Meteor Publication and Subscription

In 2011, when Meteor was not around, When u hit a site build on Rails, The client(i.e your browser) sends a request to your app, which lives on the server. The app finds out which data the client need, which could be of any size. Once the right data has been selected, the app then translates into human-readable HTML (or JSON in case of API).

Finally, the App takes the HTML code and sends it to the client’s browser. The app’s job is done here.

The Meteor Way

The feature that makes Meteor different from others is while Rails App only lives on Server, Meteor App lives both on Server and Client. Simply put, Meteor takes part of your database and copy it to client. This has two implications: Firstly, instead of sending HTML code to the client, a Meteor app will send raw data and let the client deal with it. Secondly, you’ll be able to access and even modify that data instataneously without having to wait for round-trip to the server(latency compensation).

Publishing

The App’s database can contain thousands of documents, some of which might contain sensitive and private data, So we cannot just mirror complete database to the client, for security and scalability reasons.

So we’ll need a way to tell the Meteor which subset of the data can be sent to the client which is accomplised through publications
[cc lang=”javascript”]
//on the server
Meteor.publish(‘posts’, function(author) {
return Posts.find({author: author});
});
[/cc]
The above method tells Meteor App to send Only those posts to the client which are written by author.

Subscribing

There can be thousands of authors who write posts on the site. We need a way for clients to specify which subset of that data is needed at any particular time, and that’s exactly where subscription comes in.

Any data you subscribe to will be mirrored on the client thanks to Minimongo, Meteor’s client-side implementation of MongoDB.

For example, let’s say we’re currently browsing Tom Cliff’s profile page, and only want to display his posts.

[cc lang=”javascript”]
// on the client
Meteor.subscribe(‘”posts’, ‘bob-smith’);
[/cc]

Posted

in

, ,

by

Meghna Gogna

Tags:

, ,

Recent Post

  • How to Implement In-Order, Pre-Order, and Post-Order Tree Traversal in Python?

    Tree traversal is an essential operation in many tree-based data structures. In binary trees, the most common traversal methods are in-order traversal, pre-order traversal, and post-order traversal. Understanding these tree traversal techniques is crucial for tasks such as tree searching, tree printing, and more complex operations like tree serialization. In this detailed guide, we will […]

  • Mastering Merge Sort: A Comprehensive Guide to Efficient Sorting

    Are you eager to enhance your coding skills by mastering one of the most efficient sorting algorithms? If so, delve into the world of merge sort in Python. Known for its powerful divide-and-conquer strategy, merge sort is indispensable for efficiently handling large datasets with precision. In this detailed guide, we’ll walk you through the complete […]

  • Optimizing Chatbot Performance: KPIs to Track Chatbot Accuracy

    In today’s digital age, chatbots have become integral to customer service, sales, and user engagement strategies. They offer quick responses, round-the-clock availability, and the ability to handle multiple users simultaneously. However, the effectiveness of a chatbot hinges on its accuracy and conversational abilities. Therefore, it is necessary to ensure your chatbot performs optimally, tracking and […]

  • Reinforcement Learning: From Q-Learning to Deep Q-Networks

    In the ever-evolving field of artificial intelligence (AI), Reinforcement Learning (RL) stands as a pioneering technique enabling agents (entities or software algorithms) to learn from interactions with an environment. Unlike traditional machine learning methods reliant on labeled datasets, RL focuses on an agent’s ability to make decisions through trial and error, aiming to optimize its […]

  • Understanding AI Predictions with LIME and SHAP- Explainable AI Techniques

    As artificial intelligence (AI) systems become increasingly complex and pervasive in decision-making processes, the need for explainability and interpretability in AI models has grown significantly. This blog provides a comprehensive review of two prominent techniques for explainable AI: Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These techniques enhance transparency and accountability by […]

  • Building and Deploying a Custom Machine Learning Model: A Comprehensive Guide

    Machine Learning models are algorithms or computational models that act as powerful tools. Simply put, a Machine Learning model is used to automate repetitive tasks, identify patterns, and derive actionable insights from large datasets. Due to these hyper-advanced capabilities of Machine Learning models, it has been widely adopted by industries such as finance and healthcare.  […]

Click to Copy