Redis

How to Use The Redis To Implement CRUD Operations using Spring Boot

We might have developed CRUD operations multiple times using a relational database such as My SQL, SQL Server, Oracle etc. However, this time we are going to use a NoSQL Database which is different than a relational database. Here, we will use the Redis database to implement CRUD operations using Spring Boot. Traditionally, we use a relational database to work with an application. Of course, A relational database is a structured database and contains multiple tables to maintain meaningful relations between them.
 

#spring-boot #redis #crud 

How to Implement Redis Cache using Spring Boot Step by Step 2021

In this blog, we are going to answer all the questions asked above but predominately How Redis Cache implemented Using Spring Boot?.

#redis #spring-boot 

Implementing Redis Cache in Spring Boot For Beginner 2021

Many a time, we all come to a phase when our application does not perform well as it is expected to. Redis cache helps us by minimizing the number of network calls while accessing tha data from DB. In addition, we need to download Redis Server to make Redis Cache functional in the Spring Boot Application. Moreover, apart from Cache, Redis can also be used as a database and Message Broker.
 

#spring-boot #redis 

How to Configure Redis Queue (RQ) To Handle Running Tasks in A Flask

Learn How to Configure Redis Queue (RQ) To Handle Running Tasks in A Flask

By the end of this post you should be able to:

1. Integrate Redis Queue into a Flask app and create tasks.
2. Containerize Flask and Redis with Docker.
3. Run long-running tasks in the background with a separate worker process.
4. Set up RQ Dashboard to monitor queues, jobs, and workers.
5. Scale the worker count with Docker.

#flask #redis 

How Server-side Sessions Can Be Utilized in Flask and Redis

This article looks at how server-side sessions can be utilized in Flask with Flask-Session and Redis.

This article is part of a two-part series on how sessions can be used in Flask:

1. Client-side: Sessions in Flask
2. Server-side: Server-side Sessions in Flask with Redis

#redis #flask 

Serverless For Redis Database (Explain in 3 Minutes)

Amazon DynamoDB is a great serverless database but we need more options.

In this article, we will see another, Most Loved Database as per Stackoverflow survey, Redis, that can be integrated in a pure serverless way.

#database #redis #serverless 

Learn About Distributed Lock Using Redis And Java [ With Example ]

Let’s discuss first what we cache and how multiple instances were trying to reload cache at the same time. Our microservice act as a middleware to fetch the data from multiple third party service and convert that data to our own json contract and pass it to our mobile apps. So to avoid repeating network calls and reduce the latency we decided to cache master data that does not change too often with fixed TTL. As our service has multiple nodes running in the cloud and our service scale horizontally.

When multiple user requests trying to access the same data at the same time then each service node will try to reload the cache if cache is not present in the Redis.

#redis #Java 

AWS Introduces Amazon MemoryDB for Redis

Today, I am excited to announce the general availability of Amazon MemoryDB for Redis, a new Redis-compatible, durable, in-memory database. MemoryDB makes it easy and cost-effective to build applications that require microsecond read and single-digit millisecond write performance with data durability and high availability.

#aws #redis #database 

How to implement Redis Cache in Spring Boot Application? | Making Java easy to learn

This tutorial talks about “How To Implement Redis Cache In Spring Boot Application?”

https://javatechonline.com/how-to-implement-redis-cache-in-spring-boot-application/

Redis is an open source(BSD licensed) in-memory remote data structure store(database) that offers high performance, replication, and a unique data model. The full form of Redis is Remote Directory Server. Moreover, we can use it in multiple forms. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Spring Boot supports this feature via the dependency ‘Spring Data Redis’. In addition, we need to download Redis Server to make Redis Cache functional in the Spring Boot Application. Moreover, apart from Cache, Redis can also be used as a database and Message Broker. However, in  a real time application, Redis is popular for a Cache Manager as compared to database & Message Broker. Get more details.

#springboot #redis #java #spring-framework 

Learn Better Data-Driven Applications with Spring Boot and Redis

Redis is the swiss-army knife of databases and it integrates into Spring-based workloads with ease

#redis #spring #spring-boot 

How to Manage Session Data with Flask-Session & Redis For Beginner

Instead, we can use a cloud key/value store such as Redis, and leverage a plugin called Flask-Session. Flask-Session is a Flask plugin which enables the simple integration of a server-side cache leveraging methods such as Redis, Memcached, MongoDB, relational databases, and so forth. Of these choices, Redis is an exceptionally appealing option. Redis is NoSQL datastore written in C intended to temporarily hold data in memory for users as they blaze mindlessly through your site.

Redis was designed for this very purpose, is extremely quick, and is free to use when spinning up a free instance on Redis Labs.

#redis #flask #data 

ASP.NET Core Uses Redis Cache for Distributed Caching

In my previous article A Step by Step Guide to In-Memory Caching in ASP.NET Core, I covered the basics of in-memory caching and I have shown you a practical example of implementing caching ASP.NET Web API. In-memory caching is only useful when your application is deployed on a single server. If you are planning to deploy your application on multiple servers in a typical web farm scenario, then you need a centralized caching solution. There are many ways you can implement distributed caching in ASP.NET Core and in this tutorial, I will talk about one of the most popular distributed cache called Redis Cache with some practical examples.

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What Is Distributed Caching?

A distributed cache is a cache shared by multiple application servers. Typically, it is maintained as an external service accessible to all servers. Distributed cache improve application performance and scalability because it can provide same data to multiple servers consistently and if one server restarts or crashes, the cashed data is still available to other servers as normal.

#aspdotnet #redis

Real Time Table - CoinMarketCap Clone: Django Channels + Vue.js + Celery + Redis

The video is about how to create clone of CoinMarketCap or CoinGecko with Real Time constant updates. Every 30 seconds Django Channels application performs GET-requests to the CoinGecko API to get updated data, and then sends its response to all connected clients. I use Django Channels, Celery and Redis and Vue.js as frontend.

It's a complicated Django Channels, Celery and Redis project for advanced Django "users".

#django #redis #vue

Django Channels - Celery - Redis: Real Time Broadcasting API response App

The video is about how to build Real Time application, that performs a GET-request to the API every 3 seconds, and then sends its response to all connected clients. I use Django Channels, Celery and Redis.

It's a complicated Django Channels, Celery and Redis project for advanced Django "users".

In this video:
- how to integrate Celery to a Django project,
- how to integrate Celery with Redis,
- how to execute a Django task periodically with Celery,
- how to perform a request to an API, and use the response,
- how to send data from Celery task to a Channels Consumer
- how to broadcast messages.

#django #redis

Redis vs Kafka vs RabbitMQ

Which MicroServices Message Broker Should you Choose?

When using asynchronous communication for Microservices, it is common to use a message broker. A broker ensures communication between different microservices is reliable and stable, that the messages are managed and monitored within the system and that messages don’t get lost. There are a few message brokers you can choose from, varying in scale and data capabilities. This blog post will compare the three most popular brokers: RabbitMQ, Kafka and Redis.

Microservices Communication: Synchronous and Asynchronous

There are two common ways Microservices communicate with each other: Synchronous and Asynchronous. In a Synchronous communication, the caller waits for a response before sending the next message, and it operates as a REST protocol on top of HTTP. On the contrary, in an Asynchronous communication the messages are sent without waiting for a response. This is suited for distributed systems, and usually requires a message broker to manage the messages.

The type of communication you choose should consider different parameters, such as how you structure your Microservices, what infrastructure you have in place, latency, scale, dependencies and the purpose of the communication. Asynchronous communication may be more complicated to establish and requires adding more components to stack, but the advantages of using Asynchronous communication for Microservices outweigh the cons.

#microservices #nodejs #redis