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Preview of Oracle Database 12 c In-Memory Option. Thomas Kyte http://asktom.oracle.com/.

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preview of oracle database 12 c in memory option
Preview of Oracle Database 12cIn-Memory Option

Thomas Kyte

http://asktom.oracle.com/

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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.

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Flip Flops

Core

1993 ~$25/mb; $26,214,400/tb

2014 ~$0.007/mb; $7,645/tb

ICs on board

SIMMs

DIMMS

Big Drives

Floppy

Small Drives

Flash

SSD

oracle database in memory option goals
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

optimizing transaction and query performance
Optimizing Transaction and Query Performance

Row Format Databases versus Column Format Databases

SALES

Row

  • Transactions run faster on row format
    • Fast for processing few rows, many columns
    • Example: Insert or query a sales order

ORDER

SALES

Column

  • Analytics run faster on column format
    • Fast for processing few columns, many rows
    • Example: Report on sales totals by state

STATE

Until Now You Choose One Format and Suffer Tradeoffs

breakthrough dual format in memory database
Breakthrough: Dual Format In-Memory Database

Memory

Memory

  • BOTH row and column in-memory formats forsame table
  • Simultaneously active and transactionally consistent
  • Analytics & reporting use New Column format
  • OLTP usesrow format

Sales

Sales

Analytics

OLTP

Column Format

RowFormat

Sales

oracle in memory columnar technology
Oracle In-Memory Columnar Technology
  • 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

Memory

Pure Columnar

find any business data in sub second
Find Any Business Data in Sub-second

Example: Find all sales in state of CA

  • Each CPU scans local in-memory columns
  • Scans use super fast SIMD vector instructions
  • Billions of rows/secscan rate per CPU core

In-Memory Column Store

State column

Sales

>100X Faster

“CA”

SIMD Compare all values in 1 cycle

Compare all values in 1 instruction

Load multiple State values

Vector

Register

CPU

scans and combines data from multiple tables
Scans and Combines Data from Multiple Tables

Example: Find all sales in outlet stores

  • Converts join processing into fast column scans
  • Joins up to 10x faster

Sales

Stores

Storeid in 15,38,64

STOREID

AMOUNT

TYPE

Sum

Type=outlet

oltp is slowed down by analytic indexes
OLTP is Slowed Down by Analytic 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!

1 to 3

OLTP

Indexes

5 to 15 Analytics

Indexes

Table

column store replaces analytic indexes
Column Store Replaces Analytic Indexes

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

Table

In-Memory

Column Store

extreme capacity and cost effectiveness
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 FlashSpeed of DRAM

Hottest Data

DRAM

PCI FLASH

Active Data

DISK

Cold Data

scale out in memory database to any size
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

In MemoryColumn Store

In MemoryColumn Store

In MemoryColumn Store

In MemoryColumn Store

scale up for maximum in memory performance
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
oracle in memory is trivial to deploy
Oracle In-Memory is Trivial to Deploy
  • Configure Memory Capacity
    • inmemory_size = XXXX GB
  • Configure tables or partitions to be in memory
    • alter table | partition … inmemory;
  • Later Drop analytic indexes to speed up OLTP
oracle in memory is transparent to applications
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

And All Other Apps that Support Oracle Database

Uniquely Achieves All In-Memory Benefits With No Application Changes

oracle in memory delivers extreme availability
Oracle In-Memory Delivers Extreme Availability

Data Guard & GoldenGate

  • 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

summary oracle database in memory option
Summary: Oracle Database In-Memory Option
  • 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