Real-World Performance Training Exadata and Database In-Memory

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2 Real-World Performance Training Exadata and Database In-Memory Real-World Performance Team

3 Agenda The DW/BI Death Spiral Parallel Execution Loading Data Exadata and Database In-Memory Dimensional Queries

4 Agenda 1 2 Exadata Database In-Memory

5 Exadata X5-2 Components Database Servers X5-2 Database Server Infiniband Network High-Capacity Storage Server Exadata Storage Extreme Flash Storage Server 5

6 Exadata Storage Server Smart Scan Database Server filter rows Storage Server Smart Flash Cache Flash Cards Hybrid Columnar Compression (HCC) HCC 6

7 Agenda 1 2 Exadata Database In-Memory

8 Oracle Database In-Memory: Dual Format Database Date Store Customer Product Price Row Store Format Date 1/1/02 3/4/12 8/1/10 7/4/13 Store StoreZ StoreA StoreM StoreP BOTH row and column formats for same table Simultaneously active and transactionally consistent Queries use column format OLTP uses proven row format Customer Product Price Cust5 Cust6 Cust1 CustP Prod4 Prod4 ProdX Prod Column Store Format 8

9 Why is Database In-Memory Fast? Row Format vs. Columnar Format Row directory SQL> select col1, col3, col7 from tab where.. 1) Read block header to find pointer to first row Row data Row 1 COL1 COL2 COL3 COL4 COL5 COL6 COL7 COL8 2) Find address of 1 st row 3) Read length of 1 st column 4) Read data in 1 st column Many CPU instructions Poor CPU efficiency Row 3 Row 5 Row 6 Continue until all rows in all blocks are read 5) Repeat until you ve processed the row through COL7 6) Hop back up to the row directory to find next row 7) Repeat steps 2-6 Extra data and many instructions required

10 Vector Register Why is Database In-Memory Fast? Row Format vs. Column Format Column 1 1) Process column header Column Data Column 2 dogcatbirdbirdfishcatmouse CPU Load multiple Col1/Col 2 values SIMD Compare Compare all all values values in 1 in few cycle instructions SQL> select col1, col3, col7 from tab where.. Column Data 2) Process column data, or column vector Few CPU instructions High CPU efficiency Pipeline only columns we need to CPU & use SIMD

11 Why is Database In-Memory Fast? Row Format vs. Column Format Row SALES ORDER Every row stores data for each column To access Nth column, data from columns #1 through (N-1) need to be processed Column SALES S T A T E To access Nth column, only data for Nth column needs to be read Columns processed as vectors; the entire contents of column are processed as a vector Data organized by column and accessed via columnar vector processing

12 Why is Database In-Memory Fast? In-Memory Min/Max Pruning Example: Find sales from stores with a store_id of 8 or higher Memory Min 1 Max 3 Min 4 Max 7 Each column is the made up of multiple in-memory column units Min / max value is recorded for each column unit SALES Column Format Min 8 Max 12 Min 13 Max 15 Min /max pruning provides additional filtering You have to walk before you can run! Do everything else right first, then worry about ways to cluster data

13 VECTOR REGISTER PROMO_ID Why is Database In-Memory Fast? Memory Example: Find all sales With PROMO_ID 9999 Each CPU core scans local in-memory columns Uses SIMD vector instructions CPU Load multiple PROMO_ID values Vector Compare all values an 1 cycle Can evaluate up to Billions of rows/sec per core (Row format is millions/sec)

14 Identifying In-Memory Table Scan Optimizer is aware Cost model adapted to consider INMEMORY scan New access method TABLE ACCESS IN MEMORY FULL

15 Identifying In-Memory Table Scan NEW session/system level statistics Best way to determine if In-Memory was used Best way to measure the benefits of In-Memory scan

16 DateKey Amount Date DateKey Joins and Database In-Memory Example: Find all orders placed on Christmas eve DATE_DIM LINEORDER Bloom filters used Datekey is Tried and true technology originally released in 10g Type=d.d_date='December 24, 2013' Same technique used to offload joins on Exadata Sum It s really a filter

17 Database In-Memory: Where It Helps

18 Database In-Memory: Where It Doesn t Help

19 Database In-Memory: Where It Really Doesn t Help

20 Database In-Memory Basics Fast for scans and predicate evaluation Queries must perform FULL table scans Doesn t help for index access methods Doesn t help for queries bound by sort/aggregate Know where your problem is! SQL*Monitor will tell you (and by the way, this is also true for Exadata)

21 Reference Section

22 Using Database In-Memory Validate In-Memory Configuration Configure Table(s) for the In-Memory Column Store Populate the In-Memory Column Store Validate Population Status Additional Information

23 Validate In-Memory Column Store Configuration SQL> show parameter inmemory_size Controlled by inmemory_size parameter NAME TYPE VALUE inmemory_size big integer 85G sga_target must be large enough to accommodate SQL> show sga Total System Global Area Fixed Size Variable Size Database Buffers Redo Buffers E+11 bytes bytes E+10 bytes bytes bytes Static pool In-Memory Area E+10 bytes This will have already been configured in your hands-on databases

24 Using Database In-Memory Validate In-Memory Configuration Configure Table(s) for the In-Memory Column Store Populate the In-Memory Column Store Validate Population Status Additional Information

25 Configure Table for In-Memory Column Store SQL> alter table lineorder 2 inmemory memcompress for query 3 duplicate all; Table altered. Use MEMCOMPRESS FOR QUERY for performance Use DUPLICATE ALL to ensure extents are loaded in all RAC instances on Exadata

26 Using Database In-Memory Validate In-Memory Configuration Configure Table(s) for the In-Memory Column Store Populate the In-Memory Column Store Validate Population Status Additional Information

27 Populate the In-Memory Column Store SQL> select /*+ full */ count(*) 2 from lineorder; Query the table for on-demand population Ensure the optimizer is choosing a full scan

28 Validate Population Status Validate In-Memory Configuration Configure Table(s) for the In-Memory Column Store Populate the In-Memory Column Store Validate Population Status Additional Information

29 Validate Population Status set lines 150 column name format a30 col pname format a30 column owner format a20 column segment_name format a30 column populate_status format a20 column bytes_not_populated format 999,999,999, set echo on Query GV$IM_SEGMENTS Look for populate_status= COMPLETED and bytes_not_populated=0 SELECT v.inst_id,v.owner, v.segment_name name, v.partition_name pname, v.populate_status status, v.bytes_not_populated FROM gv$im_segments v order by 3,1,2 /

30 Validate Population Status Validate In-Memory Configuration Configure Table(s) for the In-Memory Column Store Populate the In-Memory Column Store Validate Population Status Additional Information

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