How does database indexing work

Question

Given that indexing is so important as your data set increases in size  can someone explain how indexing works at a database-agnostic level   For information on queries to index a field  check out How do I index a database column

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Simple Description   The index is nothing but a data structure that stores the values for a specific column in a table  An index is created on a column of a table    Example  We have a database table called User with three columns     Name  Age and Address  Assume that the User table has thousands of rows   Now  let   s say that we want to run a query to find all the details of any users who are named  John    If we run the following query    SELECT   FROM User  WHERE Name    John    The database software would literally have to look at every single row in the User table to see if the Name for that row is    John     This will take a long time   This is where index helps us  index is used to speed up search queries by essentially cutting down the number of records rows in a table that needs to be examined     How to create an index   CREATE INDEX name index ON User  Name    An index consists of column values Eg  John  from one table  and those values are stored in a data structure        So now the database will use the index to find employees named John   because the index will presumably be sorted alphabetically by the   Users name  And  because it is sorted  it means searching for a name   is a lot faster because all names starting with a    J    will be right   next to each other in the index

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Just think of Database Index as Index of a book   If you have a book about dogs and you want to find an information about let s say  German Shepherds  you could of course flip through all the pages of the book and find what you are looking for  - but this of course is time consuming and not very fast    Another option is that  you could just go to the Index section of the book and then find what you are looking for by using the Name of the entity you are looking   in this instance  German Shepherds  and also looking at the page number to quickly find what you are looking for    In Database  the page number is referred to as a pointer which directs the database to the address on the disk where entity is located  Using the same German Shepherd analogy  we could have something like this     German Shepherd     0x77129  where 0x77129 is the address on the disk where the row data for German Shepherd is stored    In short  an index is a data structure that stores the values for a specific column in a table so as to speed up query search

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Now  let   s say that we want to run a query to find all the details of any employees who are named    Abc      SELECT   FROM Employee  WHERE Employee Name    Abc    What would happen without an index   Database software would literally have to look at every single row in the Employee table to see if the Employee Name for that row is    Abc     And  because we want every row with the name    Abc    inside it  we can not just stop looking once we find just one row with the name    Abc     because there could be other rows with the name Abc  So  every row up until the last row must be searched     which means thousands of rows in this scenario will have to be examined by the database to find the rows with the name    Abc     This is what is called a full table scan  How a database index can help performance  The whole point of having an index is to speed up search queries by essentially cutting down the number of records rows in a table that need to be examined  An index is a data structure  most commonly a B- tree  that stores the values for a specific column in a table    How does B-trees index work   The reason B- trees are the most popular data structure for indexes is due to the fact that they are time efficient     because look-ups  deletions  and insertions can all be done in logarithmic time  And  another major reason B- trees are more commonly used is because the data that is stored inside the B- tree can be sorted  The RDBMS typically determines which data structure is actually used for an index  But  in some scenarios with certain RDBMS   s  you can actually specify which data structure you want your database to use when you create the index itself   How does a hash table index work   The reason hash indexes are used is because hash tables are extremely efficient when it comes to just looking up values  So  queries that compare for equality to a string can retrieve values very fast if they use a hash index    For instance  the query we discussed earlier could benefit from a hash index created on the Employee Name column  The way a hash index would work is that the column value will be the key into the hash table and the actual value mapped to that key would just be a pointer to the row data in the table  Since a hash table is basically an associative array  a typical entry would look something like    Abc    0x28939   where 0x28939 is a reference to the table row where Abc is stored in memory  Looking up a value like    Abc    in a hash table index and getting back a reference to the row in memory is obviously a lot faster than scanning the table to find all the rows with a value of    Abc    in the Employee Name column   The disadvantages of a hash index  Hash tables are not sorted data structures  and there are many types of queries which hash indexes can not even help with  For instance  suppose you want to find out all of the employees who are less than 40 years old  How could you do that with a hash table index  Well  it   s not possible because a hash table is only good for looking up key value pairs     which means queries that check for equality   What exactly is inside a database index  So  now you know that a database index is created on a column in a table  and that the index stores the values in that specific column  But  it is important to understand that a database index does not store the values in the other columns of the same table  For example  if we create an index on the Employee Name column  this means that the Employee Age and Employee Address column values are not also stored in the index  If we did just store all the other columns in the index  then it would be just like creating another copy of the entire table     which would take up way too much space and would be very inefficient   How does a database know when to use an index  When a query like    SELECT   FROM Employee WHERE Employee Name      Abc        is run  the database will check to see if there is an index on the column s  being queried  Assuming the Employee Name column does have an index created on it  the database will have to decide whether it actually makes sense to use the index to find the values being searched     because there are some scenarios where it is actually less efficient to use the database index  and more efficient just to scan the entire table   What is the cost of having a database index   It takes up space     and the larger your table  the larger your index  Another performance hit with indexes is the fact that whenever you add  delete  or update rows in the corresponding table  the same operations will have to be done to your index  Remember that an index needs to contain the same up to the minute data as whatever is in the table column s  that the index covers   As a general rule  an index should only be created on a table if the data in the indexed column will be queried frequently   See also    What columns generally make good indexes  How do database indexes work

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Why is it needed   When data is stored on disk-based storage devices  it is stored as blocks of data  These blocks are accessed in their entirety  making them the atomic disk access operation  Disk blocks are structured in much the same way as linked lists  both contain a section for data  a pointer to the location of the next node  or block   and both need not be stored contiguously   Due to the fact that a number of records can only be sorted on one field  we can state that searching on a field that isn   t sorted requires a Linear Search which requires N 2 block accesses  on average   where N is the number of blocks that the table spans  If that field is a non-key field  i e  doesn   t contain unique entries  then the entire tablespace must be searched at N block accesses   Whereas with a sorted field  a Binary Search may be used  which has log2 N block accesses  Also since the data is sorted given a non-key field  the rest of the table doesn   t need to be searched for duplicate values  once a higher value is found  Thus the performance increase is substantial   What is indexing   Indexing is a way of sorting a number of records on multiple fields  Creating an index on a field in a table creates another data structure which holds the field value  and a pointer to the record it relates to  This index structure is then sorted  allowing Binary Searches to be performed on it   The downside to indexing is that these indices require additional space on the disk since the indices are stored together in a table using the MyISAM engine  this file can quickly reach the size limits of the underlying file system if many fields within the same table are indexed   How does it work   Firstly  let   s outline a sample database table schema     Field name       Data type      Size on disk id  Primary key  Unsigned INT   4 bytes firstName        Char 50        50 bytes lastName         Char 50        50 bytes emailAddress     Char 100       100 bytes   Note  char was used in place of varchar to allow for an accurate size on disk value   This sample database contains five million rows and is unindexed  The performance of several queries will now be analyzed  These are a query using the id  a sorted key field  and one using the firstName  a non-key unsorted field    Example 1 - sorted vs unsorted fields  Given our sample database of r   5 000 000 records of a fixed size giving a record length of R   204 bytes and they are stored in a table using the MyISAM engine which is using the default block size B   1 024 bytes  The blocking factor of the table would be bfr    B R    1024 204   5 records per disk block  The total number of blocks required to hold the table is N    r bfr    5000000 5   1 000 000 blocks    A linear search on the id field would require an average of N 2   500 000 block accesses to find a value  given that the id field is a key field  But since the id field is also sorted  a binary search can be conducted requiring an average of log2 1000000   19 93   20 block accesses  Instantly we can see this is a drastic improvement   Now the firstName field is neither sorted nor a key field  so a binary search is impossible  nor are the values unique  and thus the table will require searching to the end for an exact N   1 000 000 block accesses  It is this situation that indexing aims to correct   Given that an index record contains only the indexed field and a pointer to the original record  it stands to reason that it will be smaller than the multi-field record that it points to  So the index itself requires fewer disk blocks than the original table  which therefore requires fewer block accesses to iterate through  The schema for an index on the firstName field is outlined below     Field name       Data type      Size on disk firstName        Char 50        50 bytes  record pointer  Special        4 bytes   Note  Pointers in MySQL are 2  3  4 or 5 bytes in length depending on the size of the table   Example 2  - indexing  Given our sample database of r   5 000 000 records with an index record length of R   54 bytes and using the default block size B   1 024 bytes  The blocking factor of the index would be bfr    B R    1024 54   18 records per disk block  The total number of blocks required to hold the index is N    r bfr    5000000 18   277 778 blocks   Now a search using the firstName field can utilize the index to increase performance  This allows for a binary search of the index with an average of log2 277778   18 08   19 block accesses  To find the address of the actual record  which requires a further block access to read  bringing the total to 19   1   20 block accesses  a far cry from the 1 000 000 block accesses required to find a firstName match in the non-indexed table   When should it be used   Given that creating an index requires additional disk space  277 778 blocks extra from the above example  a  28  increase   and that too many indices can cause issues arising from the file systems size limits  careful thought must be used to select the correct fields to index   Since indices are only used to speed up the searching for a matching field within the records  it stands to reason that indexing fields used only for output would be simply a waste of disk space and processing time when doing an insert or delete operation  and thus should be avoided  Also given the nature of a binary search  the cardinality or uniqueness of the data is important  Indexing on a field with a cardinality of 2 would split the data in half  whereas a cardinality of 1 000 would return approximately 1 000 records  With such a low cardinality the effectiveness is reduced to a linear sort  and the query optimizer will avoid using the index if the cardinality is less than 30  of the record number  effectively making the index a waste of space

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Just a quick suggestion   As indexing costs you additional writes and storage space  so if your application requires more insert update operation  you might want to use tables without indexes  but if it requires more data retrieval operations  you should go for indexed table

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An index is just a data structure that makes the searching faster for a specific column in a database  This structure is usually a b-tree or a hash table but it can be any other logic structure

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Classic example  Index in Books   Consider a  Book  of 1000 pages  divided by 10 Chapters  each section with 100 pages   Simple  huh   Now  imagine you want to find a particular Chapter that contains a word  Alchemist   Without an index page  you have no other option than scanning through the entire book Chapters  i e  1000 pages    This analogy is known as  Full Table Scan  in database world     But with an index page  you know where to go  And more  to lookup any particular Chapter that matters  you just need to look over the index page  again and again  every time  After finding the matching index you can efficiently jump to that chapter by skipping the rest   But then  in addition to actual 1000 pages  you will need another  10 pages to show the indices  so totally 1010 pages      Thus  the index is a separate section that stores values of indexed   column   pointer to the indexed row in a sorted order for efficient   look-ups    Things are simple in schools  isn t it   P

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The first time I read this it was very helpful to me  Thank you   Since then I gained some insight about the downside of creating indexes  if you write into a table  UPDATE or INSERT  with one index  you have actually two writing operations in the file system  One for the table data and another one for the index data  and the resorting of it  and - if clustered - the resorting of the table data    If table and index are located on the same hard disk this costs more time  Thus a table without an index  a heap    would allow for quicker write operations   if you had two indexes you would end up with three write operations  and so on   However  defining two different locations on two different hard disks for index data and table data can decrease eliminate the problem of increased cost of time  This requires definition of additional file groups with according files on the desired hard disks and definition of table index location as desired   Another problem with indexes is their fragmentation over time as data is inserted  REORGANIZE helps  you must write routines to have it done   In certain scenarios a heap is more helpful than a table with indexes    e g - If you have lots of rivalling writes but only one nightly read outside business hours for reporting   Also  a differentiation between clustered and non-clustered indexes is rather important    Helped me - What do Clustered and Non clustered index actually mean

[sql] How does database indexing work?

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