4 ms·

I'd phrase it differently: the database is the litmus test of your supposed "good idea". If your good idea doesn't survive referential integrity or even data co

by tremon 5y ago

I'd phrase it differently: the database is the litmus test of your supposed "good idea". If your good idea doesn't survive referential integrity or even data consistency, your idea doesn't deserve the label "good". Of course, the next "good idea" is to sacrifice data integrity in the name of performance. That can work, but usually it's just a technical form of growth hacking. Sacrificing data integrity without understanding the problem space is a disaster waiting to happen (but luckily, it may not happen on your watch so you can continue hacking).

hughrr 5y ago
Very good points there. I have directly experienced the problems associated with blindly assuming that you can write your own consistency guarantees in Redis. I didn't do it but I had to write some tooling to rewrite the AOF to get some data back...
HWR_14 5y ago
I'm sorry, are you saying that a good idea should survive the loss of referential integrity or data consistency? I don't feel that's what you mean, and it's probably my fault for misreading your comment.
- erik_seaberg 5y ago
  I read it the other way: if abandoning correctness is the only way to make your idea feasible, your idea kinda sucks.
- tremon 5y ago
  Yeah, I probably could have phrased that better. The sibling comment is correct, I meant that if your idea requires abandoning referential integrity or data consistency, it's probably not a good idea. Since it's the database that actually enforces those constraints, it may seem that the database causes the problem. But in most cases, the problem is the data model or the idea itself.
  - HWR_14 5y ago
    I see. I mean, one of my ideas requires many transactions and records that require data consistency. I have no idea how to make sure it can scale to Internet scale. Do you know any good resources? Or is it just "get mysql, pay thousands a month for either a cloud provider or colocated hardware"
    - xemdetia 5y ago
      I would say that most resources would be a mixed bag in most situations but much of the advice skips over something very basic that experienced people tend to assume you know: you are always going to be limited by the number of times you can actually sync a write to a disk and/or how fast you can read back from the disk and these are the main upper bounds. One mistake I see inexperienced engineers make in modern environments is not realizing that in their particular cloud instance they are explicitly or implicitly using the cloud vendor's network storage and they are entirely hamstrung on that. The other is not configuring the database to use all the RAM- but those are usually covered by the existing tuning guides. The next from a design perspective is to leave natural places to shard/split the data up so you could represent the data consistently on multiple systems. Sometimes it's pretty bonehead things like your database tables related to login are affecting your critical path transactions and/or also tied to the referential integrity of your application data in ways that are hard to split apart. It goes back to the IO thing where if you are reading and writing login or UI traffic you sure as heck are not writing some other critical time sensitive transaction especially if it's to the same disk. The other obvious thing is to be trying to run analytic queries (aka look at many records) on a database tuned for mass INSERT/write traffic and having no plan to do that with a different resource. If you are still worried about it I would suggest just prototyping it using a currently modern API/RPC server in front of whatever datastore you come up with. As systems have gotten more distributed the straight line speed of connecting to the database directly makes less and less sense in my opinion and having an API server you control allows you to be clever later in ways that might not be obvious today (especially keeping application-aware counters so that you can see what your application is actually doing as query-style logs for databases are extremely expensive in performance). Either way if you sit down and consider the performance characteristics of the underlying system, think about each class of transactions are you considering from your application in a write/read context (hopefully from measured data), and using tools like EXPLAIN you'll quickly start finding what pieces of the puzzle might need to move to start scaling. The answer also might just be some parts of your application would be better served something other than a database like a message queue, a more straightforward ISAM or key-value store, or depending on your situation just flat files written onto the fastest local storage you can get to then be later transformed and loaded into a database sometime later.

by tremon 5y ago

hughrr 5y ago

Very good points there. I have directly experienced the problems associated with blindly assuming that you can write your own consistency guarantees in Redis. I didn't do it but I had to write some tooling to rewrite the AOF to get some data back...

HWR_14 5y ago

I'm sorry, are you saying that a good idea should survive the loss of referential integrity or data consistency? I don't feel that's what you mean, and it's probably my fault for misreading your comment.

erik_seaberg 5y ago
I read it the other way: if abandoning correctness is the only way to make your idea feasible, your idea kinda sucks.
tremon 5y ago
Yeah, I probably could have phrased that better. The sibling comment is correct, I meant that if your idea requires abandoning referential integrity or data consistency, it's probably not a good idea. Since it's the database that actually enforces those constraints, it may seem that the database causes the problem. But in most cases, the problem is the data model or the idea itself.
- HWR_14 5y ago
  I see. I mean, one of my ideas requires many transactions and records that require data consistency. I have no idea how to make sure it can scale to Internet scale. Do you know any good resources? Or is it just "get mysql, pay thousands a month for either a cloud provider or colocated hardware"
  - xemdetia 5y ago
    I would say that most resources would be a mixed bag in most situations but much of the advice skips over something very basic that experienced people tend to assume you know: you are always going to be limited by the number of times you can actually sync a write to a disk and/or how fast you can read back from the disk and these are the main upper bounds. One mistake I see inexperienced engineers make in modern environments is not realizing that in their particular cloud instance they are explicitly or implicitly using the cloud vendor's network storage and they are entirely hamstrung on that. The other is not configuring the database to use all the RAM- but those are usually covered by the existing tuning guides. The next from a design perspective is to leave natural places to shard/split the data up so you could represent the data consistently on multiple systems. Sometimes it's pretty bonehead things like your database tables related to login are affecting your critical path transactions and/or also tied to the referential integrity of your application data in ways that are hard to split apart. It goes back to the IO thing where if you are reading and writing login or UI traffic you sure as heck are not writing some other critical time sensitive transaction especially if it's to the same disk. The other obvious thing is to be trying to run analytic queries (aka look at many records) on a database tuned for mass INSERT/write traffic and having no plan to do that with a different resource. If you are still worried about it I would suggest just prototyping it using a currently modern API/RPC server in front of whatever datastore you come up with. As systems have gotten more distributed the straight line speed of connecting to the database directly makes less and less sense in my opinion and having an API server you control allows you to be clever later in ways that might not be obvious today (especially keeping application-aware counters so that you can see what your application is actually doing as query-style logs for databases are extremely expensive in performance). Either way if you sit down and consider the performance characteristics of the underlying system, think about each class of transactions are you considering from your application in a write/read context (hopefully from measured data), and using tools like EXPLAIN you'll quickly start finding what pieces of the puzzle might need to move to start scaling. The answer also might just be some parts of your application would be better served something other than a database like a message queue, a more straightforward ISAM or key-value store, or depending on your situation just flat files written onto the fastest local storage you can get to then be later transformed and loaded into a database sometime later.