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

Couchbase vs YugabyteDB

OverviewDecisionsComparisonAlternatives

Overview

Couchbase
Couchbase
Stacks505
Followers606
Votes110
YugabyteDB
YugabyteDB
Stacks50
Followers114
Votes1
GitHub Stars9.9K
Forks1.2K

Couchbase vs YugabyteDB: What are the differences?

Introduction

Couchbase and YugabyteDB are two popular distributed databases that provide high availability and scalability for managing large volumes of data. While they have some similarities, there are key differences that set them apart.

  1. Data Model: Couchbase is a NoSQL database that follows a key-value data model, where data is stored in JSON documents. On the other hand, YugabyteDB is a distributed SQL database that supports the relational data model, with tables, columns, and structured query language (SQL) capabilities.

  2. Scalability: Both Couchbase and YugabyteDB offer horizontal scalability, allowing you to add more nodes to handle increased data and traffic. However, Couchbase leverages a sharding mechanism to distribute data across nodes, while YugabyteDB uses a distributed transactional layer that automatically distributes data with consistency guarantees.

  3. Consistency Model: Couchbase supports tunable consistency, where you can choose between strong, eventual, or other levels of consistency based on your application requirements. In contrast, YugabyteDB follows a strongly consistent distributed ACID (Atomicity, Consistency, Isolation, Durability) model by default, ensuring data integrity and correctness across multiple nodes.

  4. Deployment Flexibility: Couchbase offers both cloud-based and on-premises deployment options, providing flexibility based on your infrastructure preferences. On the other hand, YugabyteDB leverages a cloud-native architecture and is designed to be deployed on cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Kubernetes, but can also be deployed on-premises.

  5. Multi-Model Support: Couchbase provides a multi-model approach, allowing you to store, retrieve, and query data using different models like key-value, document, and full-text search. In contrast, YugabyteDB focuses on the relational SQL model, with support for distributed transactions, secondary indexes, and joins.

  6. Operational Simplicity: Couchbase offers a comprehensive management interface and toolset for easy cluster setup, administration, monitoring, and troubleshooting. YugabyteDB also provides management tools, but it emphasizes an effortless, self-managing experience with automated placement, healing, and scaling of data across nodes.

In summary, Couchbase and YugabyteDB differ in their data models, scalability mechanisms, consistency models, deployment options, supported data models, and operational simplicity. These differences allow you to choose the database that best suits your specific application requirements and infrastructure preferences.

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Advice on Couchbase, YugabyteDB

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
Mike
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

Couchbase
Couchbase
YugabyteDB
YugabyteDB

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.

An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.

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
Resilience; High Performance; Scalability; Enterprise Grade; Cloud-native; Kubernetes; PostgreSQL-compatible; Geo-Distributed; Hybrid Cloud
Statistics
GitHub Stars
-
GitHub Stars
9.9K
GitHub Forks
-
GitHub Forks
1.2K
Stacks
505
Stacks
50
Followers
606
Followers
114
Votes
110
Votes
1
Pros & Cons
Pros
  • 18
    High performance
  • 18
    Flexible data model, easy scalability, extremely fast
  • 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
Pros
  • 1
    Compatible with the result of pg_dump
Integrations
Hadoop
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Golang
Golang
PHP
PHP
Java
Java
Python
Python
Spring Boot
Spring Boot
Apache Spark
Apache Spark
Node.js
Node.js
C#
C#
Kubernetes
Kubernetes
Ruby
Ruby

What are some alternatives to Couchbase, YugabyteDB?

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.

Cassandra

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

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.

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