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Oracle Active Data Guard Performance

Oracle Active Data Guard Performance. Joseph Meeks Director, Product Management Oracle High Availability Systems. Note to viewer. These slides provide various aspects of performance data for Data Guard and Active Data Guard – we are in the process of updating for Oracle Database 12c.

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Oracle Active Data Guard Performance

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  1. Oracle Active Data GuardPerformance Joseph Meeks Director, Product Management Oracle High Availability Systems

  2. Note to viewer • These slides provide various aspects of performance data for Data Guard and Active Data Guard – we are in the process of updating for Oracle Database 12c. • It can be shared with customers, but is not intended to be a canned presentation ready to go in its entirety • It provides SC’s data that can be used to substantiate Data Guard performance or to provide focused answers to particular concerns that may be expressed by customers.

  3. Note to viewer • See this FAQ for more customer and sales collateral • http://database.us.oracle.com/pls/htmldb/f?p=301:75:101451461043366::::P75_ID,P75_AREAID:21704,2

  4. Agenda – Data Guard Performance • Failover and Switchover Timings • SYNC Transport Performance • ASYNC Transport Performance • Primary Performance with Multiple Standby Databases • Redo Transport Compression • Standby Apply Performance

  5. Data Guard 12.1 Example - Faster Failover 48 seconds 2,000 sessions on both primary and standby 43 seconds 2,000 sessions on both primary and standby Preliminary # of database sessions on primary and standby # of database sessions on primary and standby

  6. Data Guard 12.1 Example – Faster Switchover 72 seconds 1,000 sessions on both primary and standby 83 seconds 500 sessions on both primary and standby Preliminary # of database sessions on primary and standby # of database sessions on primary and standby

  7. Agenda – Data Guard Performance • Failover and Switchover Timings • SYNC Transport Performance • ASYNC Transport Performance • Primary Performance with Multiple Standby Databases • Redo Transport Compression • Standby Apply Performance

  8. Synchronous Redo Transport Zero Data Loss • Primary database performance is impacted by the total round-trip time for acknowledgement to be received from the standby database • Data Guard NSS process transmits Redo to the standby directly from log buffer, in parallel with local log file write • Standby receives redo, writes to a standby redo log file (SRL), then returns ACK • Primary receives standby ACK, then acknowledges commit success to app • The following performance tests show the impact of SYNC transport on primary database using various workloads and latencies • In all cases, transport was able to keep pace with generation – no lag • We are working on test data for Fast Sync (SYNCNOAFFIRM) in Oracle Database 12c (same process as above, but standby acks primary as soon as redo is received in memory – it does not wait for SRL write.

  9. Test 1) Synchronous Redo Transport OLTP with Random Small Insert < 1ms RTT Network Latency • Workload: • Random small inserts (OLTP) to 9 tables with 787 commits per second • 132 K redo size, 1368 logical reads, 692 block changes per transaction • Sun Fire X4800 M2 (Exadata X2-8) • 1 TB RAM, 64 Cores, Oracle Database 11.2.0.3, Oracle Linux • InfiniBand, seven Exadata cells, Exadata Software 11.2.3.2 • Exadata Smart Flash, Smart Flash Logging and Write-Back flash enabled provided significant gains

  10. Test 1) Synchronous Redo Transport OLTP with Random Small Inserts and < 1ms RTT Network Latency • Local standby, <1ms RTT • 99MB/s redo rate • <1% impact on database throughput • 1% impact on transaction rate With Data Guard Synchronous Transport Enabled Data Guard Transport Disabled RTT = network round trip time

  11. Test 2) Synchronous Redo Transport Swingbench OLTP Workload with Metro-Area Network Latency • Exadata X2-8, 2-node RAC database • smart flash logging, smart write back flash • Swingbench OLTP workload • Random DMLs, 1 ms think time, 400 users, 6000+ transactions per second, 30MB/s peak redo rate (different from test 2) • Transaction profile • 5K redo size, 120 logical reads, 30 block changes per transaction • 1 and 5ms RTT network latency

  12. Test 2) Synchronous Redo Transport Swingbench OLTP Workload with Metro-Area Network Latency Transactions per/second Swingbench OLTP • 30 MB/s redo • 3% impact at 1ms RTT • 5% impact at 5ms RTT 6363 tps 6151 tps 6077 tps Baseline No Data Guard Data Guard SYNC 1ms RTT Network Latency Data Guard SYNC 5ms RTT Network Latency

  13. Test 3) Synchronous Redo Transport Large Insert OLTP Workload with Metro-Area Network Latency • Exadata X2-8, 2-node RAC database • smart flash logging, smart write back flash • Large insert OLTP workload • 180+ transactions per second, 83MB/s peak redo rate, random tables • Transaction profile • 440K redo size, 6000 logical reads, 2100 block changes per transaction • 1, 2 and 5ms RTT network latency

  14. Test 3) Synchronous Redo Transport Large Insert OLTP Workload with Metro-Area Network Latency Transactions per/second Large Insert - OLTP • 83 MB/s redo • <1%% impact at 1ms RTT • 7% impact at 2ms RTT • 12% impact at 5ms RTT 189 tps 188 tps 177 tps 167 tps 2ms RTT Network Latency Baseline No Data Guard 1ms RTT Network Latency 5ms RTT Network Latency

  15. Test 4) Synchronous Redo Transport Mixed OLTP workload with Metro-Area Network Latency • Exadata X2-8, 2-node RAC database • smart flash logging, smart write back flash • Mixed workload with high TPS • Swingbench plus large insert workloads • 26000+ txn per second and 112 MB/sec peak redo rate • Transaction profile • 4K redo size, 51 logical reads, 22 block changes per transaction • 1, 2 and 5ms RTT network latency

  16. Test 4) Synchronous Redo Transport Mixed OLTP workload with Metro-Area Network Latency Swingbench plus large insert • 112 MB/s redo • 3% impact at < 1ms RTT • 5% impact at 2ms RTT • 6% impact at 5ms RTT • Note: 0ms latency on graph represents values falling in the range <1ms

  17. Additional SYNC Configuration Details For the Previous Series of Synchronous Transport Tests • No system bottlenecks (CPU, IO or memory) were encountered during any of the test runs • Primary and standby databases had 4GB online redo logs • Log buffer was set to the maximum of 256MB • OS max TCP socket buffer size set to 128MB on both primary and standby • Oracle Net configured on both sides to send and receive 128MB with an SDU for 32k • Redo is being shipped over a 10GigE network between the two systems. • Approximately 8-12 checkpoints/log switches are occurring per run

  18. Customer References for SYNC Transport • Fannie Mae Case Study that includes performance data • Other SYNC references • Amazon • Intel • MorphoTrak – prior biometrics division of Motorola, case study, podcast, presentation • Enterprise Holdings • Discover Financial Services, podcast, presentation • Paychex • VocaLink

  19. Synchronous Redo Transport Caveat that Applies to ALL SYNC Performance Comparisons • Redo rates achieved are influenced by network latency, redo-write size, and commit concurrency – in a dynamic relationship with each other that will vary for every environment and application • Test results illustrate how an example workload can scale with minimal impact to primary database performance • Actual mileage will vary with each application and environment. • Oracle recommends customers conduct their own tests, using their workload and environment. Oracle tests are not a substitute.

  20. Agenda • Failover and Switchover Timings • SYNC Transport Performance • ASYNC Transport Performance • Primary Performance with Multiple Standby Databases • Redo Transport Compression • Standby Apply Performance

  21. Asynchronous Redo Transport Near Zero Data Loss • ASYNC does not wait for primary acknowledgement • A Data Guard NSA process transmits directly from log buffer in parallel with local log file write • NSA reads from disk (online redo log file) if log buffer is recycled before redo transmission is completed • ASYNC has minimal impact on primary database performance • Network latency has little, if any, impact on transport throughput • Uses Data Guard 11g streaming protocol & correctly sized TCP send/receive buffers • Performance tests are useful to characterize max redo volume that ASYNC is able to support without transport lag • Goal is to ship redo as fast as generated without impacting primary performance

  22. Asynchronous Test Configuration Details • 100GB online redo logs • Log buffer set to the maximum of 256MB • OS max TCP socket buffer size set to 128MB on primary and standby • Oracle Net configured on both sides to send and receive 128MB • Read buffer size set to 256 (_log_read_buffer_size=256) and archive buffers set to 256 (_log_archive_buffers=256) on primary and standby • Redo is shipped over the IB network between primary and standby nodes (insures that transport is not bandwidth constrained) • Near-zero network latency, approximate throughput of 1200MB/sec.

  23. ASYNC Redo Transport Performance Test Oracle Database 11.2. • Data Guard ASYNC transport can sustain very high rates • 484 MB/sec on single node • Zero transport lag • Add RAC nodes to scale transport performance • Each node generates its own redo thread and has a dedicated Data Guard transport process • Performance will scale as nodes are added assuming adequate CPU, I/O, and network resources • A 10GigE NIC on standby receives data at maximum of 1.2 GB/second • Standby can be configured to receive redo across two or more instances 484 Redo Transport MB/sec

  24. Data Guard 11g Streaming Network Protocol High Network Latency has Negligible Impact on Network Throughput • Streaming protocol is new with Data Guard 11g • Test measured throughput with 0 – 100ms RTT • ASYNC tuning best practices • Set correct TCP send/receive buffer size = 3 x BDP (bandwidth delay product) • BDP = bandwidth x round-trip network latency • Increase log buffer size if needed to keep NSA process reading from memory • See support note 951152.1 • X$LOGBUF_READHISTto determine buffer hit rate RedoTransportRateMB/sec NetworkLatency

  25. Agenda • Failover and Switchover Timings • SYNC Transport Performance • ASYNC Transport Performance • Primary Performance with Multiple Standby Databases • Redo Transport Compression • Standby Apply Performance

  26. Multi-Standby Configuration Primary - A Local Standby - B • A growing number of customers use multi-standby Data Guard configurations. • Additional standbys are used for: • Local zero data loss HA failover with remote DR • Rolling maintenance to reduce planned downtime • Offloading backups, reporting, and recovery from primary • Reader farms – scale read-only performance • This leads to the question: How is primary database performance affected as the number of remote transport destinations increases? SYNC ASYNC RemoteStandby - C

  27. Redo Transport in Multi-Standby Configuration Primary Performance Impact: 14 Asynchronous Transport Destinations Increase in CPU (compared to baseline) Change in redo volume(compared to baseline) 0 - 14 destinations 0 -14 destinations

  28. Redo Transport in Multi-Standby Configuration Primary Performance Impact: 1 SYNC and multiple ASYNC Destinations Increase in CPU (compared to baseline) Change in redo volume(compared to baseline) Zero Zero 1/0 1/0 1/1 1/1 1/14 1/14 # of SYNC/ASYNC destinations # of SYNC/ASYNC destinations

  29. Redo Transport for Gap Resolution • Standby databases can be configured to request log files needed to resolve gaps from other standby’s in a multi-standby configuration • A standby database that is local to the primary database is normally the preferred location to service gap requests • Local standby database are least likely to be impacted by network outages • Other standby’s are listed next • The primary database services gap requests only as a last resort • Utilizing a standby for gap resolution avoids any overhead on the primary database

  30. Agenda • Failover and Switchover Timings • SYNC Transport Performance • ASYNC Transport Performance • Primary Performance with Multiple Standby Databases • Redo Transport Compression • Standby Apply Performance

  31. Redo Transport Compression Conserve Bandwidth and Improve RPO when Bandwidth Constrained TransportLag - MB • Test configuration • 12.5 MB/second bandwidth • 22 MB/second redo volume • Uncompressed volume exceeds available bandwidth • Recovery Point Objective (RPO) impossible to achieve • perpetual increase in transport lag • 50% compression ratio results in: • volume < bandwidth = achieve RPO • ratio will vary across workloads • Requires Advanced Compression 22 MB/secuncompressed 12 MB/seccompressed Elapsed Time - Minutes

  32. Agenda • Failover and Switchover Timings • SYNC Transport Performance • ASYNC Transport Performance • Primary Performance with Multiple Standby Databases • Redo Transport Compression • Standby Apply Performance

  33. Standby Apply Performance Test • Redo apply was first disabled to accumulate a large number of log files at the standby database. Redo apply was then restarted to evaluate max apply rate for this workload. • All standby log files were written to disk in Fast Recovery Area • Exadata Write Back Flash Cache increased the redo apply rate from 72MB/second to 174MB/second using test workload (Oracle 11.2.0.3) • Apply rates will vary based upon platform and workload • Achieved volumes do not represent physical limits • They only represent the particular test case configuration and workload, higher apply rates have been achieved in practice by production customers

  34. Apply Performance at Standby Database • Test 1: no write-back flash cache • On Exadata x2-2 quarter rack • Swing bench OLTP workload • 72 MB/second apply rate • I/O bound during checkpoints • 1,762ms for checkpoint complete • 110ms DB File Parallel Write

  35. Apply Performance at Standby Database • Test 2: a repeat of the previous test but with write-back flash cache enabled • On Exadata x2-2 quarter rack • Swing bench OLTP workload • 174 MB/second apply rate • Checkpoint completes in 633ms vs 1,762ms • DB File Parallel Write is 21ms vs 110ms

  36. Two Production Customer Examples Data Guard Redo Apply Performance • Thomson-Reuters • Data Warehouse on Exadata, prior to write-back flash cache • While resolving a gap of observed an average apply rate of 580MB/second • Allstate Insurance • Data Warehouse ETL processing resulted in average apply rate over a 3 hour period of 668MB/second, with peaks hitting 900MB/second

  37. Redo Apply Performance for Different Releases • Range of Observed Apply Rates for Batch and OLTP Standby Apply Rate MB/sec

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