Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Monitoring jobs using the Apache Spark web UI

You can use the Apache Spark web UI to monitor and debug Amazon Glue ETL jobs running on the Amazon Glue job system, and also Spark applications running on Amazon Glue development endpoints. The Spark UI enables you to check the following for each job:

For more information about using the Spark Web UI, see Web UI in the Spark documentation. For guidance on how to interpret Spark UI results to improve the performance of your job, see Best practices for performance tuning Amazon Glue for Apache Spark jobs in Amazon Prescriptive Guidance.

For newer jobs, you can see the Spark UI in the Amazon Glue console. This is available when an Amazon Glue job runs on Amazon Glue 3.0 or later versions with logs generated in the Standard (rather than legacy) format, which is the default for newer jobs. For more information on finding the Spark UI in the console, see View information for recent job runs. For other versions of Amazon Glue, provision your own history server. For more information, see Launching the Spark history server.

You can enable the Spark UI by using the Amazon Glue console or the Amazon Command Line Interface (Amazon CLI). When you enable the Spark UI, Amazon Glue ETL jobs and Spark applications on Amazon Glue development endpoints can back up Spark event logs to a location that you specify in Amazon Simple Storage Service (Amazon S3). You can use the backed up event logs in Amazon S3 with the Spark UI, both in real time as the job is operating and after the job is complete. While the logs remain in Amazon S3, the Spark UI in the Amazon Glue console can view them.

Permissions

In order to use the Spark UI in the Amazon Glue console, you can use UseGlueStudio or add all the individual service APIs. All APIs are needed to use the Spark UI completely, however users can access SparkUI features by adding its service APIs in their IAM permission for fine-grained access.

RequestLogParsing is the most critical as it performs the parsing of logs. The remaining APIs are for reading the respective parsed data. For example, GetStages provides access to the data about all stages of a Spark job.

The list of Spark UI service APIs mapped to UseGlueStudio are below in the sample policy. The policy below provides access to use only Spark UI features. To add more permissions like Amazon S3 and IAM see Creating Custom IAM Policies for Amazon Glue Studio.

The list of Spark UI service APIs mapped to UseGlueStudio is below in the sample policy. When using a Spark UI service API, use the following namespace: glue:<ServiceAPI>.

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "AllowGlueStudioSparkUI",
			"Effect": "Allow",
            "Action": [
                "glue:RequestLogParsing",
                "glue:GetLogParsingStatus",
                "glue:GetEnvironment",
                "glue:GetJobs",
                "glue:GetJob",
                "glue:GetStage",
                "glue:GetStages",
                "glue:GetStageFiles",
                "glue:BatchGetStageFiles",
                "glue:GetStageAttempt",
                "glue:GetStageAttemptTaskList",
                "glue:GetStageAttemptTaskSummary",
                "glue:GetExecutors",
                "glue:GetExecutorsThreads",
                "glue:GetStorage",
                "glue:GetStorageUnit",
                "glue:GetQueries",
                "glue:GetQuery"
            ],
			"Resource": [
				"*"
			]
		}
	]
}     
    

Limitations

Example: Apache Spark web UI

This example shows you how to use the Spark UI to understand your job performance. Screen shots show the Spark web UI as provided by a self-managed Spark history server. Spark UI in the Amazon Glue console provides similar views. For more information about using the Spark Web UI, see Web UI in the Spark documentation.

The following is an example of a Spark application that reads from two data sources, performs a join transform, and writes it out to Amazon S3 in Parquet format.

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from pyspark.sql.functions import count, when, expr, col, sum, isnull
from pyspark.sql.functions import countDistinct
from awsglue.dynamicframe import DynamicFrame
 
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
 
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
 
job = Job(glueContext)
job.init(args['JOB_NAME'])
 
df_persons = spark.read.json("s3://awsglue-datasets/examples/us-legislators/all/persons.json")
df_memberships = spark.read.json("s3://awsglue-datasets/examples/us-legislators/all/memberships.json")
 
df_joined = df_persons.join(df_memberships, df_persons.id == df_memberships.person_id, 'fullouter')
df_joined.write.parquet("s3://aws-glue-demo-sparkui/output/")
 
job.commit()

The following DAG visualization shows the different stages in this Spark job.

The following event timeline for a job shows the start, execution, and termination of different Spark executors.

The following screen shows the details of the SparkSQL query plans: