Distributed Data Consistency Patterns#

In a monolith with a single database, you get ACID transactions. In microservices, each service owns its database. Cross-service consistency requires explicit patterns because distributed ACID transactions are impractical in most real systems.

CAP Theorem: Practical Implications#

The CAP theorem states that a distributed system can provide at most two of three guarantees: Consistency, Availability, and Partition tolerance. Since network partitions are inevitable, you choose between consistency and availability during partitions.

Adding and Removing Nodes#

Adding a node: start a new cockroach process with --join pointing to existing nodes. CockroachDB automatically rebalances ranges to the new node.

cockroach start --insecure --store=node4-data \
  --advertise-addr=node4:26257 \
  --join=node1:26257,node2:26257,node3:26257

Watch rebalancing in the DB Console under Metrics > Replication, or query directly:

SELECT node_id, range_count, lease_count FROM crdb_internal.kv_store_status;

Decommissioning a node moves all range replicas off before shutdown, preventing under-replication:

cockroach node decommission 4 --insecure --host=node1:26257

# Monitor progress
cockroach node status --insecure --host=node1:26257 --decommission

Do not simply kill a node. Without decommissioning, CockroachDB treats it as a failure and waits 5 minutes before re-replicating. On Kubernetes with the operator, scale by changing spec.nodes in the CrdbCluster resource.