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From david...@apache.org
Subject [37/50] [abbrv] incubator-apex-core git commit: Migrating docs
Date Fri, 04 Mar 2016 19:48:58 GMT
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-Operator Development Guide
-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
-using streams forming a Directed Acyclic Graph (DAG). In other words, a streaming
-application is represented by a DAG that consists of operations (called operators) and
-data flow (called streams).
-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.  **[Apache Apex Operators](#apex_operators)** - Introduction to operator terminology
and concepts.
-2.  **[Writing Custom Operators](#writing_custom_operators)** - Designing, coding and testing
new operators from scratch.  Includes code examples.
-3.  **[Operator Reference](#operator_reference)** - Details of operator internals, lifecycle,
and best practices and optimizations.
-* * * * *
-Apache Apex Operators <a name="apex_operators"></a>
-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 other operators in the application or by external sources,
-such as a database or a message bus. Similarly, after the tuples are
-processed, these may be passed on to other operators, or stored into an external system.

-Therea are 3 type of operators based on function: 
-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
-Operators Position in a DAG
-We may refer to operators depending on their position with respect to
-one another. For any operator opr (see image below), there are two types of operators.
-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.
-Operators in a DAG are connected together via directed flows
-called streams. Each stream has end-points located on the operators
-called ports. Therea are 2 types of ports.
-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.
-* * * * *
-How Operator Works
-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.
-Developing Custom Operators <a name="writing_custom_operators"></a>
-About this tutorial
-This tutorial will guide the user towards developing a operator from
-scratch. It includes all aspects of writing an operator including
-design, code and unit testing.
-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:
-Operator Reference <a name="operator_reference"></a>
-### 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:
--  setup(OperatorContext context)
--  beginWindow(long windowId)
--  endWindow()
--  tearDown()
-In order to simplify the creation of an operator, Apache Apex
-library also provides a base class “BaseOperator” which has empty
-implementations for these methods. Please refer to the [Apex Operators](#apex_operators) section
and the
-[Reference](#operator_reference) 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_ - Configures the output frequency 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 of all the words
-    encountered so far. Note that this variable is non transient, which
-    means that this variable is saved as part of the checkpoint 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). sendPerTuple configuration determines whether to store the most recent
or the recent
-    window worth of tuples.
-``` 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. With regards to Word Count operator,
we initialize the globalCounts variable in the constructor.
-``` java
-globalCounts = Maps.newHashMap();
-### Setup call
-The setup method is called only once during an operator lifetime and its purpose is to allow

-the operator to set itself up for processing incoming streams. Transient objects in the operator
-not serialized and checkpointed. Hence, it is essential that such objects initialized in
the setup call. 
-In case of operator failure, the operator will be redeployed (most likely on a different
container). The setup method called by the Apache Apex engine allows the operator to prepare
for execution in the new container.
-The following tasks are executed 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.
-    As a transient variable, the value will be lost when operator fails.
-### Begin Window call
-The begin window call signals the start of an application window. With 
-regards to Word Count Operator, we are expecting updated counts for the most recent window
-data if the sendPerTuple is set to false. 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. With regards to 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. With regards to our operator,
-we call the shutDown method which shuts down the operator along with any
-downstream operators.
-Testing your Operator
-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 -- initialized values from step 1 are

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