CRISP: Critical Path Analysis of Microservice Traces

Related tags

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
GitHub Repository
Minimal working example of data acquisition with nidaqmx python API

Data Aquisition using NI-DAQmx python API Based on this project It is a minimal working example for data acquisition using the NI-DAQmx python API. It

Pablo 1 Nov 05, 2021
Hidden Markov Models in Python, with scikit-learn like API

hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and

2.7k Jan 03, 2023
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8k Dec 29, 2022
Business Intelligence (BI) in Python, OLAP

Open Mining Business Intelligence (BI) Application Server written in Python Requirements Python 2.7 (Backend) Lua 5.2 or LuaJIT 5.1 (OML backend) Mong

Open Mining 1.2k Dec 27, 2022
Generate lookml for views from dbt models

dbt2looker Use dbt2looker to generate Looker view files automatically from dbt models. Features Column descriptions synced to looker Dimension for eac

lightdash 126 Dec 28, 2022
a tool that compiles a csv of all h1 program stats

h1stats - h1 Program Stats Scraper This python3 script will call out to HackerOne's graphql API and scrape all currently active programs for informati

Evan 40 Oct 27, 2022
Utilize data analytics skills to solve real-world business problems using Humana’s big data

Humana-Mays-2021-HealthCare-Analytics-Case-Competition- The goal of the project is to utilize data analytics skills to solve real-world business probl

Yongxian (Caroline) Lun 1 Dec 27, 2021
An easy-to-use feature store

A feature store is a data storage system for data science and machine-learning. It can store raw data and also transformed features, which can be fed straight into an ML model or training script.

ByteHub AI 48 Dec 09, 2022
DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN

DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN. Allowing for both categorical and numerical data, DenseClus makes it possible to incorporate all features in cluste

Amazon Web Services - Labs 53 Dec 08, 2022
Yet Another Workflow Parser for SecurityHub

YAWPS Yet Another Workflow Parser for SecurityHub "Screaming pepper" by Rum Bucolic Ape is licensed with CC BY-ND 2.0. To view a copy of this license,

myoung34 8 Dec 22, 2022
International Space Station data with Python research 🌎

International Space Station data with Python research 🌎 Plotting ISS trajectory, calculating the velocity over the earth and more. Plotting trajector

Facundo Pedaccio 41 Jun 16, 2022
Feature Detection Based Template Matching

Feature Detection Based Template Matching The classification of the photos was made using the OpenCv template Matching method. Installation Use the pa

Muhammet Erem 2 Nov 18, 2021
Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

AWS Data Wrangler Pandas on AWS Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretMana

Amazon Web Services - Labs 3.3k Jan 04, 2023
PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams

PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams Motivation When dataset freshness is critical, the annotating of high speed

4 Aug 02, 2022
BIGDATA SIMULATION ONE PIECE WORLD CENSUS

ONE PIECE is a Japanese manga of great international success. The story turns inhabited in a fictional world, tells the adventures of a young man whose body gained rubber properties after accidentall

Maycon Cypriano 3 Jun 30, 2022
A Big Data ETL project in PySpark on the historical NYC Taxi Rides data

Processing NYC Taxi Data using PySpark ETL pipeline Description This is an project to extract, transform, and load large amount of data from NYC Taxi

Unnikrishnan 2 Dec 12, 2021
Performance analysis of predictive (alpha) stock factors

Alphalens Alphalens is a Python Library for performance analysis of predictive (alpha) stock factors. Alphalens works great with the Zipline open sour

Quantopian, Inc. 2.5k Jan 09, 2023
Import, connect and transform data into Excel

xlwings_query Import, connect and transform data into Excel. Description The concept is to apply data transformations to a main query object. When the

George Karakostas 1 Jan 19, 2022
Python-based Space Physics Environment Data Analysis Software

pySPEDAS pySPEDAS is an implementation of the SPEDAS framework for Python. The Space Physics Environment Data Analysis Software (SPEDAS) framework is

SPEDAS 98 Dec 22, 2022
Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt Labs 6.3k Jan 08, 2023