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.
Related
I want to move multiple SQLite files to PostgreSQL.
Data contained in these files are monthly time-series (one month in a single *.sqlite file). Each has about 300,000 rows. There are more than 20 of these files.
My dilemma is how to organize the data in the new database:
a) Keep it in multiple tables
or
b) Merge it to one huge table with new column describing the time period (e.g. 04.2016, 05.2016, ...)
The database will be used only to pull data out of it (with the exception of adding data for new month).
My concern is that selecting data from multiple tables (join) would not perform very well and the queries can get quite complicated.
Which structure should I go for - one huge table or multiple smaller tables?
Think I would definitely go for one table - just make sure you use sensible indexes.
If you have the space and the resource 1 table, as other users have appropriately pointed out databases can handle millions of rows no problem.....Well depends on the data that is in them. The row size can make a big difference... Such as storing VARCHAR(MAX), VARBINARY(MAX) and several per row......
there is no doubt writing queries, ETL (extract transform load) is significantly easier on a single table! And maintenance of that is easier too from a archival perspective.
But if you never access the data and you need the performance in the primary table some sort of archive might make since.
There are some BI related reasons to maintain multiple tables but it doesn't sound like that is your issue here.
There is no perfect answer and will depend on your situation.
PostgreSQL is easily able to handle millions of rows in a table.
Go for option b) but..
with new column describing the time period (e.g. 04.2016, 05/2016, ...)
Please don't. Querying the different periods will become a pain, an unnecessary one. Just put the date in one column, put a index on the column and you can, probably, execute fast queries on it.
My concern is that selecting data from multiple tables (join) would not perform very well and the queries can get quite complicated.
Complicated for you to write or for the database to execute? An Example would be nice for us to get an image of your actual requirements.
We at college are making an application to generate PDF document from Excel sheet records using Java SE. I have though about two approaches to design the database. In one approach, there will be one table that will contain a lot of records (50K every year). In other approach, there will be a lot of tables created (1000 every year) at runtime and each table will contain max 50 records.
Which approach is efficient comparatively considering better overall time performance?
Multiple tables of identical structure almost never makes sense.
Databases are designed to have many records in few tables.
50K records is not "a lot" of records. You don't specify what database you will be using, but most commercial-grade databases can handle many, many millions of records in a table.
This is assuming you have proper indexes, etc. If you have to keep creating tables for you application, then there is something wrong with your design, and you need to re-think that.
When building a relational database the basic rule would be to avoid redundancy.
Look over your data and try to separate things that tend to repeat. If you notice a column or a group of columns that repeat across multiple entries create a new table for them. This way you will achieve the best performance when querying.
Otherwise, if the values are unique across the entries just keep the minimum number of tables.
You should just look for some design rules for relational databases. You will find some examples as well.
50k records is not much for a database. If it's all the same type of data (same structure), it belongs in the same table. Only if size and speed becomes an issue you should consider splitting up the data over multiple tables (or more likely: different servers).
I face the following issue. I have an extremely big table. This table is a heritage from the people who previously worked on the project. The table is in MS SQL Server.
The table has the following properties:
it has about 300 columns. All of them have "text" type but some of them eventually should represent other types (for example, integer or datetime). So one has to convert this text values in appropriate types before using them
the table has more than 100 milliom rows. The space for the table would soon reach 1 terabyte
the table does not have any indices
the table does not have any implemented mechanisms of partitioning.
As you may guess, it is impossible to run any reasonable query to this table. Now people only insert new records into the table but nobody uses it. So I need to restructure it. I plan to create a new structure and refill the new structure with the data from the old table. Obviously, I will implement partioning, but it is not the only thing to be done.
One of the most important features of the table is that those fields that are purely textual (i.e. they don't have to be converted into another type) usually have frequently repeated values. So the actual variety of values in a given column is in the range of 5-30 different values. This induces the idea to make normalization: for every such a textual column I will create an additional table with the list of all the different values that may appear in this column, then I will create a (tinyint) primary key in this additional table and then will use an appropriate foreign key in the original table instead of keeping those text values in the original table. Then I will put an index on this foreign key column. The number of the columns to be processed this way is about 100.
It raises the following questions:
would this normalization really increase the speed of the queires imposing conditions on some of those 100 fields? If we forget about the size needed to keep those columns, whether would there be any increase in the performance due to the substition of the initial text-columns with tinyint-columns? If I do not do any normalization and simply put an index on those initial text columns, whether the performace will be the same as for the index on the planned tinyint-column?
If I do the described normalization, then building a view showing the text values will require joining my main table with some 100 additional tables. A positive moment is that I'll do those joins for pairs "primary key"="foreign key". But still quite a big amount of tables should be joined. Here is the question: whether the performance of the queryes made to this view compare to the performance of the queries to the initial non-normalized table will be not worse? Whether the SQL Server Optimizer will really be able to optimize the query the way that allows taking the benefits of the normalization?
Sorry for such a long text.
Thanks for every comment!
PS
I created a related question regarding joining 100 tables;
Joining 100 tables
You'll find other benefits to normalizing the data besides the speed of queries running against it... such as size and maintainability, which alone should justify normalizing it...
However, it will also likely improve the speed of queries; currently having a single row containing 300 text columns is massive, and is almost certainly past the 8,060 byte limit for storing the row data page... and is instead being stored in the ROW_OVERFLOW_DATA or LOB_DATA Allocation Units.
By reducing the size of each row through normalization, such as replacing redundant text data with a TINYINT foreign key, and by also removing columns that aren't dependent on this large table's primary key into another table, the data should no longer overflow, and you'll also be able to store more rows per page.
As far as the overhead added by performing JOIN to get the normalized data... if you properly index your tables, this shouldn't add a substantial amount of overhead. However, if it does add an unacceptable overhead, you can then selectively de-normalize the data as necessary.
Whether this is worth the effort depends on how long the values are. If the values are, say, state abbreviations (2 characters) or country codes (3 characters), the resulting table would be even larger than the existing one. Remember, you need to include the primary key of the reference table. That would typically be an integer and occupy four bytes.
There are other good reasons to do this. Having reference tables with valid lists of values maintains database consistency. The reference tables can be used both to validate inputs and for reporting purposes. Additional information can be included, such as a "long name" or something like that.
Also, SQL Server will spill varchar columns over onto additional pages. It does not spill other types. You only have 300 columns but eventually your record data might get close to the 8k limit for data on a single page.
And, if you decide to go ahead, I would suggest that you look for "themes" in the columns. There may be groups of columns that can be grouped together . . . detailed stop code and stop category, short business name and full business name. You are going down the path of modelling the data (a good thing). But be cautious about doing things at a very low level (managing 100 reference tables) versus identifying a reasonable set of entities and relationships.
1) The system is currently having to do a full table scan on very significant amounts of data, leading to the performance issues. There are many aspects of optimisation which could improve this performance. The conversion of columns to the correct data types would not only significantly improve performance by reducing the size of each record, but would allow data to be made correct. If querying on a column, you're currently looking at the text being compared to the text in the field. With just indexing, this could be improved, but changing to a lookup would allow the ID value to be looked up from a table small enough to keep in memory and then use this to scan just integer values, which is a much quicker process.
2) If data is normalised to 3rd normal form or the like, then you can see instances where performance suffers in the name of data integrity. This is most a problem if the engine cannot work out how to restrict the rows without projecting the data first. If this does occur, though, the execution plan can identify this and the query can be amended to reduce the likelihood of this.
Another point to note is that it sounds like if the database was properly structured it may be able to be cached in memory because the amount of data would be greatly reduced. If this is the case, then the performance would be greatly improved.
The quick way to improve performance would probably be to add indexes. However, this would further increase the overall database size, and doesn't address the issue of storing duplicate data and possible data integrity issues.
There are some other changes which can be made - if a lot of the data is not always needed, then this can be separated off into a related table and only looked up as needed. Fields that are not used for lookups to other tables are particular candidates for this, as the joins can then be on a much smaller table, while preserving a fairly simple structure that just looks up the additional data when you've identified the data you actually need. This is obviously not a properly normalised structure, but may be a quick and dirty way to improve performance (after adding indexing).
Construct in your head and onto paper a normalized database structure
Construct the database (with indexes)
De-construct that monolith. Things will not look so bad. I would guess that A LOT (I MEAN A LOT) of data is repeated
Create SQL insert statements to insert the data into the database
Go to the persons that constructed that nightmare in the first place with a shotgun. Have fun.
I'm setting up a table that might have upwards of 70 columns. I'm now thinking about splitting it up as some of the data in the columns won't be needed every time the table is accessed. Then again, if I do this I'm left with having to use joins.
At what point, if any, is it considered too many columns?
It's considered too many once it's above the maximum limit supported by the database.
The fact that you don't need every column to be returned by every query is perfectly normal; that's why SELECT statement lets you explicitly name the columns you need.
As a general rule, your table structure should reflect your domain model; if you really do have 70 (100, what have you) attributes that belong to the same entity there's no reason to separate them into multiple tables.
There are some benefits to splitting up the table into several with fewer columns, which is also called Vertical Partitioning. Here are a few:
If you have tables with many rows, modifying the indexes can take a very long time, as MySQL needs to rebuild all of the indexes in the table. Having the indexes split over several table could make that faster.
Depending on your queries and column types, MySQL could be writing temporary tables (used in more complex select queries) to disk. This is bad, as disk i/o can be a big bottle-neck. This occurs if you have binary data (text or blob) in the query.
Wider table can lead to slower query performance.
Don't prematurely optimize, but in some cases, you can get improvements from narrower tables.
It is too many when it violates the rules of normalization. It is pretty hard to get that many columns if you are normalizing your database. Design your database to model the problem, not around any artificial rules or ideas about optimizing for a specific db platform.
Apply the following rules to the wide table and you will likely have far fewer columns in a single table.
No repeating elements or groups of elements
No partial dependencies on a concatenated key
No dependencies on non-key attributes
Here is a link to help you along.
That's not a problem unless all attributes belong to the same entity and do not depend on each other.
To make life easier you can have one text column with JSON array stored in it. Obviously, if you don't have a problem with getting all the attributes every time. Although this would entirely defeat the purpose of storing it in an RDBMS and would greatly complicate every database transaction. So its not recommended approach to be followed throughout the database.
Having too many columns in the same table can cause huge problems in the replication as well. You should know that the changes that happen in the master will replicate to the slave.. for example, if you update one field in the table, the whole row will be w
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.