Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 Preview of Oracle Database 12 c In-Memory Option Thomas Kyte

Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. 2

3 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 3 Flip Flops Core ICs on board DIMMS SIMMs SSD Flash Small Drives Floppy Big Drives 1993 ~$25/mb; $26,214,400/tb 2014 ~$0.007/mb; $7,645/tb

4 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Oracle Database In-Memory Option Goals 100X Faster Queries: Real-Time Analytics  Get instantaneous query results  Querying OLTP database or data warehouse Faster Transaction Processing Trivial to Deploy for All Applications and Customers 4

5 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Until Now You Choose One Format and Suffer Tradeoffs Optimizing Transaction and Query Performance Row Format Databases versus Column Format Databases Row  Transactions run faster on row format – Fast for processing few rows, many columns – Example: Insert or query a sales order Column  Analytics run faster on column format – Fast for processing few columns, many rows – Example: Report on sales totals by state ORDER SALES STATESTATE 5

6 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved.  BOTH row and column in-memory formats for same table  Simultaneously active and transactionally consistent  Analytics & reporting use New Column format  OLTP uses row format  BOTH row and column in-memory formats for same table  Simultaneously active and transactionally consistent  Analytics & reporting use New Column format  OLTP uses row format Breakthrough: Dual Format In-Memory Database Column Format Memory Row Format Memory Analytics OLTP Sales 6

7 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Oracle In-Memory Columnar Technology Memory Pure Columnar  Pure in-memory format with no logging  Near zero overhead on changes  Even for OLTP  Uses memory-optimized compression  2x to 10x memory reduction  Data loaded in-memory for active tables or partitions - on startup or first access  For in-memory tables, >90% of memory will be used for column format  Row format needs little memory  Pure in-memory format with no logging  Near zero overhead on changes  Even for OLTP  Uses memory-optimized compression  2x to 10x memory reduction  Data loaded in-memory for active tables or partitions - on startup or first access  For in-memory tables, >90% of memory will be used for column format  Row format needs little memory 7

8 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Find Any Business Data in Sub-second SIMD Compare all values in 1 cycle Compare all values in 1 instruction Load multiple State values Vector Register In-Memory Column Store State column Sales Example: Find all sales in state of CA “CA” >100X Faster Each CPU scans local in-memory columns  Scans use super fast SIMD vector instructions  Billions of rows/sec scan rate per CPU core Each CPU scans local in-memory columns  Scans use super fast SIMD vector instructions  Billions of rows/sec scan rate per CPU core CPU 8

9 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Scans and Combines Data from Multiple Tables Sales Stores Type=outlet Example: Find all sales in outlet stores TYPETYPE Storeid in 15,38,64 STOREIDSTOREID AMOUNTAMOUNT  Converts join processing into fast column scans  Joins up to 10x faster  Converts join processing into fast column scans  Joins up to 10x faster Sum 9

10 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. OLTP is Slowed Down by Analytic Indexes Table 1 to 3 OLTP Indexes 5 to 15 Analytics Indexes  Most Indexes in mixed-use OLTP (e.g. ERP) databases are only used for analytics  Indexes work well for known access patterns both in-memory and on-disk  But every change to the table requires changing all analytic indexes – Slow!  Most Indexes in mixed-use OLTP (e.g. ERP) databases are only used for analytics  Indexes work well for known access patterns both in-memory and on-disk  But every change to the table requires changing all analytic indexes – Slow! 10

11 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Column Store Replaces Analytic Indexes Table 1 to 3 OLTP Indexes  In-Memory Column Store replaces analytic indexes for tables that fit in memory  Removes analytic index overhead on changes  Both predefined and ad-hoc analytic queries run fast  Less tuning & admin needed  OLTP & batch often run 2x or more faster  In-Memory Column Store replaces analytic indexes for tables that fit in memory  Removes analytic index overhead on changes  Both predefined and ad-hoc analytic queries run fast  Less tuning & admin needed  OLTP & batch often run 2x or more faster In-Memory Column Store 11

12 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Demonstration at Oracle Openworld Performance of columnar scan vs. row scan - Both in memory

13 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Extreme Capacity and Cost Effectiveness  Size not limited by memory  Data transparently accessible across tiers  Each tier has specialized algorithms & compression Capacity of Disk IOs of Flash Speed of DRAM  Size not limited by memory  Data transparently accessible across tiers  Each tier has specialized algorithms & compression Capacity of Disk IOs of Flash Speed of DRAM DISK PCI FLASH DRAM Cold Data Hottest Data Active Data 13

14 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Scale-Out In-Memory Database to Any Size  Scale-Out across servers to grow memory and CPUs  In-Memory queries are parallelized across servers to access local column data  Direct-to-wire InfiniBand protocol speeds messaging  Scale-Out across servers to grow memory and CPUs  In-Memory queries are parallelized across servers to access local column data  Direct-to-wire InfiniBand protocol speeds messaging In Memory Column Store 14

15 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Scale-Up for Maximum In-Memory Performance  Scale-Up on large SMPs  SMP scaling removes overhead of distributing queries across servers or coordinating transactions  Inter-processor bandwidth far exceeds any network  Scale-Up on large SMPs  SMP scaling removes overhead of distributing queries across servers or coordinating transactions  Inter-processor bandwidth far exceeds any network 15

16 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Oracle In-Memory is Trivial to Deploy 1. Configure Memory Capacity  inmemory_size = XXXX GB 2. Configure tables or partitions to be in memory  alter table | partition … inmemory; 3. Later Drop analytic indexes to speed up OLTP 16

17 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Oracle In-Memory is Transparent to Applications Existing Applications Just Run Faster Full Functionality- No restrictions on SQL Trivial to Implement- No migration of data or change of product Fully Compatible- All existing applications run unchanged DB as a Service Ready- Oracle Multitenant in-memory Uniquely Achieves All In-Memory Benefits With No Application Changes And All Other Apps that Support Oracle Database 17

18 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Oracle In-Memory Delivers Extreme Availability  Pure In-Memory format does not change Oracle’s storage format, logging, backup, recovery, etc.  All Oracle’s mature availability technologies work transparently  Protection from all failures  Node, site, corruption, human error, change, etc.  Pure In-Memory format does not change Oracle’s storage format, logging, backup, recovery, etc.  All Oracle’s mature availability technologies work transparently  Protection from all failures  Node, site, corruption, human error, change, etc. RAC ASM RMAN Data Guard & GoldenGate 18

19 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Faster Queries: Real-Time Analytics  Querying OLTP database or data warehouse Increase Transaction Processing Rates Less Management and Tuning Best of Memory, Flash, Disk Scale-Out and Scale-Up Extreme Availability Trivial to Deploy for All Applications and Customers Summary: Oracle Database In-Memory Option 19

20 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

21 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.Confidential – Oracle Internal Copyright © 2013, Oracle and/or its affiliates. All rights reserved.