Kafka vs Realm React Native

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Realm React Native

Stacks45

Followers167

Votes1

GitHub Stars6.0K

Forks607

Kafka vs Realm React Native: What are the differences?

Introduction

Kafka and Realm React Native are two different technologies used in the development of software applications. While Kafka is a distributed event streaming platform, Realm React Native is a mobile database and synchronization framework.

Scalability: Kafka is designed for high scalability and can handle large amounts of data and high throughput. It is capable of handling real-time data feeds from multiple sources and distributing them to multiple consumers. On the other hand, Realm React Native is not specifically designed for scalability but provides a lightweight and efficient approach for local data storage and synchronization in mobile applications.
Use Cases: Kafka is commonly used in scenarios that involve real-time streaming of data, such as data ingestion, messaging systems, log aggregation, and real-time analytics. It is suitable for applications that require processing of large volumes of data in real-time. Realm React Native, on the other hand, is primarily used for mobile application development where data storage and synchronization across multiple devices is a requirement.
Implementation Complexity: Kafka requires a certain level of expertise in distributed systems and real-time data processing. Its setup and configuration can be complex, and it may require additional infrastructure resources to handle large data volumes effectively. In contrast, Realm React Native offers a simpler implementation approach, as it provides an easy-to-use API for storing and synchronizing data in mobile applications.
Data Persistence: Kafka is typically used for storing and processing real-time streaming data and does not provide built-in persistence for data storage. It relies on external data storage systems to store and retrieve data. In comparison, Realm React Native offers built-in persistence for mobile applications, allowing data to be stored locally on the device and providing offline access to synchronized data.
Integrations: Kafka has robust integrations with various data processing frameworks and tools, allowing seamless integration into existing data pipelines and analytics platforms. It can be integrated with popular technologies like Apache Hadoop, Apache Spark, and Apache Flink. On the other hand, Realm React Native provides integrations with React Native, allowing developers to leverage the benefits of the React Native ecosystem for mobile application development.
Concurrency and Consistency: Kafka guarantees the ordering of events within a partition but does not provide strong consistency guarantees across multiple partitions. It is designed for high throughput and low latency but sacrifices strong consistency for scalability. Realm React Native, on the other hand, provides strong consistency for data synchronization across multiple devices, ensuring that all devices have the most up-to-date data.

In summary, Kafka is a distributed event streaming platform designed for scalability and real-time data processing, while Realm React Native is a mobile database and synchronization framework focused on local data storage and synchronization in mobile applications. Kafka has a higher implementation complexity and requires external data storage, whereas Realm React Native offers a simpler implementation approach and provides built-in persistence.

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Advice on Kafka, Realm React Native

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k

Comments

Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k

Comments

Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

Reviewon

Kafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are:

you have to deal with Zookeeper
you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

10.9k views10.9k

Comments

Detailed Comparison

Kafka	Realm React Native
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Realm JavaScript enables you to efficiently write your app’s model layer in a safe, persisted and fast way. It’s designed to work with React Native and Node.js.
Written at LinkedIn in Scala;Used by LinkedIn to offload processing of all page and other views;Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled);Supports both on-line as off-line processing	-
Statistics
GitHub Stars 31.2K	GitHub Stars 6.0K
GitHub Forks 14.8K	GitHub Forks 607
Stacks 24.2K	Stacks 45
Followers 22.3K	Followers 167
Votes 607	Votes 1
Pros & Cons
Pros 126 High-throughput 119 Distributed 92 Scalable 86 High-Performance 66 Durable Cons 32 Non-Java clients are second-class citizens 29 Needs Zookeeper 9 Operational difficulties 5 Terrible Packaging	Pros 1 Reactive Database
Integrations
No integrations available	React Native

What are some alternatives to Kafka, Realm React Native?

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

Related Comparisons

Kafka vs Realm React Native: What are the differences?

Introduction

Scalability: Kafka is designed for high scalability and can handle large amounts of data and high throughput. It is capable of handling real-time data feeds from multiple sources and distributing them to multiple consumers. On the other hand, Realm React Native is not specifically designed for scalability but provides a lightweight and efficient approach for local data storage and synchronization in mobile applications.
Use Cases: Kafka is commonly used in scenarios that involve real-time streaming of data, such as data ingestion, messaging systems, log aggregation, and real-time analytics. It is suitable for applications that require processing of large volumes of data in real-time. Realm React Native, on the other hand, is primarily used for mobile application development where data storage and synchronization across multiple devices is a requirement.
Implementation Complexity: Kafka requires a certain level of expertise in distributed systems and real-time data processing. Its setup and configuration can be complex, and it may require additional infrastructure resources to handle large data volumes effectively. In contrast, Realm React Native offers a simpler implementation approach, as it provides an easy-to-use API for storing and synchronizing data in mobile applications.
Data Persistence: Kafka is typically used for storing and processing real-time streaming data and does not provide built-in persistence for data storage. It relies on external data storage systems to store and retrieve data. In comparison, Realm React Native offers built-in persistence for mobile applications, allowing data to be stored locally on the device and providing offline access to synchronized data.
Integrations: Kafka has robust integrations with various data processing frameworks and tools, allowing seamless integration into existing data pipelines and analytics platforms. It can be integrated with popular technologies like Apache Hadoop, Apache Spark, and Apache Flink. On the other hand, Realm React Native provides integrations with React Native, allowing developers to leverage the benefits of the React Native ecosystem for mobile application development.
Concurrency and Consistency: Kafka guarantees the ordering of events within a partition but does not provide strong consistency guarantees across multiple partitions. It is designed for high throughput and low latency but sacrifices strong consistency for scalability. Realm React Native, on the other hand, provides strong consistency for data synchronization across multiple devices, ensuring that all devices have the most up-to-date data.

Kafka vs Realm React Native

Overview

Kafka vs Realm React Native: What are the differences?