Isolation levels

Read committed avoids dirty reads, repeatable read also avoids values changing under you, and serializable makes transactions behave as if run one by one. Stronger levels prevent anomalies but cost throughput through locks and aborts.

Where it shows up in real systems

  • Relational database

    The primary handles every write; read replicas add read capacity but lag slightly behind, so a user may not see their own write. A Multi-AZ standby takes over in about a minute if the primary fails. Sharding multiplies write capacity, but a bad shard key creates a hot shard. Without connection pooling, many app instances exhaust the connection limit.

    In production: PostgreSQL (Amazon RDS, Aurora), MySQL, Azure SQL Database, Cloud SQL, Citus, Vitess

See it break, then fix it

Build an architecture, send it a day of traffic and outages, and learn why it holds or fails. Free to start.

Open the learning path Open the sandbox