Batch Requests Per Second

Tara Kizer explains the Batch Requests/sec metric:

WHAT IS BATCH REQUESTS/SEC?

Batch Requests/sec is a performance counter that tells us the number of T-SQL command batches received by the server per second. It is in the SQLServer:SQL Statistics performance object for a default instance or MSSQL$InstanceName:SQL Statistics for a named instance.

WHAT COMPRISES A BATCH?

If I execute a stored procedure that has multiple queries and calls to other stored procedures, what will Batch Requests/sec show? Let’s test it to find out.

Click through for the answer.

Lightweight Statistics Profiling

Arun Sirpal looks at trace flag 7412 in SQL Server 2016 SP1:

According to documentation online (https://msdn.microsoft.com/en-us/library/mt791503.aspx) it states that the new query execution statistics profiling infrastructure dramatically reduces performance overhead of collecting per-operator query execution statistics.

Read on, as Arun might have discovered a bug in it.

Performance Tuning A Streaming Application

Mathieu Dumoulin explains how he was able to get 10x performance out of a streaming application built around Kafka, Spark Streaming, and Apache Ignite:

The main issues for these applications were caused by trying to run a development system’s code, tested on AWS instances on a physical, on-premise cluster running on real data. The original developer was never given access to the production cluster or the real data.

Apache Ignite was a huge source of problems, principally because it is such a new project that nobody had any real experience with it and also because it is not a very mature project yet.

I found this article fascinating, particularly because the answer was a lot more than just “throw some more hardware at the problem.”

The Cost Of Compression

Erin Stellato looks at the COMPRESS function:

While this data is interesting, I’m more curious about how compression affects my everyday SELECT queries. I have a set of three stored procedures that each have one SELECT query, so that each index is used. I created these procedures for each table, and then wrote a script to pull values for first and last names to use for testing. Here is the script to create the procedures.

Once we have the stored procedures created, we can run the script below to call them. Kick this off and then wait a couple minutes…

To me, the COMPRESS function is most useful for compressing information you tend not to search through but need to keep the in the database, like HTML markup or long descriptions.

Filtered Indexes And Parameters

Erik Darling shows an example of what happens when you have a filtered index and parameterize the filter:

It Is Known

That when you use filtered indexes, they get ignored when your queries are parameterized. This is a Plan Caching Thing©, of course. The simplest example is a bit column with a filtered index. If your index is on WHERE Bit = 1, it doesn’t have data for WHERE Bit = 0. That index would only be suitable for one variation of the query, so caching a plan that uses an index which can’t be reused for every variation isn’t feasible.

Read on for a couple examples, and check the comments on this as well.

BatchMode Execution

Sunil Agarwal describes BatchMode execution with columnstore indexes:

You may be wondering what is this magic number 900 rows within a batch? Well, when executing a query in BatchMode, SQL Server allocates a 64k bytes structure to group the rows. The number of rows in this structure can vary between 64 to 900 depending upon number of columns selected. For the example above, there are two columns that are referenced and X marks the rows that qualified in the BatchMode structure shown in the picture below. If SCAN is part of a bigger query execution tree,  the pointer to this structure is passed to the next operator for further processing. Not all operators can be executed in BatchMode. Please refer to Industry leading analtyics query performance for details on BatchMode Operators.

Under the right circumstances, BatchMode execution can be a major performance benefit.

Columnstore Elimination

Sunil Agarwal has a two-part series on columnstore data elimination.  First up is column elimination:

Now, let us run the same query on the table with clustered columnstore index as shown in the picture below. Note, that the logical IOs for the LOB data is reduced by 3/4th for the second query as only one column needs to be fetched. You may wonder why LOB? Well, the data in each column is compressed and then is stored as BLOB. Another point to note is that the query with columnstore index runs much faster, 25x for the first query and 4x for the second query.

Next up is rowgroup elimination:

In the context of rowgroup elimination, let us revisit the previous example with sales data

  • You may not even need partitioning to filter the rows for the current quarter as rows are inserted in the SalesDate order allowing SQL Server to pick the rowgroups that contain the rows for the requested date range.
  • If you need to filter the data for a specific region within a quarter, you can partition the columnstore index at quarterly boundary and then load the data into each partition after sorting on the region. If the incoming data is not sorted on region, you can follow the steps (a) switch out the partition into a staging table T1 (b) drop the clustered columnstore index (CCI) on the T1 and create clustered btree index on T1 on column ‘region’ to order the data (c) now create the CCI while dropping the existing clustered index. A general recommendation is to create CCI with DOP=1 to keep the prefect ordering.

From these two articles, queries which hit a small percentage of columns and stick to a relatively small number of rowgroups will likely perform better.  For people who understand normal B-tree indexes, the second point seems clear enough, but the first point is at least as important.

Power Saving Mode

Mike Walsh recommends ensuring your servers are not using any form of power saving mode:

Balanced power mode has a major impact on SQL Server performance. Simply put, you’ve told Windows Server (through Control Panel) or your server hardware (through BIOS settings) to sacrifice a few performance-minded things for the sake of using a little less power. In fact, in plenty of studies and blog posts by folks in the community (including this post by Glenn Berry), you can see the effect of CPU power saving, especially. In essence, the CPUs will run at a lower clock multiplier when demand isn’t deemed high enough, and that clock speed will only increase when demand is high enough for long enough. This results in a slower CPU speed during normal operations. That setting actually works well on my laptop or tablet when I want to conserve battery life and don’t have a workload that is sensitive to CPU speed.

For a SQL Server, though? That is horrible for performance. Windows balanced mode also can cause other components to run slower or behave differently than when the server is running in High Performance mode. For instance, USB ports can be underpowered and network interfaces can be under-powered or even go to sleep. Frankly, for a SQL Serve, nothing good comes out of these modes.

Read on for a few methods for checking whether your servers are affected.

ETL With Spark

Eric Maynard demonstrates that moving data across Hadoop clusters can be sped up by using Spark:

By leveraging Spark for distribution, we can achieve the same results much more quickly and with the same amount of code. By keeping data in HDFS throughout the process, we were able to ingest the same data as before in about 36 seconds. Let’s take a look at Spark code which produced equivalent results as the bash script shown above — note that a more parameterized version of this code code and of all code referenced in this article can be found down below in the Resources section.

Read the whole thing.

Cardinality Estimator Regressions

SQL Scotsman has a great post on figuring out which of your queries have become worse as a result of the SQL Server cardinality estimator changes in 2014:

Instantly it is apparent that the most resource intensive query was the same query across both workload tests and note that the query hash is consistent too.  It is also apparent that this query performs worse under the new cardinality estimator model version 120.  To investigate and understand why this particular query behaves differently under the different cardinality estimators we’ll need to look at the actual query and the execution plans.

Looking at the information in #TempCEStats and the execution plans, the problematic query below belongs to the SLEV stored procedure.

There’s also a discussion of Query Store in there, but it’s important to understand how to figure this out even if you’re on 2014 and don’t have access to Query Store.

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