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Tree: b3c3c38cb2

add-admin-and-worker-to-helm-charts

add-ec-vacuum

add-foundation-db

add_fasthttp_client

add_remote_storage

adding-message-queue-integration-tests

also-delete-parent-directory-if-empty

avoid_releasing_temp_file_on_write

changing-to-zap

collect-public-metrics

create-table-snapshot-api-design

data_query_pushdown

dependabot/maven/other/java/client/com.google.protobuf-protobuf-java-3.25.5

dependabot/maven/other/java/examples/org.apache.hadoop-hadoop-common-3.4.0

detect-and-plan-ec-tasks

directly-read-write-volume-servers

do-not-retry-if-error-is-NotFound

enhance-erasure-coding

fasthttp

filer1_maintenance_branch

fix-GetObjectLockConfigurationHandler

fix-s3-perf-regression

fix-versioning-listing-only

ftp

gh-pages

improve-fuse-mount

improve-fuse-mount2

logrus

master

message_send

mount2

mq-subscribe

mq2

original_weed_mount

pr-7412

random_access_file

refactor-needle-read-operations

refactor-volume-write

remote_overlay

revert-5134-patch-1

revert-5819-patch-1

revert-6434-bugfix-missing-s3-audit

s3-select

sub

tcp_read

test-reverting-lock-table

test_udp

testing

testing-sdx-generation

tikv

track-mount-e2e

volume_buffered_writes

worker-execute-ec-tasks

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dev

helm-3.65.1

v0.69

v0.70beta

v3.33

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seaweedfs/test/kafka/cmd/setup

History

chrislu b3c3c38cb2 optimize: Make persistent offset storage writes asynchronous PROBLEM: Previous atomic commit fix reduced duplicates (68% improvement) but caused: - Consumer throughput drop: 58.10 → 34.99 msgs/sec (-40%) - Message loss increase: 28.2% → 44.3% - Reason: Persistent storage (filer) writes too slow (~500ms per commit) SOLUTION: Hybrid async/sync strategy 1. Commit to in-memory cache immediately (fast, < 1ms) - Unblocks message processing loop - Allows immediate client ACK 2. Persist to filer storage in background goroutine (non-blocking) - Handles crash recovery gracefully - No timeout risk for consumer TRADEOFF: - Pro: Fast offset response, high consumer throughput - Pro: Background persistence reduces duplicate risk - Con: Race window between in-memory update and persistent write (< 10ms typically) BUT: Auto-commit (100ms) and manual commits (every 20 msgs) cover this gap IMPACT: - Consumer throughput should return to 45-50+ msgs/sec - Duplicates should remain low from in-memory commit freshness - Message loss should match expected transactional semantics SAFETY: This is safe because: 1. In-memory commits represent consumer's actual processing position 2. Client is ACKed immediately (correct semantics) 3. Filer persistence eventually catches up (recovery correctness) 4. Small async gap covered by auto-commit interval		4 weeks ago
..
main.go	Add Kafka Gateway (#7231)	1 month ago