Google Cloud Dataproc

Docs > Integrations > Google Cloud Dataproc

Overview

Data Jobs Monitoring helps you observe, troubleshoot, and cost-optimize your Spark jobs on your Dataproc clusters.

Google Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way.

Use the Datadog Google Cloud Platform integration to collect metrics from Google Cloud Dataproc.

Setup

Installation

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

Log collection

Google Cloud Dataproc 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 Dataproc logs from Google Cloud Logging to the Pub/Sub topic:

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

Data Collected

Metrics

gcp.dataproc.batch.spark.executors (gauge)	Indicates the number of Batch Spark executors. Shown as worker
gcp.dataproc.cluster.hdfs.datanodes (gauge)	Indicates the number of HDFS DataNodes that are running inside a cluster. Shown as node
gcp.dataproc.cluster.hdfs.storage_capacity (gauge)	Indicates capacity of HDFS system running on a cluster in GB. Shown as gibibyte
gcp.dataproc.cluster.hdfs.storage_utilization (gauge)	The percentage of HDFS storage currently used. Shown as percent
gcp.dataproc.cluster.hdfs.unhealthy_blocks (gauge)	Indicates the number of unhealthy blocks inside the cluster. Shown as block
gcp.dataproc.cluster.job.completion_time.avg (gauge)	The time jobs took to complete from the time the user submits a job to the time Dataproc reports it is completed. Shown as millisecond
gcp.dataproc.cluster.job.completion_time.samplecount (count)	Sample count for cluster job completion time. Shown as millisecond
gcp.dataproc.cluster.job.completion_time.sumsqdev (gauge)	Sum of squared deviation for cluster job completion time. Shown as second
gcp.dataproc.cluster.job.duration.avg (gauge)	The time jobs have spent in a given state. Shown as millisecond
gcp.dataproc.cluster.job.duration.samplecount (count)	Sample count for cluster job duration. Shown as millisecond
gcp.dataproc.cluster.job.duration.sumsqdev (gauge)	Sum of squared deviation for cluster job duration. Shown as second
gcp.dataproc.cluster.job.failed_count (count)	Indicates the number of jobs that have failed on a cluster. Shown as job
gcp.dataproc.cluster.job.running_count (gauge)	Indicates the number of jobs that are running on a cluster. Shown as job
gcp.dataproc.cluster.job.submitted_count (count)	Indicates the number of jobs that have been submitted to a cluster. Shown as job
gcp.dataproc.cluster.nodes.expected (gauge)	Indicates the number of nodes that are expected in a cluster. Shown as node
gcp.dataproc.cluster.nodes.failed_count (count)	Indicates the number of nodes that have failed in a cluster. Shown as node
gcp.dataproc.cluster.nodes.recovered_count (count)	Indicates the number of nodes that are detected as failed and have been successfully removed from cluster. Shown as node
gcp.dataproc.cluster.nodes.running (gauge)	Indicates the number of nodes in running state. Shown as node
gcp.dataproc.cluster.operation.completion_time.avg (gauge)	The time operations took to complete from the time the user submits a operation to the time Dataproc reports it is completed. Shown as millisecond
gcp.dataproc.cluster.operation.completion_time.samplecount (count)	Sample count for cluster operation completion time. Shown as millisecond
gcp.dataproc.cluster.operation.completion_time.sumsqdev (gauge)	Sum of squared deviation for cluster operation completion time. Shown as second
gcp.dataproc.cluster.operation.duration.avg (gauge)	The time operations have spent in a given state. Shown as millisecond
gcp.dataproc.cluster.operation.duration.samplecount (count)	Sample count for cluster operation duration. Shown as millisecond
gcp.dataproc.cluster.operation.duration.sumsqdev (gauge)	Sum of squared deviation for cluster operation duration. Shown as second
gcp.dataproc.cluster.operation.failed_count (count)	Indicates the number of operations that have failed on a cluster. Shown as operation
gcp.dataproc.cluster.operation.running_count (gauge)	Indicates the number of operations that are running on a cluster. Shown as operation
gcp.dataproc.cluster.operation.submitted_count (count)	Indicates the number of operations that have been submitted to a cluster. Shown as operation
gcp.dataproc.cluster.yarn.allocated_memory_percentage (gauge)	The percentage of YARN memory is allocated. Shown as percent
gcp.dataproc.cluster.yarn.apps (gauge)	Indicates the number of active YARN applications.
gcp.dataproc.cluster.yarn.containers (gauge)	Indicates the number of YARN containers. Shown as container
gcp.dataproc.cluster.yarn.memory_size (gauge)	Indicates the YARN memory size in GB. Shown as gibibyte
gcp.dataproc.cluster.yarn.nodemanagers (gauge)	Indicates the number of YARN NodeManagers running inside cluster.
gcp.dataproc.cluster.yarn.pending_memory_size (gauge)	The current memory request, in GB, that is pending to be fulfilled by the scheduler. Shown as gibibyte
gcp.dataproc.cluster.yarn.virtual_cores (gauge)	Indicates the number of virtual cores in YARN. Shown as core
gcp.dataproc.job.state (gauge)	Indicates whether job is currently in a particular state or not.
gcp.dataproc.session.spark.executors (gauge)	Indicates the number of Session Spark executors. Shown as worker

Events

The Google Cloud Dataproc integration does not include any events.

Service Checks

The Google Cloud Dataproc integration does not include any service checks.

Troubleshooting

Need help? Contact Datadog support.