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How does database indexing work?

Xenph Yan
Xenph Yan Published in 2008-08-04 10:07:12Z

Given that indexing is so important as your data set increases in size, can someone explain how does indexing work at a database-agnostic level?

For information on queries to index a field, check out How do I index a database column.

Will Vousden
Will Vousden Reply to 2018-01-17 11:07:43Z

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 table space 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 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 indexes require additional space on the disk, since the indexes 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 utilise 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 indexes can cause issues arising from the file systems size limits, careful thought must be used to select the correct fields to index.

Since indexes 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.

Community Reply to 2017-05-23 11:47:36Z

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

  1. What columns generally make good indexes?
  2. How do database indexes work
hcarreras Reply to 2017-11-15 10:36:11Z

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.

For more information, I recommend this: How do database indexes work? And, how do indexes help?

mudasir Reply to 2015-01-14 06:44:51Z

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.

eFarzad Reply to 2018-01-04 10:29:34Z

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.

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 that 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!

Alf Moh
Alf Moh Reply to 2016-12-21 17:16:02Z

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.

Sankarganesh Eswaran
Sankarganesh Eswaran Reply to 2017-12-08 10:26:29Z

Classic example "Index in Books"

Consider a "Book" of 1000 pages, divided by 100 sections, each section with X pages.

Simple, huh?

Now, without an index page, to find a particular section that starts with letter "S", you have no other option than scanning through the entire book. i.e: 1000 pages

But with an index page at the beginning, you are there. And more, to read any particular section 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 the section by skipping other sections.

But then, in addition to 1000 pages, you will need another ~10 pages to display the index page, so totally 1010 pages.

Thus, index is a separate section that stores pointer of the indexed records in a sorted order for efficient look-ups.

Things are simple at schools, isn't it? :P

halcosho Reply to 2017-08-29 09:46:39Z

In a relational database using row-structured storage, a secondary index is stored in a separate storage area away from the "base table data". When you create an index, the base table is traversed to fetch the columns to be indexed, which are inserted into a stored index structure - usually a B-tree - and saved to persistent storage for persistent tables.

Index entries themselves are also "rows", containing the indexed column(s) and some sort of offset into the base table data. When an index is used to fetch a row, the index is walked until it finds the row(s) of interest, and the base table is then looked up to fetch the actual row data.

When a row is inserted, a corresponding row is written to the index, and when a row is deleted, its index row is taken out.

Note that this is why indexes take space, and why having a lot of indexes slow down write operations on the base table: the indexes have to be kept in sync with the table, so for every INSERT or DELETE to the underlying table, there will be activity in the indexes, and UPDATEs to the underlying table - even if the updated column isn't in the index - may trigger index activity as well as some base table data storage managers store row offsets that may need to be updated.

The exact behavior depends on the storage engine implementation.

Some storage engines implement "primary indexes", usually on a PRIMARY KEY. The most commonly used storage engine of this type is MySQL InnoDB - in this case, the base table data itself is stored in a B-Tree structure, and secondary indexes store the PRIMARY KEY for each row. Oracle's "Index Organized Tables" are also implemented this way.

The big advantage of this structure is lookups on the PRIMARY KEY are extremely fast as only one structure must be visited to pull up rows as opposed to two for a traditional "heap + secondary index" storage.

There are many interesting variations on indexes: "incomplete" indexes, meaning indexes that are loaded only if a property is met (these are useful if you have an enormous table but usually search on specific column values), "function-based" indexes, where the index values are computed on a function, etc. PostgreSQL has a lot of these.

There are many tools that can help, but I am currently using SQLDbm which I find most effective. Maybe you should check it out.

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