Stream pipeline

At the core of the platform is the use case. A use case is defined by user who understand the required computation and output criteria needed by the business

Overview

Stream pipeline example

Below is a fully use case that performs a basic window aggregation computation using a metrics engine and tumbling window before publishing a filter stream of events to a connected publisher.

stream:
  name: tumblingWindowQuoteStream
  eventTimeType: EVENT_TIME
  sources:
    - nasdaq_quotes_stream

  initialisation:
    sql import:
      schema: fxrates
      parquet:
        - table: quote
          asView: false
          files: [ 'fxrates.parquet' ]
          drop table: true
          index:
            fields: [ 'ccy' ]
            unique: false
            
  processing unit:
    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
              
    pipeline:
      - filter:
          expression: "symbol != 'A'"
      - timeWindow:
          emittingEventType: tumblingQuoteAnalytics
          aggregateFunctions:
            MIN: [ask, bid]
            MAX: [ask, bid]
          policy:
            type: tumblingTime
            windowSize: 5000

  emit:
    eventType: windowQuoteEvent
    select: "symbol, ask_MIN, ask_MAX, bid_MIN, bid_MAX, BidMovingAverage.avg_bid_max;WHERE symbol=${symbol}"
    having: "symbol !='A'"

  group by:
    - symbol

Core Elements

Initialisation

Joule provide the ability to prime the system with static reference data at startup. Read the initialisation documentation for further information.

Procession Unit

The main objective of the platform was to build use cases easliy and clearly. To understand how to build use case read the processing unit documentation.

Emit

Ability to select required output feilds and to perform a final filter before publishing events to downstream systems. Read the emit documentation for further information.

Group by

Provides the ability to group similair events to perform a computation in order to reduce the overall downstream output. Read the group by documentation for more information.