Consumer group failover #2
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
ref: Split init.go into consumer, producer * fix consumer topics init * minor refactors feat: Save offsets and swap consumer to start from this offset FIXME: * Handle case where group2 is lagging behind by group1 * group1 down -> swap to group2 -> group2 down -> swap to group1 causing UNKNOWN_MEMBER_ID fix: Pause and reuse topic consumption * Use list of kgo.Client instead of replacing the client ref: keep kgo.Clients in array ref: move consumer to consumerMgr ref: cleanup docs: Add docs feat: Add retryBackoffFn, cleanup
9a17569 to
3e570bd
Compare
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Given N upstream nodes,
failovermode allows us to start a consumer group on upstreamNode1 and replicate the given topics to downstreamNode. If upstreamNode1 goes down, kaf-relay automatically detects the disconnection, round-robin to the next identical kafka node and resume from where it left. This helps in deduplicating data from multiple streams without additional stores and helps us in creating an aggregated stream.