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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Cassandra vs Couchbase

Cassandra vs Couchbase

OverviewDecisionsComparisonAlternatives

Overview

Cassandra
Cassandra
Stacks3.6K
Followers3.5K
Votes507
GitHub Stars9.5K
Forks3.8K
Couchbase
Couchbase
Stacks505
Followers606
Votes110

Cassandra vs Couchbase: What are the differences?

Introduction

This markdown provides a comparison between Cassandra and Couchbase, highlighting the key differences between the two databases.

1. Data Model: Cassandra: Cassandra follows a column-oriented data model, where data is organized by columns into rows and tables. It offers a flexible schema design, allowing the addition or modification of columns without impacting existing data. It supports a wide range of data types.

Couchbase: Couchbase follows a document-oriented data model, storing data as JSON documents. It provides a flexible schema that allows changes to the document structure without affecting other documents. It supports nested and complex data structures.

2. Distribution and Scalability: Cassandra: Cassandra has a distributed architecture with no master node. It follows a peer-to-peer model, allowing it to distribute data across multiple nodes, providing high availability and scalability. It uses consistent hashing to distribute data evenly across the cluster.

Couchbase: Couchbase also has a distributed architecture without a master node. It uses a data partitioning technique called Vbuckets to distribute and replicate data across multiple nodes. Couchbase supports automatic data sharding and rebalancing for scalability.

3. Replication and Consistency: Cassandra: Cassandra offers configurable replication across multiple data centers for high availability and fault tolerance. It provides tunable consistency levels, allowing users to choose between strong or eventual consistency.

Couchbase: Couchbase supports data replication for fault tolerance and high availability. It provides various consistency models, including strong consistency for ACID compliance and eventual consistency for high performance.

4. Query Language: Cassandra: Cassandra uses CQL (Cassandra Query Language), a SQL-like language, for querying the data. It supports CRUD operations, secondary indexes, and batch processing. However, it does not provide support for joins across different tables.

Couchbase: Couchbase uses N1QL (pronounced as "nickel"), a SQL-based language, for querying data. N1QL supports CRUD operations, joins across multiple documents, and secondary indexes, enabling more flexible and complex queries.

5. Caching and In-Memory Processing: Cassandra: Cassandra does not have built-in caching mechanisms. However, it integrates with external caching solutions like Apache Ignite or Redis to improve read performance. Cassandra stores all the data on disk, limiting in-memory processing capabilities.

Couchbase: Couchbase provides built-in caching capabilities with its Memory-First architecture. It stores frequently accessed data in memory, reducing the data retrieval latency. This approach enables fast in-memory processing and improves overall performance.

6. Data Consistency and Conflict Resolution: Cassandra: In Cassandra, eventual consistency is the default consistency model, which means that updates may propagate asynchronously. It relies on conflict resolution during data synchronization and uses timestamps to resolve conflicts.

Couchbase: Couchbase offers different consistency models, including strong consistency for immediate consistency and eventual consistency for improved performance. It uses vector clocks to detect and resolve conflicts during data replication.

In summary, Cassandra follows a column-oriented data model, supports flexible schema design, and uses CQL for querying data. It has a distributed architecture, tunable consistency, and integrates with external caching solutions. On the other hand, Couchbase follows a document-oriented data model, supports JSON documents, and uses N1QL for querying. It also has a distributed architecture, different consistency models, built-in caching, and in-memory processing capabilities.

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Advice on Cassandra, Couchbase

Gabriel
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
Micha
Micha

CEO & Co-Founder at Dechea

May 27, 2022

Decided

Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.

Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.

93k views93k
Comments
Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

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

Detailed Comparison

Cassandra
Cassandra
Couchbase
Couchbase

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.

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.

-
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
9.5K
GitHub Stars
-
GitHub Forks
3.8K
GitHub Forks
-
Stacks
3.6K
Stacks
505
Followers
3.5K
Followers
606
Votes
507
Votes
110
Pros & Cons
Pros
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
Cons
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
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
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark

What are some alternatives to Cassandra, Couchbase?

MongoDB

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

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

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.

Microsoft SQL Server

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

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.

Memcached

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

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

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.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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