Return-Path: X-Original-To: apmail-hive-dev-archive@www.apache.org Delivered-To: apmail-hive-dev-archive@www.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 3842E116BA for ; Sun, 7 Sep 2014 20:14:29 +0000 (UTC) Received: (qmail 56373 invoked by uid 500); 7 Sep 2014 20:14:28 -0000 Delivered-To: apmail-hive-dev-archive@hive.apache.org Received: (qmail 56303 invoked by uid 500); 7 Sep 2014 20:14:28 -0000 Mailing-List: contact dev-help@hive.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@hive.apache.org Delivered-To: mailing list dev@hive.apache.org Received: (qmail 56292 invoked by uid 500); 7 Sep 2014 20:14:28 -0000 Delivered-To: apmail-hadoop-hive-dev@hadoop.apache.org Received: (qmail 56289 invoked by uid 99); 7 Sep 2014 20:14:28 -0000 Received: from arcas.apache.org (HELO arcas.apache.org) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Sun, 07 Sep 2014 20:14:28 +0000 Date: Sun, 7 Sep 2014 20:14:28 +0000 (UTC) From: "Prasanth J (JIRA)" To: hive-dev@hadoop.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (HIVE-7990) With fetch column stats disabled number of elements in grouping set is not taken into account MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/HIVE-7990?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Prasanth J updated HIVE-7990: ----------------------------- Resolution: Fixed Fix Version/s: 0.14.0 Status: Resolved (was: Patch Available) Committed to trunk. Thanks Gunther for review. > With fetch column stats disabled number of elements in grouping set is not taken into account > --------------------------------------------------------------------------------------------- > > Key: HIVE-7990 > URL: https://issues.apache.org/jira/browse/HIVE-7990 > Project: Hive > Issue Type: Sub-task > Components: Statistics > Affects Versions: 0.13.1 > Reporter: Mostafa Mokhtar > Assignee: Prasanth J > Fix For: 0.14.0 > > Attachments: HIVE-7990.1.patch, HIVE-7990.2.patch, HIVE-7990.3.patch > > > For queries with rollup and cube the number of rows calculation in GroupByStatsRule should be multiplied by number of elements in grouping set. > A side effect of this defect is that reducers will under estimate data size and end up with small number of tasks which negatively affects query runtime. > {code} > // since we do not know if hash-aggregation will be enabled or disabled > // at runtime we will assume that map-side group by does not do any > // reduction.hence no group by rule will be applied > // map-side grouping set present. if grouping set is present then > // multiply the number of rows by number of elements in grouping set > if (gop.getConf().isGroupingSetsPresent()) { > int multiplier = gop.getConf().getListGroupingSets().size(); > // take into account the map-side parallelism as well, default is 1 > multiplier *= mapSideParallelism; > newNumRows = multiplier * stats.getNumRows(); > long dataSize = multiplier * stats.getDataSize(); > stats.setNumRows(newNumRows); > stats.setDataSize(dataSize); > for (ColStatistics cs : colStats) { > if (cs != null) { > long oldNumNulls = cs.getNumNulls(); > long newNumNulls = multiplier * oldNumNulls; > cs.setNumNulls(newNumNulls); > } > } > } else { > // map side no grouping set > newNumRows = stats.getNumRows() * mapSideParallelism; > updateStats(stats, newNumRows, true); > } > > {code} > Query > {code} > select * > from (select i_category > ,i_class > ,i_brand > ,i_product_name > ,d_year > ,d_qoy > ,d_moy > ,s_store_id > ,sumsales > ,rank() over (partition by i_category order by sumsales desc) rk > from (select i_category > ,i_class > ,i_brand > ,i_product_name > ,d_year > ,d_qoy > ,d_moy > ,s_store_id > ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales > from store_sales > ,date_dim > ,store > ,item > where store_sales.ss_sold_date_sk=date_dim.d_date_sk > and store_sales.ss_item_sk=item.i_item_sk > and store_sales.ss_store_sk = store.s_store_sk > and d_month_seq between 1193 and 1193+11 > and ss_sold_date between '1999-06-01' and '2000-05-31' > group by i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id with rollup)dw1) dw2 > where rk <= 100 > order by i_category > ,i_class > ,i_brand > ,i_product_name > ,d_year > ,d_qoy > ,d_moy > ,s_store_id > ,sumsales > ,rk > limit 100 > {code} > Plan generated , note the data size for Map 1 > {code} > 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 5 (BROADCAST_EDGE), Map 6 (BROADCAST_EDGE), Map 7 (BROADCAST_EDGE) > Reducer 2 <- Map 1 (SIMPLE_EDGE) > Reducer 3 <- Reducer 2 (SIMPLE_EDGE) > Reducer 4 <- Reducer 3 (SIMPLE_EDGE) > DagName: mmokhtar_20140903154848_7cf1519f-e95c-47ab-9f10-6d2130cd5734:1 > Vertices: > Map 1 > Map Operator Tree: > TableScan > alias: store_sales > filterExpr: (((ss_sold_date_sk is not null and ss_store_sk is not null) and ss_item_sk is not null) and ss_sold_date BETWEEN '1999-06-01' AND '2000-05-31') (type: boolean) > Statistics: Num rows: 110339135 Data size: 4817453454 Basic stats: COMPLETE Column stats: NONE > Filter Operator > predicate: ((ss_sold_date_sk is not null and ss_store_sk is not null) and ss_item_sk is not null) (type: boolean) > Statistics: Num rows: 13792392 Data size: 602181687 Basic stats: COMPLETE Column stats: NONE > Map Join Operator > condition map: > Inner Join 0 to 1 > condition expressions: > 0 {ss_sold_date_sk} {ss_item_sk} {ss_store_sk} {ss_quantity} {ss_sales_price} {ss_sold_date} > 1 {d_date_sk} {d_month_seq} {d_year} {d_moy} {d_qoy} > keys: > 0 ss_sold_date_sk (type: int) > 1 d_date_sk (type: int) > outputColumnNames: _col0, _col2, _col7, _col10, _col13, _col23, _col27, _col30, _col33, _col35, _col37 > input vertices: > 1 Map 6 > Statistics: Num rows: 15171632 Data size: 662399872 Basic stats: COMPLETE Column stats: NONE > Map Join Operator > condition map: > Inner Join 0 to 1 > condition expressions: > 0 {_col0} {_col2} {_col7} {_col10} {_col13} {_col23} {_col27} {_col30} {_col33} {_col35} {_col37} > 1 {s_store_sk} {s_store_id} > keys: > 0 _col7 (type: int) > 1 s_store_sk (type: int) > outputColumnNames: _col0, _col2, _col7, _col10, _col13, _col23, _col27, _col30, _col33, _col35, _col37, _col58, _col59 > input vertices: > 1 Map 5 > Statistics: Num rows: 16688796 Data size: 728639872 Basic stats: COMPLETE Column stats: NONE > Map Join Operator > condition map: > Inner Join 0 to 1 > condition expressions: > 0 {_col0} {_col2} {_col7} {_col10} {_col13} {_col23} {_col27} {_col30} {_col33} {_col35} {_col37} {_col58} {_col59} > 1 {i_item_sk} {i_brand} {i_class} {i_category} {i_product_name} > keys: > 0 _col2 (type: int) > 1 i_item_sk (type: int) > outputColumnNames: _col0, _col2, _col7, _col10, _col13, _col23, _col27, _col30, _col33, _col35, _col37, _col58, _col59, _col90, _col98, _col100, _col102, _col111 > input vertices: > 1 Map 7 > Statistics: Num rows: 18357676 Data size: 801503872 Basic stats: COMPLETE Column stats: NONE > Filter Operator > predicate: (((((_col0 = _col27) and (_col2 = _col90)) and (_col7 = _col58)) and _col30 BETWEEN 1193 AND 1204) and _col23 BETWEEN '1999-06-01' AND '2000-05-31') (type: boolean) > Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE > Select Operator > expressions: _col102 (type: string), _col100 (type: string), _col98 (type: string), _col111 (type: string), _col33 (type: int), _col37 (type: int), _col35 (type: int), _col59 (type: string), _col13 (type: float), _col10 (type: int) > outputColumnNames: _col102, _col100, _col98, _col111, _col33, _col37, _col35, _col59, _col13, _col10 > Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE > Group By Operator > aggregations: sum(COALESCE((_col13 * _col10),0)) > keys: _col102 (type: string), _col100 (type: string), _col98 (type: string), _col111 (type: string), _col33 (type: int), _col37 (type: int), _col35 (type: int), _col59 (type: string), '0' (type: string) > mode: hash > outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 > Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE > Reduce Output Operator > key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: string) > sort order: +++++++++ > Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: string) > Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE > value expressions: _col9 (type: double) > Map 5 > Map Operator Tree: > TableScan > alias: store > filterExpr: s_store_sk is not null (type: boolean) > Statistics: Num rows: 212 Data size: 405680 Basic stats: COMPLETE Column stats: COMPLETE > Filter Operator > predicate: s_store_sk is not null (type: boolean) > Statistics: Num rows: 212 Data size: 22048 Basic stats: COMPLETE Column stats: COMPLETE > Reduce Output Operator > key expressions: s_store_sk (type: int) > sort order: + > Map-reduce partition columns: s_store_sk (type: int) > Statistics: Num rows: 212 Data size: 22048 Basic stats: COMPLETE Column stats: COMPLETE > value expressions: s_store_id (type: string) > Execution mode: vectorized > Map 6 > 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: 730480 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: 730480 Basic stats: COMPLETE Column stats: COMPLETE > value expressions: d_month_seq (type: int), d_year (type: int), d_moy (type: int), d_qoy (type: int) > Execution mode: vectorized > Map 7 > Map Operator Tree: > TableScan > alias: item > filterExpr: i_item_sk is not null (type: boolean) > Statistics: Num rows: 48000 Data size: 68732712 Basic stats: COMPLETE Column stats: COMPLETE > Filter Operator > predicate: i_item_sk is not null (type: boolean) > Statistics: Num rows: 48000 Data size: 18672000 Basic stats: COMPLETE Column stats: COMPLETE > Reduce Output Operator > key expressions: i_item_sk (type: int) > sort order: + > Map-reduce partition columns: i_item_sk (type: int) > Statistics: Num rows: 48000 Data size: 18672000 Basic stats: COMPLETE Column stats: COMPLETE > value expressions: i_brand (type: string), i_class (type: string), i_category (type: string), i_product_name (type: string) > Execution mode: vectorized > Reducer 2 > Reduce Operator Tree: > Group By Operator > aggregations: sum(VALUE._col0) > keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: int), KEY._col5 (type: int), KEY._col6 (type: int), KEY._col7 (type: string), KEY._col8 (type: string) > mode: mergepartial > outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 > Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE > Select Operator > expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col9 (type: double) > outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8 > Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE > Reduce Output Operator > key expressions: _col0 (type: string), _col8 (type: double) > sort order: +- > Map-reduce partition columns: _col0 (type: string) > Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE > TopN Hash Memory Usage: 0.04 > value expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: double) > Reducer 3 > Reduce Operator Tree: > Extract > Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE > PTF Operator > Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE > Filter Operator > predicate: (_wcol0 <= 100) (type: boolean) > Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE > Select Operator > expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: double), _wcol0 (type: int) > outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 > Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE > Reduce Output Operator > key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: double), _col9 (type: int) > sort order: ++++++++++ > Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE > TopN Hash Memory Usage: 0.04 > Reducer 4 > Reduce Operator Tree: > Select Operator > expressions: KEY.reducesinkkey0 (type: string), KEY.reducesinkkey1 (type: string), KEY.reducesinkkey2 (type: string), KEY.reducesinkkey3 (type: string), KEY.reducesinkkey4 (type: int), KEY.reducesinkkey5 (type: int), KEY.reducesinkkey6 (type: int), KEY.reducesinkkey7 (type: string), KEY.reducesinkkey8 (type: double), KEY.reducesinkkey9 (type: int) > outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 > Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE > Limit > Number of rows: 100 > Statistics: Num rows: 100 Data size: 4300 Basic stats: COMPLETE Column stats: NONE > File Output Operator > compressed: false > Statistics: Num rows: 100 Data size: 4300 Basic stats: COMPLETE Column stats: NONE > 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 > Execution mode: vectorized > Stage: Stage-0 > Fetch Operator > limit: 100 > Processor Tree: > ListSink > {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332)