coveralls-public
ApacheKafka
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coveralls-public | ApacheKafka | |
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10 | 104 | |
124 | 28 | |
0.0% | - | |
10.0 | 0.0 | |
about 4 years ago | 5 months ago | |
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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.
coveralls-public
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GitHub Actions for Perl Development
cpan_coverage: This calculates the coverage of your test suite and reports the results. It also uploads the results to coveralls.io
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Perl Testing in 2023
I will normally use GitHub Actions to automatically run my test suite on each push, on every major version of Perl I support. One of the test runs will load Devel::Cover and use it to upload test coverage data to Codecov and Coveralls.
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Containers for Coverage
Several years ago I got into Travis CI and set up lots of my GitHub repos so they automatically ran the tests each time I committed to the repo. Later on, I also worked out how to tie those test runs into Coveralls.io so I got pretty graphs of how my test coverage was looking. I gave a talk about what I had done.
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Comprehensive coverage Jest+Playwright in Next.js TS
This approach will create two json coverage files, which will be merged together by NYC. Therefore the results will be purely local. If You don't mind using online tools like Codecov or Coveralls for merging data from different tests, then go ahead and use them. They will probably also be more accurate. But if You still want to learn how to get coverage from E2E, then please read through
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RFC: A Full-stack Analytics Platform Architecture
Ideally, software can quickly go from development to production. Continuous deployment and delivery are some processes that make this possible. Continuous deployment means establishing an automated pipeline from development to production while continuous delivery means maintaining the main branch in a deployable state so that a deployment can be requested at any time. Predecos uses these tools. When a commit goes into master, the code is pushed directly to the public environment. Deployment also occurs when a push is made to a development branch enabling local/e2e testing before push to master. In this manner the master branch can be kept clean and ready for deployment most of the time. Problems that surface resulting from changes are visible before reaching master. Additional automated tools are used. Docker images are built for each microservice on commit to a development or master branch, a static code analysis is performed by SonarCloud revealing quality and security problems, Snyk provides vulnerability analysis and CodeClimate provides feedback on code quality while Coveralls provides test coverage. Finally, a CircleCI build is done. Each of these components use badges which give a heads-up display of the health of the system being developed. Incorporating each of these tools into the development process will keep the code on a trajectory of stability. For example, eliminating code smells, security vulnerabilities, and broken tests before merging a pull-request (PR) into master. Using Husky on development machines to ensure that code is well linted and locally tested before it is allowed to be pushed to source-control management (SCM). Applying additional processes such as writing tests around bugs meaning reintroduction of a given bug would cause a test to fail. The automated tools would then require that test to be fixed before push to SCM meaning fewer bugs will be reintroduced. Proper development processes and automation have a strong synergy.
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Any way to show cumulative code coverage using GitHub Actions for free?
There is https://coveralls.io/ and https://github.com/marketplace/codecov , but they are both priced for commercial usage. Do you know some free alternatives or approaches to have something similiar?
- Testes Unitários: Fundamentos e Qualidade de Software!
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Day 1: Project Scaffolding
Add a Code Coverage CI step using Coveralls.io Add Dependency monitoring using Snyk
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How to automate unit tests with github actions and coveralls for an npm package
Since there is no need to reinvent the wheel, I will take advantage of an existing github action in the Continuous integration workflows category: Node.js. With this action I will set up this action in one of my public repositories. I will set up Node.js action for automating my unit test and also integrate with coveralls.io for getting a badge of how much my tests covers relevant lines.
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Error with github build action
Looks like https://github.com/lemurheavy/coveralls-public/issues/632 this issue based on the log. Try going through their solutions, maybe?
ApacheKafka
- PubNubとIFTTTによるSMS通知システム
- PubNub 및 IFTTT를 사용한 SMS 알림 시스템
- Système de notification par SMS avec PubNub et IFTTT
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Wie man Ereignisse von PubNub zu RabbitMQ streamt
Senden an Kafka (d. h. Senden der Daten an Apache Kafka)
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.
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How to Use Reductstore as a Data Sink for Kafka
Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.
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🦿🛴Smarcity garbage reporting automation w/ ollama
*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)
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How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.
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Easy Guide to Integrating Kafka: Practical Solutions for Managing Blob Data
Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
What are some alternatives?
playwright-test-coverage - Playwright Test (@playwright/test) demo to collect coverage information via Istanbul
dramatiq - A fast and reliable background task processing library for Python 3.
thinkdeep - Economic analysis web application.
outbox-inbox-patterns - Repository to support the article "Building a Knowledge Base Service With Neo4j, Kafka, and the Outbox Pattern"
GoCover.io - GoCover.io offers the code coverage of any golang package as a service.
Jenkins - Jenkins automation server
lit - Lit is a simple library for building fast, lightweight web components.
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
TypeScript - TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
istio - Connect, secure, control, and observe services.
jest - Delightful JavaScript Testing.
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.