Scaling Database Performance in Azure



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Transcription:

Scaling Database Performance in Azure Results of Microsoft-funded Testing Q1 2015 2015 2014 ScaleArc. All Rights Reserved. 1

Test Goals and Background Info

Test Goals and Setup Test goals Microsoft commissioned testing to compare SQL Server performance on prem vs. paired with ScaleArc database load balancing software in Azure Testing run with and without ScaleArc caching enabled Simulate realistic, heavy loads that stress compute, disk I/O, and network evenly On-premise setup Bare metal, 24 hyper-threaded Ivy Bridge 2.6 GHz Xeon processor cores, 64 GB RAM, >300k IOPS PCIe SSD 10 Gbps LAN with ping pong latency of <11 μs @ 64K frames Azure setup SQL Server on Azure (A3) instances with 4 virtual cores and 7 GB RAM SQL Server 2014 with both synchronous and asynchronous replication ScaleArc running on A3 and A6 nodes 2015 2014 ScaleArc. All Rights Reserved. 3

What is ScaleArc? Database load balancing software Inserts transparently between apps, databases Enables an Agile Data Tier abstraction layer Available on Azure Marketplace Enables zero downtime environment and provides instant database scalability Supports SQL Server, MySQL, Oracle SQL Server 2005, 2008/R2, 2012, 2014 AlwaysOn Integration 2015 2014 ScaleArc. All Rights Reserved. 4

Without ScaleArc: Limitations of SQL Server on Azure Virtual machine size Large VMs with a lot of compute resources are unavailable / expensive I/O limitations Storage is shared / SAN-based IOPS and storage latency are a challenge vs. SSD storage Hypervisor-related losses Hypervisor layers add overhead and reduce effective resources also, bad neighbor effect Network latency Cloud machines have no guarantee of proximity Added resource utilization due to replication AlwaysOn replication needed for capacity, which may cause further performance degradation 2015 2014 ScaleArc. All Rights Reserved. 5

Benefits of Deploying ScaleArc in Azure with AlwaysOn Enables simple app migration with no performance compromise Move enterprise workloads to Azure with no app changes and no performance loss Aggregate smaller cloud database instances to behave like one large server Provides automatic high availability Auto failover with no app restarts within one locale or across regions Eliminates the need for maintenance windows to patch SQL databases Solves future scaling issues Scale up and scale out without any app modifications Delivers high-performance hybrid deployments App in Azure, with database on prem 2015 2014 ScaleArc. All Rights Reserved. 6

How ScaleArc Augments SQL Server AlwaysOn Aggregate capacity across VMs to get on premise-like performance Combine as many as nine database VMs Linear scalability Add machines as needed ramp up and down for traffic surges, with no downtime Higher availability in Azure vs. on prem, and at lower cost Auto failover + queueing supports cross-region HA, at much lower cost than two data centers Zero-downtime maintenance Perform maintenance per server, cluster or Azure zone with no application downtime Deep SQL analytics SQL query patterns and historical analytics enabling troubleshooting in minutes vs. hours 2015 2014 ScaleArc. All Rights Reserved. 7

Test Methodology and Results

Test Methodology Generate SQL load similar to SaaS, content management, and ecommerce applications Stress compute, memory, I/O, network evenly Simplistic queries so as not to overstress CPU 10% writes twice typical content loads Load profile 10:1 read:write ratio Reads = 70% point queries, 30% top/range queries Writes = 50% updates, 25% deletes, 25% inserts CREATE TABLE [dbo].[oltp1] ( [id] [int] NOT NULL, [k] [int] NOT NULL, [c] [varchar](120) COLLATE SQL_Latin1_General_CP1_CI_AS NOT NULL, [pad] [varchar](60) COLLATE SQL_Latin1_General_CP1_CI_AS NOT NULL, PRIMARY KEY NONCLUSTERED ( [id] ASC ) )ON [ PRIMARY ] Sample Read Queries SELECT c FROM oltp11 WHERE id=401 SELECT c FROM oltp13 WHERE id BETWEEN 401 AND 410 SELECT DISTINCT c FROM oltp33 WHERE id BETWEEN 401 AND 410 ORDER BY c Sample Write Queries UPDATE oltpmem32 SET k=k+1 WHERE id=5642 UPDATE oltpmem64 SET c='xv6pzw4cooufvz9k6r63muulhbw4szwadbec6u2uvci5npqm1brq8nkjdeaca2qx vilfsvudoixti6mitlvejgxeexmazkw0lhtqyt2eioe60sgz5jevajai' WHERE id=3454 DELETE FROM oltpmem25 WHERE id=328 2015 2014 ScaleArc. All Rights Reserved. 9

Benchmarks: On Premise vs. Azure Cloud Performance On premise (12 physical cores): 17K QPS, 1416 queries / physical core 3 x SQL on A3 instances + 2 x ScaleArc (A3) (20 virtual cores) 15K QPS, 750 queries / virtual core 4 x SQL on A3 instances + 2 x ScaleArc (A3) (24 virtual cores) 18.5K QPS, 770 queries / virtual core 6 x SQL on A3 instances + 2 x ScaleArc (A3) (32 virtual cores) 28K QPS, 866 queries / virtual core Queries / Second on at 4096 Concurrent Connections On-Premise 17,034 6 x Azure (A3) 27,731 5 x Azure (A3) 23,441 4 x Azure (A3) 18,499 3 x Azure (A3) 15,032 2 x Azure (A3) 10,184 1 x Azure (A3) 5,408-5,000 10,000 15,000 20,000 25,000 30,000 Note: ScaleArc + Azure enables linear scalability gains from adding readable secondaries with no app mods or downtime 2015 2014 ScaleArc. All Rights Reserved. 10

ScaleArc Cache Performance Queries / Second on at 4096 Concurrent Connections 2 x SQL on A3 + 1 x ScaleArc (A6) with read cache enabled (16 virtual cores) 31.6K QPS, 1976 queries / virtual core 2 x SQL (A3) + 1 x ScaleArc (A6) 31,619 On-premise server with 24 hyperthreaded cores (12 physical cores) 17K QPS, 1416 queries / physical core On-Premise 17,034 2 x SQL on A3 instances + 2 x ScaleArc (A3) (16 virtual cores) 9.5K QPS, 595 queries / virtual core 2 x Azure (A3) 9,531-5,000 10,000 15,000 20,000 25,000 30,000 35,000 2015 2014 ScaleArc. All Rights Reserved. 11

Automated Failover Testing ScaleArc detects failure of current AlwaysOn Primary ScaleArc queues inbound client traffic Queue shields app from errors while failover completes Traffic pauses briefly as ScaleArc queues traffic No errors are seen by app while failover completes in SQL AlwaysOn (other than the failed queries to the server that just died) Before failover, ScaleArc performed automatic read/write split, sending secondary reads and primary reads and writes No config or app changes needed, no read-intent strings After failover, newly promoted server serves all traffic 2015 2014 ScaleArc. All Rights Reserved. 12

Additional Resources on Augmenting SQL Server 2015 2014 ScaleArc. All Rights Reserved. 13