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

CRISP: Critical Path Analysis of Microservice Traces

Related tags

Overview

CRISP: Critical Path Analysis of Microservice Traces

Owner

Uber Research

Demonstrate the breadth and depth of your data science skills by earning all of the Databricks Data Scientist credentials

A Python Tools to imaging the shallow seismic structure

Project: Netflix Data Analysis and Visualization with Python

Python package to transfer data in a fast, reliable, and packetized form.

A pipeline that creates consensus sequences from a Nanopore reads. I

Statistical Analysis 📈 focused on statistical analysis and exploration used on various data sets for personal and professional projects.

A multi-platform GUI for bit-based analysis, processing, and visualization

Scraping and analysis of leetcode-compensations page.

Statsmodels: statistical modeling and econometrics in Python

A Python module for clustering creators of social media content into networks

Two phase pipeline + StreamlitTwo phase pipeline + Streamlit

Tablexplore is an application for data analysis and plotting built in Python using the PySide2/Qt toolkit.

VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

Single-Cell Analysis in Python. Scales to >1M cells.

sportsdataverse python package

COVID-19 deaths statistics around the world

Pyspark Spotify ETL

Includes all files needed to satisfy hw02 requirements

Programmatically access the physical and chemical properties of elements in modern periodic table.

A Python adaption of Augur to prioritize cell types in perturbation analysis.