首页 > 其他分享 >数仓性能优化：倾斜优化-表达式计算倾斜的hint优化

数仓性能优化：倾斜优化-表达式计算倾斜的hint优化

时间：2023-08-01 14:58:23浏览次数：49  
标签：数仓 倾斜 text 69237018 Vector type PART 1MB 优化

本文分享自华为云社区《GaussDB(DWS)性能调优：倾斜优化-表达式计算倾斜的hint优化》，作者： 譡里个檔 。

1.原始SQL

SELECT

TMP4.TAX_AMT,

CATE.L1_PUR_ITEM_CATG_CN_NAME || '-' ||

CATE.L2_PUR_ITEM_CATG_CN_NAME || '-' ||

CATE.L3_PUR_ITEM_CATG_CN_NAME AS PRODUCT_CATEGORY,

MATE.ITEM_CODE AS PRODUCT_CODE,

INVEN.INVENTORY_ORG_NAME,

TMP4.INVOICE_WITHHOLDING_TAX_GROUP,

TMP4.PAYMENT_WITHHOLDING_TAX_GROUP,

TMP4.PO_CHARGE_ACCOUNT_CODE,

TMP4.CFS_INVOICE_NUMBER,

APR.TAX_INVOICE_DATE

FROM DWLTAX.DWL_TAX_TAXDP_ERP_AP_INVOICE_TMP5 TMP4,

DWRDIM_DW1.DWR_DIM_PUR_ITEM_CATEGORY_D CATE,

DWRDIM_DW1.DWR_DIM_MATERIAL_CODE_D MATE,

DWRDIM_DW1.DWR_DIM_INVENTORY_ORG_D INVEN,

DWTAXDI.DWI_AP_INVOICE_I AP,

DWTAXDI.DWI_AP_INVOICE_REGSTN_I APR

WHERE 1 = 1

AND TMP4.ITEM_CATEGORY_KEY = CATE.PUR_ITEM_CATG_KEY(+)

AND CATE.DEL_FLAG(+) = 'N'

AND TMP4.ITEM_ID = MATE.ITEM_ID(+)

AND MATE.DEL_FLAG(+) = 'N'

AND TMP4.PO_SHIPMENT_TARGET_INV_ORG_KEY = INVEN.INVENTORY_ORG_KEY(+)

AND INVEN.DEL_FLAG(+) = 'N'

AND TMP4.AP_INVOICE_ID = AP.AP_INVOICE_ID(+)

AND 6600 || AP.ATTRIBUTE1 = TO_CHAR(APR.AP_INVOICE_REGSTN_ID(+))

执行performance，查询具体执行情况和SQL自诊断信息(详细见附件case-step1-原始执行信息.txt)

id | operation | A-time | A-rows | E-rows | E-distinct | Peak Memory | E-memory | A-width | E-width | E-costs

----+------------------------------------------------------------------------------------------------------+------------------------+------------+------------+------------+----------------+----------------+-----------+---------+-------------

1 | -> Row Adapter | 69922.773 | 69237018 | 69237018 | | 87KB | | | 573 | 15160857.61

2 | -> Vector Streaming (type: GATHER) | 65581.989 | 69237018 | 69237018 | | 536KB | | | 573 | 15160857.61

3 | -> Vector Hash Right Join (4, 6) | [61186.201, 73129.055] | 69237018 | 69237018 | | [306MB, 682MB] | 1113MB(9990MB) | | 573 | 15159431.83

4 | -> Vector Streaming(type: BROADCAST ng: LC_DL1->LC_DW1) | [554.217, 21008.078] | 1382000544 | 1381572384 | 282184 | [4MB, 4MB] | 3MB | | 16 | 7056095.88

5 | -> CStore Scan on dwifin.dwi_ap_invoice_regstn s | [5.354, 11.617] | 28791678 | 28782758 | | [1MB, 1MB] | 1MB | | 16 | 28004.18

6 | -> Vector Hash Left Join (7, 19) | [1728.008, 2017.488] | 69237018 | 69237018 | 79721 | [834KB, 834KB] | 16MB | [229,252] | 578 | 1832322.90

7 | -> Vector Hash Left Join (8, 17) | [1428.799, 1925.653] | 69237018 | 69237018 | 179 | [32MB, 32MB] | 28MB(8901MB) | | 576 | 1817105.07

8 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [996.780, 1635.826] | 69237018 | 69237018 | 4167 | [1MB, 1MB] | 2MB | | 570 | 1788113.85

9 | -> Vector Hash Left Join (10, 14) | [1086.903, 1780.641] | 69237018 | 69237018 | | [173MB, 174MB] | 227MB(9067MB) | | 570 | 1304897.12

10 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [153.628, 891.680] | 69237018 | 69237018 | 20271 | [1MB, 1MB] | 2MB | | 567 | 847160.16

11 | -> Vector Hash Left Join (12, 13) | [367.155, 465.821] | 69237018 | 69237018 | | [30MB, 30MB] | 22MB(8896MB) | | 567 | 363943.43

12 | -> CStore Scan on dwltax.dwl_tax_taxdp_erp_ap_invoice_tmp5 tmp4 | [150.676, 178.827] | 69237018 | 69237018 | 526 | [4MB, 4MB] | 1MB | | 553 | 340168.44

13 | -> CStore Scan on dwrdim_dw1.dwr_dim_pur_item_category_d cate | [14.549, 24.399] | 8228448 | 8228448 | 171426 | [2MB, 2MB] | 1MB | [104,104] | 26 | 9056.99

14 | -> Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST ng: LC_DL1->LC_DW1) | [315.926, 339.782] | 117191217 | 117191170 | 2441483 | [1MB, 1MB] | 3MB | [47,47] | 22 | 406136.10

15 | -> Vector Partition Iterator | [118.307, 151.248] | 117191170 | 117191170 | | [41KB, 41KB] | 1MB | | 22 | 300641.93

16 | -> Partitioned CStore Scan on dwifin.dwi_ap_invoice s | [86.557, 111.947] | 117191170 | 117191170 | | [6MB, 6MB] | 1MB | | 22 | 300641.93

17 | -> Vector Streaming(type: PART LOCAL PART BROADCAST) | [60.429, 99.381] | 15442613 | 15442566 | 321720 | [584KB, 584KB] | 2MB | [58,58] | 19 | 49578.19

18 | -> CStore Scan on dwrdim_dw1.dwr_dim_material_code_d mate | [19.779, 33.206] | 15442566 | 15442566 | | [1MB, 2MB] | 1MB | | 19 | 35704.02

19 | -> CStore Scan on dwrdim_dw1.dwr_dim_inventory_org_d inven | [0.383, 0.739] | 135072 | 135072 | 2814 | [1MB, 1MB] | 1MB | [53,53] | 14 | 2823.85

SQL Diagnostic Information

--------------------------------------------------------------------------------------------

Execute diagnostic information

PlanNode[4] Large Table in Broadcast "Vector Streaming(type: BROADCAST ng: LC_DL1->LC_DW1)"

Predicate Information (identified by plan id)

------------------------------------------------------------------------------------------------------------------------------

3 --Vector Hash Right Join (4, 6)

Hash Cond: (((numeric_out(s.ap_invoice_regstn_id))::character varying)::text = ('6600'::text || (s.attribute1)::text))

6 --Vector Hash Left Join (7, 19)

Hash Cond: (tmp4.po_shipment_target_inv_org_key = inven.inventory_org_key)

7 --Vector Hash Left Join (8, 17)

Hash Cond: (tmp4.item_id = mate.item_id)

Skew Join Optimized by Statistic

8 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): ((tmp4.item_id = (-999999)::numeric) OR (tmp4.item_id IS NULL))

9 --Vector Hash Left Join (10, 14)

Hash Cond: (tmp4.ap_invoice_id = s.ap_invoice_id)

Skew Join Optimized by Statistic

10 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): (tmp4.ap_invoice_id = 1001113812002::numeric)

11 --Vector Hash Left Join (12, 13)

Hash Cond: (tmp4.item_category_key = cate.pur_item_catg_key)

13 --CStore Scan on dwrdim_dw1.dwr_dim_pur_item_category_d cate

Filter: ((cate.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((cate.del_flag)::text = 'N'::text)

14 --Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST ng: LC_DL1->LC_DW1)

Skew Filter(type: BROADCAST): (s.ap_invoice_id = 1001113812002::numeric)

15 --Vector Partition Iterator

Iterations: 147

16 --Partitioned CStore Scan on dwifin.dwi_ap_invoice s

Partitions Selected by Static Prune: 1..147

17 --Vector Streaming(type: PART LOCAL PART BROADCAST)

Skew Filter(type: BROADCAST): (mate.item_id = (-999999)::numeric)

18 --CStore Scan on dwrdim_dw1.dwr_dim_material_code_d mate

Filter: ((mate.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((mate.del_flag)::text = 'N'::text)

19 --CStore Scan on dwrdim_dw1.dwr_dim_inventory_org_d inven

Filter: ((inven.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((inven.del_flag)::text = 'N'::text)

2.禁止大表广播

如上小节显示确实是id=4的这一步是一个大的结果集（2879w条）做了broadcast，并且紧接着的id=5的HashJoin耗时很长。因此通过增加hint方式禁止dwifin.dwi_ap_invoice_regstn走广播。分析发现表dwifin.dwi_ap_invoice_regstn是视图apr展开出现的，因此增加如下hint信息，其中

1. no merge (apr)是防止视图apr中的语句提升，导致的hint信息失效

2. no broadcast(apr)表示禁止apr走broadcast

EXPLAIN performance

SELECT /*+ no merge (apr) no broadcast(apr) */

TMP4.TAX_AMT,

CATE.L1_PUR_ITEM_CATG_CN_NAME || '-' ||

CATE.L2_PUR_ITEM_CATG_CN_NAME || '-' ||

CATE.L3_PUR_ITEM_CATG_CN_NAME AS PRODUCT_CATEGORY,

MATE.ITEM_CODE AS PRODUCT_CODE,

INVEN.INVENTORY_ORG_NAME,

TMP4.INVOICE_WITHHOLDING_TAX_GROUP,

TMP4.PAYMENT_WITHHOLDING_TAX_GROUP,

TMP4.PO_CHARGE_ACCOUNT_CODE,

TMP4.CFS_INVOICE_NUMBER,

APR.TAX_INVOICE_DATE

FROM DWLTAX.DWL_TAX_TAXDP_ERP_AP_INVOICE_TMP5 TMP4,

DWRDIM_DW1.DWR_DIM_PUR_ITEM_CATEGORY_D CATE,

DWRDIM_DW1.DWR_DIM_MATERIAL_CODE_D MATE,

DWRDIM_DW1.DWR_DIM_INVENTORY_ORG_D INVEN,

DWTAXDI.DWI_AP_INVOICE_I AP,

DWTAXDI.DWI_AP_INVOICE_REGSTN_I APR

WHERE 1 = 1

AND TMP4.ITEM_CATEGORY_KEY = CATE.PUR_ITEM_CATG_KEY(+)

AND CATE.DEL_FLAG(+) = 'N'

AND TMP4.ITEM_ID = MATE.ITEM_ID(+)

AND MATE.DEL_FLAG(+) = 'N'

AND TMP4.PO_SHIPMENT_TARGET_INV_ORG_KEY = INVEN.INVENTORY_ORG_KEY(+)

AND INVEN.DEL_FLAG(+) = 'N'

AND TMP4.AP_INVOICE_ID = AP.AP_INVOICE_ID(+)

AND 6600 || AP.ATTRIBUTE1 = TO_CHAR(APR.AP_INVOICE_REGSTN_ID(+))

获取如上语句的performance信息(详细见附件 case-step2-禁止大表广播.txt)

id | operation | A-time | A-rows | E-rows | E-distinct | Peak Memory | E-memory | A-width | E-width | E-costs

----+---------------------------------------------------------------------------------------------------------+------------------------+-----------+-----------+------------+----------------+----------------+-----------+---------+-------------

1 | -> Row Adapter | 15685.781 | 69237018 | 69237018 | | 87KB | | | 573 | 33341721.22

2 | -> Vector Streaming (type: GATHER) | 11361.740 | 69237018 | 69237018 | | 536KB | | | 573 | 33341721.22

3 | -> Vector Hash Left Join (4, 19) | [15269.267, 18985.791] | 69237018 | 69237018 | | [74MB, 74MB] | 101MB(9984MB) | | 573 | 33340295.43

4 | -> Vector Streaming(type: REDISTRIBUTE) | [4743.867, 18632.182] | 69237018 | 69237018 | 79721 | [1MB, 2MB] | 2MB | | 578 | 29821930.76

5 | -> Vector Hash Left Join (6, 18) | [1473.990, 15359.055] | 69237018 | 69237018 | | [866KB, 898KB] | 16MB | | 578 | 1832322.90

6 | -> Vector Hash Left Join (7, 16) | [1130.814, 15223.646] | 69237018 | 69237018 | 179 | [32MB, 32MB] | 28MB(9923MB) | | 576 | 1817105.07

7 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [681.709, 14909.424] | 69237018 | 69237018 | 4167 | [1MB, 1MB] | 2MB | | 570 | 1788113.85

8 | -> Vector Hash Left Join (9, 13) | [1049.201, 12602.796] | 69237018 | 69237018 | | [173MB, 174MB] | 227MB(10089MB) | | 570 | 1304897.12

9 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [128.704, 11737.099] | 69237018 | 69237018 | 20271 | [1MB, 1MB] | 2MB | | 567 | 847160.16

10 | -> Vector Hash Left Join (11, 12) | [368.537, 443.623] | 69237018 | 69237018 | | [30MB, 30MB] | 22MB(9918MB) | | 567 | 363943.43

11 | -> CStore Scan on dwltax.dwl_tax_taxdp_erp_ap_invoice_tmp5 tmp4 | [148.366, 175.347] | 69237018 | 69237018 | 526 | [4MB, 4MB] | 1MB | | 553 | 340168.44

12 | -> CStore Scan on dwrdim_dw1.dwr_dim_pur_item_category_d cate | [13.319, 24.442] | 8228448 | 8228448 | 171426 | [2MB, 2MB] | 1MB | [104,104] | 26 | 9056.99

13 | -> Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST ng: LC_DL1->LC_DW1) | [242.053, 294.233] | 117191217 | 117191170 | 2441483 | [1MB, 1MB] | 3MB | [47,47] | 22 | 406136.10

14 | -> Vector Partition Iterator | [118.124, 154.954] | 117191170 | 117191170 | | [41KB, 41KB] | 1MB | | 22 | 300641.93

15 | -> Partitioned CStore Scan on dwifin.dwi_ap_invoice s | [86.942, 105.441] | 117191170 | 117191170 | | [6MB, 6MB] | 1MB | | 22 | 300641.93

16 | -> Vector Streaming(type: PART LOCAL PART BROADCAST) | [83.793, 117.853] | 15442613 | 15442566 | 321720 | [584KB, 584KB] | 2MB | [58,58] | 19 | 49578.19

17 | -> CStore Scan on dwrdim_dw1.dwr_dim_material_code_d mate | [21.898, 35.895] | 15442566 | 15442566 | | [1MB, 2MB] | 1MB | | 19 | 35704.02

18 | -> CStore Scan on dwrdim_dw1.dwr_dim_inventory_org_d inven | [0.389, 0.661] | 135072 | 135072 | 2814 | [1MB, 1MB] | 1MB | [53,53] | 14 | 2823.85

19 | -> Vector Streaming(type: REDISTRIBUTE ng: LC_DL1->LC_DW1) | [30.667, 49.474] | 28791678 | 28782758 | 599641 | [2MB, 2MB] | 3MB | [75,75] | 16 | 56030.49

20 | -> Vector Subquery Scan on apr | [42.087, 61.734] | 28791678 | 28782758 | | [376KB, 376KB] | 1MB | | 16 | 30826.02

21 | -> CStore Scan on dwifin.dwi_ap_invoice_regstn s | [5.177, 8.049] | 28791678 | 28782758 | | [1MB, 1MB] | 1MB | | 16 | 28004.18

SQL Diagnostic Information

----------------------------------------------------------------------------------------------------------

Execute diagnostic information

PlanNode[4] DataSkew:"Vector Streaming(type: REDISTRIBUTE)", min_dn_tuples:257082, max_dn_tuples:47206637

Predicate Information (identified by plan id)

----------------------------------------------------------------------------------------------------------------------------------

3 --Vector Hash Left Join (4, 19)

Hash Cond: ((('6600'::text || (s.attribute1)::text)) = ((numeric_out(apr.ap_invoice_regstn_id))::character varying)::text)

5 --Vector Hash Left Join (6, 18)

Hash Cond: (tmp4.po_shipment_target_inv_org_key = inven.inventory_org_key)

6 --Vector Hash Left Join (7, 16)

Hash Cond: (tmp4.item_id = mate.item_id)

Skew Join Optimized by Statistic

7 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): ((tmp4.item_id = (-999999)::numeric) OR (tmp4.item_id IS NULL))

8 --Vector Hash Left Join (9, 13)

Hash Cond: (tmp4.ap_invoice_id = s.ap_invoice_id)

Skew Join Optimized by Statistic

9 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): (tmp4.ap_invoice_id = 1001113812002::numeric)

10 --Vector Hash Left Join (11, 12)

Hash Cond: (tmp4.item_category_key = cate.pur_item_catg_key)

12 --CStore Scan on dwrdim_dw1.dwr_dim_pur_item_category_d cate

Filter: ((cate.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((cate.del_flag)::text = 'N'::text)

13 --Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST ng: LC_DL1->LC_DW1)

Skew Filter(type: BROADCAST): (s.ap_invoice_id = 1001113812002::numeric)

14 --Vector Partition Iterator

Iterations: 147

15 --Partitioned CStore Scan on dwifin.dwi_ap_invoice s

Partitions Selected by Static Prune: 1..147

16 --Vector Streaming(type: PART LOCAL PART BROADCAST)

Skew Filter(type: BROADCAST): (mate.item_id = (-999999)::numeric)

17 --CStore Scan on dwrdim_dw1.dwr_dim_material_code_d mate

Filter: ((mate.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((mate.del_flag)::text = 'N'::text)

18 --CStore Scan on dwrdim_dw1.dwr_dim_inventory_org_d inven

Filter: ((inven.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((inven.del_flag)::text = 'N'::text)

3.表达式倾斜的hint

发现自诊断信息中倾斜告警

cke_176.png

而Plan ID为4的算子是

cke_177.png

其中是s是视图dwtaxdi.dwi_ap_invoice_i展开后的表dwifin.dwi_ap_invoice，查询此表的列attribute1的统计信息如下，发现在NULL值上存在严重倾斜

cke_178.png

因为重分布列是一个表达式6600 || AP.ATTRIBUTE1，当前DWS的倾斜的hint不支持表达式，因为我们做如下变通实现表达式的值倾斜的hint

SELECT /*+ no merge (apr) no broadcast(apr) no merge(ap) skew(ap (attr1) ('6600')) */

TMP4.TAX_AMT,

CATE.L1_PUR_ITEM_CATG_CN_NAME || '-' ||

CATE.L2_PUR_ITEM_CATG_CN_NAME || '-' ||

CATE.L3_PUR_ITEM_CATG_CN_NAME AS PRODUCT_CATEGORY,

MATE.ITEM_CODE AS PRODUCT_CODE,

INVEN.INVENTORY_ORG_NAME,

TMP4.INVOICE_WITHHOLDING_TAX_GROUP,

TMP4.PAYMENT_WITHHOLDING_TAX_GROUP,

TMP4.PO_CHARGE_ACCOUNT_CODE,

TMP4.CFS_INVOICE_NUMBER,

APR.TAX_INVOICE_DATE

FROM DWLTAX.DWL_TAX_TAXDP_ERP_AP_INVOICE_TMP5 TMP4,

DWRDIM_DW1.DWR_DIM_PUR_ITEM_CATEGORY_D CATE,

DWRDIM_DW1.DWR_DIM_MATERIAL_CODE_D MATE,

DWRDIM_DW1.DWR_DIM_INVENTORY_ORG_D INVEN,

(SELECT *, 6600 || AP.ATTRIBUTE1 AS ATTR1 FROM DWTAXDI.DWI_AP_INVOICE_I AP) AP,

DWTAXDI.DWI_AP_INVOICE_REGSTN_I APR

WHERE 1 = 1

AND TMP4.ITEM_CATEGORY_KEY = CATE.PUR_ITEM_CATG_KEY(+)

AND CATE.DEL_FLAG(+) = 'N'

AND TMP4.ITEM_ID = MATE.ITEM_ID(+)

AND MATE.DEL_FLAG(+) = 'N'

AND TMP4.PO_SHIPMENT_TARGET_INV_ORG_KEY = INVEN.INVENTORY_ORG_KEY(+)

AND INVEN.DEL_FLAG(+) = 'N'

AND TMP4.AP_INVOICE_ID = AP.AP_INVOICE_ID(+)

AND ATTR1 = TO_CHAR(APR.AP_INVOICE_REGSTN_ID(+))

其中构建了子查询 AP

SELECT *, 6600 || AP.ATTRIBUTE1 AS ATTR1 FROM DWTAXDI.DWI_AP_INVOICE_I AP

在把原始的关联列表达式放到子查询里面，然后把 6600 || AP.ATTRIBUTE1 命名为attr1。

在父查询中首先禁止AP这个子查询提升。然后在父查询中通过hint 子查询AP这个结果集的列attr1存在倾斜值'6600' 。这个倾斜值是计算出来的（NULL || 6600 = ‘6600’），并且在原始关联计算中关联表达式是如下，即 6600 || AP.ATTRIBUTE1的结果被转换为text类型（字符串类型）

cke_179.png

获取新的语句的performance如下(详细见附件 case-step3-倾斜优化.txt)

id | operation | A-time | A-rows | E-rows | E-distinct | Peak Memory | E-memory | A-width | E-width | E-costs

----+------------------------------------------------------------------------------------------------------+-----------------------+-----------+-----------+------------+----------------+----------------+-----------+---------+------------

1 | -> Row Adapter | 9045.793 | 69237018 | 69237018 | | 87KB | | | 573 | 2040755.71

2 | -> Vector Streaming (type: GATHER) | 4842.656 | 69237018 | 69237018 | | 520KB | | | 573 | 2040755.71

3 | -> Vector Hash Left Join (4, 21) | [2673.707, 11389.688] | 69237018 | 69237018 | | [1MB, 1MB] | 16MB | | 573 | 2039329.92

4 | -> Vector Hash Left Join (5, 19) | [1951.482, 10931.220] | 69237018 | 69237018 | 179 | [32MB, 32MB] | 28MB(10018MB) | | 571 | 2009687.71

5 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [1541.777, 10591.702] | 69237018 | 69237018 | 4167 | [1MB, 1MB] | 2MB | | 565 | 1980696.49

6 | -> Vector Hash Left Join (7, 18) | [1703.438, 1980.655] | 69237018 | 69237018 | | [30MB, 30MB] | 22MB(10010MB) | | 565 | 1497479.76

7 | -> Vector Hash Left Join (8, 10) | [1523.277, 1708.622] | 69237018 | 69237018 | 526 | [165MB, 166MB] | 191MB(10151MB) | | 551 | 1473704.77

8 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [94.501, 203.619] | 69237018 | 69237018 | 20271 | [1MB, 1MB] | 2MB | | 553 | 823385.17

9 | -> CStore Scan on dwltax.dwl_tax_taxdp_erp_ap_invoice_tmp5 tmp4 | [142.734, 171.486] | 69237018 | 69237018 | | [4MB, 4MB] | 1MB | | 553 | 340168.44

10 | -> Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST ng: LC_DL1->LC_DW1) | [811.192, 853.583] | 117191217 | 117191170 | 2441483 | [2MB, 2MB] | 3MB | [44,44] | 17 | 598718.74

11 | -> Vector Hash Left Join (12, 15) | [340.998, 790.399] | 117191170 | 117191170 | | [39MB, 39MB] | 27MB(10015MB) | | 17 | 493224.57

12 | -> Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN) | [53.170, 79.836] | 117191170 | 117191170 | 79721 | [2MB, 2MB] | 3MB | | 41 | 412662.90

13 | -> Vector Partition Iterator | [145.450, 171.527] | 117191170 | 117191170 | | [41KB, 41KB] | 1MB | | 22 | 303514.27

14 | -> Partitioned CStore Scan on dwifin.dwi_ap_invoice s | [112.099, 134.193] | 117191170 | 117191170 | | [6MB, 6MB] | 1MB | | 22 | 300641.93

15 | -> Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST) | [48.632, 99.230] | 28791678 | 28782758 | 282184 | [2MB, 2MB] | 3MB | [75,75] | 16 | 56928.04

16 | -> Vector Subquery Scan on apr | [41.916, 78.189] | 28791678 | 28782758 | | [376KB, 376KB] | 1MB | | 16 | 30826.02

17 | -> CStore Scan on dwifin.dwi_ap_invoice_regstn s | [5.233, 10.667] | 28791678 | 28782758 | | [1MB, 1MB] | 1MB | | 16 | 28004.18

18 | -> CStore Scan on dwrdim_dw1.dwr_dim_pur_item_category_d cate | [12.065, 20.667] | 8228448 | 8228448 | 171426 | [2MB, 2MB] | 1MB | [104,104] | 26 | 9056.99

19 | -> Vector Streaming(type: PART LOCAL PART BROADCAST) | [67.272, 97.378] | 15442613 | 15442566 | 321720 | [584KB, 584KB] | 2MB | [58,58] | 19 | 49578.19

20 | -> CStore Scan on dwrdim_dw1.dwr_dim_material_code_d mate | [18.605, 31.713] | 15442566 | 15442566 | | [1MB, 2MB] | 1MB | | 19 | 35704.02

21 | -> CStore Scan on dwrdim_dw1.dwr_dim_inventory_org_d inven | [0.378, 0.647] | 135072 | 135072 | 2814 | [1MB, 1MB] | 1MB | [53,53] | 14 | 2823.85

Predicate Information (identified by plan id)

----------------------------------------------------------------------------------------------------------------------------------

3 --Vector Hash Left Join (4, 21)

Hash Cond: (tmp4.po_shipment_target_inv_org_key = inven.inventory_org_key)

4 --Vector Hash Left Join (5, 19)

Hash Cond: (tmp4.item_id = mate.item_id)

Skew Join Optimized by Statistic

5 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): ((tmp4.item_id = (-999999)::numeric) OR (tmp4.item_id IS NULL))

6 --Vector Hash Left Join (7, 18)

Hash Cond: (tmp4.item_category_key = cate.pur_item_catg_key)

7 --Vector Hash Left Join (8, 10)

Hash Cond: (tmp4.ap_invoice_id = s.ap_invoice_id)

Skew Join Optimized by Statistic

8 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): (tmp4.ap_invoice_id = 1001113812002::numeric)

10 --Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST ng: LC_DL1->LC_DW1)

Skew Filter(type: BROADCAST): (s.ap_invoice_id = 1001113812002::numeric)

11 --Vector Hash Left Join (12, 15)

Hash Cond: ((('6600'::text || (s.attribute1)::text)) = ((numeric_out(apr.ap_invoice_regstn_id))::character varying)::text)

Skew Join Optimized by Hint

12 --Vector Streaming(type: PART REDISTRIBUTE PART ROUNDROBIN)

Skew Filter(type: ROUNDROBIN): ((('6600'::text || (s.attribute1)::text)) = '6600'::text)

13 --Vector Partition Iterator

Iterations: 147

14 --Partitioned CStore Scan on dwifin.dwi_ap_invoice s

Partitions Selected by Static Prune: 1..147

15 --Vector Streaming(type: PART REDISTRIBUTE PART BROADCAST)

Skew Filter(type: BROADCAST): ((((numeric_out(apr.ap_invoice_regstn_id))::character varying)::text) = '6600'::text)

18 --CStore Scan on dwrdim_dw1.dwr_dim_pur_item_category_d cate

Filter: ((cate.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((cate.del_flag)::text = 'N'::text)

19 --Vector Streaming(type: PART LOCAL PART BROADCAST)

Skew Filter(type: BROADCAST): (mate.item_id = (-999999)::numeric)

20 --CStore Scan on dwrdim_dw1.dwr_dim_material_code_d mate

Filter: ((mate.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((mate.del_flag)::text = 'N'::text)

21 --CStore Scan on dwrdim_dw1.dwr_dim_inventory_org_d inven

Filter: ((inven.del_flag)::text = 'N'::text)

Pushdown Predicate Filter: ((inven.del_flag)::text = 'N'::text)

 

点击关注，第一时间了解华为云新鲜技术~

标签：数仓,倾斜,text,69237018,Vector,type,PART,1MB,优化
From： https://www.cnblogs.com/huaweiyun/p/17596470.html

相关文章

  • 理解MySQL——索引与优化
    写在前面：索引对查询的速度有着至关重要的影响，理解索引也是进行数据库性能调优的起点。考虑如下情况，假设数据库中一个表有10^6条记录，DBMS的页面大小为4K，并存储100条记录。如果没有索引，查询将对整个表进行扫描，最坏的情况下，如果所有数据页都不在内存，需要读取10^4个页面，如果这10^4个页......
  • SQL优化系列之 in与range 查询
    《高性能MySQL》 里面提及用in这种方式可以有效的替代一定的range查询，提升查询效率，因为在一条索引里面，range字段后面的部分是不生效的（ps.需要考虑ICP）。MySQL优化器将in这种方式转化成 n*m 种组合进行查询，最终将返回值合并，有点类似union但是更高效。MySQL在IN()组合条件过多......
  • 关于各种优化
    发现有时候还是会对各种优化比较混乱。决策单调性对于\(i\)来说，其决策点为\(j\)，那么对于\(i'\gei\)，其决策点\(j'\gej\)。不存在依赖的可以直接分治，否则需要使用单调栈。决策点的单调性对于\(i\)来说，对于决策点\(j\)和\(j'\)，如果存在\(j\lej'\)，那么点\(j\)......
  • 数仓优劣指标化判断
    如何评价数仓的优劣，众说纷纭，其实数仓的优劣评价可以从内部、外部两个方面来评估，也可以从业务角度和技术层面来看。评价的理论很多，实际上我们可通过osm的指标体系来衡量数仓的优劣。O:数仓优劣判断;S:数据监控、元数据管理、业务流程的理解、预先计算好的中间表或者应用表;......
  • Java面试题 P28：数据库篇：MySql篇-MySql优化-索引-什么是索引？索引的底层数据结构是什么？
    什么是索引：索引（index）是帮助MySql高效获取数据的数据结构（有序）。在数据之外，数据库还维护着满足特定查找算法的数据结构（B+树），这些数据结构以某种方式引用（指向）数据，这样就可以在这些数据结构上实现高级查找算法，这种数据结构就是索引。 ......
  • Sychronized 原理，锁升级优化
    Java对象头以32位虚拟机为例普通对象所以以Integer和int为例子Integer8字节对象头+4字节int值，所以大小是int的3倍int4字节int值数组对象如Student[]s=newStudent[8]，还包括数组长度length其中markword结构为MarkWord被设计成一个非固定的......
  • Java面试题 P27：数据库篇：MySql篇-MySql优化-Sql语句执行很慢，如何分析呢？
       ......
  • Java面试题 P26：数据库篇：MySql篇-MySql优化-如何定位慢查询？
          ......
  • mysql优化--索引
    mysql优化--索引Mysql索引大概有五种类型：普通索引(INDEX)：最基本的索引，没有任何限制唯一索引(UNIQUE)：与"普通索引"类似，不同的就是：索引列的值必须唯一，但允许有空值。主键索引(PRIMARY)：它是一种特殊的唯一索引，不允许有空值。全文索引(FULLTEXT)：可用于MyISAM表，mysql5.6之后也可......
  • 深入理解Java虚拟机（JVM）：原理、结构与性能优化
    1.介绍Java虚拟机（JVM）是Java程序的核心执行引擎，负责将Java源代码编译成可执行的字节码，并在运行时负责解释执行字节码或将其编译成本地机器代码。本文将深入探讨JVM的原理、结构以及性能优化的相关技术。2.JVM原理与结构2.1JVM运行时数据区域JVM运行时数据区域由以下几部分组......

赞助商

阅读排行

网站主页关于我们联系我们网站地图

本网站内容转载自其他媒体，侵权联系[admin##ips99.com]。

Copyright © 2020-2023 IPS99 版权所有 IPS99