I get the idea of how indexing works to improve the speed of database query. But isn't it just a datastructure outside of our main table, which have pointers to our rows in the main table. We can then sort the indexing and execute Binary Search on it to get the id (for example) of a row the we need but aren't we also need to turn to the main table and execute find by id in that table, if that's the case then isn't the query also requires O(N), or I understand indexing wrong?
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In Oracle 11g, say, I have a table Task which has a column ProcessState. The values of this column can be Queued, Running and Complete (can have couple more states in future). The table will have 50M+ data with 99.9% of rows having Complete as that column value. Only a few thousand rows will have value Queued/Running.
I read that although bitmap index is good for low cardinality column, but that is used largely for static tables.
So, what index can improve the query for Queued/Running tasks? bitmap or normal non-unique b-tree index?
Also, what index can improve the query for a binary column (NUMBER(1,0) with just yes/no values) ?
Disclaimer: I am an accidental dba.
A regular (b*tree) index is fine. Just make sure there is a histogram on the column. (See METHOD_OPT parameter in DBMS_STATS.GATHER_TABLE_STATS).
With a histogram on that column, Oracle will have the data it needs to make sure it uses the index when looking for queued/running jobs but use a full table scan when looking for completed job.
Do NOT use a bitmap index, as suggested in the comments. With lots of updates, you'll have concurrency and, worse, deadlocking issues.
Also, what index can improve the query for a binary column (NUMBER(1,0) with just yes/no values)
Sorry -- I missed this part of your question. If the data in the column is skewed (i.e., almost all 1 or almost all 0), then a regular (b*tree) index as above. If the data is evenly distributed, then no index will help. Reading 50% of your table's rows via an index will be slower than a full table scan.
I guess that you are interested in selecting rows with (Queued/Running) states for updating them. So it would be nice to separate the completed rows from the others because there is no much sence in indexing completed rows. You can use paritioning here or function-based index with function returning NULL for completed rows and actual values for the others, in this case only uncompleted rows appears in an index tree.
I'm new to sql server. I'm using sql server express 2014. I have NEVER had to deal with indexes.
I've made the following table structure to experiment with indexes
Given the table is supposed to store the score a student scored for each assignment, a lecturer may want to filter the data on assignment ID just to see who scored the highest on a particular assignment for example.
The following screenshot shows the query and the nonclustered index I created. However the execution plan says it wasn't used why?
Here is the definition
The index does match the query but the table is extremely small. Scanning it accesses its only page. Using the index accesses two pages.
Make the index covering or add way more data. Both are actually good ways to try out indexes and understand them. You should try both.
When you have small amount of data, like 5 rows in here, indexes aren't really being used because the whole data is most likely just 1 page. Because you're selecting "*" the whole page must be accessed anyhow because the non-clustered index only contains the keys in it, so fetching that one page using the index would probably be twice as expensive.
You can test how many pages would be used with and without the index by first running "set statistics io on". When you run the statement you'll see number of pages in the messages tab. To compare it when the index is used you can run "select * from Submissions with (index (index_name_here)) where AssignmentID = 2"
I have a search procedure that is being passed around 15-20 (optional) parameters and the search procedure calls their respective functions to check if the value passed in parameter exists in the database. So, it is basically a Search structure based on a number of parameters.
Now, since the database is going to have millions of records, I expect the simple plain search procedure to fail right away. What are the ways that can improve query performance?
What I have tried so far:
Clustered index on FirstName column of database (as I expect it to be used very frequently)
Non Clustered index on rest of the columns that are basis of the user search and also the include keyword.
Note:
I am looking for more ways to optimize my queries.
Most of the queries are nothing but select statements checked against a condition.
One of the queries uses GroupBy clause.
I have also created a temporary table in which I am inserting all the matched entries.
First Run the query from Sql Server Management Studio and look at the query plan to see where the bottle neck is. Any place you see a "table scan" or "index scan" it has to go through all data to find what it is looking for. If you create appropriate indexes that can be used for these operations it should increase performance.
Below Listed are some tips for improving the performance of sql query..
Avoid Multiple Joins in a Single Query
Try to avoid writing a SQL query using multiple joins that includes outer joins, cross apply, outer apply and other complex sub queries. It reduces the choices for Optimizer to decide the join order and join type. Sometime, Optimizer is forced to use nested loop joins, irrespective of the performance consequences for queries with excessively complex cross apply or sub queries.
Eliminate Cursors from the Query
Try to remove cursors from the query and use set-based query; set-based query is more efficient than cursor-based. If there is a need to use cursor than avoid dynamic cursors as it tends to limit the choice of plans available to the query optimizer. For example, dynamic cursor limits the optimizer to using nested loop joins.
Avoid Use of Non-correlated Scalar Sub Query
You can re-write your query to remove non-correlated scalar sub query as a separate query instead of part of the main query and store the output in a variable, which can be referred to in the main query or later part of the batch. This will give better options to Optimizer, which may help to return accurate cardinality estimates along with a better plan.
Avoid Multi-statement Table Valued Functions (TVFs)
Multi-statement TVFs are more costly than inline TFVs. SQL Server expands inline TFVs into the main query like it expands views but evaluates multi-statement TVFs in a separate context from the main query and materializes the results of multi-statement into temporary work tables. The separate context and work table make multi-statement TVFs costly.
Create a Highly Selective Index
Selectivity define the percentage of qualifying rows in the table (qualifying number of rows/total number of rows). If the ratio of the qualifying number of rows to the total number of rows is low, the index is highly selective and is most useful. A non-clustered index is most useful if the ratio is around 5% or less, which means if the index can eliminate 95% of the rows from consideration. If index is returning more than 5% of the rows in a table, it probably will not be used; either a different index will be chosen or created or the table will be scanned.
Position a Column in an Index
Order or position of a column in an index also plays a vital role to improve SQL query performance. An index can help to improve the SQL query performance if the criteria of the query matches the columns that are left most in the index key. As a best practice, most selective columns should be placed leftmost in the key of a non-clustered index.
Drop Unused Indexes
Dropping unused indexes can help to speed up data modifications without affecting data retrieval. Also, you need to define a strategy for batch processes that run infrequently and use certain indexes. In such cases, creating indexes in advance of batch processes and then dropping them when the batch processes are done helps to reduce the overhead on the database.
Statistic Creation and Updates
You need to take care of statistic creation and regular updates for computed columns and multi-columns referred in the query; the query optimizer uses information about the distribution of values in one or more columns of a table statistics to estimate the cardinality, or number of rows, in the query result. These cardinality estimates enable the query optimizer to create a high-quality query plan.
Revisit Your Schema Definitions
Last but not least, revisit your schema definitions; keep on eye out that appropriate FORIGEN KEY, NOT NULL and CEHCK constraints are in place or not. Availability of the right constraint on the right place always helps to improve the query performance, like FORIGEN KEY constraint helps to simplify joins by converting some outer or semi-joins to inner joins and CHECK constraint also helps a bit by removing unnecessary or redundant predicates.
Reference
I have very simple query which returns 200K records very slow (20 seconds).
SELECT * FROM TABLE ORDER BY ID DESC
If I do just
SELECT * FROM TABLE
it returns quick result.
I created INDEX on that field ID (ALLOW REVERSE SCANS) but still returns very similar response.
Where can be the problem? What can be the cause of stagnation for this query?
I updated statictics and index table metadata.
I am hoping for help of db experts (administrators), I know this is not simple question.
Thank you
The bufferpool is important and the sort heap parameter (along sheapthres and sheapthres_shr) At the same time, check if there a sort overflow, because this will mean the sort will be written into disk because of lack of memory, and for this a system temporary tablespace is necesary. Check where are they stored, and if the disk are fast enough.
Take a look at the access plan, and check if the index is taking into account.
The first query is very fast because it does not need any sort, just a table scan.
For the second one, an index does not do anything, because you will retrieve all data from the table, so it does not access the index (there is nothing in the 'where', no nothing is filtered)
Both queries need table scan, but the first one needs to be sorted, and that is the problem, the sort.
I have a sproc that puts 750K records into a temp table through a query as one of its first actions. If I create indexes on the temp table before filling it, the item takes about twice as long to run compared to when I index after filling the table. (The index is an integer in a single column, the table being indexed is just two columns each a single integer.)
This seems a little off to me, but then I don't have the firmest understanding of what goes on under the hood. Does anyone have an answer for this?
If you create a clustered index, it affects the way the data is physically ordered on the disk. It's better to add the index after the fact and let the database engine reorder the rows when it knows how the data is distributed.
For example, let's say you needed to build a brick wall with numbered bricks so that those with the highest number are at the bottom of the wall. It would be a difficult task if you were just handed the bricks in random order, one at a time - you wouldn't know which bricks were going to turn out to be the highest numbered, and you'd have to tear the wall down and rebuild it over and over. It would be a lot easier to handle that task if you had all the bricks lined up in front of you, and could organize your work.
That's how it is for the database engine - if you let it know about the whole job, it can be much more efficient than if you just feed it a row at a time.
It's because the database server has to do calculations each and every time you insert a new row. Basically, you end up reindexing the table each time. It doesn't seem like a very expensive operation, and it's not, but when you do that many of them together, you start to see the impact. That's why you usually want to index after you've populated your rows, since it will just be a one-time cost.
Think of it this way.
Given
unorderedList = {5, 1,3}
orderedList = {1,3,5}
add 2 to both lists.
unorderedList = {5, 1,3,2}
orderedList = {1,2,3,5}
What list do you think is easier to add to?
Btw ordering your input before load will give you a boost.
You should NEVER EVER create an index on an empty table if you are going to massively load it right afterwards.
Indexes have to be maintained as the data on the table changes, so imagine as if for every insert on the table the index was being recalculated (which is an expensive operation).
Load the table first and create the index after finishing with the load.
That's were the performance difference is going.
After performing large data manipulation operations, you frequently have to update the underlying indexes. You can do that by using the UPDATE STATISTICS [table] statement.
The other option is to drop and recreate the index which, if you are doing large data insertions, will likely perform the inserts much faster. You can even incorporate that into your stored procedure.
this is because if the data you insert is not in the order of the index, SQL will have to split pages to make room for additional rows to keep them together logically
This due to the fact that when SQL Server indexes table with data it is able to produce exact statistics of values in indexed column. At some moments SQL Server will recalculate statistics, but when you perform massive inserts the distribution of values may change after the statistics was calculated last time.
The fact that statistics is out of date can be discovered on Query Analyzer. When you see that on a certain table scan number of rows expected differs to much from actual numbers of rows processed.
You should use UPDATE STATISTICS to recalculate distribution of values after you insert all the data. After that no performance difference should be observed.
If you have an index on a table, as you add data to the table SQL Server will have to re-order the table to make room in the appropriate place for the new records. If you're adding a lot of data, it will have to reorder it over and over again. By creating an index only after the data is loaded, the re-order only needs to happen once.
Of course, if you are importing the records in index order it shouldn't matter so much.
In addition to the index overhead, running each query as a transaction is a bad idea for the same reason. If you run chunks of inserts (say 100) within 1 explicit transaction, you should also see a performance increase.