Implement clustered, nonclustered, filtered, and columnstore indexes

-
SQL Server would resort to do a table scan operation, which is essentially a full read of all rows in a table. A table scan is typical on tables with no clustered indexes, also known as heap tables. A table scan is the most inefficient read operation that SQL Server performs while all data pages in the table are read. Table scans increase memory, disk, and CPU resource utilization and cause queries to perform slow.

To illustrate the performance impact of table scans, compare the execution plans of a query executed against a table with a clustered index and a table without a clustered index (heap table). For this comparison, use the [Dimension].[City] table in the Wide World Importers DW database.




















Execute the following query to return all cities in the state of Florida from the [Dimension].[City] table:

SELECT [City], [State Province],[Country]
FROM [WideWorldImportersDW].[Dimension].[City]
WHERE [State Province] = 'Florida'

The execution plan and read operation tooltip with a clustered index


















Notice that 116,295 rows were read (Number of Rows Read) to retrieve 4,024 rows (Actual Numbers of Rows).

The following script provides an example of a nonclustered index that can be defined to cover the query in the previous example in the [Dimension].[City] table:

CREATE NONCLUSTERED INDEX NC_IX_StateProvince
ON [Dimension].[City] ([State Province])
INCLUDE ([City])

Notice that the key column is [State Province] and the included column is [City]. The key column is used in the WHERE clause and the included column for the SELECT portion.

The execution plan and read operation tooltip for the query with a covering index


















After the covering nonclustered index was added, SQL Server performed an index seek instead. After the covering nonclustered index was added, SQL Server performed an index seek instead.

Another feature of nonclustered indexes is the ability to add a filter to include or exclude rows based on a column filter predicate. These types of nonclustered indexes are referred to as filtered indexes.

SELECT [City]
FROM [WideWorldImportersDW].[Dimension].[City]
where [State Province] = 'Florida'
and [Latest Recorded Population] > 0

The definition of the filtered nonclustered index to cover the query is as follows:

CREATE NONCLUSTERED INDEX NC_IX_StateProvice2
ON [Dimension].[City] ([State Province])
INCLUDE ([City])
WHERE [Latest Recorded Population] > 0

As a general rule, query performance is improved by reducing the number of rows that SQL Server has to read. Table and index scans should be avoided during query execution for selective queries.

Comments

Post a Comment

Popular Posts

Non-blocking, semi-blocking, and blocking transforms in SSIS

-
Not all Data Flow transforms are created equal. In fact, some are much faster and require a  lot less memory (non-blocking transforms). Others are slower while consuming much more  memory (blocking transforms), and some are in between (semi-blocking transforms). We don’t  always have a choice if we need to use a certain transform to do a certain specified task, but  it is good to know the differences between these types of transforms so that we can better  understand performance and resource utilization issues. Synchronous and asynchronous There is another related aspect about Data Flow transforms, which is their ability to quickly  process a row as they are coming into the transform, independently of any other rows that  came before or after (synchronous transforms). The other type of transform needs to be  dependent on some or all of the rows that come before and after (asynchronous transforms). On the whole, non-blocking transforms are also synchronous, thus are fast and
Read more

Implement additive, semi-additive, and non-additive measures

-
Additive Additive measures are numeric values that can be summed up by all the dimensions in the  fact table, for example the Sales Amount. These are the most common measures and they are  the easiest to understand and implement. Semi-additive Semi-additive measures are numeric values that can be summed up by some dimensions  in the fact table, but not all. In most cases, they can be aggregated by any other dimension  except time.  An example of a semi-additive measure is Account Balance. You can determine  the ending balance of an account at the end of a month, but adding up these monthly account  balances do not make sense. Semi-additive measures are usually found in fact tables that store  periodic snapshots of data. Non-additive Non-additive measures, as the name implies, are measures that cannot be summed up.  These measures include percentages such as Discount Percent, ratios such as Profit Margin, and  averages such as Average Unit Cost. Exam 70-767
Read more

Design an appropriate storage solution, including hardware, disk, and file layout

-
Image
The data warehouse storage’s performance can be measured by  analyzing the time it takes for read and write operations to complete, known as latency , and by  analyzing how much data volume it can read or write in each time frame, known as throughput .  High latency and low throughput can be an indication of an improper selection for the  storage infrastructure or incorrect hardware and software configurations. Performance of a storage subsystem can  be estimated based on several criteria: Volume of data Rate of change and growth of the data Data retention policy Number of concurrent users Typical workloads expected Two important metrics associated with storage are throughput and Input/Output Per  Second (IOPS) . Throughput is a measure of the amount of data that can be transferred in a  period. This is typically measured in megabytes or gigabytes per second. IOPS is the number of  I/O read or write operations that can complete in a second.  It is important
Read more