Page Comparison

...

I want the ability to pass the custom data (such as metric, status, error codes etc.) from one program in the Workflow to the next subsequent programs in the form of a token.
At any node in the Workflow, I want ability to query the data from the token.
I want ability to fetch the data from the token which was set by a specific node.
I want ability to find the name of the node which most recently set the token value for the a specific key; e.g., the node who last set the ERROR flag in the token, so that I can take appropriate action (such as logging or improving its code) on it.
I want to have the conditional execution in the Workflow based on the information contained in the token.
I want to terminate the execution if some a node in the Workflow produce produces unexpected results.

As an admin/support person/developer of the Workflow application -

I want the ability to query the WorkflowToken from the past runs for running analysis such as which node is executed more frequently and why.
I want the ability to query the token values which were added by the a specific node in the Workflow to debug the flow of execution.

...

WorkflowToken interface changes

Code Block

/**
 * Interface to represent the data that is transferred from one node to the next nodes in the {@link Workflow}.
 */
@Beta
public interface WorkflowToken {

  /**
   * Keys in the {@link WorkflowToken} can be added by user, using the
   * {@link WorkflowToken#put} method. These keys are added under the {@link Scope#USER} scope.
   * CDAP also adds some keys to the {@link WorkflowToken}. for e.g. MapReduce counters.
   * The keys added by CDAP gets added under {@link Scope#SYSTEM} scope.
   */
  public enum Scope {
    USER,
    SYSTEM
  }

  /**
   * Put the specified key and value into the {@link WorkflowToken}.
   * The token may store additional information about the context in which
   * this key is being set, for example, the unique name of the workflow node.
   * @param key the key representing the entry
   * @param value the value for the key
   */
  void put(String key, String value);

  /**
   * Put the specified key and {@link Value} into the {@link WorkflowToken}.
   * The token may store additional information about the context in which
   * this key is being set, for example, the unique name of the workflow node.
   * @param key the key representing entry
   * @param value the {@link Value} for the key
   */
  void put(String key, Value value);

  /**
   * Get the most recent value added for the specified key for a {@link Scope#USER} scope.
   * @param key the key to be searched
   * @return the {@link Value} for the key or <code>null</code> if the key does not
   * exist in the {@link Scope#USER} scope
   */
  @Nullable
  Value get(String key);

  /**
   * Get the most recent value for the specified key for a given scope.
   * @param key the key to be searched
   * @param scope the {@link WorkflowToken.Scope} for the key
   * @return the {@link Value} for the key from the specified scope or <code>null</code> if the key
   * does not exist in the given scope
   */
  @Nullable
  Value get(String key, Scope scope);

  /**
   * Get the value set for the specified key by the specified node for a {@link Scope#USER} scope.
   * @param key the key to be searched
   * @param nodeName the name of the node
   * @return the {@link Value} set for the key by nodeName or <code>null</code> if the key is not
   * added by the nodeName in the {@link Scope#USER} scope
   */
  @Nullable
  Value get(String key, String nodeName);

  /**
   * Get the value set for the specified key by the specified node for a given scope.
   * @param key the key to be searched
   * @param nodeName the name of the node
   * @param scope the {@link WorkflowToken.Scope} for the key
   * @return the {@link Value} set for the key by nodeName for a given scope or <code>null</code>
   * if the key is not added by the nodeName in the given scope
   */
  @Nullable
  Value get(String key, String nodeName, Scope scope);

  /**
   * Same key can be added to the {@link WorkflowToken} by multiple nodes.
   * This method returns the {@link List} of {@link NodeValue}, where
   * each entry represents the unique node name and the {@link Value} that it set
   * for the specified key for a {@link Scope#USER} scope.
   * <p>
   * The list maintains the order in which the values were
   * inserted in the WorkflowToken for a specific key except in the case of fork
   * and join. In case of fork in the Workflow, copies of the WorkflowToken are made
   * and passed along each branch. At the join, all copies of the
   * WorkflowToken are merged together. While merging, the order in which the values were
   * inserted for a specific key is guaranteed within the same branch, but not across
   * different branches.
   * @param key the key to be searched
   * @return the list of {@link NodeValue} from node name to the value that node
   * added for the input key
   */
  List<NodeValue> getAll(String key);

  /**
   * Same key can be added to the WorkflowToken by multiple nodes.
   * This method returns the {@link List} of {@link NodeValue}, where
   * each entry represents the unique node name and the {@link Value} that it set
   * for the specified key for a given scope.
   * <p>
   * The list maintains the order in which the values were
   * inserted in the WorkflowToken for a specific key except in the case of fork
   * and join. In case of fork in the Workflow, copies of the WorkflowToken are made
   * and passed along each branch. At the join, all copies of the
   * WorkflowToken are merged together. While merging, the order in which the values were
   * inserted for a specific key is guaranteed within the same branch, but not across
   * different branches.
   * @param key the key to be searched
   * @param scope the {@link WorkflowToken.Scope} for the key
   * @return the list of {@link NodeValue} from node name to the value that node
   * added for the input key for a given scope
   */
  List<NodeValue> getAll(String key, Scope scope);

  /**
   * Get the {@link Map} of key to {@link Value}s that were added to the {@link WorkflowToken}
   * by specific node for a {@link Scope#USER} scope.
   * @param nodeName the unique name of the node
   * @return the map of key to values that were added by the specified node
   */
  Map<String, Value> getAllFromNode(String nodeName);

  /**
   * Get the {@link Map} of key to {@link Value}s that were added to the {@link WorkflowToken}
   * by specific node for a given scope.
   * @param nodeName the unique name of the node
   * @param scope the {@link WorkflowToken.Scope} for the key
   * @return the map of key to values that were added by the specified node for a given scope
   */
  Map<String, Value> getAllFromNode(String nodeName, Scope scope);

  /**
   * Same key can be added to the WorkflowToken by multiple nodes.
   * This method returns the key to {@link List} of {@link NodeValue}
   * added in the {@link Scope#USER} scope.
   * @return the {@link Map} of key to {@link List} of {@link NodeValue} added for
   * the given scope
   */
  Map<String, List<NodeValue>> getAll();

  /**
   * Same key can be added to the WorkflowToken by multiple nodes.
   * This method returns the key to {@link List} of {@link NodeValue}
   * added in the {@link WorkflowToken.Scope} provided.
   * @param scope the scope for the key
   * @return the {@link Map} of key to {@link List} of {@link NodeValue} added for
   * the given scope
   */
  Map<String, List<NodeValue>> getAll(Scope scope);

  /**
   * This method is deprecated as of release 3.1.
   * Get the Hadoop counters from the previous MapReduce program in the Workflow.
   * The method returns null if the counters are not set.
   * @return the Hadoop MapReduce counters set by the previous MapReduce program
   */
  @Deprecated
  @Nullable
  Map<String, Map<String, Long>> getMapReduceCounters();
}

The method getAll(String key) in the above interface returns the List of NodeValue objects. NodeValue class represents nodeName and value that the node put for the specific key.

Code Block

/**
 * Multiple nodes in the Workflow can add the same key to the {@link WorkflowToken}.
 * This class provides a mapping from node name to the {@link Value} which was set for the
 * specific key.
 */
public final class NodeValue implements Serializable {

  private static final long serialVersionUID = 6157808964174399650L;

  private final String nodeName;
  private final Value value;

  public NodeValue(String nodeName, Value value) {
    this.nodeName = nodeName;
    this.value = value;
  }

  public String getNodeName() {
    return nodeName;
  }

  public Value getValue() {
    return value;
  }

  ...
  // other methods like toString(), equals() and hashCode()		 
  ...
}

The details of the Value class are as follows:

Code Block

/**
 * Class representing the value of the key in the {@link WorkflowToken}.
 */
public class Value implements Serializable {

  private static final long serialVersionUID = -3420759818008526875L;

  private final String value;

  private Value(String value) {
    this.value = value;
  }

  /**
   * @return the boolean value
   */
  public boolean getAsBoolean() {
    return Boolean.parseBoolean(value);
  }

  /**
   * @return the int value
   */
  public int getAsInt() {
    return Integer.parseInt(value);
  }

  /**
   * @return the long value
   */
  public long getAsLong() {
    return Long.parseLong(value);
  }

  /**
   * @return the String value
   */
  @Override
  public String toString() {
    return value;
  }

}

Ability to include same program multiple times in the Workflow (Most of this part is now not required, since user can specify unique names in the existing API)

This can be achieved without making any changes to the API. Consider the following use case -

Use Case: Email campaign generates two categories of events - send events (SUCCESS, FAIL) and tracking events (OPEN, CLICK etc.). Records representing the send event and tracking event have different number of fieldsschema. These two categories of the events are sent to CDAP using streams "send" and "tracking".

Tracking event format:

audience_id,event_type,ip_address,device_type,event_time,link

Example records:

bob,CLICK,192.168.29.10,android,1436311150092,http://www.somedomain.com

adam,CLICK,192.168.29.18,ipad,1436311232276,http://www.anotherdomain.com

Send event format:

audience_id::event_sub_type::ip_address::deliveryCode::event_time

Example records:

bob::SEND::192.168.29.10::SUCCESS::1436311232276

adam::SEND::192.168.29.9::SUCCESS::1436311434476Solution:

Same MapReduce program "EventParser" can be used in the Workflow to parse these two categories of the events in parallel and create the list Event object per audience id.

Step 1: Add same MapReduce/Spark program multiple times in the Application.

API changes for the ApplicationConfigurer to allow adding MapReduce/Spark program in the Application with the explicit name.EventParser application:

Code Block

language	java

/**public class *EventParserApp Addsextends aAbstractApplication {@link
MapReduce} to the@Override
Application.  *public @paramvoid nameconfigure() the{
name to be given	// Stream to thereceive {@linksend MapReduce} program
 * @param mapReduce the {@link MapReduce} program to be included in the Application
 */
void addMapReduce(String name, MapReduce mapReduce);
 
/**
 * Adds a {@link Spark} to the Application.
 * @param name the name to be given to the {@link Spark} program
 * @param spark the {@link Spark} program to be included in the Application
 */
void addSpark(String name, Spark spark);

EventParser application:

Code Block

language	java

public class EventParserApp extends AbstractApplication {
  @Override
  public void configure() {
	// Stream to receive send events
	addStream(new Stream("send"));
 	// Stream to receive tracking events
	addStream(new Stream("tracking"));
 
    // Add EventParser MapReduce program multiple times in the application with different properties
  	Map<String, String> properties = Maps.newHashMap();
	properties.put("input.stream", "tracking");
	// 'trackingParser' is instance of the EventParser which will read the 'tracking' stream 
	addMapReduce("trackingParser", new EventParser(properties));

	properties = Maps.newHashMap();
	properties.put("input.stream", "send");
	// 'sendParser' is instance of the EventParser which will read the 'send' stream 
	addMapReduce("sendParser", new EventParser(properties));
 
	// Add Workflow which will process the tracking and send events in parallel
	addWorkflow(new EventParserWorkflow());
  }
}

EventParser MapReduce program:

Code Block

language	java

public class EventParser extends AbstractMapReduce {

  private final Map<String, String> properties;
  public EventParser(Map<String, String> properties) {
    this.properties = propertiesevents
	addStream(new Stream("send"));
 	// Stream to receive tracking events
	addStream(new Stream("tracking"));
 
    // Add EventParser MapReduce program multiple times in the application with different properties
  	Map<String, String> properties = Maps.newHashMap();
	properties.put("input.stream", "tracking");
	// 'trackingParser' is instance of the EventParser which will read the 'tracking' stream 
	addMapReduce(new EventParser("trackingParser", properties));

	properties = Maps.newHashMap();
	properties.put("input.stream", "send");
	// 'sendParser' is instance of the EventParser which will read the 'send' stream 
	addMapReduce(new EventParser("sendParser", properties));
 
	// Add Workflow which will process the tracking and send events in parallel
	addWorkflow(new EventParserWorkflow());
  }
}

EventParser MapReduce program:

Code Block

language	java

public class EventParser extends AbstractMapReduce {
  private final String name;
  private final Map<String, String> properties;

  public EventParser(String name, Map<String, String> properties) {
    this.name = name;
    this.properties = properties;
  }
 
  @Override
  public void configure() {
    setName(name);
    setDescription("MapReduce program for parsing the email events and storing them in the dataset.");
    // Serialize the properties
  	setProperties(properties);
  	setOutputDataset("events");
  }
 
  @Override
  public void configurebeforeSubmit(MapReduceContext context) throws Exception {
  	Job job setDescription("MapReduce program for parsing the email events and storing them in the dataset.");
    // Serialize the properties
  	setProperties(properties);
  	setOutputDataset("events");
  }
 
  @Override
  public void beforeSubmit(MapReduceContext context) throws Exception {
  	Job job = context.getHadoopJob();
  	job.setMapperClass(EventParserMapper.class);
  	job.setReducerClass(EventParserReducer.class);

  	job.setMapOutputKeyClass(Text.class);
  	job.setOutputValueClass(Event.class);

  	job.setNumReduceTasks(1);
	String streamToVerify = context.getSpecification().getProperties().get("input.stream");
  	job.getConfiguration().set("input.stream", streamToVerify);

  	// Read the purchase events from the last 60 minutes as input to the mapper.
  	final long endTime = context.getLogicalStartTime();
  	final long startTime = endTime - TimeUnit.MINUTES.toMillis(60);
  	StreamBatchReadable.useStreamInput(context, streamToVerify, startTime, endTime);
  }
}
 
// EventParserMapper
public static class EventParserMapper extends Mapper<LongWritable, Text, Text, Event>  {

  @Override
  public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    String logEvent = value.toString();
    if (logEvent.isEmpty()) {
      return= context.getHadoopJob();
  	job.setMapperClass(EventParserMapper.class);
  	job.setReducerClass(EventParserReducer.class);

  	job.setMapOutputKeyClass(Text.class);
  	job.setOutputValueClass(Event.class);

  	job.setNumReduceTasks(1);
	String streamToVerify = context.getSpecification().getProperties().get("input.stream");
  	job.getConfiguration().set("input.stream", streamToVerify);

  	// Read the purchase events from the last 60 minutes as input to the mapper.
  	final long endTime = context.getLogicalStartTime();
  	final long startTime = endTime - TimeUnit.MINUTES.toMillis(60);
  	StreamBatchReadable.useStreamInput(context, streamToVerify, startTime, endTime);
  }
}
 
// EventParserMapper
public static class EventParserMapper extends Mapper<LongWritable, Text, Text, Event>  {

  @Override
  public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    String logEvent = value.toString();
    if (logEvent.isEmpty()) {
      return;
    }
    
    String inputStream = context.getConfiguration().get("input.stream");
    Event event;
    if(inputStream.equals("send")) {
      event = getSendEvent(logEvent);
    } else {
      event = getTrackingEvent(logEvent);
    }
    if (event != null) {
String inputStream    = context.getConfiguration().get("input.stream");write(new Text(event.getAudienceId()), event);
    }
  }
 Event event;
  private Event if(inputStream.equals("send"))getSendEvent(String logEvent) {
    String  eventseperator = getSendEvent(logEvent)"::";
    }int elsefieldLength {= 5;
    String[] eventfields = getTrackingEventlogEvent.split(logEventseperator);
    }
    if (eventfields.length != nullfieldLength) {
       context.write(new Text(event.getAudienceId()), event)return null;
    }
  }  String audienceId   private Event getSendEvent(String logEvent) {= fields[0];
    String seperatoreventType = "::"fields[1];
    intString fieldLengthipAddress = 5fields[2];
    String[] fieldsdeliveryCode = logEvent.split(seperator)fields[3];
    if (fields.length != fieldLength) {String eventTime = fields[4];
    
 return null;  return new  }
 Event(audienceId, eventType, ipAddress, eventTime, deliveryCode)
  String}
audienceId = fields[0];
  private Event getTrackingEvent(String eventType = fields[1];logEvent) {
    String ipAddressseperator = fields[2]",";
    Stringint deliveryCodefieldLength = fields[3]6;
    String[] eventTimefields = fields[4]logEvent.split(seperator);
    if (fields.length != fieldLength) {
return new Event(audienceId, eventType, ipAddress, eventTime, deliveryCode)return null;
 }   }
  private Event getTrackingEvent(String logEvent) {
    String seperatoraudienceId = ","fields[0];
    intString fieldLengtheventType = 6fields[1];
    String[] fieldsipAddress = logEvent.split(seperator);
    if (fields.length != fieldLength) {
      return null;
    }fields[2];
    String audienceIddeviceType = fields[03];
    String eventTypeeventTime = fields[14];
    String ipAddresslink = fields[25];
    String deviceType = fields[3];
    String eventTime = fields[4];
    String link = fields[5];
    returnreturn new Event(audienceId, eventType, ipAddress, eventTime, deviceType + "&&" + link);
  }
}

EventParserWorkflow:

Code Block

language	java

public class EventParserWorkflow extends AbstractWorkflow {
  @Override
  protected void configure() {
    fork()
      .addMapReduce("trackingParser")
    .also()
      .addMapReduce("sendParser")
    .join();
  }
}

Step 2: WorkflowConfigurer interface changes

With the API changes mentioned in the above section, same program (MapReduce/Spark) can be added multiple times in the Application with different names and parameters. These programs can be referred to in the Workflow. However in order to add same action multiple times in the Workflow, we still need to specify the unique name.

The WorkflowConfigurer API can be updated to allow a user to set a unique name for the Workflow action, if it occurs multiple times in a Workflow and use that unique name to retrieve the token.

Code Block

Provide ability to set and get information in the WorkflowToken

1. MapReduce program: Users should be able to access and modify WorkflowToken from "beforeSubmit" and "onFinish" methods of the MapReduce program. Since these methods get the MapReduceContext, we will need to update the MapReduceContext interface to get the WorkflowToken.

Code Block

/**
 * If * {@link WorkflowMapReduce} consistsprogram ofis multipleexecuted {@link WorkflowNode}s.
   * Same Workflow action can be added multiple times inas a part of the {@link Workflow} at
 * then *get differentthe {@link WorkflowNodeWorkflowToken}s. associated with the *current <p>run, otherwise return  *null. This method
allows associating the uniqueName to* @return the {@link WorkflowNodeWorkflowToken} if available
 */
which@Nullable
represents the Workflow action. The uniqueName helps querying for the
   * values that were added to the {@link WorkflowToken} by the particular node.
   * <p>
   * The uniqueName must be unique across all {@link WorkflowNode} in the Workflow,
   * otherwise the Application deployment will fail.
   * @param uniqueName the uniqueName to be assigned to the {@link WorkflowNode}
   *                   which represents the Spark program
   * @param action the action to be added to the {@link Workflow}
   * @return the configurer for the current condition
   */
  WorkflowForkConfigurer<T> addAction(String uniqueName, WorkflowAction action);

WorkflowToken can also be updated from a predicate on the condition node. In the presence of multiple condition nodes in a Workflow, we will need the ability to specify unique names for the conditions as well so that token values from specific condition nodes can be fetched.

WorkflowToken getWorkflowToken();

Consider the following code sample to update the WorkflowToken in the MapReduce program:

Code Block

@Override
public void beforeSubmit(MapReduceContext context) throws Exception {
  ...
  WorkflowToken workflowToken = context.getWorkflowToken();
  if (workflowToken != null) {
    // Put the action type in the WorkflowToken
    workflowToken.put("action.type", "MAPREDUCE");
    // Put the start time for the action
    workflowToken.put("start.time", String.valueOf(System.currentTimeMillis()));
  }
  ...
}
 
@Override
public void onFinish(boolean succeeded, MapReduceContext context) throws Exception {
  ...
  WorkflowToken workflowToken = context.getWorkflowToken();
  if (workflowToken != null) {
    // Put the end time for the action 
    workflowToken.put("end.time", String.valueOf(System.currentTimeMillis()));
  }
  ...
}

2. Spark program: Users should be able to access and modify WorkflowToken from "beforeSubmit" and "onFinish" methods of the Spark program. Since these methods get the SparkContext, we will need to update the SparkContext interface to get the WorkflowToken.

Code Block

/**
 * Adds a condition with the unique name to If {@link Spark} program is executed as a part of the {@link Workflow}.
 * @paramthen conditionNameget the unique{@link nameWorkflowToken} to be assigned toassociated with the conditioncurrent run, *otherwise @paramreturn conditionnull.
 *  @return the {@link PredicateWorkflowToken} to be evaluated for the condition
 * @return the configurer for the conditionif available
 */
WorkflowConditionConfigurer<?@Nullable
extendsWorkflowToken WorkflowConfigurer> condition(String conditionName, Predicate<WorkflowContext> condition);

Provide ability to set and get information in the WorkflowToken
1. MapReduce program: Users should be able to access and modify WorkflowToken from "beforeSubmit" and "onFinish" methods of the MapReduce program. Since these methods get the MapReduceContext, we will need to update the MapReduceContext interface to get the WorkflowToken.

Code Block

/**
 * If {@link MapReduce} program is executed as a part of the {@link Workflow} 
 * then get the {@link WorkflowToken} associated with the current run, otherwise return null.  
 * @return the {@link WorkflowToken} if available
 */
@Nullable
WorkflowToken getWorkflowToken();

Consider the following code sample to update the WorkflowToken in the MapReduce program:

Code Block

@Override
public void beforeSubmit(MapReduceContext context) throws Exception {
  ...
  WorkflowToken workflowToken = context.getWorkflowToken();
  if (workflowToken != null) {
    // Put the action type in the WorkflowToken
    workflowToken.put("action_type", "MAPREDUCE");
    // Put the start time for the action
    workflowToken.put("startTime", String.valueOf(System.currentTimeMillis()));
  }
  ...
}
 
@Override
public void onFinish(boolean succeeded, MapReduceContext context) throws Exception {
  ...
  WorkflowToken workflowToken = context.getWorkflowToken();
  if (workflowToken != null) {
    // Put the end time for the action 
    workflowToken.put("endTime", String.valueOf(System.currentTimeMillis()));
  }
  ...
}

2. Spark program: Users should be able to access and modify WorkflowToken from "beforeSubmit" and "onFinish" methods of the Spark program. Since these methods get the SparkContext, we will need to update the SparkContext interface to get the WorkflowToken.

Code Block

/**
 * If {@link Spark} program is executed as a part of the {@link Workflow}
 * then get the {@link WorkflowToken} associated with the current run, otherwise return null.
 * @return the {@link WorkflowToken} if available
 */
@Nullable
WorkflowToken getWorkflowToken();

3. Custom Workflow action: Since custom workflow actions already receive WorkflowContext, no changes are anticipated in the interface.

Following is the sample code to get values from the WorkflowToken in custom action:

Code Block

@Override
public void run() {
  ...
  WorkflowToken token = getContext().getToken();
  // set the type of the action of the current node	
  token.put("action_type", "CUSTOM_ACTION");
 
  // Assume that we have the following Workflow 
 
  //												|------->PurchaseByCustomer------->|
  //										True	|								   |	
  // Start---->RecordVerifier---->Predicate-------->|								   |------------->StatusReporter----->End 	
  //								  |				| 								   |	    |
  //								  | False		|------->PurchaseByProduct-------->|        |
  //                                  |                        									|
  //                                getWorkflowToken();

3. Custom Workflow action: Since custom workflow actions already receive WorkflowContext, no changes are anticipated in the interface.

Following is the sample code to get values from the WorkflowToken in custom action:

Code Block

@Override
public void run() {
  ...
  WorkflowToken token = getContext().getToken();
  // set the type of the action of the current node	
  token.put("action.type", "CUSTOM_ACTION");
 
  // Assume that we have the following Workflow 
 
  //												|------->PurchaseByCustomer------->|
  //										True	|								   |	
  // Start---->RecordVerifier---->Predicate-------->|								   |------------->StatusReporter----->End 	
  //								  |				| 								   |	    |
  //								  | False		|------->PurchaseByProduct-------->|        |
  //                                  |                        									|
  //                                  |--------------------->ProblemLogger--------------------->|
 
  
  // Use case 1: Predicate can add the key "branch" in the WorkflowToken with value as "true" if true branch will be executed
  // or "false" otherwise. In "StatusReporter" in order to get which branch in the Workflow was executed
  boolean bTrueBranch = Boolean.parseBoolean(token.get("branch"));
 
  // Use case 2: User may want to compare the records emitted by "PurchaseByCustomer" and "PurchaseByProduct", in order to find which job
  // is generating more records. 
  String flattenReduceOutputRecordsCounterName = "org.apache.hadoop.mapreduce.TaskCounter.REDUCE_OUTPUT_RECORDS";
  String purchaseByCustomerCounterValue = token.get(flattenReduceOutputRecordsCounterName, "PurchaseByCustomer", WorkflowToken.Scope.SYSTEM);
  String purchaseByProductCounterValue = token.get(flattenReduceOutputRecordsCounterName, "PurchaseByProduct", WorkflowToken.Scope.SYSTEM);
  
  // Use case 3: Since Workflow can have multiple complex conditions and forks in its structure, in the "StatusReporter", 
  // user may want to  know how many actions were executed as a part of this run. If the number of nodes executed were below
  // certain threshold send an alert. Assuming that every node in the Workflow adds the key "action_.type" with the value as action
  // type for node in the WorkflowToken, user can further figure out the break down by action type in the particular Workflow run.
  List<NodeValueEntry> nodeValues = token.getAll("action_.type");
  int totalNodeExecuted = nodeValues.size();
  int mapReduceNodes = 0;
  int sparkNodes = 0;
  int customActionNodes = 0;
  int conditions = 0;
  for (NodeValueEntry entry : nodeValues) {
    if (entry.getValue().equals("MAPREDUCE")) {
      mapReduceNodes++;
    } 
    if (entry.getValue().equals("SPARK")) {
      sparkNodes++;
    } 
    if (entry.getValue().equals("CUSTOM_ACTION")) {
      customActionNodes++;
    }
    if (entry.getValue().equals("CONDITION")) {
      conditions++;
    }
  }

  // Use case 4: To get the name of the last node which set the "ERROR" flag in the WorkflowToken
  List<NodeValueEntry> errorNodeValueList = token.getAll("ERROR");
  String nodeNameWhoSetTheErrorFlagLast = errorNodeValueList.get(errorNodeValueList.size() - 1);
 
  // To get the start time of the MapReduce program with unique name "PurchaseHistoryBuilder"
  String startTime = token.get("startTimestart.time", "PurchaseHistoryBuilder");
 
  // To get the most recent value of counter with group name
  // 'org.apache.hadoop.mapreduce.TaskCounter' and counter name 'MAP_INPUT_RECORDS'
  
  String flattenCounterKey = "mr.counters.org.apache.hadoop.mapreduce.TaskCounter.MAP_INPUT_RECORDS";
  workflowToken.get(flattenCounterKey, WorkflowToken.Scope.SYSTEM);

  // To get the value of counter with group name 'org.apache.hadoop.mapreduce.TaskCounter'
  // and counter name 'MAP_INPUT_RECORDS' as set by MapReduce program with unique name 'PurchaseHistoryBuilder'
  workflowToken.get(flattenCounterKey, "PurchaseHistoryBuilder", WorkflowToken.Scope.SYSTEM);
 ...
}

WorkflowToken in presence of Fork and Join
When a fork is encountered in the Workflow, we make a copy of the WorkflowToken and pass it along to each branch. At the join, we create a new WorkflowToken, which will be a merge of the WorkflowTokens associated with each of the branches of the fork. Since we are storing the information in the token at the node level, there will not be any conflicts during the merge process.
Persisting the WorkflowToken
The RunRecord for the Workflow will contain the WorkflowToken as its property. This token will be updated before the execution of the action in the Workflow. We will add a version field to the RunRecord itself which will help in the upgrade process.
RESTful end-points to access the value of the WorkflowToken that was received by an individual node in the Workflow
We will expose a RESTful end point to retrieve the token values that were set by a particular node as identified by its unique name.
level, there will not be any conflicts during the merge process.
Persisting the WorkflowToken
The RunRecord for the Workflow will contain the WorkflowToken as its property. This token will be updated before the execution of the action in the Workflow. We will add a version field to the RunRecord itself which will help in the upgrade process.

RESTful end-points to access the value of the WorkflowToken that was received by an individual node in the Workflow

1. To get the values that user put in the WorkflowToken for a particular run

Code Block

language	java

/apps/{app-id}/workflows/{workflow-id}/runs/{run-id}/token

2. To get the values that CDAP put (e.g. MapReduce counters for MapReduce nodes) in the WorkflowToken for a particular run

Code Block
/apps/{app-id}/workflows/{workflow-id}/runs/{run-id}/token?scope=system

3. To get the key values in the USER scope that particular node added to the WorkflowToken

Code Block
/apps/{app-id}/workflows/{workflow-id}/runs/{run-id}/nodes/{node-id}/token

4. To get the key values in the SYSTEM scope that particular node added to the WorkflowToken

Code Block
/apps/{app-id}/workflows/{workflow-nameid}/runs/{run-id}/nodes/{unique-node-nameid}/token?scope=system

REST API

Response

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/namespaces/{namespace-id}/apps/{app-id}/workflows/{workflow-name}/runs/{run-id}/token

Json containing the entire workflow token for a particular workflow run e.g.

Code Block
{ "tokenValueMap": { "key1": [ { "nodeName": "node1", "value": "value1" }, { "nodeName": "node2", "value": "value2" } ], "key2": [ { "nodeName": "node2", "value": "v2" } ] } }

Response Codes:

200 if successful
404 if app/workflow not found
500 if there is an internal error

/namespaces/{namespace-id}/apps/{app-id}/workflows/{workflow-name}/runs/{run-id}/nodes/{unique-node-name}/token

Code Block
{ "key1": "value1", "key2": "value2 }

Response Codes:

200 if successful
404 if app/workflow not found
500 if there is an internal error

...

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