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By Xenph Yan
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.
Classic example "Index in Books"
Consider a "Book" of 1000 pages, divided by 100 sections, each section with X pages.
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, 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
really nice analogy! funny i didn't make the connection between a book index and a db index
This makes me think Library or Grocery Store Could you image not having an index at a grocery store? Where's The Beef?!? Oh its next to the Restrooms, a mop, and makeup
Where's The Beef?!? Oh its next to the Restrooms, a mop, and makeup
"But with an index page at the beginning, you are there." What does "you are there" mean?
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 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.
r = 5,000,000
R = 204
B = 1,024
bfr = (B/R) = 1024/204 = 5
N = (r/bfr) = 5000000/5 = 1,000,000
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.
N/2 = 500,000
log2 1000000 = 19.93 = 20
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.
N = 1,000,000
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.
R = 54
bfr = (B/R) = 1024/54 = 18
N = (r/bfr) = 5000000/18 = 277,778
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.
log2 277778 = 18.08 = 19
19 + 1 = 20
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.
binary search can be done when the data is unique, am i right? although you mentioned that minimum cardinality is important, the algorithm wouldn't be a simple binary search, how would this approximation (~log2 n) affect the process time?
This is also a great read: kylebanker.com/blog/2010/09/21/the-joy-of-mongodb-indexes
@XenphYan - So an index is just a way to sort data in a column and keep that sort order handy for quickly accessing the column elements ? If we update a non-indexed column, then performance should not be affected, right ? Related question - stackoverflow.com/questions/16124690/…
"it stands to reason that indexing fields used only for output would be simply a waste" This is not entirely true if you consider covering indexes. A covering index will respond to queries by pulling values only from the index without needing to look up matching records.
@AbhishekShivkumar:Great question!I think the index table will have as many rows as there are in the data table. And as this field will have only 2 values(boolean with true/false) & say you want a record with value true,then you can only halve the result set in first pass, in second pass all your records have value true so there is no basis to differentiate,now you have to search the data table in linear fashion-hence he said cardinality should be considered while deciding the indexed column. In this case,it's worthless to index on such a column. Hope I'm correct :)
shouldn't the number of block accesses in the average case be (N+1)/2. If we sum the number of block accesses for all possible cases, and divide it by the number of cases, then we have N*(N+1)/(2*n) which comes out to be (N+1)/2.
Higher cardinality fields can make use of bitmap indexes. It is designed specifically for fields which hold lots of duplicate values, e.g., a gender field.
Finally clarified. I have one more question. If a table is small and will stay small (example a country list) it looks like not indexing at all is both a win in time and disk space. On huge tables indexing CHAR fields should be avoided if possible, especially if searches on CHAR fields are rather rare. That's probably why big forums only allow one keyword search every minute.
I think there are a few typos in this answer, for example, in the sentence: "a far cry from the 277,778 block accesses required by the non-indexed table." doesn't the author mean 1,000,000 block accesses? 277,778 is the number of blocks required by the index itself. There seems to be a couple of other inaccuracies too :(
The answer doesn't seem to explain that the index itself is sorted? I.e. they just jump to this explanation: "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" - the index MUST be sorted in order for binary search to be possible, but I can't see that explained anywhere.
It would also be great if there was an explanation of how the indexing works on multiple fields, the answer only explains indexing of a single field, the firstName
@jcm He explained it in the "What is indexing section" - "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."
In the second example, why do we need to store the firstName? Isn't the pointer enough? We'll just have to simply change the comparison function to (*pointer).firstName.
Can you tell us the references from which one can learn these ?
fyi, binary search is not used on b-tree lookups. The performance would be terrible when the index does not fit in memory. The reason for 'b-tree indexes' is to be fast even when not all the index fits in available memory. This is about 'fan out' of keys to where to find data. Binary tree has a 'fan out' of 2. 'b-trees' can have fan-outs of 'rather more' than that 10's or greater - it depends on key size and 'page size'.
Can you explain why "searching on a field that isn’t sorted requires a Linear Search which requires N/2 block accesses (on average)"? Why is it N/2 and not N?
@HeyJude I think he means Average.
@jcm (and future readers) Re: comment about 1,000,00 vs 277,778 - I just reversed an edit making this change because I thought the reference was correct. On re-reading both the answer and your comment more carefully though, you are absolutely correct, it's talking about the original unsorted field and not the index size. I changed the number back (oops) to 1 million and re-worded things slightly (e.g replaced 'table' with 'index') to clarify the context of the 277,778
Am I missing something or When should it be used? point doesn't answer the question. It rather describes "When should't it be used?". So when it should be used?
Just to be clean when we say 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 is for non clustered index. For clustered index which can be only one for a table this is not true since the original data itself is stored in sorted order using it..
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.
How can we run binary serch on sorted data if they are not stored in contiguous blocks ?
I tried indexing on a non-key field, things are going worse for select query. How do I understand this?
I lost you after first paragraph Due to the fact that a number of records can only be sorted on one field, can you explain this or correct the sentence.
Due to the fact that a number of records can only be sorted on one field,
index can be hash index, not necessary has to be sorted. What you mentioned is just a kind of index.
Thank you for the brilliant, thorough explanation. If I could posit an explanation of my own (probably inaccurate and wrong): indexing on firstName is like adding a list of sorted ids to firstName that correlate to the entries of that field, which enables Binary Search?
Can someone explain why searching on a field that isn’t sorted but has unique entries requires N/2 block accesses on average and searching on a field that is a non-key field (doesn't contain unique entries) requires N block accesses? Wouldn't they both require N since it's a linear search?
@mouscous Suppose you have a list of unique numbers in random order. If you want to know if a value is in there you look at the 1st one, then the 2nd one, 3rd etc... until you find a match. Once found there is no need to continue looking, the value is unique and only present once. Sometimes you'll have a match early on, sometimes it takes a while to find it but on average you'll traverse half the list. If the values are not unique you may find it in the first spot, but it could be there's another occurrence further on! To find them all you need to go through the entire list every time. Thus N.
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: How do database indexes work? And, how do indexes help?
+1 times a million for this answer, as I found this listing while trying to find a simple explanation what indexing essentially is.
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 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!
An index doesn't imply sorting order on the column
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.
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.
"a database index does not store the values in the other columns " -- not true.
@mustaccio: Index stores reference of row with the indexed columns only (as far I know). I might be wrong. Do you have any reference which says index stores other columns values?
@To Downvoters : Can you just explain what's wrong so that I can improve?
Check for example SQL Server clustering indexes or DB2's CREATE INDEX ... INCLUDE clause. You have too many generalizations in your answer, in my view.
CREATE INDEX ... INCLUDE
@mustaccio: So by default create index does not include the other columns and why it should. 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.. This is more generalized version of indexes. CREATE INDEX ... INCLUDE is the newer version by considering other columns. Post I have explained is considering more generalized version. How indexes work would be one book if we consider all the databases? Isn't it? Do you think answer deserves downvote?
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.
This is basically extracted from programmerinterview.com/index.php/database-sql/what-is-an-index Perhaps you could add that
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.
This is a comment, not an answer.
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?
I think, these indexing issues can be resolved by maintaining two different databases, just as Master and Slave. Where Master can be used to insert or update records. Without indexing. And slave can be used to read with proper indexing right???
no, wrong, sorry. not just the content of the tables must be updated, but also the index structure and content (b-tree, nodes). your concept of master and slave makes no sense here. what can be feasable though is replicating or mirroring to a second database on which analytics take place to take that workload away from the first database. that second database would hold copies of data and indexes on that data.
Ya...! Try to read my comment and understand it properly. I also said the same, I referred to master and slave (whatever) as "eplicating or mirroring to a second database on which analytics take place to take that workload away from the first database. that second database would hold copies of data and indexes on that data"
the second database - to which mirroring or replicating is done, the slave - would experience all the data manipulation as the first one does. with each dml-operation the indexes on that second database would experience "these indexing issues". i don't see the gain in that, where ever the indexes are needed and built for quick analysis they need to be kept up to date.