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Sliding window analytics
Sliding windows are useful to gain analytic insights over a period whereby some of the events contribute to the insight, these windows are either time-based or a fixed count of events.
This example provides a demonstration of the available OOTB aggregate functions Joule provides that work with sliding windows.
This pipeline will compute a sliding window function on a single event type based on symbol
sources: [ nasdaq_quotes_stream ]
- time window:
emitting type: slidingQuoteAnalytics
LAST: [ ask ]
MIN: [ ask ]
MAX: [ bid ]
SUM: [ volume ]
MEAN: [ volatility ]
HARMONIC_MEAN: [ volatility ]
VARIANCE: [ volatility ]
STDEV: [ volatility ]
window size: 2500
expression: "symbol, ask_FIRST, ask_LAST, ask_MIN, bid_MAX, volume_SUM, volatility_MEAN, volatility_HARMONIC_MEAN, volatility_VARIANCE, volatility_STDEV"