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https://issues.apache.org/jira/browse/HIVE-8517?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14178981#comment-14178981
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Vikram Dixit K commented on HIVE-8517:
--------------------------------------
+1 for 0.14
> When joining on partition column NDV gets overridden by
> StatsUtils.getColStatisticsFromExpression
> -------------------------------------------------------------------------------------------------
>
> Key: HIVE-8517
> URL: https://issues.apache.org/jira/browse/HIVE-8517
> Project: Hive
> Issue Type: Bug
> Components: Physical Optimizer
> Affects Versions: 0.14.0
> Reporter: Mostafa Mokhtar
> Assignee: Mostafa Mokhtar
> Priority: Critical
> Fix For: 0.14.0
>
> Attachments: HIVE-8517.1.patch, HIVE-8517.2.patch, HIVE-8517.3.patch
>
>
> When joining on partition column number of partitions is used as NDV which
> gets overridden by StatsUtils.getColStatisticsFromExpression and the number
> of partitions used as NDV is replaced by number of rows which results in the
> same behavior as explained in
> https://issues.apache.org/jira/browse/HIVE-8196. "Joining on partition
> columns with fetch column stats enabled results it very small CE which
> negatively affects query performance "
> This is the call stack.
> {code}
> StatsUtils.getColStatisticsFromExpression(HiveConf, Statistics, ExprNodeDesc)
> line: 1001
> StatsRulesProcFactory$ReduceSinkStatsRule.process(Node, Stack<Node>,
> NodeProcessorCtx, Object...) line: 1479
> DefaultRuleDispatcher.dispatch(Node, Stack<Node>, Object...) line: 90
> PreOrderWalker(DefaultGraphWalker).dispatchAndReturn(Node, Stack<Node>) line:
> 94
> PreOrderWalker(DefaultGraphWalker).dispatch(Node, Stack<Node>) line: 78
> PreOrderWalker.walk(Node) line: 54
> PreOrderWalker.walk(Node) line: 59
> PreOrderWalker.walk(Node) line: 59
> PreOrderWalker(DefaultGraphWalker).startWalking(Collection<Node>,
> HashMap<Node,Object>) line: 109
> AnnotateWithStatistics.transform(ParseContext) line: 78
> TezCompiler.runStatsAnnotation(OptimizeTezProcContext) line: 248
> TezCompiler.optimizeOperatorPlan(ParseContext, Set<ReadEntity>,
> Set<WriteEntity>) line: 120
> TezCompiler(TaskCompiler).compile(ParseContext, List<Task<Serializable>>,
> HashSet<ReadEntity>, HashSet<WriteEntity>) line: 99
> SemanticAnalyzer.analyzeInternal(ASTNode) line: 10037
> SemanticAnalyzer(BaseSemanticAnalyzer).analyze(ASTNode, Context) line: 221
> ExplainSemanticAnalyzer.analyzeInternal(ASTNode) line: 74
> ExplainSemanticAnalyzer(BaseSemanticAnalyzer).analyze(ASTNode, Context) line:
> 221
> Driver.compile(String, boolean) line: 415
> {code}
> Query
> {code}
> select
> ss_item_sk item_sk, d_date, sum(ss_sales_price),
> sum(sum(ss_sales_price))
> over (partition by ss_item_sk order by d_date rows between unbounded
> preceding and current row) cume_sales
> from store_sales
> ,date_dim
> where ss_sold_date_sk=d_date_sk
> and d_month_seq between 1193 and 1193+11
> and ss_item_sk is not NULL
> group by ss_item_sk, d_date
> {code}
> Plan
> Notice in the Map join operator the number of rows drop from 82,510,879,939
> to 36524 after the join.
> {code}
> OK
> STAGE DEPENDENCIES:
> Stage-1 is a root stage
> Stage-0 depends on stages: Stage-1
> STAGE PLANS:
> Stage: Stage-1
> Tez
> Edges:
> Map 1 <- Map 4 (BROADCAST_EDGE)
> Reducer 2 <- Map 1 (SIMPLE_EDGE)
> Reducer 3 <- Reducer 2 (SIMPLE_EDGE)
> DagName: mmokhtar_20141019131818_086d663a-5621-456c-bf25-8ccb7112ee3b:6
> Vertices:
> Map 1
> Map Operator Tree:
> TableScan
> alias: store_sales
> filterExpr: ss_item_sk is not null (type: boolean)
> Statistics: Num rows: 82510879939 Data size: 6873789738208
> Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: ss_item_sk is not null (type: boolean)
> Statistics: Num rows: 82510879939 Data size: 652315818272
> Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> condition expressions:
> 0 {ss_item_sk} {ss_sales_price} {ss_sold_date_sk}
> 1 {d_date_sk} {d_date} {d_month_seq}
> keys:
> 0 ss_sold_date_sk (type: int)
> 1 d_date_sk (type: int)
> outputColumnNames: _col1, _col12, _col22, _col26,
> _col28, _col29
> input vertices:
> 1 Map 4
> Statistics: Num rows: 36524 Data size: 4163736 Basic
> stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: (((_col22 = _col26) and _col29 BETWEEN
> 1193 AND 1204) and _col1 is not null) (type: boolean)
> Statistics: Num rows: 9131 Data size: 1040934 Basic
> stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: _col1 (type: int), _col28 (type:
> string), _col12 (type: float)
> outputColumnNames: _col1, _col28, _col12
> Statistics: Num rows: 9131 Data size: 1040934 Basic
> stats: COMPLETE Column stats: COMPLETE
> Group By Operator
> aggregations: sum(_col12)
> keys: _col1 (type: int), _col28 (type: string)
> mode: hash
> outputColumnNames: _col0, _col1, _col2
> Statistics: Num rows: 4565 Data size: 483890
> Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int), _col1
> (type: string)
> sort order: ++
> Map-reduce partition columns: _col0 (type:
> int), _col1 (type: string)
> Statistics: Num rows: 4565 Data size: 483890
> Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col2 (type: double)
> Execution mode: vectorized
> Map 4
> Map Operator Tree:
> TableScan
> alias: date_dim
> filterExpr: (d_date_sk is not null and d_month_seq BETWEEN
> 1193 AND 1204) (type: boolean)
> Statistics: Num rows: 73049 Data size: 81741831 Basic
> stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: (d_date_sk is not null and d_month_seq BETWEEN
> 1193 AND 1204) (type: boolean)
> Statistics: Num rows: 36524 Data size: 3725448 Basic
> stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: d_date_sk (type: int)
> sort order: +
> Map-reduce partition columns: d_date_sk (type: int)
> Statistics: Num rows: 36524 Data size: 3725448 Basic
> stats: COMPLETE Column stats: COMPLETE
> value expressions: d_date (type: string), d_month_seq
> (type: int)
> Select Operator
> expressions: d_date_sk (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 36524 Data size: 0 Basic stats:
> PARTIAL Column stats: COMPLETE
> Group By Operator
> keys: _col0 (type: int)
> mode: hash
> outputColumnNames: _col0
> Statistics: Num rows: 36524 Data size: 0 Basic stats:
> PARTIAL Column stats: COMPLETE
> Dynamic Partitioning Event Operator
> Target Input: store_sales
> Partition key expr: ss_sold_date_sk
> Statistics: Num rows: 36524 Data size: 0 Basic
> stats: PARTIAL Column stats: COMPLETE
> Target column: ss_sold_date_sk
> Target Vertex: Map 1
> Execution mode: vectorized
> Reducer 2
> Reduce Operator Tree:
> Group By Operator
> aggregations: sum(VALUE._col0)
> keys: KEY._col0 (type: int), KEY._col1 (type: string)
> mode: mergepartial
> outputColumnNames: _col0, _col1, _col2
> Statistics: Num rows: 4565 Data size: 502150 Basic stats:
> COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int), _col1 (type: string)
> sort order: ++
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 4565 Data size: 502150 Basic stats:
> COMPLETE Column stats: COMPLETE
> value expressions: _col0 (type: int), _col1 (type: string),
> _col2 (type: double)
> Execution mode: vectorized
> Reducer 3
> Reduce Operator Tree:
> Extract
> Statistics: Num rows: 4565 Data size: 502150 Basic stats:
> COMPLETE Column stats: COMPLETE
> PTF Operator
> Statistics: Num rows: 4565 Data size: 502150 Basic stats:
> COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: _col0 (type: int), _col1 (type: string),
> _col2 (type: double), _wcol0 (type: double)
> outputColumnNames: _col0, _col1, _col2, _col3
> Statistics: Num rows: 4565 Data size: 36520 Basic stats:
> COMPLETE Column stats: COMPLETE
> File Output Operator
> compressed: false
> Statistics: Num rows: 4565 Data size: 36520 Basic
> stats: COMPLETE Column stats: COMPLETE
> table:
> input format:
> org.apache.hadoop.mapred.TextInputFormat
> output format:
> org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
> serde:
> org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
> Stage: Stage-0
> Fetch Operator
> limit: -1
> Processor Tree:
> ListSink
> {code}
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