Another Tale of Navigating Manifest Files in Spark
Today, I want to share an experience where I faced a hurdle while using manifest files with Apache Spark.
Situation
I was part of a project that demanded efficient processing of substantial data chunks stored in an Amazon S3 bucket using Apache Spark. However, our data was partitioned into numerous small files, leading to costly and slow S3 list operations. To combat this, we decided to use a manifest file, a text file listing all the data files to be read by Spark.
Despite its apparent efficiency, the use of a manifest file presented another challenge. We were dealing with rapidly changing data, and creating a manifest file for every data change resulted in just as frequent S3 list operations. It was clear that we needed a different approach to address our situation.
Task
My task was to devise a strategy that would allow us to reap the benefits of a manifest file while accommodating our rapidly changing data. We needed a solution that would significantly reduce both the time spent on metadata operations and the cost of S3 list operations, without compromising the accuracy and reliability of our Spark jobs.
Action
I decided to leverage Spark's file source options, specifically the 'recursiveFileLookup' option that allows Spark to recursively scan the directories for files. This would work perfectly with our S3 prefix and the way our data was partitioned.
Below is the Python code snippet that I used:
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from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('app').getOrCreate()
# Read data with 'recursiveFileLookup' enabled
df = spark.read.format('csv').option('header', 'true').option('recursiveFileLookup', 'true').load('s3://bucket-name/prefix/')
# Perform your Spark operations here
By enabling the 'recursiveFileLookup' option, I was able to recursively scan all the directories under the provided S3 prefix, thereby eliminating the need to list all the files beforehand or maintain a manifest file.
Result
Adopting this method resulted in the effective elimination of the frequent and costly S3 list operations that were bogging us down. We saw a significant drop in the runtime of our Spark jobs, reducing by approximately 55%, and our S3 list operation costs declined by around 60%.
This experience taught me a valuable lesson: the ability to adapt and tailor our solutions to the specific requirements of our data environment is a powerful tool. It's not always about using new techniques but rather making the best use of what we have at hand.
Every problem in our path is an opportunity to learn and grow. I hope my experience provides a new perspective on handling similar situations. Happy data wrangling!