I don't keep track. All microservices use continuous deployment pipelines. If you check in code and it passes all the tests, it will make it out to prod some time in the next few hours.
Short version: have DB1 hold the transactional data (data generated while running the system). Have DB2a have the release-bound data (data about and connected to the code itself-settings, prices, whatever).
Have DB2a have views onto DB1 tables. Version a code only “knows about” DB2a but any transactional CRUD ops hit the tables on DB1.
Now version b of the code just needs to ship/create a DB2b and both a and b can run in parallel.
If you need to change the shape of DB1 tables, those changes need to be backward compatible (can only add nullable columns, no use of "select *", etc).
There’s a few details about how to make it fully practical, but that’s the gist and we ran than for about 12 years on a moderately heavily trafficked e-commerce site.
What ends up happening is we end up separating the two more with "cache this one, not that one" rather than two different databases.
I will explore this idea on my next greenfield project, whenever that happens.
I'll look again later tonight more thoroughly to see if I've posted the mechanisms and restrictions publicly anywhere before. If I haven't, I'll try to dig it out of our old dev doc system and post them here, but I can't make any promises as the docs I recall are now over a decade old, so I'm not fully sure they exist any more. :)
This requires a little discipline, but if you follow a few simple rules it's not really that arduous:
- when adding a new column, it must have a default value set, or be nullable
- don't drop any columns
- don't rename any columns
- release 1: remove dependencies on the column from the app/API/service
- release 2: performs the database migration with destructive changes
Same here. A forward only approach works best for us too. if you need to clean up a mess, it is a new migration script. It's too complex to try an work backwards. What if multiple scripts were ran? Then you have to roll back say script number 2 out of 5 and there were destructive operations. It becomes really hairy really quickly. So forward-only is the easiest to reason about.
Please do make sure that you have snapshots for restoring if you really mess up badly. I know its not always feasible to do snapshots before every deploy, but having a daily snapshot can bring you a lot of comfort.
If you built your own migration tool (highly encourage it, its not that hard to build a forward-only migration tool), then you can trigger selective snapshots/table dumps for only the tables that gets changed, and only for specific operations (updating schema, dropping columns, dropping table) before your migration scripts touches the db - that way you have a path to restore. You don't always need a full DB dump (say you have 500+ tables but only changing 2, 1 of which is destructive, thus the backup is tiny and quick). It also helps if different data sets live in isolation to help manage this kind of admin.
So ideally you have some kind of monitoring that reports/shows how many services are alive (and where they live in a cluster), how many errors they generate etc. Then based on some thresholds you can take them out of circulation and let them cool down. If certain kinds of errors occurs, or at a certain frequency, the system can notify a site reliability engineer (or equivalent) to check it out. Then they can decide if it should be permanently removed and to log an internal support ticket and so forth for the developers or product teams.
Production issues are a part of life. You need to have some visibility on issues and their severity. Every company and tech stack is different, also depending on their SLA's and uptime promises.
Ads not rendering in an app might be less severe than a pump failure at a fuel station, so they have different kinds of monitoring and and reaction times to faults. Obviously things like hospitals, banks, airlines/aircraft manufacturers have way different requirements and infrastructure from say a system that manages all school libraries for a state/province.
There are too many products and approaches to mention here if you were looking for a list of those. I have one or two favorite approaches and a handful of tools for this kind of stuff, half of which is homemade, so not something you can google. But you can google it and see a few different approaches. "microservices monitoring java" or "microservices monitoring best practice" or something along those lines will get you on a path. Try to find 5 different approaches and reflect what each one is missing or how they may help you, and then ponder what would you like to see from a reporting system with hundreds/thousands of services.
And then obviously the the best lessons will come from production itself.
Good luck!
Only if you accept them. The alternative is to do very few, rigorously tested releases per year. This way you don't have production issues. That's how industries like banking make sure bank transfers and card payments work and people's money is not randomly lost... It's a shame many other industries just accept their product failing for users as something normal/inevitable.
We did four releases a year, across the entire organization (so mainframe and more modern platforms), on Saturday nights/early Sunday mornings. There was plenty of testing but there was still plenty of errors only found on the day of, and rushed to fix in the wee hours or daylight hours of Sunday morning.
The only thing that seemed to correlate with release quality was the overall risk of the release, i.e. the complexity and number of new features written during that quarter.
This way, you had bugs in prod for less than a day once every quarter, as opposed to having buggy prod all the time, as is common in organizations doing Continuous Deployment.
Luckily, a good CI/CD pipeline makes reversions just as easy as deployments. So even when you have errors, it's easier to correct than if you suddenly discovered "our deployment bash script / ansible playbook isn't as reversible as we thought it was"
The idea of deploying every commit all the way to prod is is very questionable.