Glossary

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Glossary / Evaluation and implementation guide

CAP Theorem

The CAP theorem is a foundational result of distributed systems: when a network partition occurs, a system must choose between consistency (every read returns the latest write) and availability (every read gets a response).

It cannot guarantee both. Partition tolerance — surviving network breaks — is not optional in cloud systems, so the real choice is how a tool behaves during sync failures.

This is why bidirectional CRM syncs are typically eventually consistent: brief windows where the two systems disagree are a design tradeoff, not a defect.

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A practical example

Example: a rep updates a lead's stage in the CRM while the warehouse is mid-sync and unreachable.

The change queues and lands minutes later; during that window, a dashboard built on the warehouse shows the old stage.

What to evaluate before investing

  • How does the vendor describe sync behavior during outages — queued and replayed, or dropped — and is there a conflict-resolution rule?
  • Can you see sync lag or pending-change counts, so stale dashboard data is detectable rather than silent?
  • Is there a documented order of precedence when the same field is edited in both systems nearly simultaneously?

Limitations and tradeoffs

The theorem explains tradeoffs, not product quality; every distributed tool faces it. Treat 'always in sync' marketing claims with skepticism and ask specifically what happens during a partition or failed sync job.

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Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.

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