Query Plan Table Scan,Woodworking Bench Plans Uk,Wood Burning Alphabet Set - Tips For You

02.07.2015, admin  
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In this article we will first try to understand what is a SQL plan, how is it created and then we will move towards understanding how to read the SQL plan. Parse: - The first phase is to parse the SQL query for syntaxes and create a query processor tree which defines logical steps to execute the SQL. Before we try to understand table scan operator let’s try to see how can we see a SQL plan. Now that we have understood how to see a query plan let try to understand the first basic operator i.e.
If there is a primary key table scan operator scans row by row every record until it finds the exact record.
Seek scan does not scan all the rows to go to a record, it uses indexes and b-tree logic to get to a record. On the same above sample lets create a clustered index on the company code field and see the SQL plan again by hitting the SQL plan icon. If you view the SQL plan for the select query with company code you will see a clustered index seek operator as shown in the below figure. In order to understand the RID look up we need to first understand how non-clustered indexes work with clustered indexes and heap tables.
Scenario 2 :- if the table which is having the primary does not have a clustered index then the non-clustered index leaf node ‘rowid’ will point to actual row on the heap table. As discussed in the previous section, when query searches on a column which is not a part of non-clustered index a lookup is required.
Let us make a statement before we answer this question:- ‘The more the size of the index key, performance will also degrade accordingly’. Now if you fire the above query you can see the number of pages have increased which means SQL engine has to jump between pages to fetch the data row.
Below is a simple query which calculates count as per ‘registerdate’ from the customers table.
In order to understand the same we will do the following:-• We will run the above aggregate select query on the table without indexes and measure logical reads for the same.
Benchmarking with clustered indexesNow let’s go and create a clustered index on the ‘customers’ table , set the statistics io on and see the plan.
If you see the plan you will see that there are no aggregate logical operators because it’s pre-calculated in the indexed views.


So concluding indexed views are good for select aggregate queries they increase performance. Performance is impacted in insert’s on indexed views because any updates to the base table needs to be also reflected back to indexed views which decreases performance. As we read the SQL plan we will try to understand different operators like table scan, index seek scan, clustered scan, RID lookup etc. Each operator performs basic operations like insertion, search, scan, updation, aggregation etc. Once you are in your SQL management studio, click on new query and write the SQL for which you want to see the SQL plan.
You may be wondering what the RID lookup and nested loop is in the query plan, we will come to that later on.The below figure indicates that its using the non-clustered index logical operator. As mentioned earlier either you need to lookup on the clustered index or you need to lookup on the heap tables i.e.
To avoid RID lookup or clustered lookup we can use covering indexes.Below is a simple table which has two fields customer code and customer name.
Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Scan count 0, logical reads 1, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
You can see that it uses the index scan and uses the stem aggregate logical operator to give the results. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Scan count 0, logical reads 6, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Scan count 3, logical reads 12, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
On the other hand if your table is highly transactional then indexed views can decrease your performance.Below is a simple performance impact graph which plotted using the above benchmark data. Optimize: - The next step is to find a optimized way of executing the query processor tree defined by the ‘algebrizer’. As discussed previously operator’s form the basic unit of you SQL plan, if we are able to understand them we can optimize our SQL to a great extent.


In order to understand the same we have created a simple companies table with company code and description. So right click on the table field and create a unique key on customer code field as shown in the below figure. Below is a simple customer table with customer code and customer name field and below are the data types as shown in table. Let’s demonstrate how creating clustered indexes on unique columns increases performances as compared to creating indexes on columns with non-unique values.Below is a simple query which is fired on the customer table which has two fields ‘active’ and ‘customercode’. So when we query a view it queries the underlying base table.But when we create indexed views, result set is persisted on the hard disk which can save lot of overheads. You can see how logical scan is decreased with select plus indexed views and how insert logical is increased with indexed views.
This task is done by using ‘Optimizer’.’Optimizer’ takes data statistics like how many rows, how many unique data exist in the rows, do the table span over more than one page etc. If you are seeing SQL plan using SQL 2000 you should see a bookmark lookup in your query plan. Now write a simple SQL with a select clause on one of the properties, we have selected company code currently as shown in the above figure.If you hit on the query plan icon you will see table scan in your query plan. As it’s less than 40 it loops through the leaf nodes which belong to 40 nodes.In table scan it scans all rows while in seek scan it scans less number of rows comparatively.
For instance to get to the record 39 it only scanned 9 records rather than travelling through all 50 records. The optimizer arrives to an estimated plan, for this estimated plan it tries to find an actual execution plan in the cache. Estimated plan is basically which comes out from the optimizer and actual plan is the one which is generated once the query is actually executed.




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