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Writing code in comment? Please use ide. Load Comments. What's New. Most popular in DBMS. More related articles in DBMS. By comparing keys to the index it is possible to find one or more database records with the same value. It is essential the correct indexes are defined for each table, since an index drastically speeds up data retrieval.
I was once working on a database where a series of operations took about eight days to complete. By looking at the longest-running queries and running them through a query plan generator we realized the database could benefit from a new index. The optimizer estimated the query cost would drop from , operations to 30! We implemented the index and took the entire operation from eight days to two hours. Needless to say, we were very happy to get a performance boost. For this example consider the index in the back of a book.
The keys to this index are the subject words we reference. The index entries consist of the key and page numbers. The keys are in alphabetical order, which makes really easy for us to scan the index, find an entry, note the pages, and then flip the book to the correct pages. Consider an alternative. A book with no index may have the subject words listed at the bottom of each page. This makes looking up subjects really slow! Only until you got to the very end of the book would you know you have seen every page about the subject.
Practically speaking, this saves hours of page-flipping! Consider that you have a deck of 52 cards: four suits, Ace through King. If the deck is shuffled into a random order, and I asked you to pick out the 8 of hearts, to do so you would individually flip through each card until you found it. On average you would have to go through half the deck, which is 26 cards.
Now, instead, consider that we separated the cards into four piles by suit, each pile randomly shuffled. Now if I asked you to pick out the 8 of hearts you would first select the hearts pile, which would take on average two to find, and then flip through the 13 cards.
On average it would take seven flips to find, thus nine total. This is seventeen flips faster than just scanning the whole deck. We could take it one step further and split the individual piles into two groups one Ace through 6, the other 7 through King.
In this case, the average search would decrease to 6. This is the power of an index. By segregating and sorting our data on keys, we can use a piling method to drastically reduce the number of flips it takes to find our data. As the name implies, the piles, technically called nodes, are connected in a tree-like fashion. Each pile drastically reduces the number of items you need to scan; actually exponentially so.
In this way, by walking down the nodes, doing comparisons along the way we can avoid scanning thousands of records, in just a few easy node scans. Hopefully, this diagram helps to illustrate the idea….
In the example above consider you need to retrieve the record corresponding to the key-value To do so the following comparisons are made:. As the number of lookups is directly related to the height of the tree, it is imperative to ensure all the branches are of equal height. This spreads out the data across the entire tree, making it more efficient to look up data within any range.
Each time records are added, removed, or keys updates, special algorithms shift data and key values from block to block to ensure no one part of the tree is more than one level higher than the other. If you are interested in the gritty detail, I would start with the Wikipedia article. I an actual example, each node dark blue would contain many key values light blue. In fact, each node is the size of a block of a disk, which is traditionally the smallest amount of data that can be read from a hard drive.
This is a pretty complicated subject. I would really like to know. Please leave a comment. Kris Wenzel has been working with databases over the past 30 years as a developer, analyst, and DBA. Kris has written hundreds of blog articles and many online courses. He loves helping others learn SQL. Thank you so much for the example with the book index!
It really helped solidify the concept of indexes in my mind. 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. This is why indexes are typically applied to databases in data warehouses that get new data updated on a scheduled basis off-peak hours and not production databases which might be receiving new writes all the time.
NOTE: The newest version of Postgres that is currently in beta will allow you to query the database while the indexes are being updated. To test if indexes will begin to decrease query times, you can run a set of queries on your database, record the time it takes those queries to finish, and then begin creating indexes and rerunning your tests.
This output will tell you which method of search from the query plan was chosen and how long the planning and execution of the query took. What is Indexing? Visualization for finding the last entry : If the table was ordered alphabetically, searching for a name could happen a lot faster because we could skip looking for the data in certain rows.
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