Wednesday, November 29, 2017

Adaptive Query Processing and Automatic Tuning - Adaptive Joins - 2

Continuing from the introduction written over here, let’s look at one of the aspects of Adaptive Query Processing which is Adaptive Joins.

Before we start Adaptive Joins, SQL Server's Query optimizer internally has 3 join types.

Nested Loop Join - Usually picked by optimizer, when tables participating in the joins are small or when one of the table is big and indexed on joining column
Hash Join: One of the table is big but doesn't have an index
Merge Join: Both the tables are big, and the joining column is indexed

So, the size of the table or the rows participating in the join plays a major role SQL's Query optimizer picking the correct join type. Let’s say, SQL Server estimates few rows participating in the join at compile time and picks "nested join" but during runtime, the join extracts larger number of rows and thereby making the choice of plan ineffective. 

SQL Server 2017's adaptive join addresses this problem. With Adaptive Join, SQL Server comes with plan attribute called "Adaptive Row Threshold" which is a row count threshold estimated by SQL Server.  "Adaptive Row Threshold" will help SQL Server dynamically alter the plan choice at run time. Adaptive join in short will work in the following way

  • If the number of rows participating in the join are greater than "Adaptive Row Threshold" rows, then "Hash Join" operator is used for join operation
  • If the number of rows participating in the join are lesser than "Adaptive Row Threshold" rows, then "Nested loop Join" operator is used for join operation
Consider the following query:

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] = 360;

Observe the picture below:

Query Optimizer fixes 68 rows as Adaptive join threshold.
Estimated number of rows for the outer table in the join was 213. As 213 is greater than 68, estimated join type is Hash Match Join. During runtime, 206 rows from outer table participated in the join and as it was greater than "Adaptive join threshold" of 68, Hash match join was selected.
Sharing another example where Adaptive Join switches the join type in runtime. Refer to pic below

In this case, threshold was 46 rows and estimate were 87 rows. However, runtime row count was just 36 rows hence switching the join type to Nested loop join from an estimate of "Hash Join"
 The ability to alter execution plan operator in runtime would make SQL Server's query optimizer weed out most of the incorrect plan choices during runtime.

Saturday, November 25, 2017

Tricks - Copy T SQL Result to Excel

Sometimes, some of the silliest of tricks save so much of time and effort. Very common scenario is to copy a query result to excel. Most take either of the 2 approaches

* Copy the result from result grid of management studio and paste to Excel as shown below

The problem with above approach is sometimes, when one pastes the result to excel, the formatting can go haywire, especially if the result contains carriage return (new line) and commas as shown below.

* Export to excel via export wizard

Little tedious and sometimes getting the mapping right can be a pain.

Over the years, the simplest and effective technique, I have used is running T SQL query directly from excel (Yes, you can do that :)). Not just that, it gives a cleanly formatted result all the time!!! Step by step instructions provided below

How to run TSQL Query from Excel:

Open Excel and move to data tab and click on the icon "Other Data Sources" and select "SQL Server" as shown below.

Enter the server to connect to and the credentials

Select the database against which you want to run the query. Pick a random table (doesn't matter if you are not going to query it. Any table is fine)

Click next and finish (we are not done yet :))

Click on properties

Uncheck background refresh check box

Click on the "Definition" tab and change the command type to "SQL" from "Table"

Paste the query you want to run on the "Command Text" box as shown below

Result would be a nice formatted excel sheet as shown below

From excel once can not only get select query results but stored procedures as well. Excel also servers as an excellent tool to query database when one needs to access the database on a machine that doesn't have SQL Server Management Studio. Hope the tip was useful!!!

Sunday, November 12, 2017

Adaptive Query Processing and Automatic Tuning - Introduction - 1

SQL Server 2017 and Azure SQL Database are being touted as the self learning, artificial intelligent database. One of the main reasons for the same is due to release of the following features

 1) Adaptive Query Processing

2) Automatic Tuning
Adaptive Query Processing is SQL Server's first attempt using artificial intelligence to fix poor performing queries due to incorrect cardinality estimates. Query optimizer produces better plans using "Adaptive Query Processing" which is enabled by default when one is on SQL 2017's compatibility mode (CE 140). Queries can benefit via Adaptive Query Processing in the following scenarios.

1) Adaptive Join -> Query optimizer makes a intelligent choice between Nested Loop and Hash Join in runtime

2) Memory Grant Feedback -> Query optimizer adjusts the memory granted to a query depending upon the previous run's execution results

3) Interleaved Execution -> Portion of the query is executed while generating the query plan to come with the estimates. Applies currently for Multi Statement Table Valued Functions alone

 The above scenarios and operators are likely to expand in upcoming releases or patches

 Other aspect of auto driven query tuning is "Automatic Tuning" feature. Automatic Query tuning helps in the following scenarios
 Automatic Tuning:

1) Force Last Good Plan: Attempts to fix query regression by detecting any sudden change in query performance due to plan change and forcing the last good performing plan. one can turn it on using the command below
2) Automatic Index creation and deletion: Feature available only in Azure SQL Database. By studying the workload, the database engine automatically deletes and creates indexes!!!
This is just a introduction post. As usual,will be writing a series of posts to cover the individual sections in detail.