Spark Stage Details
This tab displays the summary metrics of the tasks that completed within the selected Spark stage.
| Metric | Description |
|---|---|
| Percentile | A number where certain percentage of jobs fall below that number. For example, 95th percentile indicates that a certain amount of jobs executed within xx seconds, which is faster than 95% of the jobs in queue. |
| Duration | Time taken by the stages to complete. |
| Executor CpuTime | Total CPU time taken by the executor to run the task (in milliseconds). |
| Executor DeserializeTime | Time taken by the executor to deserialize tasks. |
| Jvm GcTime | Time spent by the JVM in garbage collection while executing a task. |
| Result SerializationTime | Time spent to serialize a task result. |
| Peak ExecutionMemory | The memory used during shuffles, aggregations, and joins by internal data structures. |
| Shuffle writeTime | Time spent to write serialized data on all executors. |
| Shuffle BytesWritten | Bytes written to the host. These bytes are read by a shuffle later when needed. |
Tasks Analysis by Metrics
note
To see task analysis by the Details metrics, click any row in Details table. This displays the data represented in bar charts for each metric.
The bar chart represents two kinds of values.
- The percentile value.
- The average percentile value.
Executors
This tab displays the following aggregated metrics of executors in hosts for the selected stage ID.
| Metric | Description |
|---|---|
| eid | The executor ID. |
| Host | The host on which the executors are running. |
| failedTasks | The number of failed tasks in the executor. |
| killedTasks | The number of terminated tasks in the executor. |
| SucceededTasks | The number of successfully completed tasks in the executor. |
| taskTime | The time spent on the tasks. |
| memoryBytesSpilled | The amount of deserialized form of the data in memory at the time it is spilt. |
| Input Bytes | The number of bytes read from the executor in that stage. |
| Output Bytes | The number of bytes written to the executor in that stage. |
| shuffleRead | The amount of serialized data read on the executor. |
| ShuffleWrite | The nmount of serialized data written to the executor. |
note
To view the host details, click the host name.
Task
| Metric | Description |
|---|---|
| taskId | The ID of the task. |
| status | The status of the task, which can be one of the following. - running, succeeded, failed, unknown. |
| taskLocality | The type of task in the host which can be on of the following. PROCESS_LOCAL NODE_LOCAL RACK_LOCAL NO_PREF |
| host | The hostname where the task resides on. |
| Duration | Time elapsed in completing the task. |
| Jvm GcTime | Time spent by the JVM in garbage collection while executing a task. |
| Result SerializationTime | Time spent to serialize a task result. |
| Peak ExecutionMemory | The memory used during shuffles, aggregations, and joins by internal data structures. |
| Input Read Bytes | Bytes read from the executor in that stage. |
| Shuffle Read Blocked Time | The time spent by tasks staying blocked and waiting for shuffle data to be read from remote machines. |
| Shuffle Records Read | Number of records written to the host. These records are read by a shuffle later when needed. |
| Shuffle Remote Reads | The shuffle bytes read from remote executors. |
Trace
The Trace tab displays the logs of that Stage ID. You can detect the internal state of the jobs running in the stage you are currently viewing.
This tab also displays the log for any errors that occur in the tasks of that stage.
DAG
The Direct Acyclic Graph (DAG) displays a flow diagram of the Spark job.
DAG is a work scheduling graph with finite elements connected in edges and vertices. These elements are also called RDDs (Resilient Distributed Datasets). The RDDs are fault-tolerant in nature.
The order of execution of the jobs in DAG is specified by the directions of the edges in the graph. The graph is acyclic as it has no loops or cycles.
