![]() ![]() ![]() Your application can't have features that are made or broken by features specific to a particular database. This can be tricky when you have regressions in query performance from one database flavor to the next.ģ. Such projects would need to test or otherwise verify that all SQL queries execute in the ways they're expected to. You need authors of queries to understand the least common denominator of supported SQL features and syntax (probably ANSI SQL, but there may be cases where it isn't)Ģ. I haven't spoken to too many folks who've had to make this decision, but off the top of my head I think there's a number of factors that aren't obvious at first glance that factor into this equation:ġ. With the exception of enterprise software built to run in customer datacenters with whatever infrastructure that may entail, practice seems to dictate that the overhead of making something DB agnostic isn't a worthwhile trade off. There's a reason why SQLite can reputably claim to be the "Most Widely Deployed and Used Database Engine" with an estimated one trillion SQL database in use. It can be populated and queried similar to SQL-based data warehouses, and it also includes secondary indexes, an efficient query engine, and fast random writes with durability and atomic transactions. There are other alternatives such as Apache Arrow, but SQLite is tried and tested option. Even ~100 MB datasets can benefit from SQLite if you're performing a lot of random writes/reads or want to execute complex queries. Adding an RDBMS would complicate installation and support.įurther, researchers and data analysts benefit from SQLite databases when the data is too large to hold in memory, yet not large enough to warrant a proper data warehouse. Additionally, there's desktop software that has to work with relatively large datasets, which require reliable persistence and efficient searching/reading. In this case, an RDBMS would add unnecessary overhead. An RDBMS cluster is the obvious choice.īut for mobile/embedded, all the data is local to a single device and commonly also a single process. Our workloads involve multiple readers/writers for both scaling and availability. This is not a challenge commonly faced on on the enterprise server side, hence why many of us cannot imagine a use case for SQLite. Like an application that would otherwise persist state in XML/JSON/protobuf and have to manage the challenges of regularly persisting and reading state challenges with become unwieldly as the data size grows. (AFAIK, there’s no ANSI SQL standardization on what casts must be assignment casts so lowest-common-denominator SQL-standard-compliant queries shouldn’t be relying on the presence of any defined assignment casts.)Įxactly! There are numerous applications where SQLite is replacing an ad hoc file-based solution, with nebulous at best durability and atomicity, let alone scalability. Presumably, in any ANSI SQL-compliant DBMS, you could redefine whatever assignment casts are annoying you to be non-assignment casts. (We generally use the term assignment cast to describe this kind of cast.) > will be allowed if the cast from type integer to type text is marked AS ASSIGNMENT, otherwise not. For example, supposing that foo.f1 is a column of type text, then: > If the cast is marked AS ASSIGNMENT then it can be invoked implicitly when assigning a value to a column of the target data type. Quoting Postgres docs (because ain’t nobody paying for the ANSI SQL standard just to quote it): table-row column, composite-value member field, stored-proc parameter) of type Y, and an expression-value being passed into it of type X. ![]() ANSI SQL provides “CREATE CAST AS ASSIGNMENT”, which will define a cast that gets used implicitly to get from type X to type Y when you have a tuple-field (e.g. ![]()
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