The BITMAP index is recommended when the column has less than 100 options, and let's assume I have a varchar column, and that column can store more than 100 options.
Okay, we should create a B-Tree index for this column, but how would the B-Tree index be executed?
For example, here's how a B-Tree index on a column that does NOT have Low Cardinality executes:
And here we have how the index BITMAP on a column that HAS Low Cardinality is executed:
My question is: How is the index B-Tree on a column that HAS Low Cardinality executed?
Related
Azure SQL Server has a simple table with three columns, all of which are part of the Clustered Index. The select query that filters using two of those columns and returns the third column uses Index Scan instead of Index Seek. A total of 500k records are being scanned and the CPU usage is high and the execution is slow
Clustered Index
Are there any reasons to not use Index Seek? Should I create the non-clustered index as suggested?
We have a query where the table is partitioned on column Adate.
Row count: 56595943, partition scheme - yearly, no of partitions - 300
Clustered index columns : empid, Adate
Query :
select top 1 Adate
from emp
where empid = 134556 and Adate <= {ts '7485-09-01 00:00:00.0'}
order by Adate desc
The actual execution plan returns a clustered index seek operation with 93% of the total query cost on clustered index key.
But why is the optimizer recommending a missing index with 92% of cost?
missing index details: Improve query cost:92%
create nonclustered index IDX_NC on dbo.emp([empid], [Adate])
The missing index has an improvement measure of 14755268, as per Microsoft the improvement measure baseline is 1,000,000
Why is this happening? Do you recommend to have a nonclustered index on already clustered index columns?
Well - consider this:
you do have the clustered index on (empid, adate)
the clustered index contains the whole data, e.g. the leaf level pages of the clustered index contain the whole data records (all the columns in your table)
If you are searching and the query uses the clustered index, it might still need to load much more data than is actually needed.... the whole record, as many times as your criteria is found.
If you have a non-clustered index on just (empid, Adate), and your query really only requires Adate (in its SELECT list of columns), then this index will be much smaller - it contains only those two columns (none of the overhead of all the other columns, which are not needed for your current query). So scanning this index, or loading these index pages, will load much less data compared to the clustered index.
From that point of view, yes, even having a nonclustered index on the same columns that make up your clustered index can be beneficial for certain query scenarios - that's probably what the SQL Server query optimizer picks up here.
As I know, heap tables are tables without clustered index and has no physical order.
I have a heap table "scan" with 120k rows and I am using this select:
SELECT id FROM scan
If I create a non-clustered index for the column "id", I get 223 physical reads.
If I remove the non-clustered index and alter the table to make "id" my primary key (and so my clustered index), I get 515 physical reads.
If the clustered index table is something like this picture:
Why Clustered Index Scans workw like the table scan? (or worse in case of retrieving all rows). Why it is not using the "clustered index table" that has less blocks and already has the ID that I need?
SQL Server indices are b-trees. A non-clustered index just contains the indexed columns, with the leaf nodes of the b-tree being pointers to the approprate data page. A clustered index is different: its leaf nodes are the data page itself and the clustered index's b-tree becomes the backing store for the table itself; the heap ceases to exist for the table.
Your non-clustered index contains a single, presumably integer column. It's a small, compact index to start with. Your query select id from scan has a covering index: the query can be satisfied just by examining the index, which is what is happening. If, however, your query included columns not in the index, assuming the optimizer elected to use the non-clustered index, an additional lookup would be required to fetch the data pages required, either from the clustering index or from the heap.
To understand what's going on, you need to examine the execution plan selected by the optimizer:
See Displaying Graphical Execution Plans
See Red Gate's SQL Server Execution Plans, by Grant Fritchey
A clustered index generally is about as big as the same data in a heap would be (assuming the same page fullness). It should use just a little more reads than a heap would use because of additional B-tree levels.
A CI cannot be smaller than a heap would be. I don't see why you would think that. Most of the size of a partition (be it a heap or a tree) is in the data.
Note, that less physical reads does not necessarily translate to a query being faster. Random IO can be 100x slower than sequential IO.
When to use Clustered Index-
Query Considerations:
1) Return a range of values by using operators such as BETWEEN, >, >=, <, and <= 2) Return large result sets
3) Use JOIN clauses; typically these are foreign key columns
4) Use ORDER BY, or GROUP BY clauses. An index on the columns specified in the ORDER BY or GROUP BY clause may remove the need for the Database Engine to sort the data, because the rows are already sorted. This improves query performance.
Column Considerations :
Consider columns that have one or more of the following attributes:
1) Are unique or contain many distinct values
2) Defined as IDENTITY because the column is guaranteed to be unique within the table
3) Used frequently to sort the data retrieved from a table
Clustered indexes are not a good choice for the following attributes:
1) Columns that undergo frequent changes
2) Wide keys
When to use Nonclustered Index-
Query Considerations:
1) Use JOIN or GROUP BY clauses. Create multiple nonclustered indexes on columns involved in join and grouping operations, and a clustered index on any foreign key columns.
2) Queries that do not return large result sets
3) Contain columns frequently involved in search conditions of a query, such as WHERE clause, that return exact matches
Column Considerations :
Consider columns that have one or more of the following attributes:
1) Cover the query. For more information, see Index with Included Columns
2) Lots of distinct values, such as a combination of last name and first name, if a clustered index is used for other columns
3) Used frequently to sort the data retrieved from a table
Database Considerations:
1) Databases or tables with low update requirements, but large volumes of data can benefit from many nonclustered indexes to improve query performance.
2) Online Transaction Processing applications and databases that contain heavily updated tables should avoid over-indexing. Additionally, indexes should be narrow, that is, with as few columns as possible.
Try running
DBCC DROPCLEANBUFFERS
Before the queries...
If you really want to compare them.
Physical reads don't mean the same as logical reads when optimizing a query
I have a Postgres table with about 4.5 million rows. The columns are basically just
low BIGINT,
high BIGINT,
data1,
data2,
...
When you query this table, you have a long integer, and want to find the data corresponding to the range between low and high that includes that value. What would be the best way to index this table for fast lookups?
A multi-column index with reversed sort order:
CREATE INDEX tbl_low_high_idx on tbl(low, high DESC);
This way, the index can be scanned forward to where low is high enough, then take all rows until high is too low - all in one scan. That's the main reason why sort order is implemented for indexes to begin with: to combine different sort orders in a multi-column index with different order. Basically, a b-tree index can be traversed in both directions at practically the same speed, so ASC / DESC would hardly be needed for single-column indexes.
You may also be interested in the new range types of PostgreSQL 9.2. Can be indexed with a GiST index like this:
CREATE INDEX tbl_idx ON tbl USING gist (low_high);
Should make a query of this form perform very fast:
SELECT * FROM tbl WHERE my_value <# low_high;
<# being the "element is contained by" operator.
Since both a Table Scan and a Clustered Index Scan essentially scan all records in the table, why is a Clustered Index Scan supposedly better?
As an example - what's the performance difference between the following when there are many records?:
declare #temp table(
SomeColumn varchar(50)
)
insert into #temp
select 'SomeVal'
select * from #temp
-----------------------------
declare #temp table(
RowID int not null identity(1,1) primary key,
SomeColumn varchar(50)
)
insert into #temp
select 'SomeVal'
select * from #temp
In a table without a clustered index (a heap table), data pages are not linked together - so traversing pages requires a lookup into the Index Allocation Map.
A clustered table, however, has it's data pages linked in a doubly linked list - making sequential scans a bit faster. Of course, in exchange, you have the overhead of dealing with keeping the data pages in order on INSERT, UPDATE, and DELETE. A heap table, however, requires a second write to the IAM.
If your query has a RANGE operator (e.g.: SELECT * FROM TABLE WHERE Id BETWEEN 1 AND 100), then a clustered table (being in a guaranteed order) would be more efficient - as it could use the index pages to find the relevant data page(s). A heap would have to scan all rows, since it cannot rely on ordering.
And, of course, a clustered index lets you do a CLUSTERED INDEX SEEK, which is pretty much optimal for performance...a heap with no indexes would always result in a table scan.
So:
For your example query where you select all rows, the only difference is the doubly linked list a clustered index maintains. This should make your clustered table just a tiny bit faster than a heap with a large number of rows.
For a query with a WHERE clause that can be (at least partially) satisfied by the clustered index, you'll come out ahead because of the ordering - so you won't have to scan the entire table.
For a query that is not satisified by the clustered index, you're pretty much even...again, the only difference being that doubly linked list for sequential scanning. In either case, you're suboptimal.
For INSERT, UPDATE, and DELETE a heap may or may not win. The heap doesn't have to maintain order, but does require a second write to the IAM. I think the relative performance difference would be negligible, but also pretty data dependent.
Microsoft has a whitepaper which compares a clustered index to an equivalent non-clustered index on a heap (not exactly the same as I discussed above, but close). Their conclusion is basically to put a clustered index on all tables. I'll do my best to summarize their results (again, note that they're really comparing a non-clustered index to a clustered index here - but I think it's relatively comparable):
INSERT performance: clustered index wins by about 3% due to the second write needed for a heap.
UPDATE performance: clustered index wins by about 8% due to the second lookup needed for a heap.
DELETE performance: clustered index wins by about 18% due to the second lookup needed and the second delete needed from the IAM for a heap.
single SELECT performance: clustered index wins by about 16% due to the second lookup needed for a heap.
range SELECT performance: clustered index wins by about 29% due to the random ordering for a heap.
concurrent INSERT: heap table wins by 30% under load due to page splits for the clustered index.
http://msdn.microsoft.com/en-us/library/aa216840(SQL.80).aspx
The Clustered Index Scan logical and physical operator scans the clustered index specified in the Argument column. When an optional WHERE:() predicate is present, only those rows that satisfy the predicate are returned. If the Argument column contains the ORDERED clause, the query processor has requested that the rows' output be returned in the order in which the clustered index has sorted them. If the ORDERED clause is not present, the storage engine will scan the index in the optimal way (not guaranteeing the output to be sorted).
http://msdn.microsoft.com/en-us/library/aa178416(SQL.80).aspx
The Table Scan logical and physical operator retrieves all rows from the table specified in the Argument column. If a WHERE:() predicate appears in the Argument column, only those rows that satisfy the predicate are returned.
A table scan has to examine every single row of the table. The clustered index scan only needs to scan the index. It doesn't scan every record in the table. That's the point, really, of indices.