What is JSON and what are its top alternatives?
Top Alternatives to JSON
- YAML
A human-readable data-serialization language. It is commonly used for configuration files, but could be used in many applications where data is being stored or transmitted. ...
- Protobuf
Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler. ...
- Avro
It is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format. ...
- MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...
- OData
It is an ISO/IEC approved, OASIS standard that defines a set of best practices for building and consuming RESTful APIs. It helps you focus on your business logic while building RESTful APIs without having to worry about the various approaches to define request and response headers, status codes, HTTP methods, URL conventions, media types, payload formats, query options, etc. ...
- MessagePack
It is an efficient binary serialization format. It lets you exchange data among multiple languages like JSON. But it's faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. ...
- 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. ...
- 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. ...
JSON alternatives & related posts
YAML
related YAML posts
related Protobuf posts
We've already been monitoring Agones for a few years now, but we only adapted Kubernetes in mid 2021, so we could never use it until then. Transitioning to Kubernetes has overall been a blast. There's definitely a steep learning curve associated with it, but for us, it was certainly worth it. And Agones plays definitely a part in it.
We previously scheduled our game servers with Docker Compose and Docker Swarm, but that always felt a little brittle and like a really "manual" process, even though everything was already dockerized. For matchmaking, we didn't have any solution yet.
After we did tons of local testing, we deployed our first production-ready Kubernetes cluster with #kubespray and deployed Agones (with Helm) on it. The installation was very easy and the official chart had just the right amount of knobs for us!
The aspect, that we were the most stunned about, is how seamless Agones integrates into the Kubernetes infrastructure. It reuses existing mechanisms like the Health Pings and extends them with more resource states and other properties that are unique to game servers. But you're still free to use it however you like: One GameServer per Game-Session, one GameServer for multiple Game-Sessions (in parallel or reusing existing servers), custom allocation mechanisms, webhook-based scaling, ... we didn't run into any dead ends yet.
One thing, that I was a little worried about in the beginning, was the SDK integration, as there was no official one for Minecraft/Java. And the two available inofficial ones didn't satisfy our requirements for the SDK. Therefore, we went and developed our own SDK and ... it was super easy! Agones does publish their Protobuf files and so we could generate the stubs with #Protoc. The existing documentation regarding Client-SDKs from Agones was a great help in writing our own documentation for the interface methods.
And they even have excellent tooling for testing your own SDK implementations. With the use of Testcontainers we could just spin up the local SDK testing image for each of the integration tests and could confirm that our SDK is working fine. We discovered a very small inconsistency for one of the interface methods, submitted an issue and a corresponding PR and it was merged within less than 24 hours.
We've now been using Agones for a few months and it has proven to be very reliable, easy to manage and just a great tool in general.
related Avro posts
- Document-oriented storage828
- No sql593
- Ease of use553
- Fast464
- High performance410
- Free255
- Open source218
- Flexible180
- Replication & high availability145
- Easy to maintain112
- Querying42
- Easy scalability39
- Auto-sharding38
- High availability37
- Map/reduce31
- Document database27
- Easy setup25
- Full index support25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Support for many languages through different drivers4
- Schemaless3
- Aggregation Framework3
- Drivers support is good3
- Fast2
- Managed service2
- Easy to Scale2
- Awesome2
- Consistent2
- Good GUI1
- Acid Compliant1
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language2
related MongoDB posts
Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.
We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient
Based on the above criteria, we selected the following tools to perform the end to end data replication:
We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.
We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.
In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.
Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.
In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!
We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.
As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).
When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.
- Patterns for paging, sorting, filtering7
- ISO Standard5
- Query Language4
- RESTful3
- No overfetching, no underfetching3
- Get many resources in a single request2
- Self-documenting2
- Batch requests2
- Bulk requests ("array upsert")2
- Ask for what you need, get exactly that2
- Evolve your API by following the compatibility rules1
- Resource model defines conventional operations1
- Resource Modification Language1
- Overwhelming, no "baby steps" documentation1
related OData posts
- Lightweight1
related MessagePack posts
JavaScript
- Can be used on frontend/backend1.7K
- It's everywhere1.5K
- Lots of great frameworks1.2K
- Fast898
- Light weight746
- Flexible425
- You can't get a device today that doesn't run js392
- Non-blocking i/o286
- Ubiquitousness237
- Expressive191
- Extended functionality to web pages55
- Relatively easy language49
- Executed on the client side46
- Relatively fast to the end user30
- Pure Javascript25
- Functional programming21
- Async15
- Full-stack13
- Future Language of The Web12
- Setup is easy12
- Its everywhere12
- Because I love functions11
- JavaScript is the New PHP11
- Like it or not, JS is part of the web standard10
- Easy9
- Can be used in backend, frontend and DB9
- Expansive community9
- Everyone use it9
- Easy to hire developers8
- Most Popular Language in the World8
- For the good parts8
- Can be used both as frontend and backend as well8
- No need to use PHP8
- Powerful8
- Evolution of C7
- Its fun and fast7
- It's fun7
- Nice7
- Versitile7
- Hard not to use7
- Popularized Class-Less Architecture & Lambdas7
- Agile, packages simple to use7
- Supports lambdas and closures7
- Love-hate relationship7
- Photoshop has 3 JS runtimes built in7
- 1.6K Can be used on frontend/backend6
- Client side JS uses the visitors CPU to save Server Res6
- It let's me use Babel & Typescript6
- Easy to make something6
- Can be used on frontend/backend/Mobile/create PRO Ui6
- Client processing5
- What to add5
- Everywhere5
- Scope manipulation5
- Function expressions are useful for callbacks5
- Stockholm Syndrome5
- Promise relationship5
- Clojurescript5
- Only Programming language on browser4
- Because it is so simple and lightweight4
- Easy to learn and test1
- Easy to understand1
- Not the best1
- Subskill #41
- Hard to learn1
- Test21
- Test1
- Easy to learn1
- Hard 彤0
- A constant moving target, too much churn22
- Horribly inconsistent20
- 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
- HORRIBLE DOCUMENTS, faulty code, repo has bugs0
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
Python
- Great libraries1.2K
- Readable code963
- Beautiful code847
- Rapid development788
- Large community691
- Open source438
- Elegant393
- Great community282
- Object oriented273
- Dynamic typing221
- Great standard library77
- Very fast60
- Functional programming55
- Easy to learn50
- Scientific computing46
- Great documentation35
- Productivity29
- Matlab alternative28
- Easy to read28
- Simple is better than complex24
- It's the way I think20
- Imperative19
- Very programmer and non-programmer friendly18
- Free18
- Machine learning support17
- Powerfull language17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- Import antigravity8
- It's lean and fun to code8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- High Documented language6
- I love snakes6
- Readability counts6
- Rapid Prototyping6
- Now is better than never6
- Although practicality beats purity6
- Flat is better than nested6
- Great for tooling6
- There should be one-- and preferably only one --obvious6
- Fast coding and good for competitions6
- Web scraping5
- Lists, tuples, dictionaries5
- Great for analytics5
- Beautiful is better than ugly4
- Easy to learn and use4
- Easy to setup and run smooth4
- Multiple Inheritence4
- CG industry needs4
- Socially engaged community4
- Complex is better than complicated4
- Plotting4
- Simple and easy to learn4
- List comprehensions3
- Powerful language for AI3
- Flexible and easy3
- 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
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- No cruft3
- Generators3
- Import this3
- Batteries included2
- Securit2
- Can understand easily who are new to programming2
- Should START with this but not STICK with This2
- A-to-Z2
- Because of Netflix2
- Only one way to do it2
- Better outcome2
- Good for hacking2
- Best friend for NLP1
- Sexy af1
- Procedural programming1
- Automation friendly1
- Slow1
- Keep it simple0
- Powerful0
- Ni0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- 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