Joule
  • Welcome to Joule's Docs
  • Why Joule?
    • Joule capabilities
  • What is Joule?
    • Key features
    • The tech stack
  • Use case enablement
    • Use case building framework
  • Concepts
    • Core concepts
    • Low code development
    • Unified execution engine
    • Batch and stream processing
    • Continuous metrics
    • Key Joule data types
      • StreamEvent object
      • Contextual data
      • GeoNode
  • Tutorials
    • Getting started
    • Build your first use case
    • Stream sliding window quote analytics
    • Advanced tutorials
      • Custom missing value processor
      • Stateless Bollinger band analytics
      • IoT device control
  • FAQ
  • Glossary
  • Components
    • Pipelines
      • Use case anatomy
      • Data priming
        • Types of import
      • Processing unit
      • Group by
      • Emit computed events
      • Telemetry auditing
    • Processors
      • Common attributes
      • Filters
        • By type
        • By expression
        • Send on delta
        • Remove attributes
        • Drop all events
      • Enrichment
        • Key concepts
          • Anatomy of enrichment DSL
          • Banking example
        • Metrics
        • Dynamic contextual data
          • Caching architecture
        • Static contextual data
      • Transformation
        • Field Tokeniser
        • Obfuscation
          • Encryption
          • Masking
          • Bucketing
          • Redaction
      • Triggers
        • Change Data Capture
        • Business rules
      • Stream join
        • Inner stream joins
        • Outer stream joins
        • Join attributes & policy
      • Event tap
        • Anatomy of a Tap
        • SQL Queries
    • Analytics
      • Analytic tools
        • User defined analytics
          • Streaming analytics example
          • User defined analytics
          • User defined scripts
          • User defined functions
            • Average function library
        • Window analytics
          • Tumbling window
          • Sliding window
          • Aggregate functions
        • Analytic functions
          • Stateful
            • Exponential moving average
            • Rolling Sum
          • Stateless
            • Normalisation
              • Absolute max
              • Min max
              • Standardisation
              • Mean
              • Log
              • Z-Score
            • Scaling
              • Unit scale
              • Robust Scale
            • Statistics
              • Statistic summaries
              • Weighted moving average
              • Simple moving average
              • Count
            • General
              • Euclidean
        • Advanced analytics
          • Geospatial
            • Entity geo tracker
            • Geofence occupancy trigger
            • Geo search
            • IP address resolver
            • Reverse geocoding
            • Spatial Index
          • HyperLogLog
          • Distinct counter
      • ML inferencing
        • Feature engineering
          • Scripting
          • Scaling
          • Transform
        • Online predictive analytics
        • Model audit
        • Model management
      • Metrics engine
        • Create metrics
        • Apply metrics
        • Manage metrics
        • Priming metrics
    • Contextual data
      • Architecture
      • Configuration
      • MinIO S3
      • Apache Geode
    • Connectors
      • Sources
        • Kafka
          • Ingestion
        • RabbitMQ
          • Further RabbitMQ configurations
        • MQTT
          • Topic wildcards
          • Session management
          • Last Will and Testament
        • Rest endpoints
        • MinIO S3
        • File watcher
      • Sinks
        • Kafka
        • RabbitMQ
          • Further configurations
        • MQTT
          • Persistent messaging
          • Last Will and Testament
        • SQL databases
        • InfluxDB
        • MongoDB
        • Geode
        • WebSocket endpoint
        • MinIO S3
        • File transport
        • Slack
        • Email
      • Serialisers
        • Serialisation
          • Custom transform example
          • Formatters
        • Deserialisers
          • Custom parsing example
    • Observability
      • Enabling JMX for Joule
      • Meters
      • Metrics API
  • DEVELOPER GUIDES
    • Setting up developer environment
      • Environment setup
      • Build and deploy
      • Install Joule
        • Install Docker demo environment
        • Install with Docker
        • Install from source
        • Install Joule examples
    • Joulectl CLI
    • API Endpoints
      • Mangement API
        • Use case
        • Pipelines
        • Data connectors
        • Contextual data
      • Data access API
        • Query
        • Upload
        • WebSocket
      • SQL support
    • Builder SDK
      • Connector API
        • Sources
          • StreamEventParser API
        • Sinks
          • CustomTransformer API
      • Processor API
      • Analytics API
        • Create custom metrics
        • Define analytics
        • Windows API
        • SQL queries
      • Transformation API
        • Obfuscation API
        • FieldTokenizer API
      • File processing
      • Data types
        • StreamEvent
        • ReferenceDataObject
        • GeoNode
    • System configuration
      • System properties
  • Deployment strategies
    • Deployment Overview
    • Single Node
    • Cluster
    • GuardianDB
    • Packaging
      • Containers
      • Bare metal
  • Product updates
    • Public Roadmap
    • Release Notes
      • v1.2.0 Join Streams with stateful analytics
      • v1.1.0 Streaming analytics enhancements
      • v1.0.4 Predictive stream processing
      • v1.0.3 Contextual SQL based metrics
    • Change history
Powered by GitBook
On this page
  • Overview
  • Metrics API
  • Available Methods
  • Metric types
  • Example

Was this helpful?

  1. Components
  2. Observability

Metrics API

Tracks component events and data flow with counters

Overview

Joule provides a simple Monitoring API, which tracks various operational metrics by incrementing or decrementing counters for different components, including custom processors, connectors and storage components.

Currently the monitoring API is limited to only publishing counter metrics using the below method

Metrics API

Every component within the Joule system has the ability to be monitored. This means any custom processor, transport or storage component inherits this feature within the component context.

The API includes methods to adjust counters for metrics (i.e., increment or decrement a value, set an initial value).

Available Methods

// Increment a metric by 1
void incrementMetric(Metric metric)
// Increment a metric by a delta
void incrementMetric(Metric metric, int delta)
// Decrement a metric by 1
void decrementMetric(Metric metric)
// Increment a metric by a delta
void decrementMetric(Metric metric, int delta)
// Initialises a metric to a given start value
void setMetric(Metric metric, long value)

Metric types

Metrics cover different system aspects:

  1. Components Tracks counts for events received, processed, failed, discarded, etc.

  2. Data stores Records data read / write operations and byte volume.

  3. Processing stream Logs start, shutdown, suspension and resumption times.

This is an enum data type which represents various classes of metrics which can be tracked over time.

// Component
RECEIVED("received"),
PROCESSED("processed"),
PROCESS_FAILED("process_failed"),
DISCARDED("discard"), QUEUED_EVENT("queued_event"),
AVG_LATENCY("averageLatency"),
IGNORED("ignored"),

// Data stores
READ_STORE("read"), WRITE_STORE("write"),
BYTES_READ("bytes_read"), BYTES_WRITE("bytes_write"),

// Processing stream
START_TIME("start_time"), SHUTDOWN_TIME("stop_time")
SUSPENDED_TIME("suspended_time"), RESUMED_TIME("resumed_time")

Example

This example demonstrates how to apply metrics in a filter processor, incrementing specific counters based on processing outcomes.

try(var filterContext = prepareEnvironment(event, context)){
    var val = filterContext.eval(expressionSource);
    if (!val.isNull() && val.isBoolean() && val.asBoolean()) {
        // PROCESSED METRIC
        metrics.incrementMetric(Metric.PROCESSED);
    } else {
        // DISCARDED METRIC    
        metrics.incrementMetric(Metric.DISCARDED);
        return null;
    }
} catch (Exception e) {
    // FAILED METRIC
    metrics.incrementMetric(Metric.PROCESS_FAILED);
    throw new StreamsException("Failed processing ....", e);
}
return event;
PreviousMetersNextSetting up developer environment

Last updated 6 months ago

Was this helpful?