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.
Related
Even after reading SQLite limits I could not find the maximum number of tables a SQLite database file can hold. So, I'd like to know if
There is a maximum number of tables a SQLite database can hold?
It is an issue to have thousands of small tables in a SQLite database file?
Many tables in a SQLite database file can impact performance of queries?
The list of limits in SQLite is documented at this page. There is no maximum number of tables per database given, so there is likely no limit implemented by SQLite. There is a limit of 64 tables per JOIN.
4. Maximum Number Of Tables In A Join
SQLite does not support joins containing more than 64 tables. This
limit arises from the fact that the SQLite code generator uses bitmaps
with one bit per join-table in the query optimizer.
SQLite uses an efficient query planner algorithm and so even a large
join can be prepared quickly. Hence, there is no mechanism to raise or
lower the limit on the number of tables in a join.
15. Maximum Number Of Tables In A Schema
Each table and index requires at least one page in the database file.
An "index" in the previous sentence means an index created explicitly
using a CREATE INDEX statement or implicit indices created by UNIQUE
and PRIMARY KEY constraints. Since the maximum number of pages in a
database file is 2147483646 (a little over 2 billion) this is also
then an upper bound on the number of tables and indices in a schema.
Whenever a database is opened, the entire schema is scanned and parsed
and a parse tree for the schema is held in memory. That means that
database connection startup time and initial memory usage is
proportional to the size of the schema.
Are the table identical in structure? If so, it's generally considered a better practice to store them in a single table with an identifying column.
I believe the number of tables is constrained only by the size of the database. There can be at most 2,147,483,646 pages in a single SQLite database. So I'd guess that would also be the maximum number of tables in a single SQLite database.
That's based on the assumption that database pages are used only for tables, which probably isn't a very useful assumption.
To answer your questions 2 and 3, although having multiple tables with similar structure goes against the principles of database normalisation, there are many practical reasons why it would be preferred over a single table or virtual table - the biggest of course being than in SQLite it's much easier to drop tables than columns. It also takes up less space than having "tableX" in every row of a single table if you take the simple approach and don't do "proper" normalized relational tables.
In terms of performance, you won't see any issues with using hundreds of thousands of tables compared to a single table with hundreds of thousands entries in the "table" column, and that column indexed. In fact the index on that single normalized table could be far larger than the table indexing mechanisms SQLite uses, and less efficient.
Having said all of that, i cannot with a healthy conscience end this post without saying that much like exec() being used to assign variables with variable names being a common beginner mistake in programming, making multiple tables which should be in a single normalized table (virtual or otherwise) is a common beginner mistake in database architecture. There are, in both areas, instances where the circumstances make using exec or many tables the correct option. If for example, your data is all very similar but you are sure you will not be doing any joining whatsoever on the data, then many tables is fine. Just make sure that you really do see the data as totally unrelated, despite being of a similar structure.
I know the question does not make that good of a sense.
What is a typical number of columns in a small, medium and large table (in a database) in professional environment.
Want to get an idea what is a good design, how big I can grow my table column wise. Is 100 columns or 200 columns in a table OK?
It totally depends upon the nature of the subject you are modeling. If you need one column, you need one; if you need 500, then you need 500. Properly designed, the size of the tables you end up with will always be "just right".
How big can they be, what performs well, what if you need more columns than you can physically stuff into SQL... those are all implementation, maintenance, and/or performance questions, and that's a different and secondary subject. Get the models right first, then worry about implementation.
Well, for SQL Server 2005, according to the Max Capacity Specifications, the maximum number of columns per base table are 1024. So that's a hard upper limit.
You should normalize your tables to at least third normal form (3NF). That should be your primary guide.
You should consider the row size. 100 sparse columns is differnt from 100 varchar (3000) columns. It is almost always better to make a related table (with an enforced 1-1 relationship) when you are starting to get past the record size that SQL Server can store on one page.
You also should consider how the data will be queried. Are many of those fields ones that will not frequently need to be returned? Do they have a natural grouping (think a person record vice a user login record) and will that natural grouping dictate how they will be queried? In that case it might be better to separate them out.
And of course you should consider normalization. If you are doing multiple columns to avoid having a one-to-many relationship and a join, then you should not do that even if you only have 6 columns. Joins (with the key fields indexed) are preferable to denormalized tables except in data warehousing situations in general. It is better if you don't have to add new columns because you need to store a new phone type.
Is there any performance benefit to splitting a large table with roughly 100 columns into 2 separate tables? This would be in terms of inserting, deleting and selecting tasks? I'm using SQL Server 2008.
If one of the fields is a CLOB or BLOB and you anticipate it holding a huge amount of data and you won't need that field very often and the result set will transmitted over a long pipe (like server to a web-based client), then I think putting that field in a separate table would be appropriate.
But just returning 100 regular fields probably won't tax your system so much as to justify a separate table and a join.
The only benefit you might see is if a number of columns are only occasionally populated. In which case putting those into their own table and only adding a row when there is data might make sense in terms of overall row overhead and, depending on the number of rows, overall page count for the table(s). That said, this is one of the reasons they introduced sparse columns in SQL Server 2008.
For the maintenance and other overhead of managing two tables instead of one (especially given that people can act on individual tables if they choose), it's unlikely it would be worth it.
Can you describe what type of entity needs to have over 100 columns? Perhaps the data model is just wrong in the first place.
I would say no as it would take more execution time to join the 2 tables whenever you wanted to do something.
I depends if you use these fields in the same time in your application.
These kind of performance improvements are really bad : you make your source code impossible to understand. If you have performance trouble with this table, add something (like a table containing the 15 fields you'll use in a request that'll updated via trigger), don't modify your clean solution.
If you don't have performance problem, don't do anything, you'll see later !
I always tried to make my sql database as simple and as understandable as possible.
Until now I always used a limited number of columns, I think I never had more than 20. Now, there is one thing, that would make my life easier, if I had much more columns. Let´s say 200 columns. (not rows). What do you think about it?
I just want to know, if it is a bad idea, not why i´m doing this or if there are other possibilities, just if somebody has already experienced something like that and if it is a bad idea to do such a table.
Fewer, smaller width columns is better than lots of columns and/or large width columns.
Why? Because the narrower the row size, the more rows you fit on a 8K page. That means you do less I/O and use less memory to buffer pages. That is always a good thing.
In those (hopefully) rare cases, where the domain requires many attributes on an object (with the assumption of 1-1 object-table mapping), you should consider splitting into two tables ina 1-1 relationship, one containing the frequently used columns.
I don't think it is black and white. Having a large row size (implied by the large number of columns) will hurt performance (i.e., more I/O) -- but there are cases where taking a small hit in performance in one place will be offset by increased performance in others.
I'd say it depends on how many rows you expect this table to have, how often will it will be queried, how many of those additional columns will really be accessed, and how it would compare to your alternative design in terms of efficiency and complexity.
Luke--
It really depends on the type of the system you are working with. Example in transactional systems, most tables have at most 50 columns or so with almost no redundant data attrributes ( If you have a process date, you would not need the Process Month or the process year as a seperate column). This of course is because the records are updated/inserted frequently and you'll need to update all the redundant attributes everytime you update one row.
In Data Warehouse/reporting environments, for Dimension tables (which have the attributes for an entity) it is typical to have 100+ columns as there are could be various ways you want to categorize a given entity.The Updates here are not so much a problem as data is typically loaded once during off-peak hours and then is used mostly in selects.
Take a look at these links to know more..
http://en.wikipedia.org/wiki/Database_normalization
http://en.wikipedia.org/wiki/Star_schema
So the answer is it depends... If you want a perfectly relational system, then may be 200+ columns is kind of a red flag indicating you should look at normalize your data (May be not). Updates and Indexes are two things that you should be concerned with in such a system.
You are using SQL Server, which I think defaults to row-oriented storage (all fields in a row are stored together in a page), which can be a problem with large number of columns. However, if you use column-oriented storage, the number of columns per table does not matter because each column is stored together. I don't know if this is possible with SQL Server.
I want to know optimization techniques for databases that has nearly 80,000 records,
list of possibilities for optimizing
i am using for my mobile project in android platform
i use sqlite,i takes lot of time to retreive the data
Thanks
Well, with only 80,000 records and assuming your database is well designed and normalized, just adding indexes on the columns that you frequently use in your WHERE or ORDER BY clauses should be sufficient.
There are other more sophisticated techniques you can use (such as denormalizing certain tables, partitioning, etc.) but those normally only start to come into play when you have millions of records to deal with.
ETA:
I see you updated the question to mention that this is on a mobile platform - that could change things a bit.
Assuming you can't pare down the data set at all, one thing you might be able to do would be to try to partition the database a bit. The idea here is to take your one large table and split it into several smaller identical tables that each hold a subset of the data.
Which of those tables a given row would go into would depend on how you choose to partition it. For example, if you had a "customer_id" field that could range from 0 to 10,000 you might put customers 0 - 2500 in table1, 2,500 - 5,000 in table2, etc. splitting the one large table into 4 smaller ones. You would then have logic in your app that would figure out which table (or tables) to query to retrieve a given record.
You would want to partition your data in such a way that you generally only need to query one of the partitions at a time. Exactly how you would partition the data would depend on what fields you have and how you are using them, but the general idea is the same.
Create indexes
Delete indexes
Normalize
DeNormalize
80k rows isn't many rows these days. Clever index(es) with queries that utlise these indexes will serve you right.
Learn how to display query execution maps, then learn to understand what they mean, then optimize your indices, tables, queries accordingly.
Such a wide topic, which does depend on what you want to optimise for. But the basics:
indexes. A good indexing strategy is important, indexing the right columns that are frequently queried on/ordered by is important. However, the more indexes you add, the slower your INSERTs and UPDATEs will be so there is a trade-off.
maintenance. Keep indexes defragged and statistics up to date
optimised queries. Identify queries that are slow (using profiler/built-in information available from SQL 2005 onwards) and see if they could be written more efficiently (e.g. avoid CURSORs, used set-based operations where possible
parameterisation/SPs. Use parameterised SQL to query the db instead of adhoc SQL with hardcoded search values. This will allow better execution plan caching and reuse.
start with a normalised database schema, and then de-normalise if appropriate to improve performance
80,000 records is not much so I'll stop there (large dbs, with millions of data rows, I'd have suggested partitioning the data)
You really have to be more specific with respect to what you want to do. What is your mix of operations? What is your table structure? The generic advice is to use indices as appropriate but you aren't going to get much help with such a generic question.
Also, 80,000 records is nothing. It is a moderate-sized table and should not make any decent database break a sweat.
First of all, indexes are really a necessity if you want a well-performing database.
Besides that, though, the techniques depend on what you need to optimize for: Size, speed, memory, etc?
One thing that is worth knowing is that using a function in the where statement on the indexed field will cause the index not to be used.
Example (Oracle):
SELECT indexed_text FROM your_table WHERE upper(indexed_text) = 'UPPERCASE TEXT';