I have a large domain set of tables in a database - over 100 tables. Every single one uses a uniqueidentifier as a PK.
I'm realizing now that my mistake is that these are also by default, the clustered index.
Consider a table with this type of structure:
Orders
Id (uniqueidentifier) Primary Key
UserId (uniqueidentifier)
.
.
.
.
Other columns
Most queries are going to be something like "Get top 10 orders for user X sorted by OrderDate".
In this case, would it make sense to create a clustered index on UserId,Id...that way the data is physically stored sorted by UserId?
I'm not too concerned about Inserts and Updates - those will be few enough that performance loss there isn't a big deal. I'm mostly concerned with READs.
A clustered index means that data is physically stored in the order of the values. By default, the primary key is used for the clustered index.
The problem with GUIDs is that they are generated is (essentially) random order. That means that inserts are happening "in the middle" of the table. And, such inserts result in fragmentation.
Without getting into database internals, this is a little hard to explain. But what it means is that inserts require much more work than just inserting the values "at the end" of the table, because new rows go in the middle of a data page so the other rows have to be moved around.
SQL Server offers a solution for this, newsequentialid(). On a given server, this returns a sequential value which is inserted at the end. Often, this is an excellent compromise if you have to use GUIDs.
That said, I have a preference for just plain old ints as ids -- identity columns. These are smaller, so they take up less space. This is particularly true for indexes. Inserts work well because new values go at the "end" of the table. I also find integers easier to work with visually.
Using identity columns for primary keys and foreign key references still allows you to have unique GUID columns for each identity, if that is a requirement for the database (say for interfacing to other applications).
Clustered index is when you want to retrieve rows for a range of values for a given column. As data is physically arranged in that order, the rows can be extracted very efficiently.
a GUID, while excellent for a primary key, could be positively detrimental to performance, as there will be additional cost for inserts and no perceptible benefit on selects.
So yes, don't cluster an index on GUID.
The problem itself is quite simple:
I have a large number of timestamp and value pairs
The timestamps with a few exceptions (<1%) are ever increasing and unique
I use the timestampt as clustered index
How can I enforce that the DB system try to insert the value to the end, and if it fails (does not go rightmost (large side) in the B-tree, that can be checked const time) only then do the binary search for the correct placement?
Target system: MSSQL 2016 or 2017
Either you want timestampt to be a clustered index or you do not. There is no "half-way" clustered index.
So, if you want it clustered, then leave extra space on each page in case a new value gets inserted later. You can control this by using fill_factor (documented here). This allows a clustered index to (more) efficiently insert values that are not at the end.
If you don't want a clustered index on timestampt, then use an identity column to identify each row. This will ensure that rows are only inserted at the "end" (i.e. last page) of the table, making inserts more efficient. You can still have a regular index on timestampt for efficient access.
Actually, I prefer the second method. I would be concerned about duplicates in timestampt, and I prefer having a clustered index that uniquely identifies each row.
Clustering factor - A Awesome Simple Explanation on how it is calculated:
Basically, the CF is calculated by performing a Full Index Scan and
looking at the rowid of each index entry. If the table block being
referenced differs from that of the previous index entry, the CF is
incremented. If the table block being referenced is the same as the
previous index entry, the CF is not incremented. So the CF gives an
indication of how well ordered the data in the table is in relation to
the index entries (which are always sorted and stored in the order of
the index entries). The better (lower) the CF, the more efficient it
would be to use the index as less table blocks would need to be
accessed to retrieve the necessary data via the index.
My Index statistics:
So, here are my indexes(index over just one column) under analysis.
Index starting PK_ is my Primary Key and UI is a Unique key. (Ofcourse both hold unique values)
Query1:
SELECT index_name,
UNIQUENESS,
clustering_factor,
num_rows,
CEIL((clustering_factor/num_rows)*100) AS cluster_pct
FROM all_indexes
WHERE table_name='MYTABLE';
Result:
INDEX_NAME UNIQUENES CLUSTERING_FACTOR NUM_ROWS CLUSTER_PCT
-------------------- --------- ----------------- ---------- -----------
PK_TEST UNIQUE 10009871 10453407 96 --> So High
UITEST01 UNIQUE 853733 10113211 9 --> Very Less
We can see the PK having the highest CF and the other unique index is not.
The only logical explanation that strikes me is, the data beneath is stored actually by order of column over the Unique index.
1) Am I right with this understanding?
2) Is there any way to give the PK , the lowest CF number?
3) Seeing the Query cost using both these index, it is very fast for single selects. But still, the CF number is what baffle us.
The table is relatively huge over 10M records, and also receives real time inserts/updates.
My Database version is Oracle 11gR2, over Exadata X2
You are seeing the evidence of a heap table indexed by an ordered tree structure.
To get extremely low CF numbers you'd need to order the data as per the index. If you want to do this (like SQL Server or Sybase clustered indexes), in Oracle you have a couple of options:
Simply create supplemental indexes with additional columns that can satisfy your common queries. Oracle can return a result set from an index without referring to the base table if all of the required columns are in the index. If possible, consider adding columns to the trailing end of your PK to serve your heaviest query (practical if your query has small number of columns). This is usually advisable over changing all of your tables to IOTs.
Use an IOT (Index Organized Table) - It is a table, stored as an index, so is ordered by the primary key.
Sorted hash cluster - More complicated, but can also yield gains when accessing a list of records for a certain key (like a bunch of text messages for a given phone number)
Reorganize your data and store the records in the table in order of your index. This option is ok if your data isn't changing, and you just want to reorder the heap, though you can't explicitly control the order; all you can do is order the query and let Oracle append it to a new segment.
If most of your access patterns are random (OLTP), single record accesses, then I wouldn't worry about the clustering factor alone. That is just a metric that is neither bad nor good, it just depends on the context, and what you are trying to accomplish.
Always remember, Oracle's issues are not SQL Server's issues, so make sure any design change is justified by performance measurement. Oracle is highly concurrent, and very low on contention. Its multi-version concurrency design is very efficient and differs from other databases. That said, it is still a good tuning practice to order data for sequential access if that is your common use case.
To read some better advice on this subject, read Ask Tom: what are oracle's clustered and nonclustered indexes
I have a table Item with autoinc int primary key Id and a foreign key UserId.
And I have a table User with autoinc int primary key Id.
Default is that the index for Item.Id gets clustered.
I will mostly query items on user-id so my question is: Would it be better to set the UserId foreign key index to be clustered instead?
Having the clustered index on the identity field has the advantage that the records will be stored in the order that they are created. New records are added at the end of the table.
If you use the foreign key as clustered index, the records will be stored in that order instead. When you create new records the data will be fragmented as records are inserted in the middle, which can reduce performance.
If you want an index on the foreign key, then just add a non-clustered index for it.
The answer depends only on usage scenario. For example, Guffa tolds that data will be fragmented. That's wrong. If your queries depends mostly on UserId, then data clustered by ItemId is fragmented for you, because items for same user may be spreaded over a lot of pages.
Of course, compared to sequential ItemId (if it is sequential in your schema), using UserId as clustered key can cause page splits while inserting. This is two additional page writes at maximum. But when you're selecting by some user, his items may be fragmented over tens of pages (depends on items per user, item size, insertion strategy, etc) and therefor a lot of page reads. If you have a lof of such selects per single single insert (very often used web/olap scenarios), you can face hundreds of IO operations compared to few ones spent on page splitting. That was the clustering index was created for, not only for clustering by surrogate IDs.
So there is no clear answer, are the clustered UserId in your case good or bad, because this is highly depends on context. What is ratio between selects/inserts operations? How fragmented user ids are if clustered by itemid? How many additional indicies are on the table, because there is a pitfall (below) in sql server.
As you might know, clustered index requires unique values. This is not a big problem, because you can create index on pair (UserId, ItemId). Clustered index isn't itself stored on disk, so no matter how many fields are there. But non-clustered indices store clustered index values in their leaves. So if you have clustered index on UserId+ItemId (lets imagine their type is [int] and size is 8 bytes) and non-clustered index on ItemId, then this index will have twice size (8 bytes per a b-tree leaf) compared to just the ItemId as clustered index (4 bytes per a leaf).
In general, you want to cluster on the most frequently accessed index. But you're not required to have a clustering index at all. You (or your DBAs) need to evaluate things and weigh the advantages and disadvantages so as to choose the most appropriate indexing strategy.
If you cluster on a monotonic counter like an identity column, all new rows are going to be inserted at the end of the table: that means a "hot spot" is created that is likely to cause lock contention on inserts, since every SPID doing an insert is hitting the same data page.
Tables without a clustering index have their data pages organized as a heap, pretty much just a linked list of data pages.
SQL Server indexes are B-trees. For non-clustered indexes, the leaf nodes of the B-tree are pointers to the appropriate data page. That means if the index is used and doesn't cover the query's columns, an additional look aside has to be done to fetch the data page. That means additional I/O and paging.
Clustered indices are different: their leaf nodes are the data pages themselves, meaning the heap essentially goes away: a table scan means a traversal of the clustering index's B-tree. The advantage is that once you've found what you need in the clustered index, you already have the data page you need, thus avoiding the additional I/O that a seek on a non-clustered index is likely to requir. The disadvantage, of course, is that the clustered index is larger, since it carrys the entire table with it, so traversals of the clustered index are more expensive.
clustered index is created on primary key so what you can do is leave that as clustered and then create a non clustered index on the user Id of item. This will still be very fast as user. Id column will be clustered index.
Possibly.
Is the item.user-id column a unique column within your item table? If not you'd need to make this a clustered primary key by adding a second (possibly more) column to the key to make it unique / possibly this will add additional overhead that you'd not anticipated.
Are there any relationships with the item.id column? If so those may be important to the performance of your application so should be taken into account.
How often is the item.user-id value likely to change? If not at all that counts in its favour; the more often it's likely to be updated the worse, since that leads to fragmentation.
My recommendation would be to build you app with the regular item.id as clustered key, the later once you've got some data try (in a test system using a copy of your production data) switching the clustered index and testing its impact; that way you can easily see real results rather than trying to guess the multitude of possibilities. This avoids premature optimisation / ensures you make the correct choice.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 12 months ago.
The community reviewed whether to reopen this question yesterday and left it closed:
Original close reason(s) were not resolved
Improve this 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.
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.