faker
Hey
faker | Hey | |
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9 | 38 | |
17,101 | 17,294 | |
- | - | |
9.5 | 0.0 | |
6 days ago | 8 days ago | |
Python | Go | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
faker
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Leveling up your custom fake data with Faker.js
Faker was originally written in Perl and is also available as a library for Ruby, Java, and Python.
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The Uncreative Software Engineer's Compendium to Testing
Faker: a library that generates fake data that can be useful when you need data to test for various components.
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Exploring LLMs for Data Synthesizing & Anonymization: looking for Insights on Current & Future Solutions
Don't get me wrong, LLMs are awesome but totally unsuited for what you are describing. Classic data science tools like faker will be better for the task in pretty much every aspect. They can generate synthetic datasets and anonymize existing ones faster and far more reliable than any LLM.
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Undercover work
The Python package, Faker, is just what you're looking for!
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Is there a way to automate testing in python? In my case :
for datatypes like string/date and other stuff, there is a Python library called faker, which you can use. It can generate fake names, fake phone numbers, dates, and addresses. here is the link to the documentation. https://faker.readthedocs.io/ here is a link to a blog post explaining Faker. https://levelup.gitconnected.com/pythons-faker-library-your-go-to-solution-for-test-data-generation-3a070065cc04
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Testing files in Python like a pro
Then test cases became more complex. Primary data sources were often files. We needed to test pipelines. Faker still helped a lot, but it was not convenient to copy your last-best-approach for files and reinvent the wheel over and over with each project.
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Database automation challenges and how to solve them
For a cloud-based solution, one can write their own Terraform or CloudFormation for installation as soon as their RDS instance boots up with appropriate security and authentication details. For a local dev environment, one can rely on Faker to create mock database data for your database.
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How to create a 1M record table with a single query
Creating realistic fake data is useful in lower environments and for load testing. Outside of SQL I like faker: https://github.com/joke2k/faker
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DuckDB: an embedded DB for data wrangling
To test a database, first you need some data. So I created a python script and used Faker to create the following CSV files:
Hey
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AWS SnapStart - Part 19 Measuring cold starts and deployment time with Java 17 using different Lambda memory settings
The results of the experiment below were based on reproducing approximately 100 cold starts for the duration of our experiment which ran for approximately 1 hour. For it (and all experiments from my previous articles) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman
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Data API for Amazon Aurora Serverless v2 with AWS SDK for Java - Part 5 Basic cold and warm starts measurements
The results of the experiment to retrieve the existing product from the database by its id see GetProductByIdViaAuroraServerlessV2DataApiHandler with Lambda function with 1024 MB memory setting were based on reproducing more than 100 cold and approximately 10.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. We won't enable SnapStart on the Lambda function first.
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AWS SnapStart - Part 15 Measuring cold and warm starts with Java 21 using different synchronous HTTP clients
The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. I ran all these experiments for all 3 scenarios using 2 different compilation options in template.yaml each:
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AWS SnapStart - Part 13 Measuring warm starts with Java 21 using different Lambda memory settings
In our experiment we'll re-use the application introduced in part 9 for this. There are basically 2 Lambda functions which both respond to the API Gateway requests and retrieve product by id received from the API Gateway from DynamoDB. One Lambda function GetProductByIdWithPureJava21Lambda can be used with and without SnapStart and the second one GetProductByIdWithPureJava21LambdaAndPriming uses SnapStart and DynamoDB request invocation priming. We'll measure cold and warm starts using the following memory settings in MBs : 256, 512, 768, 1024, 1536 and 2048. I also put the cold starts measured in the part 12 into the tables to see both cold and warm starts in one place. The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts for the duration of our experiment which ran for approximately 1 hour. Here is the code for the sample application. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. Abbreviation c is for the cold start and w is for the warm start.
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Diagnósticos usando dotnet-monitor + prometheus + grafana
Por último, podemos executar os testes de carga usando hey.
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Amazon DevOps Guru for the Serverless applications - Part 2 Setting up the Sample Application for the Anomaly Detection
For running our experiments to provoke anomalies we'll use the stress test tool. You can use the tool of your choice (like Gatling, JMeter, Fiddler or Artillery), I personally prefer to use the tool hey as it is easy to use and similar to curl. On Linux this tool can be installed by executing
- Threadpool no aspnet e problemas de performance
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The Uncreative Software Engineer's Compendium to Testing
Hey: is a fast HTTP load testing tool used to test web applications and APIs. It provides a CLI (command-line interface) and supports concurrent requests.
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The TCP receiver only ack the minimum bytes of MSS one by one
The client and server nodes are CentOS7.9/X86_64. If the HTTP POST requests were sent directly to the server with hey -c 1, there are about 0.2% of cases that may timeout. If the HTTP POST requests were sent through an NGINX proxy on the client node, there are about 20% of cases will timeout. I've confirmed that only one backend node has this problem. All other nodes are 100% succeeded even with higher throughput.
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Benchmarking SQLite Performance in Go. Using Go's awesome built-in simple benchmarking tools to investigate SQLite database performance in a couple of different benchmarks, plus a comparison to Postgres.
64 concurrent requests isn't a lot. Modern web apps can typically handle much more than that (depending on what the request does, of course). Try it yourself with a load tester like https://github.com/rakyll/hey against a Go HTTP server, for example the one I've built in https://www.golang.dk/articles/go-and-sqlite-in-the-cloud
What are some alternatives?
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
Vegeta - HTTP load testing tool and library. It's over 9000!
FauxFactory - Generates random data for your tests.
k6 - A modern load testing tool, using Go and JavaScript - https://k6.io
fake2db - create custom test databases that are populated with fake data
siege - Siege is an http load tester and benchmarking utility
picka - pip install picka - Picka is a python based data generation and randomization module which aims to increase coverage by increasing the amount of tests you _dont_ have to write by hand.
ddosify - Effortless Kubernetes Monitoring and Performance Testing. Available on CLI, Self-Hosted, and Cloud
PyRestTest - Python Rest Testing
grpcurl - Like cURL, but for gRPC: Command-line tool for interacting with gRPC servers
radar
kubernetes - Production-Grade Container Scheduling and Management