And then what does PlanetScale add on top of Vitess hosted anywhere else?
Sorry, the linked blog post is both very abstract and assumes a high level of preexisting knowledge about database scaling.
The assumption that tenants are perfectly isolated is actually the original sin of early Slack infrastructure that we adopted Vitess to migrate away from. From some earlier features in the Enterprise product (which joins lots of "little Slacks" into a corporate-wide entity) to more post-modern features like Slack Connect (https://slack.com/help/articles/1500001422062-Start-a-direct...) or Network Shared Channels (https://slack.com/blog/news/shared-channels-growth-innovatio...), the idea that each tenant is fully isolated was increasingly false.
Vitess is a meta-layer on top of MySQL shards that asks, per table, which key to shard on. It then uses that information to maintain some distributed indexes of its own, and to plan the occasional scatter/gather query appropriately. In practice, simply migrating code from our application-sharded, per-tenant old way into the differently-sharded Vitess storage system was not a simple matter of pointing to a new database; we had to change data access patterns to avoid large fan-out reads and writes. The team did a great write-up about it here: https://slack.engineering/scaling-datastores-at-slack-with-v...
> Today, we serve 2.3 million QPS at peak. 2M of those queries are reads and 300K are writes.
I think the "today" QPS numbers are still doable with a properly tuned single-writer galera cluster running on machines with TBs of memory. Of course, with Slack workload, there would be too much historical data to fit into a single host, so I can see the reasons to shard into multiple clusters/hosts.
Still, the numbers seem a little off. Let's say back in fall 2016 there were already 200K write QPS at peak, with 200 sharded hosts accepting write. That's just 1K write QPS at peak per host on average, and let's say 20K write QPS at peak for a particularly hot shard. What could be the bottleneck? Replication lag? Data size? I don't think any of the articles from Slack has talked about this.
What Vitess provides is invaluable, especially the very solid implementation of secondary index. But sometimes I feel like it is being used/advocated as a sledgehammer ("just keep sharding") without looking at what could be done better at the lower MySQL/InnODB level, in exchange for a much more costly cloud bill.
CockroachDB (aka CRDB) is completely custom and compatible with Postgres wire/datatype protocols, which operates directly on its own key/value store called Pebble (but originally was also RocksDB): https://www.cockroachlabs.com/blog/distributed-sql-key-value...
Both systems are foundationally the same SQL-on-KV but implement it very differently.
You always need a lot of attention to sharding otherwise you'll have poor performance.
Implementations vary but there are the 2 major architectures: systems like Vitess/Proxy SQL/Citus/Timescale that act as a proxy layer on top of existing RDBMS running on multiple servers to make them look like a single database, and entirely custom projects like CockroachDB/TiDB/Yugabyte/Cloud Spanner which have their own native processing and data layers.
OLAP relational data warehouses like Vertica/Greenplum/MemSQL/Redshift/Bigquery are also natively distributed but focus on large-scale analytics with features like column-oriented storage and vectorized processing.
I wouldn't say there's too much "magic" in there, but it does a lot of known difficult things (schema management, sharding/resharding, connection pooling, query optimization, DB administration, monitoring, backup/failover) which are generally painful and expensive to do yourself.
Rarely is it the case that someone's actually discovered e.g. novel math or something to make the hard part easier. Better tools (to do well-understood things more easily for this use case) and restrictions (so you don't use it in ways the tools can't handle) are the usual way.
Admittedly, that view left out the considerable challenge of actually deploying and running the infrastructure, designing and optimizing that schema, along with all the joys of managing large cluster environments.
That's what PlanetScale, the product, aims to solve. Dealing with clustering infrastructure IS a hurdle for most teams to overcome, and though Vitess' feature set and compatibility has expanded greatly to accommodate some of the most demanding use cases on the web today, a lot of its functionality can still be out of reach for a developer just trying to merge some code and a schema change.
Abstracting as much of that complexity away from the end user is the goal, as well as making their lives easier with a ton of the functionality we've always wanted to see built with Vitess. I can confirm that that is not an "easy" job on our end. :)
Isn’t that the same for a normal sharded MySQL database though?
EDIT: To clarify, sharding is not a standard feature included in Community Edition MySQL. Over the years, there have been various Oracle-initiated attempts at providing it as an enterprise scaling strategy through MySQL (NDB) Cluster, MySQL Fabric, etc., but these have either ended up having limited applicability outside very specific use cases and are not widely in use.
Most large MySQL users (e.g. Facebook or YouTube) ended up rolling their own frameworks, like Vitess, which has since been open sourced and adapted to more diverse environments. Until that became more accessible, though, the rest of the world mostly made do with wobbly multi-master setups relying on circular replication, behind some kind of proxy, or had to implement the sharding logic itself into their application code.
Thanks for the correction!