#1290. Oracle Core: InMemory Column Performing Databases GmbH Mitterteich / Germany

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2 #1290 Oracle Core: InMemory Column Store Martin Klier Performing Databases GmbH Mitterteich / Germany 2/40

3 Speaker Martin Klier Solution Architect and Database Expert My focus Performance Optimization High Availability Architecture DBMS Linux since 1997 Oracle Database since /40

4 Speaker Meet & Greet Regionalgruppen Fachkonferenzen April 2016 Contact: Weblog: (English) 4/40

5 Conference + Exhibition #DOAG Visitors Nuremberg, Germany November /40

6 Performing Databases Experts for Database Technology Concept Planning & Sizing Licensing Implementation and Troubleshooting Get in touch Performing Databases GmbH Wiesauer Straße Mitterteich, GERMANY Web: Twitter: 6/40

7 International Design Licensing Implementation Tuning Troubleshooting Service Upgrade Migration 7/40

8 Agenda Let's refresh Oracle Architecture Row- and Column based data System architecture w/ InMemory Column Store How it works Reading Transactions and consistency Usability and Limitations 8/40

9 Basics 9/40

10 Oracle Architecture (simplified) SGA Listener :1521 Server Server Server Shared Pool PGAs Buffer Cache Log Buffer PMON CKPT LGWR SMON DBWn ARCn TEMP Online Tablespace Undo Tbs. Redo Logs Archived Redo Logs Blocks 10/40

11 Rows & Columns 11/40

12 Row Data SELECT * WHERE ID IN (3,36). ID NAME1 NAME2 STREET ZIP CITY 2 John Doe 2 Wilbury Way Wesley 3 Alice Allison 108 Agora Alley Amberg 4 Sally Salinger 17 Samson St Salisbury Bob Bobson 42 Century Ct Taccanooga 12/40

13 Row Data ID NAME1 NAME2 STREET ZIP CITY 2 John Doe 2 Wilbury Way Wesley 3 Alice Allison 108 Agora Alley Amberg 4 Sally Salinger 17 Samson St Salisbury 8 Bob Bobson 42 Century Ct Taccanooga Header / ITL Block / Buffer Row Store 13/40

14 Reading a Row Buffer SGA select. ; Listener :1521 Server Shared Pool Sort, Hash, Merge Buffer Cache PGAs TEMP Tablespace Blöcke 14/40

15 Reading a Row SGA Listener :1521 Server Buffer Cache Maximum 1,000,000 rows per second by design. (Tirthankar Lahiri, Oracle VP) 15/40

16 Buffer Cache Access Cache Buffer Chains: Is this block in the BC? Chains Latches CBC 4F BH 1 BH 77 CBC 51 BH 99 BH 32 Buffer Headers (references in Shared Pool) 16/40

17 Buffer Cache Access Locks the chain and looks for a buffer Session 1 CBC 4F BH 1 BH 77 CBC 51 BH 99 BH 32 Spin Session 2 Same or diff. Buffer (Chain), same latch :( 17/40

18 Rows & Columns 18/40

19 Columnar Data SELECT ID. WHERE ZIP.; SELECT COUNT(*). WHERE ZIP.; ID NAME1 NAME2 STREET ZIP CITY 2 John Doe 2 Wilbury Way Wesley 3 Alice Allison 108 Agora Alley Amberg 4 Sally Salinger 17 Samson St Salisbury Bob Bobson 42 Century Ct Taccanooga 19/40

20 Columnar Data ID NAME1 NAME2 STREET ZIP CITY 2 John Doe 2 Wilbury Way Wesley 3 Alice Allison 108 Agora Alley Amberg 4 Sally Salinger 17 Samson St Salisbury IMCU ROWID ID ZIP City AABBCC Wesley BBCCDD Amberg DDEEFF Salisbury Column Compression Units (CU) 20/40

21 In-Memory Architecture 21/40

22 In-Memory Population SGA In-Memory Compression Unit (1MB) Buffer Cache In-Memory Area IMCO W001 W002 Tablespace IMCU- or 1MB pool In-Memory Coordinator Process OrderPriority per table Blocks critical medium low high none (=on demand?) 22/40

23 In-Memory Data Change SGA x Server Snapshot Metadata Unit (64k) Buffer Cache In-Memory Area SMU- or 64kB pool IMCU Tablespace Blocks 23/40

24 Lesevorgang Columnar SGA x Server Buffer Cache SMU Sort, Hash, Merge? PGAs In-Memory Area IMCU Optimizations! Tablespace Blocks 24/40

25 In-Memory RePopulation SGA x Buffer Cache SMU In-Memory Area IMCO W001 W002 IMCU When? Tablespace Blocks Threshold per IMCU Trickle (constant) 25/40

26 InMemory Area IMCU SMU IMCU- or 1MB pool SMU- or 64kB pool Segregated in 1MB-Pool (IMCUs) 64k-Pool (SMUs) In-Memory Area NO Least-Recently-Used-Mechanism (LRU) First come, first serve! => Think: Tablespace not Cache 26/40

27 Using Column Store 27/40

28 With Or Without You 28/40

29 When and When Not 29/40

30 Operation Pushdown I 30/40

31 Operation Pushdown II 31/40

32 Optimizations Vector Processing (Single Instruction Multiple Data SIMD) z.b. Intel AVX / 256bit Register Picture: Wikimedia >1,000,000,000 rows per second (Tirthankar Lahiri, Oracle VP) 32/40

33 Optimizations Operation Pushdown: InMemory Scan (*) (like Exadata Smart Scan) Predicate check in Column Store (*) Aggregation during Scan Bloom Filter in IMCU Scan (*) In-Memory Storage Index (Skip unneeded IMCUs during In-Memory Scan) Predicate Optimization (if an IMCU is needed as a whole, the predicate is not checked for each row) 33/40

34 Finish run 34/40

35 Advantages IM Column St. Faster for columnar operations (obviously) Optimizer uses Shortcuts in the architecture Fully transparent, mixed operations are normal OLAP OLTP 35/40

36 Not fully deterministic: Advantage depends heavily on Workload Content Condition of data yo u Content maintenance => Advisor! w Ressources RAM CPU for Compression Kn o rn ee ds! Disadvantages IMC Store Scalability in RAC crazy and w/o redundancy (available in Exadata only) 36/40

37 InMemory ist cool. Cool is not enough. You need a UseCase. #FiveWordTechHorrors InMemory solves all performance problems 37/40

38 Q&A Download Presentation and Whitepaper 38/40

39 Conference + Exhibition #DOAG Visitors Nuremberg, Germany November /40

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