Some say performance troubleshooting is a difficult science that blends just the right amount of patience, knowledge and experience. But I say forget all that, a few bullet points can get you a long way in fixing any problem you encounter. Is more important to find a google SEO friendly result that gives simplistic advice. Most importantly, good advice never contains the words ‘It depends’. Without further ado, here is my bulletproof SQL Server optimization guide:
- Always trust your gut feeling. Avoid doing costly and unnecessary measurements. They may lead down the treacherous path of the scientific method. A gut feeling is always easier to explain and this improves communication. Measurements require use of complicated notions not everybody understands, so they lead to conflicts in the team.
- High CPU utilization is caused by index fragmentation. Because the distance between database pages increases, the processor needs more cycles to reference the pages in the buffer pool.
- Low CPU utilization is caused by index fragmentation. As the index fragments get smaller, they fit better into the processor L2 cache and this results in fewer cycles needed to access the row slots in the page. Because the data is in the cache the processor idles next cycles, resulting in low CPU utilization.
- High Avg. Disk Sec. per Transfer is caused by index fragmentation. When indexes are fragmented the disk controller has to reorder the IO scatter-gather requests to put them in descending order. Needles to say, this operation increases the transfer times in geometric progression, because all the commercial disk controllers use bubble sort for this operation.
- High memory consumption is caused by index fragmentation. This is fairly trivial and well known, but I’ll repeat it here: as the number of index fragments increases more pointers are needed to keep track of each fragment. Pointers are stored in virtual memory and virtual memory is very large, and this causes high memory consumption.
- Syntax errors are caused by index fragmentation. Because the syntax is verified using the metadata catalogs, high fragmentation in the database can leave gaps in the syntax. This is turn causes the parser to generate syntax errors on perfectly valid statements like SECLET and UPTADE.
- Covering indexes can lead to index fragmentation. Covering indexes are the indexes used by the query optimizer to cover itself in case the plan has execution faults. Because they are so often read they wear off and start to fragment.
- Index fragmentation can be resolved by shrinking the database. As the data pages are squeezed tighter during the shrinking, they naturally realign themselves in the correct order.
There you have it, the simplest troubleshooting guide. Since most performance problems are caused by index fragmentation, all you have to do is shrink the database to force the pages to re-align correctly, and this will resolve the performance problem.
Happy April 1st everyone!
A very common question asked on all programming forums is how to implement queues based on database tables. This is not a trivial question actually. Implementing a queue backed by a table is notoriously difficult, error prone and susceptible to deadlocks. Because queues are usually needed as a link between various processing stages in a workflow they operate in highly concurrent environments where multiple processes enqueue rows into the table while multiple processes attempt to dequeue these rows. This concurrency creates correctness, scalability and performance challenges.
But since SQL Server 2005 introduced the OUTPUT clause, using tables as queues is no longer a hard problem. This fact is called out in the OUTPUT Clause topic in BOL:
You can use OUTPUT in applications that use tables as queues, or to hold intermediate result sets. That is, the application is constantly adding or removing rows from the table… Other semantics may also be implemented, such as using a table to implement a stack.
The reason why OUTPUT clause is critical is that it offers an atomic destructive read operation that allows us to remove the dequeued row and return it to the caller, in one single statement.
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The question of comparing the MAX types (VARCHAR, NVARCHAR, VARBINARY) with their non-max counterparts is often asked, but the answer usually gravitate around the storage differences. But I’d like to address the point that these types have inherent, intrinsic performance differences that are not driven by different storage characteristics. In other words, simply comparing and manipulating variables and columns in T-SQL can yield different performance when VARCHAR(MAX) is used vs. VARCHAR(N).
First comparing simple assignment, assign a value to a VARBINARY(8000) variable in a tight loop:
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On a project I’m currently involved with we have to handle a constant influx of audit messages for processing. The messages come from about 50 SQL Express instances located in data centers around the globe, delivered via Service Broker into a processing queue hosted on a mirrored database where an activated procedure shreds the message payload into relational tables. These tables are in turn replicated with transactional replication into a data warehouse database. after that the messages are deleted from the processing servers, as replication is set up not to replicate deletes. The system must handle a constant average rate of about 200 messages per second, 24×7, with spikes going up to 2000-3000 messages per second over periods of minutes to an hour.
When dealing with these relatively high volumes, it is inevitable that queues will grow during the 2000 msgs/sec spikes and drain back to empty when the incoming rate stabilizes again at the normal 200 msgs/sec rate. Service Broker does an excellent job at handling these non-connectivity periods, retains the audit messages and quickly delivers them when connectivity is restored.
What I noticed though is that sometimes the processing of the received messages could hit a threshold from where it could not recover. The queue processing would slow down to a rate that was bellow the incoming rate, and from that point forward the queue could just grow. I want to detail a bit the reason why this can happen and what I did to alleviate the problem.
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