Question 1

What Slack data can I load into ClickHouse?

Accepted Answer

Six tables: channels, channel members, users, messages, threads, and events if Slack event subscriptions are configured. Reactions and files are not separate tables, arriving nested on messages and threads instead, with lists becoming native Array columns and objects landing as JSON on ClickHouse 24.8 or later. The users table carries Slack's deleted flag so deactivated accounts stay visible.

Question 2

Should I add LowCardinality to these tables?

Accepted Answer

Often yes, and you will need to do it yourself since Polytomic does not emit LowCardinality by default. Slack data is full of columns that suit it, channel identifier, user identifier, message subtype, where the same handful of values repeat across millions of rows. Existing LowCardinality columns are recognised and left in place when reading an existing schema, so applying it after the table exists is safe and will not be undone by the next sync.

Question 3

What makes a first Slack load take days?

Accepted Answer

The Slack API tier, essentially always, rather than anything about ClickHouse. On the restricted non Marketplace tier the API returns roughly 15 messages a page and permits about one history request a minute, which caps throughput no matter how fast the destination is. Check which tier your app sits on before committing to a timeline. The other frequent cause is that the bot was never added to the channel, so there is nothing to read.

Question 4

Are deleted Slack messages removed?

Accepted Answer

Only with event updates enabled, since polling will not catch a deletion reliably. Turn on Slack event updates for bulk syncs with the matching subscriptions configured, and deletes come through for messages and replies, channels and private groups, and channel memberships. They arrive as tombstone versions carrying a deleted at timestamp. Webhooks are not delivery guaranteed, so add a periodic full read if your counts have to be exact.

Question 5

Why analyse conversation data in ClickHouse?

Accepted Answer

Because message volume behaves like event volume, and that is the workload ClickHouse is built for. A busy workspace produces millions of rows a year, which is uncomfortable in a general purpose warehouse and unremarkable here. ETL into a data warehouse turns those messages into rows you can group, filter and join to accounts, so questions like which customers talk to you most become fast rather than expensive.

Sync Slack to ClickHouse

Sync Slack to ClickHouse with Polytomic

Six source tables

Tune cardinality yourself

Picks up edited messages

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Connect Slack in Polytomic

Connect ClickHouse in Polytomic

Create a sync between Slack and ClickHouse

Map fields and enable the sync

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