Tips to Improve Spark Job Execution Speed

Here's how to systematically think about data lake query optimizations: First think about business needs: - Ask "what is the impact of sampling this dataset on downstream consumers?" If the answer to this is minimal, then add sampling to your pipeline and that will probably make all other optimizations unnecessary. - Ask "what are all the query patterns you're looking for?" Adding in long-tail query patterns can sometimes make your pipeline and data model extremely bloated without adding much additional value. Then think about logic bugs/flaws: - Ask "what are my join types?" You might have a bad join condition or could minimize the number of comparisons each JOIN operation is making. Remember string comparisons are much slower than integer comparisons - Ask "am I bringing in too make additional columns?" Select * belongs in adhoc queries only. Production pipelines should always list out every column - Ask "am I filtering as soon as I can?" Leveraging predicate pushdown with Spark and Trino will allow you to avoid shuffling so much data. The earlier you can slap a WHERE clause in, the better Then think about window sizing errors: - Ask "how many partitions am I scanning?" If the answer is a lot (>30), then your query could most definitely benefit from cumulative table design. Tutorial: https://lnkd.in/gqJgnZ6K - Ask "Can I run this pipeline more often? Maybe hourly? to make it more reliable" Tutorial on how to run an hourly dedupe batch pipeline as efficiently as possible: https://lnkd.in/gHDm6P7t Then think about production problems: - Ask "does my Spark job balance partitioning and memory correctly?" Bumping up spark.sql.shuffle.partitions will increase the parallelism of your Spark job. But it also increases the network overhead so it's a balancing act with spark.executor.memory - Ask "does my Spark job need to be skew aware?" If your job is heavily skewed, adding adaptive execution with spark. Setting this setting to true will fix your problems immediately: spark.sql.adaptive.enabled - Ask "do my upstream data sets produce enough files or splittable files to keep my initial parallelism high" Slow initial reads can be a painful bottle neck for some Spark jobs. Working with your upstream producers so they output many files or splittable files will keep the job zooming along.

Efficient partitioning is critical for performance in Apache Spark. Poor partitioning leads to data skew, excessive shuffling, and slow query execution. Key considerations when defining partitions: Data Distribution – Uneven partitions create stragglers. Use range or hash partitioning to balance workload. Partition Size – Aim for 100–200MB per partition. Smaller partitions incur overhead from task scheduling, while larger partitions risk memory issues and slow serialization. This range strikes a balance between parallelism and task efficiency. Shuffle Reduction – Use coalesce() to reduce partitions efficiently for narrow transformations and repartition() when a full shuffle is necessary. Storage Partitioning – When writing to Parquet or ORC, partitioning by frequently filtered columns improves query performance. Default settings often lead to suboptimal performance. Fine-tuning partitioning strategies based on workload characteristics is essential for scalable and efficient Spark jobs.

𝐌𝐨𝐬𝐭 𝐖𝐢𝐝𝐞𝐥𝐲 𝐔𝐬𝐞𝐝 𝐒𝐩𝐚𝐫𝐤 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐟𝐨𝐫 𝐅𝐚𝐬𝐭𝐞𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 . . ➤ 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗲𝘀𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Spark spends a lot of time converting objects. Using Kryo instead of Java serialization makes things faster and memory-efficient. ➤ 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Too much data shuffling? Try broadcast joins, reduce shuffle partitions, and avoid unnecessary groupBy operations. ➤ 𝐏𝐫𝐞𝐝𝐢𝐜𝐚𝐭𝐞 𝐏𝐮𝐬𝐡𝐝𝐨𝐰𝐧: Why scan extra data? Push filters down to the data source (Parquet, ORC, or databases) so Spark reads only what’s needed. ➤ 𝐁𝐫𝐨𝐚𝐝𝐜𝐚𝐬𝐭 𝐉𝐨𝐢𝐧𝐬: If one dataset is small, broadcast it instead of shuffling huge amounts of data. It’s a game-changer for performance. ➤ 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 & 𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐞:Reusing data? Cache or persist it in memory to skip recomputation and speed up queries. But don’t overuse it! ➤ 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠 & 𝐁𝐮𝐜𝐤𝐞𝐭𝐢𝐧𝐠:Splitting data smartly reduces shuffle and improves query performance. Bucketing is great for frequent joins on the same column. ➤ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐅𝐢𝐥𝐞 𝐅𝐨𝐫𝐦𝐚𝐭𝐬: Always go for Parquet or ORC. These columnar formats are faster, compressed, and support predicate pushdown. ➤ 𝐀𝐯𝐨𝐢𝐝𝐢𝐧𝐠 𝐔𝐃𝐅𝐬: Spark’s built-in functions are way faster than UDFs. If you must use a UDF, consider Pandas UDFs for better performance. ➤ 𝐒𝐤𝐞𝐰 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠:If some partitions are overloaded, balance them using techniques like salting or increasing partitions to avoid slow queries. ➤ 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐲 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 (𝐀𝐐𝐄): Let Spark auto-tune shuffle partitions and optimize joins dynamically. Spark 3.x does this out of the box! ➤ 𝐌𝐞𝐦𝐨𝐫𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭:Set executor memory wisely and tune storage fraction to avoid out-of-memory issues and excessive garbage collection. ➤ 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐬𝐦 𝐓𝐮𝐧𝐢𝐧𝐠:If Spark isn’t using all resources, increase spark.default.parallelism and spark.sql.shuffle.partitions to make full use of the cluster. #SparkOptimization #ApacheSpark #BroadcastJoin #PredicatePushdown #DataSkewHandling #CatalystOptimizer #PySparkPerformance #FasterSparkJobs #SparkBestPractices #ETLPerformance

Many high-paying data engineering jobs require expertise with distributed data processing, usually Apache Spark. Distributed data processing systems are inherently complex; add to the fact that Spark provides us with multiple optimization features (knobs to use), and it becomes tricky to know what the right approach is. Trying to understand all of the components of Spark feels like fighting an uphill battle with no end in sight; there is always something else to learn or know about. What if you knew precisely how Apache Spark works internally and the optimization techniques that you can use? Distributed data processing system's optimization techniques (partitioning, clustering, sorting, data shuffling, join strategies, task parallelism, etc.) are like knobs, each with its tradeoffs. When it comes to gaining Spark (& most distributed data processing system) mastery, the fundamental ideas are: 1. Reduce the amount of data (think raw size) to be processed. 2. Reduce the amount of data that needs to be moved between executors in the Spark cluster (data shuffle). I recommend thinking about reducing data to be processed and shuffled in the following ways: 1. Data Storage: How you store your data dictates how much it needs to be processed. Does your query often use a column in its filter? Partition your data by that column. Ensure that your data uses file encoding (e.g., Parquet) to store and use metadata when processing. Co-locate data with bucketing to reduce data shuffle. If you need advanced features like time travel, schema evolution, etc., use table format (such as Delta Lake). 2. Data Processing: Filter before processing (Spark automatically does this with Lazy loading), analyze resource usage (with UI) to ensure maximum parallelism, know the type of code that will result in data shuffle, and identify how Spark performs joins internally to optimize its data shuffle. 3. Data Model: Know how to model your data for the types of queries to expect in a data warehouse. Analyze tradeoffs between pre-processing and data freshness to store data as one big table. 4. Query Planner: Use the query plan to check how Spark plans to process the data. Ensure metadata is up to date with statistical information about your data to help Spark choose the optimal way to process it. 5. Writing efficient queries: While Spark performs many optimizations under the hood, writing efficient queries is a key skill. Learn how to write code that is easily readable and able to perform necessary computations. Here is a visual representation (zoom in for details) of how the above concepts work together: ------------------- If you want to learn about the above topics in detail, watch out for my course “Efficient Data Processing in Spark,” which will be releasing soon! #dataengineering #datajobs #apachespark

🔹𝗘𝗧𝗟 & 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 : 𝗤1: 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗘𝗧𝗟 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸? Answer: To design a scalable ETL pipeline using Apache Spark, I follow these steps: Data Ingestion: Use Kafka, AWS Kinesis, or Azure Event Hubs for streaming data, and tools like Apache Sqoop or AWS Glue for batch ingestion. Data Processing: Utilize Spark structured streaming for real-time processing and Spark SQL/DataFrame API for batch workloads. Orchestration: Implement Apache Airflow, AWS Step Functions, or Azure Data Factory to schedule and monitor ETL jobs. 2. 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲, 𝗟𝗮𝗺𝗯𝗱𝗮, 𝗞𝗮𝗽𝗽𝗮): Q2: Can you explain the differences between the Lambda and Kappa architectures? When would you use each? Answer: Lambda Architecture (Batch + Streaming): Uses two separate layers: batch processing (Hadoop, Spark) and stream processing (Kafka, Flink, Spark Streaming). Suitable when historical batch processing is crucial, such as fraud detection and analytics. Kappa Architecture (Streaming-Only): Relies entirely on a streaming system where new data is continuously processed and stored in a scalable data lake or warehouse. Suitable when low latency and real-time decision-making are critical, such as recommendation systems and IoT analytics. 3. 𝗖𝗹𝗼𝘂𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗚𝗖𝗣) Q3: How would you build a scalable data pipeline in the cloud (AWS/Azure/GCP)? Answer: A scalable data pipeline in the cloud consists of: Data Ingestion: Batch: AWS Glue, Azure Data Factory, Google Dataflow Streaming: Kafka, Kinesis, Event Hubs Data Processing: Use Databricks (Spark) or AWS EMR for large-scale processing Implement serverless solutions like AWS Lambda or Azure Functions for lightweight tasks Data Storage: Store raw data in S3 (AWS), ADLS (Azure), GCS (Google) 4. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗘𝗧𝗟: Q4: How do you optimize Spark jobs for performance and scalability? Answer: I optimize Spark jobs using the following techniques: Data Partitioning & Bucketing: Avoid data skew and optimize shuffling. Broadcast Joins: Use broadcast() for smaller tables to prevent costly shuffle joins. Cache & Persist: Use df.cache() and persist(StorageLevel.MEMORY_AND_DISK) to avoid recomputation. Reduce Shuffle Operations: Minimize groupBy and leverage reduceByKey for better efficiency.