Metrics
Enrich events with pre-calculated metrics to facilitate further advanced processing
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Enrich events with pre-calculated metrics to facilitate further advanced processing
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Advanced use cases may need pre-computed metrics as inputs for calculations, event triggers, or additional context.
The enricher processor uses the query interface to query and bind metrics to an event using additional DSL attributes
There are various uses for this filter such as:
In retail Utilise real-time sales data and competitor pricing metrics to adjust product prices dynamically, maximising profit margins and ensuring competitiveness.
In manufacturing Use sensor data from machinery to predict failures before they occur, enabling proactive maintenance scheduling and reducing unplanned downtime.
In e-commerce Combine user behaviour data with product performance metrics to generate personalised product recommendations, enhancing the customer shopping experience.
This example defines three enrichment strategies:
bid_ask_avg
Retrieves avg_ask
and avg_bid
values by using the AskBidMovingAverage
metric family and symbol
as the key from the MetricsDB
.
market_depth_metrics
Fetches all attributes related to marketDepth
for a given symbol
from the MetricsDB
.
trading_triggers
Retrieves tradingTriggers
data as an object for a given symbol
from the MetricsDB
.
All enrichments use the MetricsDB
for data retrieval.
To direct the enricher to use the computed in-memory metrics database two attributes must be set.
by metric family
Metrics family to query against. This must be defined in the use case
String
using: MetricsDB
Directs the processor to bind to the metrics engine
String