Overview

Kafka

Stacks24.0K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Couchbase

Stacks480

Followers606

Votes110

Couchbase vs Kafka: What are the differences?

Introduction

Couchbase and Kafka are both widely used technologies in the field of data management and processing. While they serve different purposes, there are key differences between the two. This Markdown code provides an overview of the differences between Couchbase and Kafka in a concise and structured format for website usage.

Data Storage and Management: Couchbase is a NoSQL database that offers flexible document-based data storage and management capabilities. It allows for schema-less data modeling and supports CRUD operations. On the other hand, Kafka is a distributed streaming platform that provides a fault-tolerant, high-throughput, and scalable system for handling real-time data streams. Kafka stores data as immutable streams of records, using a publish-subscribe model.
Data Processing Model: Couchbase primarily focuses on providing fast and efficient data retrieval, storage, and querying capabilities. It supports various querying mechanisms, including N1QL (SQL-like query language). Kafka, on the other hand, is designed for data streaming and processing. It enables real-time processing of data streams, allowing for data transformation, filtering, and analysis in a distributed manner.
Data Scalability and Replication: Couchbase offers built-in data replication and automatic data sharding for scalable and high-availability deployments. It uses a distributed architecture to ensure data durability and fault tolerance. Kafka, on the other hand, provides distributed messaging and storage capabilities, allowing for horizontally scalable deployments. It uses topic partitions and replication to achieve fault tolerance and provide scalable data processing.
Data Persistence and Durability: Couchbase stores data persistently on disk, providing durability for long-term data storage. It ensures durability through replica placement and replication across multiple nodes. Kafka, while it also persists data to disk, does not focus on long-term data storage. It primarily serves as a streaming platform, where data is stored temporarily for real-time processing and analysis.
Data Consistency and Concurrency: Couchbase offers strong consistency guarantees, ensuring that all clients accessing the data see the most recent update. It supports concurrent access and provides a flexible data consistency model. In contrast, Kafka provides eventual consistency. It allows for high-concurrency data streaming and processing while ensuring that data is eventually replicated to all subscribers.
Data Use Cases: Couchbase is suitable for a wide range of use cases, including high-performance web applications, caching layers, session stores, and real-time analytics. It provides a flexible data model and robust querying capabilities. Kafka, on the other hand, is ideal for building real-time streaming data pipelines, event-driven architectures, fault-tolerant messaging systems, and data processing applications. It excels in scenarios that require real-time data ingestion, processing, and analysis.

In summary, Couchbase is a NoSQL database that focuses on data storage, retrieval, and management, offering flexible data modeling and querying capabilities. Kafka, on the other hand, is a distributed streaming platform designed for real-time data streaming, processing, and analysis. Couchbase provides strong consistency, while Kafka prioritizes high-throughput data streaming and eventual consistency. Both technologies have their unique use cases and strengths in the data management and processing landscape.

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Advice on Kafka, Couchbase

Gabriel

CEO at Naologic

Nov 2, 2020

Decided

After using couchbase for over 4 years, we migrated to MongoDB and that was the best decision ever! I'm very disappointed with Couchbase's technical performance. Even though we received enterprise support and were a listed Couchbase Partner, the experience was horrible. With every contact, the sales team was trying to get me on a $7k+ license for access to features all other open source NoSQL databases get for free.

Here's why you should not use Couchbase

Full-text search Queries The full-text search often returns a different number of results if you run the same query multiple types

N1QL queries Configuring the indexes correctly is next to impossible. It's poorly documented and nobody seems to know what to do, even the Couchbase support engineers have no clue what they are doing.

Community support I posted several problems on the forum and I never once received a useful answer

Enterprise support It's very expensive. $7k+. The team constantly tried to get me to buy even though the community edition wasn't working great

Autonomous Operator It's actually just a poorly configured Kubernetes role that no matter what I did, I couldn't get it to work. The support team was useless. Same lack of documentation. If you do get it to work, you need 6 servers at least to meet their minimum requirements.

Couchbase cloud Typical for Couchbase, the user experience is awful and I could never get it to work.

Minimum requirements The minimum requirements in production are 6 servers. On AWS the calculated monthly cost would be ~$600. We achieved better performance using a $16 MongoDB instance on the Mongo Atlas Cloud

writing queries is a nightmare While N1QL is similar to SQL and it's easier to write because of the familiarity, that isn't entirely true. The "smart index" that Couchbase advertises is not smart at all. Creating an index with 5 fields, and only using 4 of them won't result in Couchbase using the same index, so you have to create a new one.

Couchbase UI The UI that comes with every database deployment is full of bugs, barely functional and the developer experience is poor. When I asked Couchbase about it, they basically said they don't care because real developers use SQL directly from code

Consumes too much RAM Couchbase is shipped with a smaller Memcached instance to handle the in-memory cache. Memcached ends up using 8 GB of RAM for 5000 documents! I'm not kidding! We had less than 5000 docs on a Couchbase instance and less than 20 indexes and RAM consumption was always over 8 GB

Memory allocations are useless I asked the Couchbase team a question: If a bucket has 1 GB allocated, what happens when I have more than 1GB stored? Does it overflow? Does it cache somewhere? Do I get an error? I always received the same answer: If you buy the Couchbase enterprise then we can guide you.

247k views247k

Comments

Gabriel

CEO at Naologic

Jan 2, 2020

Decidedon

CouchDB

Couchbase

Memcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

Kafka	Couchbase
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
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	JSON document database; N1QL (SQL-like query language); Secondary Indexing; Full-Text Indexing; Eventing/Triggers; Real-Time Analytics; Mobile Synchronization for offline support; Autonomous Operator for Kubernetes and OpenShift
Statistics
GitHub Stars 31.2K	GitHub Stars -
GitHub Forks 14.8K	GitHub Forks -
Stacks 24.0K	Stacks 480
Followers 22.3K	Followers 606
Votes 607	Votes 110
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 18 Flexible data model, easy scalability, extremely fast 18 High performance 9 Mobile app support 7 You can query it with Ansi-92 SQL 6 All nodes can be read/write Cons 3 Terrible query language
Integrations
No integrations available	Hadoop Elasticsearch Kubernetes Apache Spark

What are some alternatives to Kafka, Couchbase?

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.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

RabbitMQ

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

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.