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Siddhi 5.1 Features

  • Retrieving Events
    • From various data sources supporting multiple message formats
  • Mapping Events
    • Mapping events with various data formats to Stream for processing
    • Mapping streams to multiple data formats for publishing
  • Processing Streams
    • Filter
      • Filtering stream based on conditions
    • Window
      • Support for sliding and batch (tumbling) and many other type of windows
    • Aggregation
      • Supporting Avg, Sum, Min, Max, etc
      • For long running aggregations and aggregation over windows
      • Ability to perform aggregate processing with Group by and filter aggregated data with Having conditions
    • Incremental Aggregation
      • Support for processing and retrieving long running Aggregation
      • Supports data processing in seconds, minutes, hours, days, months, and years granularity
    • Table and Stores
      • For storing events for future processing and retrieving them on demand
      • Supporting storage in in-memory, RDBMs, Solr, mongoDb, etc
    • Join
      • Joining two streams, two windows based on conditions
      • Joining stream/window with table or incremental aggregation based on conditions
      • Supports inner joins, and left, right & full outer joins
    • Pattern
      • Identifies event occurrence patterns among streams over time
      • Identify non occurrence of events
      • Supports repetitive matches of event pattern occurrences with logical conditions and time bound
    • Sequence processing
      • Identifies continuous sequence of events from streams
      • Supports zero to many, one to many, and zero to one event matching conditions
    • Partitions
      • Grouping queries and based on keywords or value ranges for isolated parallel processing
    • Scripting
      • Support writing scripts like JavaScript, Scala and R within Siddhi Queries
    • Process Based on event time
      • Whole execution driven by the event time
  • Publishing Events
    • To various data sources with various message formats
    • Supporting load balancing and failover data publishing
  • Error handling
    • Support errors and exceptions through error streams
    • Automatic backoff retries to external data stores, sources and sinks.
  • Parallel processing
    • Support parallel processing through asynchronous multithreading at streams
  • Snapshot and restore
    • Support for periodic state persistence and restore capabilities to allow state restore during failures
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