Monitoring Your Workload#

This section helps you debug and monitor the execution of your Dataset by viewing the:

Ray Data Dashboard#

Ray Data emits Prometheus metrics in real-time while a Dataset is executing. These metrics are tagged by both dataset and operator, and are displayed in multiple views across the Ray dashboard.


Most metrics are only available for physical operators that use the map operation. For example, physical operators created by map_batches(), map(), and flat_map().

Ray Data overview#

For an overview of all datasets that have been running on your cluster, see the Ray Data Overview in the jobs view. This table appears once the first dataset starts executing on the cluster, and shows dataset details such as:

  • execution progress (measured in blocks)

  • execution state (running, failed, or finished)

  • dataset start/end time

  • dataset-level metrics (for example, sum of rows processed over all operators)


For a more fine-grained overview, each dataset row in the table can also be expanded to display the same details for individual operators.



To evaluate a dataset-level metric where it’s not appropriate to sum the values of all the individual operators, it may be more useful to look at the operator-level metrics of the last operator. For example, to calculate a dataset’s throughput, use the “Rows Outputted” of the dataset’s last operator, because the dataset-level metric contains the sum of rows outputted over all operators.

Ray dashboard metrics#

For a time-series view of these metrics, see the Ray Data section in the Metrics view. This section contains time-series graphs of all metrics emitted by Ray Data. Execution metrics are grouped by dataset and operator, and iteration metrics are grouped by dataset.

The metrics recorded are:

  • Bytes spilled by objects from object store to disk

  • Bytes of objects allocated in object store

  • Bytes of objects freed in object store

  • Current total bytes of objects in object store

  • Logical CPUs allocated to dataset operators

  • Logical GPUs allocated to dataset operators

  • Bytes outputted by dataset operators

  • Rows outputted by dataset operators

  • Time spent generating blocks

  • Time user code is blocked during iteration.

  • Time spent in user code during iteration.


To learn more about the Ray dashboard, including detailed setup instructions, see Ray Dashboard.

Ray Data logs#

During execution, Ray Data periodically logs updates to ray-data.log.

Every five seconds, Ray Data logs the execution progress of every operator in the dataset. For more frequent updates, set RAY_DATA_TRACE_SCHEDULING=1 so that the progress is logged after each task is dispatched.

Execution Progress:
0: - Input: 0 active, 0 queued, 0.0 MiB objects, Blocks Outputted: 200/200
1: - ReadRange->MapBatches(<lambda>): 10 active, 190 queued, 381.47 MiB objects, Blocks Outputted: 100/200

When an operator completes, the metrics for that operator are also logged.

Operator InputDataBuffer[Input] -> TaskPoolMapOperator[ReadRange->MapBatches(<lambda>)] completed. Operator Metrics:
{'num_inputs_received': 20, 'bytes_inputs_received': 46440, 'num_inputs_processed': 20, 'bytes_inputs_processed': 46440, 'num_outputs_generated': 20, 'bytes_outputs_generated': 800, 'rows_outputs_generated': 100, 'num_outputs_taken': 20, 'bytes_outputs_taken': 800, 'num_outputs_of_finished_tasks': 20, 'bytes_outputs_of_finished_tasks': 800, 'num_tasks_submitted': 20, 'num_tasks_running': 0, 'num_tasks_have_outputs': 20, 'num_tasks_finished': 20, 'obj_store_mem_alloc': 800, 'obj_store_mem_freed': 46440, 'obj_store_mem_cur': 0, 'obj_store_mem_peak': 23260, 'obj_store_mem_spilled': 0, 'block_generation_time': 1.191296085, 'cpu_usage': 0, 'gpu_usage': 0, 'ray_remote_args': {'num_cpus': 1, 'scheduling_strategy': 'SPREAD'}}