Due to some recent database problems we've been having recently, I've been looking at the state of our indexes and trying to reduce the fragmentation.
The app has several tables of names such as BoysNames and GirlsNames which we use to set attributes on User objects we created. The obvious attribute here would be Gender. These tables can have anywhere from a few hundred to 10,000 rows.
These tables are structured like this:
Name - nvarchar(50) - PK & Clustered Index
DateCreated - datetime
When I tell Sql Server to reorganize or rebuild the indexes on all my tables, most table fragmentaiton goes down to 0%, but some of these Name tables are at 50% fragemented straight away.
I only access these tables in 2 places:
The first is when I select every name from the table and store it in
memory to use against new users coming into the system so I can do something like this: if
(boysNames.Contains(user.Name)) {user.Gender = "M"}; This happens quite
often.
The second is when I'm adding new names to the list, I check for the
existance of a name, if it doesn't exist, I add it. This happens
rarely.
So what I need to know is:
Is this high level of fragmentation going to be causing me problems? How can I reduce the the index fragmentation to 0% when it's being set to 50% straight after a reorganize/rebuild?
Should I have used an int as the primary key and put an index on Name, or was nvarchar the right choice for primary key?
If the index pages are resident in memory, which is likely with that few rows, fragmentation does not matter. You can benchmark that with a count(*) query. Starting with the 2nd execution, you should see in-memory speeds. If you now compare the results for a 100% and a 0% fragmented table, you should see no difference.
I don't think you have a problem. If you insist, you can set a fill factor lower than 100 so that there is room for new rows when you insert rows at random places. Start with 90 and lower it in increments of 5 until you are happy with the rate fragmentation develops.
Clustering on an IDENTITY field would remove the fragmentation for the clustered index, but you'd probably need an index on Name which again fragments. If you do not need any index at all, make this a heap table and be done with it. I recommend very much against this, though.
I have a large table (~40 mill records) in SQL Server 2008 R2 that has high traffic on it (constantly growing, selected and edited...)
Up to now I was accessing rows on this table by its id (simple identity key), I have a column let's call it GUID, that is unique for most of the rows but some of the rows has the same value for that column.
That GUID column is nvarchar(max) and the table contains about 10 keys and constrains, index just on the simple identity key column.
I want to set an index on this column without causing anything to crash or making the table unavailable.
How can I do so ?
Please keep in mind this is a large table that has high traffic on, and it must stay online and available
Thanks
Well, the answer to this one is easy (but you probably won't like it): You can't.
SQL Server requires the index key to be less then 800 bytes. It also requires the key to be stored "in-row" at all times. As a NVARCHAR(MAX) column can grow significantly larger then 800 bytes (up to 2GB) and is also most often stored outside of the standard row-data-pages SQL Server does not allow an index key to include a NVARCHAR(MAX) column.
One option you have is to make this GUID column an actual UNIQUEIDENTIFIER datatype (or at least a CHAR(32). Indexing GUIDs is still not recommended because they cause high fragmentation, but at least with that it is possible. However, that is not a quick nor simple thing to do and if you need the table to stay online during this change, I strongly recommend you get outside help.
I have a simple little table (just 8 fields) an hour ago I rebuilt one of the indexes on the table, which reset it to 0% fragmentation, but now it’s up to 38%.
The table itself has 400k records in it, but only 158 new ones have been inserted since I rebuilt the index, there have been no updates to records but perhaps a couple of deletes.
Why should the index be getting so fragmented?
The index is non-unique, non-clustered just on one field.
The database is running on SQL Server 2005 but with a compatibility level of SQL Server 2000.
Thanks
Check the Fill Factor for that index when it is re-built. The fill factor may be too high. If this is the case, the index pages will be too full when the index is re-built and adding new rows will soon start to cause page splits (fragmentation). Reducing the fill factor on rebuild will allow more new records to be inserted into the index pages before page splitting starts to occur.
http://msdn.microsoft.com/en-us/library/aa933139%28SQL.80%29.aspx
Fill factor 0 is equal to 100, so you are not allowing any room for inserts. You should be choosing a lower fill factor if you will be inserting.
I have a big table in SQL Server 2005 that's taking about 3.5 GB of space (according to sp_spaceused). It has 10 million records, and several indexes.
I just dropped a bunch of columns from it, such that the record length got reduced to a half, and to my surprise it took zero time to do that. Obviously, sp_spaceused was still reporting the same taken space, SQL server hadn't really done anything when dropping the columns, other than marking them as "dropped".
So I moved all the data from this table into another new table, truncated it, and moved all the data back, so that it'd get all reconstructed.
Now, after that, data is taking 2.8 GB, which IS less than before, but I expected a bigger drop.
Is it possible that the fact that this table originally had these columns is still leaving something there?
Was truncating it not enough? Should I drop it and create it again with the smaller column set?
Or is the data really taking 2.8 GB?
Thanks!
You will need to rebuild the clustered index (assuming you have one - by default, your primary key is the clustered key).
ALTER INDEX (your clustered index) ON TABLE (your table) REBUILD
The data is really the leaf level of your clustered index - once you rebuild it, it will be "compacted" and the rows should be stored on much fewer data pages, reducing your database size, too.
If that doesn't help at all, you might also need to run a DBCC SHRINKDATABASE on your database to really reclaim the space. These two steps together should really get you some smaller database file!
Marc
How did you calculate that "expected a bigger drop"? Note that the data comes in 8K pages, which means that even if individual rows are smaller, that does not always mean you need less pages to store them.
For example (an extreme example), if your rows used to be 7.5K each, only one row per page would fit. You drop some columns, your row is 5K, but still it is one row per page.
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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.
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+1)/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.
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
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.
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?
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
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!
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.