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Aggregates and user defined time based window functions
Combining aggregates, stateful and stateless window functions is a powerful mix to provide group-by analytics over a stream of events
This example computes basic aggregates and a mix of stateless and stateful analytic functions or time base sliding windows.
File: app-aggregatesAndEMA.env
SOURCEFILE=${PWD}/conf/sources/stockQuoteStream.yaml
ENGINEFILE=${PWD}/conf/usecases/slidingEventWindowAnalyticsAggregatesAndEMA.yaml
PUBLISHFILE=${PWD}/conf/publishers/fileStandardAnalytics.yaml
This pipeline configures a sliding window of 2.5 second with a 500ms slide which computes aggregates and three window listener functions, all using the same event window.
processing unit:
pipeline:
- filter:
expression: "symbol == 'CVCO'"
- time window:
emitting type: slidingQuoteAnalytics
aggregate functions:
SUM: [ volume ]
MEAN: [ volatility ]
window listeners:
- exponential moving average:
attributes: [ ask, bid ]
parameters:
emaFactor: 0.33333
- minmax normalization:
attributes: [ ask, bid ]
- zscore normalization:
attributes: [ ask, bid ]
policy:
type: slidingTime
slide: 500
window size: 2500
emit:
select: "symbol, ask_EMA, bid_EMA, volume_SUM, volatility_MEAN, ask_MINMAX_NORM, bid_MINMAX_NORM, ask_ZSCORE, bid_ZSCORE"
group by:
- symbol
Last modified 10mo ago