Andrés
@andalm
Let the technical be tangible, free of any mystical aura ✨
Andrés
github.com
GitHub - readysettech/readyset: Readyset is a MySQL and Postgres wire-compatible caching layer that sits in front of existing databases to speed up queries and horizontally scale read throughput. Under the...
readysettechgithub.com
Lobby Code — AI Code Generation by Lobby
lobby.so
Grand Theft Audio
notes.catalog.works
Northflank — Deploy any project in seconds, in our cloud or yours.
northflank.com
Dynamo: Amazon’s Highly Available Key-value Store
allthingsdistributed.comDatabase Partitioning is when you split the data inside the database into smaller "databases". One partition holds a part of your data. All partitions together form the entire database.
Partitioning is often used with replication, which means each partition's data is copied onto multiple nodes. This way, even though each record is in only one partition, it can be stored on different nodes for fault tolerance. A node may hold more than one partition.
When using a leader-follower replication model, the mix of partitioning and replication might look like this:
The interesting function therefore is the red line, the one that adds the up-front invest to the potential liability. That's your total cost and the thing you should be minimizing. In most cases, with increasing up-front invest, you'll move towards an optimum range. Additional investment into reducing lock-in actually leads to higher total cost. The reason is simple: the returns on investment diminish, especially for switches that carry a small probability. If we make our architecture ever-so-flexible, we are likely stuck in this zone of over-investment. The Yagni (you ain't gonna need it) folks may aim for the other end of the spectrum - as so often, the trick is to find the happy medium.