Tobias Petry

Scaling Databases

Stop over-engineering your database. Learn the simple scaling patterns that can handle massive traffic before you ever need to shard.

Scaling Databases
#1about 2 minutes

The database is the real bottleneck in scaling applications

Your programming language or framework is rarely the performance problem; the database is almost always the component that limits growth.

#2about 1 minute

Optimize queries and add caching before scaling out

Before distributing your database across multiple servers, you must first fix slow queries, add proper indexes, and implement caching to avoid amplifying existing problems.

#3about 1 minute

Scaling up is the simplest and most effective first step

Instead of immediately adding complexity with multiple servers, simply upgrading your existing database server with more memory and CPU cores is a cost-effective solution.

#4about 4 minutes

Understanding the trade-offs of multi-master replication

While multi-master replication allows writes to any server and guarantees read-after-write consistency, it often suffers from performance degradation due to write conflicts on the same data.

#5about 5 minutes

Using read replication to scale read-heavy workloads

Read replication uses a single primary for writes and multiple secondaries for reads, but requires careful application design to handle asynchronous replication lag and potential stale data.

#6about 5 minutes

The power and complexity of database sharding

Sharding provides near-infinite scalability by distributing data across multiple independent databases, but introduces significant complexity like cross-shard joins and managing multiple database systems.

#7about 2 minutes

Keep your database scaling strategy as simple as possible

Avoid premature optimization by choosing the simplest scaling solution that meets your current needs, as overly complex systems are difficult to manage and can lead to costly rewrites.

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