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Computation graph

How it works

All data passing through this graph is represented as arrow::RecordBatch - a columnar format of Apache Arrow library. It has typed fields (columns) and records (rows), can be serialized and deserialized from arrow::Buffer - an object containing a pointer to a piece of contiguous memory with a particular size.

The main unit of the library is NodePipeline consisting of three parts: Producer, Node and number of Consumers. In the certain pipeline Producer provides data for Node from some kind of data source: external TCP endpoint, another pipeline, etc. After that, Node is responsible for handling the data according to the Node's type. And finally, Node passes the handled data to Consumers, which send it to the next pipeline or write it to the file or something else.

Such design were used to separate parts that are responsible for data handling and data transfer. It allows user to create very flexible and configurable computation graph.

Node

The Node is used to mutate data as soon as it arrives. It doesn't have any internal state so it is easy to understand how it works. The EvalNode uses provided in the constructor DataHandler to handle arriving data. There are two types of data handlers that are currently implemented:

  • DataParser - parses data arriving in the certain format. For example, CSV or Graphite output data format.
  • SerializedRecordBatchHandler - deserialize arriving data from arrow::Buffer to the vector of arrow::RecordBatch that can be handled by provided RecordBatchHandler.

RecordBatchHandler

There is a full list of currently available handlers:

  • AggregateHandler - aggregates data using provided aggregate functions (first, last, mean, min, max).
  • DefaultHandler - sets default values for columns. Analog of the Kapacitor node of the same name.
  • FilterHandler - filters rows with provided conditions. Use arrow::gandiva library to create conditions tree.
  • GroupHandler - splits record batches into groups with the same values in columns.
  • MapHandler - evaluates expressions with present columns as arguments. Use arrow::gandiva library to create expressions.
  • SortHandler - sorts rows by the certain column.
  • JoinHandler - joins received record batches on the set of columns.
  • WindowHandler - analogue of Kapacitor WindowNode.
  • ThresholdStateMachine - sets a threshold level adjusting it to the incoming data.
  • GroupDispatcher - splits incoming data into groups according to metadata and uses another RecordBatchHandler type to handle each group separately.
  • LogHandler - logs incoming data using spdlog library.

Producer

Producer provides data to the certain Node. There are two types of data producers have been implemented:

  • TCPProducer - listens on the certain endpoint for arriving data. It is mostly used to receive data from the external data source.
  • SubscriberProducer - producer based on ZeroMQ PUB-SUB pattern. Created for transferring data between pipelines. As argument it takes transport_utils::Subscriber class containing two ZMQ sockets: subscriber socket and synchronize socket. It needs for proper PUB-SUB communicating (for more details see ZMQ Guide).

Consumer

As opposite to Producer this class consumes data from Node and pass it to the next destination. Available types of consumer:

  • PrintConsumer - write record batches to the output stream. PrintFileConsumer subclass is more convenient way of writing to the file.
  • TCPConsumer - writes data to the TCP socket.
  • PublisherConsumer - the second part of PUB-SUB pattern.

Helpers

  • As configuring PUB-SUB consumers and producers appears to be unhandy and massive the NodePipeline::subscribeTo method was implemented. It can be used after nodes of two pipelines have been set to create PublisherConsumer/SubscriberProducer pair for these pipelines without manual creating ZMQ sockets.
  • src/utils directory is full of useful instruments if you are going to implement some additional functionality by yourself.