SAP HANA SPS 09 - What s New? SAP HANA Scalability

SAP HANA SPS 09 - What s New? SAP HANA Scalability (Delta from SPS08 to SPS09) SAP HANA Product Management November, 2014 2014 SAP AG or an SAP affiliate company. All rights reserved. 1

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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, #Ds 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 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 3

Scale-up

Micro-Architecture Trends NUMA (Non Uniform Memory Access) remote CPU 1 CPU 1 local each socket has its own memory controller (IMC) Core1 Core2 Core1 Core2 scales well, but there are NUMA-effects Core3 Core4 remote Core3 Core4 memory access is non-uniform -> local vs. remote access high-speed interconnect CPU 1 CPU 1 Core1 Core2 Core1 Core2 Core3 Core4 Core3 Core4 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 5

HANA Parallel Query Processing multi-user multi-query ( multi-plan ) intra-plan-parallelism ( multi-pops ) intra-pop-parallelism ( multi-threads ) Core 1: Query 1: Join Aggregate Sort Core 2: Query 2: Join Aggregate Sort Core 3: Core 4: Query 3: Join Aggregate Core 5: Core 6: Jobs over time HANA global optimizer generates efficient execution plan for each query (rule-based, cost-based) Execution plans are cached and reused by HANA Each execution plan is split into multiple plan operators (e.g. join, agg, sort) that are executed in parallel Plan operators generate job graphs, containing job nodes Job nodes are dispatched for execution to available treads by the Job Executor 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 6

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 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 7

Scale-out

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 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 9

HANA s Shared-Nothing Architecture A shared-nothing architecture helps to minimize the inter-node communication in distributed environments Topology Each node runs on its own local data (shared-nothing) Data Volume Master node HDB net Standby node(s) without own persistence Log Volume 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 Slave node Standby node(s) Auto fail-over 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 10

Minimize Inter-Node Communication with Data Distribution Data distribution aims to find the best balance between data model, data usage and actual workload to minimize internode communication Analyze Distribute 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 Table co-location Locate interdependent tables on the same node, e.g. for referential integrity Locate tables within the same statement on the same node, e.g. for collocated joins or transactions Horizontal Partitioning Locate interdependent table partitions on the same node Table Replication Duplicate tables to multiple nodes, e.g. for master data referenced by many tables 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 11

HANA Data Distribution Table Placement Table Placement is the definition of generic table distribution patterns to control the physical location of (column-)tables and how many partitions of a table are to be created in a distributed HANA system 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. initial_partitions, 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 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 12

HANA Data Distribution Table Redistribution Save and restore old table locations Typical operations use a dedicated user with required privileges 1. Generate Plan Review Plan 2. Execute Plan 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 SYS.REORG_PLAN_ Read plan Parallel execution of plan items Plan is always executed completely No automatic rollback, but restore of old table locations possible Execute Plan 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 13

HANA Scale-out Solutions BW on HANA BW workload characteristics Mostly OLAP workload in rather static distribution environment OLAP load benefits from 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 Symmetric scale-out across the system Master-Node OLTP tables Slave-Node 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 OLAP tables Slave-Node OLAP tables Standby-Node 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 14

HANA Scale-out Solutions Suite on HANA Symmetric hardware across the system 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) Suite ABAP App-Server Master-Node Table groups Slave-Node Table groups Slave-Node Table groups Standby-Node 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 15

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 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 16

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 at http://help.sap.com/hana_platform. The knowledge centers are structured according to the product lifecycle: installation, security, administration, development: SAP HANA Platform SPS What s New Release Notes Installation Administration Development References Documentation sets for SAP HANA options can be found at http://help.sap.com/hana_options: SAP HANA Options SAP HANA Advanced Data Processing SAP HANA Dynamic Tiering SAP HANA Enterprise Information Management SAP HANA Predictive SAP HANA Real-Time Replication SAP HANA Smart Data Streaming SAP HANA Spatial 2014 SAP SE or an SAP affiliate company. All rights reserved. Public 17

Thank you Contact information Ruediger Karl SAP HANA Product Management AskSAPHANA@sap.com 2014 SAP SE or an SAP affiliate company. All rights reserved.

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