This check monitors Spark through the Datadog Agent. Collect Spark metrics for:
Note: Spark Structured Streaming metrics are not supported.
The Spark check is included in the Datadog Agent package. No additional installation is needed on your Mesos master (for Spark on Mesos), YARN ResourceManager (for Spark on YARN), or Spark master (for Spark Standalone).
To configure this check for an Agent running on a host:
Edit the spark.d/conf.yaml file, in the conf.d/ folder at the root of your Agent’s configuration directory. The following parameters may require updating. See the sample spark.d/conf.yaml for all available configuration options.
init_config:
instances:
- spark_url: http://localhost:8080 # Spark master web UI
# spark_url: http://<Mesos_master>:5050 # Mesos master web UI
# spark_url: http://<YARN_ResourceManager_address>:8088 # YARN ResourceManager address
spark_cluster_mode: spark_yarn_mode # default
# spark_cluster_mode: spark_mesos_mode
# spark_cluster_mode: spark_yarn_mode
# spark_cluster_mode: spark_driver_mode
# required; adds a tag 'cluster_name:<CLUSTER_NAME>' to all metrics
cluster_name: "<CLUSTER_NAME>"
# spark_pre_20_mode: true # if you use Standalone Spark < v2.0
# spark_proxy_enabled: true # if you have enabled the spark UI proxy
For containerized environments, see the Autodiscovery Integration Templates for guidance on applying the parameters below.
| Parameter | Value |
|---|---|
<INTEGRATION_NAME> | spark |
<INIT_CONFIG> | blank or {} |
<INSTANCE_CONFIG> | {"spark_url": "%%host%%:8080", "cluster_name":"<CLUSTER_NAME>"} |
Collecting logs is disabled by default in the Datadog Agent, enable it in your datadog.yaml file:
logs_enabled: true
Uncomment and edit the logs configuration block in your spark.d/conf.yaml file. Change the type, path, and service parameter values based on your environment. See the sample spark.d/conf.yaml for all available configuration options.
logs:
- type: file
path: <LOG_FILE_PATH>
source: spark
service: <SERVICE_NAME>
# To handle multi line that starts with yyyy-mm-dd use the following pattern
# log_processing_rules:
# - type: multi_line
# pattern: \d{4}\-(0?[1-9]|1[012])\-(0?[1-9]|[12][0-9]|3[01])
# name: new_log_start_with_date
See [Datadog’s documentation][6] for additional information on how to configure the Agent for log collection in Docker environments.
Run the Agent’s [status subcommand][7] and look for spark under the Checks section.
| spark.job.count (count) | Number of jobs Shown as task |
| spark.job.num_tasks (count) | Number of tasks in the application Shown as task |
| spark.job.num_active_tasks (count) | Number of active tasks in the application Shown as task |
| spark.job.num_skipped_tasks (count) | Number of skipped tasks in the application Shown as task |
| spark.job.num_failed_tasks (count) | Number of failed tasks in the application Shown as task |
| spark.job.num_completed_tasks (count) | Number of completed tasks in the application Shown as task |
| spark.job.num_active_stages (count) | Number of active stages in the application Shown as stage |
| spark.job.num_completed_stages (count) | Number of completed stages in the application Shown as stage |
| spark.job.num_skipped_stages (count) | Number of skipped stages in the application Shown as stage |
| spark.job.num_failed_stages (count) | Number of failed stages in the application Shown as stage |
| spark.stage.count (count) | Number of stages Shown as task |
| spark.stage.num_active_tasks (count) | Number of active tasks in the application's stages Shown as task |
| spark.stage.num_complete_tasks (count) | Number of complete tasks in the application's stages Shown as task |
| spark.stage.num_failed_tasks (count) | Number of failed tasks in the application's stages Shown as task |
| spark.stage.executor_run_time (count) | Time spent by the executor in the application's stages Shown as millisecond |
| spark.stage.input_bytes (count) | Input bytes in the application's stages Shown as byte |
| spark.stage.input_records (count) | Input records in the application's stages Shown as record |
| spark.stage.output_bytes (count) | Output bytes in the application's stages Shown as byte |
| spark.stage.output_records (count) | Output records in the application's stages Shown as record |
| spark.stage.shuffle_read_bytes (count) | Number of bytes read during a shuffle in the application's stages Shown as byte |
| spark.stage.shuffle_read_records (count) | Number of records read during a shuffle in the application's stages Shown as record |
| spark.stage.shuffle_write_bytes (count) | Number of shuffled bytes in the application's stages Shown as byte |
| spark.stage.shuffle_write_records (count) | Number of shuffled records in the application's stages Shown as record |
| spark.stage.memory_bytes_spilled (count) | Number of bytes spilled to disk in the application's stages Shown as byte |
| spark.stage.disk_bytes_spilled (count) | Max size on disk of the spilled bytes in the application's stages Shown as byte |
| spark.driver.rdd_blocks (count) | Number of RDD blocks in the driver Shown as block |
| spark.driver.memory_used (count) | Amount of memory used in the driver Shown as byte |
| spark.driver.disk_used (count) | Amount of disk used in the driver Shown as byte |
| spark.driver.active_tasks (count) | Number of active tasks in the driver Shown as task |
| spark.driver.failed_tasks (count) | Number of failed tasks in the driver Shown as task |
| spark.driver.completed_tasks (count) | Number of completed tasks in the driver Shown as task |
| spark.driver.total_tasks (count) | Number of total tasks in the driver Shown as task |
| spark.driver.total_duration (count) | Time spent in the driver Shown as millisecond |
| spark.driver.total_input_bytes (count) | Number of input bytes in the driver Shown as byte |
| spark.driver.total_shuffle_read (count) | Number of bytes read during a shuffle in the driver Shown as byte |
| spark.driver.total_shuffle_write (count) | Number of shuffled bytes in the driver Shown as byte |
| spark.driver.max_memory (count) | Maximum memory used in the driver Shown as byte |
| spark.executor.count (count) | Number of executors Shown as task |
| spark.executor.rdd_blocks (count) | Number of persisted RDD blocks in the application's executors Shown as block |
| spark.executor.memory_used (count) | Amount of memory used for cached RDDs in the application's executors Shown as byte |
| spark.executor.max_memory (count) | Max memory across all executors working for a particular application Shown as byte |
| spark.executor.disk_used (count) | Amount of disk space used by persisted RDDs in the application's executors Shown as byte |
| spark.executor.active_tasks (count) | Number of active tasks in the application's executors Shown as task |
| spark.executor.failed_tasks (count) | Number of failed tasks in the application's executors Shown as task |
| spark.executor.completed_tasks (count) | Number of completed tasks in the application's executors Shown as task |
| spark.executor.total_tasks (count) | Total number of tasks in the application's executors Shown as task |
| spark.executor.total_duration (count) | Time spent by the application's executors executing tasks Shown as millisecond |
| spark.executor.total_input_bytes (count) | Total number of input bytes in the application's executors Shown as byte |
| spark.executor.total_shuffle_read (count) | Total number of bytes read during a shuffle in the application's executors Shown as byte |
| spark.executor.total_shuffle_write (count) | Total number of shuffled bytes in the application's executors Shown as byte |
| spark.executor_memory (count) | Maximum memory available for caching RDD blocks in the application's executors Shown as byte |
| spark.executor.id.rdd_blocks (count) | Number of persisted RDD blocks in this executor Shown as block |
| spark.executor.id.memory_used (count) | Amount of memory used for cached RDDs in this executor. Shown as byte |
| spark.executor.id.max_memory (count) | Total amount of memory available for storage for this executor Shown as byte |
| spark.executor.id.disk_used (count) | Amount of disk space used by persisted RDDs in this executor Shown as byte |
| spark.executor.id.active_tasks (count) | Number of active tasks in this executor Shown as task |
| spark.executor.id.failed_tasks (count) | Number of failed tasks in this executor Shown as task |
| spark.executor.id.completed_tasks (count) | Number of completed tasks in this executor Shown as task |
| spark.executor.id.total_tasks (count) | Total number of tasks in this executor Shown as task |
| spark.executor.id.total_duration (count) | Time spent by the executor executing tasks Shown as millisecond |
| spark.executor.id.total_input_bytes (count) | Total number of input bytes in the executor Shown as byte |
| spark.executor.id.total_shuffle_read (count) | Total number of bytes read during a shuffle in the executor Shown as byte |
| spark.executor.id.total_shuffle_write (count) | Total number of shuffled bytes in the executor Shown as byte |
| spark.rdd.count (count) | Number of RDDs |
| spark.rdd.num_partitions (count) | Number of persisted RDD partitions in the application |
| spark.rdd.num_cached_partitions (count) | Number of in-memory cached RDD partitions in the application |
| spark.rdd.memory_used (count) | Amount of memory used in the application's persisted RDDs Shown as byte |
| spark.rdd.disk_used (count) | Amount of disk space used by persisted RDDs in the application Shown as byte |
| spark.streaming.statistics.avg_input_rate (gauge) | Average streaming input data rate Shown as byte |
| spark.streaming.statistics.avg_processing_time (gauge) | Average application's streaming batch processing time Shown as millisecond |
| spark.streaming.statistics.avg_scheduling_delay (gauge) | Average application's streaming batch scheduling delay Shown as millisecond |
| spark.streaming.statistics.avg_total_delay (gauge) | Average application's streaming batch total delay Shown as millisecond |
| spark.streaming.statistics.batch_duration (gauge) | Application's streaming batch duration Shown as millisecond |
| spark.streaming.statistics.num_active_batches (gauge) | Number of active streaming batches Shown as job |
| spark.streaming.statistics.num_active_receivers (gauge) | Number of active streaming receivers Shown as object |
| spark.streaming.statistics.num_inactive_receivers (gauge) | Number of inactive streaming receivers Shown as object |
| spark.streaming.statistics.num_processed_records (count) | Number of processed streaming records Shown as record |
| spark.streaming.statistics.num_received_records (count) | Number of received streaming records Shown as record |
| spark.streaming.statistics.num_receivers (gauge) | Number of streaming application's receivers Shown as object |
| spark.streaming.statistics.num_retained_completed_batches (count) | Number of retained completed application's streaming batches Shown as job |
| spark.streaming.statistics.num_total_completed_batches (count) | Total number of completed application's streaming batches Shown as job |
| spark.structured_streaming.input_rate (gauge) | Average streaming input data rate Shown as record |
| spark.structured_streaming.latency (gauge) | Average latency for the structured streaming application. Shown as millisecond |
| spark.structured_streaming.processing_rate (gauge) | Number of received streaming records per second Shown as row |
| spark.structured_streaming.rows_count (gauge) | Count of rows. Shown as row |
| spark.structured_streaming.used_bytes (gauge) | Number of bytes used in memory. Shown as byte |
The Spark check does not include any events.
The Agent submits one of the following service checks, depending on how you’re running Spark:
spark.standalone_master.can_connect
Returns CRITICAL if the Agent is unable to connect to the Spark instance’s Standalone Master. Returns OK otherwise.
spark.mesos_master.can_connect
Returns CRITICAL if the Agent is unable to connect to the Spark instance’s Mesos Master. Returns OK otherwise.
spark.application_master.can_connect
Returns CRITICAL if the Agent is unable to connect to the Spark instance’s ApplicationMaster. Returns OK otherwise.
spark.resource_manager.can_connect
Returns CRITICAL if the Agent is unable to connect to the Spark instance’s ResourceManager. Returns OK otherwise.
spark.driver.can_connect
Returns CRITICAL if the Agent is unable to connect to the Spark instance’s ResourceManager. Returns OK otherwise.
To receive metrics for Spark on AWS EMR, [use bootstrap actions][8] to install the [Datadog Agent] [10]:
For Agent v5, create the /etc/dd-agent/conf.d/spark.yaml configuration file with the [proper values on each EMR node][9].
For Agent v6/7, create the /etc/datadog-agent/conf.d/spark.d/conf.yaml configuration file with the [proper values on each EMR node] [11].
Additional helpful documentation, links, and articles:
[6]: [7]: https://docs.datadoghq.com/agent/guide/agent-commands/#agent-status-and-information [8]: https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-bootstrap.html [9]: https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-connect-master-node-ssh.html [10]: https://www.datadoghq.com/blog/monitoring-spark
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