Oracle Database in-Memory Overivew

Oracle Database In-Memory
Overview
• Maria Colgan
• Master Product Manager
• Mission Critical Database Technologies
• April 2017
Copyright © 2019, Oracle and/or its affiliates. All rights reserved.

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
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.
Safe Harbor Statement

What is Database
In-Memory
3

Oracle Database In-Memory Goals
4
Real-Time Analytics Trivial to Implement
No Application Changes
Not Limited by Memory
100X
Accelerate Mixed
Workload
AnalyticsTransactions
Run analytics on
Operational Systems
Enable Real-Time
Business Decisions
Real-Time Analytics
100X
Risk-Free
Proven Scale-Out,
Availability, Security

Row Format Databases vs. Column Format Databases
Rows Stored
Contiguously
Transactions run faster on row format
– Example: Query or Insert a sales order
– Fast processing few rows, many columns
Columns
Stored
Contiguously
Analytics run faster on column format
– Example : Report on sales totals by region
– Fast accessing few columns, many rows
SALES
SALES
5
Until Now Must Choose One Format and Suffer Tradeoffs
QueryQuery
Query

Breakthrough: Dual Format Database
• BOTH row and column
formats for same table
• Simultaneously active and
transactionally consistent
• Analytics & reporting use new
in-memory Column format
• OLTP uses proven row format
6
Buffer Cache
New In-Memory
Column Store
SALES SALES
Row
Format
Column
Format
SALES

Oracle In-Memory Columnar Technology
• Pure in-memory column format
• Not persistent, and no logging
• Quick to change data: fast OLTP
• Enabled at table or partition
• Only active data in-memory
• 2x to 20x compression typical
• Available on all hardware platforms
7
SALES
SALES
Pure In-Memory Columnar

Orders of Magnitude Faster Analytic Data Scans
• Each CPU core scans local
in-memory columns
• Scans use super fast SIMD
vector instructions
• Originally designed for
graphics & science
• Billions of rows/sec scan
rate per CPU core
• Row format is millions/sec
8
Memory
Example:
Find sales in
California region
> 100x Faster
VectorRegister
Load
multiple
region
values
Vector
Compare
all values
an 1 cycle
CPU
CA
CA
CA
CA
REGION

• Speed of memory
• Scan and Filter only
the needed Columns
• Vector Instructions
Improvements to All Aspects of Analytic Query
9
Data Scans In-Memory Aggregation
•Create In-Memory
Report Outline that is
Populated during Fast Scan
•Runs Reports Instantly
Joins
•Convert Star Joins into 10X
Faster Column Scans
•Search large table for values
that match small table
HASH JOIN
Table A Table B
VectorRegister
Load
multiple
region
values
Vector
Compare
all values
an 1 cycle
CPU
CA
CA
CA
CA

• Complex OLTP is Slowed by Analytic Indexes
• Inserting one row into a table requires updating
10-20 analytic indexes: Slow!
• Column Store Replaces Analytic Indexes
• Fast analytics on any columns
• Column Store not persistent so update cost
is much lower
10
Database In-Memory Accelerates Mixed Workloads
Table
1 – 3
OLTP
Indexes
10 – 20
Analytic
Indexes
REPLACE

Database In-Memory Scales to Any Size
11
Scale-Up
•Scale-Up on large SMPs
•NUMA Optimized
•Scale-Out Across Servers to
Grow Memory and CPUs
•In-Memory Queries
Parallelized Across Servers
Scale-Out Combine with Flash and Disk
•Easily place data on most
cost effective tier
•Simultaneously Achieve:
•Speed of DRAM
•I/Os of Flash
•Cost of Disk
DISK
Cold Data
DRAM
Hottest
Data
PCI FLASH
Active
Data

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 12
RAC
ASM
RMAN
Data Guard & GoldenGate
• Pure In-Memory format does not
change Oracle’s storage format,
logging, backup, recovery, etc.
• All Oracle’s proven availability
technologies work transparently
• Protection from all failures
• Node, site, corruption, human error, etc.
Database In-Memory: Industrial Strength Availability

• Similar to storage mirroring
• Duplicate in-memory
columns on another node
• Enabled per table/partition
• E.g. only recent data
• Application transparent
• Downtime eliminated by
using duplicate after failure
13
Only Available on Engineered Systems
Database In-Memory: Unique Fault Tolerance

Database In-Memory: Trivial to Implement
14
Full Functionality
No SQL restrictions
100% Compatible
No application
changes
No data migration
Easy to Deploy Easy to Use
No complex setup
1. Set column store size
2. Declare In-Memory tables

What’s New
15

SPARC M7 Software in Silicon
• Traditional DB algorithms too complex for chips
• Big Change: In-memory algorithms are much simpler
• 5 years ago Oracle initiated a revolutionary project
–Build fastest ever microprocessor
• Most processing cores (32) and concurrent threads (256)
• Fastest Memory Bandwidth (160 GB/sec)
–Add In-Memory DB operations directly on chip
• Only high-volume CPU with native SQL optimizations
16

Database Algorithms Natively Implemented in SPARC CPU
17
Software-in-Silicon adds revolutionary new capabilities:
• SQL in Silicon - Database In-Memory Acceleration Engines (DAX)
• Capacity in Silicon - Real-time decompression of in-memory data
• Security in Silicon - Fine-grained low-overhead memory protection

SQL in Silicon: Database In-Memory Acceleration Engines
• SIMD Vectors instructions are fast, but were designed for
graphics, not database
• New SPARC M7 chip has 32 optimized database
acceleration engines (DAX) built on chip
• Independently process streams of columns
– E.g. find all values that match ‘California’
– Up to 170 Billion rows per second!
• Like adding 32 additional specialized cores to chip
– Using less than 1% of chip space
18
Core
Shared Cache
Core Core Core
DB
Accel
DB
Accel
DB
Accel
DB
Accel
SPARC M7

Capacity in Silicon: Decompression Engines
• Compression is key to putting more data in-memory
• Decompression is far more import for databases than compression
– Data is loaded once, queried many times
• Bit pattern decompression in normal cores is slow
– 64 CPU cores needed to decompress at full memory speed
• SPARC M7 adds 32 optimized decompress engines
– Run bit-pattern decompress at memory speed
19
Doubles Memory
Capacity

Silicon Secured Memory: Fine Grained Memory Protection
• Database In-memory places terabytes of data in memory
– More vulnerable to corruption by bugs/attacks than storage
• SPARC M7 locks memory as it is allocated so only the owner
can access it
– Hidden “color” bits added to pointers (key), and content (lock)
– Pointer color (key) must match content color or program is aborted
– Hardware support eliminates performance impact
• Helps prevent access off end of structure, stale pointer access,
malicious attacks, etc. plus improves developer productivity
20
Memory
Pointers
Memory
Content
STOP

What’s New in 12.2
21

What’s new in 12.2 for Database In-Memory
2X Faster Joins
5X Faster Expressions
Real-Time Analytics Automation
Dynamic Data
Movement Between
Storage & Memory
Massive Capacity
In-Memory on
Exadata Flash
Mixed Workload
Active Data
Guard Support
Multi-model
Native support for
JSON Data type
22
{}
JSON
Column

• Analytic queries have complex joins
with no filter predicates specified
• Join Group specifies columns used to
join tables
– Column share compression dictionary
– Joins occur on dictionary values rather
than data
• Enables 2-3x faster join processing
Real-Time Analytics: Faster In-Memory Joins
Sales
VEHICLENAME
Example: Find sales price of each Vehicle
SalesVehicle
NAME
is join
column
NAME
CREATE INMEMORY JOIN GROUP V_name_jg
(VEHICLES(NAME),SALES(NAME));
23

• Analytic queries contain complex
expressions
– Originally evaluated for every row
• Expressions pre-computed and
cached in-memory
–User defined via virtual columns
–Or expressions automatic detected
• All In-Memory optimizations apply
• 3-5x faster complex queries
Real-Time Analytics: In-Memory Expressions
Net = Price + Price * Tax
In-Memory Column Store
Sales
Example: Compute total sales price
Tax
Price
24
Price
Price+PriceXTaxPrice+Price*Tax

• Real-time analytics with no impact
on production database
• Make productive use of standby
database resources
• Can populate different data from
production database
– Use new DISTRIBUTE BY SERVICE to
determine where to populate a table
– Increase total columnar capacity
1 Month
In-Memory
Mixed Workload: In-Memory on Active Data Guard
Production
25
Standby
1 Year
In-Memory

Massive Capacity: IMC Format in Columnar Flash Cache
• In-Memory format now used in Smart Columnar Flash Cache
– Enables in-memory optimizations on data in Exadata flash
– E.g. multiple column values evaluated in single vector
instruction
• In-memory performance seamlessly extended from DB node
DRAM memory to 10x larger flash in storage
– Huge advantage over all-flash arrays and other in-memory DBs
In-Memory
Columnar scans
26
In-Flash
Columnar scans

• Full JSON documents populated using
an optimized binary format
• Additional expressions can be
created on JSON columns (e.g.
JSON_VALUE) & stored in column store
• Queries on JSON content or
expressions automatically directed to
In-Memory format
• E.g. Find movies where movie.name
contains “Jurassic”
• 60x performance gains observed
Multi-Model Analytics: In-Memory JSON
Relational
In-Memory Colum Store
In-Memory
Virtual Columns
In-Memory
JSON Format
{
"Theater":"AMC 15",
"Movie":”Sully",
"Time“:2016-09-09T18:45:00",
"Tickets":{
"Adults":2
}
}
Relational Virtual JSON
27

• Heat map tracks data access frequency
• Policies can be defined to
• Bring data into the IM column store
• Increase compression levels as data cools
• Evict cold data from IM column store
28
Sales_Q3
Sales_Q2
Sales_Q4
Sales _Q1
Automation: In-Memory Data Auto Population Policies

Population Performance: In-Memory Fast-Start
• IM column format persisted to storage
• In-Memory column store contents
checkpointed to secure file lob on
populate
• When DB restarts population is faster
as population process reads the
column format directly from storage
• Faster restore (2-5x) of column store
since no need to reformat data
DBFILE1
Table
Index Table
Table
Index
DBFILE2SALES
TABLESPACE
FAST START
TABLESPACE
Fast Start
Data
29
In-Memory
Column Store
Buffer
Cache

When and How
Should I Use
In-Memory
30

Getting The Most From In-Memory
• Fast cars speed up travel, not meetings
• In-Memory speeds up analytic data access, not:
– Network round trips, logon/logoff
– Parsing, PL/SQL, complex functions
– Data processing (as opposed to access)
• Complex joins or aggregations where not much data is filtered before processing
– Load and select once – Staging tables, ETL, temp tables
Understand Where it Helps
31
Know your bottleneck!

Getting The Most From In-Memory
• Avoid stop and go traffic
– Process data in sets of rows in the Database
– Not one row at a time in the application
• Plan ahead, take shortest route
– Help the optimizer help you: Gather representative statistics using DBMS_STATS
• Use all your cylinders
– Enable parallel execution
– In-Memory removes storage bottlenecks allowing parallelism to increase
The Driver Matters
32

• Star-schema and pre-calculated KPIs
• Improves performance of dash-boards
• All or a subset of Foundation Layer
• For time-sensitive analytics on 3rd normal form
• Staging/ETL/Temp not good candidates
• Write once, read once
33
Where to use In-Memory
ODS
ETL
SALES
ReportingOLTP System
• Enables real-time reporting directly on OLTP data
• Speeds data extraction part of ETL process
• Removes need for separate ODS
Reporting
Data Warehouse
Foundation LayerStaging Layer Performance Layer
STAR SCHEMA
Pre-Cal KPIs
3rd Normal Form

Where can I get
more information
34

Join the Conversation
35
https://twitter.com/db_inmemory
https://blogs.oracle.com/in-memory/
Related White Papers
• Oracle Database In-Memory White Paper
•Oracle Database In-Memory Aggregation Paper
• When to use Oracle Database In-Memory
• Oracle Database In-Memory Advisor
Related Videos
• In-Memory YouTube Channel
• Managing Oracle Database In-Memory
• Database In-Memory and Oracle Multitenant
• Industry Experts Discuss Oracle Database In-Memory
• Software on Silicon
Any Additional Questions
• Oracle Database In-Memory Blog
https://www.facebook.com/OracleDatabase
http://www.oracle.com/goto/dbim.html
Additional
Resources
https://sqlmaria.com

Q & A
If you have more questions later, feel free to ask
36

Oracle Database in-Memory Overivew

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