Spark History MCP Server

{ "app_id": "application_1768524288842_0023", "skew_analysis": [], "spill_analysis": [], "resource_analysis": [], "partitioning_analysis": [], "join_analysis": [], "recommendations": [ { "category": "Configuration", "issue": "Kryo serialization is generally faster and more compact than Java serialization, potentially reducing network I/O and memory usage, especially beneficial for reduceByKey operations. Requires registering custom classes for optimal performance.", "suggestion": "Set spark.serializer to org.apache.spark.serializer.KryoSerializer", "evidence": "Current: org.apache.spark.serializer.JavaSerializer", "impact_level": "High" }, { "category": "Configuration", "issue": "Setting `spark.kryo.registrationRequired` to true forces you to register your classes with Kryo. This prevents runtime exceptions during serialization and often leads to better performance. Only enable this if all classes can be registered.", "suggestion": "Set spark.kryo.registrationRequired to true", "evidence": "Current: Not Set", "impact_level": "High" }, { "category": "Configuration", "issue": "Reduce the size of reduce tasks to reduce the memory pressure during shuffle. If the task is reduced too much, then increase slightly till optimal memory and performance trade-off is achieved.", "suggestion": "Set spark.reducer.maxSizeInFlight to 24m", "evidence": "Current: 48m", "impact_level": "High" }, { "category": "Configuration", "issue": "Increasing the shuffle file buffer size can improve shuffle performance by reducing the number of disk I/O operations. A larger buffer can hold more data before writing to disk. This may slightly increase memory footprint.", "suggestion": "Set spark.shuffle.file.buffer to 64k", "evidence": "Current: 32k", "impact_level": "High" }, { "category": "Configuration", "issue": "Enabling compression for shuffle spill data reduces disk I/O and storage space, especially if spilling is observed, which often happens during reduceByKey. This setting is already enabled, but ensure that the compression codec is efficient (e.g., lz4 or snappy).", "suggestion": "Set spark.shuffle.spill.compress to true", "evidence": "Current: true", "impact_level": "High" }, { "category": "Configuration", "issue": "Increase memory available for spark execution.", "suggestion": "Set spark.memory.fraction to 0.7", "evidence": "Current: 0.6", "impact_level": "High" }, { "category": "Configuration", "issue": "Reduce memory reserved for caching RDDs and DataFrames to allow Spark to spill less.", "suggestion": "Set spark.memory.storageFraction to 0.3", "evidence": "Current: 0.5", "impact_level": "High" }, { "category": "Configuration", "issue": "Enable the G1 garbage collector for potentially better and more predictable GC pauses. Also explicitly disable explicit GC calls, which can sometimes hinder G1's performance, and reduce the InitiatingHeapOccupancyPercent to trigger garbage collection earlier.", "suggestion": "Set spark.driver.extraJavaOptions to -Djava.net.preferIPv6Addresses=false -XX:+UseG1GC -XX:InitiatingHeapOccupancyPercent=35 -XX:+DisableExplicitGC -XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED -Djdk.reflect.useDirectMethodHandle=false", "evidence": "Current: -Djava.net.preferIPv6Addresses=false -XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED -Djdk.reflect.useDirectMethodHandle=false", "impact_level": "High" }, { "category": "Configuration", "issue": "Enable the G1 garbage collector for potentially better and more predictable GC pauses. Also explicitly disable explicit GC calls, which can sometimes hinder G1's performance, and reduce the InitiatingHeapOccupancyPercent to trigger garbage collection earlier.", "suggestion": "Set spark.executor.extraJavaOptions to -Djava.net.preferIPv6Addresses=false -XX:+UseG1GC -XX:InitiatingHeapOccupancyPercent=35 -XX:+DisableExplicitGC -XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED -Djdk.reflect.useDirectMethodHandle=false", "evidence": "Current: -Djava.net.preferIPv6Addresses=false -XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED -Djdk.reflect.useDirectMethodHandle=false", "impact_level": "High" }, { "category": "Code", "issue": "Hardcoded partitions during RDD creation. This limits parallelism and might not be optimal for different cluster sizes.", "suggestion": "Pass the number of partitions to `parallelize` based on cluster size or data volume. e.g., `rdd = spark.sparkContext.parallelize(data, numSlices=spark.sparkContext.defaultParallelism)`", "evidence": "Line: 12", "impact_level": "Medium" }, { "category": "Code", "issue": "Using `collect()` to bring the entire result set to the driver. This is inefficient and can cause driver memory issues with large datasets.", "suggestion": "Avoid `collect()` if the result set is large. Consider writing the results to persistent storage (e.g., Parquet, CSV) or processing them in batches on the driver if absolutely necessary. If the intention is just to get the count, use `sums.count()` instead.", "evidence": "Line: 16", "impact_level": "Medium" }, { "category": "Code", "issue": "Creating an RDD and DataFrame from the same data. Only one is required to achieve the result. The DataFrame approach is usually faster.", "suggestion": "Choose either the RDD or DataFrame approach based on performance testing. In general DataFrames/Datasets provide better optimized execution plans through the Catalyst optimizer. DataFrame code is provided as an alternative, it should not be present together.", "evidence": "Line: 19", "impact_level": "Medium" }, { "category": "Code", "issue": "`collect()` is used to bring the entire DataFrame result set to the driver. This is inefficient and can cause driver memory issues with large datasets.", "suggestion": "Similar to RDDs, avoid `collect()` on DataFrames if the result set is large. Write to persistent storage or process in batches if the result set is very large. Use `df_sums.count()` to get count only.", "evidence": "Line: 21", "impact_level": "Medium" } ] }