Friday, March 30, 2018

Azure SQL Database – Automatic Index Tuning

Introduction: -

We live in an era of “Automation”.  Planes run on auto pilot mode, cars will soon be auto driven, so why not have auto indexed databases? This article will discuss the automatic index tuning feature available in Azure SQL Database.

Problem Statement:

Application development is moving at a rapid pace and its very common for application teams to develop a template database design and deliver it to multiple customers.  It is even more common in cloud where solutions are delivered as “software as a service” and it is way too challenging for application development team to analyze the workload of individual database, design specific indexes for them and tune them.

Automatic tuning in Azure SQL Database

Azure SQL Database, the cloud Platform as a service (PAAS) offering of SQL Server has attempted to address the above problem by introducing “Automatic tuning”. As of date, “automatic tuning” has the following features

·         Automatic plan correction – Plan regressions or sudden change in query plan resulting in performance degradation are tracked and last good plan available is forced. Automatic plan correction is already well explained over here

·         Automatic Index Tuning:  Automatic index tuning feature of Azure SQL Database will study the workload executed against the database, identify useful indexes and create them automatically. Automatic index tuning will also drop indexes that are unused over 93 days or any duplicate indexes present.

How to enable automatic index tuning

Once logged in to Azure Portal ( one can enable the same by navigating to SQL Databases-> Click on the database -> Automatic tuning

·         Force plan -> Enables Automatic Plan Correction
        ·         Create / Drop Index -> enables automatic index tuning

Features can be enabled at server level and can be inherited at the database level as well.

TSQL Script for enabling automatic index tuning provided below
      DROP_INDEX = ON)


To test how Auto Indexing works, following things were performed
·         A sample database with 5 tables with no indexes was created
·         10 to 15 select queries against the tables were executed repeatedly few thousand times over 3 to 4 hours
·         After one day, automatic indexing found the recommendations and rolled them out when there was less or no workload on the database.  Auto Indexing requires minimum one day of monitoring to identify recommendations
 This can be verified on Azure Portal by clicking on the “Database name” -> “Performance Recommendation”

The same can also be verified via the DMV sys.dm_db_tuning_recommendations, documented clearly over here
Key pointers of Automatic Index Tuning

·         Indexing recommendations are obtained using advanced artificial intelligence rules applied on the monitored workload. The recommendations are expected to be lot more accurate than earlier missing index recommendations from SSMS
·         The best part of the feature is it is non-blocking during the rollout of new indexes. 
·         Index changes wouldn’t be rolled out and will be postponed, if DTU usage was above 80% anytime in the last 20 minutes
·         Automatically created indexes don’t enforce any dependency on the columns. When the column with automatically created index is dropped, index is dropped as well
·         Auto created indexes can be tracked via sys.indexes – auto_created column
Learn – Adapt - Verify

One of the key features of Automatic Index tuning is the ability to monitor the impact of the changes done via automatic tuning. Azure SQL Database closely monitors the automatically created or dropped indexes and verifies if it has achieved the desired performance improvement. If it doesn’t then the change is automatically reverted to prevent any negative impact to the database. The reverted changes can be tracked from sys.dm_db_tuning_recommendations DMV as well.

Suggested enhancements

·         Automatic indexing is available only as an Azure SQL Database feature and is not available on SQL Server on premise. If would be wonderful if the feature could be extended to on premise SQL Server.
·         Automatic Index tuning should be extended to fixing fragmentation issues of the indexes as well.
 Automatic Index Tuning does simplify one of the major tasks of a database administrator and a developer. Automatic Index Tuning may not be 100% perfect to get the choices correct all the time but it is certainly a step in the right direction. Expect Microsoft to release more of such features in the days to come not just in indexing but also all areas of database maintenance

Sunday, March 18, 2018

Webinar on Adaptive Query Processing for Data Platform Geeks

My session on Adaptive Query Processing has been finding significant interest in Data Platform events :) Delivered 2 webinars for Data Platform Geeks community, one of them at American time and other with Indian time. Indian time webinar gave me massive attendance with 100 enthusiasts joining in. Recording of one of the webinars shared below for people who couldn't join in.

Data Platform Geeks have quite a few webinars going on. Do check out their website over here and join them.  

Friday, February 9, 2018

Data Channel Interview - 10 - Dinesh Priyankara on Azure Data Lake Analytics

Here we go for the 10th Interview of Data Channel. In this interview, Dinesh Priyankara, a 12-time MVP from Sri Lanka, demystifies Azure Data Lake and ADL Analytics. This interview is an excellent starting point if you looking to learn Azure Data Lake Analytics. Personally, Imo, one of my best performances as an interviewer asking the right questions. Happy Learning!!!

Sunday, January 21, 2018

Configuration Manager Missing - Windows 10

SQL Server Configuration Manager is one of the fundamental tools for a DBA to do basic tasks like starting or stopping SQL Service, knowing how many instances and what components of SQL Server are been installed on the machine.

Imagine this, suddenly you find your configuration manager missing. Pretty annoying and can be one of those things which can give that 15 minutes of extreme irritating moments. Not sure, why it happens ( Windows update perhaps ? ) but have seen this in couple of machines now. You can find it at "C:\Windows\SysWOW64\" directory

For SQL Server 2017 it is : C:\Windows\SysWOW64\SQLServerManager14.msc 
For SQL Server 2016/2014 it is : C:\Windows\SysWOW64\SQLServerManager13.msc / SQLServerManager12.msc

To add it back to the start menu, copy it from the original location provided above and paste it to

C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Microsoft SQL Server 2017\Configuration Tools\

This would put back the configuration manager under start menu. Hope it helps!!!

Sunday, December 31, 2017

Azure SQL Database - Backup

Most of you must be knowing that backup in Azure SQL Database fully automatic. After the database is created and the database is automatically backed up by Azure. One can restore to any point in time for a period of 7 days for the basic subscription and 35 days for standard and premium.

Most of the above are already known to you. But, what can be little surprising for someone learning Azure SQL Database is, one can never access backup files used for recovering the database. Meaning, you can only restore the database as another azure sql database but you can never download the automatic backup files like you would do when you configure backups on your on-premise database.

So, what do you do when you need the backup file of Azure SQL Database for reference?

Create a bacpac file backup (similar to your full back up on - premise database ) of Azure SQL Database

1) Connect to the Azure SQL Database via SSMS
2) Right click tasks-> Export -Data-Tier Application. Follow the screenshots below to manually take a backup of Azure SQL Database

So, if you need an older backup of the azure database (yesterday 8:00 am backup), then recover the older version of the database using the Azure portal to a different azure sql database and extract the bacpac file using the steps explained above.

Saturday, December 23, 2017

My Interview at Data Platform Geeks 2017

Dear All,

Earlier this year in August, I had the privilege of speaking at Data Platform Summit 2017 at Bangalore. As always a terrific experience to hangout, interact and learn from the best in the business. Here is my interview to DPS Team about my experience at the summit

Adaptive Query Processing - Adaptive Joins - Plan resue - 3

Continuing on the series on Adaptive Query Processing covered over here, this post would cover what happens to adaptive joins when plans are reused.

To give a little bit of context to the post, the earlier posts in the series explained how SQL Server 2017's Adaptive joins made it possible for SQL Server to switch between nested loop join and hash join in query plan during runtime depending upon the number of rows returned by one of the joining tables. "Adaptive join threshold" is a row count threshold used to decide whether to go for Nested loop join or hash join.
So, the question is what happens when the query plan is reused? Does the Query plan dynamically switch between the nested loop and hash join when the query plan is reused or does it just go with the last choice? What is the role of "Adaptive join threshold" when the plan is reused? To check it out, let's test it



EXECUTE sp_executesql

         N'SELECT  [fo].[Order Key], [si].[Lead Time Days], [fo].[Quantity]

FROM    [Fact].[Order] AS [fo]

INNER  JOIN [Dimension].[Stock Item] AS [si]

       ON [fo].[Stock Item Key] = [si].[Stock Item Key]

WHERE   [fo].[Quantity] = @quantity', N'@quantity int',@quantity = 360

EXECUTE sp_executesql

         N'SELECT  [fo].[Order Key], [si].[Lead Time Days], [fo].[Quantity]

FROM    [Fact].[Order] AS [fo]

INNER  JOIN [Dimension].[Stock Item] AS [si]

       ON [fo].[Stock Item Key] = [si].[Stock Item Key]

WHERE   [fo].[Quantity] = @quantity', N'@quantity int',@quantity = 130

Step 1: Clearing the cache
Step 2: Run the query using sp_executesql and pass the variable, so that plan is reused
Step 3: Run the same query with a different parameter value, so that the plan is reused with a different value.

Don't forget to turn on the Show query plan option to see the query plan.
Query plans for both the queries provided below

Both use Adaptive Joins which is a good news.
Now, let's compare the properties of Adaptive Join operator to understand more

First observation: Actual join type for both executions are different. So, Adaptive Join switches the join operator dynamically even when the plan is reused
 Second Observation: Adaptive threshold of rows remains the same and this implies it is fixed at the compile time or when the plan was first generated. Hence "Estimated Join Type" also remains the same.

 So, to summarize, Adaptive Join threshold is computed at the compile time. So, when the plans are reused, the same adaptive join threshold is used for all executions. However, Adaptive join still switches the join operator at each execution, using the adaptive join threshold calculated at the first time the plan was generated.