12c In Memory Management - Saurabh Gupta

Oracle Database 12c
In-Memory
Saurabh K. Gupta
Database Product Management

Who am I?
• Principal Technologist, Database Product
Management
• Author of “Oracle Advanced PL/SQL
Developer Professional Guide”, Technical
Reviewer, blogger
• Experience in Oracle Databases, design,
development and administration, High
Availability solutions, Exadata
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
3

Oracle Database In-Memory Goals
Real Time Accelerate OLTP
Analytics
No Changes to
Applications
Exploit latest
generation
hardware
CPU
100x 2x

Become a Real-Time Enterprise
• Data-Driven
• Rapidly make decisions based on real-time
data
• Agile
AGILE
DATA-DRIVEN
• 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
SSAtoLErSes
INSERT
INSERT
INSERT
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
• Analytics reporting use new
in-memory Column format
• OLTP uses proven row format
8
Row
Format
Column
Format

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
Indexes
In-Memory
Column Store
• 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
Uniquely Achieves All In-Memory Benefits With No Application Changes
11

Program Agenda
Configuring the In-Memory Column Store
Populating the In-Memory Column Store
Querying the In-Memory Column Store
Monitoring the In-Memory Column Store

Configuring : In-Memory Column Store
System Global Area SGA
Buffer Cache
Shared Pool Redo Buffer
Large Pool
Other shared
Memory
Components
In-Memory
Area

Configuring : In-Memory Column Store
• Controlled by INMEMORY_SIZE
parameter
•Minimum size of 100MB
• SGA_TARGET must be large
SELECT * FROM V$SGA;
NAME VALUE
------------------ ---------
enough to accommodate
• Static Pool
Fixed Size 2927176
Variable Size 570426808
Database Buffers 4634022912
Redo Buffers 13848576
In-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
• 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
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;
• 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 INMEMORY
NO INMEMORY (PROD_ID);
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 CRITICAL
NO 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))
INMEMORY
MEMCOMPRESS 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 MEMCOMPRESS
PARTITION p2 ……
INMEMORY MEMCOMPRESS FOR DML,
Default mode for most tables
• FOR CAPACITY
For less frequently accessed
segments
• Easy to switch levels as part of
ILM strategy
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, inmemory
FROM USER_TABLES;
TABLE_NAME INMEMORY
• USER_TABLESdoesn’t display
------------ --------
CHANNELS DISABLED
COSTS
CUSTOMERS DISABLED
PRODUCTS ENABLED
SALES
TIMES DISABLED
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$IM_SEGMENTS
• Indicate:
• Objects populated in memory
• Current population status
SELECT segment_name name,
population_status status
FROM v$IM_SEGMENTS;
• Can also be used to determine
compression ratio achieved
NAME STATUS
------------ --------
PRODUCTS COMPLETED
SALES 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
• 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
populated into IM column
store

Program Agenda

Why is an In-Memory scan faster than the buffer cache?
SELECT COL4 FROM MYTABLE;
X
X
Buffer Cache
X
X
X
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
X
X
X
X

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
Example: Find sales from stores with a store_id of 8 or higher
Memory
Min 1
Max 3
Min 4
• Storage index provides
partition pruning like
performance for ALL queries
29
SALES
Column
Format
Max 7
Min 8
Max 12
Min 13
Max 15

Orders of Magnitude Faster Analytic Data Scans
CPU
PROMO_ID
Example:
Find all sales
With PROMO_ID 9999
Memory
• Each CPU core scans local in-memory
columns
• Scans use super fast SIMD
vector instructions
Load
multiple
PROMO_ID
values
Vector
Compare
all values
an 1 cycle
9999
9999
9999
9999
VECTOR
REGISTER
• 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
• NEWSession 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
DATE_DIM LINEORDER
Datekey is
24122013
Date
DateKey
• Joins tables 10x faster
33
DateKey
Type=d.d_date='December 24, 2013'
Amount
Sum

Identifying : INMEMORY Joins
• Bloom filters enable joins to be
converted into fast column
scans
• Tried and true technology
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_datekey
And l.lo_partkey = p.p_partkey
Optimizer
• Simultaneously joining to all
of the dimension tables while
scanning the fact table
And l.lo_suppkey = s.s_suppkey
And p.p_mfgr= 'MFGR#12’
And s.s_region = 'AMERICA’
Group by d.d_year, s.s_nation
Order 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
Sales
Products
In-Memory
Report Outline
Footwear
$
• Reports run much faster
without predefined cubes
37
Stores
Outlets
Footwear
Outlets
Sales
$$
$
$$$

Identifying : INMEMORY Aggregation
Hash Group By
MMoonniittoorriinngg Monitoring tthhee the IInn--In-MMeemmoorryy CCoolluummnn SSttoorree
Vector Group By

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

New In-Memory Entries in CPU Wait Class

In-Memory Query in SQL Monitor

12c In Memory Management - Saurabh Gupta

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