Deterministic rules engine for Spark job analysis
Project description
spark-advisor-rules
Deterministic rules engine for Apache Spark job analysis. Part of the spark-advisor ecosystem.
Install
pip install spark-advisor-rules
What it detects
11 rules that identify common Spark performance problems:
| Rule | Detects |
|---|---|
| DataSkewRule | Task duration skew (max/median > 5x) |
| SpillToDiskRule | Disk spill indicating insufficient memory |
| GCPressureRule | GC time > 20% of task time |
| ShufflePartitionsRule | Partition count far from optimal (128MB target) |
| ExecutorIdleRule | Slot utilization < 40% |
| TaskFailureRule | Failed tasks (OOM, fetch failures) |
| SmallFileRule | Avg input bytes per task < 10MB |
| BroadcastJoinThresholdRule | Broadcast join disabled or too low |
| SerializerChoiceRule | Java serializer used with shuffle stages |
| DynamicAllocationRule | Missing min/max bounds or disabled |
| ExecutorMemoryOverheadRule | High GC + high memory utilization |
All thresholds are configurable via Thresholds model.
Usage
from spark_advisor_rules.static_analysis import StaticAnalysisService
service = StaticAnalysisService()
results = service.analyze(job_analysis)
Links
License
Apache 2.0
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