In 2015, I was able to get to 1 million read/write queries per second on only a couple nodes and tested this with multiple databases, it required (at the time) decent network tuning and node placement inside AWS but it cost me about 10 to 15 dollars per run if I recall correctly, obviously there is the matter of scaling such performance and so I want to recognize the engineering effort gone into this but this is too much money. This reminds of when one of my teams used Hadoop to process only a a few terabytes of offline data and were able to process the WHOLE THING in only a few hours. I did not have the heart or courage to tell them during the demo that this was overkill, but I did write a very simple (and small) piece of code that could extract all the signals from the offline files in mere seconds with careful network planning and storage optimization and invited them for a demo/lunch and learn next week.
87.3 % served from cache. Does that mean it returned a result existing in the cache because the very same query was executed before? Probably still a relevant result, if you have to process millions of queries every second, it seems not unlikely that you will see a lot of repeated queries. But at that point you are measuring cache performance more than query performance. But unless you run some standardized query benchmark, a single queries per second number is not that informative anyway because query complexity and therefore execution time can span many others of magnitude. Looking up a name by ID and aggregating across a billion rows from seventeen tables joined together are both a single query.
That's roughly 250x more than it would cost to perform this stunt using on-demand Cloud Bigtable, if my math checks out (~1150 nodes @ 85¢/hour for 1h).
I estimated the cluster to achieve this was ~$3-4k per-hour. I am thinking there is a typo on the r8g.16xlarge and they are actually r8gd.16xlarge (notice the d) which comes with directly attached nvme disks.
I'm curious why the test needed so many router hosts:
512 shards, each with one Postgres primary each on an r8g.16xlarge
480 Neki routers, each on its own 8xlarge instance
That's ~250K queries/sec per router which seems lowish for this type of workload? The routers won't be doing very much (parse query, route it to proper shard?).
that's over 13k queries/sec per router core, about 50% of it is spent doing syscalls, the other 50%: parsing, doing grpc, tls, go gc, resolving the shards, waiting for the responses
neki is still in platform preview, this experimentation allowed us to collect profiles at such scale and ship some nice optimizations, more are coming
I believe multigress is the similarly aged open equivalent from Supabase. Haven't used it myself and don't know what the differences are in usability, but I'm a bit more interested in that since it's open.
Someone posted a twitch conversation yesterday about this, I poked around on the page realised there was no open source version and noped out immediately.
I'm sure it's a great product (it seems like planetscale do good engineering and the folks I know who use them seem fine with it) but I don't do vendor lock-in as a service personally, I'll use whatever employer uses because that's the deal but for personal stuff, well this isn't designed for that really, wrong order of magnitude on scaling.
yeah, this is a definitely a "best case" workload for a sharded database. Single row reads on the key used to shard with no hotspots (no shard to shard network traffic at all).
512 shards, each with one Postgres primary each on an r8g.16xlarge
480 Neki routers, each on its own 8xlarge instance
That's ~250K queries/sec per router which seems lowish for this type of workload? The routers won't be doing very much (parse query, route it to proper shard?).
neki is still in platform preview, this experimentation allowed us to collect profiles at such scale and ship some nice optimizations, more are coming
Multiple live shards: yes
Query routing across shards: yes
Online shard splitting: yes
Zero-downtime resharding: yes
Multiple independent shard groups: yes
Data topology management: yes
HA / automated failover: yes
Multi-AZ: yes
Connection pooling: yes
Online schema changes: yes
Workflow-driven migrations/cutovers: yes
Zero-downtime imports: yes
CDC / logical replication: yes
Online Postgres version upgrade workflows: yes
Cross-shard transactions: coming
Multigres today:
Multiple live shards: no
Query routing across shards: no
Online shard splitting: no
Resharding: no
Multiple shard groups: no
HA / failover: yes
Multi-AZ: yes
Connection pooling: yes
Logical replication/import work: in progress
Distributed migration/resharding workflows: no
How it is an it's an alternative? Do you just say things without validating?
I'm sure it's a great product (it seems like planetscale do good engineering and the folks I know who use them seem fine with it) but I don't do vendor lock-in as a service personally, I'll use whatever employer uses because that's the deal but for personal stuff, well this isn't designed for that really, wrong order of magnitude on scaling.