Ylem documentation
  • 🗒️General information
    • Introduction to Ylem
    • Quick start guide
    • Release notes
  • 🔬Open-source edition
    • Installation
    • Usage of Apache Kafka
    • Task processing architecture
    • Configuring integrations with .env variables
  • 💡Integrations
    • Connecting an integration
    • Library of integrations
      • Amazon Redshift
      • Apache Kafka
      • APIs
      • Atlassian Jira
      • AWS Lambda
      • AWS RDS
      • AWS S3
      • ClickHouse
      • ElasticSearch
      • E-mail
      • Google Big Query
      • Google Cloud SQL
      • Google Pub/Sub
      • Google Sheets
      • Immuta
      • Incident.io
      • Jenkins
      • Hubspot
      • Microsoft Azure SQL
      • MySQL
      • OpenAI ChatGPT
      • Opsgenie
      • PostgreSQL
      • PlanetScale
      • RabbitMQ
      • Salesforce
      • Slack
      • Snowflake
      • Tableau
      • Twilio. SMS
      • WhatsApp (through Twilio)
    • Initial demo data source
  • 🚡Pipelines
    • Pipeline management
    • Tasks
      • Aggregator
      • API Call
      • Code
      • Condition
      • External trigger
      • Filter
      • For each
      • GPT
      • Merge
      • Notification
      • Query
      • Pipeline runner
      • Processor
      • Transformer
    • Running and scheduling pipelines
    • Library of templates
    • Environment variables
    • Mathematical functions and operations
    • Formatting of messages
  • 📈Statistics and profiling
    • Statistics of runs
    • Slow tasks
  • 📊Metrics
    • Metric management
    • Using previous values of a metric
  • 💼Use cases, patterns, templates, examples
    • Use cases
    • Messaging patterns
      • Datatype Channel
      • Message Dispatcher
      • Messaging Bridge
      • Message Bus
      • Message Filter
      • Message Router
      • Point-to-Point Channel
      • Publish-Subscribe Channel
      • Pull-Push
    • Functional use cases
      • Streaming from Apache Kafka and messaging queues
      • Streaming from APIs
      • Streaming from databases
      • Data orchestration, transformation and processing
      • Usage of Python and Pandas
      • KPI Monitoring
      • OKRs and custom metrics
      • Data Issues & Incidents
      • Reporting
      • Other functional use cases
    • Industry-specific use cases
      • Finance and Payments
      • E-commerce & Logistics
      • Customer Success
      • Security, Risk, and Anti-Fraud
      • Anti-Money Laundering (AML)
  • 🔌API
    • OAuth clients
    • API Reference
  • 👁️‍🗨️Other resources
    • FAQ
    • Our blog on Medium
Powered by GitBook
On this page
  • Give metric a name
  • Define how to retrieve data for your metric
  • Define how to calculate your metric
  • Define how often or when to calculate your metric
  • Define thresholds and pipelines to run

Was this helpful?

Edit on GitHub
  1. Metrics

Metric management

PreviousSlow tasksNextUsing previous values of a metric

Last updated 1 year ago

Was this helpful?

The functionality of metrics allows you to quickly create OKRs, KPIs, SLAs, and other custom metrics, schedule them, monitor their value, and execute various pipelines based on them.

consists 5 simple steps:

Give metric a name

Define how to retrieve data for your metric

As same as in the case of the pipeline task "Query" you can choose between using a visual UI or an SQL interface:

Define how to calculate your metric

Define how often or when to calculate your metric

Define thresholds and pipelines to run

And now as the final and most important step, you can define which pipeline to execute depending on the value of your metric:

are supported here:

More scheduling information can be found .

Some of the more detailed examples of metrics can be found here in the list of our use cases. Our also contains multiple ones, which you can use for configuring your metrics.

📊
Mathematical functions
library of templates
Management of metrics
here