A few years ago I learned about ACID, BASE, and the CAP theorem. Recently I learned about an extension, the PACELC theorem. So some recap:


ACID is about database transaction operation. It means:

  • atomicity: transaction is either fully succeed or entirely rolled back
  • consistency: database state should never be invalidated. All necessary constraints, triggers, cascades should be applied
  • isolation: parallel execution of transactions should as-if executed sequentially. Incomplete transaction may even invisible to other transactions.
  • durability: completed transactions persist, committed to non-volatile memory, so we can even sustain from power failure

The common confusion is on the term consistency, which here means not self-contradicting in different part of the database state constraints. The other use of the keyword is to mean identical data in a distributed system, or convergence to the same state between different hosts.

BASE transaction is at the other extreme, which use the other meaning of consistency:

  • basically available: system responsive even without guarantee on data consistency
  • soft-state: database state may change over time while no user update
  • eventual consistency: the soft-state of database will converge to a stable state when data propagated to all hosts

ACID is the standard mode of operation for RDBMS to guarantee data accuracy. So usually mutex is used and hurts I/O throughput. BASE, however, is common for NoSQL database cluster to optimize for availability and best-effort response time while sacrificing correctness.


Connecting ACID and BASE paradigm is the CAP theorem. It concerns the following attributes of a distributed data store:

  • consistency: read reflects the most recent write (A and D in ACID, same meaning of consistency in BASE)
  • availability: respond to every request without above consistency guarantee
  • partition tolerance: the system continue to operate despite network failure, e.g., partially disconnected, packet drop, severe delay

We cannot avoid partition in a distributed system, so CAP theorem means a distributed system should choose between consistency or availability. ACID database chose consistency (refuse response if cannot check with peers) while BASE database chose availability (respond with local data without ensuring it is the latest with its peers).

PACELC theorem give further detail on what happen when there is no partitioning (i.e., network is healthy). The acronym means if we suffer from network partitioning (P), we have to choose between availability (A) or consistency (C), else (E) we have to choose between latency (L) or consistency (C). The PAC is same as the CAP theorem and the ELC is the extension.

The whole thesis is assuming we maintain high availability by replication. So when there is failure, CAP theorem prevails. But if not, we still have to consider the tradeoff between consistency and latency of a replicated system. Now we can classify come database systems:

  • MySQL cluster: PC+EC, such RDBMS always prioritize for consistency of data
  • Amazon DynamoDB: PA+EL, it aimed for fast respond time by trading off consistency of data


Daniel Abadi, Consistency Tradeoffs in Modern Distributed Database System Design. IEEE Computer, pp.37–42, Feburary 2012.