Class-Leading In-Memory Technology4)In... · In-Memory Database Technology Facts • Next Generation Enterprises must be real-time –In-Memory is essentialfor real-time processing

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |

Oracle In-Memory Processing

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Tirthankar LahiriVice President Data and In-Memory Technologies Oracle Database

Class-Leading In-Memory Technology

@dbinmemory


Safe Harbor StatementThe 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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Database Platform of the Future – Complete and Integrated

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REST{JSON}


In-Memory Database Technology Facts

• Next Generation Enterprises must be real-time

– In-Memory is essential for real-time processing

– In-Memory is essential in next-gen database platforms

• Current Memory sizes enable In-Memory today–Oracle X7-2 server: Up to 1.5TB of DRAM

– Not using in-memory is like using Windows 98 in 2018

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The Time is NOW

Real-Time Enterprise


In-Memory Across Tiers in the Oracle Database Stack• TimesTen In-Memory Database– Latency Critical custom OLTP applications– Microsecond response time– Standalone or Cache for Oracle Database

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Application

SALES

RowFormat

ColumnFormat

SALESDatabase-Tier

Storage-Tier

Application-Tier

SALES• In-Memory on Exadata Storage – In-memory column format on Exadata Flash Cache– 5-10x faster smart scan in storage – 15x increase in total columnar capacity

• Database In-Memory– Dual Format In-Memory Database– Billions of Rows/sec analytic data processing– 2-3x Faster Mixed Workloads


Relational Database– Pure in-memory– ACID compliant– Standard SQL– Entire database in DRAM

Persistent and Recoverable– Database and Transaction logs

persisted on local disk or flash storage

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Extremely Fast– Microseconds response time– Very high throughput

Highly Available– Active-Standby and multi-master

replication – Very high performance parallel

replication

Oracle TimesTen In-Memory Database


Application-Tier Database Cache for Oracle DatabaseTelco ServicesFinancial Services

Real-Time Analytics –Dashboard, Scorecard

Data MarteCommerce,

Personalization

TimesTen Application Tier Cache

• Cache subset of Oracle Database tables in TimesTen for better response time – With full persistence to local storage

• Read-write caching – Transaction execution and persistence in TimesTen

• Read-only caching– Transactions executed in Oracle Database

• HA and fault tolerance in the application-tier

ApplicationApplicationApplication

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 8

New Release: TimesTen Scaleout In-Memory Database

Single Image In-Memory Database

Proven TimesTen technology with Scale-Out for High Performance, and Multi-Copy for High Availability

• For High-Velocity Extreme OLTP applications– IOT, trading, mobile, click stream, billing, orders, fraud, etc.

• Performance-Oriented Design – Pure In-Memory, Full SQL, Full ACID Transactions– Fault-Tolerant Scale-Out• All nodes active for read/writes• Multiple data copies for HA

– Sophisticated and Parallel SQL for reporting and batch

• Extremely easy to install and operate – Less than 15 mins to install, deploy, and run application


Scale-Out Shared Nothing In-Memory Database

• Appears as a single DB to applications- Not a sharded database

• Adding and removing DB nodes (elements)- Data automatically redistributed - Workload automatically uses new nodes

• Built-in HA via fully-active node copies– Node copies automatically kept in sync

• Highly compatible with Oracle (subset)- Data types, SQL & PLSQL, Oracle Call Interface

Single-Image DB with High Availability and Elasticity

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A

A

B

BC

C

D

D


Workload: • YCSB version 0.15.0• 1KB record

(100-byte x 10 Fields)• 100M records / Replica Set• Uniform Distribution

Configuration• Oracle Bare Metal Cloud• 32 x DenseIO.52 hosts • Oracle Linux x86 64-bit • Intel Platinum 8167M @2GHz• 25G Ethernet• NVMe disk storage

YCSB Workload A (50% Read 50% Update)

10

432.929 878.512

1.715.569

3.294.530

6.428.732

-

1.000.000

2.000.000

3.000.000

4.000.000

5.000.000

6.000.000

7.000.000

1x2 2x2 4x2 8x2 16x2

Tran

sact

ions

per

Sec

ond

TimesTen Scaleout Configuration


YCSB Workload B (95% Read 5% Update)

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2.772.366 5.505.610

10.661.407

20.466.127

38.154.715

-

5.000.000

10.000.000

15.000.000

20.000.000

25.000.000

30.000.000

35.000.000

40.000.000

45.000.000

1x2 2x2 4x2 8x2 16x2

Tran

sact

ions

per

Sec

ond






YCSB Workload C (100% Read)

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5.112.763 9.854.235

19.831.099

37.646.889

65.440.298

-

10.000.000

20.000.000

30.000.000

40.000.000

50.000.000

60.000.000

70.000.000

1x2 2x2 4x2 8x2 16x2

Tra

nsa

ctio

ns

pe

r S

eco

nd






Linear and Massive Transaction Scalability

5.003.22510.213.637

19.803.43639.244.125

76.167.743

144.211.475

0

20.000.000

40.000.000

60.000.000

80.000.000

100.000.000

120.000.000

140.000.000

160.000.000

0 10 20 30 40 50 60 70

SQL Transactions/secWorkload• TPTBM telco workload• Shipped with TimesTen for

10+ years• 80% read / 20% write

workload

Configuration• Oracle Bare Metal Cloud• 64 x BM.HighIO1.36• Oracle Linux x86 64-bit• E5-2699 v3 @ 2.30GHz• 10G Ethernet• NVMe Disk storage

# Elements

TPS


Linear and Massive Query Scalability

39.241.44678.705.103

154.432.274

308.344.070

616.833.030

1.236.703.781

0

200.000.000

400.000.000

600.000.000

800.000.000

1.000.000.000

1.200.000.000

1.400.000.000

0 10 20 30 40 50 60 70

1.2 Billion SQL Selects/secWorkload• TPTBM telco workload• Shipped with TimesTen for

10+ years• 100% read workload

Configuration• Oracle Bare Metal Cloud• 64 x BM.HighIO1.36• Oracle Linux x86 64-bit • E5-2699 v3 @ 2.30GHz• 10G Ethernet• NVMe Disk storage

# Elements

TPS


In-Memory Across Tiers

• TimesTen In-Memory Database– Latency Critical custom OLTP applications

– Microsecond response time

– Standalone or Cache for Oracle Database

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Application

SALES

RowFormat

ColumnFormat

SALESDatabase-Tier

Storage-Tier

Application-Tier

SALES

• In-Memory on Exadata Storage – In-memory column format on Exadata Flash Cache

– 5-10x faster smart scan in storage

– 15x increase in total columnar capacity

• Database In-Memory– Dual Format In-Memory Database

– Billions of Rows/sec analytic data processing

– 2-3x Faster Mixed Workloads


Until Now Must Choose One Format and Suffer Tradeoffs

Row Format Databases vs. Column Format Databases

Row

§ Transactions run faster on row format

– Example: Insert or query a sales order– Fast processing for few rows, many columns

Column

§ Analytics run faster on column format

– Example : Report on sales totals by region– Fast accessing few columns, many rows

SALES

SALES

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What is a Controversy?

“A discussion marked especially by the expression of opposing views”

Merriam Webster


Decades Long Controversy in Database Systems

Row FormatColumn Format

“A discussion marked especially by the expression of opposing views”

Merriam Webster


Oracle Database In-Memory: Dual Format Architecture

• BOTH row and column formats for same table

• Simultaneously active and consistent

• OLTP uses existing row format

• Analytics uses new In-Memory Column format

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Existing Buffer Cache

New In-MemoryFormat

SALES SALES

RowFormat

ColumnFormat


In-Memory Columnar Format

• Pure in-memory column format• Cheap to maintain – no logging or IO• Allows efficient OLTP

• No changes to disk format• Transparent to Applications

• Can be enabled for any subset of database• Partitions of tables• Tables• Tablespaces• Full database if needed

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SALES

Pure In-Memory Columnar


Vector Processing: Additional Advantage of Column Format

• Each CPU core scans only required columns

• SIMD vector instructions used to process multiple values in each instruction • E.g. Intel AVX instructions

• Billions of rows/sec scan rate per CPU core• Row format is millions/sec

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Vect

or R

egist

erLoadmultipleregion values

VectorCompare all valuesin 1 instruction

CPU

Memory

CA

CACA

CA

Example:Find all salesin state of CA

> 100x Faster

STAT

E


Joining and Combining Data Also Dramatically Faster

• Converts joins of data in multiple tables into fast filtered column scans

• Joins tables 10x faster

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Example: Find total sales in outlet stores

SalesStores

Stor

e ID

StoreID in15, 38, 64

Type=‘Outlet’

Type

Sum

Stor

e ID

Amou

nt


Generates Reports Instantly

• Dynamically creates in-memory report outline

• Then report outline filled-in during fast fact scan

• Reports run 10x faster• Without predefined cubes

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Example: Report sales of footwear in outlet stores

Sales

Stores

ProductsIn-Memory

Report Outline

Footwear

Out

lets

$$$

$$$$

Footwear

SalesOutlets


Scale-Out In-Memory Database to Any Size

• Scale-Out across servers to grow memory and CPUs

• In-Memory queries parallelized across servers to access local column data

• Direct-to-wire InfiniBand protocol speeds messaging on Engineered Systems

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In-Memory Across Tiers

• TimesTen In-Memory Database– Latency Critical custom OLTP applications

– Microsecond response time

– Standalone or Cache for Oracle Database

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Application

SALES

RowFormat

ColumnFormat

SALESDatabase-Tier

Storage-Tier

Application-Tier

SALES

• In-Memory on Exadata Storage – In-memory column format on Exadata Flash Cache

– 5-10x faster smart scan in storage

– 15x increase in total columnar capacity

• Database In-Memory– Dual Format In-Memory Database

– Billions of Rows/sec analytic data processing

– 2-3x Faster Mixed Workloads


Exadata: The Ultimate Database Platform

• Ideal Database Hardware - Scale-out, database optimized compute, networking, and storage

• Smart Database Software – Specialized algorithms for Analytics, OLTP, and Consolidation • Full-Stack Automation – Automation and

optimization of configuration, updates, performance, resource management

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DB Servers

InfiniBand

Storage Servers


Exadata: Transparent Storage TieringIn-Memory Speed + Flash Bandwidth + Disk Capacity

DISK

NVMe FLASH

DRAM

Cold Data

Hottest Data

Active Data

• Size not limited by memory

• Data transparently accessed across tiers

• Each tier has specialized algorithms & compression

• Simultaneously Achieve:

• Speed of DRAM

• I/Os of Flash

• Cost of Disk

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Exadata Achieves Memory Performance with Shared Flash

• Exadata X7 delivers 400GB/sec flash bandwidth– Approaches 800GB/sec aggregate DRAM bandwidth of DB servers

• Must move compute to data to achieve full flash potential– Push query processing into storage tier– Requires owning full stack, can’t be solved in storage or DB alone

• Flash storage arrays scale flash capacity but not performance– Even with next gen scale-out, PCIe networks, or NVMe over fabric

• Shared storage with memory-level bandwidth is a paradigm change in the industry– Get near DRAM throughput, with the capacity of shared flash

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ExadataDB Servers

Exadata Smart Storage

InfiniBand

CPU

PCIeNVMe

Flash Chips

QueryOffload


In-Memory Columnar Format in Columnar Flash Cache

• In-Memory format in Smart Columnar Flash

– Enables SAME in-memory optimizations on data in Exadata flash as available on Exadata DB compute nodes DRAM

• Extends in-memory from DB compute nodes to Storage nodes

– 15x Columnar Capacity (100s of TB possible on full rack X7)

– New in-memory format – offloaded queries 10x faster

• Does not require any user intervention

– Powers the new Autonomous Data Warehouse

In-Memory Columnar scans

In-FlashColumnar scans

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Summary: In-Memory is a Cross-Tier Technology • TimesTen In-Memory Database– Latency Critical custom OLTP applications– Microsecond response time– Standalone or Cache for Oracle Database

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Application

SALES

RowFormat

ColumnFormat

SALESDatabase-Tier

Storage-Tier

Application-Tier

SALES• In-Memory on Exadata Storage – In-memory column format on Exadata Flash Cache– 5-10x faster smart scan in storage – 15x increase in total columnar capacity

• Database In-Memory– Dual Format In-Memory Database– Billions of Rows/sec analytic data processing– 2-3x Faster Mixed Workloads


The Forrester WaveTM: In-Memory Databases, Q1 2017

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Oracle In-Memory DatabasesScored Highest by Forrester

on both Current Offering and Strategy

http://www.oracle.com/us/corporate/analystreports/forrester-imdb-wave-2017-3616348.pdf

The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.

http://www.oracle.com/us/corporate/analystreports/forrester-imdb-wave-2017-3616348.pdf


§ Data Driven – Rapidly make decisions based on real-time data

§ Agile – Respond quickly to change

§ Efficient – Continually improve processes and profitability

Real-Time Enterprise

AGILE

EFFICIENT

DATA-DRIVEN AGILE

In-Memory Enables the Real Time Enterprise


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Class-Leading In-Memory Technology4)In... · In-Memory Database Technology Facts • Next Generation Enterprises must be real-time –In-Memory is essentialfor real-time processing