CRISP: Critical Path Analysis of Microservice Traces

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Data AnalysisCRISP
Overview

CRISP: Critical Path Analysis of Microservice Traces

This repo contains code to compute and present critical path summary from Jaeger microservice traces. To use first collect the microservice traces of a specific endpoint in a directory (say traces). Let the traces be for OP operation and SVC service (these are Jaeger termonologies). python3 process.py --operationName OP --serviceName SVC -t <path to trace> -o . --parallelism 8 will produce the critical path summary using 8 concurrent processes. The summary will be output in the current directory as an HTML file with a heatmap, flamegraph, and summary text in criticalPaths.html. It will also produce three flamegraphs flame-graph-*.svg for three different percentile values.

The script accepts the following options:

python3 process.py --help
usage: process.py [-h] -a OPERATIONNAME -s SERVICENAME [-t TRACEDIR] [--file FILE] -o OUTPUTDIR
                  [--parallelism PARALLELISM] [--topN TOPN] [--numTrace NUMTRACE] [--numOperation NUMOPERATION]

optional arguments:
  -h, --help            show this help message and exit
  -a OPERATIONNAME, --operationName OPERATIONNAME
                        operation name
  -s SERVICENAME, --serviceName SERVICENAME
                        name of the service
  -t TRACEDIR, --traceDir TRACEDIR
                        path of the trace directory (mutually exclusive with --file)
  --file FILE           input path of the trace file (mutually exclusivbe with --traceDir)
  -o OUTPUTDIR, --outputDir OUTPUTDIR
                        directory where output will be produced
  --parallelism PARALLELISM
                        number of concurrent python processes.
  --topN TOPN           number of services to show in the summary
  --numTrace NUMTRACE   number of traces to show in the heatmap
  --numOperation NUMOPERATION
                        number of operations to show in the heatmap
Owner
Uber Research
Uber's research projects. Projects in this organization are not built for production usage. Maintainance and supports are limited.
Uber Research
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