SQL Server has automatically chosen a name for the index and if needed this can be changed but why not go with the defaults. To complete the index creation just hit the OK button one more time:. This time, we got 1 physical, 13 logical, and 11 read-ahead reads which is significantly less than before.
Notice that we got the exact same result back with much less disk activity and access to the cache within the addition of a single index and SQL Server fulfilled the request of this query but did significantly less work. Why is that? Well, we can look at a few things to explain this.
So, after we created the index, SQL Server could execute the same query by only reading the index because in this case the index is 7. We can also look at the statistics.
Statistics play a significant role in the database performance world. Once the index is created, SQL Server automatically creates statistics.
Back to Object Explorer, expand the table, expend the Statistics folder and right-click the statistic with the exact same name as previously created index and choose Properties :.
Again, this will open the statistics properties window in which we can switch to Details page under which we can see that the index is broken into different ranges:. Usually, it will start at some percentage thru and just read to the end of it.
Bear in mind that the indexes provide for a performance increase when the data is being read from a database but they can also lead to a performance decrease when the data is being written. Simply because when inserting data into a table, SQL Server would have to update both the table and the index values increasing the writing resources.
The general rule of thumb is to be aware of how often a table is being read vs how often is written to. Tables that are primarily read-only can have many indexes and tables that are written to often should have fewer indexes.
The following createIndex method creates an index on item and quantity named query for inventory :. You can view index names using the db. You cannot rename an index once created. Instead, you must drop and re-create the index with a new name. MongoDB provides a number of different index types to support specific types of data and queries. For a single-field index and sort operations, the sort order i. MongoDB also supports user-defined indexes on multiple fields, i.
The order of fields listed in a compound index has significance. For compound indexes and sort operations, the sort order i. See Sort Order for more information on the impact of index order on results in compound indexes. MongoDB uses multikey indexes to index the content stored in arrays. If you index a field that holds an array value, MongoDB creates separate index entries for every element of the array. These multikey indexes allow queries to select documents that contain arrays by matching on element or elements of the arrays.
MongoDB automatically determines whether to create a multikey index if the indexed field contains an array value; you do not need to explicitly specify the multikey type. To support efficient queries of geospatial coordinate data, MongoDB provides two special indexes: 2d indexes that uses planar geometry when returning results and 2dsphere indexes that use spherical geometry to return results.
See 2d Index Internals for a high level introduction to geospatial indexes. MongoDB provides a text index type that supports searching for string content in a collection. These text indexes do not store language-specific stop words e. See Text Indexes for more information on text indexes and search. To support hash based sharding , MongoDB provides a hashed index type, which indexes the hash of the value of a field. You can imagine the enhancement that can be gained in the case of large tables, as shown below:.
The previous Non-clustered index is created over a heap table. Recall what we mentioned in the previous articles of this series that the Non-clustered index will be rebuilt automatically when creating a Clustered index on the table in order to point to the Clustered key instead of pointing to the base table.
You will see that the same execution plan will be created without any change from the previous one, as shown below:. In addition, the TIME and IO statistics will show that the time required to execute the query and the CPU time consumed by the query is somehow similar to the previous result, with a small enhancement in reducing the IO operations performed to retrieve the data, due to working with a small table, as shown below:.
If we drop all indexes available on that table and replace it with only one Clustered index, using the T-SQL script below:. You will see from the generated execution plan, that a Clustered Index Seek operation will be performed to retrieve the requested data, as shown below:. With no noticeable enhancement in the TIME and IO statistics generated from executing the query, in the case of our small table, as shown below:. In addition, a green message will be displayed in the execution plan showing a suggested index from the SQL Server to enhance the performance of the query, as shown below:.
Right-click on that execution plan and choose Missing Index Details option, to show the suggested index. When you execute the query to be tuned, the query statistics will be caught in the opened SQL Server Profiler session, as shown in the snapshot below:. Save the previous trace in order to use it as the workload for the Database Engine Tuning Advisor.
From the opened Database Engine Tuning Advisor window, connect to the target SQL Server, the database from which the query will retrieve data and finally allocate the workload file that contains the query trace, then click on as shown below:.
Once the analysis operation completed successfully, recommendations that include indexes and statistics that could enhance the performance of the query, with the estimated enhancement percentage will be displayed on the generated report.
In our case, the same previously suggested index will be displayed in the recommendations report, as shown below:. You will see from the generated execution plan, that the previous slow Table Scan operation changed to a fast Index Seek operation, as shown below:.
In addition, the TIME and IO statistics will show a slight enhancement in the logical reads number and the time required to execute the query, as we are working with a small table, as shown below:. That is the clustered index that was referenced earlier in the article that is automatically created based off of the primary key. That index was created similarly to the names index:. This provides a way for our database to swiftly query city names. After your non-clustered indexes are created you can begin querying with them.
Indexes use an optimal search method known as binary search. Binary searches work by constantly cutting the data in half and checking if the entry you are searching for comes before or after the entry in the middle of the current portion of data. This works well with B-trees because they are designed to start at the middle entry; to search for the entries within the tree you know the entries down the left path will be smaller or before the current entry and the entries to the right will be larger or after the current entry.
In a table this would look like:. Comparing this method to the query of the non-indexed table at the beginning of the article, we are able to reduce the total number of searches from eight to three. Using this method, a search of 1,, entries can be reduced down to just 20 jumps in a binary search. Indexes are meant to speed up the performance of a database, so use indexing whenever it significantly improves the performance of your database.
As your database becomes larger and larger, the more likely you are to see benefits from indexing. When data is written to the database, the original table the clustered index is updated first and then all of the indexes off of that table are updated. Every time a write is made to the database, the indexes are unusable until they have updated. If the database is constantly receiving writes then the indexes will never be usable.
0コメント