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背景

写这个文章的原因是为了了解一下Starrocks对各个算子的代价是怎么计算的，以便在后续对Starrocks做优化的时候，能够起到辅助作用
本文基于 Starrocks 3.3.5

结论

StatisticsCalculator 通过获取底层数据源的统计信息而进行自上而下的数据信息的统计，这些统计信息的计算大部分也是估算获得的。

分析

直接到 StatisticsCalculator 类中(该类会被DeriveStatsTask调用)，这个类采用的典型的Visitor模式，对于不同的算子，会进入到不同的方法中去：
这里我们分析三种算子:

1. Scan olap
2. Filter
3. Projection

其他的算子可以看源码实现。

1. Scan olap算子

   @Overridepublic Void visitLogicalOlapScan(LogicalOlapScanOperator node, ExpressionContext context) {return computeOlapScanNode(node, context, node.getTable(), node.getSelectedPartitionId(),node.getColRefToColumnMetaMap());}@Overridepublic Void visitPhysicalOlapScan(PhysicalOlapScanOperator node, ExpressionContext context) {return computeOlapScanNode(node, context, node.getTable(), node.getSelectedPartitionId(),node.getColRefToColumnMetaMap());}private Void computeOlapScanNode(Operator node, ExpressionContext context, Table table,Collection<Long> selectedPartitionIds,Map<ColumnRefOperator, Column> colRefToColumnMetaMap) {Preconditions.checkState(context.arity() == 0);// 1. get table row countlong tableRowCount = StatisticsCalcUtils.getTableRowCount(table, node, optimizerContext);// 2. get required columns statisticsStatistics.Builder builder = StatisticsCalcUtils.estimateScanColumns(table, colRefToColumnMetaMap, optimizerContext);if (tableRowCount <= 1) {builder.setTableRowCountMayInaccurate(true);}// 3. deal with column statistics for partition pruneOlapTable olapTable = (OlapTable) table;adjustPartitionColsStatistic(selectedPartitionIds, olapTable, builder, colRefToColumnMetaMap);builder.setOutputRowCount(tableRowCount);if (isRewrittenMvGE(node, table, context)) {adjustNestedMvStatistics(context.getGroupExpression().getGroup(), (MaterializedView) olapTable, builder);if (node.getProjection() != null) {builder.setShadowColumns(node.getProjection().getOutputColumns());}}// 4. estimate cardinalitycontext.setStatistics(builder.build());return visitOperator(node, context);}

这个也是统计信息的来源，其他算子的统计信息都是基于此类算子计算出来的。

• StatisticsCalcUtils.getTableRowCount 首先计算行数
  该方法会首先获取到扫描的分区数，之后再从CachedStatisticStorage获取到对应分区的行数信息，从而累加，最小为1行
• StatisticsCalcUtils.estimateScanColumns 获取到对应列的统计信息
  该方法也是从CachedStatisticStorage 获取ColumnStatistics和HistogramStatistics信息
• 如果行数只有一行，那就标记为统计信息不准确
• 调增分区列的统计信息
• visitOperator 这里会对scan涉及到的谓词以及 limit projection 做进一步的统计分析
  对于不同的谓词，利用 BaseCalculatingVisitor / LargeOrCalculatingVisitor 进行统计，比如说 in / or / and等
  对于projection 利用 ExpressionStatisticVisitor类进行统计

2. Filter算子

    @Overridepublic Void visitLogicalFilter(LogicalFilterOperator node, ExpressionContext context) {return computeFilterNode(node, context);}@Overridepublic Void visitPhysicalFilter(PhysicalFilterOperator node, ExpressionContext context) {return computeFilterNode(node, context);}private Void computeFilterNode(Operator node, ExpressionContext context) {Statistics inputStatistics = context.getChildStatistics(0);Statistics.Builder builder = Statistics.builder();builder.addColumnStatistics(inputStatistics.getColumnStatistics());builder.setOutputRowCount(inputStatistics.getOutputRowCount());context.setStatistics(builder.build());return visitOperator(node, context);}

• 对于filter操作来说 ，也是沿用了子节点的统计信息，所以说filter这一层级统计信息和子节点是一致的

3. Projection算子

@Overridepublic Void visitLogicalProject(LogicalProjectOperator node, ExpressionContext context) {return computeProjectNode(context, node.getColumnRefMap());}@Overridepublic Void visitPhysicalProject(PhysicalProjectOperator node, ExpressionContext context) {return computeProjectNode(context, node.getColumnRefMap());}private Void computeProjectNode(ExpressionContext context, Map<ColumnRefOperator, ScalarOperator> columnRefMap) {Preconditions.checkState(context.arity() == 1);Statistics.Builder builder = Statistics.builder();Statistics inputStatistics = context.getChildStatistics(0);builder.setOutputRowCount(inputStatistics.getOutputRowCount());Statistics.Builder allBuilder = Statistics.builder();allBuilder.setOutputRowCount(inputStatistics.getOutputRowCount());allBuilder.addColumnStatistics(inputStatistics.getColumnStatistics());for (ColumnRefOperator requiredColumnRefOperator : columnRefMap.keySet()) {ScalarOperator mapOperator = columnRefMap.get(requiredColumnRefOperator);if (mapOperator instanceof SubfieldOperator && context.getOptExpression() != null) {Operator child = context.getOptExpression().inputAt(0).getOp();if (child instanceof LogicalScanOperator || child instanceof PhysicalScanOperator) {addSubFiledStatistics(child, ImmutableMap.of(requiredColumnRefOperator,(SubfieldOperator) mapOperator), builder);continue;}}ColumnStatistic outputStatistic =ExpressionStatisticCalculator.calculate(mapOperator, allBuilder.build());builder.addColumnStatistic(requiredColumnRefOperator, outputStatistic);allBuilder.addColumnStatistic(requiredColumnRefOperator, outputStatistic);}context.setStatistics(builder.build());return visitOperator(context.getOp(), context);}

对于Projection来说，也是根据不同的 Projection 算子来做区别对待的（也是用ExpressionStatisticVisitor类），如case when操作，函数操作等，这些对应的列的最大值和最小值都是不一样的