Tag Archives: sql

What the Checkpoint does?

Checkpoints flush dirty data pages from the buffer cache of the current database to disk. This minimizes the active portion of the log that must be processed during a full recovery of a database. During a full recovery, the following types of actions are performed:

  • The log records of modifications not flushed to disk before the system stopped are rolled forward.
  • All modifications associated with incomplete transactions, such as transactions for which there is no COMMIT or ROLLBACK log record, are rolled back.

Checkpoints occur in the following situations:

  • A CHECKPOINT statement is explicitly executed. A checkpoint occurs in the current database for the connection.
  • A minimally logged operation is performed in the database; for example, a bulk-copy operation is performed on a database that is using the Bulk-Logged recovery model.
  • Database files have been added or removed by using ALTER DATABASE.
  • An instance of SQL Server is stopped by a SHUTDOWN statement or by stopping the SQL Server (MSSQLSERVER) service. Either action causes a checkpoint in each database in the instance of SQL Server.
  • An instance of SQL Server periodically generates automatic checkpoints in each database to reduce the time that the instance would take to recover the database.
  • A database backup is taken.
  • An activity requiring a database shutdown is performed. For example, AUTO_CLOSE is ON and the last user connection to the database is closed, or a database option change is made that requires a restart of the database.


VLF (Virtual Log Files)


To see how many VLFs you have solely look at the number of rows returned by DBCC LOGINFO.

The size and number of VLFs you’ll have depends largely on the size that the chunk is when it’s added to you transaction log.

There is no general rule how to determine the best values for the auto-growth option, as these vary from case to case. Having too many or too little virtual log files causes bad performance.

Having an excessive number of VLFs can negatively impact all transaction log related activities and you may even see degradation in performance when transaction log backups occur.

Most of the time excessive VLF fragmentation is brought about by excessive file growth at small intervals. For example, a database that is set to grow a transaction log file by 5mb at a time is going to have a large number of VLFs should the log decide to grow.

Growth Number of VLFs created
<= 64Mb 4
>64 but <=1Gb 8
>1Gb 16

SQL Operations Studio Linux

Great news, a visual way to manage SQL Server from Linux.

SQL Operations Studio came to fill a gap we were waiting to develop and manage SQL Server databases on Linux. In the link https://github.com/Microsoft/sqlopsstudio we can see more information like the features below.

Feature Highlights

  • Cross-Platform DB management for Windows, macOS and Linux with simple XCopy deployment
  • SQL Server Connection Management with Connection Dialog, Server Groups, and Registered Servers
  • Object Explorer supporting schema browsing and contextual command execution
  • T-SQL Query Editor with advanced coding features such as autosuggestions, error diagnostics, tooltips, formatting and peek definition
  • Query Results Viewer with advanced data grid supporting large result sets, export to JSON\CSV\Excel, query plan and charting
  • Management Dashboard supporting customizable widgets with drill-through actionable insights
  • Visual Data Editor that enables direct row insertion, update and deletion into tables
  • Backup and Restore dialogs that enables advanced customization and remote filesystem browsing, configured tasks can be executed or scripted
  • Task History window to view current task execution status, completion results with error messages and task T-SQL scripting
  • Scripting support to generate CREATE, SELECT and DROP statements for database objects
  • Workspaces with full Git integration and Find In Files support to managing T-SQL script libraries
  • Modern light-weight shell with theming, user settings, full screen support, integrated terminal and numerous other features

Let’s see how it works:

The process is very simple. First step download de file in this link https://go.microsoft.com/fwlink/?linkid=862646

After download the next step is to extract the files in a folder wherever you want and run the sqlops file.

My first impression is quite good. A good visual tool to work with SQL Server on Linux. I like the execution plan view and how to manage the databases.



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Comparing with SQL Server Management Studio I miss some options such as:

  • LinkedServer Objects
  • Maintenance Plans
  • Replication
  • Database Mail
  • Extended Events

The conclusion is I really can work on that and run all my scripts and create objects like linkedservers, extended events or replications. That helps a lot to try a new platform concepts.


Storage – Part II

Logical Structures
How, where, when you need to think in data storage? Well, this is the first step after you modeling your database and most companies do not think about it, I have saw companies with a large data into one disk or small data separated in the wrong way, but what is the best to do?

We have some best practices and you can read more about logical structures in SQL Server called filegroups in MSDN page. Every DBA needs to know to create and maintain filegroups because they are part of every SQL Server database. Filegroups affect the performance, maintenance, and security of your data and they are logical structures to group files together.filegoups

At a minimum, every SQL Server database has two operating system files: a data file and a log file. Data files contain data and objects such as tables, indexes, stored procedures, and views. Log files contain the information that is required to recover all transactions in the database. Data files can be grouped together in filegroups for allocation and administration purposes.

Filegroups can be created when the database is first created or created later when more files are added to the database. However, you cannot move files to a different filegroup after the files have been added to the database.

A file cannot be a member of more than one filegroup. Tables, indexes, and large object (LOB) data can be associated with a specific filegroup. This means that all their pages are allocated from the files in that filegroup.

Why use filegroups?

  • you have one or more objects tha have heavy read/write activity
  • you’ve already tuned through indexes and query writing
  • you need a performance boost
  • you have additional storage you can utilize to separate the objects
  • you want to separate tha data so administration tasks such as backups take less time
  • you have a large database (>1TB)
  • disaster and recovery

Best Practices

  • separate data and log files onto separate disks
  • separate tempdb onto its own disk
  • at least two filegroups – primary and one user-defined (default)
  • files in a filegroup should be equally sized for equal proportion of writes
  • use filegroups to isolate objects with heavy read and write activity from each other

Search Arguments

SARG or Search Arguments are very important for a good query, even if the predicate is very selective and supported by an index, does not guarantee that SQL Server is going to use an index. You need to write an appropriate predicate to allow the Query Optimizer to take advantage of the indexes. The Query Optimizer is not omnipotent. It can decide to use an index only when the arguments in the predicate are searchable. You have to learn how to write appropriate search arguments (SARGs).


To write an appropriate SARG, you must ensure that a column that has an index on it appears in the predicate alone, not as a function parameter. SARGs must take the form of column inclusive_operator <value> or <value> inclusive_operator column. The column name is alone on one side of the expression, and the constant or calculated value appears on the other side. Inclusive operators include the operators =, >, <, =>, <=, BETWEEN, and LIKE.
However, the LIKE operator is inclusive only if you do not use a wildcard % or _ at the beginning of the string you are comparing the column to.

Above the query returns two rows only; therefore, the WHERE predicate is very selective. There is a nonclustered index on the orderdate column. However, SQL Server did not use the index, as you can see.

The orderdate in the predicate does not appear alone; it is instead an argument of a function. You can rewrite such a query many times. The following query produces the same result, but this time the predicate is a SARG and we can see the different cost for both querys.

difference sarg

Using the AND operator in the WHERE clause predicate means that each part of the predicate limits the result set even more than the previous part. For example, if the first condition limits a query to five rows, then the next condition connected to the first one with the logical AND operator limits the query to five rows at most. The Query Optimizer understands how the logical AND operator works, and can use appropriate indexes.

However, the logical OR operator is inclusive. For example, if the first condition in a predicate would limit the query to 5 rows and the next condition connected to the first condition with the logical OR operator would limit the query to 6 rows, then the result set could have anything between 6 and 11 rows. If the two conditions use two different columns, then SQL Server conservatively takes the worst case and estimates that the query would return 11 rows.

Having multiple conditions in a predicate connected with the OR operator lowers the possibility for SQL Server to use indexes. You should consider rewriting the predicate to a logically equivalent predicate that uses the AND operato.

Another thing you have in mind is that SQL Server executing the IN and the OR operators in the same way. For example the following two queries return the same two rows and are internally treated as equal.

sarg or

Temporary Tables

Sometimes we need to use temporary tables to do aggregations, joins or to simplify the job. SQL Server supports a number of options that you can use to store data temporarily. You can use temporary tables and table variables, before we start with codes we need know some concepts.

Local temporary tables are named with a single number sign as a prefix; for example, #T1.

They are visible only to the session that created them. Different sessions can actually create temporary tables with the same name, and each session will see only its own table. Behind the scenes, SQL Server adds unique suffixes to make the names unique in the database, but this is transparent to the sessions.

Local temporary tables are visible throughout the level that created them, across batches, and in all inner levels of the call stack. So if you create a temporary table in a specific level in your code and then execute a dynamic batch or a stored procedure, the inner batch can access the temporary table. If you don’t drop the temporary table explicitly, it is destroyed when the level that created it terminates.

Global temporary tables are named with two number signs as a prefix; for example, ##T1.
They are visible to all sessions. They are destroyed when the session that created them terminates and there are no active references to them.

Table variables are declared, as opposed to being created. They are named with the at sign (@) as a prefix; for example, @T1. They are visible only to the batch that declared them and are destroyed automatically at the end of the batch. They are not visible across batches in the same level, and are also not visible to inner levels in the call stack.

Temporary tables are created in tempdb in the dbo schema. As already mentioned, you can create two temporary tables with the same name in different sessions, because SQL Server internally adds a unique suffix to each. But if you create temporary tables in different sessions with the same constraint name, only one will be created and the other attempts will fail.

Physical Representation in tempdbtempDB

There’s a common misconception that only temporary tables have a physical representation in tempdb and that table variables reside only in memory. This isn’t true. Both temporary tables and table variables have a physical representation in tempdb.

You can find entries in the sys.objects view for the internal tables that SQL Server creates in tempdb to implement your temporary tables and table variables. As an example, the following code creates a temporary table called #T1 and then queries the sys.objects view in tempdb looking for table names starting with #.

As we can see, both the variable as a temporary table are created implicitly and explicitly in tempdb.

So when should I use one or the other?

When it comes to performance, there’s a very important difference between temporary tables and table variables. SQL Server maintains distribution statistics (histograms) for temporary tables but not for table variables. This means that, generally speaking, you tend to get more optimal plans for temporary tables. This comes at the cost of maintaining histograms, and at the cost of recompilations that are associated with histogram refreshes.


This plan is very efficient. The optimizer examined the histogram on col2 and estimated that a very small number of rows are supposed to be filtered. The plan decided to use the index on col2 because the filter is very selective. Only a small number of key lookups are required to obtain the respective data rows. For such a selective filter, this plan is preferable to one that does a full clustered index scan.


Unlike with temporary tables, SQL Server doesn’t maintain histograms for table variables. Not being able to accurately estimate the selectivity of the filter, the optimizer relies on hardcoded estimates that assume fairly low selectivity (30 percent). It just doesn’t realize that the filter is actually very selective, and that a plan more similar to the one shown earlier in the temporary table would have been much more efficient.

A common question is whether table expressions such as common table expressions (CTEs) also get persisted like temporary tables and table variables. The answer is no. When SQL Server optimizes a query against a table expression, it unnests the inner query’s logic and interacts directly with the underlying tables. This means that unlike temporary tables and table variables, table expressions have no physical side to them.

The conclusion from this example is that when the plan efficiency depends on existence of histograms, you should use temporary tables. Table variables are fine to use in two general cases. One is when the volume of data is so small, like a page or two, that the efficiency of the plan isn’t important. The other case is when the plan is trivial. A trivial plan means that there’s only one sensible plan and the optimizer doesn’t really need histograms to come up with this conclusion. An example for such a plan is a range scan in a clustered index or a covering index. Such a plan is not dependent on selectivity of the filter—it’s simply always a better option
than a full scan.

The Meaning of “Set-Based”

The term set-based is used to describe an approach to handle querying tasks and is based on principles from the relational model. Remember that the relational model is based in part on mathematical set theory. Set-based solutions use T-SQL queries, which operate on the input tables as sets of rows. Such solutions are contrasted with iterative solutions that use cursors or other iterative constructs to handle one row at a time.

According to set theory, a set should be considered as a whole. This means that your attention should be focused on the set and not on its individual elements. With iterative solutions, you break this principle by operating on one element (row) at a time. Also, a set has no particular order to its elements. So when you use set-based solutions, you cannot make any assumptions about the order of the data. Similarly, unless you add an ORDER BY clause to the query, you’re not guaranteed that the data will be returned in any particular order. With iterative solutions, you process one row at a time and you can do so in a specific order.

When you use set-based solutions, you provide your request as a declarative plain language query. In your request, you focus on the “what” part of the request and let the database engine worry about the “how” part. With iterative solutions, you need to implement both the what and the how parts in your code. As a result, iterative solutions tend to be much longer than set-based ones and harder to follow and maintain.

Another reason why you should stick to set-based solutions is a very pragmatic one—performance. Iterative constructs in T-SQL are very slow. For one thing, loops in T-SQL are much slower than those in other programming languages such as in Microsoft .NET code. Secondly, each record fetch from a cursor by using the FETCH NEXT command has quite a high overhead associated with it. There’s no such overhead when SQL Server processes a set-based solution, even if internally the execution plan for the query involves iterations. As a result, if you know how to tune queries, you are often able to achieve much better performance compared to using iterative solutions.

For example, I have seen something like this:
actid INT,
mx MONEY );

@actid AS INT,
@val AS MONEY,
@prevactid AS INT,
@prevval AS MONEY;

SELECT actid, val
  FROM dbo.Transactions
  ORDER BY actid, val;

SELECT TOP(1) @prevactid = actid, @prevval = val
FROM #tbTemp;

WHILE @prevactid NOT IS NULL
IF @actid @prevactid
     INSERT INTO @Result(actid, mx) VALUES(@prevactid, @prevval);

  DELETE #tbTemp
  WHERE actid = @actid;

  SELECT TOP(1) @prevactid = @actid, @prevval = @val
  FROM #tbTemp;

IF @prevactid  IS NOT NULL
INSERT INTO @Result(actid, mx) VALUES(@prevactid, @prevval);

SELECT actid, mx
FROM @Result;

Some people think this solution is set-based because it doesn’t explicitly declare and use a cursor object (we talk about this later or never 🙂 ). However, recall that one of the principles implemented by set-based solutions is that they treat the set as a whole as opposed to handling one element at a time. This principle is violated here. Also, set-based solutions do not rely on the order of the data, and this one does.

How we can do this with a set-based rule. Well, lets see the following code:
T1.actid, T1.tranid, T1.val,
SUM(T2.val) AS balance
FROM dbo.Transactions AS T1
JOIN dbo.Transactions AS T2 ON T2.actid = T1.actid AND T2.tranid <= T1.tranid
GROUP BY T1.actid, T1.tranid, T1.val;

The code above use a recursive join, recursive querys are commonly used with CTE (we talk later) and for this time this works well, but the best way in SQL Server 2012 is using Window Functions.

SELECT actid, tranid, val,
               ORDER BY tranid
AS balance
FROM dbo.Transactions;

The conclusion is, always have a way to work with sets, the first intent wasn’t set-based because works one by one element at time.
I used that code with a temporary table (#tbTemp) because I will discuss this subject in the next post.