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SAP HANA SPS 09 - Whats NewSAP HANA Scalability

SAP HANA Product Management (Delta from SPS08 to SPS09)

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Disclaimer

This presentation outlines our general product direction and should not be rel

a purchase decision. This presentation is not subject to your license agreemeagreement with SAP.

SAP has no obligation to pursue any course of business outlined in this prese

develop or release any functionality mentioned in this presentation. This prese

SAPs strategy and possible future developments are subject to change and m

by SAP at any time for any reason without notice.

This document is provided without a warranty of any kind, either express or im

but not limited to, the implied warranties of merchantability, fitness for a partic

non-infringement. SAP assumes no responsibility for errors or omissions in th

except if such damages were caused by SAP intentionally or grossly negligent

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Scalability

Setting the stage

Scalability is the ability of a computer hardware/software to take full

advantage of its changed context in a re-scaled situation, e.g. by adding or

reducing resources

Vertical Scalability (Scale-up)

Adding resources within the same computing unit, e.g. #CPUs, #DRAMs

Horizontal Scalability (Scale-out)

Adding multiple computing units and making them work as one logical

computing unit

SAP HANA is designed for scale-up and scale-out since the beginning

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Scale-up

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Micro-Architecture Trends

Core1 Core2

Core4Core3

CPU 1

Core1 Core2

Core4Core3

CPU 1

Core1 Core2

Core4Core3

CPU 1

Core1 Core2

Core4Core3

CPU 1

RAM

RAM

RAM

RAM

RAM

RAM

RAM

RAM

RAM

RAM

RAM

RAM

NUMA (Non Uniform MemoryAccess)

high-speed interconnect

each socket has its own memory con

scales well, but there are NUMA-effe

memory access is non-uniform -> lo

local

remote

remote

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HANA Parallel Query Processing

multi-user multi-query (multi-plan)

Join

Join

Core 1:

Query 2: Aggregate

Aggregate

Sort

Sort

intra-plan-parallelism (multi-pops)

JoinQuery 3: Aggregate

intra-p

Core 2:

Core 3:

Core 4:

Core 5:

Core 6:

Query 1:

Each execution plan is split into multiple plan

operators (e.g. join, agg, sort) that are

executed in parallel

HANA global optimizer generates

efficient execution plan for each

query (rule-based, cost-based)

Execution plans are cached and re-

used by HANA

Plan op

contain

Job nod

availabl

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Summary: Vertical Scalability in HANA (scale-up)

Parallelism in HANA

HANA is heavily optimized for state-of-the-art HW architectures

HANA uses massive intra-plan and intra-operator parallelism for query execution

HANA tries to use all available resources for maximal parallelization

Challenge: NUMA-effects on large multi-socket systems

Remark: Virtualization of CPU resources can reinforce NUMA-effects

Job Scheduler

Central place in HANA to execute plan-operator jobs

Re-adjust dynamically the concurrency level avoiding over-parallelization

Can consider additional .ini configuration data to limit the concurrency level

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Scale-out

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Horizontal Scalability (scale-out)

General

Scale-out means expanding to multiple servers rather than a single, bigger server in one database system Multiple servers (nodes) are switched together to one logical, but physically distributed database system

When to scale-out ?

Overcome hardware limitations of one single server (memory, cpu)

Scale-out aims to distribute data and workload

Attention

Scale-out requires deep knowledge about data, application and hardware

Scale-out increases data center operation costs

Scale-out affects your scaling factor by inter-node communication

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HANAs Shared-Nothing Architecture

Topology

Each node runs on its own local data (shared-nothing)

Standby node(s) without own persistence

Shared file system for node fail-over and recovery (HW partner

implementation)

Nodes communicate via internal HDB net protocol

All nodes belonging to the same HANA system must have the

same HW setup

Data

Volume

Log

Volume

Data

Volume

Log

Volume

Auto fail-over

A shared-nothing architecture helps to minimize the inter-node communication in distributed environm

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Minimize Inter-Node Communication with Data Distribut

Data model

Types of data, e.g. master data, transactional data and

configuration data

Referential integrity between tables

Data usage Data interdependencies, e.g. table groups

Data access paths

Workload

Mixed workloads, i.e. read/ write, analytical/transactional

Update frequency

Data distribution aims to find the best balance between data model, data usage and actual workload to

node communication

Analyze

Table co-location

Locate interdependent tables on th

referential integrity

Locate tables within the same state

node, e.g. for collocated joins or tr

Horizontal Partitioning

Locate interdependent table partitio

Table Replication

Duplicate tables to multiple nodes,

referenced by many tables

Distribute

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Step 1. Classification

In HANA it is possible to classify a table by additional metadata, like schema_name, group name, group_type

The table classification can be set with the create / alter table statement

The table classifications are stored in the table sys.table_groups_

Step 2. Table Placement Configuration

For each table classification you can assign a table placement configuration

A table placement configuration defines, on which node type, disk-volume (SPS09) a table and its partitions shall be located

A table placement configuration also defines when a table shall be partitioned (works only if the table has a partition specification), e.g.

min_rows_partitioning and repartitioning_threshold

The table placement values are inserted via SQL commands into table _sys_rt.table_placement (HANA studio -> SQL-console)

When is the Table Placement Configuration evaluated

During create/alter table and table redistribute operations

The table placement can be overruled with the location_clause, partition_clause or move_clause information in the DDL statement

HANA Data DistributionTable Placement

Table Placement is the definition of generic table distribution patterns to control the physical location of

and how many partitions of a table are to be created in a distributed HANA system

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HANA Data DistributionTable Redistribution

Collects information about landscape and database

Evaluates table placement information and .ini-parameter

Apply heuristic algorithm to optimize table placement

Save execution plan with execution order

Execute Plan

1. Generate Plan 2. Execute Pla

Typical operations

Review Plan

Read plan

Parallel execution of plan items

Plan is always executed completely

No automatic rollback, but restore of SYS.REORG_PLAN_

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Ma

OL

tab

Sla

OL

tab

Sla

O

ta

Stan

BW workload characteristics

Mostly OLAP workload in rather static distribution environment

OLAP load benefits f rom parallel processing on distributed partitions

Master Node

Handles OLTP load and DDL statements: ABAP system tables, meta data,

operational tables and all row tables are stored

Slave Nodes

will handle OLAP load exclusively:

Master data + cubes/DSOs/PSAs are distributed evenly across all slaves

Cube, DSO and PSA tables are partitioned dependent on the table placement rules

Useful information

global.ini [table_placement] method= 2 (keeps row-store tables on the master

node)

note #1908075 (BW scale-out configuration recommendations)

BW ABAP

App-Server

Symmetric scale-

HANA Scale-out SolutionsBW on HANA

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Master-N

Table

group

Slave-No

Slave-No

Standby-N

Suite ABAP

App-Server

Symmetric hardware a

Table

group

Tabl

group

HANA Scale-out SolutionsSuite on HANA

Suite workload characteristics Mixed OLTP/OLAP workloads across all nodes (2PC, Cross-joins)

Table access patterns dynamic and differ from customer to customer

Dynamic Table distribution

Buffered SQL statements in the statement cache are parsed and prioritized

based on DB statistics

Determine disjoint table groups

Table groups are placed on same node while balancing memory and CPU

Useful information

note #1899817 (Suite scale-out configuration recommendations)

Note #1781986 (Suite on distributed HANA database)

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Takeaways

Scale-up first !

Scale-up Most common and easiest way for databases to scale

No changes in the database or application required

HANA parallelism strongly supports latest multi-cpu, multi-core HW architectures

HANA NUMA-aware scheduling and memory allocation is under development

Scale-out

Requires deep knowledge about data and workload to achieve good scaling

Scale-out works good in static distribution environments, like OLAP

Scale-out in mixed OLAP/OLTP environments can be challenging

Optimizing scale-out performance is an iterative task

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How to find SAP HANA documentation on this topic?

In addition to this learning material, you can find SAP HANA

platform documentation on SAP Help Portal knowledge center athttp://help.sap.com/hana_platform.

The knowledge centers are structured according to the product

lifecycle: installation, security, administration, development:SAP HANA Options

SAP HANA Advanced Data Process

SAP HANA Dynamic Tiering

SAP HANA Enterprise Information M

SAP HANA Predictive

SAP HANA Real-Time Replication

SAP HANA Smart Data Streaming

SAP HANA Spatial

Documentation sets for SAP HANA option

http://help.sap.com/hana_options :

SAP HANA Platform SPS

Whats New Release Notes

Installation

Administration

Development

References

http://help.sap.com/hana_platformhttp://help.sap.com/hana_optionshttp://help.sap.com/hana_optionshttp://help.sap.com/hana_platform

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Thank you

Contact information

Ruediger Karl

SAP HANA Product Management

AskSAPHANA@sap.com

mailto:AskSAPHANA@sap.commailto:AskSAPHANA@sap.com

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