Recommending Materialized Views and Indexes with the IBM DB2 Design Advisor (Automating Physical Database Design) Jarek Gryz

Agenda Motivation Indexes in DB2 Materialized query tables in DB2 Problem definition How does the DB2 Design Advisor tool work ? Experiments

Motivation

Why have an index? Performance, Performance, Performance Provides order for example : Joins, GROUP BY, ORDER BY, DISTINCT Limits I/O and data retrieved due to filtering with predicates Range of values (start/stop keys) Join predicates Provides index-only access Enforces uniqueness or other constraints Provides statistics useful to the optimizer for cardinality estimation for example: statistics on number of keys

Why have Materialized query tables ? The MQT feature is a powerful feature in DB2 that allows you to precompute and materialize a query result into a table Full refresh or incremental refresh possible Subsequently it allows similar queries to automatically use the precomputed data from the MQT to improve performance

Problem Definition Given: Determine: Workload information System configuration Database characteristics Determine: An Index and MQT set that will lead to good workload performance in a reasonable or specified maximum time considering disk space and maintenance constraints and be easy to use

The DB2 Design Advisor Automatically capture : Determines: A representative query workload (potentially compressing it to reduce its size) The existing database characteristics and environment System information Determines: An Index and MQT set that will lead to good ESTIMATED workload response time Using DB2's Query Rewriter/Optimizer to suggest candidates Using DB2's Optimizer to provide cost / benefit information Using a combinatorial algorithm to perform a cost-benefit analysis observing constraints of (1) advisor execution time, (2) disk space and (3) anticipated DB2 costs of creating the entities plus overhead during INSERT / UPDATE / DELETE activity. Using sampled or estimated statistics of new entities Providing both GUI and command line options for initiating

Issues in automating physical DB design selection When to initiate the design algorithm? Health monitor with health indicators such as number of sort overflows to initiate the advisor What data to use to make the decision? Automatically capture workload, DB, and system information Allow work on real data or just statistics How to make the decision? Method to be described How are the recommendations implemented? Little user interaction to ask if or when to initiate to gain DBA trust Online methods to reduce implementation cost E.g., online index creation

The various steps within the Advisor

Index candidate generation During optimization generate virtual candidates when: Predicate exists but no index (e.g., R.A > 5 or R.A=S.B) Ordering required Uniqueness required Winning candidates are the virtuals in the final optimized query plan Provides candidates we know the optimizer will use

MQT Candidate Generation Candidates are generated from original queries, logical views and common expressions which are formed by matching multiple queries. Uses multiquery optimization (MQO) Provides candidates we know the queries will use Candidates can contain table references in a federated DB (tables on different servers)

Combinatorial search algorithm The search phase uses a knapsack algorithm and random swap method to choose the recommended index and MQT set Requires each candidate to have a cost-benefit ratio (cbratio) Benefit based on estimated cost with and without MQT usage (updates have negative benefit) Cost based on disk space usage REFRESH DEFERRED or IMMEDIATE MQTs recommended. Assumption (DEFERRED): estimated time for population = full refresh cost one refresh cost included in the calculation IMMEDIATE changes added in plans for insert/update/deletes If indexes on candidate MQT are selected, then the MQT must be selected as well

Experiments Detect what MQO candidates adds to performance improvement OLAP DB and workload Workload estimated execution time (WET) Type of MQT Selection WET without MQTs WET with MQTs % diff in WETs Num. of MQTs MQTs from queries 493.7 seconds 353.0 seconds 28.5% 7 MQTs from MQO 352.0 seconds 28.4% 4

Autonomic capabilities in DB2 Stinger Health Monitor Recommendation Advisor Automatic page write integrity checking Automatic index reorganization Recovery Expert Fault Monitor Backup Self-tuning Automated HADR DB2/Websphere Integration log and trace analyzer Configuration Advisor Design Advisor advises: Indexes, MDCs, MQTs, Partitioning Self-configuring Self-healing Query compiler query rewrite cost based optimization Automatic query parallelism degree Self-configuring/optimizing utilities Adaptive utility throttling Runstats Performance Expert Query patroller workload manager Self-tuning load Self-optimizing Automated Table Maintenance Runstats Reorg Statistics profiling