User stories documented (Albert/Alvin)
User stories reviewed (Nitin)
Design documented (Albert/Alvin)
Design reviewed (Andreas/Terence)
Feature merged (Albert/Alvin)
Examples and guides (Albert/Alvin)
Integration tests (Albert/Alvin)
Documentation for feature (Albert/Alvin)
Blog post

The developer to load webpage click and view data (customer id, timestamp, action, url) into a partitioned fileset. After loading the data, the developer wants to de-duplicate records and calculate how many times each customer clicked and viewed over the past hour, past day, and past month.

User Stories:

(3.4) A developer should be able to create pipelines that contain aggregations (GROUP BY -> count/sum/unique)
(3.5) A developer should be able to create a pipeline with multiple sourcesA control some parts of the pipeline running before others. For example, one source -> sink branch running before another source -> sink branch.
(3.4) A developer should be able to use a Spark ML job as a pipeline stage
A (3.4) A developer should be able to rerun failed pipeline runs without reconfiguring the pipeline
A developer (3.4) A developer should be able to de-duplicate records in a pipeline
(3.5) A developer should be able to join multiple branches of a pipeline
A (3.5) A developer should be able to use an Explore action as a pipeline stage
A (3.5) A developer should be able to create pipelines that contain Spark Streaming jobs
A (3.5) A developer should be able to create pipelines that run based on various conditions, including input data availability and Kafka events

Design:

Story 1: Group By -> Aggregations

Option 1:

Introduce a new plugin type "aggregation"

...

aggregator". In general, to support more and more plugin types in a generic way, we want to refactor the config:

Code Block

{
  "stages": [
    {
      "name": "inputTable",
      "plugin": {
        "name": "Table",
        "type": "batchsource",  // new field
        "properties": {
        }
      }
    },
    {
      "name": "aggStage",
      "plugin": {
        "name": "RecordAggregatorGroupByAggregate",
        "type": "aggregationaggregator",  // new plugin type
        "properties": {
          "groupBy": "id",
          "functions": "{[
            "totalPrice": {
              "namecolumnName": "sumtotalPrice",
              "propertiesplugin": {
                "columnname": "pricesum",
              }  "properties": {
         },             "numTransactionscolumn": {"price"
              "name": "count" }
           }   }
       }"     },
   }       }  {
  }   ],   "connections": [     { "fromcolumnName": "inputTablenumTransactions",
 "to": "aggStage" }    ] }   Option 2: {   "sourcesplugin": [{
         {       "name": "inputTablecount",
      "plugin": {       }
 "name": "Table",         "type": "batchsource", }
 // new field         "properties": {]"
        }
      }
    }
  ],
  "aggregationsconnections": [
    {
      "namefrom": "aggStageinputTable",
      "groupByto": "idaggStage", } 
     "aggregations": [
        {
          "columnName": "totalPrice",
   ]
}

Some problems with this is that the plugin property "functions" is itself a json describing plugins to use. This is not easy for somebody to configure, but maybe it could be simplified by a UI widget type.

Java APIs for plugin developers. It is basically mapreduce, 'Aggregation' is probably a bad name for this. Need to see if this fits into Spark. Would we have to remove the emitters?

Code Block

public abstract class Aggregation<GROUP_BY, RECORD_TYPE, OUTPUT_TYPE> {
 
  public abstract void groupBy(RECORD_TYPE input, Emitter<GROUP_BY> emitter);
 
  public abstract void aggregate(GROUP_BY groupKey, Iterable<RECORD_TYPE> groupRecords, Emitter<OUTPUT_TYPE> emitter);
 
}

@Plugin(type = "aggregation")
@Name("GroupByAggregate")
public GroupByAggregate extends Aggregation<StructuredRecord, StructuredRecord, StructuredRecord> {
  private static final AggConfig config;
  
  public static class AggConfig extends PluginConfig {
    private String groupBy;
 
    // ideally this would be Map<String, FunctionConfig> functions
    private String functions;
  }
 
  public void configurePipeline(PipelineConfigurer configurer) {
    Map<String, FunctionConfig> functions = gson.fromJson(config.functions, MAP_TYPE);
    for each function:
      usePlugin(id, type, name, properties);
  }
 
  public groupBy(StructuredRecord input, Emitter<StructuredRecord> emitter) {
    // key = new record from input with only fields in config.groupBy
    Set<String> fields = config.groupBy.split(",");
    emitter.emit(recordSubset(input, fields));
  }
 
  public void initialize() {
    Map<String, FunctionConfig> functions = gson.fromJson(config.functions, MAP_TYPE);
    for each function:
      val = function.aggregate(groupRecords);
  }
 
  public void aggregate(StructuredRecord groupKey, Iterable<StructuredRecord> groupRecords, Emitter<StructuredRecord> emitter) {
    // reset all functions
    for (StructuredRecord record : groupRecords) {
      foreach function:
        function.update(record);
    }
    // build record from group key and function values
    for each function:
      val = function.aggregate();
    // emit record
  }
 
}
 
public abstract class AggregationFunction<RECORD_TYPE, OUTPUT_TYPE> {
  public abstract void reset();
  public abstract void update(RECORD_TYPE record);
  public abstract OUTPUT_TYPE aggregate();
}
 
@Plugin(type = "aggregationFunction")
@Name("sum")
public SumAggregation extends AggregationFunction<StructuredRecord, Number> {
  private final SumConfig config;
  private Number sum;
  
  public static class SumConfig extends PluginConfig {
    private String column;
  }
 
  public void update(StructuredRecord record) {
    // get type of config.column, initialize sum to right type based on that
    sum += (casted to correct thing) record.get(config.column);
  }
 
  public Number aggregate() {
    return sum;
  }
}

Note: This would also satisfy user story 5, where a unique can be implemented as a Aggregation plugin, where you group by the fields you want to unique, and ignore the Iterable<> in aggregate and just emit the group key.

Story 2: Control Flow (Not Reviewed, WIP)

Option 1: Introduce different types of connections. One for data flow, one for control flow

Code Block

{
  "stages": [
    {
      "name": "customersTable",
      "plugin": {
        "name": "Database",
        "type": "batchsource", ...
      }
    },    
    {
      "name": "customersFiles",
      "plugin": {
        "name": "TPFSParquet",
        "type": "batchsink", ...
      }
    },
    {
      "name": "purchasesTable",
      "plugin": {
        "name": "Database",
        "type": "batchsource"
      }
    },
    {
      "name": "purchasesFiles",
      "plugin": {
        "name": "TPFSParquet",
        "type": "batchsink", ...
      }
    },
  ],
  "connections": [
    { "from": "customersTable", "to": "customersFiles", "type": "data" },
    { "from": "customersFiles", "to": "purchasesTable", "type": "control" },
    { "from": "purchasesTable", "to": "purchasesFiles", "type": "data" }
  ]
}

An alternative could be to introduce the concept of "phases", with connections between phases always control flow, and connections within phases as data flow

Code Block

{
  "phases": [
    {
      "name": "phase1",
      "stages": [
        {
          "name": "customersTable",
          "plugin": {
            "name": "Database",
            "type": "batchsource", ...
          }
        },   
        {
          "name": "customersFiles",
          "plugin": {
            "name": "TPFSParquet",
            "type": "batchsink", ...
        }
      ],
      "connections": [
        { "from": "customersTable", "to": "customersFiles" }
      ]
    },
    {
      "name": "phase2",
      "stages": [
        {
          "name": "purchasesTable",
          "plugin": {
            "name": "Database",
            "type": "batchsource"
          }
        },
        {
          "name": "purchasesFiles",
          "plugin": {
            "name": "TPFSParquet",
            "type": "batchsink", ...
          }
        }
      ],
      "connections": [
        { "from": "purchasesTable", "to": "purchasesFiles" }
      ]
    }
  ]
  "connections": [
    { "from": "phase1", "to": "phase2" }
  ]
}

Option2: Make it so that connections into certain plugin types imply control flow rather than data flow. For example, introduce "condition" plugin type. Connections into a condition imply control flow rather than data flow. Similarly, connections into an "action" plugin type would imply control flow

Code Block

{
  "stages": [
    {
      "name": "customersTable",
      "plugin": {
        "name": "Database",
        "type": "batchsource", ...
      }
    },    
    {
      "name": "customersFiles",
      "plugin": {
        "name": "TPFSParquet",
        "type": "batchsink", ...
      }
    },
    {
      "name": "afterDump",
      "plugin": {
        "name": "AlwaysRun",
        "type": "condition"
      }
    },
    {
      "name": "purchasesTable",
      "plugin": {
        "name": "Database",
        "type": "batchsource"
      }
    },
    {
      "pluginname": {"purchasesFiles",
      "plugin": {
        "name": "sumTPFSParquet",
        "type": "batchsink", ...
      }
  "properties": { },
  ],
  "connections": [
    {  "columnfrom": "pricecustomersTable", "to": "customersFiles" },
    { "from": "customersFiles", "to": "afterDump" },
          }
       { "from": "afterDump", "to": "purchasesTable" },

       { "from": "purchasesTable", "to": "purchasesFiles" }
     "columnName": "numTransactions",
          "plugin": {]
}

You could also say that connections into a source imply control flow, or connections into an action imply control flow.

Story 3: Spark ML in a pipeline

Add a plugin type "sparksink" that is treated like a sink. When present, a spark program will be used to read data, transform it, then send all transformed results to the sparksink plugin.

Code Block

{
  "stages": [
     {
      "name": "countcustomersTable",
      "plugin": {
  }         }
      ]
    }
  ],
  "connections": ["name": "Database",
     { "from": "inputTable", "totype": "aggStage"batchsource", ...
      }
   ] },   public abstract class
Aggregation<INPUT_TYPE, GROUP_BY, RECORD_TYPE, OUTPUT_TYPE> {
     public abstract groupBy(INPUT_TYPE input, Emitter<KeyValue<GROUP_BY, RECORD_TYPE>> emitter);
 
  public abstract aggregate(GROUP_BY groupKey, Iterable<RECORD_TYPE> groupRecords, Emitter<OUTPUT_TYPE> emitter);
 
}

@Plugin(type = "aggregation")
@Name("record")
public RecordAggregation extends Aggregation<StructuredRecord, StructuredRecord, StructuredRecord, StructuredRecord> {
  private static final AggConfig config;
  
  public static class AggConfig extends PluginConfig {
    private String groupBy;
 
    // ideally this would be Map<String, FunctionConfig> functions
    private String functions;
  }
 
  public void configurePipeline(PipelineConfigurer configurer) {
    Map<String, FunctionConfig> functions = gson.fromJson(config.functions, MAP_TYPE);
    for each function:
      usePlugin(id, type, name, properties);
  }
 
  public groupBy(StructuredRecord input, Emitter<KeyValue<StructuredRecord, StructuredRecord>> emitter) {
    // key = new record from input with only fields in config.groupBy
    // emitter.emit(new KeyValue<>(key, input));
  }
 
  public aggregate(StructuredRecord groupKey, Iterable<StructuredRecord> groupRecords, Emitter<StructuredRecord> emitter) {
    Map<String, FunctionConfig> functions = gson.fromJson(config.functions, MAP_TYPE);
    for each function:
      val = function.aggregate(groupRecords);
    for (StructuredRecord record : groupRecords) {
      function.update(record);
    }
    // build record from group key and function values
    for each function:
      val = function.aggregate();
    // emit record
  }
 
}
 
public abstract class AggregationFunction<RECORD_TYPE, OUTPUT_TYPE> {
 
  public abstract void update(RECORD_TYPE record);
  public abstract OUTPUT_TYPE aggregate();
}
 
@Plugin(type = "aggregationFunction")
@Name("sum")
public SumAggregation extends AggregationFunction<StructuredRecord, Number> {
  private final SumConfig config;
  private Number sum;
  
  public static class SumConfig extends PluginConfig {
    private String column;
  }
 
  public void update(StructuredRecord record) {
    // get type of config.column, initialize sum to right type based on that
    sum += record;
  }
 
  public Number aggregate() {
    return sum;
  }
}

...

 "name": "categorizer",
      "plugin": {
        "name": "SVM",
        "type": "sparksink", ...
      }
    },
    {
      "name": "models",
      "plugin": {
        "name": "Table",
        "type": "batchsink", ...
      }
    },
  ],
  "connections": [
    { "from": "customersTable", "to": "categorizer" }
  ]
}

Story 6: Join (Not Reviewed, WIP)

Add a join plugin type. Different implementations could be inner join, left outer join, etc.

Code Block

{
  "stages": [
    {
      "name": "customers",
      "plugin": {
        "name": "Table",
        "type": "batchsource", ...
      }
    },
    {
      "name": "purchases",
      "plugin": {
        "name": "Table",
        "type": "batchsource", ...
      }
    },
    {
      "name": "customerPurchaseJoin",
      "plugin": {
        "name": "inner",
        "type": "join",
        "properties": {
          "left": "customers.id",
          "right": "purchases.id",
          "rename": "customers.name:customername,purchases.name:itemname"
        }
      }
    },
    ...
  ],
  "connections": [
    { "from": "customers", "to": "customerPurchaseJoin" },
    { "from": "purchases", "to": "customerPurchaseJoin" },
    { "from": "customerPurchaseJoin", "to": "sink" },
  ]
}

Java API for join plugin type: these might just be built into the app. Otherwise the interface is basically MapReduce.

Versions Compared

Old Version 27

New Version Current

Key

User Stories:

Design:

Story 1: Group By -> Aggregations

Story 2: Control Flow (Not Reviewed, WIP)

Story 3: Spark ML in a pipeline

Story 6: Join (Not Reviewed, WIP)

Page Comparison

Versions Compared

Old Version 27

New Version Current

Key

User Stories:

Design:

Story 1: Group By -> Aggregations

Story 2: Control Flow (Not Reviewed, WIP)

Story 3: Spark ML in a pipeline

Story 6: Join (Not Reviewed, WIP)