Does SQL Server scalability top your system priority list? Have you been tasked with identifying ways to optimize your organization’s SQL Server database performance? If scaling out does not seem to be a feasible option, you can follow these six steps and make the scale-up process easy and effective for your enterprise:
1. Maximize the Existing Components in Your SQL Server
Scaling SQL Server is a tedious and time-consuming project that involves multiple systems, partitioned databases, memory, storage, CPU and network adapters. Start with maximizing the memory, as it’s the easiest component to expand. If you have any server instances that are currently running on a 32-bit Windows version, your first step should be migrating them to 64-bit machines. This step will enable your systems to support additional memory and deliver improved performance.
2. Optimize Disc Storage
Disc storage also plays a vital role in boosting SQL Server scalability. Before your processors or network adapters become an issue, the database server is sure to become I/O bound and cause storage issues. You need a storage system that has the capacity to handle your database needs — in other words, it must be fast. You can achieve this gain by using fast SAN protocols on your fiber-optic connections when communicating with disks.
3. Examine Your Network Connectivity
Running multiple network adapters is known to deliver better performance. When you employ one adapter for every major server in use, you will boost the scalability of your SQL Server database and keep it from getting performance-bound at the adapter level. If your existing network can support 10 Gbps Ethernet adapters, then go for it!
4. Go Virtual
Virtualization is a clever move to boost the performance of your SQL Server database. It allows you to run multiple workloads simultaneously on a single host. Convert all of the databases that require less than one physical server into a virtual machine. SQL Server gives you the flexibility of separating your database across multiple instances. When you use virtual machine guest applications, you can quickly and easily move a machine from one host to another while ensuring optimum utilization of hardware, depending on the workload demands. With a SQL Server database, you can shift instances as and when needed, with negligible interruption to your database availability. Virtualization supports dynamic scaling and delivers improved performance by shifting resources from the cluster node that is being scaled up, allowing the instances being hammered to use idle resources. Virtualization is the easiest way to ensure that your hardware is working at peak performance while delivering a seamless end-user experience.
5. Add Caching
The majority of applications are read-heavy apps — that is, about 80% of the database queries are reads, and 20% are writes. So many applications benefit from caching, since you can serve a lot of read traffic directly from the cache rather than hit the database. The challenge is to implement caching in a way that’s least intrusive for the application. One approach is to use the caching in database load balancing software to enable this server offload with making code changes. However, if you want to avoid rewriting code, see step 6.
6. Prepare for Scale Out – Invest in Database Load Balancing Software
You eventually reach a point where you cannot squeeze any more from your database -- your memory is maxed out, your processor slot is jam-packed and you’ve got no room for expansion, and you’re using caching to the fullest. At that point, you need to look at scaling horizontally vs. vertically – scaling out. The easiest way to transition to a scaled-out infrastructure is to invest in database load-balancing software. That software can perform a wide range of SQL management capabilities including replication monitoring, auto failover, read/write split and traffic management – all of which make the transition from a single-server architecture to scale out much easier. With the right database load balancing software, you can let your apps take advantage of scaled-out databases without writing a single line of code.