Use the Dataflow job monitoring interface Stay organized with collections Save and categorize content based on your preferences.
When you run your pipeline by using Dataflow,you can view that job and any others by using the Dataflow monitoringinterface. The monitoring interface lets you see andinteract with your Dataflow jobs.
You can access the Dataflow monitoring interface in theGoogle Cloud console.
Tasks that you can perform by using the monitoring interface include thefollowing:
- See a list of running, completed, and failed jobs.
- View a graphical representation of a job's stages and the progress of eachstage
- View graphs of job metrics, such as data freshness, resource utilization, andI/O requests.
- Monitor the estimated cost of a job.
- View pipeline logs.
- Identify which steps might cause pipeline lag.
- Identify causes of latency in your sources and sinks.
- Understand pipeline errors.
Monitoring interface components
The monitoring interface contains the following visualizers and charts:
- Project monitoring dashboard
- A dashboard that monitors your Dataflow jobs at the projectlevel.
- Jobs list
- A list of all running Dataflow jobs and all jobs run within thelast 30 days, along with their status, region, elapsed time, and otherinformation.
- Job graph
- A graphical representation of a pipeline. The job graph also provides a jobsummary, a job log, and information about each step in the pipeline.
- Execution details
- Shows the execution stages of a job, data freshness for streaming jobs, andworker progress for batch jobs.
- Job metrics
- Charts that display metrics over the duration of a job.
- Estimated cost
- The estimated cost of your Dataflow job, based on resourceusage metrics.
- Recommendations
- Recommendations for improving job performance, reducing cost, andtroubleshooting errors.
- Autoscaling
- A set of charts that help you to understand the autoscaling behavior ofstreaming jobs.
- Pipeline logs
- Logs emitted by your pipeline and by the Dataflow service.
- Data sampling
- A tool that lets you observe sampled data at each step of a pipeline.
What's next
- UseCloud Monitoring to create alerts and view Dataflow metrics, including custom metrics
- Learn more aboutbuilding production-ready data pipelines
- Learn how totroubleshoot your pipeline
Except as otherwise noted, the content of this page is licensed under theCreative Commons Attribution 4.0 License, and code samples are licensed under theApache 2.0 License. For details, see theGoogle Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-02-19 UTC.