Skip to content

Latest commit

 

History

History
159 lines (133 loc) · 9.27 KB

README.md

File metadata and controls

159 lines (133 loc) · 9.27 KB
Raw

Spark on Kubernetes benchmark utility

This repository provides a general tool to benchmark Spark performance. If you want to use the prebuild docker image based on a prebuild OSS spark_3.1.2_hadoop_3.3.1, you can skip the build section and jump to Run Benchmark directly. If you want to build your own, follow the steps in the build section.

Prerequisite

  • eksctl is installed ( >= 0.143.0)
curl --silent --location "https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_$(uname -s)_amd64.tar.gz" | tar xz -C /tmp
sudo mv -v /tmp/eksctl /usr/local/bin
eksctl version
  • Update AWS CLI to the latest (requires aws cli version >= 2.11.23) on macOS. Check out the link for Linux or Windows
curl "https://awscli.amazonaws.com/AWSCLIV2.pkg" -o "AWSCLIV2.pkg"
sudo installer -pkg ./AWSCLIV2.pkg -target /
aws --version
rm AWSCLIV2.pkg
  • Install kubectl on macOS, check out the link for Linux or Windows.( >= 1.26.4 )
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl
kubectl version --short --client
  • Helm CLI ( >= 3.2.1 )
curl -sSL https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash
helm version --short
brew cask install docker
docker --version

Set up test environment

The script creates a new EKS cluster, enables EMR on EKS and builds a private ECR for the eks-spark-benchmark utility docker image. Change the region if needed.

export EKSCLUSTER_NAME=eks-nvme
export AWS_REGION=us-east-1
./provision.sh

Build benchmark utility docker image

This repo has the auto-build workflow enabled, which builds the eks-spark-benchmark image triggered by code changes in the main branch. It is a docker image based on Apache Spark base image.

To build manually, run the command:

# stay in the project root directory
cd emr-on-eks-benchmark

# Login to ECR
ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
ECR_URL=$ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com
aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $ECR_URL
aws ecr create-repository --repository-name spark --image-scanning-configuration scanOnPush=true

# Build Spark base image
docker build -t $ECR_URL/spark:3.1.2_hadoop_3.3.1 -f docker/hadoop-aws-3.3.1/Dockerfile --build-arg HADOOP_VERSION=3.3.1 --build-arg SPARK_VERSION=3.1.2 .
docker push $ECR_URL/spark:3.1.2_hadoop_3.3.1

# Build benchmark utility based on the Spark
docker build -t $ECR_URL/eks-spark-benchmark:3.1.2 -f docker/benchmark-util/Dockerfile --build-arg SPARK_BASE_IMAGE=$ECR_URL/spark:3.1.2_hadoop_3.3.1 .

If you need to build the image based on a different Spark image, for example EMR Spark runtime, run the command:

# get EMR on EKS base image
export SRC_ECR_URL=755674844232.dkr.ecr.us-east-1.amazonaws.com
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin $SRC_ECR_URL
docker pull $SRC_ECR_URL/spark/emr-6.5.0:latest

# Custom an image on top of the EMR Spark
docker build -t $ECR_URL/eks-spark-benchmark:emr6.5 -f docker/benchmark-util/Dockerfile --build-arg SPARK_BASE_IMAGE=$SRC_ECR_URL/spark/emr-6.5.0:latest .

Finally, push it to your ECR. Replace the default docker images in examples if needed:

aws ecr create-repository --repository-name eks-spark-benchmark --image-scanning-configuration scanOnPush=true
# benchmark utility image based on Apache Spark3.1.2
docker push $ECR_URL/eks-spark-benchmark:3.1.2
# benchmark utility image based on EMR Spark runtime
docker push $ECR_URL/eks-spark-benchmark:emr6.5

Run Benchmark

Generate the TCP-DS data (OPTIONAL)

Before run the data gen job, check the docker image name in the example yaml file, change it accordingly. Alternatively, copy the 3TB data from a publicaly available dataset s3://blogpost-sparkoneks-us-east-1/blog/BLOG_TPCDS-TEST-3T-partitioned to your S3 bucket.

kubectl apply -f examples/tpcds-data-generation.yaml

Run TPC-DS benchmark for OSS Spark on EKS

The benchmark file contains a configmap that dynamically map your S3 bucket to the environment variable codeBucket in EKS. Run the command to set up the configmap:

app_code_bucket=<S3_BUCKET_HAS_TPCDS_DATASET>
kubectl create -n oss configmap special-config --from-literal=codeBucket=$app_code_bucket

# start the benchmark test job
kubectl apply -f examples/tpcds-benchmark.yaml

Run EMR on EKS benchmark

# set EMR virtual cluster name, change the region if needed
export EMRCLUSTER_NAME=emr-on-eks-nvme
export AWS_REGION=us-east-1
bash examples/emr6.5-benchmark.sh

Benchmark for EMR on EC2

Few notes for the set up:

  1. Use the same instance type c5d.9xlarge as in the EKS cluster.
  2. If choosing an EBS-backed instance, check the default instance storage setting by EMR on EC2, and attach the same number of EBS volumes to your EKS cluster before running EKS related benchmarks.

The benchmark utility app was compiled to a jar file during an automated GitHub workflow process. If you already have a running Kubernetes container, the quickest way to get the jar is using kubectl cp command as shown below:

# Download the jar and ignore the warning message
kubectl cp oss/oss-spark-tpcds-exec-1:/opt/spark/examples/jars/eks-spark-benchmark-assembly-1.0.jar eks-spark-benchmark-assembly-1.0.jar

However if you are running a benchmark just for EMR on EC2, you probably don't have a running container. To copy the jar file from a docker container, you need two terminals. In the first terminal, spin up a docker container based on your image built:

docker run --name spark-benchmark -it $ECR_URL/eks-spark-benchmark:3.1.2 bash
# you are logged in to the container now, find the jar file
hadoop@9ca5b2afe778: ls -alh /opt/spark/examples/jars/eks-spark-benchmark-assembly-1.0.jar

Keep the container running then go to the second terminal, run the command to copy the jar file from the container to your local directory:

docker cp spark-benchmark:/opt/spark/examples/jars/eks-spark-benchmark-assembly-1.0.jar .

# Upload to s3
S3BUCKET=<S3_BUCKET_HAS_TPCDS_DATASET>
aws s3 cp eks-spark-benchmark-assembly-1.0.jar s3://$S3BUCKET

Submit the benchmark job via EMR Step on the AWS console. Make sure the EMR on EC2 cluster can access the $S3BUCKET:

# Step type: Spark Application
# JAR location: s3://$S3BUCKET/eks-spark-benchmark-assembly-1.0.jar
# Spark-submit options:
--class com.amazonaws.eks.tpcds.BenchmarkSQL --conf spark.driver.cores=4 --conf spark.driver.memory=5g --conf spark.executor.cores=4 --conf spark.executor.memory=6g --conf spark.executor.instances=47 --conf spark.network.timeout=2000 --conf spark.executor.heartbeatInterval="300s" --conf spark.executor.memoryOverhead=2G --conf spark.driver.memoryOverhead=1000 --conf spark.dynamicAllocation.enabled=false --conf spark.shuffle.service.enabled=false
# Arguments:
s3://blogpost-sparkoneks-us-east-1/blog/BLOG_TPCDS-TEST-3T-partitioned s3://$S3BUCKET/EMRONEC2_TPCDS-TEST-3T-RESULT /opt/tpcds-kit/tools parquet 3000 1 false q1-v2.4,q10-v2.4,q11-v2.4,q12-v2.4,q13-v2.4,q14a-v2.4,q14b-v2.4,q15-v2.4,q16-v2.4,q17-v2.4,q18-v2.4,q19-v2.4,q2-v2.4,q20-v2.4,q21-v2.4,q22-v2.4,q23a-v2.4,q23b-v2.4,q24a-v2.4,q24b-v2.4,q25-v2.4,q26-v2.4,q27-v2.4,q28-v2.4,q29-v2.4,q3-v2.4,q30-v2.4,q31-v2.4,q32-v2.4,q33-v2.4,q34-v2.4,q35-v2.4,q36-v2.4,q37-v2.4,q38-v2.4,q39a-v2.4,q39b-v2.4,q4-v2.4,q40-v2.4,q41-v2.4,q42-v2.4,q43-v2.4,q44-v2.4,q45-v2.4,q46-v2.4,q47-v2.4,q48-v2.4,q49-v2.4,q5-v2.4,q50-v2.4,q51-v2.4,q52-v2.4,q53-v2.4,q54-v2.4,q55-v2.4,q56-v2.4,q57-v2.4,q58-v2.4,q59-v2.4,q6-v2.4,q60-v2.4,q61-v2.4,q62-v2.4,q63-v2.4,q64-v2.4,q65-v2.4,q66-v2.4,q67-v2.4,q68-v2.4,q69-v2.4,q7-v2.4,q70-v2.4,q71-v2.4,q72-v2.4,q73-v2.4,q74-v2.4,q75-v2.4,q76-v2.4,q77-v2.4,q78-v2.4,q79-v2.4,q8-v2.4,q80-v2.4,q81-v2.4,q82-v2.4,q83-v2.4,q84-v2.4,q85-v2.4,q86-v2.4,q87-v2.4,q88-v2.4,q89-v2.4,q9-v2.4,q90-v2.4,q91-v2.4,q92-v2.4,q93-v2.4,q94-v2.4,q95-v2.4,q96-v2.4,q97-v2.4,q98-v2.4,q99-v2.4,ss_max-v2.4 true

Note: We use 3TB dataset in the examples, if you'd like to change to 100G or 1T, don't forget to change the parameter Scale factor (in GB) in the job submission scripts. Spark executor configuration should also be adjusted correspondingly.

Cleanup

export EKSCLUSTER_NAME=eks-nvme
export AWS_REGION=us-east-1
./deprovision.sh