How to efficiently filter large datasets with Spark DataFrames in Scala 2.12 - performance optimization
I need some guidance on I'm wondering if anyone has experience with I'm migrating some code and I'm attempting to set up I've searched everywhere and can't find a clear answer... I'm working on a Spark application using Scala 2.12, and I'm experiencing important performance optimization when filtering large datasets using DataFrames. I'm trying to filter a DataFrame to only include records where a specific column, `status`, is equal to `"active"`. The dataset is quite large (over 10 million rows), and the filtering operation seems to take an excessive amount of time. Here's the relevant code snippet: ```scala import org.apache.spark.sql.{SparkSession, DataFrame} val spark: SparkSession = SparkSession.builder() .appName("FilterExample") .getOrCreate() val df: DataFrame = spark.read.parquet("hdfs://path/to/dataset") val filteredDF = df.filter(df("status") === "active") filteredDF.show() // This takes too long ``` I've tried various optimizations like using `broadcast` joins and caching the DataFrame after initial loading, but the filtering still seems slow. I've also checked the Spark UI and noticed that the query stages are taking a long time, especially on the shuffle operations. I suspect that I might not be leveraging DataFrame optimizations effectively. Could anyone suggest strategies to improve the performance of this filtering operation or provide insights into what might be going wrong? Are there specific configurations or practices in Spark that could help in such scenarios? Additionally, should I be considering partitioning the data differently? Any advice would be greatly appreciated! Am I missing something obvious? This is part of a larger CLI tool I'm building. Has anyone else encountered this? I'm developing on Ubuntu 22.04 with Scala. My development environment is Linux. Thanks in advance! For reference, this is a production application. I appreciate any insights! Any feedback is welcome!