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SQLite provides developers with the freedom to store content in any desired format, regardless of the declared datatype of the column. Some people find this feature troublesome. Some developers are shocked to discover that it is possible to insert text into a column marked INTEGER.
This article advocates in favor of the flexible type rules in SQLite.
Details regarding the flexible type system of SQLite are found in the separate Datatypes In SQLite document. Here is a quick summary:
Datatype names on column definitions are optional. A column definition can consist of just the column name and nothing else.
When datatype names are provided, they can be just about any text. SQLite attempts to deduce the preferred datatype for the column based on the datatype name in the column definition, but that preferred datatype is advisory, not mandatory. The preferred datatype is known as the "column affinity".
An attempt is made to transform incoming data into the preferred datatype of the column. (All SQL database engines do this, not just SQLite.) If this transformation is successful, all is well. But if unsuccessful, instead of raising an error, SQLite just stores the content using its original datatype.
The above can lead to situations that advocates of rigid typing find incommodious:
| Column Datatype | Types Allowed In That Column |
|---|---|
| INTEGER | INTEGER, REAL, TEXT, BLOB |
| REAL | REAL, TEXT, BLOB |
| TEXT | TEXT, BLOB |
| BLOB | INTEGER, REAL, TEXT, BLOB |
Note that an INTEGER or REAL value will never end up being stored in a TEXT column, since an INTEGER or REAL value can and always will be converted into its equivalent TEXT representation. Similarly, an INTEGER will never be stored in a REAL column because it will always be converted into a REAL. But TEXT does not always look like an INTEGER or REAL value and so cannot always be converted. And a BLOB cannot be converted into anything and nothing else can be converted into a BLOB.
Some readers, upon first encountering flexible typing in SQLite, ask themselves "how could this ever be useful?" Here is an attempt to answer that question:
Many applications, especially those that use SQLite as an application file format, need a place to store miscellaneous attributes such as thumbnail images (as BLOB values), short pieces of text (such as the user's name), as well as numeric, date, and JSON values. It is convenient to create a single table to handle this storage:
CREATE TABLE attribute(name TEXT PRIMARY KEY, value) WITHOUT ROWID;
Without flexible typing, such a table would need to be more complex, with separate columns for each possible type of data. Flexible typing of the "value" column makes the table conceptually simpler, more space-efficient, and easier to access and update.
In the Fossil version control system, each repository has a CONFIG table that is used to store all kinds of settings with every possible datatype. The user-specific configuration file for Fossil (the ~/.fossil file) is a separate SQLite database that contains a single attribute table hold the user-specific state across all repositories.
Some applications use an SQLite database as a pure key-value store The database schema contains a single table that looks something like this:
CREATE TABLE storage(name TEXT PRIMARY KEY, value ANYTHING);
The json_tree and json_each table-valued functions that are built into SQLite both have a "value" column that can hold values of type INTEGER, REAL, or TEXT depending on the type of the corresponding JSON field. For example:
SELECT typeof(value) FROM json_each('{"a":1,"b":2.5,"c":"hello"}');
The query above returns three rows of one column with values "integer", "real", and "text", respectively.
Analysts sometimes encounter CSV files where some columns contain a mixture of integer, real, and text data. CSV files that are obtained from Excel spreadsheet exports commonly have this trait, for example. When importing such "dirty data" into an SQL database, it is convenient to have flexibly typed columns to import into.
Dirty data is not restricted to CSV files coming out of Excel, of course. There are many data sources in which a single field might contain a mix of types. For example, a data column might contain the number of seconds since 1970 sometimes, or a text date string in other cases. It is desirable to clean up these inconsistent representations, but at the same time it is convenient to be able to store all the different representations in the same column of the intermediate database while the cleanup is underway.
SQLite began as a TCL extension that later escaped into the wild. TCL is a dynamic language in the sense that the programmer does not need to be aware of datatypes. Under the hood, TCL keeps careful track of the datatype of every value, but to the developer and user of a TCL program, everything looks like a string. Flexible typing is a natural fit for use with dynamic programming languages like TCL and others, since with a dynamic programming language, you can not always predict in advance what datatype a variable will hold. So when you need to store the value of that variable into the database, having a database that supports flexible typing makes storage much easier.
Every SQL database engine seems to have its own unique set of supported datatype names:
The fact that SQLite will accept any of these names as a valid typename, and let you store any kind of content into the column, increases the chances that a script written to run on some other SQL database engine will also work in SQLite.
Because an SQLite database file is a single file on disk, some applications use SQLite as an application file format. This means that a single instance of the application might, over the course of its life, talk to hundreds or thousands of separate databases, each in a separate file. When such applications evolve over years, some columns in the underlying database will have their meanings altered subtly. Or, it might be desirable to repurpose an existing column to serve two or more purposes. This is much easier to do if the column has a flexible datatype.
The following perceived disadvantages of flexible typing were gleaned and compiled from countless posts on Hacker News and Reddit and similar forums where developers discuss these sorts of things. If you can think of other reasons why flexible typing is a bad idea, please contact the SQLite developers or leave a post on the SQLite Forum so that your idea can be added to the list.
Many skeptics of flexible typing simply express shock and disbelief, without offering any rationale for why they think flexible typing is a bad idea. Without supporting arguments, one must assume their reason for not liking flexible typing is that it is different from what they are used to.
Presumably, many developers who are aghast at SQLite's flexible typing feel this way because they have just never encountered anything like it before. All prior exposure to databases and especially SQL databases has involved rigid typing, and the readers mental model of SQL includes rigid typing as a fundamental feature. Flexible typing upsets their world-view.
Yes, flexible typing is a new way of thinking about data in an SQL database. But new is not necessary bad. Sometimes, and I think especially in the case of flexible typing, innovation leads to improvement.
It has become a point of doctrine among many programmers that the best way to prevent application bugs is strict type enforcement. But I find no evidence in support of this.
To be sure, strict type enforcement does help prevent some kinds of bugs in lower-level languages like C and C++ that present a model that is close to machine hardware. But this does not seem to be the case for higher-abstraction languages in which all data is passed around in a "Value" superclass of some kind which is subclassed for the various lower-level data types. When everything is a Value object, specific datatypes cease to be important.
This technical note is authored by the original author of SQLite. I having been writing TCL programs for 27 years. TCL has no type enforcement whatsoever. The "Value" class in TCL (called Tcl_Obj) can hold many different datatypes, but it presents the content to the program and to the application user as a string. And I've had a lot of bugs in those TCL programs over the years. But I do not recall a single instance where the bugs might have been caught by a rigid type system. I have also written a lot of C code over a span of 35 years, not the least of which is SQLite itself. I have found the type system in C to be very helpful at finding and preventing problems. For the Fossil Version Control System, which is written in C, I have even implemented supplemental static analysis programs that scan the Fossil source code prior to compilation, looking for problems that compilers miss. This works well for compiled programs.
The SQL language model is a higher-level abstraction than C/C++. In SQLite, every data item is stored in memory as an "sqlite3_value" object. There are subclasses of this object for strings, integer, floating-point numbers, blobs, and other representations. Everything is passed around inside the SQL language implemented by SQLite as "sqlite3_value" objects so the underlying datatype does not really matter. I have never found rigid type enforcement to be helpful in languages like TCL and SQLite that have a single "Value" superclass used to represent any data element. Fossil makes extensive use of SQLite in its implementation. There have been many bugs in Fossil over its 14-years history, but I cannot recall a single bug that might have been prevented by rigid type enforcement in the SQLite. Some C-language bugs might have been caught by better type enforcement (which is why I wrote the supplemental source code scanners), but no SQL bugs.
Based on decades of experience, I reject the thesis that rigid type enforcement helps prevent application bugs. I will accept and believe a slightly modified thesis: Rigid type enforcement helps to prevent applications bugs in languages that lack a single top-level "Value" superclass. But SQLite does have the single "sqlite3_value" superclass, so that proverb does not apply.
Some people contend that if you have rigorous constraints on the schema, and especially strict enforcement of column datatypes, this will help prevent incorrect data from being added to the database. This is not true. It is true that type enforcement might help prevent egregiously incorrect data from getting into the system. But type enforcement is no help in prevent subtly incorrect data from being recorded.
So, for example, rigid type enforcement can successfully prevent the customer name (text) from being inserted into the integer Customer.creditScore column. On the other hand, if that mistake occurs, it is very easy to spot the problem and find all affected rows. But type enforcement is no help in preventing a bug where the customer family name and given name are reversed, since both are text fields.
(Aside: Decades ago, I worked on a team where there was a woman named "Merritt Tracy". "Merritt" was her given name and "Tracy" was her family name. She reported that she spent an inordinate amount of time and energy trying to correct databases that had "Tracy" as her given name and "Merritt" as her family name.)
By suppressing easy-to-detect errors and passing through only the hard-to-detect errors, rigid type enforcement can actually make it more difficult to find and fix bugs. Data errors tend to cluster. If you have 20 different data sources, most of the data errors will usually come from just 2 or 3 of those sources. The presence of egregious errors (such as text in an integer column) is a convenient early warning signal that something is amiss. The source of the problem can be tracked quickly and extra scrutiny applied to the source of the egregious errors, thus hopefully also fixing the subtle errors too. When egregious errors are suppressed, you lose an important signal that helps you to detect and fix the subtle errors.
Data errors are inevitable. They will happen regardless of how much type checking is done. Rigid type enforcement can catch only a small subset of those cases - the most obvious cases. It does nothing to help find and fix the more subtle cases. And, by suppressing the signal of which data sources are problematic, it can sometimes make the subtle errors more difficult to locate.
Because SQLite is less restrictive and allows you to do more things, SQL scripts that work on other database engines will also usually work on SQLite, but script written initially for SQLite might not work on more restrictive database engines. This can cause problems when developers use SQLite for prototyping and testing and then migrate their application to a more restrictive SQL engine for deployment. If the application was (unintentionally) taking advantage of the flexible typing available in SQLite, then it will fail when migrated.
People use this problem to argue that SQLite should be more restrictive about datatypes. But you could just as easily turn that argument around and say that other database engines should be more flexible with regard to datatypes. The application was working correctly under SQLite, prior to be migrated, after all. If rigid type enforcement is really all that useful, why did it break an application that was previously working?
As of SQLite version 3.37.0 (2021-11-27), SQLite supports this development style using STRICT tables.
If you find a real-world case where STRICT tables prevented or would have prevented a bug in an application, please post a message to the SQLite Forum so that we can add your story to this document.
If flexible typing in an SQL database is a new concept to you, I encourage you to give it a try. It probably will not cause you any problems and it might make your program simpler and easier to write and maintain. I think that even if you are skeptical at first, if you will just give flexible typing a try, you will eventually come to realize that it is a better approach and will start encouraging other database vendors to support at least an ANY datatype if not complete SQLite-style type flexibility.
Most of the time, flexible typing does not matter because a column stores a single well-defined type. But occasionally you will run across situations where having a flexible type system makes the solution to your problem cleaner and easier.
This page last modified on 2024-04-06 13:49:08 UTC
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