Question 1

How well does Plain data fit MongoDB?

Accepted Answer

Unusually well, because both sides are document shaped. Nested values such as label arrays, thread custom fields and assignee objects are written as native BSON arrays and subdocuments rather than being serialized into strings or exploded into columns, so a stored thread looks much like the record Plain returned. That means queries against nested paths work directly instead of needing a decoding step first.

Question 2

Which Plain objects can be synced?

Accepted Answer

A fixed set: customers, companies, tenants, threads, users, tiers and label types, plus the conversation tables of messages, notes, message attachments, message components, thread links, thread timeline events and thread discussions. There is no ticket object in Plain, so threads is the collection that represents a support conversation. Seven prebuilt reports are also exposed, covering daily thread counts, agent activity and SLA compliance by label.

Question 3

How does incremental syncing work?

Accepted Answer

Threads, customers, companies, tenants and several reports are read using Plain's updated timestamp rather than a change stream. Each run starts its window ten minutes before the previous run began so records updated near a boundary are not lost, then fetches child records for the threads that changed. Because that window overlaps, the same document can arrive twice, so configure the sync to upsert on the record identifier rather than inserting.

Question 4

Are deletions reflected in MongoDB?

Accepted Answer

Only partially. Plain exposes deletion flags on company records, so those arrive marked and can be filtered. Threads carry no equivalent, so a conversation deleted in Plain simply stops appearing in later reads and its document stays in the collection. If exact parity matters for what you are building, schedule a periodic full resync of the affected collections rather than depending on incremental runs to remove anything.

Connect Plain with MongoDB

Connect Plain and MongoDB with Polytomic

Native BSON arrays and documents

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