When dealing with small projects, what do you feel is the break even point for storing data in simple text files, hash tables, etc., versus using a real database? For small projects with simple data management requirements, a real database is unnecessary complexity and violates YAGNI. However, at some point the complexity of a database is obviously worth it. What are some signs that your problem is too complex for simple ad-hoc techniques and needs a real database?
Note: To people used to enterprise environments, this will probably sound like a weird question. However, my problem domain is bioinformatics. Most of my programming is prototypes, not production code. I’m primarily a domain expert and secondarily a programmer. Most of my code is algorithm-centric, not data management-centric. The purpose of this question is largely for me to figure out how much work I might save in the long run if I learn to use proper databases in my code instead of the more ad-hoc techniques I typically use.
1) Concurrency. Do you have multiple people accessing the same dataset? Then it’s going to get pretty involved to broker all of the different readers and writers in a scalable fashion if you roll your own system.
2) Formatting and relationships: Is your data something that doesn’t fit neatly into a table structure? Long nucleotide sequences and stuff like that? That’s not really conveniently tabular data.
Another example: Nobody would consider implementing software like Photoshop to store PSDs in a relational format, because the data structures don’t really lend themselves to that type of storage or query pattern.
3) ACID (sort of a corollary to #1): If Atomicity, Consistency, Integrity, and Durability are not challenges with a flat file, then go with a flat file.