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Siddhi 4.x 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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