facebook/kuduraft
Apr 2026 – May 2026
2 Months active
Languages Used
C++
Technical Skills
C++backend developmentdistributed systemsmetrics instrumentationmetrics monitoring
Serena Chan
PROFILE
Serena Chan
Worked on the facebook/kuduraft repository to enhance backend observability for distributed systems using C++. Focused on expanding metrics instrumentation, this developer introduced detailed latency metrics for raft consensus replication and Kudu RPC paths, enabling granular monitoring of log persistence and majority acknowledgment phases. Leveraging fb303, they exported new counters and standardized metric naming to improve data scraping and facilitate SLA tracking. The technical approach emphasized telemetry and validation through local thrift server tests, supporting proactive performance analysis and incident response. Their work improved operational visibility and monitoring readiness, aligning with performance goals and supporting SRE practices for distributed backend services.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
109
Activity Months2
Your Network
3052 peopleSame Organization
@meta.com3012
Peter RongMember
Zain RizviMember
Aaditya NairMember
Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron FarberMember
Aaron PollackMember
Aaryaman SagarMember
Work History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026: Implemented observability enhancement by exporting RPC handler latency metrics to fb303 and standardizing metric naming for easier scraping and analysis. This work improves service performance visibility and supports SLA tracking for Kuduraft RPCs.
1 Commits • 1 Features
May 1, 2026May 2026: Implemented observability enhancement by exporting RPC handler latency metrics to fb303 and standardizing metric naming for easier scraping and analysis. This work improves service performance visibility and supports SLA tracking for Kuduraft RPCs.
May 2026
April 2026
2 Commits • 2 Features
Apr 1, 2026April 2026 (2026-04) focused on expanding observability for critical raft consensus and Kudu RPC paths to enable data-driven performance tuning, incident response, and capacity planning. Delivered two major metrics-driven features via fb303: - Raft Consensus Latency Metrics: Added two fb303 counters (raft_leader_flush_latency and raft_leader_quorum_latency) to measure log persistence time and majority-ACK latency, with a per-round boundary timestamp in ConsensusRound to separate flush and quorum phases. These changes provide granular latency distributions (p50/ p99, etc.) and verify via local thrift server tests. - Kudu RPC Observability: Reintroduced fb303 dynamic statistics for Kudu RPC metrics (reactor_load_percent, reactor_active_latency_us, rpc_incoming_queue_time_us, timeout_connection_kill) to improve monitoring and data scraping for ODS via dbexecd. Includes validation of counters and time-series data. Impact: Improves operational visibility, supports proactive tuning and faster MTTR, and enables data-driven capacity planning. The work emphasizes telemetry over feature toggles, aligning with performance goals and SRE readiness.
April 2026
2 Commits • 2 Features
Apr 1, 2026April 2026 (2026-04) focused on expanding observability for critical raft consensus and Kudu RPC paths to enable data-driven performance tuning, incident response, and capacity planning. Delivered two major metrics-driven features via fb303: - Raft Consensus Latency Metrics: Added two fb303 counters (raft_leader_flush_latency and raft_leader_quorum_latency) to measure log persistence time and majority-ACK latency, with a per-round boundary timestamp in ConsensusRound to separate flush and quorum phases. These changes provide granular latency distributions (p50/ p99, etc.) and verify via local thrift server tests. - Kudu RPC Observability: Reintroduced fb303 dynamic statistics for Kudu RPC metrics (reactor_load_percent, reactor_active_latency_us, rpc_incoming_queue_time_us, timeout_connection_kill) to improve monitoring and data scraping for ODS via dbexecd. Includes validation of counters and time-series data. Impact: Improves operational visibility, supports proactive tuning and faster MTTR, and enables data-driven capacity planning. The work emphasizes telemetry over feature toggles, aligning with performance goals and SRE readiness.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture86.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++backend developmentdistributed systemsmetrics instrumentationmetrics monitoring
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