12c In Memory Management - Saurabh Gupta

Oracle Database 12c In-Memory

Saurabh K. GuptaDatabase Product Management

Who am I?

• Principal Technologist, Database Product Management• Author of “Oracle Advanced PL/SQL

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |

• Author of “Oracle Advanced PL/SQL Developer Professional Guide”, Technical Reviewer, blogger• Experience in Oracle Databases, design, development and administration, High Availability solutions, Exadata

3

Accelerate OLTPReal Time

Analytics

No Changes to

Applications

Exploit latest

generation

hardware

Oracle Database In-Memory Goals


CPU100x 2x

Become a Real-Time Enterprise

• Data-Driven

• Rapidly make decisions based on real-time data

• Agile

AGILEDATA-

DRIVEN


• Agile

• Respond quickly to change

• Efficient

• Continually improve processes and profitability

5

EFFICIENT

Row Format Databases vs. Column Format Databases

Row

� Transactions run faster on row format

– Example: Insert or query a sales order

– Fast processing few rows, many columns

SALES


Column

� Analytics run faster on column format

– Example : Report on sales totals by region

– Fast accessing few columns, many rows

SALES

6

Row Format Databases vs. Column Format Databases

Row

� Insert a new sales order on row format

– One row insert = FAST

SALES

INSERT

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 7

Column

� Insert a new sales order in Column Format

– Many column inserts = S L O W

StoresSALES

INSERT

INSERTINSERT

INSERT

Until Now Must Choose One Format and Suffer Tradeoffs

Breakthrough: Dual Format Database

• BOTH row and column formats for same table

• Simultaneously active and transactionally consistent

Memory Memory

SALES SALES


transactionally consistent

• Analytics & reporting use new in-memory Column format

• OLTP uses proven row format

8

SALES SALES

RowFormat

ColumnFormat

OLTP is Slowed Down by Analytic Indexes

Insert rate decreases as

number of indexes

increases


Column Store Replaces Analytic Indexes

• Fast analytics on any columns

• Better for unpredictable analytics

• Less tuning & administration

Table

1 – 3 OLTP

IndexesIn-Memory

Column Store


• Less tuning & administration

• Column Store not persistent so update cost is much lower

• OLTP & batch run faster

10

Oracle In-Memory Requires Zero Application Changes

Full Functionality - No restrictions on SQL

Easy to Implement - No migration of data

Fully Compatible - All existing applications run unchanged

Fully Multitenant - Oracle In-Memory is Cloud Ready


Fully Multitenant - Oracle In-Memory is Cloud Ready

Uniquely Achieves All In-Memory Benefits With No Application Changes

11

� Configuring the In-Memory Column Store

� Populating the In-Memory Column Store

� Querying the In-Memory Column Store

Program Agenda



� Monitoring the In-Memory Column Store

System Global Area SGA

Buffer Cache Shared Pool Redo Buffer

Configuring : In-Memory Column Store


Buffer Cache

Large PoolOther shared

Memory

Components

In-Memory

Area

Configuring : In-Memory Column Store

• Controlled by INMEMORY_SIZEparameter

•Minimum size of 100MB

• SGA_TARGET must be large

SELECT * FROM V$SGA;

NAME VALUE------------------ ---------


• SGA_TARGET must be largeenough to accommodate

• Static Pool

------------------ ---------Fixed Size 2927176Variable Size 570426808Database Buffers 4634022912Redo Buffers 13848576In-Memory Area 1024483648




Program Agenda




Oracle In-Memory Columnar Technology

• Pure in-memory column format

• Not persistent, and no logging

• Quick to change data: fast OLTP

• 2x to 20x compression

Pure In-Memory Columnar


• 2x to 20x compression

• Enabled at table or partition level

• Available on all hardware platforms

SALES

Populating : In-Memory Column Store

• Populate is the term used to bring data into the In-Memory column store

• Populate is used instead of load because load is commonly used to mean inserting new data into the database

• Populate doesn’t bring new data into the database, it brings existing data into


• Populate doesn’t bring new data into the database, it brings existing data into memory and formats it in an optimized columnar format

• Population is completed by a new set of background processes

–ORA_W001_orcl

–Number of processes controlled by INMEMORY_MAX_POPULATE_SERVERS

17


• New INMEMORY ATTRIBUTE

• Following segment types are

eligible

• Tables

• Partitions

ALTER TABLE sales INMEMORY;

ALTER TABLE sales NO INMEMORY;


• Partitions

• Subpartition

• Materialized views

• Following segment types not

eligible

• IOTs

• Hash clusters

• Out of line LOBs

CREATE TABLE customers …… PARTITION BY LIST(PARTITION p1 …… INMEMORY,(PARTITION p2 …… NO INMEMORY); Pure OLTP

Features


• Possible to populate only

certain columns from a table or

partition

• Order in which objects are

populated controlled by

ALTER TABLE sales INMEMORYNO INMEMORY (PROD_ID);


populated controlled by

PRIORITY subclause

• Critical, high, medium, low

• Default – none (populate on

first access)

• Does not control the speed of

population

CREATE TABLE orders (c1 number,c2 varchar(20),c3 number)

INMEMORY PRIORITY CRITICALNO INMEMORY (c1);


• Objects compressed during

population

• New compression techniques

• Focused on scan performance

ALTER MATERIALIZED VIEW mv1 INMEMORY MEMCOMPRESS FOR QUERY;


• Controlled by MEMCOMPRESS

subclause

• Multiple levels of compression

• Possible to use a different level for

different partitions in a table

CREATE TABLE trades (Name varchar(20),Desc varchar(200))

INMEMORYMEMCOMPRESS FOR DML(desc);


• Different levels

• FOR DML

Use on tables or partitions with

very active DML activity

• FOR QUERY

CREATE TABLE ORDERS …… PARTITION BY RANGE ……(PARTITION p1 ……INMEMORY NO MEMCOMPRESSPARTITION p2 ……

INMEMORY MEMCOMPRESS FOR DML,


• FOR QUERY

Default mode for most tables

• FOR CAPACITY

For less frequently accessed

segments

• Easy to switch levels as part of

ILM strategy

INMEMORY MEMCOMPRESS FOR DML,PARTITION p3 ……

INMEMORY MEMCOMPRESS FOR QUERY,:

PARTITION p200 …… INMEMORY MEMCOMPRESS FOR CAPACITY);

Identifying : Tables With INMEMORY Attribute

• New INMEMORY column in

*_TABLES dictionary tables

• INMEMORY is a segment

attribute

SELECT table_name, inmemoryFROM USER_TABLES;

TABLE_NAME INMEMORY• USER_TABLESdoesn’t display


------------ --------CHANNELS DISABLEDCOSTSCUSTOMERS DISABLEDPRODUCTS ENABLEDSALESTIMES DISABLED

• USER_TABLESdoesn’t display

segment attributes for logical

objects

• Both COSTS & SALES are

partitioned => logical objects

• INMEMORY attribute also

reported in *_TAB_PARTITIONS

Identifying : Tables With INMEMORY Attribute

• New view v$IM_SEGMENTS

• Indicate:

• Objects populated in memory

• Current population status

SELECT segment_name name, population_status statusFROM v$IM_SEGMENTS;


• Can also be used to determine

compression ratio achieved

NAME STATUS------------ --------PRODUCTS COMPLETEDSALES STARTED

Oracle Compression Advisor And In-Memory

• Easy way to determine

memory requirements

• Use DBMS_COMPRESSION

• Applies MEMCOMPRESS to

sample set of data from a table


sample set of data from a table

• Returns estimated

compression ratio

Oracle In-Memory Advisor

• New In-Memory Advisor

• Analyzes existing DB workload

via AWR & ASH repositories

• Provides list of objects that

would benefit most from being


would benefit most from being

populated into IM column

store




Program Agenda




Why is an In-Memory scan faster than the buffer cache?

SELECT COL4 FROM MYTABLE;

XX

Buffer Cache


XXXX

RESULT

Row Format

Why is an In-Memory scan faster than the buffer cache?

SELECT COL4 FROM MYTABLE;

IM Column Store


RESULT

Column Format

RESULT

XXXX

Oracle In-Memory Column Store Storage Index

• Each column is the made up of multiple column units

• Min / max value is recorded for each column unit in a storage index

Memory

Min 1

Max 3

Min 4

Example: Find sales from stores with a store_id of 8 or higher


for each column unit in a storage index

• Storage index provides partition pruning like performance for ALL queries

29

SALES

ColumnFormat

Min 4

Max 7

Min 8

Max 12

Min 13

Max 15

Orders of Magnitude Faster Analytic Data Scans

CPU

PR

OM

O_

ID

Example:Find all salesWith PROMO_ID 9999

Memory• Each CPU core scans local in-

memory columns

• Scans use super fast SIMD vector instructions


LoadmultiplePROMO_ID

values

VectorCompare all valuesan 1 cycle

CPU9999

99999999

9999VE

CTO

RR

EG

IST

ER

vector instructions

• Originally designed for graphics & science

• Billions of rows/sec scan rate per CPU core

• Row format is millions/sec

Identifying : INMEMORY Table Scan

• Optimizer fully aware

• Cost model adapted to consider INMEMORY scan


• New access method TABLE ACCESS IN MEMORY FULL

• Can be disabled via new parameter

• INMEMORY_QUERY

Identifying : INMEMORY Table Scan

• NEW Session level statistics

• Best way to determine if In-Memory was use


• Best way to measure the benefits of In-Memory scan

Joining and Combining Data Also Dramatically Faster

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

Example:Find all orders placed on Christmas eve

LINEORDERDATE_DIM

Datekey is

24122013

Da

te

Da

teK

ey


scans

• Joins tables 10x faster

33

Da

teK

ey

Type=d.d_date='December 24, 2013'

Da

te

Da

teK

ey

Am

ou

nt

Sum

Identifying : INMEMORY Joins• Bloom filters enable joins to be

converted into fast column scans

• Tried and true technology originally released in 10g


originally released in 10g

• Same technique used to offload joins on Exadata

Multiple bloom filter

• Multi-table joins are common in analytic queries

• Possible to apply multiple bloom filters to the fact table due to sophisticated

Select d.d_year, s.s_nation, sum(lo_revenue - lo_supplycost)

From LINEORDER l, DATE_DIM d, PART p, SUPPLIER s

Where l.lo_orderdate = d.d_datekeyAnd l.lo_partkey = p.p_partkey


bloom filters to the fact table due to sophisticated Optimizer

• Simultaneously joining to all of the dimension tables while scanning the fact table

And l.lo_partkey = p.p_partkeyAnd l.lo_suppkey = s.s_suppkeyAnd p.p_mfgr= 'MFGR#12’And s.s_region = 'AMERICA’Group by d.d_year, s.s_nationOrder by d.d_year, s.s_nation;

Multiple Bloom Filter


In-Memory Aggregation

• Dynamically creates in-memory report outline

• Then report outline filled-in during fast fact scan

Example: Report sales of footwear in outlet stores

SalesProducts

In-Memory Report Outline

Footwear

$


during fast fact scan

• Reports run much faster without predefined cubes

37

Stores

Ou

tle

tsFootwear

OutletsSales

$$$

$$$$

Identifying : INMEMORY Aggregation

Hash Group By

� Monitoring the In-Memory Column Store� Monitoring the In-Memory Column Store


Vector Group By

� Monitoring the In-Memory Column Store� Monitoring the In-Memory Column Store




Program Agenda




New In-Memory Central Screen in EM

In-Memory Area (GB) :4.00

In-Memory Area Used (GB) :1.79

Object map integrated with

12c heatmap feature



In-Memory Area Used (GB) :1.79





New In-Memory Entries in CPU Wait Class


New In-Memory Entries in CPU Wait Class


In-Memory Query in SQL Monitor


Date post:	24-May-2015
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