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From sas...@apache.org
Subject [23/26] incubator-apex-site git commit: Updating docs
Date Sat, 19 Mar 2016 02:33:08 GMT
http://git-wip-us.apache.org/repos/asf/incubator-apex-site/blob/2ea69c2b/content/docs/apex/application_development/index.html
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-                <h1 id="application-developer-guide">Application Developer Guide</h1>
-<p>The Apex platform is designed to process massive amounts of
-real-time events natively in Hadoop.  It runs as a YARN (Hadoop 2.x) 
-application and leverages Hadoop as a distributed operating
-system.  All the basic distributed operating system capabilities of
-Hadoop like resource management (YARN), distributed file system (HDFS),
-multi-tenancy, security, fault-tolerance, and scalability are supported natively 
-in all the Apex applications.  The platform handles all the details of the application 
-execution, including dynamic scaling, state checkpointing and recovery, event 
-processing guarantees, etc. allowing you to focus on writing your application logic without
-mixing operational and functional concerns.</p>
-<p>In the platform, building a streaming application can be extremely
-easy and intuitive.  The application is represented as a Directed
-Acyclic Graph (DAG) of computation units called <em>Operators</em> interconnected
-by the data-flow edges called  <em>Streams</em>. The operators process input
-streams and produce output streams. A library of common operators is
-provided to enable quick application development.  In case the desired
-processing is not available in the Operator Library, one can easily
-write a custom operator. We refer those interested in creating their own
-operators to the <a href="../operator_development/">Operator Development Guide</a>.</p>
-<h1 id="running-a-test-application">Running A Test Application</h1>
-<p>If you are starting with the Apex platform for the first time,
-it can be informative to launch an existing application and see it run.
-One of the simplest examples provided in <a href="https://github.com/apache/incubator-apex-malhar">Apex-Malhar repository</a> is a Pi demo application,
-which computes the value of PI using random numbers.  After <a href="../apex_development_setup/">setting up development environment</a>
-Pi demo can be launched as follows:</p>
-<ol>
-<li>Open up Apex Malhar files in your IDE (for example Eclipse, IntelliJ, NetBeans, etc)</li>
-<li>Navigate to <code>demos/pi/src/test/java/com/datatorrent/demos/ApplicationTest.java</code></li>
-<li>Run the test for ApplicationTest.java</li>
-<li>View the output in system console</li>
-</ol>
-<p>Congratulations, you just ran your first real-time streaming demo :) 
-This demo is very simple and has four operators. The first operator
-emits random integers between 0 to 30, 000. The second operator receives
-these coefficients and emits a hashmap with x and y values each time it
-receives two values. The third operator takes these values and computes
-x**2+y**2. The last operator counts how many computed values from
-the previous operator were less than or equal to 30, 000**2. Assuming
-this count is N, then PI is computed as N/number of values received.
-Here is the code snippet for the PI application. This code populates the
-DAG. Do not worry about what each line does, we will cover these
-concepts later in this document.</p>
-<pre><code class="java">// Generates random numbers
-RandomEventGenerator rand = dag.addOperator(&quot;rand&quot;, new RandomEventGenerator());
-rand.setMinvalue(0);
-rand.setMaxvalue(30000);
-
-// Generates a round robin HashMap of &quot;x&quot; and &quot;y&quot;
-RoundRobinHashMap&lt;String,Object&gt; rrhm = dag.addOperator(&quot;rrhm&quot;, new RoundRobinHashMap&lt;String, Object&gt;());
-rrhm.setKeys(new String[] { &quot;x&quot;, &quot;y&quot; });
-
-// Calculates pi from x and y
-JavaScriptOperator calc = dag.addOperator(&quot;picalc&quot;, new Script());
-calc.setPassThru(false);
-calc.put(&quot;i&quot;,0);
-calc.put(&quot;count&quot;,0);
-calc.addSetupScript(&quot;function pi() { if (x*x+y*y &lt;= &quot;+maxValue*maxValue+&quot;) { i++; } count++; return i / count * 4; }&quot;);
-calc.setInvoke(&quot;pi&quot;);
-dag.addStream(&quot;rand_rrhm&quot;, rand.integer_data, rrhm.data);
-dag.addStream(&quot;rrhm_calc&quot;, rrhm.map, calc.inBindings);
-
-// puts results on system console
-ConsoleOutputOperator console = dag.addOperator(&quot;console&quot;, new ConsoleOutputOperator());
-dag.addStream(&quot;rand_console&quot;,calc.result, console.input);
-</code></pre>
-
-<p>You can review the other demos and see what they do. The examples
-given in the Demos project cover various features of the platform and we
-strongly encourage you to read these to familiarize yourself with the
-platform. In the remaining part of this document we will go through
-details needed for you to develop and run streaming applications in
-Malhar.</p>
-<h2 id="test-application-yahoo-finance-quotes">Test Application: Yahoo! Finance Quotes</h2>
-<p>The PI application was to
-get you started. It is a basic application and does not fully illustrate
-the features of the platform. For the purpose of describing concepts, we
-will consider the test application shown in Figure 1. The application
-downloads tick data from  <a href="http://finance.yahoo.com">Yahoo! Finance</a>  and computes the
-following for four tickers, namely <a href="http://finance.yahoo.com/q?s=IBM">IBM</a>,
-<a href="http://finance.yahoo.com/q?s=GOOG">GOOG</a>, <a href="http://finance.yahoo.com/q?s=YHOO">YHOO</a>.</p>
-<ol>
-<li>Quote: Consisting of last trade price, last trade time, and
-    total volume for the day</li>
-<li>Per-minute chart data: Highest trade price, lowest trade
-    price, and volume during that minute</li>
-<li>Simple Moving Average: trade price over 5 minutes</li>
-</ol>
-<p>Total volume must ensure that all trade volume for that day is
-added, i.e. data loss would result in wrong results. Charting data needs
-all the trades in the same minute to go to the same slot, and then on it
-starts afresh, so again data loss would result in wrong results. The
-aggregation for charting data is done over 1 minute. Simple moving
-average computes the average price over a 5 minute sliding window; it
-too would produce wrong results if there is data loss. Figure 1 shows
-the application with no partitioning.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image00.png" /></p>
-<p>The operator StockTickerInput: StockTickerInput<a href="http://docs.google.com/../apidocs/com/datatorrent/demos/yahoofinance/StockTickInput.html"> </a>is
-the input operator that reads live data from Yahoo! Finance once per
-interval (user configurable in milliseconds), and emits the price, the
-incremental volume, and the last trade time of each stock symbol, thus
-emulating real ticks from the exchange.  We utilize the Yahoo! Finance
-CSV web service interface.  For example:</p>
-<pre><code>$ GET 'http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO&amp;f=sl1vt1'
-&quot;IBM&quot;,203.966,1513041,&quot;1:43pm&quot;
-&quot;GOOG&quot;,762.68,1879741,&quot;1:43pm&quot;
-&quot;AAPL&quot;,444.3385,11738366,&quot;1:43pm&quot;
-&quot;YHOO&quot;,19.3681,14707163,&quot;1:43pm&quot;
-</code></pre>
-
-<p>Among all the operators in Figure 1, StockTickerInput is the only
-operator that requires extra code because it contains a custom mechanism
-to get the input data.  Other operators are used unchanged from the
-Malhar library.</p>
-<p>Here is the class implementation for StockTickInput:</p>
-<pre><code class="java">package com.datatorrent.demos.yahoofinance;
-
-import au.com.bytecode.opencsv.CSVReader;
-import com.datatorrent.annotation.OutputPortFieldAnnotation;
-import com.datatorrent.api.Context.OperatorContext;
-import com.datatorrent.api.DefaultOutputPort;
-import com.datatorrent.api.InputOperator;
-import com.datatorrent.lib.util.KeyValPair;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.util.*;
-import org.apache.commons.httpclient.HttpClient;
-import org.apache.commons.httpclient.HttpStatus;
-import org.apache.commons.httpclient.cookie.CookiePolicy;
-import org.apache.commons.httpclient.methods.GetMethod;
-import org.apache.commons.httpclient.params.DefaultHttpParams;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/**
- * This operator sends price, volume and time into separate ports and calculates incremental volume.
- */
-public class StockTickInput implements InputOperator
-{
-  private static final Logger logger = LoggerFactory.getLogger(StockTickInput.class);
-  /**
-   * Timeout interval for reading from server. 0 or negative indicates no timeout.
-   */
-  public int readIntervalMillis = 500;
-  /**
-   * The URL of the web service resource for the POST request.
-   */
-  private String url;
-  public String[] symbols;
-  private transient HttpClient client;
-  private transient GetMethod method;
-  private HashMap&lt;String, Long&gt; lastVolume = new HashMap&lt;String, Long&gt;();
-  private boolean outputEvenIfZeroVolume = false;
-  /**
-   * The output port to emit price.
-   */
-  @OutputPortFieldAnnotation(optional = true)
-  public final transient DefaultOutputPort&lt;KeyValPair&lt;String, Double&gt;&gt; price = new DefaultOutputPort&lt;KeyValPair&lt;String, Double&gt;&gt;();
-  /**
-   * The output port to emit incremental volume.
-   */
-  @OutputPortFieldAnnotation(optional = true)
-  public final transient DefaultOutputPort&lt;KeyValPair&lt;String, Long&gt;&gt; volume = new DefaultOutputPort&lt;KeyValPair&lt;String, Long&gt;&gt;();
-  /**
-   * The output port to emit last traded time.
-   */
-  @OutputPortFieldAnnotation(optional = true)
-  public final transient DefaultOutputPort&lt;KeyValPair&lt;String, String&gt;&gt; time = new DefaultOutputPort&lt;KeyValPair&lt;String, String&gt;&gt;();
-
-  /**
-   * Prepare URL from symbols and parameters. URL will be something like: http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO&amp;f=sl1vt1
-   *
-   * @return the URL
-   */
-  private String prepareURL()
-  {
-    String str = &quot;http://download.finance.yahoo.com/d/quotes.csv?s=&quot;;
-    for (int i = 0; i &lt; symbols.length; i++) {
-      if (i != 0) {
-        str += &quot;,&quot;;
-      }
-      str += symbols[i];
-    }
-    str += &quot;&amp;f=sl1vt1&amp;e=.csv&quot;;
-    return str;
-  }
-
-  @Override
-  public void setup(OperatorContext context)
-  {
-    url = prepareURL();
-    client = new HttpClient();
-    method = new GetMethod(url);
-    DefaultHttpParams.getDefaultParams().setParameter(&quot;http.protocol.cookie-policy&quot;, CookiePolicy.BROWSER_COMPATIBILITY);
-  }
-
-  @Override
-  public void teardown()
-  {
-  }
-
-  @Override
-  public void emitTuples()
-  {
-
-    try {
-      int statusCode = client.executeMethod(method);
-      if (statusCode != HttpStatus.SC_OK) {
-        System.err.println(&quot;Method failed: &quot; + method.getStatusLine());
-      }
-      else {
-        InputStream istream = method.getResponseBodyAsStream();
-        // Process response
-        InputStreamReader isr = new InputStreamReader(istream);
-        CSVReader reader = new CSVReader(isr);
-        List&lt;String[]&gt; myEntries = reader.readAll();
-        for (String[] stringArr: myEntries) {
-          ArrayList&lt;String&gt; tuple = new ArrayList&lt;String&gt;(Arrays.asList(stringArr));
-          if (tuple.size() != 4) {
-            return;
-          }
-          // input csv is &lt;Symbol&gt;,&lt;Price&gt;,&lt;Volume&gt;,&lt;Time&gt;
-          String symbol = tuple.get(0);
-          double currentPrice = Double.valueOf(tuple.get(1));
-          long currentVolume = Long.valueOf(tuple.get(2));
-          String timeStamp = tuple.get(3);
-          long vol = currentVolume;
-          // Sends total volume in first tick, and incremental volume afterwards.
-          if (lastVolume.containsKey(symbol)) {
-            vol -= lastVolume.get(symbol);
-          }
-
-          if (vol &gt; 0 || outputEvenIfZeroVolume) {
-            price.emit(new KeyValPair&lt;String, Double&gt;(symbol, currentPrice));
-            volume.emit(new KeyValPair&lt;String, Long&gt;(symbol, vol));
-            time.emit(new KeyValPair&lt;String, String&gt;(symbol, timeStamp));
-            lastVolume.put(symbol, currentVolume);
-          }
-        }
-      }
-      Thread.sleep(readIntervalMillis);
-    }
-    catch (InterruptedException ex) {
-      logger.debug(ex.toString());
-    }
-    catch (IOException ex) {
-      logger.debug(ex.toString());
-    }
-  }
-
-  @Override
-  public void beginWindow(long windowId)
-  {
-  }
-
-  @Override
-  public void endWindow()
-  {
-  }
-
-  public void setOutputEvenIfZeroVolume(boolean outputEvenIfZeroVolume)
-  {
-       this.outputEvenIfZeroVolume = outputEvenIfZeroVolume;
-  }
-
-}
-</code></pre>
-
-<p>The operator has three output ports that emit the price of the
-stock, the volume of the stock and the last trade time of the stock,
-declared as public member variables price, volume and  time of the class.  The tuple of the
-price output port is a key-value
-pair with the stock symbol being the key, and the price being the value.
- The tuple of the volume output
-port is a key value pair with the stock symbol being the key, and the
-incremental volume being the value.  The tuple of the  time output port is a key value pair with the
-stock symbol being the key, and the last trade time being the
-value.</p>
-<p>Important: Since operators will be
-serialized, all input and output ports need to be declared transient
-because they are stateless and should not be serialized.</p>
-<p>The method setup(OperatorContext)
-contains the code that is necessary for setting up the HTTP
-client for querying Yahoo! Finance.</p>
-<p>Method emitTuples() contains
-the code that reads from Yahoo! Finance, and emits the data to the
-output ports of the operator.  emitTuples() will be called one or more times
-within one application window as long as time is allowed within the
-window.</p>
-<p>Note that we want to emulate the tick input stream by having
-incremental volume data with Yahoo! Finance data.  We therefore subtract
-the previous volume from the current volume to emulate incremental
-volume for each tick.</p>
-<p>The operator
-DailyVolume: This operator
-reads from the input port, which contains the incremental volume tuples
-from StockTickInput, and
-aggregates the data to provide the cumulative volume.  It uses the
-library class  SumKeyVal&lt;K,V&gt; provided in math package.  In this case,
-SumKeyVal&lt;String,Long&gt;, where K is the stock symbol, V is the
-aggregated volume, with cumulative
-set to true. (Otherwise if  cumulativewas set to false, SumKeyVal would
-provide the sum for the application window.)  Malhar provides a number
-of built-in operators for simple operations like this so that
-application developers do not have to write them.  More examples to
-follow. This operator assumes that the application restarts before the
-market opens every day.</p>
-<p>The operator Quote:
-This operator has three input ports, which are price (from
-StockTickInput), daily_vol (from
-Daily Volume), and time (from
- StockTickInput).  This operator
-just consolidates the three data items and and emits the consolidated
-data.  It utilizes the class ConsolidatorKeyVal&lt;K&gt; from the
-stream package.</p>
-<p>The operator HighLow: This operator reads from the input port,
-which contains the price tuples from StockTickInput, and provides the high and the
-low price within the application window.  It utilizes the library class
- RangeKeyVal&lt;K,V&gt; provided
-in the math package. In this case,
-RangeKeyVal&lt;String,Double&gt;.</p>
-<p>The operator MinuteVolume:
-This operator reads from the input port, which contains the
-volume tuples from StockTickInput,
-and aggregates the data to provide the sum of the volume within one
-minute.  Like the operator  DailyVolume, this operator also uses
-SumKeyVal&lt;String,Long&gt;, but
-with cumulative set to false.  The
-Application Window is set to one minute. We will explain how to set this
-later.</p>
-<p>The operator Chart:
-This operator is very similar to the operator Quote, except that it takes inputs from
-High Low and  Minute Vol and outputs the consolidated tuples
-to the output port.</p>
-<p>The operator PriceSMA:
-SMA stands for - Simple Moving Average. It reads from the
-input port, which contains the price tuples from StockTickInput, and
-provides the moving average price of the stock.  It utilizes
-SimpleMovingAverage&lt;String,Double&gt;, which is provided in the
- multiwindow package.
-SimpleMovingAverage keeps track of the data of the previous N
-application windows in a sliding manner.  For each end window event, it
-provides the average of the data in those application windows.</p>
-<p>The operator Console:
-This operator just outputs the input tuples to the console
-(or stdout).  In this example, there are four console operators, which connect to the output
-of  Quote, Chart, PriceSMA and VolumeSMA.  In
-practice, they should be replaced by operators that use the data to
-produce visualization artifacts like charts.</p>
-<p>Connecting the operators together and constructing the
-DAG: Now that we know the
-operators used, we will create the DAG, set the streaming window size,
-instantiate the operators, and connect the operators together by adding
-streams that connect the output ports with the input ports among those
-operators.  This code is in the file  YahooFinanceApplication.java. Refer to Figure 1
-again for the graphical representation of the DAG.  The last method in
-the code, namely getApplication(),
-does all that.  The rest of the methods are just for setting up the
-operators.</p>
-<pre><code class="java">package com.datatorrent.demos.yahoofinance;
-
-import com.datatorrent.api.ApplicationFactory;
-import com.datatorrent.api.Context.OperatorContext;
-import com.datatorrent.api.DAG;
-import com.datatorrent.api.Operator.InputPort;
-import com.datatorrent.lib.io.ConsoleOutputOperator;
-import com.datatorrent.lib.math.RangeKeyVal;
-import com.datatorrent.lib.math.SumKeyVal;
-import com.datatorrent.lib.multiwindow.SimpleMovingAverage;
-import com.datatorrent.lib.stream.ConsolidatorKeyVal;
-import com.datatorrent.lib.util.HighLow;
-import org.apache.hadoop.conf.Configuration;
-
-/**
- * Yahoo! Finance application demo. &lt;p&gt;
- *
- * Get Yahoo finance feed and calculate minute price range, minute volume, simple moving average of 5 minutes.
- */
-public class Application implements StreamingApplication
-{
-  private int streamingWindowSizeMilliSeconds = 1000; // 1 second (default is 500ms)
-  private int appWindowCountMinute = 60;   // 1 minute
-  private int appWindowCountSMA = 5 * 60;  // 5 minute
-
-  /**
-   * Get actual Yahoo finance ticks of symbol, last price, total daily volume, and last traded price.
-   */
-  public StockTickInput getStockTickInputOperator(String name, DAG dag)
-  {
-    StockTickInput oper = dag.addOperator(name, StockTickInput.class);
-    oper.readIntervalMillis = 200;
-    return oper;
-  }
-
-  /**
-   * This sends total daily volume by adding volumes from each ticks.
-   */
-  public SumKeyVal&lt;String, Long&gt; getDailyVolumeOperator(String name, DAG dag)
-  {
-    SumKeyVal&lt;String, Long&gt; oper = dag.addOperator(name, new SumKeyVal&lt;String, Long&gt;());
-    oper.setType(Long.class);
-    oper.setCumulative(true);
-    return oper;
-  }
-
-  /**
-   * Get aggregated volume of 1 minute and send at the end window of 1 minute.
-   */
-  public SumKeyVal&lt;String, Long&gt; getMinuteVolumeOperator(String name, DAG dag, int appWindowCount)
-  {
-    SumKeyVal&lt;String, Long&gt; oper = dag.addOperator(name, new SumKeyVal&lt;String, Long&gt;());
-    oper.setType(Long.class);
-    oper.setEmitOnlyWhenChanged(true);
-dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);
-    return oper;
-  }
-
-  /**
-   * Get High-low range for 1 minute.
-   */
-  public RangeKeyVal&lt;String, Double&gt; getHighLowOperator(String name, DAG dag, int appWindowCount)
-  {
-    RangeKeyVal&lt;String, Double&gt; oper = dag.addOperator(name, new RangeKeyVal&lt;String, Double&gt;());
-    dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);
-    oper.setType(Double.class);
-    return oper;
-  }
-
-  /**
-   * Quote (Merge price, daily volume, time)
-   */
-  public ConsolidatorKeyVal&lt;String,Double,Long,String,?,?&gt; getQuoteOperator(String name, DAG dag)
-  {
-    ConsolidatorKeyVal&lt;String,Double,Long,String,?,?&gt; oper = dag.addOperator(name, new ConsolidatorKeyVal&lt;String,Double,Long,String,Object,Object&gt;());
-    return oper;
-  }
-
-  /**
-   * Chart (Merge minute volume and minute high-low)
-   */
-  public ConsolidatorKeyVal&lt;String,HighLow,Long,?,?,?&gt; getChartOperator(String name, DAG dag)
-  {
-    ConsolidatorKeyVal&lt;String,HighLow,Long,?,?,?&gt; oper = dag.addOperator(name, new ConsolidatorKeyVal&lt;String,HighLow,Long,Object,Object,Object&gt;());
-    return oper;
-  }
-
-  /**
-   * Get simple moving average of price.
-   */
-  public SimpleMovingAverage&lt;String, Double&gt; getPriceSimpleMovingAverageOperator(String name, DAG dag, int appWindowCount)
-  {
-    SimpleMovingAverage&lt;String, Double&gt; oper = dag.addOperator(name, new SimpleMovingAverage&lt;String, Double&gt;());
-    oper.setWindowSize(appWindowCount);
-    oper.setType(Double.class);
-    return oper;
-  }
-
-  /**
-   * Get console for output.
-   */
-  public InputPort&lt;Object&gt; getConsole(String name, /*String nodeName,*/ DAG dag, String prefix)
-  {
-    ConsoleOutputOperator oper = dag.addOperator(name, ConsoleOutputOperator.class);
-    oper.setStringFormat(prefix + &quot;: %s&quot;);
-    return oper.input;
-  }
-
-  /**
-   * Create Yahoo Finance Application DAG.
-   */
-  @Override
-  public void populateDAG(DAG dag, Configuration conf)
-  {
-    dag.getAttributes().put(DAG.STRAM_WINDOW_SIZE_MILLIS,streamingWindowSizeMilliSeconds);
-
-    StockTickInput tick = getStockTickInputOperator(&quot;StockTickInput&quot;, dag);
-    SumKeyVal&lt;String, Long&gt; dailyVolume = getDailyVolumeOperator(&quot;DailyVolume&quot;, dag);
-    ConsolidatorKeyVal&lt;String,Double,Long,String,?,?&gt; quoteOperator = getQuoteOperator(&quot;Quote&quot;, dag);
-
-    RangeKeyVal&lt;String, Double&gt; highlow = getHighLowOperator(&quot;HighLow&quot;, dag, appWindowCountMinute);
-    SumKeyVal&lt;String, Long&gt; minuteVolume = getMinuteVolumeOperator(&quot;MinuteVolume&quot;, dag, appWindowCountMinute);
-    ConsolidatorKeyVal&lt;String,HighLow,Long,?,?,?&gt; chartOperator = getChartOperator(&quot;Chart&quot;, dag);
-
-    SimpleMovingAverage&lt;String, Double&gt; priceSMA = getPriceSimpleMovingAverageOperator(&quot;PriceSMA&quot;, dag, appWindowCountSMA);
-       DefaultPartitionCodec&lt;String, Double&gt; codec = new DefaultPartitionCodec&lt;String, Double&gt;();
-    dag.setInputPortAttribute(highlow.data, PortContext.STREAM_CODEC, codec);
-    dag.setInputPortAttribute(priceSMA.data, PortContext.STREAM_CODEC, codec);
-    dag.addStream(&quot;price&quot;, tick.price, quoteOperator.in1, highlow.data, priceSMA.data);
-    dag.addStream(&quot;vol&quot;, tick.volume, dailyVolume.data, minuteVolume.data);
-    dag.addStream(&quot;time&quot;, tick.time, quoteOperator.in3);
-    dag.addStream(&quot;daily_vol&quot;, dailyVolume.sum, quoteOperator.in2);
-
-    dag.addStream(&quot;quote_data&quot;, quoteOperator.out, getConsole(&quot;quoteConsole&quot;, dag, &quot;QUOTE&quot;));
-
-    dag.addStream(&quot;high_low&quot;, highlow.range, chartOperator.in1);
-    dag.addStream(&quot;vol_1min&quot;, minuteVolume.sum, chartOperator.in2);
-    dag.addStream(&quot;chart_data&quot;, chartOperator.out, getConsole(&quot;chartConsole&quot;, dag, &quot;CHART&quot;));
-
-    dag.addStream(&quot;sma_price&quot;, priceSMA.doubleSMA, getConsole(&quot;priceSMAConsole&quot;, dag, &quot;Price SMA&quot;));
-
-    return dag;
-  }
-
-}
-</code></pre>
-
-<p>Note that we also set a user-specific sliding window for SMA that
-keeps track of the previous N data points.  Do not confuse this with the
-attribute APPLICATION_WINDOW_COUNT.</p>
-<p>In the rest of this chapter we will run through the process of
-running this application. We assume that  you are familiar with details
-of your Hadoop infrastructure. For installation
-details please refer to the <a href="http://docs.datatorrent.com/installation/">Installation Guide</a>.</p>
-<h2 id="running-a-test-application_1">Running a Test Application</h2>
-<p>We will now describe how to run the yahoo
-finance application described above in different modes
-(local mode, single node on Hadoop, and multi-nodes on Hadoop).</p>
-<p>The platform runs streaming applications under the control of a
-light-weight Streaming Application Manager (STRAM). Each application has
-its own instance of STRAM. STRAM launches the application and
-continually provides run time monitoring, analysis, and takes action
-such as load scaling or outage recovery as needed.  We will discuss
-STRAM in more detail in the next chapter.</p>
-<p>The instructions below assume that the platform was installed in a
-directory &lt;INSTALL_DIR&gt; and the command line interface (CLI) will
-be used to launch the demo application. An application can be run in
-local mode (in IDE or from command line) or on a Hadoop cluster.</p>
-<p>To start the dtCli run</p>
-<pre><code>&lt;INSTALL_DIR&gt;/bin/dtcli
-</code></pre>
-<p>The command line prompt appears.  To start the application in local mode (the actual version number in the file name may differ)</p>
-<pre><code>dt&gt; launch -local &lt;INSTALL_DIR&gt;/yahoo-finance-demo-3.2.0-SNAPSHOT.apa
-</code></pre>
-<p>To terminate the application in local mode, enter Ctrl-C</p>
-<p>Tu run the application on the Hadoop cluster (the actual version
-number in the file name may differ)</p>
-<pre><code>dt&gt; launch &lt;INSTALL_DIR&gt;/yahoo-finance-demo-3.2.0-SNAPSHOT.apa
-</code></pre>
-<p>To stop the application running in Hadoop, terminate it in the dtCli:</p>
-<pre><code>dt&gt; kill-app
-</code></pre>
-<p>Executing the application in either mode includes the following
-steps. At a top level, STRAM (Streaming Application Manager) validates
-the application (DAG), translates the logical plan to the physical plan
-and then launches the execution engine. The mode determines the
-resources needed and how how they are used.</p>
-<h2 id="local-mode">Local Mode</h2>
-<p>In local mode, the application is run as a single-process with multiple threads. Although a
-few Hadoop classes are needed, there is no dependency on a Hadoop
-cluster or Hadoop services. The local file system is used in place of
-HDFS. This mode allows a quick run of an application in a single process
-sandbox, and hence is the most suitable to debug and analyze the
-application logic. This mode is recommended for developing the
-application and can be used for running applications within the IDE for
-functional testing purposes. Due to limited resources and lack  of
-scalability an application running in this single process mode is more
-likely to encounter throughput bottlenecks. A distributed cluster is
-recommended for benchmarking and production testing.</p>
-<h2 id="hadoop-cluster">Hadoop Cluster</h2>
-<p>In this section we discuss various Hadoop cluster setups.</p>
-<h3 id="single-node-cluster">Single Node Cluster</h3>
-<p>In a single node Hadoop cluster all services are deployed on a
-single server (a developer can use his/her development machine as a
-single node cluster). The platform does not distinguish between a single
-or multi-node setup and behaves exactly the same in both cases.</p>
-<p>In this mode, the resource manager, name node, data node, and node
-manager occupy one process each. This is an example of running a
-streaming application as a multi-process application on the same server.
-With prevalence of fast, multi-core systems, this mode is effective for
-debugging, fine tuning, and generic analysis before submitting the job
-to a larger Hadoop cluster. In this mode, execution uses the Hadoop
-services and hence is likely to identify issues that are related to the
-Hadoop environment (such issues will not be uncovered in local mode).
-The throughput will obviously not be as high as on a multi-node Hadoop
-cluster. Additionally, since each container (i.e. Java process) requires
-a significant amount of memory, you will be able to run a much smaller
-number of containers than on a multi-node cluster.</p>
-<h3 id="multi-node-cluster">Multi-Node Cluster</h3>
-<p>In a multi-node Hadoop cluster all the services of Hadoop are
-typically distributed across multiple nodes in a production or
-production-level test environment. Upon launch the application is
-submitted to the Hadoop cluster and executes as a  multi-processapplication on multiple nodes.</p>
-<p>Before you start deploying, testing and troubleshooting your
-application on a cluster, you should ensure that Hadoop (version 2.2.0
-or later) is properly installed and
-you have basic skills for working with it.</p>
-<hr />
-<h1 id="apache-apex-platform-overview">Apache Apex Platform Overview</h1>
-<h2 id="streaming-computational-model">Streaming Computational Model</h2>
-<p>In this chapter, we describe the the basics of the real-time streaming platform and its computational model.</p>
-<p>The platform is designed to enable completely asynchronous real time computations done in as unblocked a way as possible with
-minimal overhead .</p>
-<p>Applications running in the platform are represented by a Directed
-Acyclic Graph (DAG) made up of  operators and streams. All computations
-are done in memory on arrival of
-the input data, with an option to save the output to disk (HDFS) in a
-non-blocking way. The data that flows between operators consists of
-atomic data elements. Each data element along with its type definition
-(henceforth called  schema) is
-called a tuple. An application is a
-design of the flow of these tuples to and from
-the appropriate compute units to enable the computation of the final
-desired results. A message queue (henceforth called
- buffer server) manages tuples streaming
-between compute units in different processes.This server keeps track of
-all consumers, publishers, partitions, and enables replay. More
-information is given in later section.</p>
-<p>The streaming application is monitored by a decision making entity
-called STRAM (streaming application
-manager). STRAM is designed to be a light weight
-controller that has minimal but sufficient interaction with the
-application. This is done via periodic heartbeats. The
-STRAM does the initial launch and periodically analyzes the system
-metrics to decide if any run time action needs to be taken.</p>
-<p>A fundamental building block for the streaming platform
-is the concept of breaking up a stream into equal finite time slices
-called streaming windows. Each window contains the ordered
-set of tuples in that time slice. A typical duration of a window is 500
-ms, but can be configured per application (the Yahoo! Finance
-application configures this value in the  properties.xml file to be 1000ms = 1s). Each
-window is preceded by a begin_window event and is terminated by an
-end_window event, and is assigned
-a unique window ID. Even though the platform performs computations at
-the tuple level, bookkeeping is done at the window boundary, making the
-computations within a window an atomic event in the platform.  We can
-think of each window as an  atomic
-micro-batch of tuples, to be processed together as one
-atomic operation (See Figure 2).  </p>
-<p>This atomic batching allows the platform to avoid the very steep
-per tuple bookkeeping cost and instead has a manageable per batch
-bookkeeping cost. This translates to higher throughput, low recovery
-time, and higher scalability. Later in this document we illustrate how
-the atomic micro-batch concept allows more efficient optimization
-algorithms.</p>
-<p>The platform also has in-built support for
-application windows.  An application window is part of the
-application specification, and can be a small or large multiple of the
-streaming window.  An example from our Yahoo! Finance test application
-is the moving average, calculated over a sliding application window of 5
-minutes which equates to 300 (= 5 * 60) streaming windows.</p>
-<p>Note that these two window concepts are distinct.  A streaming
-window is an abstraction of many tuples into a higher atomic event for
-easier management.  An application window is a group of consecutive
-streaming windows used for data aggregation (e.g. sum, average, maximum,
-minimum) on a per operator level.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image02.png" /></p>
-<p>Alongside the platform, a set of
-predefined, benchmarked standard library operator templates is provided
-for ease of use and rapid development of application. These
-operators are open sourced to Apache Software Foundation under the
-project name “Malhar” as part of our efforts to foster community
-innovation. These operators can be used in a DAG as is, while others
-have properties that can be set to specify the
-desired computation. Those interested in details, should refer to
-<a href="https://github.com/apache/incubator-apex-malhar">Apex-Malhar operator library</a>.</p>
-<p>The platform is a Hadoop YARN native
-application. It runs in a Hadoop cluster just like any
-other YARN application (MapReduce etc.) and is designed to seamlessly
-integrate with rest of Hadoop technology stack. It leverages Hadoop as
-much as possible and relies on it as its distributed operating system.
-Hadoop dependencies include resource management, compute/memory/network
-allocation, HDFS, security, fault tolerance, monitoring, metrics,
-multi-tenancy, logging etc. Hadoop classes/concepts are reused as much
-as possible.  The aim is to enable enterprises
-to leverage their existing Hadoop infrastructure for real time streaming
-applications. The platform is designed to scale with big
-data applications and scale with Hadoop.</p>
-<p>A streaming application is an asynchronous execution of
-computations across distributed nodes. All computations are done in
-parallel on a distributed cluster. The computation model is designed to
-do as many parallel computations as possible in a non-blocking fashion.
-The task of monitoring of the entire application is done on (streaming)
-window boundaries with a streaming window as an atomic entity. A window
-completion is a quantum of work done. There is no assumption that an
-operator can be interrupted at precisely a particular tuple or window.</p>
-<p>An operator itself also
-cannot assume or predict the exact time a tuple that it emitted would
-get consumed by downstream operators. The operator processes the tuples
-it gets and simply emits new tuples based on its business logic. The
-only guarantee it has is that the upstream operators are processing
-either the current or some later window, and the downstream operator is
-processing either the current or some earlier window. The completion of
-a window (i.e. propagation of the  end_window event through an operator) in any
-operator guarantees that all upstream operators have finished processing
-this window. Thus, the end_window event is blocking on an operator
-with multiple outputs, and is a synchronization point in the DAG. The
- begin_window event does not have
-any such restriction, a single begin_window event from any upstream operator
-triggers the operator to start processing tuples.</p>
-<h2 id="streaming-application-manager-stram">Streaming Application Manager (STRAM)</h2>
-<p>Streaming Application Manager (STRAM) is the Hadoop YARN native
-application master. STRAM is the first process that is activated upon
-application launch and orchestrates the streaming application on the
-platform. STRAM is a lightweight controller process. The
-responsibilities of STRAM include</p>
-<ol>
-<li>
-<p>Running the Application</p>
-<ul>
-<li>Read the logical plan of the application (DAG) submitted by the client</li>
-<li>Validate the logical plan</li>
-<li>Translate the logical plan into a physical plan, where certain operators may  be partitioned (i.e. replicated) to multiple operators for  handling load.</li>
-<li>Request resources (Hadoop containers) from Resource Manager,
-    per physical plan</li>
-<li>Based on acquired resources and application attributes, create
-    an execution plan by partitioning the DAG into fragments,
-    each assigned to different containers.</li>
-<li>Executes the application by deploying each fragment to
-    its container. Containers then start stream processing and run
-    autonomously, processing one streaming window after another. Each
-    container is represented as an instance of the  StreamingContainer class, which updates
-    STRAM via the heartbeat protocol and processes directions received
-    from STRAM.</li>
-</ul>
-</li>
-<li>
-<p>Continually monitoring the application via heartbeats from each StreamingContainer</p>
-</li>
-<li>Collecting Application System Statistics and Logs</li>
-<li>Logging all application-wide decisions taken</li>
-<li>Providing system data on the state of the application via a  Web Service.</li>
-<li>
-<p>Supporting Fault Tolerance</p>
-<p>a.  Detecting a node outage
-b.  Requesting a replacement resource from the Resource Manager
-    and scheduling state restoration for the streaming operators
-c.  Saving state to Zookeeper</p>
-</li>
-<li>
-<p>Supporting Dynamic Partitioning: Periodically evaluating the SLA and modifying the physical plan if required
-    (logical plan does not change).</p>
-</li>
-<li>Enabling Security: Distributing security tokens for distributed components of the execution engine
-    and securing web service requests.</li>
-<li>Enabling Dynamic modification of DAG: In the future, we intend to allow for user initiated
-    modification of the logical plan to allow for changes to the
-    processing logic and functionality.</li>
-</ol>
-<p>An example of the Yahoo! Finance Quote application scheduled on a
-cluster of 5 Hadoop containers (processes) is shown in Figure 3.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image01.png" /></p>
-<p>An example for the translation from a logical plan to a physical
-plan and an execution plan for a subset of the application is shown in
-Figure 4.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image04.png" /></p>
-<h2 id="hadoop-components">Hadoop Components</h2>
-<p>In this section we cover some aspects of Hadoop that your
-streaming application interacts with. This section is not meant to
-educate the reader on Hadoop, but just get the reader acquainted with
-the terms. We strongly advise readers to learn Hadoop from other
-sources.</p>
-<p>A streaming application runs as a native Hadoop 2.2 application.
-Hadoop 2.2 does not differentiate between a map-reduce job and other
-applications, and hence as far as Hadoop is concerned, the streaming
-application is just another job. This means that your application
-leverages all the bells and whistles Hadoop provides and is fully
-supported within Hadoop technology stack. The platform is responsible
-for properly integrating itself with the relevant components of Hadoop
-that exist today and those that may emerge in the future</p>
-<p>All investments that leverage multi-tenancy (for example quotas
-and queues), security (for example kerberos), data flow integration (for
-example copying data in-out of HDFS), monitoring, metrics collections,
-etc. will require no changes when streaming applications run on
-Hadoop.</p>
-<h3 id="yarn">YARN</h3>
-<p><a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site">YARN</a>is
-the core library of Hadoop 2.2 that is tasked with resource management
-and works as a distributed application framework. In this section we
-will walk through Yarn's components. In Hadoop 2.2, the old jobTracker
-has been replaced by a combination of ResourceManager (RM) and
-ApplicationMaster (AM).</p>
-<h4 id="resource-manager-rm">Resource Manager (RM)</h4>
-<p><a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html">ResourceManager</a>(RM)
-manages all the distributed resources. It allocates and arbitrates all
-the slots and the resources (cpu, memory, network) of these slots. It
-works with per-node NodeManagers (NMs) and per-application
-ApplicationMasters (AMs). Currently memory usage is monitored by RM; in
-upcoming releases it will have CPU as well as network management. RM is
-shared by map-reduce and streaming applications. Running streaming
-applications requires no changes in the RM.</p>
-<h4 id="application-master-am">Application Master (AM)</h4>
-<p>The AM is the watchdog or monitoring process for your application
-and has the responsibility of negotiating resources with RM and
-interacting with NodeManagers to get the allocated containers started.
-The AM is the starting point of your application and is considered user
-code (not system Hadoop code). The AM itself runs in one container. All
-resource management within the application are managed by the AM. This
-is a critical feature for Hadoop 2.2 where tasks done by jobTracker in
-Hadoop 1.0 have been distributed allowing Hadoop 2.2 to scale much
-beyond Hadoop 1.0. STRAM is a native YARN ApplicationManager.</p>
-<h4 id="node-managers-nm">Node Managers (NM)</h4>
-<p>There is one <a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html">NodeManager</a>(NM)
-per node in the cluster. All the containers (i.e. processes) on that
-node are monitored by the NM. It takes instructions from RM and manages
-resources of that node as per RM instructions. NMs interactions are same
-for map-reduce and for streaming applications. Running streaming
-applications requires no changes in the NM.</p>
-<h4 id="rpc-protocol">RPC Protocol</h4>
-<p>Communication among RM, AM, and NM is done via the Hadoop RPC
-protocol. Streaming applications use the same protocol to send their
-data. No changes are needed in RPC support provided by Hadoop to enable
-communication done by components of your application.</p>
-<h3 id="hdfs">HDFS</h3>
-<p>Hadoop includes a highly fault tolerant, high throughput
-distributed file system (<a href="http://hadoop.apache.org/docs/r1.0.4/hdfs_design.html">HDFS</a>).
-It runs on commodity hardware, and your streaming application will, by
-default, use it. There is no difference between files created by a
-streaming application and those created by map-reduce.</p>
-<h1 id="developing-an-application">Developing An Application</h1>
-<p>In this chapter we describe the methodology to develop an
-application using the Realtime Streaming Platform. The platform was
-designed to make it easy to build and launch sophisticated streaming
-applications with the developer having to deal only with the
-application/business logic. The platform deals with details of where to
-run what operators on which servers and how to correctly route streams
-of data among them.</p>
-<h2 id="development-process">Development Process</h2>
-<p>While the platform does not mandate a specific methodology or set
-of development tools, we have recommendations to maximize productivity
-for the different phases of application development.</p>
-<h4 id="design">Design</h4>
-<ul>
-<li>Identify common, reusable operators. Use a library
-    if possible.</li>
-<li>Identify scalability and performance requirements before
-    designing the DAG.</li>
-<li>Leverage attributes that the platform supports for scalability
-    and performance.</li>
-<li>Use operators that are benchmarked and tested so that later
-    surprises are minimized. If you have glue code, create appropriate
-    unit tests for it.</li>
-<li>Use THREAD_LOCAL locality for high throughput streams. If all
-    the operators on that stream cannot fit in one container,
-    try NODE_LOCAL locality. Both THREAD_LOCAL and
-    NODE_LOCAL streams avoid the Network Interface Card (NIC)
-    completly. The former uses intra-process communication to also avoid
-    serialization-deserialization overhead.</li>
-<li>The overall throughput and latencies are are not necessarily
-    correlated to the number of operators in a simple way -- the
-    relationship is more nuanced. A lot depends on how much work
-    individual operators are doing, how many are able to operate in
-    parallel, and how much data is flowing through the arcs of the DAG.
-    It is, at times, better to break a computation down into its
-    constituent simple parts and then stitch them together via streams
-    to better utilize the compute resources of the cluster. Decide on a
-    per application basis the fine line between complexity of each
-    operator vs too many streams. Doing multiple computations in one
-    operator does save network I/O, while operators that are too complex
-    are hard to maintain.</li>
-<li>Do not use operators that depend on the order of two streams
-    as far as possible. In such cases behavior is not idempotent.</li>
-<li>Persist key information to HDFS if possible; it may be useful
-    for debugging later.</li>
-<li>Decide on an appropriate fault tolerance mechanism. If some
-    data loss is acceptable, use the at-most-once mechanism as it has
-    fastest recovery.</li>
-</ul>
-<h4 id="creating-new-project">Creating New Project</h4>
-<p>Please refer to the <a href="../application_packages/">Apex Application Packages</a> for
-the basic steps for creating a new project.</p>
-<h4 id="writing-the-application-code">Writing the application code</h4>
-<p>Preferably use an IDE (Eclipse, Netbeans etc.) that allows you to
-manage dependencies and assists with the Java coding. Specific benefits
-include ease of managing operator library jar files, individual operator
-classes, ports and properties. It will also highlight and assist to
-rectify issues such as type mismatches when adding streams while
-typing.</p>
-<h4 id="testing">Testing</h4>
-<p>Write test cases with JUnit or similar test framework so that code
-is tested as it is written. For such testing, the DAG can run in local
-mode within the IDE. Doing this may involve writing mock input or output
-operators for the integration points with external systems. For example,
-instead of reading from a live data stream, the application in test mode
-can read from and write to files. This can be done with a single
-application DAG by instrumenting a test mode using settings in the
-configuration that is passed to the application factory
-interface.</p>
-<p>Good test coverage will not only eliminate basic validation errors
-such as missing port connections or property constraint violations, but
-also validate the correct processing of the data. The same tests can be
-re-run whenever the application or its dependencies change (operator
-libraries, version of the platform etc.)</p>
-<h4 id="running-an-application">Running an application</h4>
-<p>The platform provides a commandline tool called dtcli for managing applications (launching,
-killing, viewing, etc.). This tool was already discussed above briefly
-in the section entitled Running the Test Application. It will introspect
-the jar file specified with the launch command for applications (classes
-that implement ApplicationFactory) or property files that define
-applications. It will also deploy the dependency jar files from the
-application package to the cluster.</p>
-<p>Dtcli can run the application in local mode (i.e. outside a
-cluster). It is recommended to first run the application in local mode
-in the development environment before launching on the Hadoop cluster.
-This way some of the external system integration and correct
-functionality of the application can be verified in an easier to debug
-environment before testing distributed mode.</p>
-<p>For more details on CLI please refer to the <a href="../dtcli/">dtCli Guide</a>.</p>
-<h2 id="application-api">Application API</h2>
-<p>This section introduces the API to write a streaming application.
-The work involves connecting operators via streams to form the logical
-DAG. The steps are</p>
-<ol>
-<li>
-<p>Instantiate an application (DAG)</p>
-</li>
-<li>
-<p>(Optional) Set Attributes</p>
-<ul>
-<li>Assign application name</li>
-<li>Set any other attributes as per application requirements</li>
-</ul>
-</li>
-<li>
-<p>Create/re-use and instantiate operators</p>
-<ul>
-<li>Assign operator name that is unique within the  application</li>
-<li>Declare schema upfront for each operator (and thereby its ports)</li>
-<li>(Optional) Set properties  and attributes on the dag as per specification</li>
-<li>Connect ports of operators via streams<ul>
-<li>Each stream connects one output port of an operator to one or  more input ports of other operators.</li>
-<li>(Optional) Set attributes on the streams</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-<p>Test the application.</p>
-</li>
-</ol>
-<p>There are two methods to create an application, namely Java, and
-Properties file. Java API is for applications being developed by humans,
-and properties file (Hadoop like) is more suited for DAGs generated by
-tools.</p>
-<h3 id="java-api">Java API</h3>
-<p>The Java API is the most common way to create a streaming
-application. It is meant for application developers who prefer to
-leverage the features of Java, and the ease of use and enhanced
-productivity provided by IDEs like NetBeans or Eclipse. Using Java to
-specify the application provides extra validation abilities of Java
-compiler, such as compile time checks for type safety at the time of
-writing the code. Later in this chapter you can read more about
-validation support in the platform.</p>
-<p>The developer specifies the streaming application by implementing
-the ApplicationFactory interface, which is how platform tools (CLI etc.)
-recognize and instantiate applications. Here we show how to create a
-Yahoo! Finance application that streams the last trade price of a ticker
-and computes the high and low price in every 1 min window. Run above
- test application to execute the
-DAG in local mode within the IDE.</p>
-<p>Let us revisit how the Yahoo! Finance test application constructs the DAG:</p>
-<pre><code class="java">public class Application implements StreamingApplication
-{
-
-  ...
-
-  @Override
-  public void populateDAG(DAG dag, Configuration conf)
-  {
-    dag.getAttributes().attr(DAG.STRAM_WINDOW_SIZE_MILLIS).set(streamingWindowSizeMilliSeconds);
-
-    StockTickInput tick = getStockTickInputOperator(&quot;StockTickInput&quot;, dag);
-    SumKeyVal&lt;String, Long&gt; dailyVolume = getDailyVolumeOperator(&quot;DailyVolume&quot;, dag);
-    ConsolidatorKeyVal&lt;String,Double,Long,String,?,?&gt; quoteOperator = getQuoteOperator(&quot;Quote&quot;, dag);
-
-    RangeKeyVal&lt;String, Double&gt; highlow = getHighLowOperator(&quot;HighLow&quot;, dag, appWindowCountMinute);
-    SumKeyVal&lt;String, Long&gt; minuteVolume = getMinuteVolumeOperator(&quot;MinuteVolume&quot;, dag, appWindowCountMinute);
-    ConsolidatorKeyVal&lt;String,HighLow,Long,?,?,?&gt; chartOperator = getChartOperator(&quot;Chart&quot;, dag);
-
-    SimpleMovingAverage&lt;String, Double&gt; priceSMA = getPriceSimpleMovingAverageOperator(&quot;PriceSMA&quot;, dag, appWindowCountSMA);
-
-    dag.addStream(&quot;price&quot;, tick.price, quoteOperator.in1, highlow.data, priceSMA.data);
-    dag.addStream(&quot;vol&quot;, tick.volume, dailyVolume.data, minuteVolume.data);
-    dag.addStream(&quot;time&quot;, tick.time, quoteOperator.in3);
-    dag.addStream(&quot;daily_vol&quot;, dailyVolume.sum, quoteOperator.in2);
-
-    dag.addStream(&quot;quote_data&quot;, quoteOperator.out, getConsole(&quot;quoteConsole&quot;, dag, &quot;QUOTE&quot;));
-
-    dag.addStream(&quot;high_low&quot;, highlow.range, chartOperator.in1);
-    dag.addStream(&quot;vol_1min&quot;, minuteVolume.sum, chartOperator.in2);
-    dag.addStream(&quot;chart_data&quot;, chartOperator.out, getConsole(&quot;chartConsole&quot;, dag, &quot;CHART&quot;));
-
-    dag.addStream(&quot;sma_price&quot;, priceSMA.doubleSMA, getConsole(&quot;priceSMAConsole&quot;, dag, &quot;Price SMA&quot;));
-
-    return dag;
-  }
-}
-</code></pre>
-
-<h3 id="property-file-api">Property File API</h3>
-<p>The platform also supports specification of a DAG via a property
-file. The aim here to make it easy for tools to create and run an
-application. This method of specification does not have the Java
-compiler support of compile time check, but since these applications
-would be created by software, they should be correct by construction.
-The syntax is derived from Hadoop properties and should be easy for
-folks who are used to creating software that integrated with
-Hadoop.</p>
-<p>Create an application (DAG): myApplication.properties</p>
-<pre><code># input operator that reads from a file
-dt.operator.inputOp.classname=com.acme.SampleInputOperator
-dt.operator.inputOp.fileName=somefile.txt
-
-# output operator that writes to the console
-dt.operator.outputOp.classname=com.acme.ConsoleOutputOperator
-
-# stream connecting both operators
-dt.stream.inputStream.source=inputOp.outputPort
-dt.stream.inputStream.sinks=outputOp.inputPort
-</code></pre>
-
-<p>Above snippet is intended to convey the basic idea of specifying
-the DAG without using Java. Operators would come from a predefined
-library and referenced in the specification by class name and port names
-(obtained from the library providers documentation or runtime
-introspection by tools). For those interested in details, see later
-sections and refer to the  Operation and
-Installation Guide mentioned above.</p>
-<h3 id="attributes">Attributes</h3>
-<p>Attributes impact the runtime behavior of the application. They do
-not impact the functionality. An example of an attribute is application
-name. Setting it changes the application name. Another example is
-streaming window size. Setting it changes the streaming window size from
-the default value to the specified value. Users cannot add new
-attributes, they can only choose from the ones that come packaged and
-pre-supported by the platform. Details of attributes are covered in the
- Operation and Installation
-Guide.</p>
-<h2 id="operators">Operators</h2>
-<p>Operators are basic compute units.
-Operators process each incoming tuple and emit zero or more tuples on
-output ports as per the business logic. The data flow, connectivity,
-fault tolerance (node outage), etc. is taken care of by the platform. As
-an operator developer, all that is needed is to figure out what to do
-with the incoming tuple and when (and which output port) to send out a
-particular output tuple. Correctly designed operators will most likely
-get reused. Operator design needs care and foresight. For details, refer
-to the  <a href="../operator_development/">Operator Developer Guide</a>. As an application developer you need to connect operators
-in a way that it implements your business logic. You may also require
-operator customization for functionality and use attributes for
-performance/scalability etc.</p>
-<p>All operators process tuples asynchronously in a distributed
-cluster. An operator cannot assume or predict the exact time a tuple
-that it emitted will get consumed by a downstream operator. An operator
-also cannot predict the exact time when a tuple arrives from an upstream
-operator. The only guarantee is that the upstream operators are
-processing the current or a future window, i.e. the windowId of upstream
-operator is equals or exceeds its own windowId. Conversely the windowId
-of a downstream operator is less than or equals its own windowId. The
-end of a window operation, i.e. the API call to endWindow on an operator
-requires that all upstream operators have finished processing this
-window. This means that completion of processing a window propagates in
-a blocking fashion through an operator. Later sections provides more
-details on streams and data flow of tuples.</p>
-<p>Each operator has a unique name within the DAG as provided by the
-user. This is the name of the operator in the logical plan. The name of
-the operator in the physical plan is an integer assigned to it by STRAM.
-These integers are use the sequence from 1 to N, where N is total number
-of physically unique operators in the DAG.  Following the same rule,
-each partitioned instance of a logical operator has its own integer as
-an id. This id along with the Hadoop container name uniquely identifies
-the operator in the execution plan of the DAG. The logical names and the
-physical names are required for web service support. Operators can be
-accessed via both names. These same names are used while interacting
-with  dtcli to access an operator.
-Ideally these names should be self-descriptive. For example in Figure 1,
-the node named “Daily volume” has a physical identifier of 2.</p>
-<h3 id="operator-interface">Operator Interface</h3>
-<p>Operator interface in a DAG consists of ports, properties, and attributes.
-Operators interact with other components of the DAG via ports. Functional behavior of the operators
-can be customized via parameters. Run time performance and physical
-instantiation is controlled by attributes. Ports and parameters are
-fields (variables) of the Operator class/object, while attributes are
-meta information that is attached to the operator object via an
-AttributeMap. An operator must have at least one port. Properties are
-optional. Attributes are provided by the platform and always have a
-default value that enables normal functioning of operators.</p>
-<h4 id="ports">Ports</h4>
-<p>Ports are connection points by which an operator receives and
-emits tuples. These should be transient objects instantiated in the
-operator object, that implement particular interfaces. Ports should be
-transient as they contain no state. They have a pre-defined schema and
-can only be connected to other ports with the same schema. An input port
-needs to implement the interface  Operator.InputPort and
-interface Sink. A default
-implementation of these is provided by the abstract class DefaultInputPort. An output port needs to
-implement the interface  Operator.OutputPort. A default implementation
-of this is provided by the concrete class DefaultOutputPort. These two are a quick way to
-implement the above interfaces, but operator developers have the option
-of providing their own implementations.</p>
-<p>Here are examples of an input and an output port from the operator
-Sum.</p>
-<pre><code class="java">@InputPortFieldAnnotation(name = &quot;data&quot;)
-public final transient DefaultInputPort&lt;V&gt; data = new DefaultInputPort&lt;V&gt;() {
-  @Override
-  public void process(V tuple)
-  {
-    ...
-  }
-}
-@OutputPortFieldAnnotation(optional=true)
-public final transient DefaultOutputPort&lt;V&gt; sum = new DefaultOutputPort&lt;V&gt;(){ … };
-</code></pre>
-
-<p>The process call is in the Sink interface. An emit on an output
-port is done via emit(tuple) call. For the above example it would be
-sum.emit(t), where the type of t is the generic parameter V.</p>
-<p>There is no limit on how many ports an operator can have. However
-any operator must have at least one port. An operator with only one port
-is called an Input Adapter if it has no input port and an Output Adapter
-if it has no output port. These are special operators needed to get/read
-data from outside system/source into the application, or push/write data
-into an outside system/sink. These could be in Hadoop or outside of
-Hadoop. These two operators are in essence gateways for the streaming
-application to communicate with systems outside the application.</p>
-<p>Port connectivity can be validated during compile time by adding
-PortFieldAnnotations shown above. By default all ports have to be
-connected, to allow a port to go unconnected, you need to add
-“optional=true” to the annotation.</p>
-<p>Attributes can be specified for ports that affect the runtime
-behavior. An example of an attribute is parallel partition that specifes
-a parallel computation flow per partition. It is described in detail in
-the Parallel Partitions section. Another example is queue capacity that specifies the buffer size for the
-port. Details of attributes are covered in  Operation and Installation Guide.</p>
-<h4 id="properties">Properties</h4>
-<p>Properties are the abstractions by which functional behavior of an
-operator can be customized. They should be non-transient objects
-instantiated in the operator object. They need to be non-transient since
-they are part of the operator state and re-construction of the operator
-object from its checkpointed state must restore the operator to the
-desired state. Properties are optional, i.e. an operator may or may not
-have properties; they are part of user code and their values are not
-interpreted by the platform in any way.</p>
-<p>All non-serializable objects should be declared transient.
-Examples include sockets, session information, etc. These objects should
-be initialized during setup call, which is called every time the
-operator is initialized.</p>
-<h4 id="attributes_1">Attributes</h4>
-<p>Attributes are values assigned to the operators that impact
-run-time. This includes things like the number of partitions, at most
-once or at least once or exactly once recovery modes, etc. Attributes do
-not impact functionality of the operator. Users can change certain
-attributes in runtime. Users cannot add attributes to operators; they
-are pre-defined by the platform. They are interpreted by the platform
-and thus cannot be defined in user created code (like properties).
-Details of attributes are covered in  <a href="http://docs.datatorrent.com/configuration/">Configuration Guide</a>.</p>
-<h3 id="operator-state">Operator State</h3>
-<p>The state of an operator is defined as the data that it transfers
-from one window to a future window. Since the computing model of the
-platform is to treat windows like micro-batches, the operator state can
-be checkpointed every Nth window, or every T units of time, where T is significantly greater
-than the streaming window.  When an operator is checkpointed, the entire
-object is written to HDFS.  The larger the amount of state in an
-operator, the longer it takes to recover from a failure. A stateless
-operator can recover much quicker than a stateful one. The needed
-windows are preserved by the upstream buffer server and are used to
-recompute the lost windows, and also rebuild the buffer server in the
-current container.</p>
-<p>The distinction between Stateless and Stateful is based solely on
-the need to transfer data in the operator from one window to the next.
-The state of an operator is independent of the number of ports.</p>
-<h4 id="stateless">Stateless</h4>
-<p>A Stateless operator is defined as one where no data is needed to
-be kept at the end of every window. This means that all the computations
-of a window can be derived from all the tuples the operator receives
-within that window. This guarantees that the output of any window can be
-reconstructed by simply replaying the tuples that arrived in that
-window. Stateless operators are more efficient in terms of fault
-tolerance, and cost to achieve SLA.</p>
-<h4 id="stateful">Stateful</h4>
-<p>A Stateful operator is defined as one where data is needed to be
-stored at the end of a window for computations occurring in later
-window; a common example is the computation of a sum of values in the
-input tuples.</p>
-<h3 id="operator-api">Operator API</h3>
-<p>The Operator API consists of methods that operator developers may
-need to override. In this section we will discuss the Operator APIs from
-the point of view of an application developer. Knowledge of how an
-operator works internally is critical for writing an application. Those
-interested in the details should refer to  Malhar Operator Developer Guide.</p>
-<p>The APIs are available in three modes, namely Single Streaming
-Window, Sliding Application Window, and Aggregate Application Window.
-These are not mutually exclusive, i.e. an operator can use single
-streaming window as well as sliding application window. A physical
-instance of an operator is always processing tuples from a single
-window. The processing of tuples is guaranteed to be sequential, no
-matter which input port the tuples arrive on.</p>
-<p>In the later part of this section we will evaluate three common
-uses of streaming windows by applications. They have different
-characteristics and implications on optimization and recovery mechanisms
-(i.e. algorithm used to recover a node after outage) as discussed later
-in the section.</p>
-<h4 id="streaming-window">Streaming Window</h4>
-<p>Streaming window is atomic micro-batch computation period. The API
-methods relating to a streaming window are as follows</p>
-<pre><code class="java">public void process(&lt;tuple_type&gt; tuple) // Called on the input port on which the tuple arrives
-public void beginWindow(long windowId) // Called at the start of the window as soon as the first begin_window tuple arrives
-public void endWindow() // Called at the end of the window after end_window tuples arrive on all input ports
-public void setup(OperatorContext context) // Called once during initialization of the operator
-public void teardown() // Called once when the operator is being shutdown
-</code></pre>
-
-<p>A tuple can be emitted in any of the three streaming run-time
-calls, namely beginWindow, process, and endWindow but not in setup or
-teardown.</p>
-<h4 id="aggregate-application-window">Aggregate Application Window</h4>
-<p>An operator with an aggregate window is stateful within the
-application window timeframe and possibly stateless at the end of that
-application window. An size of an aggregate application window is an
-operator attribute and is defined as a multiple of the streaming window
-size. The platform recognizes this attribute and optimizes the operator.
-The beginWindow, and endWindow calls are not invoked for those streaming
-windows that do not align with the application window. For example in
-case of streaming window of 0.5 second and application window of 5
-minute, an application window spans 600 streaming windows (5*60*2 =
-600). At the start of the sequence of these 600 atomic streaming
-windows, a beginWindow gets invoked, and at the end of these 600
-streaming windows an endWindow gets invoked. All the intermediate
-streaming windows do not invoke beginWindow or endWindow. Bookkeeping,
-node recovery, stats, UI, etc. continue to work off streaming windows.
-For example if operators are being checkpointed say on an average every
-30th window, then the above application window would have about 20
-checkpoints.</p>
-<h4 id="sliding-application-window">Sliding Application Window</h4>
-<p>A sliding window is computations that requires previous N
-streaming windows. After each streaming window the Nth past window is
-dropped and the new window is added to the computation. An operator with
-sliding window is a stateful operator at end of any window. The sliding
-window period is an attribute and is a multiple of streaming window. The
-platform recognizes this attribute and leverages it during bookkeeping.
-A sliding aggregate window with tolerance to data loss does not have a
-very high bookkeeping cost. The cost of all three recovery mechanisms,
- at most once (data loss tolerant),
-at least once (data loss
-intolerant), and exactly once (data
-loss intolerant and no extra computations) is same as recovery
-mechanisms based on streaming window. STRAM is not able to leverage this
-operator for any extra optimization.</p>
-<h3 id="single-vs-multi-input-operator">Single vs Multi-Input Operator</h3>
-<p>A single-input operator by definition has a single upstream
-operator, since there can only be one writing port for a stream.  If an
-operator has a single upstream operator, then the beginWindow on the
-upstream also blocks the beginWindow of the single-input operator. For
-an operator to start processing any window at least one upstream
-operator has to start processing that window. A multi-input operator
-reads from more than one upstream ports. Such an operator would start
-processing as soon as the first begin_window event arrives. However the
-window would not close (i.e. invoke endWindow) till all ports receive
-end_window events for that windowId. Thus the end of a window is a
-blocking event. As we saw earlier, a multi-input operator is also the
-point in the DAG where windows of all upstream operators are
-synchronized. The windows (atomic micro-batches) from a faster (or just
-ahead in processing) upstream operators are queued up till the slower
-upstream operator catches up. STRAM monitors such bottlenecks and takes
-corrective actions. The platform ensures minimal delay, i.e processing
-starts as long as at least one upstream operator has started
-processing.</p>
-<h3 id="recovery-mechanisms">Recovery Mechanisms</h3>
-<p>Application developers can set any of the recovery mechanisms
-below to deal with node outage. In general, the cost of recovery depends
-on the state of the operator, while data integrity is dependant on the
-application. The mechanisms are per window as the platform treats
-windows as atomic compute units. Three recovery mechanisms are
-supported, namely</p>
-<ul>
-<li>At-least-once: All atomic batches are processed at least once.
-    No data loss occurs.</li>
-<li>At-most-once: All atomic batches are processed at most once.
-    Data loss is possible; this is the most efficient setting.</li>
-<li>Exactly-once: All atomic batches are processed exactly once.
-    No data loss occurs; this is the least efficient setting since
-    additional work is needed to ensure proper semantics.</li>
-</ul>
-<p>At-least-once is the default. During a recovery event, the
-operator connects to the upstream buffer server and asks for windows to
-be replayed. At-least-once and exactly-once mechanisms start from its
-checkpointed state. At-most-once starts from the next begin-window
-event.</p>
-<p>Recovery mechanisms can be specified per Operator while writing
-the application as shown below.</p>
-<pre><code class="java">Operator o = dag.addOperator(“operator”, …);
-dag.setAttribute(o,  OperatorContext.PROCESSING_MODE,  ProcessingMode.AT_MOST_ONCE);
-</code></pre>
-
-<p>Also note that once an operator is attributed to AT_MOST_ONCE,
-all the operators downstream to it have to be AT_MOST_ONCE. The client
-will give appropriate warnings or errors if that’s not the case.</p>
-<p>Details are explained in the chapter on Fault Tolerance below.</p>
-<h2 id="streams">Streams</h2>
-<p>A stream is a connector
-(edge) abstraction, and is a fundamental building block of the platform.
-A stream consists of tuples that flow from one port (called the
-output port) to one or more ports
-on other operators (called  input ports) another -- so note a potentially
-confusing aspect of this terminology: tuples enter a stream through its
-output port and leave via one or more input ports. A stream has the
-following characteristics</p>
-<ul>
-<li>Tuples are always delivered in the same order in which they
-    were emitted.</li>
-<li>Consists of a sequence of windows one after another. Each
-    window being a collection of in-order tuples.</li>
-<li>A stream that connects two containers passes through a
-    buffer server.</li>
-<li>All streams can be persisted (by default in HDFS).</li>
-<li>Exactly one output port writes to the stream.</li>
-<li>Can be read by one or more input ports.</li>
-<li>Connects operators within an application, not outside
-    an application.</li>
-<li>Has an unique name within an application.</li>
-<li>Has attributes which act as hints to STRAM.</li>
-<li>
-<p>Streams have four modes, namely in-line, in-node, in-rack,
-    and other. Modes may be overruled (for example due to lack
-    of containers). They are defined as follows:</p>
-<ul>
-<li>THREAD_LOCAL: In the same thread, uses thread
-    stack (intra-thread). This mode can only be used for a downstream
-    operator which has only one input port connected; also called
-    in-line.</li>
-<li>CONTAINER_LOCAL: In the same container (intra-process); also
-    called in-container.</li>
-<li>NODE_LOCAL: In the same Hadoop node (inter processes, skips
-    NIC); also called in-node.</li>
-<li>RACK_LOCAL: On nodes in the same rack; also called
-    in-rack.</li>
-<li>unspecified: No guarantee. Could be anywhere within the
-    cluster</li>
-</ul>
-</li>
-</ul>
-<p>An example of a stream declaration is given below</p>
-<pre><code class="java">DAG dag = new DAG();
- …
-dag.addStream(&quot;views&quot;, viewAggregate.sum, cost.data).setLocality(CONTAINER_LOCAL); // A container local  stream
-dag.addStream(“clicks”, clickAggregate.sum, rev.data); // An example of unspecified locality
-</code></pre>
-
-<p>The platform guarantees in-order delivery of tuples in a stream.
-STRAM views each stream as collection of ordered windows. Since no tuple
-can exist outside a window, a replay of a stream consists of replay of a
-set of windows. When multiple input ports read the same stream, the
-execution plan of a stream ensures that each input port is logically not
-blocked by the reading of another input port. The schema of a stream is
-same as the schema of the tuple.</p>
-<p>In a stream all tuples emitted by an operator in a window belong
-to that window. A replay of this window would consists of an in-order
-replay of all the tuples. Thus the tuple order within a stream is
-guaranteed. However since an operator may receive multiple streams (for
-example an operator with two input ports), the order of arrival of two
-tuples belonging to different streams is not guaranteed. In general in
-an asynchronous distributed architecture this is expected. Thus the
-operator (specially one with multiple input ports) should not depend on
-the tuple order from two streams. One way to cope with this
-indeterminate order, if necessary, is to wait to get all the tuples of a
-window and emit results in endWindow call. All operator templates
-provided as part of Malhar operator library follow these principles.</p>
-<p>A logical stream gets partitioned into physical streams each
-connecting the partition to the upstream operator. If two different
-attributes are needed on the same stream, it should be split using
-StreamDuplicator operator.</p>
-<p>Modes of the streams are critical for performance. An in-line
-stream is the most optimal as it simply delivers the tuple as-is without
-serialization-deserialization. Streams should be marked
-container_local, specially in case where there is a large tuple volume
-between two operators which then on drops significantly. Since the
-setLocality call merely provides a hint, STRAM may ignore it. An In-node
-stream is not as efficient as an in-line one, but it is clearly better
-than going off-node since it still avoids the potential bottleneck of
-the network card.</p>
-<p>THREAD_LOCAL and CONTAINER_LOCAL streams do not use a buffer
-server as this stream is in a single process. The other two do.</p>
-<h2 id="validating-an-application">Validating an Application</h2>
-<p>The platform provides various ways of validating the application
-specification and data input. An understanding of these checks is very
-important for an application developer since it affects productivity.
-Validation of an application is done in three phases, namely</p>
-<ol>
-<li>Compile Time: Caught during application development, and is
-    most cost effective. These checks are mainly done on declarative
-    objects and leverages the Java compiler. An example is checking that
-    the schemas specified on all ports of a stream are
-    mutually compatible.</li>
-<li>Initialization Time: When the application is being
-    initialized, before submitting to Hadoop. These checks are related
-    to configuration/context of an application, and are done by the
-    logical DAG builder implementation. An example is the checking that
-    all non-optional ports are connected to other ports.</li>
-<li>Run Time: Validations done when the application is running.
-    This is the costliest of all checks. These are checks that can only
-    be done at runtime as they involve data. For example divide by 0
-    check as part of business logic.</li>
-</ol>
-<h3 id="compile-time">Compile Time</h3>
-<p>Compile time validations apply when an application is specified in
-Java code and include all checks that can be done by Java compiler in
-the development environment (including IDEs like NetBeans or Eclipse).
-Examples include</p>
-<ol>
-<li>Schema Validation: The tuples on ports are POJO (plain old
-    java objects) and compiler checks to ensure that all the ports on a
-    stream have the same schema.</li>
-<li>Stream Check: Single Output Port and at least one Input port
-    per stream. A stream can only have one output port writer. This is
-    part of the addStream api. This
-    check ensures that developers only connect one output port to
-    a stream. The same signature also ensures that there is at least one
-    input port for a stream</li>
-<li>Naming: Compile time checks ensures that applications
-    components operators, streams are named</li>
-</ol>
-<h3 id="initializationinstantiation-time">Initialization/Instantiation Time</h3>
-<p>Initialization time validations include various checks that are
-done post compile, and before the application starts running in a
-cluster (or local mode). These are mainly configuration/contextual in
-nature. These checks are as critical to proper functionality of the
-application as the compile time validations.</p>
-<p>Examples include</p>
-<ul>
-<li>
-<p><a href="http://docs.oracle.com/javaee/6/tutorial/doc/gircz.html">JavaBeans Validation</a>:
-    Examples include</p>
-<ul>
-<li>@Max(): Value must be less than or equal to the number</li>
-<li>@Min(): Value must be greater than or equal to the
-    number</li>
-<li>@NotNull: The value of the field or property must not be
-    null</li>
-<li>@Pattern(regexp = “....”): Value must match the regular
-    expression</li>
-<li>Input port connectivity: By default, every non-optional input
-    port must be connected. A port can be declared optional by using an
-    annotation:     @InputPortFieldAnnotation(name = "...", optional
-    = true)</li>
-<li>Output Port Connectivity: Similar. The annotation here is:    
-    @OutputPortFieldAnnotation(name = "...", optional = true)</li>
-</ul>
-</li>
-<li>
-<p>Unique names in application scope: Operators, streams, must have
-    unique names.</p>
-</li>
-<li>Cycles in the dag: DAG cannot have a cycle.</li>
-<li>Unique names in operator scope: Ports, properties, annotations
-    must have unique names.</li>
-<li>One stream per port: A port can connect to only one stream.
-    This check applies to input as well as output ports even though an
-    output port can technically write to two streams. If you must have
-    two streams originating from a single output port, use  a streamDuplicator operator.</li>
-<li>Application Window Period: Has to be an integral multiple the
-    streaming window period.</li>
-</ul>
-<h3 id="run-time">Run Time</h3>
-<p>Run time checks are those that are done when the application is
-running. The real-time streaming platform provides rich run time error
-handling mechanisms. The checks are exclusively done by the application
-business logic, but the platform allows applications to count and audit
-these. Some of these features are in the process of development (backend
-and UI) and this section will be updated as they are developed. Upon
-completion examples will be added to demos to illustrate these.</p>
-<p>Error ports are output ports with error annotations. Since they
-are normal ports, they can be monitored and tuples counted, persisted
-and counts shown in the UI.</p>
-<hr />
-<h1 id="multi-tenancy-and-security">Multi-Tenancy and Security</h1>
-<p>Hadoop is a multi-tenant distributed operating system. Security is
-an intrinsic element of multi-tenancy as without it a cluster cannot be
-reasonably be shared among enterprise applications. Streaming
-applications follow all multi-tenancy security models used in Hadoop as
-the

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