Google Cloud Dataflow

Docs > Integrations > Google Cloud Dataflow

Overview

Google Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness.

Use the Datadog Google Cloud integration to collect metrics from Google Cloud Dataflow.

Setup

Metric collection

Installation

If you haven’t already, set up the Google Cloud Platform integration first. There are no other installation steps.

Log collection

Google Cloud Dataflow logs are collected with Google Cloud Logging and sent to a Dataflow job through a Cloud Pub/Sub topic. If you haven’t already, set up logging with the Datadog Dataflow template.

Once this is done, export your Google Cloud Dataflow logs from Google Cloud Logging to the Pub/sub:

Go to the Google Cloud Logging page and filter the Google Cloud Dataflow logs.
Click Create Sink and name the sink accordingly.
Choose “Cloud Pub/Sub” as the destination and select the pub/sub that was created for that purpose. Note: The pub/sub can be located in a different project.
Click Create and wait for the confirmation message to show up.

Data Collected

Metrics

gcp.dataflow.job.billable_shuffle_data_processed (gauge)	The billable bytes of shuffle data processed by this Dataflow job. Shown as byte
gcp.dataflow.job.current_num_vcpus (gauge)	The number of vCPUs currently being used by this Dataflow job. Shown as cpu
gcp.dataflow.job.current_shuffle_slots (gauge)	The current shuffle slots used by this Dataflow job.
gcp.dataflow.job.data_watermark_age (gauge)	The age (time since event timestamp) of the most recent item of data that has been fully processed by the pipeline. Shown as second
gcp.dataflow.job.elapsed_time (gauge)	Duration that the current run of this pipeline has been in the Running state so far, in seconds. When a run completes, this stays at the duration of that run until the next run starts. Shown as second
gcp.dataflow.job.element_count (count)	Number of elements added to the pcollection so far. Shown as item
gcp.dataflow.job.estimated_byte_count (count)	An estimated number of bytes added to the pcollection so far. Shown as byte
gcp.dataflow.job.is_failed (gauge)	Has this job failed.
gcp.dataflow.job.system_lag (gauge)	The current maximum duration that an item of data has been awaiting processing, in seconds. Shown as second
gcp.dataflow.job.total_memory_usage_time (gauge)	The total GB seconds of memory allocated to this Dataflow job. Shown as gibibyte
gcp.dataflow.job.total_pd_usage_time (gauge)	The total GB seconds for all persistent disk used by all workers associated with this Dataflow job. Shown as gibibyte
gcp.dataflow.job.total_shuffle_data_processed (gauge)	The total bytes of shuffle data processed by this Dataflow job. Shown as byte
gcp.dataflow.job.total_streaming_data_processed (gauge)	The total bytes of streaming data processed by this Dataflow job. Shown as byte
gcp.dataflow.job.total_vcpu_time (gauge)	The total vCPU seconds used by this Dataflow job.
gcp.dataflow.job.user_counter (gauge)	A user-defined counter metric.
gcp.dataflow.quota.region_endpoint_shuffle_slot.exceeded (count)	Number of attempts to exceed the limit on quota metric dataflow.googleapis.com/regionendpointshuffle_slot.
gcp.dataflow.quota.region_endpoint_shuffle_slot.limit (gauge)	Current limit on quota metric dataflow.googleapis.com/regionendpointshuffle_slot.
gcp.dataflow.quota.region_endpoint_shuffle_slot.usage (gauge)	Current usage on quota metric dataflow.googleapis.com/regionendpointshuffle_slot.

When using Google Cloud Dataflow to monitor Apache Beam pipeline metrics, note that metrics generated from Gauge static methods are not collected. If you need to monitor these metrics, you can use Micrometer.