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Finance Quotes</a></li> + + <li><a class="toctree-l4" href="#running-a-test-application_1">Running a Test Application</a></li> + + <li><a class="toctree-l4" href="#local-mode">Local Mode</a></li> + + <li><a class="toctree-l4" href="#hadoop-cluster">Hadoop Cluster</a></li> + + + <li class="toctree-l3"><a href="#apache-apex-platform-overview">Apache Apex Platform Overview</a></li> + + <li><a class="toctree-l4" href="#streaming-computational-model">Streaming Computational Model</a></li> + + <li><a class="toctree-l4" href="#streaming-application-manager-stram">Streaming Application Manager (STRAM)</a></li> + + <li><a class="toctree-l4" href="#hadoop-components">Hadoop Components</a></li> + + + <li class="toctree-l3"><a href="#developing-an-application">Developing An Application</a></li> + + <li><a class="toctree-l4" href="#development-process">Development Process</a></li> + + <li><a class="toctree-l4" href="#application-api">Application API</a></li> + + <li><a class="toctree-l4" href="#operators">Operators</a></li> + + <li><a class="toctree-l4" href="#streams">Streams</a></li> + + <li><a class="toctree-l4" href="#validating-an-application">Validating an Application</a></li> + + + <li class="toctree-l3"><a href="#multi-tenancy-and-security">Multi-Tenancy and Security</a></li> + + <li><a class="toctree-l4" href="#security">Security</a></li> + + <li><a class="toctree-l4" href="#resource-limits">Resource Limits</a></li> + + + <li class="toctree-l3"><a href="#scalability-and-partitioning">Scalability and Partitioning</a></li> + + <li><a class="toctree-l4" href="#partitioning">Partitioning</a></li> + + <li><a class="toctree-l4" href="#nxm-partitions">NxM Partitions</a></li> + + <li><a class="toctree-l4" href="#parallel">Parallel</a></li> + + <li><a class="toctree-l4" href="#parallel-partitions-with-streams-modes">Parallel Partitions with Streams Modes</a></li> + + <li><a class="toctree-l4" href="#skew-balancing-partition">Skew Balancing Partition</a></li> + + <li><a class="toctree-l4" href="#skew-unifier-partition">Skew Unifier Partition</a></li> + + <li><a class="toctree-l4" href="#cascading-unifier">Cascading Unifier</a></li> + + <li><a class="toctree-l4" href="#sla">SLA</a></li> + + + <li class="toctree-l3"><a href="#fault-tolerance">Fault Tolerance</a></li> + + <li><a class="toctree-l4" href="#state-of-the-application">State of the Application</a></li> + + <li><a class="toctree-l4" href="#checkpointing">Checkpointing</a></li> + + <li><a class="toctree-l4" href="#recovery-mechanisms_1">Recovery Mechanisms</a></li> + + + <li class="toctree-l3"><a href="#dynamic-application-modifications">Dynamic Application Modifications</a></li> + + + <li class="toctree-l3"><a href="#demos">Demos</a></li> + + + </ul> + + </li> + + + + <li class="toctree-l1 "> + <a class="" href="../application_packages/">Packages</a> + + </li> + + + + <li class="toctree-l1 "> + <a class="" href="../operator_development/">Operators</a> + + </li> + + + + <li class="toctree-l1 "> + <a class="" href="../autometrics/">AutoMetric API</a> + + </li> + + + </ul> +<li> + + <li> + <ul class="subnav"> + <li><span>Operations</span></li> + + + + <li class="toctree-l1 "> + <a class="" href="../dtcli/">dtCli</a> + + </li> + + + </ul> +<li> + + </ul> + </div> + + </nav> + + <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"> + + + <nav class="wy-nav-top" role="navigation" aria-label="top navigation"> + <i data-toggle="wy-nav-top" class="fa fa-bars"></i> + <a href="..">Apache Apex Documentation</a> + </nav> + + + <div class="wy-nav-content"> + <div class="rst-content"> + <div role="navigation" aria-label="breadcrumbs navigation"> + <ul class="wy-breadcrumbs"> + <li><a href="..">Docs</a> »</li> + + + + <li>Development »</li> + + + + <li>Applications</li> + <li class="wy-breadcrumbs-aside"> + + </li> + </ul> + <hr/> +</div> + <div role="main"> + <div class="section"> + + <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("rand", new RandomEventGenerator()); +rand.setMinvalue(0); +rand.setMaxvalue(30000); + +// Generates a round robin HashMap of "x" and "y" +RoundRobinHashMap<String,Object> rrhm = dag.addOperator("rrhm", new RoundRobinHashMap<String, Object>()); +rrhm.setKeys(new String[] { "x", "y" }); + +// Calculates pi from x and y +JavaScriptOperator calc = dag.addOperator("picalc", new Script()); +calc.setPassThru(false); +calc.put("i",0); +calc.put("count",0); +calc.addSetupScript("function pi() { if (x*x+y*y <= "+maxValue*maxValue+") { i++; } count++; return i / count * 4; }"); +calc.setInvoke("pi"); +dag.addStream("rand_rrhm", rand.integer_data, rrhm.data); +dag.addStream("rrhm_calc", rrhm.map, calc.inBindings); + +// puts results on system console +ConsoleOutputOperator console = dag.addOperator("console", new ConsoleOutputOperator()); +dag.addStream("rand_console",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&f=sl1vt1' +"IBM",203.966,1513041,"1:43pm" +"GOOG",762.68,1879741,"1:43pm" +"AAPL",444.3385,11738366,"1:43pm" +"YHOO",19.3681,14707163,"1:43pm" +</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<String, Long> lastVolume = new HashMap<String, Long>(); + private boolean outputEvenIfZeroVolume = false; + /** + * The output port to emit price. + */ + @OutputPortFieldAnnotation(optional = true) + public final transient DefaultOutputPort<KeyValPair<String, Double>> price = new DefaultOutputPort<KeyValPair<String, Double>>(); + /** + * The output port to emit incremental volume. + */ + @OutputPortFieldAnnotation(optional = true) + public final transient DefaultOutputPort<KeyValPair<String, Long>> volume = new DefaultOutputPort<KeyValPair<String, Long>>(); + /** + * The output port to emit last traded time. + */ + @OutputPortFieldAnnotation(optional = true) + public final transient DefaultOutputPort<KeyValPair<String, String>> time = new DefaultOutputPort<KeyValPair<String, String>>(); + + /** + * Prepare URL from symbols and parameters. URL will be something like: http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO&f=sl1vt1 + * + * @return the URL + */ + private String prepareURL() + { + String str = "http://download.finance.yahoo.com/d/quotes.csv?s="; + for (int i = 0; i < symbols.length; i++) { + if (i != 0) { + str += ","; + } + str += symbols[i]; + } + str += "&f=sl1vt1&e=.csv"; + return str; + } + + @Override + public void setup(OperatorContext context) + { + url = prepareURL(); + client = new HttpClient(); + method = new GetMethod(url); + DefaultHttpParams.getDefaultParams().setParameter("http.protocol.cookie-policy", 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("Method failed: " + method.getStatusLine()); + } + else { + InputStream istream = method.getResponseBodyAsStream(); + // Process response + InputStreamReader isr = new InputStreamReader(istream); + CSVReader reader = new CSVReader(isr); + List<String[]> myEntries = reader.readAll(); + for (String[] stringArr: myEntries) { + ArrayList<String> tuple = new ArrayList<String>(Arrays.asList(stringArr)); + if (tuple.size() != 4) { + return; + } + // input csv is <Symbol>,<Price>,<Volume>,<Time> + 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 > 0 || outputEvenIfZeroVolume) { + price.emit(new KeyValPair<String, Double>(symbol, currentPrice)); + volume.emit(new KeyValPair<String, Long>(symbol, vol)); + time.emit(new KeyValPair<String, String>(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<K,V> provided in math package.  In this case, +SumKeyVal<String,Long>, 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<K> 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<K,V> provided +in the math package. In this case, +RangeKeyVal<String,Double>.</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<String,Long>, 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<String,Double>, 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. <p> + * + * 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<String, Long> getDailyVolumeOperator(String name, DAG dag) + { + SumKeyVal<String, Long> oper = dag.addOperator(name, new SumKeyVal<String, Long>()); + 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<String, Long> getMinuteVolumeOperator(String name, DAG dag, int appWindowCount) + { + SumKeyVal<String, Long> oper = dag.addOperator(name, new SumKeyVal<String, Long>()); + 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<String, Double> getHighLowOperator(String name, DAG dag, int appWindowCount) + { + RangeKeyVal<String, Double> oper = dag.addOperator(name, new RangeKeyVal<String, Double>()); + dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount); + oper.setType(Double.class); + return oper; + } + + /** + * Quote (Merge price, daily volume, time) + */ + public ConsolidatorKeyVal<String,Double,Long,String,?,?> getQuoteOperator(String name, DAG dag) + { + ConsolidatorKeyVal<String,Double,Long,String,?,?> oper = dag.addOperator(name, new ConsolidatorKeyVal<String,Double,Long,String,Object,Object>()); + return oper; + } + + /** + * Chart (Merge minute volume and minute high-low) + */ + public ConsolidatorKeyVal<String,HighLow,Long,?,?,?> getChartOperator(String name, DAG dag) + { + ConsolidatorKeyVal<String,HighLow,Long,?,?,?> oper = dag.addOperator(name, new ConsolidatorKeyVal<String,HighLow,Long,Object,Object,Object>()); + return oper; + } + + /** + * Get simple moving average of price. + */ + public SimpleMovingAverage<String, Double> getPriceSimpleMovingAverageOperator(String name, DAG dag, int appWindowCount) + { + SimpleMovingAverage<String, Double> oper = dag.addOperator(name, new SimpleMovingAverage<String, Double>()); + oper.setWindowSize(appWindowCount); + oper.setType(Double.class); + return oper; + } + + /** + * Get console for output. + */ + public InputPort<Object> getConsole(String name, /*String nodeName,*/ DAG dag, String prefix) + { + ConsoleOutputOperator oper = dag.addOperator(name, ConsoleOutputOperator.class); + oper.setStringFormat(prefix + ": %s"); + 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("StockTickInput", dag); + SumKeyVal<String, Long> dailyVolume = getDailyVolumeOperator("DailyVolume", dag); + ConsolidatorKeyVal<String,Double,Long,String,?,?> quoteOperator = getQuoteOperator("Quote", dag); + + RangeKeyVal<String, Double> highlow = getHighLowOperator("HighLow", dag, appWindowCountMinute); + SumKeyVal<String, Long> minuteVolume = getMinuteVolumeOperator("MinuteVolume", dag, appWindowCountMinute); + ConsolidatorKeyVal<String,HighLow,Long,?,?,?> chartOperator = getChartOperator("Chart", dag); + + SimpleMovingAverage<String, Double> priceSMA = getPriceSimpleMovingAverageOperator("PriceSMA", dag, appWindowCountSMA); + DefaultPartitionCodec<String, Double> codec = new DefaultPartitionCodec<String, Double>(); + dag.setInputPortAttribute(highlow.data, PortContext.STREAM_CODEC, codec); + dag.setInputPortAttribute(priceSMA.data, PortContext.STREAM_CODEC, codec); + dag.addStream("price", tick.price, quoteOperator.in1, highlow.data, priceSMA.data); + dag.addStream("vol", tick.volume, dailyVolume.data, minuteVolume.data); + dag.addStream("time", tick.time, quoteOperator.in3); + dag.addStream("daily_vol", dailyVolume.sum, quoteOperator.in2); + + dag.addStream("quote_data", quoteOperator.out, getConsole("quoteConsole", dag, "QUOTE")); + + dag.addStream("high_low", highlow.range, chartOperator.in1); + dag.addStream("vol_1min", minuteVolume.sum, chartOperator.in2); + dag.addStream("chart_data", chartOperator.out, getConsole("chartConsole", dag, "CHART")); + + dag.addStream("sma_price", priceSMA.doubleSMA, getConsole("priceSMAConsole", dag, "Price SMA")); + + 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 <INSTALL_DIR> 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><INSTALL_DIR>/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> launch -local <INSTALL_DIR>/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> launch <INSTALL_DIR>/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> 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 âMalharâ 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("StockTickInput", dag); + SumKeyVal<String, Long> dailyVolume = getDailyVolumeOperator("DailyVolume", dag); + ConsolidatorKeyVal<String,Double,Long,String,?,?> quoteOperator = getQuoteOperator("Quote", dag); + + RangeKeyVal<String, Double> highlow = getHighLowOperator("HighLow", dag, appWindowCountMinute); + SumKeyVal<String, Long> minuteVolume = getMinuteVolumeOperator("MinuteVolume", dag, appWindowCountMinute); + ConsolidatorKeyVal<String,HighLow,Long,?,?,?> chartOperator = getChartOperator("Chart", dag); + + SimpleMovingAverage<String, Double> priceSMA = getPriceSimpleMovingAverageOperator("PriceSMA", dag, appWindowCountSMA); + + dag.addStream("price", tick.price, quoteOperator.in1, highlow.data, priceSMA.data); + dag.addStream("vol", tick.volume, dailyVolume.data, minuteVolume.data); + dag.addStream("time", tick.time, quoteOperator.in3); + dag.addStream("daily_vol", dailyVolume.sum, quoteOperator.in2); + + dag.addStream("quote_data", quoteOperator.out, getConsole("quoteConsole", dag, "QUOTE")); + + dag.addStream("high_low", highlow.range, chartOperator.in1); + dag.addStream("vol_1min", minuteVolume.sum, chartOperator.in2); + dag.addStream("chart_data", chartOperator.out, getConsole("chartConsole", dag, "CHART")); + + dag.addStream("sma_price", priceSMA.doubleSMA, getConsole("priceSMAConsole", dag, "Price SMA")); + + 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 âDaily volumeâ 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 = "data") +public final transient DefaultInputPort<V> data = new DefaultInputPort<V>() { + @Override + public void process(V tuple) + { + ... + } +} +@OutputPortFieldAnnotation(optional=true) +public final transient DefaultOutputPort<V> sum = new DefaultOutputPort<V>(){ ⦠}; +</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 +âoptional=trueâ 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(<tuple_type> 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(âoperatorâ, â¦); +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 thatâs 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("views", viewAggregate.sum, cost.data).setLocality(CONTAINER_LOCAL); // A container local stream +dag.addStream(âclicksâ, 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 = â....â): 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 +they are native Hadoop
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