How to prevent data spills in Apache Spark

🚀 Master Apache Spark Performance: 21 Proven Optimization Techniques! Working with large-scale data in Spark? Then you know performance optimization isn’t optional — it’s essential. Here’s a concise summary of key techniques to supercharge your Spark jobs: 🔥 Top Spark Optimizations 1️⃣ Partition your data smartly to balance workloads. 2️⃣ Use caching & persistence to avoid recomputation. 3️⃣ Leverage broadcast variables for small lookup tables. 4️⃣ Minimize shuffles — they’re performance killers! 5️⃣ Store data in columnar formats (Parquet, ORC). 6️⃣ Enable predicate pushdown to filter data at source. 7️⃣ Use vectorized (Pandas) UDFs for faster row processing. 8️⃣ Coalescepartitions efficiently. 9️⃣ Avoid unnecessary explode operations. 🔟 Rely on DataFrames/Datasets API for Catalyst optimization. …and don’t forget these advanced ones 👇 💡 Adaptive Query Execution (AQE) 💡 Dynamic Partition Pruning 💡 Speculative Execution 💡 Kryo Serialization 💡 Broadcast Joins 💡 Resource & Skew Optimization ⚙️ Every tweak counts — applying the right combination can make your Spark pipelines run 2x to 10x faster! If you work with big data, this list is your go-to optimization checklist. 📘 Reference: [codeinspark.com] #ApacheSpark #BigData #DataEngineering #PerformanceOptimization #PySpark #SparkTuning Follow Dakshitha Reddy For More Content

🚀 Day 27 of #100DaysOfDataengineering 💡 What is Data Skew in Apache Spark? Data skew happens when data is unevenly distributed across partitions, causing some tasks to process much more data than others. This leads to performance bottlenecks. 1️⃣ Why It Happens • Highly repetitive keys in joins/aggregations • Uneven partitioning logic • Hotspot values dominating the dataset 2️⃣ Problems Caused by Data Skew • Slower job execution • Straggler tasks (some tasks finish quickly, others take too long) • Increased resource usage & costs 3️⃣ How to Handle Data Skew • Use salting techniques (adding randomness to keys) • Apply broadcast joins for smaller tables • Repartition data with balanced keys • Use skew join optimization in Spark ✨ Recognizing and fixing data skew is critical for scaling big data pipelines efficiently. #DataEngineering #ApacheSpark #BigData #DataSkew #100DaysOfDataengineering

👉 𝗗𝗜𝗦𝗔𝗣𝗣𝗘𝗔𝗥 𝗨𝗡𝗧𝗜𝗟 𝗗𝗘𝗖𝗘𝗠𝗕𝗘𝗥 𝟮𝟬𝟮𝟱 and 𝗦𝗛𝗢𝗖𝗞 𝗘𝗩𝗘𝗥𝗬𝗢𝗡𝗘 𝗟𝗔𝗧𝗘𝗥,,, In the world of Big Data, performance matters the most. I’ve seen many people struggling with performance issues during joins in Apache Spark — especially when the data size is huge.?? So I wanted to share something that really helps in optimizing joins — 𝗕𝗥𝗢𝗔𝗗𝗖𝗔𝗦𝗧 𝗝𝗢𝗜𝗡 𝗕𝗥𝗢𝗔𝗗𝗖𝗔𝗦𝗧 𝗝𝗢𝗜𝗡???? A Broadcast Join is one of the most powerful Spark optimizations. Instead of shuffling both datasets, Spark broadcasts the smaller dataset to all the executors in the cluster. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:: Why Broadcast Join? Why not a normal join?” Whenever Spark performs a normal join between two big datasets, it needs to shuffle data across the cluster. This shuffle is done so that matching keys come to the same node for joining — but here’s the problem: 👉 Shuffling is very expensive✅ That’s exactly where the hero enters: 𝗕𝗥𝗢𝗔𝗗𝗖𝗔𝗦𝗧 𝗝𝗢𝗜𝗡🔥 Now imagine one dataset is very large, and the other one is small (like a lookup or reference table). “Instead of moving both datasets around, Spark can simply send the small dataset to every executor. That way, each node already has a local copy of that small dataset, and the join happens locally — no shuffle needed! Let’s say we have: • transactions → 100 GB • country_mapping → 50 MB Normally Spark will shuffle both, but with broadcast join, it’s super simple:” Now Spark broadcasts the smaller country_mapping table to all executors — and joins happen locally. End result? ✅ No shuffle ✅ Less memory usage ✅ Much faster job execution 💪 Why 𝗕𝗥𝗢𝗔𝗗𝗖𝗔𝗦𝗧 𝗝𝗢𝗜𝗡?????? Removes shuffle → reduces execution time • Saves cluster resources → less network & CPU load • Improves job stability → no random OOM errors • Ideal for small reference or lookup tables And Remember???? tables is small enough to fit in memory (usually < 100MB). If both are large, it can backfire.

Wide & Narrow Dependencies in Apache Spark 👨💻 When we process big data using Apache Spark, transformations play a major role. There are two types of dependencies that decide performance: 🔹 Narrow Transformation Each partition depends on only one parent partition. No data movement between partitions. Faster execution (example: map, filter). 🔹 Wide Transformation Data from multiple partitions is required to create the next stage. Causes shuffle (data moves across the cluster). Slower and more resource heavy (example: groupByKey, reduceByKey). ✅ Key takeaway: Use narrow transformations wherever possible. Wide transformations are powerful but should be used carefully to avoid performance bottlenecks. Understanding this helps us design faster, scalable, and optimized data pipelines in Spark. #ApacheSpark #BigData #DataEngineering #ETL #DataPipelines #SparkOptimization #DataProcessing #LearningSpark

🔍 A Query’s Journey in Apache Spark: From SQL to Executors When we write a simple Spark SQL query, a lot happens behind the scenes before results appear on our dashboard. Most people talk about Spark’s Driver, Executors, and Cluster Manager — but fewer dive into the Catalyst optimizer, physical plans, and Adaptive Query Execution (AQE). Here’s a breakdown of the query journey I visualized in the diagram 👇 #ApacheSpark #PySpark #BigData #DataEngineering #ETL #DistributedComputing

💡 Think of it like a highway: 16 lanes of road, but only one car driving. That’s your unpartitioned query. Now partition it: 16 lanes filled with cars moving at once, that’s your Spark cluster fully utilized. 🚀 From 8 Hours to 20 Minutes We had a Glue job (Spark) pulling data from SQL Server. 2 DPUs → 8 hours 10 minutes → timeout 16 DPUs → still timeout after 8hours 👉 Why? Because Spark can only parallelize the work it’s given. One giant unpartitioned query = one giant task = wasted executors. ✅ The fix: Partitioned the extraction by the date column Optimized SELECTs to fetch per partition Reran with 16 DPUs ⏱ Result: 8 hours → 20 minutes 🧑💻 Spark insight: Unpartitioned = 1 task → idle cluster Partitioned = many tasks → full utilization 💡 Lesson: Partition first. Scale later. Throwing more DPUs at a badly designed job just makes inefficiency more expensive. #AWSGlue #ApacheSpark #DataEngineering #BigData #CloudComputing #ETL #Scalability

Exciting news for the open data community: Variant, the native data type for semi-structured data, is now ratified in the Apache Parquet™ community — with support across Delta Lake, Apache Iceberg™, and Apache Spark™. Variant brings a unified, open standard to how the lakehouse stores and queries flexible data—making it faster, simpler, and more consistent across formats and engines. https://lnkd.in/gESP97Nm

Exciting milestone for the open data ecosystem! 🚀 The ratification of Variant as a native data type in Apache Parquet™ is a big step forward for managing semi-structured data. With unified support across Delta Lake, Apache Iceberg™, and Apache Spark™, this truly strengthens interoperability in the open lakehouse world. A major leap toward simplifying how we store, process, and query flexible data — making analytics faster and more consistent across platforms. #DataEngineering #ApacheParquet #DeltaLake #ApacheSpark #OpenSource #Lakehouse

Databricks Variant type sets a new standard for speed and efficiency with semi-structured data, offering up to 8x faster performance compared to traditional JSON string storage. - What stands out most is both the remarkable query speed and reduced storage requirements, Variant uses 22% less storage than plain strings, saving significant time and cost. - Real-world benchmarks show dramatic gains: ETL jobs that once took hours now finish in minutes, and 1TB queries dropped from over 4 hours to just 20 minutes. This combination of ultra-fast querying and lower storage overhead makes Variant a clear leader in big data analytics.

Huge news for anyone building a Security Lakehouse. Security data like logs, events, and telemetry from various sources (endpoints, cloud infra, SaaS ) is inherently semi-structured. A single security event might contain dozens of nested fields, which can change frequently with new product versions or changes to logging config. Before Variant, handling this required kludgey workarounds: 👎 Storing all the JSON as a massive, opaque string. This is slow to query and wastes compute power. 👎 Trying to force a rigid schema on the data. This leads to brittle pipelines that constantly break when a new field appears. The new Variant type solves this by providing a unified, open standard for storing this kind of data natively and efficiently within Parquet. This means you can now: 🔥 Simplify Ingestion: Security pipelines become more resilient, as you don't need to preemptively flatten or strictly validate every piece of semi-structured data. 🔥Accelerate Investigations: You can query nested or evolving fields much faster without complex JSON parsing at query time. Quicker queries mean faster threat detection and response. 🔥Reduce Costs: More efficient storage and faster queries often translate directly into lower compute costs for your security platform. This move brings the flexibility needed for modern security data alongside the high performance and open standards of the Lakehouse architecture.