What is Liquibase and what are its top alternatives?
Liquibase is an open-source database schema change management solution that automates and simplifies the process of tracking, managing, and applying database changes. It supports various databases, version control systems, and deployment tools, allowing teams to work collaboratively and efficiently. Key features include rollback functionality, SQL and XML support, and the ability to generate detailed change logs. However, Liquibase may have a steeper learning curve for beginners, and its configuration options can be complex for some users.
- Flyway: Flyway is another popular open-source database migration tool that focuses on simplicity and ease of use. Its key features include automatic schema versioning, seamless integration with CI/CD pipelines, and support for both SQL and Java-based migrations. Pros: Simple setup and configuration, strong support for Java environments. Cons: Limited support for complex migration scenarios.
- Sqitch: Sqitch is a database change management application that emphasizes a VCS-centric workflow. It uses native SQL scripts for database migrations and supports various database platforms. Key features include dependency management, robust auditing, and support for custom extension scripts. Pros: Git-like version control, flexible deployment options. Cons: Requires a solid understanding of SQL and database principles.
- DBDeploy: DBDeploy is a lightweight database change management tool that focuses on simplicity and easy integration with existing processes. It allows users to track changes using plain SQL scripts and apply them in a structured manner. Key features include automatic script ordering, database-independent deployments, and support for multiple environments. Pros: Lightweight and easy to set up, clear separation of concerns. Cons: Limited features compared to more advanced tools.
- Redgate SQL Change Automation: SQL Change Automation by Redgate is a comprehensive database release tool that encompasses both schema changes and data deployment. It integrates with popular DevOps tools like Azure DevOps and Jenkins and supports SQL Server databases. Key features include automated testing, customizable deployment workflows, and built-in support for rollback scripts. Pros: Rich feature set, seamless integration with Redgate's ecosystem. Cons: Commercial license required for advanced features.
- Alembic: Alembic is a lightweight database migration tool for Python applications that leverages SQLAlchemy's ORM capabilities. It provides a powerful migration API for generating and applying database changes in a version-controlled manner. Key features include schema autogeneration, dynamic migration scripts, and support for multiple databases. Pros: Pythonic syntax, tight integration with SQLAlchemy. Cons: Limited support for non-Python environments.
- Migrate: Migrate is a database migration tool written in Go that offers a simple and flexible approach to managing schema changes. It supports SQL migrations, version tracking, and rollbacks, making it suitable for a wide range of projects. Key features include concurrent migrations, environment-specific configuration, and seamless integration with Go applications. Pros: Lightweight and fast, robust Go integration. Cons: Lack of advanced features compared to more established tools.
- TorqueBox: TorqueBox is a Ruby-based database schema migration tool that provides a Ruby DSL for defining database changes. It offers version control, rollback support, and integration with popular Ruby frameworks like Rails. Key features include reusable migration templates, automatic schema diffs, and database-agnostic migrations. Pros: Ruby-centric design, easy integration with Ruby projects. Cons: Limited adoption outside of the Ruby community.
- LiquiBase Pro: LiquiBase Pro is the commercial edition of LiquiBase that offers additional features and support for enterprise deployments. It provides advanced capabilities like database testing, schema comparison, and role-based access control. Key features include audit logging, fine-grained permissions, and enterprise-grade support. Pros: Extended feature set, dedicated customer support. Cons: Higher cost compared to the open-source version.
- ActiveRecord Migrations: ActiveRecord Migrations is a built-in database migration tool for Ruby on Rails applications that follows a convention-over-configuration approach. It allows developers to define database changes using Ruby DSL and manage schema updates within the Rails framework. Key features include schema versioning, transactional migrations, and built-in schema conventions. Pros: Seamless integration with Rails, automatic schema management. Cons: Limited flexibility for non-Rails projects.
- Talend Open Studio for Data Integration: Talend Open Studio is a data integration tool that includes database schema migration capabilities as part of its feature set. It provides a visual interface for designing data workflows and automating database changes across multiple platforms. Key features include drag-and-drop design, job scheduling, and support for big data technologies. Pros: Comprehensive data integration functionality, user-friendly interface. Cons: Overkill for projects that only require database schema management.
Top Alternatives to Liquibase
- Flyway
It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore. ...
- Hibernate
Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper. ...
- Sqitch
It is a standalone change management system with no opinions about your database engine, application framework, or development environment. Native scripting. Changes are implemented as scripts native to your selected database engine. ...
- JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...
- Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...
- GitHub
GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...
- Python
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...
- jQuery
jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML. ...
Liquibase alternatives & related posts
- Superb tool, easy to configure and use13
- Very easy to config, great support on plain sql scripts9
- Is fantastic and easy to install even with complex DB6
- Simple and intuitive4
- Easy tool to implement incremental migration1
- "Undo Migrations" requires pro version, very expensive3
related Flyway posts
Flyway vs Liquibase #Migration #Backwards-compatible
We were looking for a tool to help us integrating the migration scripts as part of our Deployment. At first sight both tools look very alike, are well integrated with Spring, have a fairly frequent development activity and short release cycles.
Liquibase puts a lot of emphasis on independence with the DB, allowing you to create the scripts on formats like JSON and YML, abstracting away from SQL, which it's also supported. Since we only work with one DB type across services we wouldn't take much advantage of this feature.
Flyway on the other hand has the advantage on being actively working on the integration with PostgreSQL 11, for it's upcoming version 6. Provides a more extensive set of properties that allow us to define what's allowed on what's not on each different environment.
Instead of looking for a tool that will allow us to rollback our DB changes automatically, we decided to implement backwards-compatible DB changes, for example adding a new column instead of renaming an existing one, postponing the deletion of the deprecated column until the release has been successfully installed.
MSSQL to PostgreSQL database migration can be done through Flyway? Please advise the steps if possible.
- Easy ORM22
- Easy transaction definition8
- Is integrated with spring jpa3
- Can't control proxy associations when entity graph used3
related Hibernate posts
I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.
I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).
As per my work experience and knowledge, I have chosen the followings stacks to this mission.
UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.
Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.
Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.
Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.
Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.
Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.
Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.
Happy Coding! Suggestions are welcome! :)
Thanks, Ganesa
Material Design for Angular Angular 2 Node.js TypeScript Spring-Boot RxJS Microsoft SQL Server Hibernate Spring MVC
We built our customer facing portal application using Angular frontend backed by Spring boot.
Sqitch
- Interacts with git super well2
- Native scripting1
related Sqitch posts
JavaScript
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- Non-blocking i/o286
- Ubiquitousness236
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- Async15
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- JavaScript is the New PHP11
- Because I love functions11
- Like it or not, JS is part of the web standard10
- Expansive community9
- Everyone use it9
- Can be used in backend, frontend and DB9
- Easy9
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- No need to use PHP8
- For the good parts8
- Can be used both as frontend and backend as well8
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- Love-hate relationship7
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- 1.6K Can be used on frontend/backend6
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- What to add5
- Because it is so simple and lightweight4
- Only Programming language on browser4
- Test21
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- Hard to learn1
- Subskill #41
- Test1
- Hard 彤0
- A constant moving target, too much churn22
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- Javascript is the New PHP15
- No ability to monitor memory utilitization9
- Shows Zero output in case of ANY error8
- Thinks strange results are better than errors7
- Can be ugly6
- No GitHub3
- Slow2
related JavaScript posts
Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.
But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.
But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.
Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
- Distributed version control system1.4K
- Efficient branching and merging1.1K
- Fast959
- Open source845
- Better than svn726
- Great command-line application368
- Simple306
- Free291
- Easy to use232
- Does not require server222
- Distributed27
- Small & Fast22
- Feature based workflow18
- Staging Area15
- Most wide-spread VSC13
- Role-based codelines11
- Disposable Experimentation11
- Frictionless Context Switching7
- Data Assurance6
- Efficient5
- Just awesome4
- Github integration3
- Easy branching and merging3
- Compatible2
- Flexible2
- Possible to lose history and commits2
- Rebase supported natively; reflog; access to plumbing1
- Light1
- Team Integration1
- Fast, scalable, distributed revision control system1
- Easy1
- Flexible, easy, Safe, and fast1
- CLI is great, but the GUI tools are awesome1
- It's what you do1
- Phinx0
- Hard to learn16
- Inconsistent command line interface11
- Easy to lose uncommitted work9
- Worst documentation ever possibly made7
- Awful merge handling5
- Unexistent preventive security flows3
- Rebase hell3
- When --force is disabled, cannot rebase2
- Ironically even die-hard supporters screw up badly2
- Doesn't scale for big data1
related Git posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).
It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up
or vagrant reload
we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.
I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up
, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.
We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.
If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.
The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).
Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.
GitHub
- Open source friendly1.8K
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- Nice UI1.3K
- Great for team collaboration1.1K
- Easy setup867
- Issue tracker504
- Great community486
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- Pull request and features planning442
- Just works147
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- Network effect56
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- Organizations43
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- Developer Profiles34
- Git Powered Wikis32
- Great for collaboration30
- It's fun24
- Clean interface and good integrations23
- Community SDK involvement22
- Learn from others source code20
- Because: Git16
- It integrates directly with Azure14
- Standard in Open Source collab10
- Newsfeed10
- It integrates directly with Hipchat8
- Fast8
- Beautiful user experience8
- Easy to discover new code libraries7
- Smooth integration6
- Cloud SCM6
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- Graphs6
- Integrations6
- It's awesome6
- Quick Onboarding5
- Reliable5
- Remarkable uptime5
- CI Integration5
- Hands down best online Git service available5
- Uses GIT4
- Version Control4
- Simple but powerful4
- Unlimited Public Repos at no cost4
- Free HTML hosting4
- Security options4
- Loved by developers4
- Easy to use and collaborate with others4
- Ci3
- IAM3
- Nice to use3
- Easy deployment via SSH3
- Easy to use2
- Leads the copycats2
- All in one development service2
- Free private repos2
- Free HTML hostings2
- Easy and efficient maintainance of the projects2
- Beautiful2
- Easy source control and everything is backed up2
- IAM integration2
- Very Easy to Use2
- Good tools support2
- Issues tracker2
- Never dethroned2
- Self Hosted2
- Dasf1
- Profound1
- Owned by micrcosoft53
- Expensive for lone developers that want private repos37
- Relatively slow product/feature release cadence15
- API scoping could be better10
- Only 3 collaborators for private repos8
- Limited featureset for issue management3
- GitHub Packages does not support SNAPSHOT versions2
- Does not have a graph for showing history like git lens2
- No multilingual interface1
- Takes a long time to commit1
- Expensive1
related GitHub posts
I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.
I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!
I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.
Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.
Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.
With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.
If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.
StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.
Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!
#StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit
Python
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- It's lean and fun to code8
- Import antigravity8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- Although practicality beats purity6
- Flat is better than nested6
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- There should be one-- and preferably only one --obvious6
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- Great for analytics5
- Lists, tuples, dictionaries5
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- Web scraping4
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- Plotting4
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
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- It is Very easy , simple and will you be love programmi3
- Many types of collections3
- If the implementation is easy to explain, it may be a g3
- Batteries included2
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- A-to-Z2
- Because of Netflix2
- Only one way to do it2
- Better outcome2
- Sexy af1
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- Still divided between python 2 and python 353
- Performance impact28
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- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
- Cross-browser1.3K
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related jQuery posts
The client-side stack of Shopify Admin has been a long journey. It started with HTML templates, jQuery and Prototype. We moved to Batman.js, our in-house Single-Page-Application framework (SPA), in 2013. Then, we re-evaluated our approach and moved back to statically rendered HTML and vanilla JavaScript. As the front-end ecosystem matured, we felt that it was time to rethink our approach again. Last year, we started working on moving Shopify Admin to React and TypeScript.
Many things have changed since the days of jQuery and Batman. JavaScript execution is much faster. We can easily render our apps on the server to do less work on the client, and the resources and tooling for developers are substantially better with React than we ever had with Batman.
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I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.
I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).
As per my work experience and knowledge, I have chosen the followings stacks to this mission.
UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.
Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.
Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.
Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.
Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.
Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.
Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.
Happy Coding! Suggestions are welcome! :)
Thanks, Ganesa