Processing unit

Quickly build custom business use cases

Overview

At its simplest form, a processing unit provides two key functions; a stream processing pipeline and a metrics engine that generate metrics based on a time schedule policy. This forms the core of data handling in Joule.

The processing unit is composed of two main components:

1. Stream processing pipeline A configurable series of processors that perform transformations, filters, and aggregations on event data. Pipelines are built from Joule’s set of core processors and allow users to construct sophisticated processing workflows.

2. Metrics engine This engine calculates complex metrics on a defined schedule using SQL-based queries. Metrics are generated and updated at regular intervals, allowing for real-time insights into data.

Example

stream:
  ...
  processing unit:              
      pipeline:
        # Here comes the list of processors

      metrics engine:
        # Here comes the metrics declarations            

Pipeline

A pipeline is a sequence of processors that compute functions on events. Joule provides out-of-the-box a set of core processors to enable you to build useful use cases, learn more about the available processors and analytic tools.

Processors

Processors are the core of the Joule platform, each performing a specific task. These create use case when linked together

Analytics tools

Define math expressions or provide as a file using Joule supported languages and APIs

Example

This example defines a pipeline that filters, groups, and aggregates events:

1. Filter Filters events that match where symbol != 'A'.

2. Tumbling time window Groups events into 5-second windows.

3. Aggregations Calculates MIN and MAX values for ask and bid fields, grouped by symbol.

4. Event emission Outputs one event per symbol with aggregated values at the end of each window.

stream:
  ...
  pipeline:
    - filter:
        expression: "symbol != 'A'"
    - time window:
        emitting type: tumblingQuoteAnalytics
        aggregate functions:
          MIN: [ask, bid]
          MAX: [ask, bid]
        policy:
          type: tumblingTime
          window size: 5000

Metrics engine

The metrics engine provides the ability to compute complex metrics based upon SQL queries, the embedded SQL engine provides.

This feature which is enabled by default

Example

stream:
  ...
  metrics engine:
      policy:
        timeUnit: MINUTES
        frequency: 1
        startup delay: 2
  
      foreach metric compute:
        metrics:
          - name: BidMovingAverage
            metric key: symbol
            table definition: standardQuoteAnalyticsStream.BidMovingAverage (symbol VARCHAR, avg_bid_min FLOAT, avg_bid_avg FLOAT,avg_bid_max FLOAT)                          
            query:
              SELECT symbol, 
              AVG(bid_MAX) AS mt_avg_bid_min,
              AVG(bid_MAX) AS mt_avg_bid_max
              OVER (
              PARTITION BY symbol ORDER BY eventTime ASC
              RANGE BETWEEN INTERVAL 1 SECONDS PRECEDING
              AND INTERVAL 1 SECONDS FOLLOWING)
              AS 'bid max Moving Average'
              FROM quotesStream.tumblingQuoteAnalytics_View
              ORDER BY 1;
            truncate on start: true
            compaction:
              frequency: 8
              timeUnit: HOURS

policy

The policy block defines the schedule for executing metrics calculations and managing data within the metrics engine.

The following table describes each attribute.

Attribute	Description	Required	Default	Supported values
policy	Defines the scheduling policy for metric calculations, setting the timing and intervals for updates		N/A	N/A
timeUnit	Sets the time unit used for the frequency and startup delay intervals		MINUTES	SECONDS, MINUTES, HOURS
frequency	Specifies how often metric calculations are performed		1 Minute	Any positive integer in timeUnit
startup delay	Delay before the initial metric calculation		5 Minutes	Any positive integer in timeUnit

Metrics computations

The foreach metric compute syntax defines a metric table, computation, management and assigns it to a named metric family.

The following table describes each attribute.

Attributes	Description	Required	Default	Supported values
name	A unique identifier for the metric; also referred to as the metrics family name		N/A	N/A
metric key	Generates optimised metric queries for user lookups and management functions		N/A	N/A
table definition	Defines the SQL table for storing and accessing metrics, including the schema as part of the table name		N/A	N/A
query	An ANSI SQL query executed periodically, with results inserted into the defined metric table		N/A	N/A
Management	Default management processes on startup for efficient memory and housekeeping. Enabled hourly		N/A	N/A
truncate on start	Truncates the table on startup if set to true		true	true, false
compaction	Removes outdated metrics according to a set period, ensuring efficient use of storage		hourly	N/A
frequency	Defines the interval between metric compaction processes		hourly	N/A
timeUnits	Specifies the time units for frequency, supporting HOURS and MINUTES		HOURS	SECONDS, MINUTES, HOURS