The size of each record of table is a performance parameter. that means if size of record was small SQL Server fetch more records in each read from physical hard.
In most of our queries we not use all column of table, and may be some column use only in specific query. Is it possible for we partitioning columns of each table to have better performance.
I use SQL Server 2008 R2.
Thank you.
True column level partitioning comes with column oriented storage, see Inside the SQL Server 2012 Columnstore Indexes, but that is available only in SQL Server 2012 and addresses specific BI workloads, not general SQL Server apps.
In row oriented storage the vertical partitioning is actually another name for designing proper covering indexes. If the engine has an alternative narrow index it will use it instead of the base table, when possible.
The last alternative, manually splinting the table and joining the vertical 'shards' in queries (or defining joining views, same thing) is usually ill advised and seldom pays off.
At the moment with SQL Server 2008, you cannot partition tables horizontally. If you have a large number of columns, you would need to chop it into horizontal chunk tables that share a common key and then skin them with an update-able view to give the illusion of one very wide table.
If there are just a few large columns (e.g. VARCHAR(1000)), you can normalize your data into unique value tables.
The one exception to the no column partitioning rule are character columns declared as max (varchar(max), for example).
These are stored on a separate data page. I believe this page is not read in unless the column is referenced in the query. If I am wrong, I'am sure more knowledge people will correct me.
Related
I’m trying to evaluate which type of indexes to use on our tables in the SQL Server 2014 data mart, which we are using to power our OLAP cube in SSAS. I have read the documentation on MSDN and still a bit unclear which is the right strategy for our use case with the ultimate goal of speeding up the queries on SQL Server that the cube issues when people browse the cube.
I have the tables related to each other as shown in the following snow flake dimensional model. The majority of the calculations that we are going to do in the cube, is COUNT DISTINCT of the users (UserInfoKey) based on different combination of dimensions (both filters and pivots). Keeping that in mind, what would the SQL experts suggest I do in terms of creating indexes on the tables?. I have the option of creating COLUMN STORE INDEXES on all my tables (partitioned by the HASH of primary keys) or create the regular primary keys (clustered indexes) on all my tables. Which one is better for my scenario? From my understanding the cube will be doing a lot of joins and groupby’s under the covers based on the dimensions selected by the user.
I tried both versions with some sample data and the performance isn’t that different in both cases. Now before I do the same experiment with real data (it’s going to take a lot of time to produce the real data and load it into our data mart), I wanted to check with the experts about their suggestions.
We are also evaluating if we should use PDW( Parallel Datawarehouse) as our data mart instead of vanilla SQL Server 2014.
Just to give an idea on the scale of data we are dealing with
The 2 largest tables are
ActivityData fact table : 784+ million rows
DimUserInfo dimension table: 30 + million rows
Any help or pointers are appreciated
I have a SQL Server 2008 R2 database containing a very huge table that we use for reporting. Every night around 40,000 records are inserted into the table. I read in many articles that Indexed views are suitable for OLAP or Warehouse databases, not for transaction tables.
My goal is not to query the whole table, but to query a subset, say last 3 months data. Don't want to use triggers to create a subset. Would an indexed view be suitable for my scenario ? If not, any better ideas ?
You might need to check some repercussions about using an indexed view. Here are some details of some items to consider before. http://msdotnetbuddy.blogspot.com/2010/12/indexed-view-in-mssql-server.html
You could also partition your big table, into let's say having only quarterly data. You would only query on a subset. If that is not an option, you could also create a temporary cache table, that only contains data specific for this report.
You could use an indexed view, you will need to use the "with schemabinding" keywords, you can put this into any search engine to find the implications of using this.
I'm Working on My Program that Works With SQL Server.
for Store Data in Database Table, Which of the below approaches is correct?
Store Many Rows Just in One Table (10 Million Record)
Store Fewer Rows in Several Table (500000 Record) (exp: for each Year Create One Table)
It depends on how often you access data.If you are not using the old records, then you can archive those records. Splitting up of tables is not desirable as it may confuse you while fetching data.
I would say to store all the data in a single table, but implement a table partition on the older data. Partioning the data will increase query performance.
Here are some references:
http://www.mssqltips.com/sqlservertip/1914/sql-server-database-partitioning-myths-and-truths/
http://msdn.microsoft.com/en-us/library/ms188730.aspx
http://blog.sqlauthority.com/2008/01/25/sql-server-2005-database-table-partitioning-tutorial-how-to-horizontal-partition-database-table/
Please note that this table partioning functionality is only available in Enterprise Edition.
Well, it depends!
What are you going to do with the data? If you are querying this data a lot of times it could be a better solution to split the data in (for example) year tables. That way you would have a better performance since you have to query smaller tables.
But on the other side. With a bigger table and with good query's you might not even see a performance issue. If you only need to store this data it would be better to just use 1 table.
BTW For loading this data into the database you could use BCP (bulkcopy), which is a fast way of inserting a lot of rows.
DataColumn, DataColumn, DateColumn
Every so often we put data into the table via date.
So everything seems great at first, but then I thought: What happens when there are a million or billion rows in the table? Should I be breaking up the tables by date? This way the query performance will never degrade? How do people deal with this sort of thing?
You can use partitioned tables starting with SQL 2K5: Partitioned Tables
This way you gain the benefits of keeping the logical design pure while being able to move old data into a different file group.
You should not break your tables because of data. Instead, you should worry about your indexes, normalization and so on.
Update
A little deeper explanation. Let's suppose you have a table with a million records. If you have different dates on [DateColumn], your greatest ally will be the indexes that work with the [DateColumn]. Then you make sure your queries always filter by at least [DateColumn].
This way, you will be fine.
This easily qualifies as premature optimization, which is tough to achieve in db design IMHO, because optimization is/should be closer to the surface in data modeling.
But all you need to do is create an index on the DateColumn field. An index is actually a much better performance solution than any kind of table splitting/breaking up and keeps your design and therefore all of you programming much simpler. (And you can decide to use partitioning w/o affecting your design in the future if it helps.)
Sounds like you could use a history table. If you are mostly going to query the current date's data, then migrate the old data to the history table and your main table will not grow so much.
If I understand you question correctly, you have a table with some data and a date. Your question is -- will I see improved performance if I make a new table say, every year. This way the queries will never have to look at more than one years worth of data.
This is wrong. Instead what you should do is set the date field as an index. The server will be able to give you the performance gain you need if it is an index.
If you don't do this your program's logic will get crazy and ultimately slow down your system.
Keep it simple.
(NB - There are some advanced partitioning features you can make use of, but these can be layered in later if needed -- it is unlikely you will need these features but the simple design should be able to migrate to them if needed.)
When tables and indexes become very
large, partitioning can help by
partitioning the data into smaller,
more manageable sections.
Microsoft SQL Server 2005 allows you
to partition your tables based on
specific data usage patterns using
defined ranges or lists. SQL Server
2005 also offers numerous options for
the long-term management of
partitioned tables and indexes by the
addition of features designed around
the new table and index structure.
Furthermore, if a large table exists
on a system with multiple CPUs,
partitioning the table can lead to
better performance through parallel
operations.
You might need considering the
following too: In SQL Server 2005,
related tables (such as Order and
OrderDetails tables) that are
partitioned to the same partitioning
key and the same partitioning function
are said to be aligned. When the
optimizer detects that two partitioned
and aligned tables are joined, SQL
Server 2005 can join the data that
resides on the same partitions first
and then combine the results. This
allows SQL Server 2005 to more
effectively use multiple-CPU
computers.
Read about Partitioned Tables and Indexes in SQL Server 2005
What is the maximum number of tables that can be created in sql and what is the maximum number of columns for a single table?
As seen here: http://msdn.microsoft.com/en-us/library/ms143432.aspx
MS SQL Server can contain 2,147,483,647 objects. Objects include tables, views, stored procedures, user-defined functions, triggers, rules, defaults, constraints and so on. In short you're unlikely to run out of room for objects though.
1024 columns per non-wide table, 30,000 for wide tables.
If you're talking about another database platform then I'm sure google will help.
That would differ by database but I can tell you that if you are thinking of anything close to the max number of columns in a table, you need to redesign. You have an absolute limit on the bytes per record as well and while you can create columns that together would add to more bytes than the record would allow in some databases (I know SQL Server will let you do this), you will eventually have to redisgn becasue one record can't fit the limit. If you are planning for each record to have a lot of null columns, you probaly need related tables instead. Less wide tables also tend to perform better and are far easier to maintain.
SQL as a language dictates no such restrictions, and assumes an unlimited amount of columns, tables and what-have-yous. Also, SQL, as a language, thinks that all operations are constant and instant in time-efficiency.
As the other answerers already have mentioned, different RDBMSes implement things very differently from each others. And few implement SQL in its entirety.