Spark History MCP Server

{ "app_id": "application_1768524288842_0014", "skew_analysis": [], "spill_analysis": [], "resource_analysis": [], "partitioning_analysis": [], "join_analysis": [], "recommendations": [ { "category": "Configuration", "issue": "Kryo serialization is often faster and more compact than Java serialization, potentially reducing serialization overhead and GC pressure, especially beneficial considering the noted high executorRunTime in Stage 0 and other stages. The current JavaSerializer is known to be slow.", "suggestion": "Set spark.serializer to org.apache.spark.serializer.KryoSerializer", "evidence": "Current: org.apache.spark.serializer.JavaSerializer", "impact_level": "High" }, { "category": "Configuration", "issue": "Enabling Kryo registration can improve performance and prevent unexpected serialization issues, which are hard to debug. Though it requires upfront configuration, it's generally beneficial for long-running applications.", "suggestion": "Set spark.kryo.registrationRequired to true", "evidence": "Current: false", "impact_level": "High" }, { "category": "Configuration", "issue": "Setting default parallelism can improve resource utilization. Without knowing the specifics of the data, a good starting point is to set it to 2-3 times the number of cores available in your cluster. This can prevent tasks from being too large and causing excessive GC or OOM issues, as the executorRunTime is high on multiple stages.", "suggestion": "Set spark.default.parallelism to <number of cores> * 2 or 3", "evidence": "Current: N/A", "impact_level": "High" }, { "category": "Configuration", "issue": "This configuration controls the number of partitions for shuffles, specifically in Spark SQL. Reducing the number of shuffle partitions may decrease shuffle time and overall executor run time, which is high on stages 36 and 33. Set it to a similar value as spark.default.parallelism.", "suggestion": "Set spark.sql.shuffle.partitions to <number of cores> * 2 or 3", "evidence": "Current: 200", "impact_level": "High" }, { "category": "Configuration", "issue": "With high executorRunTime across stages, consider increasing the executor memory. This could reduce spills to disk. Monitor GC behavior after increasing to ensure it doesn't negatively impact performance. If the job is CPU-bound, increasing memory will have limited impact.", "suggestion": "Set spark.executor.memory to <Increase if possible, e.g., 2g or higher depending on cluster resources>", "evidence": "Current: N/A", "impact_level": "High" }, { "category": "Configuration", "issue": "Add G1GC as garbage collector in JVM. G1GC is designed for large heaps and can often improve GC performance, reducing JVM GC time in stages 0, 6, and 12. Also, lower InitiatingHeapOccupancyPercent down to 35% can trigger earlier Garbage collections, preventing long pauses with large heaps. Consider disabling explicit GC as well using -XX:+DisableExplicitGC. Remove the add-opens JVM options as they are not needed in most cases and could impact performance. The current values are default.", "suggestion": "Set spark.driver.extraJavaOptions to -Djava.net.preferIPv6Addresses=false -XX:+UseG1GC -XX:InitiatingHeapOccupancyPercent=35 -XX:+DisableExplicitGC -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": "Add G1GC as garbage collector in JVM. G1GC is designed for large heaps and can often improve GC performance, reducing JVM GC time in stages 0, 6, and 12. Also, lower InitiatingHeapOccupancyPercent down to 35% can trigger earlier Garbage collections, preventing long pauses with large heaps. Consider disabling explicit GC as well using -XX:+DisableExplicitGC. Remove the add-opens JVM options as they are not needed in most cases and could impact performance. The current values are default.", "suggestion": "Set spark.executor.extraJavaOptions to -Djava.net.preferIPv6Addresses=false -XX:+UseG1GC -XX:InitiatingHeapOccupancyPercent=35 -XX:+DisableExplicitGC -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": "Ensure the driver has sufficient memory, especially if it's collecting or broadcasting large datasets. This is important, since, in client deploy mode, the driver process is running on the local machine, and can easily run out of memory.", "suggestion": "Set spark.driver.memory to <Increase if possible, e.g., 2g or higher depending on cluster resources>", "evidence": "Current: N/A", "impact_level": "High" }, { "category": "Code", "issue": "Action inside loop (count). This triggers a full job execution for each iteration, which is extremely inefficient.", "suggestion": "Calculate all the filtered counts in a single Spark operation using `groupBy` or `aggregate` functions. Then collect the results outside the loop for printing.", "evidence": "Line: 16", "impact_level": "Medium" }, { "category": "Code", "issue": "Multiple actions (`count`, `agg(max)`, `agg(min)`) are performed on the same DataFrame, resulting in multiple full scans of the data.", "suggestion": "Calculate `count`, `max`, and `min` in a single `agg` operation to minimize data scans. Use `df.agg(F.count('*'), F.max('value'), F.min('value')).collect()`.", "evidence": "Line: 24", "impact_level": "Medium" } ] }