email: [email protected]
I am an expert in performance testing and data engineering.
I create performance tests and build processes for finding performance issues. I help companies to understand why their programs work slowly. I teach people to look for and prevent such kind of issues.
I am looking for problems not only in programs but also in processes. I define the requirements, and I create test scripts based on those requirements for various test types and load profiles. I setup a pipeline of scenarios in a continuous integration system and setup a system for automatic analysis and anomaly search using machine learning. I keep human interaction with tests to a minimum and provide developers with the fastest possible feedback after their commits.
Companies contact me when they need to understand:
- why the program is slow;
- when it is necessary to build a process for detecting performance issues from scratch;
- when it needs to teach the system automatically find issues during the development process.
I can work with many performances, monitoring, and profiling tools. I can find errors in desktop applications, web applications, and databases. I can independently analyze large volumes of logs and find hidden correlations between several data sources. I can use machine learning to find problems of balancing, memory leaks, and predict future resource consumption by servers.
I’m not a wizard, and I can’t find errors and bottlenecks just by looking at the application. Load testing is scrupulous research that required long preparation and gives accurate results. However, we can always find a compromise by sacrificing accuracy for the reason of speed (I prefer not to do so).
My mission is to create one button - which can say why the application is slow
- Performance testing of the neural network's services
- Devolop reporting API for generated yearly/monthly reports about system usage
- Develop performance testing framework for elastic search engine
- Anomaly detection in performance testing (static)
- Anomaly detection for monitoring system (dynamic via Plumber)
- Add machine learning in performance testing
- Add automatic results analysis of performance testing
- Generate recommendation system for performance testing engineer for a faster gathering of performance testing results
- Create interactive shiny-dashboard for performance testing results
- Create a robust regression model for faster gathering of performance testing results
- Create CI performance framework in a company
- Invent performance testing pipeline in a company
- Structured performance methodology in a company
- Build perfomance testing framework for legacy technologies (fat client performance testing)
- Develop performance and profiling tools for legacy technologies
- NLP: QA-fest review analysis
- Regression: Predict performance behavior on high load based on low load.
- Forecasting: Create expected requirements for performance testing based on historical vales
- Other: detect anomalies in time series for bug and route cause detection on performance testing
- Develop methodology for economical efficiency performance benchmark of different CPUs for DBs
- Develop methodology for AI performance testing
- Develop performance testing process for release certification jobs
- Develop methodology for performance testing and benchmarking services with neural network on backend
- Develop methodology of performance testing management: "From chaos to application performance management"
- Develop a methodology for faster gathering performance testing results using machine learning
- Develop strategy (algorithm) about getting information about changes in application performance in one performance run
- Develop an approach for predicting expected result without defined requirements (based on historical results and ARIMA forecasting)
- Educate students in National aviation university for performance testing
- Mentoring and teaching junior performance test engineers
programming languages | level |
---|---|
R | ⚫⚫⚫⚫⚫ |
Python | ⚫⚫⚫⚫⚪ |
JAVA | ⚫⚫⚫⚪⚪ |
groovy | ⚫⚫⚫⚪⚪ |
bash, powerShell | ⚫⚫⚫⚪⚪ |
- data engineering: PySpark, Dask, Twisted, Tornado, asyncio, flask, rest, pytest, unittest,Tensorflow, pandas, plotly, dash, keras, numpy, scipy, mathplotlib, tesseract,
- frameworks: Selenium webDriver, selenoid, zeep, requests, tidyverse, shiny, Scikit-learn, pandas, pytest, testNg, Junit,
- testing tools: Load runner, jmeter, Gatling, tsung, ReadyAPI/SoapUI, postman
- DB: mysql, postreSql, MSsql, Oracle SQL, mongoDB, sybase SQL
- virtulization: vmware esxi
- conteinerization: kubernetes, docker
- CI/CD tools: teamcity, jenkins, hudson, azure devops, docker
- monitoring: Dynatrace, appDynamics, newRelic, DataDog
- cloud: AWS, AZURE
year | company / projects | roles |
---|---|---|
2023 | Crown Castle | Senior Cloud platform engineer |
2022 | Casenet llc | Senior performance engineer |
2022 | N-ix | Senior R developer |
2021 | PropellMinds | Senior performance engineer - consulting |
2021 | Percona | Performance engineer - consulting |
2020 | Datarobot | Senior AI performance engineer (prev. Senior performance engineer) |
2019 | HintMD | Senior performance engineer |
2019 | DNVGL | Performance architect - consulting |
2018 | RB | Performance architect - consulting |
2018 | NDA-banki | Senior Performance Engineer |
2016 | Islandsbanki | Senior Performance Engineer |
2016 | Itera | Data engineer / Data scientiest / Performance engineer (prev. Senior Performance Engineer) |
2016 | Luxoft | Senior performance test engineer |
2014 | Terrasoft | System analyst / Performance test Engineer / QA Engineer |
2013 | SRS | QA engineer |
2011 | Apeal court | System administratod / Deputy IT department |
2009 | SDICSED | System administrator |
2008 | Geochemincal insitute | System administrator |
years | university | specialization |
---|---|---|
2018 | Moscow Institute of Physics and Technology | Machine learning and data analysis |
2017 | John Hopkins University | Data Science / Executive Data Science |
2015 | «STRATOPLAN» Management School | Project management |
2012 | Institute of postgraduate education of Taras Shevchenko National University of Kyiv | Psychology |
2010 | National Academy of security service of Ukraine | System and technology of protection restricted access information |
- Comparing Graviton (ARM) Performance to Intel and AMD for MySQL (Part 1) - m5
- Comparing Graviton (ARM) Performance to Intel and AMD for MySQL (Part 2) - c5
- Comparing Graviton (ARM) Performance to Intel and AMD for MySQL (Part 3) - m6
- Economical Comparison of AWS CPUs for MySQL (ARM vs Intel vs AMD)
- QA FEST - How to test app which is using machine learning - (RUS) Как тестровать приложения которые используют машинное обучение
- QA Fest - How save time on performance testing report analysis - (RUS) Как сэкономить время на анализе отчетов о нагрузочном тестировании
- QA Fest - ML usage methodology in performance testing - (RUS) Методология использоания машинного обучения в нагрузочном тестировании