3/13/2023 0 Comments Small filesafe![]() It’s important to quantify how many small data files are contained in folders that are queried frequently. Cloudera does a great job examining this problem as well. Hadoop’s small file problem has been well documented for quite some time. Need to finish the rest of this section… Small file problem in Hadoop Let’s use the AWS CLI to identify the small files in a S3 folder. Programatically compacting the small files ![]() Here’s what s3://some-bucket/nhl_game_shifts contains after this code is run: aws s3 rm s3://some-bucket/nhl_game_shifts/game_shiftsC.csvĪws s3 rm s3://some-bucket/nhl_game_shifts/game_shiftsD.csvĪws s3 rm s3://some-bucket/nhl_game_shifts/game_shiftsE.csvĪws s3 rm s3://some-bucket/nhl_game_shifts/game_shiftsF.csv Let’s run some AWS CLI commands to delete files C, D, E, and F. csv(s"/mnt/some-bucket/nhl_game_shifts/") Let’s read game_shiftsC, game_shiftsD, game_shiftsE, and game_shiftsF into a DataFrame, shuffle the data to a single partition, and write out the data as a single file. Let’s split up this CSV into 6 separate files and store them in the nhl_game_shifts S3 directory: Kaggle has an open source CSV hockey dataset called game_shifts.csv that has 5.56 million rows of data and 5 columns. All of our code that references with s3_path_with_the_data will still work. This approach is nice because the data isn’t written to a new directory. Here’s what s3_path_with_the_data will look like after the small files have been compacted. run a S3 command to delete fileA, fileB, fileC, fileD, fileE val df = ("fileA, fileB, fileC, fileD, fileE") We can read in the small files, write out 2 files with 0.8 GB of data each, and then delete all the small files. Only repartitioning the small filesįiles F, G, and H are already perfectly sized, so it’ll be more performant to simply repartition Files A, B, C, D, and E (the small files). ![]() The repartition() method makes it easy to build a folder with equally sized files. Let’s use the repartition() method to shuffle the data and write it to another directory with five 0.92 GB files. Let’s look at a folder with some small files (we’d like all the files in our data lake to be 1GB): This blog will describe how to get rid of small files using Spark. Garren Staubli wrote a great blog does a great job explaining why small files are a big problem for Spark analyses. The small problem get progressively worse if the incremental updates are more frequent and the longer incremental updates run between full refreshes. The “small file problem” is especially problematic for data stores that are updated incrementally. You can make your Spark code run faster by creating a job that compacts small files into larger files. Spark runs slowly when it reads data from a lot of small files in S3.
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