Spark History MCP Server

{ "app_id": "application_1768320005356_0008", "skew_analysis": [], "spill_analysis": [ { "has_spill": true, "total_disk_spill": 0, "total_memory_spill": 0, "stage_id": 2 }, { "has_spill": true, "total_disk_spill": 0, "total_memory_spill": 0, "stage_id": 0 } ], "resource_analysis": [], "partitioning_analysis": [], "join_analysis": [], "recommendations": [ { "category": "Configuration", "issue": "Enable Adaptive Query Execution (AQE). While no explicit skew is flagged in the metrics, AQE can dynamically handle skew and optimize joins, potentially alleviating issues in stages 0, 2, and 5 if skew is present. It also generally improves query performance.", "suggestion": "Set spark.sql.adaptive.enabled to true", "evidence": "Current: false (default)", "impact_level": "High" }, { "category": "Configuration", "issue": "Consider switching to Kryo serialization, especially with Java 17. Kryo is often faster and more compact than Java serialization. This helps reduce shuffle data size and improve serialization/deserialization performance, impacting stages 0, 2, and 5.", "suggestion": "Set spark.serializer to org.apache.spark.serializer.KryoSerializer", "evidence": "Current: org.apache.spark.serializer.JavaSerializer (default)", "impact_level": "High" }, { "category": "Configuration", "issue": "Increase the Kryo serializer buffer size to handle potentially large objects in serialization, preventing exceptions and improving serialization performance. Applicable if KryoSerializer is enabled, as recommended above, and if you are serializing large objects.", "suggestion": "Set spark.kryoserializer.buffer.max to 128m", "evidence": "Current: 64m (default)", "impact_level": "High" }, { "category": "Configuration", "issue": "Increase executor memory to reduce GC pressure. The current memory is quite small which could be contributing to the GC overhead in stages 0 and 2. Tune in conjunction with `spark.executor.cores` to optimize resource allocation.", "suggestion": "Set spark.executor.memory to 2g", "evidence": "Current: 1024m (inferred from resource profile)", "impact_level": "High" }, { "category": "Configuration", "issue": "Increase the number of cores per executor to increase the number of parallel tasks. Tune in conjunction with `spark.executor.memory` to optimize resource allocation. Consider increasing this only if you have sufficient CPU resources in your cluster.", "suggestion": "Set spark.executor.cores to 2", "evidence": "Current: 1 (inferred from resource profile)", "impact_level": "High" }, { "category": "Configuration", "issue": "Stage 5's single task suggests low parallelism. Increasing the default parallelism will increase the number of partition of each data frame. This allows to better use the resources and parallel processing. Update the stage 5 to leverage increased resources.", "suggestion": "Set spark.default.parallelism to 200", "evidence": "Current: based on cluster size", "impact_level": "High" }, { "category": "Configuration", "issue": "Switch to G1GC garbage collector. The default collector might not be optimal for Spark workloads. Add `-XX:+UseG1GC` to `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions`. This is especially useful given the Java 17 environment and could improve GC performance observed in stages 0 and 2.", "suggestion": "Set spark.driver.extraJavaOptions to -Djava.net.preferIPv6Addresses=false -XX:+UseG1GC", "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": "Switch to G1GC garbage collector. The default collector might not be optimal for Spark workloads. Add `-XX:+UseG1GC` to `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions`. This is especially useful given the Java 17 environment and could improve GC performance observed in stages 0 and 2.", "suggestion": "Set spark.executor.extraJavaOptions to -Djava.net.preferIPv6Addresses=false -XX:+UseG1GC", "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 partition count in `repartition(10)`. This might be insufficient or excessive depending on cluster size and data volume.", "suggestion": "Use a dynamic partition count based on data size and cluster resources. Consider `df.repartition(spark.sparkContext.defaultParallelism)` or calculating an appropriate number based on the input data size. If you are using Spark 3.0 or later, adaptive query execution (AQE) can help with this automatically.", "evidence": "Line: 20", "impact_level": "Medium" }, { "category": "Code", "issue": "Using `collect()` after `groupBy()`. This pulls all the grouped data to the driver, which can cause driver OOM errors, especially with large datasets. It defeats the purpose of distributed processing.", "suggestion": "Avoid `collect()` after aggregations unless the result set is guaranteed to be small. Instead, consider writing the aggregated data to a distributed storage system (e.g., Parquet on S3/HDFS) or processing it further in a distributed manner using other transformations.", "evidence": "Line: 24", "impact_level": "Medium" }, { "category": "Code", "issue": "Manual loop to create skewed data. This is inefficient and makes the code harder to read. The data creation logic is not Spark-native.", "suggestion": "Use Spark's `range` and `flatMap` to create the skewed data within Spark. For example:```python\nnum_skewed = 100000\nnum_uniform = 10000\n\nskewed_df = spark.range(num_skewed).withColumn('id', F.lit(1)).withColumn('val', F.lit('skewed'))\nuniform_df = spark.range(num_uniform).withColumn('id', F.col('id') + num_skewed).withColumn('val', F.lit('uniform'))\ndf = skewed_df.union(uniform_df).repartition(10)\n```", "evidence": "Line: 11-14", "impact_level": "Medium" } ] }