4 ms·

It depends a lot on the backend architecture. Number of DB requests per web request can also be high due to the pathological cases in some ORMs which can result

It depends a lot on the backend architecture. Number of DB requests per web request can also be high due to the pathological cases in some ORMs which can result in N+1 query problems or eagerly fetching entire object hierarchies. Such problems in application code can get brushed under the carpet due to "magical" autoscaling (be it RDS or K8s). There can also be fanout to async services/job queues which will in turn run even more DB queries.

AccountAccount1 4y ago
Hey, this is not a problem for us at Nhost since most of the interfacing with Postgres is through Hasura (a GraphQL SQL-to-GraphQL) it solves the n+1 issue by compiling a performant sql statement from the gql query (it's also written in haskell, you can read more here https://hasura.io/blog/architecture-of-a-high-performance-graphql-to-sql-server-58d9944b8a87/ https://hasura.io/blog/architecture-of-a-high-performance-gr...)
robertlagrant 4y ago
I don't think K8s at least will autoscale quickly enough to mask something like that.
- raffraffraff 4y ago
  Autoscaling is slow. If you're using AWS autoscaling group, decisions are based on several different metrics that are typically averaged over a period. If the instance pool size is increased, that fact gets picked up by yet another event loop that runs periodically, and actually starts instances. So there are multiple chained delays before the instance is actually launched. In practice, even if your instances have extremely fast start-up and can begin processing the queue quickly, the job in the queue could be waiting 4+ minutes to get picked up, in a scale-to-zero situation. You've also got things like cooldown periods to ensure that you are not flapping. With k8s you have more control over knobs and switches, and you don't have an instance start-up delay, but the same type of metrics and event loops are used, particularly if you're using an external metric (eg SQS queue depth) in your calculations. Some type of predictive and/or scheduled scaling can reduce delays at the expense of potentially higher cost.

AccountAccount1 4y ago

Hey, this is not a problem for us at Nhost since most of the interfacing with Postgres is through Hasura (a GraphQL SQL-to-GraphQL) it solves the n+1 issue by compiling a performant sql statement from the gql query (it's also written in haskell, you can read more here https://hasura.io/blog/architecture-of-a-high-performance-graphql-to-sql-server-58d9944b8a87/ https://hasura.io/blog/architecture-of-a-high-performance-gr...)

robertlagrant 4y ago

I don't think K8s at least will autoscale quickly enough to mask something like that.

raffraffraff 4y ago
Autoscaling is slow. If you're using AWS autoscaling group, decisions are based on several different metrics that are typically averaged over a period. If the instance pool size is increased, that fact gets picked up by yet another event loop that runs periodically, and actually starts instances. So there are multiple chained delays before the instance is actually launched. In practice, even if your instances have extremely fast start-up and can begin processing the queue quickly, the job in the queue could be waiting 4+ minutes to get picked up, in a scale-to-zero situation. You've also got things like cooldown periods to ensure that you are not flapping. With k8s you have more control over knobs and switches, and you don't have an instance start-up delay, but the same type of metrics and event loops are used, particularly if you're using an external metric (eg SQS queue depth) in your calculations. Some type of predictive and/or scheduled scaling can reduce delays at the expense of potentially higher cost.