Oracle RAC Oracle RAC provides two main features: Availability Scalability May operate in two modes: All nodes are active (load distributed between nodes) Active/Passive RAC scaling/performance considerations are similar to OpenVMS clustering scaling/performance considerations Interconnect Locks Sharing
RAC Scaling – Maklees Golden Rules Application that does not scale on a standalone node – will not scale on RAC Start with single instance tuning shutdown all nodes measure scaling test scaling by adding CPUs Add one node at a time to measure scalability
Scalability Benchmark 2 nodes cluster 1.3 Ghz rx2600, running OpenVMS V8.3-1H1 Oracle 10gR2 RAC Latest set of patches Test database contains information about 50,000 customers 200,000 customer orders 200,000 ordered items
Scalability Benchmark PL/SQL procedure to fetch data about 2000 random customers Read only test All data in SGA No I/O CPU Bound
Scalability Benchmark Elapsed time (seconds per job) to complete the test Less is better Elapsed time (seconds per job) to complete the test Less is better
RAC Proof Of Concept MAKLEE Engineering recently performed a RAC proof of concept installation at a large chain of department stores in Switzerland. Benchmarked a single Alpha GS1280 (production node) vs. a RAC cluster running 2 Integrity servers rx6600. The goals were: Install RAC Get hands on experience with RAC Perform RAC scaling tests Make a go/no go decision on implementing RAC in production
27 Parallel Database Import Jobs Minutes to complete database import less is better
Database Import Itanium outperformed Alpha Operating in RAC environment does not increase the throughput of the import operation Spreading the jobs across two nodes or running all jobs on one node yields identical performance/throughput No performance degradation witnessed
Batch Processing Benchmark Minutes to complete batch processing cycle Less is better Minutes to complete batch processing cycle Less is better
Batch Processing Benchmark Itanium outperformed Alpha RAC allows scaling outside of the box Second RAC node adds 40% more throughput
Another Example – European Bank European Bank migrating from Alpha to Itanium 2 nodes AlphaServer ES47 -> 2 nodes rx7640 Migrating to Oracle 10gR2 RAC Availability is main concern Interactive users will be distributed between nodes No plans to distributed batch load between nodes Needed to verify that RAC does not degrade performance
Another Example – European Bank Benchmarked various batch jobs – focusing on one specific batch job. Initial results did not favor Itanium.
Batch Processing Benchmark Minutes to complete selected batch job Less is better Minutes to complete selected batch job Less is better
European Bank - Summary Tuning is critical for achieving optimal performance Dont run out of the box. 66% improvement after (minimal) tuning The specific benchmark is running 52% faster on Itanium comparing to Alpha.
European Bank - Summary All other batch jobs/applications witnessed similar improvement. RAC increases availability and does not degrade performance. RAC will go into production in few weeks
CRS Base Priority CRS is running in batch Usually, runs in a dedicated batch queue By default, base priority of a batch queue is 4 On a system with thousands of processes, CRS may need to compete (and sometimes lose) for CPU resources CRS should be given high priority Set base priority of CRS queue to 12
RAC Cluster Interconnect The performance of the cluster interconnect is critical to the performance of the RAC. Interconnect used for Cluster management Locks Cache Fusion Oracle requires (at least one) dedicated cluster interconnect Gigabit Ethernet is highly recommended Enable Jumbo Frames Transfer rate of ~ 25MB per second (faster than some disks ;-)
Cluster interconnect Performance Latency is CRITICAL for RAC performance Measure the latency of the interconnect: set numwidth 20 column "AVG CR BLOCK RECEIVE TIME (ms)" format select b1.inst_id, b2.value "GCS CR BLOCKS RECEIVED", b1.value "GCS CR BLOCK RECEIVE TIME", ((b1.value/b2.value) * 10) "AVG CR BLOCK RECEIVE TIME (ms)" from gv$sysstat b1, gv$sysstat b2 where b1.name='gc cr block receive time' and b2.name='gc cr blocks received' and b1.inst_id=b2.inst_id;
Cluster interconnect Performance Latency should be lower than 15ms OpenVMS achieved 0.5ms on blades RAC (BL860) V8.3-1H1 Gigabit Ethernet Jumbo Frames enabled
Load distribution between instances set pagesize 60 space 2 numwidth 8 linesize 132 verify off feedback off column service_name format a20 truncated heading 'Service' column instance_name heading 'Instance' format a10 column service_time heading 'Service Time|mSec/Call' format select service_name, instance_name, elapsedpercall service_time, cpupercall cpu_time, dbtimepercall db_time, callspersec throughput from gv$instance gvi, gv$active_services gvas, gv$servicemetric gvsm where gvas.inst_id=gvsm.inst_id and gvas.name_hash=gvsm.service_name_hash and gvi.inst_id=gvsm.inst_id and gvsm.group_id=10 order by service_name, gvi.inst_id;
Standalone Database Import Minutes to complete database import less is better 37% Improvement
Database import Install imp.exe as resident image with shared address space $ install add imp.exe/resident/share=addr Increase default quotas for BEQs mailboxes $ define/sys ORA_BEQ_MBXSIZ $ define/sys ORA_BEQ_MBXSBFQ Set DEFMBXBUFQUO to Set DEFMBXMXMSG to 64000
DBMS_STATS.GATHER_SCHEMA_STATS Minutes to gather database statistics (350GB database) Less is better Minutes to gather database statistics (350GB database) Less is better
DBMS_STATS.GATHER_SCHEMA_STATS Calling gather_schema_stats results in a database server process being created The server process in not multithreaded Typically consumes 100% of one CPU Performance improvement achieved by affinitizing the server process to one CPU and increasing QUANTUM to 20.
SORT Analyze the efficiency of sort operations Determine the number of optimal, one pass and multipass operations SELECT optimal_count, round(optimal_count*100/total, 2) optimal_perc, onepass_count, round(onepass_count*100/total, 2) onepass_perc, multipass_count, round(multipass_count*100/total, 2) multipass_perc FROM (SELECT decode(sum(total_executions), 0, 1, sum(total_executions)) total, sum(OPTIMAL_EXECUTIONS) optimal_count, sum(ONEPASS_EXECUTIONS) onepass_count, sum(MULTIPASSES_EXECUTIONS) multipass_count FROM v$sql_workarea_histogram WHERE low_optimal_size > 64*1024);
Sizing the SGA Reserve memory for the SGA (SYSMAN) Avoid automatic memory management in the SGA whenever possible. The following query will help properly size the SGA select sga_size, sga_size_factor as size_factor, estd_physical_reads as estimated_physical_reads from v$sga_target_advice order by sga_size_factor;
Sizing the SGA SQL> select sga_size, sga_size_factor as size_factor, 2 estd_physical_reads as estimated_physical_reads 3 from v$sga_target_advice order by sga_size_factor; SGA_SIZE SIZE_FACTOR ESTIMATED_PHYSICAL_READS , , , , SQL>
Whats wrong in this picture? $ show memory System Memory Resources on 1-APR :32:35.62 Physical Memory Usage (bytes): Total Free In Use Modified Main Memory (GB) Extended File Cache (Time of last reset: 31-MAR :14:46.99) Allocated (MBytes) Maximum size (MBytes) Free (MBytes) Minimum size (MBytes) 3.12 In use (MBytes) Percentage Read I/Os 77% Read hit rate 99% Write hit rate 0% Read I/O count Write I/O count Read hit count Write hit count 0 Reads bypassing cache 79 Writes bypassing cache Files cached open 739 Files cached closed 2255 Vols in Full XFC mode 0 Vols in VIOC Compatible mode 52 Vols in No Caching mode 0 Vols in Perm. No Caching mode Of the physical memory in use, 8.52 GB are permanently allocated to OpenVMS. $
The next step in improving performance SQL Tuning ! With previous Alpha Vs. Itanium benchmarks we had to play it fare Not a single SQL statement was changed. SQL tuning may improve performance by magnitudes
SQL Tuning All the tools that are required for SQL tuning are shipping with the database: Automatic Workload Repository (AWR) Endless amount of performance related information Enhanced version of statpak Active Session History (ASH) Automatic Database Diagnostic Monitor (ADDM) SQL Access Advisor SQL Tuning Advisor Statspack analyzer (not part of the DB but available for free)
The power of SQL tuning AWR was used to analyze the scalability benchmark 97% of the time was spent executing single SQL statement After SQL tuning – elapsed time of the benchmark was reduced from 411 seconds to 3.18 seconds ! 130 times faster!!!!
The power of SQL tuning Real life example rx6600, Oracle 10g, DWH DB Single SQL statement required 140 minutes to complete By biasing the optimizer, elapsed time reduced to 10 minutes
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