Launch a cluster - Step 1
In this step we’ll launch our first cluster. This will be a transient cluster that will be shut down after it finishes running the application we submit to it at cluster creation time, and will run solely on Spot Instances. The application is a simple wordcount that will run against a public data set of Amazon product reviews, located in an Amazon S3 bucket in the N. Virginia region. If you want to know more about the Amazon Customer Reviews Dataset, click here
Normally our dataset on S3 would be located on the same region where we are going to run our EMR clusters. In this workshop, for educational purposes, it is fine if you are running EMR in a different region, and the Spark application will work against the dataset which is located in the N. Virginia region.
To launch the cluster, follow these steps:
- Open the EMR console in the region where you are looking to launch your cluster.
- Click “Create Cluster“
- Click “Go to advanced options“
- Select the latest EMR release, and in the list of components, only leave Hadoop checked and also check Spark and Ganglia (we will use it later to monitor our cluster)
- Under “Add steps (Optional)” -> Step type drop down menu, select “Spark application” and click Configure, then add the following details in the Add step dialog window:
- Spark-submit options: here we will configure the memory and core count for each executor, as described in the previous section. Use these settings (make sure you have two ‘-’ chars):
--executor-memory 18G --executor-cores 4 - Application location: here we will configure the location of our Spark application. Save the following python code to a file (or download it from the Attachment box) and upload it to your S3 bucket using the AWS management console. You can refer to the S3 Getting Started guide for detailed instructions
import sys
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('Amazon reviews word count').getOrCreate()
df = spark.read.parquet("s3://amazon-reviews-pds/parquet/")
df.selectExpr("explode(split(lower(review_body), ' ')) as words").groupBy("words").count().write.mode("overwrite").parquet(sys.argv[1])
exit()Then add the location of the file under the Application location field, i.e: s3://<your-bucket-name>/script.py
- Arguments: Here we will configure the location of where Spark will write the results of the job. Enter: s3://<your-bucket-name>/results/
- Action on failure: Leave this on Continue and click Add to save the step.
Check the Auto-terminate cluster after the last step is completed option. Since we are looking to run a transient cluster just for running our Spark application, this will terminate the cluster once our submitted step (Spark Application) has completed.
If you are not running through the workshop in one sitting, then don’t use Auto-terminate cluster after the last step is completed, otherwise your cluster will be terminated before you examine it, later in the workshop.
Click Next to continue setting up the EMR cluster and move from “Step 1: Software and steps”” to “Step 2: Hardware”.