Wow, I wrote migration script for a couple of table rationalisation and it took me a week, to make it error-prone and to provide a revert option. Not to mention that you usually need to work with production data on your local to test if the migration will work without any errors.

And If you happen to have a migration error on your production database. It is a terrible experience, which usually includes downtime.

Maybe my lack of experience on writing SQL migrations increased that time, but isn't this the whole point of the OP in terms of dev first.

Even when you use a schemaless db like MongoDB, you still have to manage your data (let's say the schema is implicit).

If for example you decide that the "phone number" field is no longer a string but a list of objects (containing each the phone number and the kind of number), you will have to update the old data to adapt it to the new model, incurring in the risk of messing up production data.

Of course you can (and it is what many people do) avoid the problem at the data level and "fix it" at the application level (for example, checking every time if you have a single string or the new list), but if you really want you can do the same with a SQL database (keep the "phone" field on the table, but add the new table for the list of numbers).

A disadvantage of the SQL approach is that many are less versed in it than in the application language, but there are many tools available to handle SQL migrations in your framework's language.

Yes, the "secret" to MongoDB not having to write data migrations is simple: you get to simply have them as technical debt for later.

What could go wrong ?

Well, that's easy too: an accounting system where each method has a switch with 10+ cases, each case having 30+ lines of code inside of it, depending on something like "record version" or worse, on implicit tests.

And what could be worse ? Well, having a switch with only 9 cases in one of those methods ...

This is essentially how my team handles things while using HBase as our main DB. At first it felt weird to not write a migration at the DB level and maintaining compatibility is certainly something that you have to keep in mind at all times, but we have a pretty solid internal library for handling de/serialization in Scala.

> A disadvantage of the SQL approach is that many are less versed in it than in the application language, but there are many tools available to handle SQL migrations in your framework's language.

I was surprised by this at my previous job. One of the first things I learned about when starting to do web development was database migrations (Miguel Grinberg's Flask tutorial). Sure, I had to learn Alembic when I started the job, but it's a pretty straight forward API and the main issue was the lack of transactional DDL in MySQL that we constantly had to keep in mind. Besides that it was just writing SQL statements wrapped in Python.

What about removing a column? What about if you've decided that you both current and next version of the code to work (then removing a column is a 3 step process, copy to new and use new, skip a version, remove old).

Migration can and is often hard.

Either you kick the can to the code, or do a migration script. Is there some magic style of coding that allows me to use the old data structures with the new code without having to do any special checks?

Well, there's your problem right there!

...if you're making a relatively small change to a non-mission-critical, non-distributed system that can be taken offline briefly while you migrate; then you can just shut everything down, migrate, roll new versions, and start up again.

If you need to keep everything running while you do a gradual rollout across thousands of DBs, this gets more complicated very quickly - and that's not even taking into account more complex migrations that involve more than just "add / remove this column".

You're definitely right, however, to point out that implicit schemas don't make this better: you have all the same problems, but with less documentation / validation helping you not screw things up. It's also a good point that many businesses / applications never reach this latter stage where migrations become much, much more complex; when you can get away with the former approach, you definitely should.

I have worked on a number of large enterprise projects where there were hundreds and hundreds of migrations over the lifespan. I have never seen it take minutes of time. Or maybe you're just one of those fun developers who never properly tested their work.

But I have definitely seen production outages and subsequent failures to rollback because of edge cases between various individual migrations.

And I have written many migrations in systems with tens of different db`s/schemas in the same project which could be different based on the geographical region or some other requirement and never had any big problem

Especially since you need to write the rollback script as well. And then test it with data. And then ensure there are no weird interactions with other migrations. Because if there is a problem with any one of them then it can be a huge headache to unwind everything.

But often you are adding to reference data or running a transform SQL in order to produce some new denormalised table or the huge array of changes needed to support new use cases. These can't be automated and are hugely error prone.

Unless you migration involves "disable all checks, add new columns/tables, move and delete old data, enable all checks"

If you are migrating to a denormalised table then it should obviously crash when your conversion tool is doing something wrong and the RDBMS just prevented it from succeeding in doing something wrong.

If your migration is actually error prone as in "mangling and corrupting" data I suggest you are doing something wrong and you probably disabled the safety checks in your RDBMS.

The company I work for has 10's of TB's of data on most customer systems, and migration is either impossible as they have no place to store it, or too costly in time to move.

So while writing the code is not hard, the difficulty (or even possibility) of field upgrades can vastly influence whether or not a feature is considered.