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 sources, with one happening after the otherA 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 (3.4) A developer should be able to de-duplicate records in a pipeline
A (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

Option 1:
{
  "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": [
    {       "name"from": "inputTable", "to": "aggStage", } 
     "groupBy": "id",
      "aggregations": [
        {
  ]
}

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  "columnName": "totalPrice",
 void groupBy(RECORD_TYPE input, Emitter<GROUP_BY> emitter);
 
  public abstract void  "plugin": {
  aggregate(GROUP_BY groupKey, Iterable<RECORD_TYPE> groupRecords, Emitter<OUTPUT_TYPE> emitter);
 
}

@Plugin(type =    "nameaggregation":)
@Name("sumGroupByAggregate",)
public GroupByAggregate extends Aggregation<StructuredRecord, StructuredRecord, StructuredRecord> {
  private static final "properties": {AggConfig config;
  
  public static class AggConfig extends PluginConfig {
  "column": "price" 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,  "columnName": "numTransactions",MAP_TYPE);
    for each function:
     "plugin": {
   usePlugin(id, type, name, properties);
  }
 
  public  "name": "count"
 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));
  ],}
 
  "connections": [
 public void initialize() {
  { "from": "inputTable" Map<String, "to": "aggStage" } 
  ]
}
 
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);
 FunctionConfig> functions = gson.fromJson(config.functions, MAP_TYPE);
    for each function:
      val = function.aggregate(groupRecords);
  }
 
  public abstractvoid aggregate(GROUP_BYStructuredRecord groupKey, Iterable<RECORD_TYPE>Iterable<StructuredRecord> groupRecords, Emitter<OUTPUT_TYPE>Emitter<StructuredRecord> emitter); {
    } // @Plugin(typereset = "aggregation")
@Name("record")
public RecordAggregation extends Aggregation<StructuredRecord, StructuredRecord, StructuredRecord, StructuredRecord>all functions
    for (StructuredRecord record : groupRecords) {
  private static final AggConfig config;foreach function:
    public static class AggConfig extends PluginConfig { function.update(record);
    }
 private String groupBy;  // build record from group //key ideallyand thisfunction wouldvalues
be Map<String, FunctionConfig> functions for each function:
 private String functions;   }val  
= function.aggregate();
 public void configurePipeline(PipelineConfigurer configurer) {
 // emit record
  }
 
}
Map<String, FunctionConfig>
functionspublic = gson.fromJson(config.functions, MAP_TYPE);
 abstract class AggregationFunction<RECORD_TYPE, OUTPUT_TYPE> {
  forpublic eachabstract function:
void reset();
  public abstract void usePlugin(id, type, name, propertiesupdate(RECORD_TYPE record);
  }public abstract OUTPUT_TYPE aggregate();
}
 public
groupBy@Plugin(StructuredRecordtype input, Emitter<KeyValue<StructuredRecord, StructuredRecord>> emitter) {
    // key = new record from input with only fields in config.groupBy= "aggregationFunction")
@Name("sum")
public SumAggregation extends AggregationFunction<StructuredRecord, Number> {
  private final SumConfig config;
  private Number sum;
  
  // emitter.emit(new KeyValue<>(key, input))public static class SumConfig extends PluginConfig {
    private String column;
  }
 
  public void aggregateupdate(StructuredRecord groupKey, Iterable<StructuredRecord> groupRecords, Emitter<StructuredRecord> emitterrecord) {
    Map<String,// FunctionConfig>get functionstype =of gson.fromJson(config.functionscolumn, MAP_TYPE);initialize sum to right type forbased on that
each function:   sum += (casted to valcorrect =thing) functionrecord.aggregateget(groupRecordsconfig.column);
  }
 
for (StructuredRecord recordpublic :Number groupRecordsaggregate() {
      function.update(record)return sum;
    }
    // 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;
  }
}

Story 2: Multiple Sources

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

Code Block
{ "stages": [ {} }

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": "customersTablepurchasesFiles",
          "plugin": {
            "name": "DatabaseTPFSParquet",
            "type": "batchsourcebatchsink", ...
      }     },
        {}
      "name": "customersFiles"],
      "pluginconnections": {[
        { "namefrom": "TPFSParquetpurchasesTable", "to": "purchasesFiles" }
      "type": "batchsink", ...]
    }
  ]
}  "connections": [
 },   {  {
  "from": "phase1", "to": "phase2" }
   "name": "purchasesTable",
      "plugin": {
        "name": "Database",
        "type": "batchsource"
      }
    },
    ]
}

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": "purchasesFilescustomersTable",
      "plugin": {
        "name": "TPFSParquetDatabase",
        "type": "batchsinkbatchsource", ...
      }
    },   ], 
  "connections": [ {
   { "from": "customersTable", "toname": "customersFiles",
      "typeplugin": "data" },
 {
   { "from": "customersFiles", "to": "purchasesTable", "typename": "controlTPFSParquet",
},     { "from": "purchasesTable", "totype": "purchasesFilesbatchsink", "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": "phase1afterDump",
      "stagesplugin": [{
        {
 "name": "AlwaysRun",
        "nametype": "customersTablecondition",
      }
   "plugin": { },
     {
      "name": "DatabasepurchasesTable",
      "plugin": {
        "typename": "batchsourceDatabase",
...        "type": "batchsource"
 }
        },   
  
    },
{    {
      "name": "customersFilespurchasesFiles",
   
      "plugin": {
            "name": "TPFSParquet",
            "type": "batchsink", ...
      }
 }   },
   ],
      "connections": [
        { "from": "customersTable", "to": "customersFiles" }
      ]
    },
    {
      "namefrom": "phase2customersFiles",       "stages": [
   "to": "afterDump" },
    { "from": "afterDump", "to": "purchasesTable" },
    { "namefrom": "purchasesTable",           "plugin": {
            "name": "Database",
            "type": "batchsource"
          }
        },
        {
 "to": "purchasesFiles" }
  ]
}

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": "purchasesFilescustomersTable",
   
      "plugin": {
 
          "name": "TPFSParquetDatabase",
            "type": "batchsinkbatchsource", ...
      }
    },    
    {
}       ]"name": "categorizer",
      "connectionsplugin": [{
        { "fromname": "purchasesTableSVM",
"to        "type": "purchasesFilessparksink", }...
      ]}
    },
  ]  {
      "connectionsname": "models",
[     { "fromplugin": "phase1",{
        "toname": "phase2Table",
 }   ] }

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.

Code Block
{ "stagestype": [ "batchsink", ... { } "name": "customersTable" }, ], "pluginconnections": {[ { "from": "customersTable", "nameto": "Databasecategorizer", } ] }

Story 6: Join (Not Reviewed, WIP)

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

Code Block

{
    "typestages": "batchsource", ...[
       }
    },    
    {
      "name": "customersFilescustomers",
      "plugin": {
        "name": "TPFSParquetTable",
        "type": "batchsinkbatchsource", ...
      }
    },
    {
      "name": "afterDumppurchases",
      "plugin": {
        "name": "AlwaysRunTable",
        "type": "conditionbatchsource", ...
      }
    },
    {
      "name": "purchasesTablecustomerPurchaseJoin",
      "plugin": {
        "name": "Databaseinner",
        "type": "batchsourcejoin",
      }     },
"properties": {
   {       "nameleft": "purchasesFilescustomers.id",
  
   "plugin": {         "nameright": "TPFSParquetpurchases.id",
          "typerename": "batchsink", ...customers.name:customername,purchases.name:itemname"
        }
      },

 ],   "connections": [},
    ...
{ "from": "customersTable", "to ],
  "connections": "customersFiles" },[
    { "from": "customersFilescustomers", "to": "afterDumpcustomerPurchaseJoin" },
    { "from": "afterDumppurchases", "to": "purchasesTablecustomerPurchaseJoin" },
    { "from": "purchasesTablecustomerPurchaseJoin", "to": "purchasesFilessink" },
  ]
}

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

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

Versions Compared

Old Version 33

New Version Current

Key

User Stories:

Design:

Story 1: Group By -> Aggregations

Story 2: Multiple Sources

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 33

New Version Current

Key

User Stories:

Design:

Story 1: Group By -> Aggregations

Story 2: Multiple Sources

Story 2: Control Flow (Not Reviewed, WIP)

Story 3: Spark ML in a pipeline

Story 6: Join (Not Reviewed, WIP)