override def hashCode()

in spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala [103:180]
75 lines of code
1 McCabe index (conditional complexity)

  override def hashCode(): Int = Objects.hashCode(originalPlan, serializedPlanOpt)

  override lazy val metrics: Map[String, SQLMetric] = {
    // We don't append CometMetricNode.baselineMetrics because
    // elapsed_compute has no counterpart on the native side.
    Map(
      "output_rows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"),
      "time_elapsed_opening" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Wall clock time elapsed for file opening"),
      "time_elapsed_scanning_until_data" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Wall clock time elapsed for file scanning + " +
            "first record batch of decompression + decoding"),
      "time_elapsed_scanning_total" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Elapsed wall clock time for for scanning " +
            "+ record batch decompression / decoding"),
      "time_elapsed_processing" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Wall clock time elapsed for data decompression + decoding"),
      "file_open_errors" ->
        SQLMetrics.createMetric(sparkContext, "Count of errors opening file"),
      "file_scan_errors" ->
        SQLMetrics.createMetric(sparkContext, "Count of errors scanning file"),
      "predicate_evaluation_errors" ->
        SQLMetrics.createMetric(
          sparkContext,
          "Number of times the predicate could not be evaluated"),
      "row_groups_matched_bloom_filter" ->
        SQLMetrics.createMetric(
          sparkContext,
          "Number of row groups whose bloom filters were checked and matched (not pruned)"),
      "row_groups_pruned_bloom_filter" ->
        SQLMetrics.createMetric(sparkContext, "Number of row groups pruned by bloom filters"),
      "row_groups_matched_statistics" ->
        SQLMetrics.createMetric(
          sparkContext,
          "Number of row groups whose statistics were checked and matched (not pruned)"),
      "row_groups_pruned_statistics" ->
        SQLMetrics.createMetric(sparkContext, "Number of row groups pruned by statistics"),
      "bytes_scanned" ->
        SQLMetrics.createSizeMetric(sparkContext, "Number of bytes scanned"),
      "pushdown_rows_pruned" ->
        SQLMetrics.createMetric(
          sparkContext,
          "Rows filtered out by predicates pushed into parquet scan"),
      "pushdown_rows_matched" ->
        SQLMetrics.createMetric(sparkContext, "Rows passed predicates pushed into parquet scan"),
      "row_pushdown_eval_time" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Time spent evaluating row-level pushdown filters"),
      "statistics_eval_time" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Time spent evaluating row group-level statistics filters"),
      "bloom_filter_eval_time" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Time spent evaluating row group Bloom Filters"),
      "page_index_rows_pruned" ->
        SQLMetrics.createMetric(sparkContext, "Rows filtered out by parquet page index"),
      "page_index_rows_matched" ->
        SQLMetrics.createMetric(sparkContext, "Rows passed through the parquet page index"),
      "page_index_eval_time" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Time spent evaluating parquet page index filters"),
      "metadata_load_time" ->
        SQLMetrics.createNanoTimingMetric(
          sparkContext,
          "Time spent reading and parsing metadata from the footer"))
  }