Spark History MCP Server

{ "app_id": "application_1768524288842_0008", "skew_analysis": [], "spill_analysis": [], "resource_analysis": [], "partitioning_analysis": [], "join_analysis": [], "recommendations": [ { "category": "Code", "issue": "Loop with `cache()` and `unpersist()`: Although caching is handled, repeatedly creating, caching, counting, and unpersisting the same DataFrame within a loop is inefficient. The `count()` action triggers a full computation each time. This might not be the intended behavior if the same data transformations need to be used later.", "suggestion": "Consider materializing the `df` DataFrame *before* the loop if `df` remains unchanged. If the goal is to repeatedly perform a similar computation with the same base data, extract the random column generation from the loop. If `df` is indeed re-used, cache it only *once* before the loop and `unpersist` after the loop completes.", "evidence": "Line: 21-25", "impact_level": "Medium" }, { "category": "Code", "issue": "`temp_df = df.withColumn(f\"col_{i}\", F.rand() * 100)`: Calling `F.rand()` within a loop without a seed will produce different random numbers each time the job is run, which makes debugging difficult.", "suggestion": "Use a seed to make the random number generation reproducible. Consider adding `F.rand(seed=some_integer)`.", "evidence": "Line: 22", "impact_level": "Medium" }, { "category": "Code", "issue": "Potential for large shuffle writes: Repeatedly creating new columns without optimizing the underlying execution plan can lead to increased shuffle writes as each `withColumn` operation might not be efficiently combined.", "suggestion": "If all columns are used together at some point, consider building up the final dataframe with a single `selectExpr` or `select` call with all columns defined in a single call instead of iteratively creating columns.", "evidence": "Line: 22", "impact_level": "Medium" } ] }