The Amazon EMR runtime for Apache Spark gives a high-performance runtime surroundings whereas sustaining 100% API compatibility with open supply Apache Spark and Apache Iceberg desk format. Amazon EMR on EC2, Amazon EMR Serverless, Amazon EMR on Amazon EKS, Amazon EMR on AWS Outposts and AWS Glue all use the optimized runtimes.
On this publish, we exhibit the efficiency advantages of utilizing the Amazon EMR 7.5 runtime for Spark and Iceberg in comparison with open supply Spark 3.5.3 with Iceberg 1.6.1 tables on the TPC-DS 3TB benchmark v2.13.
Iceberg is a well-liked open supply high-performance format for giant analytic tables. Our benchmarks exhibit that Amazon EMR can run TPC-DS 3 TB workloads 3.6 occasions sooner, lowering the runtime from 1.54 hours to 0.42 hours. Moreover, the associated fee effectivity improves by 2.9 occasions, with the entire price lowering from $16.00 to $5.39 when utilizing Amazon Elastic Compute Cloud (Amazon EC2) On-Demand r5d.4xlarge cases, offering observable beneficial properties for knowledge processing duties.
It is a additional 32% enhance from the optimizations shipped in Amazon EMR 7.1 lined in a earlier publish, Amazon EMR 7.1 runtime for Apache Spark and Iceberg can run Spark workloads 2.7 occasions sooner than Apache Spark 3.5.1 and Iceberg 1.5.2. Since then now we have continued including extra assist for DataSource V2 for eight extra current question optimizations within the EMR runtime for Spark.
Along with these DataSource V2 particular enhancements, now we have made extra optimizations to Spark operators since Amazon EMR 7.1 that additionally contribute to the extra speedup.
Benchmark outcomes for Amazon EMR 7.5 in contrast to4 open supply Spark 3.5.3 and Iceberg 1.6.1
To evaluate the Spark engine’s efficiency with the Iceberg desk format, we carried out benchmark exams utilizing the 3 TB TPC-DS dataset, model 2.13 (our outcomes derived from the TPC-DS dataset aren’t instantly akin to the official TPC-DS outcomes on account of setup variations). Benchmark exams for the EMR runtime for Spark and Iceberg have been performed on Amazon EMR 7.5 EC2 clusters vs open supply Spark 3.5.3 and Iceberg 1.6.1 on EC2 clusters.
The setup directions and technical particulars can be found in our GitHub repository. To reduce the affect of exterior catalogs like AWS Glue and Hive, we used the Hadoop catalog for the Iceberg tables. This makes use of the underlying file system, particularly Amazon S3, because the catalog. We are able to outline this setup by configuring the property spark.sql.catalog.<catalog_name>.sort. The actual fact tables used the default partitioning by the date column, which have a lot of partitions various from 200–2,100. No precalculated statistics have been used for these tables.
We ran a complete of 104 SparkSQL queries in three sequential rounds, and the common runtime of every question throughout these rounds was taken for comparability. The common runtime for the three rounds on Amazon EMR 7.5 with Iceberg enabled was 0.42 hours, demonstrating a 3.6-fold pace enhance in comparison with open supply Spark 3.5.3 and Iceberg 1.6.1. The next determine presents the entire runtimes in seconds.
The next desk summarizes the metrics.
| Metric | Amazon EMR 7.5 on EC2 | Amazon EMR 7.1 on EC2 | Open Supply Spark 3.5.3 and Iceberg 1.6.1 |
| Common runtime in seconds | 1535.62 | 2033.17 | 5546.16 |
| Geometric imply over queries in seconds | 8.30046 | 10.13153 | 20.40555 |
| Price* | $5.39 | $7.18 | $16.00 |
*Detailed price estimates are mentioned later on this publish.
The next chart demonstrates the per-query efficiency enchancment of Amazon EMR 7.5 relative to open supply Spark 3.5.3 and Iceberg 1.6.1. The extent of the speedup varies from one question to a different, with the quickest as much as 9.4 occasions sooner for q93, with Amazon EMR outperforming open supply Spark with Iceberg tables. The horizontal axis arranges the TPC-DS 3TB benchmark queries in descending order primarily based on the efficiency enchancment seen with Amazon EMR, and the vertical axis depicts the magnitude of this speedup as a ratio.
Price comparability
Our benchmark offers the entire runtime and geometric imply knowledge to evaluate the efficiency of Spark and Iceberg in a posh, real-world choice assist state of affairs. For added insights, we additionally look at the associated fee side. We calculate price estimates utilizing formulation that account for EC2 On-Demand cases, Amazon Elastic Block Retailer (Amazon EBS), and Amazon EMR bills.
- Amazon EC2 price (consists of SSD price) = variety of cases * r5d.4xlarge hourly fee * job runtime in hours
- r5d.4xlarge hourly fee = $1.152 per hour in us-east-1
- Root Amazon EBS price = variety of cases * Amazon EBS per GB-hourly fee * root EBS quantity measurement * job runtime in hours
- Amazon EMR price = variety of cases * r5d.4xlarge Amazon EMR price * job runtime in hours
- 4xlarge Amazon EMR price = $0.27 per hour
- Whole price = Amazon EC2 price + root Amazon EBS price + Amazon EMR price
The calculations reveal that the Amazon EMR 7.5 benchmark yields a 2.9-fold price effectivity enchancment over open supply Spark 3.5.3 and Iceberg 1.6.1 in working the benchmark job.
| Metric | Amazon EMR 7.5 | Amazon EMR 7.1 | Open Supply Spark 3.5.1 and Iceberg 1.5.2 |
| Runtime in hours | 0.426 | 0.564 | 1.540 |
|
Variety of EC2 cases (Consists of main node) |
9 | 9 | 9 |
| Amazon EBS Dimension | 20gb | 20gb | 20gb |
|
Amazon EC2 (Whole runtime price) |
$4.35 | $5.81 | $15.97 |
| Amazon EBS price | $0.01 | $0.01 | $0.04 |
| Amazon EMR price | $1.02 | $1.36 | $0 |
| Whole price | $5.38 | $7.18 | $16.01 |
| Price financial savings | Amazon EMR 7.5 is 2.9 occasions higher | Amazon EMR 7.1 is 2.2 occasions higher | Baseline |
Along with the time-based metrics mentioned to this point, knowledge from Spark occasion logs present that Amazon EMR scanned roughly 3.4 occasions much less knowledge from Amazon S3 and 4.1 occasions fewer information than the open supply model within the TPC-DS 3 TB benchmark. This discount in Amazon S3 knowledge scanning contributes on to price financial savings for Amazon EMR workloads.
Run open supply Spark benchmarks on Iceberg tables
We used separate EC2 clusters, every outfitted with 9 r5d.4xlarge cases, for testing each open supply Spark 3.5.3 and Amazon EMR 7.5 for Iceberg workload. The first node was outfitted with 16 vCPU and 128 GB of reminiscence, and the eight employee nodes collectively had 128 vCPU and 1024 GB of reminiscence. We performed exams utilizing the Amazon EMR default settings to showcase the everyday person expertise and minimally adjusted the settings of Spark and Iceberg to take care of a balanced comparability.
The next desk summarizes the Amazon EC2 configurations for the first node and eight employee nodes of sort r5d.4xlarge.
| EC2 Occasion | vCPU | Reminiscence (GiB) | Occasion Storage (GB) | EBS Root Quantity (GB) |
| r5d.4xlarge | 16 | 128 | 2 x 300 NVMe SSD | 20 GB |
Conditions
The next stipulations are required to run the benchmarking:
- Utilizing the directions within the emr-spark-benchmark GitHub repo, arrange the TPC-DS supply knowledge in your S3 bucket and in your native pc.
- Construct the benchmark software following the steps offered in Steps to construct spark-benchmark-assembly software and duplicate the benchmark software to your S3 bucket. Alternatively, copy spark-benchmark-assembly-3.5.3.jar to your S3 bucket.
- Create Iceberg tables from the TPC-DS supply knowledge. Observe the directions on GitHub to create Iceberg tables utilizing the Hadoop catalog. For instance, the next code makes use of an EMR 7.5 cluster with Iceberg enabled to create the tables:
Be aware the Hadoop catalog warehouse location and database identify from the previous step. We use the identical iceberg tables to run benchmarks with Amazon EMR 7.5 and open supply Spark.
This benchmark software is constructed from the department tpcds-v2.13_iceberg. In the event you’re constructing a brand new benchmark software, change to the proper department after downloading the supply code from the GitHub repo.
Create and configure a YARN cluster on Amazon EC2
To check Iceberg efficiency between Amazon EMR on Amazon EC2 and open supply Spark on Amazon EC2, observe the directions within the emr-spark-benchmark GitHub repo to create an open supply Spark cluster on Amazon EC2 utilizing Flintrock with eight employee nodes.
Based mostly on the cluster choice for this take a look at, the next configurations are used:
Ensure to switch the placeholder <non-public ip of main node>, within the yarn-site.xml file, with the first node’s IP deal with of your Flintrock cluster.
Run the TPC-DS benchmark with Spark 3.5.3 and Iceberg 1.6.1
Full the next steps to run the TPC-DS benchmark:
- Log in to the open supply cluster main node utilizing
flintrock login $CLUSTER_NAME. - Submit your Spark job:
- Select the proper Iceberg catalog warehouse location and database that has the created Iceberg tables.
- The outcomes are created in
s3://<YOUR_S3_BUCKET>/benchmark_run. - You possibly can monitor progress in
/media/ephemeral0/spark_run.log.
Summarize the outcomes
After the Spark job finishes, retrieve the take a look at consequence file from the output S3 bucket at s3://<YOUR_S3_BUCKET>/benchmark_run/timestamp=xxxx/abstract.csv/xxx.csv. This may be finished both by the Amazon S3 console by navigating to the required bucket location or by utilizing the AWS Command Line Interface (AWS CLI). The Spark benchmark software organizes the information by making a timestamp folder and putting a abstract file inside a folder labeled abstract.csv. The output CSV recordsdata comprise 4 columns with out headers:
- Question identify
- Median time
- Minimal time
- Most time
With the information from three separate take a look at runs with one iteration every time, we will calculate the common and geometric imply of the benchmark runtimes.
Run the TPC-DS benchmark with the EMR runtime for Spark
A lot of the directions are just like Steps to run Spark Benchmarking with just a few Iceberg-specific particulars.
Conditions
Full the next prerequisite steps:
- Run
aws configureto configure the AWS CLI shell to level to the benchmarking AWS account. Discuss with Configure the AWS CLI for directions. - Add the benchmark software JAR file to Amazon S3.
Deploy the EMR cluster and run the benchmark job
Full the next steps to run the benchmark job:
- Use the AWS CLI command as proven in Deploy EMR on EC2 Cluster and run benchmark job to spin up an EMR on EC2 cluster. Ensure to allow Iceberg. See Create an Iceberg cluster for extra particulars. Select the proper Amazon EMR model, root quantity measurement, and similar useful resource configuration because the open supply Flintrock setup. Discuss with create-cluster for an in depth description of the AWS CLI choices.
- Retailer the cluster ID from the response. We want this for the following step.
- Submit the benchmark job in Amazon EMR utilizing
add-stepsfrom the AWS CLI:- Exchange <cluster ID> with the cluster ID from Step 2.
- The benchmark software is at
s3://<your-bucket>/spark-benchmark-assembly-3.5.3.jar. - Select the proper Iceberg catalog warehouse location and database that has the created Iceberg tables. This ought to be the identical because the one used for the open supply TPC-DS benchmark run.
- The outcomes shall be in
s3://<your-bucket>/benchmark_run.
Summarize the outcomes
After the step is full, you possibly can see the summarized benchmark consequence at s3://<YOUR_S3_BUCKET>/benchmark_run/timestamp=xxxx/abstract.csv/xxx.csv in the identical approach because the earlier run and compute the common and geometric imply of the question runtimes.
Clear up
To forestall any future costs, delete the sources you created by following the directions offered within the Cleanup part of the GitHub repository.
Abstract
Amazon EMR is constantly enhancing the EMR runtime for Spark when used with Iceberg tables, attaining a efficiency that’s 3.6 occasions sooner than open supply Spark 3.5.3 and Iceberg 1.6.1 with EMR 7.5 on TPC-DS 3 TB, v2.13. It is a additional enhance of 32% from EMR 7.1. We encourage you to maintain updated with the newest Amazon EMR releases to completely profit from ongoing efficiency enhancements.
To remain knowledgeable, subscribe to the AWS Huge Knowledge Weblog’s RSS feed, the place you’ll find updates on the EMR runtime for Spark and Iceberg, in addition to tips about configuration greatest practices and tuning suggestions.
Concerning the Authors
Atul Felix Payapilly is a software program improvement engineer for Amazon EMR at Amazon Net Providers.
Udit Mehrotra is an Engineering Supervisor for EMR at Amazon Net Providers.
