Contextual SQL based metrics

Define SQL based metrics to drive advanced use case insights

Version 1.0.3


This release brings a number of new features, bug fixes, optimisations and general usability enhancements. The focus of this release has been providing a solid foundation for in-memory SQL support, metrics processing and multi language scripting support.


  • SQL Support

  • Metrics engine

  • Dynamic Rest APIs

  • Multi-Language scripting support

  • Parquet support

  • Database publisher

  • Documentation

SQL Support

Joule ships with an embedded in-memory modern SQL engine, DuckDB. This is used to capture events flowing through the processing pipeline along with supporting the metrics engine implementation.

  • SQL Tap for event capture and storage

  • Metrics Engine to provide SQL analytics

  • Rest API provides data access and export functions

Metrics Engine

The metrics engine computes SQL-defined metrics using events stored by the SQL Tap and scheduled using a runtime policy.

metrics engine:
  runtime policy:
    frequency: 1
    startup delay: 2
    time unit: MINUTES

  foreach metric compute:
      - name: BidMovingAverage
          metric key: symbol
          table definition: standardQuoteAnalyticsStream.BidMovingAverage 
                           (symbol VARCHAR, avg_bid_min FLOAT, 
                            avg_bid_avg FLOAT,avg_bid_max FLOAT)
            SELECT symbol,
            MIN(bid) AS 'avg_bid_min',
            AVG(bid) AS 'avg_bid_avg',
            MAX(bid) AS 'avg_bid_max'
            FROM standardQuoteAnalyticsStream.quote
            ingestTime >= date_trunc('minutes',now() - INTERVAL 2 MINUTES) AND ingestTime <= date_trunc('minutes',now())
            GROUP BY symbol
            ORDER BY 1;
          truncate on start: true
          compaction policy:
            frequency: 8
            time unit: HOURS

Dynamic Rest APIs

All SQL tables created by a Joule process are accessible through a well-defined Rest API.

Multi-Language scripting support

Joule provides a flexible scripting processor implemented using GraalVM. This enables the developer to integrate code written using Python, Node.JS, R, Javascript and Ruby within a streaming context.

Parquet import/export

Data can be stored within the Joule process and can be exported as Parquet files for further analytics use cases. Also, Parquet files can be imported into the Joule process to drive user-defined functionality.

  sql import:
    schema: banking
        table: fxrates
        asView: false
        files: [ 'fxrates.parquet' ]
        drop table: true
          fields: [ 'ccy' ]
          unique: false

Database publisher

Publisher transport persists processed events to a configured SQL database and table. The insert statement is dynamically generated from an event, attribute names and types need to match the table definition.

This feature is an idea for offline analytics, business reporting, dashboards and process testing.


Joule is now shipping with online documentation.

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