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fa1be8b2b0
add-admin-and-worker-to-helm-charts
add-ec-vacuum
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
copilot/fix-helm-chart-installation
copilot/fix-s3-object-tagging-issue
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
do-not-retry-if-error-is-NotFound
ec-disk-type-support
enhance-erasure-coding
fasthttp
feature/tus-protocol
filer1_maintenance_branch
fix-GetObjectLockConfigurationHandler
fix-mount-http-parallelism
fix-s3-object-tagging-issue-7589
fix-versioning-listing-only
ftp
gh-pages
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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
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revert-6434-bugfix-missing-s3-audit
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test-reverting-lock-table
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upgrade-versions-to-4.00
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dev
helm-3.65.1
v0.69
v0.70beta
v3.33
${ noResults }
chrislu
fa1be8b2b0
CRITICAL: Gateway was creating Avro codecs and inferring RecordTypes on
EVERY fetch request for schematized topics!
Problem (from CPU profile):
- NewCodec (Avro): 17.39% CPU (2.35s out of 13.51s)
- inferRecordTypeFromAvroSchema: 20.13% CPU (2.72s)
- Total schema overhead: 37.52% CPU
- Called during EVERY fetch to check if topic is schematized
- No caching - recreating expensive goavro.Codec objects repeatedly
Root Cause:
In the fetch path, isSchematizedTopic() -> matchesSchemaRegistryConvention()
-> ensureTopicSchemaFromRegistryCache() -> inferRecordTypeFromCachedSchema()
-> inferRecordTypeFromAvroSchema() was being called.
The inferRecordTypeFromAvroSchema() function created a NEW Avro decoder
(which internally calls goavro.NewCodec()) on every call, even though:
1. The schema.Manager already has a decoder cache by schema ID
2. The same schemas are used repeatedly for the same topics
3. goavro.NewCodec() is expensive (parses JSON, builds schema tree)
This was wasteful because:
- Same schema string processed repeatedly
- No reuse of inferred RecordType structures
- Creating codecs just to infer types, then discarding them
Solution:
Added inferredRecordTypes cache to Handler:
Changes to handler.go:
- Added inferredRecordTypes map[string]*schema_pb.RecordType to Handler
- Added inferredRecordTypesMu sync.RWMutex for thread safety
- Initialize cache in NewTestHandlerWithMock() and NewSeaweedMQBrokerHandlerWithDefaults()
Changes to produce.go:
- Added glog import
- Modified inferRecordTypeFromAvroSchema():
* Check cache first (key: schema string)
* Cache HIT: Return immediately (V(4) log)
* Cache MISS: Create decoder, infer type, cache result
- Modified inferRecordTypeFromProtobufSchema():
* Same caching strategy (key: "protobuf:" + schema)
- Modified inferRecordTypeFromJSONSchema():
* Same caching strategy (key: "json:" + schema)
Cache Strategy:
- Key: Full schema string (unique per schema content)
- Value: Inferred *schema_pb.RecordType
- Thread-safe with RWMutex (optimized for reads)
- No TTL - schemas don't change for a topic
- Memory efficient - RecordType is small compared to codec
Performance Impact:
With 250 fetches/sec across 5 topics (1-3 schemas per topic):
- Before: 250 codec creations/sec + 250 inferences/sec = ~5s CPU
- After: 3-5 codec creations total (one per schema) = ~0.05s CPU
- Reduction: 99% fewer expensive operations
Expected CPU Reduction:
- Before: 13.51s total, 5.07s schema operations (37.5%)
- After: ~8.5s total (-37.5% = 5s saved)
- Benefit: 37% lower gateway CPU, more capacity for message processing
Cache Consistency:
- Schemas are immutable once registered in Schema Registry
- If schema changes, schema ID changes, so safe to cache indefinitely
- New schemas automatically cached on first use
- No need for invalidation or TTL
Additional Optimizations:
- Protobuf and JSON Schema also cached (same pattern)
- Prevents future bottlenecks as more schema formats are used
- Consistent caching approach across all schema types
Testing:
- ✅ Compiles successfully
- Ready to deploy and measure CPU improvement under load
Priority: HIGH - Eliminates major performance bottleneck in gateway schema path
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2 months ago | |
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