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For instance, to perform the operations illustrated in Fig. 1 on a stream of messages, a user can write the Samza app in listing 1 using Samza high-level API:

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Fig. 1 — A logical workflow of stream processing operations

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public class StreamStreamJoinApp implements StreamApplication {  
   @Override
    public void init(StreamGraph graph, Config config) {
      MessageStream s1 = graph
          .getInputStream("S1")
          .filter(/* Omitted for brevity */);

      MessageStream s2 = graph
          .getInputStream("S2");

      OutputStream s3 = graph.getOutputStream("S3");

      s1.join(s2, /* Omitted for brevity */)
        .sendTo(s3);
    }
}

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The Execution Planner is the core Samza module responsible for verifying that all streams participating in any given Join operation agree in partition count. To achieve this, it traverses the graph of OperatorSpecs produced by Samza High-Level API to verify compliance to this requirement among all such sets of streams.

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1. Any intermediate stream joined with an input stream gets assigned the same partition count as that input stream.

2. Any intermediate stream not covered by the first rule is assigned the partition count value specified by the Samza config property job.intermediate.stream.partitions.

3. If no value is specified for job.intermediate.stream.partitions, the Execution Planner falls back to using the maximum partition count among all input and output streams, capped at a maximum hard-coded value of 256.

It is important to realize there are situations where it is not possible to enforce agreement between an intermediate stream and the input streams it is joined with, a scenario that would cause the Execution Planner to signal an error and reject the whole application. Fig. 5 illustrates one such case.

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public class StreamTableJoinApp implements StreamApplication {  
   @Override
    public void init(StreamGraph graph, Config config) {

      MessageStream s1 = graph.getInputStream("S1");
      MessageStream s1Prime = s1.partitionBy(/* Omitted for brevity */);

      // Assume local table
      Table t = graph.getTable(/* Omitted for brevity */);

      s1Prime.sendTo(t);

      MessageStream s2 = graph.getInputStream("S2");
      OutputStream s3 = graph.getOutputStream("S3");

      s2.join(t, /* Omitted for brevity */)
        .sendTo(s3);
    }
}

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public class StreamTableJoinApp implements StreamApplication {  
   @Override
    public void init(StreamGraph graph, Config config) {
      Table<KV<Integer, String>> t = graph
        .getTable(/* Omitted for brevity */);

      MessageStream s1 = graph
          .getInputStream("S1")
          .filter(/* Omitted for brevity*/);
      
      s1.sendTo(t);  
      
      MessageStream s2 = graph.getInputStream("S2");

      OutputStream s3 = graph.getOutputStream("S3");

      s2.join(t, /* Omitted for brevity*/)
        .sendTo(s3);
    }
}

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Fig. 7 — A graph representing the OperatorSpec graph generated by Samza for the application in listing 3. As usual, OperatorSpecs associated with input/output streams are highlighted in yellow.

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To extend Samza’s ExecutionPlanner to support Tables, we need to address the disconnect between a SendToTableOperatorSpec and all relevant StreamTableJoinOperatorSpecs. One possibility that does not require changing Samza’s High-Level APIs is to modify the OperatorSpec graph traversal such that virtual connections are assumed between every SendToTableOperatorSpec and all the StreamTableJoinOperatorSpecs that reference the same table (TableSpec) in the entire OperatorSpec graph.

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1. OperatorSpec graph analysis

2. Conversion to StreamEdges

3. Join input validation

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Details of each one of these steps are outlined below.

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