Press "Enter" to skip to content

Category: Performance Tuning

Automatic Plan Correction

Published 2017-05-17 by Kevin Feasel

Jovan Popovic introduces a new tuning feature in SQL Server 2017:

How to identify plans that should be corrected?

SQL Server 2017 provides a new system view called sys.dm_db_tuning_recommendations that shows all identified plan regressions. You can select data from this view, find the difference between last known good plan and regressed plan, and the script that you can execute to manually fix regression.

Automatic plan correction

As a next step, you can let SQL Server 2017 to automatically correct any plan that regressed.

I like it when the database engine gets smarter, but I get the feeling I’d like there to be some finer-grained options around what gets considered a regression and when a sub-optimal plan gets swapped out.

Comments closed

Optimizing Kafka

Published 2017-05-16 by Kevin Feasel

Yeva Byzek explains different tuning options available within Apache Kafka:

Without needing to make any changes to Kafka configuration parameters, you can setup a development Kafka environment and test basic functionality. Yet the fact that Kafka runs straight off the shelf does not mean you won’t want to do some tuning before you go into production. The reason to tune is that different use cases will have different sets of requirements that will drive different service goals. To optimize for those service goals, there are Kafka configuration parameters that you should change. In fact, the Kafka design itself provides configuration flexibility to users, and to make sure your Kafka deployment is optimized for your service goals, you absolutely should investigate tuning the settings of some configuration parameters and benchmarking in your own environment. Ideally, you should do that before you go to production, or at least before you scale out to a larger cluster size.

We have written a white paper to help you identify those service goals, configure your Kafka deployment to optimize for them, and ensure that you are achieving them through monitoring.

Read the whole thing, especially the part about throughput versus latency.

Comments closed

UNION ALL Ordering

Published 2017-05-12 by Kevin Feasel

Paul White shows how UNION ALL concatenation has changed since SQL Server 2008 R2:

The concatenation of two or more data sets is most commonly expressed in T-SQL using the UNION ALL clause. Given that the SQL Server optimizer can often reorder things like joins and aggregates to improve performance, it is quite reasonable to expect that SQL Server would also consider reordering concatenation inputs, where this would provide an advantage. For example, the optimizer could consider the benefits of rewriting A UNION ALL B as B UNION ALL A.

In fact, the SQL Server optimizer does not do this. More precisely, there was some limited support for concatenation input reordering in SQL Server releases up to 2008 R2, but this was removed in SQL Server 2012, and has not resurfaced since.

It’s an interesting article about an edge case.

Comments closed

Improving Performance For JSON In SQL Server

Published 2017-05-11 by Kevin Feasel

Bert Wagner shows how to use indexed, non-persisted columns to pre-parse JSON data in SQL Server:

This is basically a cheat code for indexing computed columns.

SQL will only compute the “Make” value on a row’s insert or update into the table (or during the initial index creation) — all future retrievals of our computed column will come from the pre-computed index page.

This is how SQL is able to parse indexed JSON properties so fast; instead of needing to do a table scan and parsing the JSON data for each row of our table, SQL Server can go look up the pre-parsed values in the index and return the correct data incredibly fast.

Personally, I think this makes JSON that much easier (and practical) to use in SQL Server 2016. Even though we are storing large JSON strings in our database, we can still index individual properties and return results incredibly fast.

It’s great that the database engine is smart enough to do this, but I’m not really a big fan of storing data in JSON and parsing it within SQL Server, as that violates first normal form.  If you know you’re going to use Make as an attribute and query it in SQL, make it a real attribute instead of holding multiple values in a single attribute.

Comments closed

SQL As A Limiting Agent

Published 2017-05-03 by Kevin Feasel

Bert Wagner has advice for application developers:

Basically, if you are running code similar to above, the reason your job is slow is because you are not optimizing where your work is being performed:

  • Every time you write SELECT * you probably are bringing back more data than you actually need — you are hurting your performance.

  • Every time you don’t have a WHERE clause, you are hurting your performance.

  • Every time your process queries the database multiple times (ie. multiple SELECT statements in your job to bring back data), you are hurting your performance.

It’s nothing new for data professionals, but for application developers who avoid the database as much as possible due to a lack of knowledge, this might be a good wake-up call.

Comments closed

Temp Table Caching

Published 2017-05-03 by Kevin Feasel

Paul White explains how to cache temporary objects:

Table variables and local temporary tables are both capable of being cached. To qualify for caching, a local temporary table or table variable must be created in a module:

  • Stored procedure (including a temporary stored procedure)
  • Trigger
  • Multi-statement table-valued function
  • Scalar user-defined function

The return value of a multi-statement table-valued function is a table variable, which may itself be cached. Table-valued parameters (which are also table variables) can be cached when the parameter is sent from a client application, for example in .NET code using SqlDbType.Structured. When the statement is parameterized, table-valued parameter structures can only be cached on SQL Server 2012 or later.

The first time I heard about this was a SQL Saturday presentation that Eddie Wuerch did.  Paul does a great job talking about the requirements (and noting that table variables are eligible as well), making this well worth the time to read.

Comments closed

SQL Data Warehouse Distribution Keys

Published 2017-05-01 by Kevin Feasel

Simon Whiteley explains the different distribution key options available in Azure SQL Data Warehouse and SQL Server APS:

Each record that is inserted goes onto the next available distribution. This guarantees that you will have a smooth, even distribution of data, but it means you have no way of telling which data is on which distribution. This isn’t always a problem!

If I wanted to perform a count of records, grouped by a particular field, I can perform this on a round-robin table. Each distribution will run the query in parallel and return it’s grouped results. The results can be simply added together as a second part of the query, and adding together 60 smaller datasets shouldn’t be a large overhead. For this kind of single-table aggregation, round-robin distribution is perfectly adequate!

However, the issues arise when we have multiple tables in our query. In order to join two tables. Let’s take a very simple join between a fact table and a dimension. I’ve shown 6 distributions for simplicity, but this would be happening across all 60.

Figuring out which distribution key to use can make a huge difference in performance.

Comments closed

Stopwatches

Published 2017-04-28 by Kevin Feasel

Drew Furgiuele explains how to use the .NET Stopwatch class in Powershell:

We can see I cleared the history buffer of my session ( Clear-History ), then ran a script. It’s nothing fancy; just connecting to my local instance of SQL Server and outputting a list of tables to a text file. With Get-History , I can see every command I put in the buffer, and using expressions I can calculate how long a command took.

And that’s great and all, but that’s the entire execution time. If there are multiple steps to your function, how long does each step take? If your script execution is 186 seconds, how much of time is spent on a database query? What about a loop? How long does each iteration take? Are you writing to a network share, and want to know what the latency is? Get-History  isn’t the tool of this, but thankfully we have other methods.

I use the stopwatch a lot for similar things; it’s a useful tool.

Comments closed

Adaptive Query Processing In CTP 2.0

Published 2017-04-20 by Kevin Feasel

Joe Sack has a couple blog posts on adaptive query processing enhancements in SQL Server 2017 CTP 2.0.  First, Batch Mode Adaptive Joins:

We have seen numerous cases where providing a specific join hint solved query performance issues for our customers.  However, the drawback of adding a hint is that we remove join algorithm decisions from the optimizer for that statement. While fixing a short-term issue, the hard-coded hint may not be the optimal decision as data distributions shift over time.

Another scenario is where we do not know up front what the optimal join should be, for example, with a parameter sensitive query where a low or high number of rows may flow through the plan based on the actual parameter value.

With these scenarios in mind, the Query Processing team introduced the ability to sense a bad join choice in a plan and then dynamically switch to a better join strategy during execution.

That one’s really cool.  Joe also talks about interleaved execution for multi-statement TVFs:

SQL Server has historically used a unidirectional “pipeline” for optimizing and executing queries.  During optimization, the cardinality estimation process is responsible for providing row count estimates for operators in order to derive estimated costs.  The estimated costs help determine which plan gets selected for use in execution.  If cardinality estimates are incorrect, we will still end up using the original plan despite the poor original assumptions.

Interleaved execution changes the unidirectional boundary between the optimization and execution phases for a single-query execution and enables plans to adapt based on the revised estimates. During optimization if we encounter a candidate for interleaved execution, which for this first version will be multi-statement table valued functions (MSTVFs), we will pause optimization, execute the applicable subtree, capture accurate cardinality estimates and then resume optimization for downstream operations.

The goal here is to make Table-Valued Functions viable from a performance perspective.  The team has a long way to go there, but they’re working on it.  Also, Joe gives a shout out to Arun Sirpal, frequent curatee.

Comments closed

Transactional Replication Procedures

Published 2017-04-12 by Kevin Feasel

Drew Furgiuele offers up warnings when thinking about rolling your own transactional replication stored procedures:

In the above picture, we can see that it did replicate the execute statement, and that it affected 19,972 rows on the replica, and it only took 67ms! Sounds awesome, doesn’t it? Here’s a way to handle large batch updates at your publishers without overwhelming your replication setup. But before you go changing everything, you should probably understand that this has some really, really bad side effects if you’re not careful. Let’s look at three really big ones.

All in all, it’s a fairly risky move but might be worth the performance improvements.

Comments closed