tuples of type String,
+count the number of occurrences for each word appearing in the tuple and
+send out the updated counts for all the words encountered in the tuple.
+Further, the operator will also accept a file path on HDFS which will
+contain the stop-words which need to be ignored when counting
+Design of the operator must be finalized before starting to write an
+operator. Many aspects including the functionality, the data sources,
+the types involved etc. need to be first finalized before writing the
+operator. Let us dive into each of these while considering the Word
+Count operator.
+### Functionality
+We can define the scope of operator functionality using the following
+1.  Parse the input tuple to identify the words in the tuple
+2.  Identify the stop-words in the tuple by looking up the stop-word
+    file as configured
+3.  For each non-stop-word in the tuple, count the occurrences in that
+    tuple and add it to a global counts
+Let’s consider an example. Suppose we have the following tuples flow
+into the Word Count operator.
+1.  _Humpty dumpty sat on a wall_
+2.  _Humpty dumpty had a great fall_
+Initially counts for all words is 0. Once the first tuple is processed,
+the counts that must be emitted are:
+``` java
+humpty - 1
+dumpty - 1
+sat - 1
+wall - 1
+Note that we are ignoring the stop-words, “on” and “a” in this case.
+Also note that as a rule, we’ll ignore the case of the words when
+counting occurrences.
+Similarly, after the second tuple is processed, the counts that must be
+emitted are:
+``` java
+humpty - 2
+dumpty - 2
+great - 1
+fall - 1
+Again, we ignore the words _“had”_ and _“a”_ since these are stop-words.
+Note that the most recent count for any word is correct count for that
+word. In other words, any new output for a word, invalidated all the
+previous counts for that word.
+### Inputs
+As seen from the example above, the following inputs are expected for
+the operator:
+1.  Input stream whose tuple type is String
+2.  Input HDFS file path, pointing to a file containing stop-words
+Only one input port is needed. The stop-word file will be small enough
+to be read completely in a single read. In addition this will be a one
+time activity for the lifetime of the operator. This does not need a
+separate input port.
+### Outputs
+We can define the output for this operator in multiple ways.
+1.  The operator may send out the set of counts for which the counts
+    have changed after processing each tuple.
+2.  Some applications might not need an update after every tuple, but
+    only after a certain time duration.
+Let us try and implement both these options depending on the
+configuration. Let us define a boolean configuration parameter
+_“sendPerTuple”_. The value of this parameter will indicate whether the
+updated counts for words need to be emitted after processing each
+tuple (true) or after a certain time duration (false).
+The type of information the operator will be sending out on the output
+port is the same for all the cases. This will be a _< key, value >_ pair,
+where the key is the word while, the value is the latest count for that
+word. This means we just need one output port on which this information
+will go out.
+We have the following configuration parameters:
+1.  _stopWordFilePath_ - This parameter will store the path to the stop
+    word file on HDFS as configured by the user.
+2.  _sendPerTuple_ - This parameter decides whether we send out the
+    updated counts after processing each tuple or at the end of a
+    window. When set to true, the operator will send out the updated
+    counts after each tuple, else it will send at the end of
+    each window.
+The source code for the tutorial can be found here:
+### The Operator Class
+The operator will exist physically as a class which implements the
+Operator interface. This interface will require implementations for the
+following method calls:
+``` java
+-   setup(OperatorContext context)
+-   beginWindow(long windowId)
+-   endWindow()
+-   tearDown()
+In order to simplify the creation of an operator, the Apache Apex
+library also provides a base class “BaseOperator” which has empty
+implementations for these methods. Please refer to the [Getting
+Started](#h.dbmfyx6yasqs) section and the
+[Reference](#h.z10k75daf2xl) section for details on these.
+We extend the class “BaseOperator” to create our own operator
+``` java
+public class WordCountOperator extends BaseOperator
+### Class (Operator) properties
+We define the following class variables:
+-   _sendPerTuple_ - Needed for configuring the frequency of output from
+    the operator
+``` java
+private boolean sendPerTuple = true; // default
+-   _stopWordFilePath_ - Stores the path to the stop words file on HDFS
+``` java
+private String stopWordFilePath; // no default
+-   _stopWords_ - Stores the stop words read from the configured file
+``` java
+private transient String[] stopWords;
+-   _globalCounts_ - A Map which stores the counts for all the words
+    encountered so far. Note that this variable is non transient, which
+    means that this variable is saved as part of the checkpointing state
+    and can be recovered in event of a crash.
+``` java
+private Map<String, Long> globalCounts;
+-   _updatedCounts_ - A map which stores the counts for only the most
+    recent tuple(s). Whether to store the most recent or the recent
+    window worth of tuples will be determined by the configuration
+    parameter sendPerTuple.
+``` java
+private transient Map<String, Long> updatedCounts;
+-   _input_ - The input port for the operator. The type of this input port
+    is String which means it will only accept tuples of type String. The
+    definition of an input port requires implementation of a method
+    called process(String tuple), which should have the processing logic
+    for the input tuple which  arrives at this input port. We delegate
+    this task to another method called processTuple(String tuple). This
+    helps in keeping the operator classes extensible by overriding the
+    processing logic for the input tuples.
+``` java
+public transient DefaultInputPort<String> input = new    
+    @Override
+    public void process(String tuple)
+    {
+        processTuple(tuple);
+    }
+-   output - The output port for the operator. The type of this port is
+    Entry < String, Long >, which means the operator will emit < word,
+    count > pairs for the updated counts.
+``` java
+public transient DefaultOutputPort <Entry<String, Long>> output = new
+### The Constructor
+The constructor is the place where we initialize the non-transient data
+structures, since
+constructor is called just once per activation of an operator. In case
+of Word Count operator, we initialize the globalCounts variable in the
+``` java
+globalCounts = Maps.newHashMap();
+### Setup call
+The setup method is called only once during the lifetime of an operator.
+The purpose of the setup call is to allow the operator to set itself up
+for processing incoming streams. Transient objects in the operator are
+not serialized and checkpointed. Hence it is essential that such objects
+must be initialized in the setup call. In case of operator failure, the
+operator will be redeployed, most likely on a different container. In
+this case, it is the setup method which will be called by the Apache
+Apex engine to allow the operator to prepare for execution in the new
+We perform the following tasks as part of the setup call:
+1.  Read the stop-word list from HDFS and store it in the
+    stopWords array
+2.  Initialize updatedCounts variable. This will store the updated
+    counts for words in most recent tuples processed by the operator.
+    This is a transient variable, hence the value of this variable will
+    be lost in case of operator failure.
+### Begin Window call
+The begin window call signals the start of an application window. In
+case of Word Count Operator, if the sendPerTuple is set to false, it
+means that we are expecting updated counts for the most recent window of
+data. Hence, we clear the updatedCounts variable in the begin window
+call and start accumulating the counts till the end window call.
+### Process Tuple call
+The processTuple method is called by the process method of the input
+port, input. This method defines the processing logic for the current
+tuple that is received at the input port. As part of this method, we
+identify the words in the current tuple and update the globalCounts and
+the updatedCounts variables. In addition, if the sendPerTuple variable
+is set to true, we also emit the words and corresponding counts in
+updatedCounts to the output port. Note that in this case (sendPerTuple =
+true), we clear the updatedCounts variable in every call to
+### End Window call
+This call signals the end of an application window. In case of Word
+Count Operator, we emit the updatedCounts to the output port if the
+sendPerTuple flag is set to false.
+### Teardown call
+This method allows the operator to gracefully shut down itself after
+releasing the resources that it has acquired. In case of our operator,
+we call the shutDown method which shuts down the operator along with any
+downstream operators.
+Testing your Operator
+Testing an operator after development is essential to ensure that he
+required functionality is indeed correctly implemented. As part of
+testing our operator, we test the following two facets:
+1.  Test output of the operator after processing a single tuple
+2.  Test output of the operator after processing of a window of tuples
+The unit tests for the WordCount operator are available in the class
+WordCountOperatorTest.java. We simulate the behavior of the engine by
+using the test utilities provided by Apache Apex libraries. We simulate
+the setup, beginWindow, process method of the input port and
+endWindow calls and compare the output received at the simulated output
+1. Invoke constructor; non-transients initialized.
+2. Copy state from checkpoint -- initalized values from step 1 are

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