Amazon EMR Serverless lets you run open supply massive knowledge frameworks reminiscent of Apache Spark and Apache Hive with out managing clusters and servers. With EMR Serverless, you possibly can run analytics workloads at any scale with automated scaling that resizes assets in seconds to fulfill altering knowledge volumes and processing necessities.
We’ve launched job employee metrics in Amazon CloudWatch for EMR Serverless. This characteristic lets you monitor vCPUs, reminiscence, ephemeral storage, and disk I/O allocation and utilization metrics at an combination employee degree on your Spark and Hive jobs.
This submit is a part of a collection about EMR Serverless observability. On this submit, we focus on easy methods to use these CloudWatch metrics to watch EMR Serverless staff in close to actual time.
CloudWatch metrics for EMR Serverless
On the per-Spark job degree, EMR Serverless emits the next new metrics to CloudWatch for each driver and executors. These metrics present granular insights into job efficiency, bottlenecks, and useful resource utilization.
| WorkerCpuAllocated | The full numbers of vCPU cores allotted for staff in a job run |
| WorkerCpuUsed | The full numbers of vCPU cores utilized by staff in a job run |
| WorkerMemoryAllocated | The full reminiscence in GB allotted for staff in a job run |
| WorkerMemoryUsed | The full reminiscence in GB utilized by staff in a job run |
| WorkerEphemeralStorageAllocated | The variety of bytes of ephemeral storage allotted for staff in a job run |
| WorkerEphemeralStorageUsed | The variety of bytes of ephemeral storage utilized by staff in a job run |
| WorkerStorageReadBytes | The variety of bytes learn from storage by staff in a job run |
| WorkerStorageWriteBytes | The variety of bytes written to storage from staff in a job run |
The next are the advantages of monitoring your EMR Serverless jobs with CloudWatch:
- Optimize useful resource utilization – You may acquire insights into useful resource utilization patterns and optimize your EMR Serverless configurations for higher effectivity and value financial savings. For instance, underutilization of vCPUs or reminiscence can reveal useful resource wastage, permitting you to optimize employee sizes to realize potential price financial savings.
- Diagnose widespread errors – You may establish root causes and mitigation for widespread errors with out log diving. For instance, you possibly can monitor the utilization of ephemeral storage and mitigate disk bottlenecks by preemptively allocating extra storage per employee.
- Acquire close to real-time insights – CloudWatch gives close to real-time monitoring capabilities, permitting you to trace the efficiency of your EMR Serverless jobs as and when they’re operating, for fast detection of any anomalies or efficiency points.
- Configure alerts and notifications – CloudWatch allows you to arrange alarms utilizing Amazon Easy Notification Service (Amazon SNS) based mostly on predefined thresholds, permitting you to obtain notifications by e-mail or textual content message when particular metrics attain essential ranges.
- Conduct historic evaluation – CloudWatch shops historic knowledge, permitting you to investigate developments over time, establish patterns, and make knowledgeable choices for capability planning and workload optimization.
Resolution overview
To additional improve this observability expertise, we’ve got created an answer that gathers all these metrics on a single CloudWatch dashboard for an EMR Serverless software. You have to launch one AWS CloudFormation template per EMR Serverless software. You may monitor all the roles submitted to a single EMR Serverless software utilizing the identical CloudWatch dashboard. To study extra about this dashboard and deploy this answer into your personal account, discuss with the EMR Serverless CloudWatch Dashboard GitHub repository.
Within the following sections, we stroll you thru how you should utilize this dashboard to carry out the next actions:
- Optimize your useful resource utilization to avoid wasting prices with out impacting job efficiency
- Diagnose failures attributable to widespread errors with out the necessity for log diving and resolve these errors optimally
Stipulations
To run the pattern jobs supplied on this submit, you could create an EMR Serverless software with default settings utilizing the AWS Administration Console or AWS Command Line Interface (AWS CLI), after which launch the CloudFormation template from the GitHub repo with the EMR Serverless software ID supplied because the enter to the template.
You have to submit all the roles on this submit to the identical EMR Serverless software. If you wish to monitor a distinct software, you possibly can deploy this template on your personal EMR Serverless software ID.
Optimize useful resource utilization
When operating Spark jobs, you typically begin with the default configurations. It may be difficult to optimize your workload with none visibility into precise useful resource utilization. A number of the commonest configurations that we’ve seen prospects modify are spark.driver.cores, spark.driver.reminiscence, spark.executor.cores, and spark.executors.reminiscence.
For instance how the newly added CloudWatch dashboard worker-level metrics may also help you fine-tune your job configurations for higher price-performance and enhanced useful resource utilization, let’s run the next Spark job, which makes use of the NOAA Built-in Floor Database (ISD) dataset to run some transformations and aggregations.
Use the next command to run this job on EMR Serverless. Present your Amazon Easy Storage Service (Amazon S3) bucket and EMR Serverless software ID for which you launched the CloudFormation template. Make certain to make use of the identical software ID to submit all of the pattern jobs on this submit. Moreover, present an AWS Identification and Entry Administration (IAM) runtime position.
Now let’s verify the executor vCPUs and reminiscence from the CloudWatch dashboard.
This job was submitted with default EMR Serverless Spark configurations. From the Executor CPU Allotted metric within the previous screenshot, the job was allotted 396 vCPUs in complete (99 executors * 4 vCPUs per executor). Nevertheless, the job solely used a most of 110 vCPUs based mostly on Executor CPU Used. This means oversubscription of vCPU assets. Equally, the job was allotted 1,584 GB reminiscence in complete based mostly on Executor Reminiscence Allotted. Nevertheless, from the Executor Reminiscence Used metric, we see that the job solely used 176 GB of reminiscence through the job, indicating reminiscence oversubscription.
Now let’s rerun this job with the next adjusted configurations.
| Authentic Job (Default Configuration) | Rerun Job (Adjusted Configuration) | |
| spark.executor.reminiscence | 14 GB | 3 GB |
| spark.executor.cores | 4 | 2 |
| spark.dynamicAllocation.maxExecutors | 99 | 30 |
| Whole Useful resource Utilization |
6.521 vCPU-hours 26.084 memoryGB-hours 32.606 storageGB-hours |
1.739 vCPU-hours 3.688 memoryGB-hours 17.394 storageGB-hours |
| Billable Useful resource Utilization |
7.046 vCPU-hours 28.182 memoryGB-hours 0 storageGB-hours |
1.739 vCPU-hours 3.688 memoryGB-hours 0 storageGB-hours |
We use the next code:
Let’s verify the executor metrics from the CloudWatch dashboard once more for this job run.
Within the second job, we see decrease allocation of each vCPUs (396 vs. 60) and reminiscence (1,584 GB vs. 120 GB) as anticipated, leading to higher utilization of assets. The unique job ran for 4 minutes, 41 seconds. The second job took 4 minutes, 54 seconds. This reconfiguration has resulted in 79% decrease price financial savings with out affecting the job efficiency.
You need to use these metrics to additional optimize your job by growing or reducing the variety of staff or the allotted assets.
Diagnose and resolve job failures
Utilizing the CloudWatch dashboard, you possibly can diagnose job failures attributable to points associated to CPU, reminiscence, and storage reminiscent of out of reminiscence or no house left on the system. This allows you to establish and resolve widespread errors shortly with out having to verify the logs or navigate by Spark Historical past Server. Moreover, as a result of you possibly can verify the useful resource utilization from the dashboard, you possibly can fine-tune the configurations by growing the required assets solely as a lot as wanted as an alternative of oversubscribing to the assets, which additional saves prices.
Driver errors
For instance this use case, let’s run the next Spark job, which creates a big Spark knowledge body with a couple of million rows. Usually, this operation is completed by the Spark driver. Whereas submitting the job, we additionally configure spark.rpc.message.maxSize, as a result of it’s required for activity serialization of information frames with a lot of columns.
After a couple of minutes, the job failed with the error message “Encountered errors when releasing containers,” as seen within the Job particulars part.
When encountering non-descriptive error messages, it turns into essential to analyze additional by inspecting the motive force and executor logs to troubleshoot additional. However earlier than additional log diving, let’s first verify the CloudWatch dashboard, particularly the motive force metrics, as a result of releasing containers is usually carried out by the motive force.
We will see that the Driver CPU Used and Driver Storage Used are properly inside their respective allotted values. Nevertheless, upon checking Driver Reminiscence Allotted and Driver Reminiscence Used, we are able to see that the motive force was utilizing the entire 16 GB reminiscence allotted to it. By default, EMR Serverless drivers are assigned 16 GB reminiscence.
Let’s rerun the job with extra driver reminiscence allotted. Let’s set driver reminiscence to 27 GB as the start line, as a result of spark.driver.reminiscence + spark.driver.memoryOverhead must be lower than 30 GB for the default employee sort. park.rpc.messsage.maxSize can be unchanged.
The job succeeded this time round. Let’s verify the CloudWatch dashboard to watch driver reminiscence utilization.
As we are able to see, the allotted reminiscence is now 30 GB, however the precise driver reminiscence utilization didn’t exceed 21 GB through the job run. Subsequently, we are able to additional optimize prices right here by decreasing the worth of spark.driver.reminiscence. We reran the identical job with spark.driver.reminiscence set to 22 GB, and the job nonetheless succeeded with higher driver reminiscence utilization.
Executor errors
Utilizing CloudWatch for observability is right for diagnosing driver-related points as a result of there is just one driver per job and driver assets used is the precise useful resource utilization of the one driver. Alternatively, executor metrics are aggregated throughout all the employees. Nevertheless, you should utilize this dashboard to supply solely an sufficient quantity of assets to make your job succeed, thereby avoiding oversubscription of assets.
For instance, let’s run the next Spark job, which simulates uniform disk over-utilization throughout all staff by processing very giant NOAA datasets from a number of years. This job additionally transiently caches a really giant knowledge body on disk.
After a couple of minutes, we are able to see that the job failed with “No house left on system” error within the Job particulars part, which signifies that among the staff have run out of disk house.
Checking the Working Executors metric from the dashboard, we are able to establish that there have been 99 executor staff operating. Every employee comes with 20 GB storage by default.
As a result of this can be a Spark activity failure, let’s verify the Executor Storage Allotted and Executor Storage Used metrics from the dashboard (as a result of the motive force gained’t run any duties).
As we are able to see, the 99 executors have used up a complete of 1,940 GB from the whole allotted executor storage of two,126 GB. This contains each the information shuffled by the executors and the storage used for caching the information body. We don’t see the total 2,126 GB being utilized from this graph as a result of there is perhaps a couple of executors out of the 99 executors that weren’t holding a lot knowledge when the job failed (earlier than these executors may begin processing duties and retailer the information body chunks).
Let’s rerun the identical job however with elevated executor disk dimension utilizing the parameter spark.emr-serverless.executor.disk. Let’s strive with 40 GB disk per executor as a place to begin.
This time, the job ran efficiently. Let’s verify the Executor Storage Allotted and Executor Storage Used metrics.
Executor Storage Allotted is now 4,251 GB as a result of we’ve doubled the worth of spark.emr-serverless.executor.disk. Though there’s now twice as a lot aggregated executors’ storage, the job nonetheless used solely a most of 1,940 GB out of 4,251 GB. This means that our executors have been possible operating out of disk house solely by a couple of GBs. Subsequently, we are able to attempt to set spark.emr-serverless.executor.disk to an excellent decrease worth like 25 GB or 30 GB as an alternative of 40 GB to avoid wasting storage prices as we did within the earlier situation. As well as, you possibly can monitor Executor Storage Learn Bytes and Executor Storage Write Bytes to see in case your job is I/O intensive. On this case, you should utilize the Shuffle-optimized disks characteristic of EMR Serverless to additional improve your job’s I/O efficiency.
The dashboard can also be helpful to seize details about transient storage used whereas caching or persisting the information frames, together with spill-to-disk eventualities. The Storage tab of Spark Historical past Server data any caching actions, as seen within the following screenshot. Nevertheless, this knowledge can be misplaced from Spark Historical past Server after the cache is evicted or when the job finishes. Subsequently, Executor Storage Used can be utilized to do an evaluation of a failed job run attributable to transient storage points.
On this explicit instance, the information was evenly distributed among the many executors. Nevertheless, when you have an information skew (for, instance only one–2 executors out of 99 course of probably the most quantity of information, and because of this, your job runs out of disk house), the CloudWatch dashboard gained’t precisely seize this situation as a result of the storage knowledge is aggregated throughout all of the executors for a job. For diagnosing points on the particular person executor degree, we have to observe per-executor-level metrics. We discover extra superior examples of how per-worker-level metrics may also help you establish, mitigate, and resolve hard-to-find points by EMR Serverless integration with Amazon Managed Service for Prometheus.
Conclusion
On this submit, you discovered easy methods to successfully handle and optimize your EMR Serverless software utilizing a single CloudWatch dashboard with enhanced EMR Serverless metrics. These metrics can be found in all AWS Areas the place EMR Serverless is obtainable. For extra particulars about this characteristic, discuss with Job-level monitoring.
Concerning the Authors
Kashif Khan is a Sr. Analytics Specialist Options Architect at AWS, specializing in massive knowledge providers like Amazon EMR, AWS Lake Formation, AWS Glue, Amazon Athena, and Amazon DataZone. With over a decade of expertise within the massive knowledge area, he possesses intensive experience in architecting scalable and strong options. His position entails offering architectural steering and collaborating intently with prospects to design tailor-made options utilizing AWS analytics providers to unlock the total potential of their knowledge.
Veena Vasudevan is a Principal Associate Options Architect and Knowledge & AI specialist at AWS. She helps prospects and companions construct extremely optimized, scalable, and safe options; modernize their architectures; and migrate their massive knowledge, analytics, and AI/ML workloads to AWS.
