Couchbase vs ObjectBox

Overview

Couchbase

Stacks505

Followers606

Votes110

ObjectBox

Stacks9

Followers20

Votes0

Couchbase vs ObjectBox: What are the differences?

Introduction: Couchbase and ObjectBox are both popular databases but have key differences that set them apart.

Data Modeling: Couchbase utilizes a flexible JSON data model that allows for dynamic schema updates, making it suitable for agile development. On the other hand, ObjectBox employs a more traditional object-oriented data model that provides strong data consistency and relationships.
Querying: While Couchbase supports queries using N1QL (SQL for JSON), ObjectBox offers a simpler query API that focuses on efficiency and speed by leveraging index-optimized query execution.
Scalability: Couchbase is designed for horizontal scalability with built-in sharding and replication capabilities to handle large volumes of data across multiple nodes. ObjectBox, on the other hand, provides a lightweight and embedded solution that is more suited for local storage or edge computing scenarios.
Supported Platforms: Couchbase provides support for a wide range of platforms including cloud services, mobile devices, and IoT devices. ObjectBox primarily targets mobile and IoT applications with a focus on performance and minimal resource consumption.
Data Sync: Couchbase offers robust data synchronization capabilities with features like bi-directional conflict resolution, making it ideal for applications requiring seamless offline and online data access. ObjectBox has a sync solution but primarily focuses on local database needs without as much emphasis on sync functionality.
Community and Support: Couchbase has a larger community and offers comprehensive documentation, tutorials, and support resources for developers. ObjectBox, being a newer player in the market, has a growing community and support but may have fewer resources compared to Couchbase.

In Summary, Couchbase excels in scalability and data synchronization, while ObjectBox focuses on efficiency in querying and working well in local storage or edge computing scenarios.

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Advice on Couchbase, ObjectBox

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

Couchbase	ObjectBox
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.	It is for developers who look for performance and ease of use. We are committed to providing you with the easiest APIs for you to keep your code short and maintainable. No SQL under the hood-Simply faster. Unlike other databases, it has been built from the ground up using key-value storage instead of column storage. The resulting performance is 10x faster than the leading alternative, we welcome you to try it yourself. It is fast regardless of the amount of data or operating system you are using.
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	10x faster than any alternative; Reduces cloud costs up to 60%; Supports Linux, Windows, Mac/iOS, Android, Raspbian, etc. Embedded or Containerized; Develop applications that work on- and offline, independently from a constant Internet connection, providing an “always-on”-feeling; Accelerate time-to-market, save development and lifecycle costs, save precious developer time for tasks that bring value
Statistics
Stacks 505	Stacks 9
Followers 606	Followers 20
Votes 110	Votes 0
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	No community feedback yet
Integrations
Hadoop Kafka Elasticsearch Kubernetes Apache Spark	Linux Android OS macOS iOS Windows Raspbian

What are some alternatives to Couchbase, ObjectBox?

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.

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.

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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Couchbase vs ObjectBox: What are the differences?

Introduction: Couchbase and ObjectBox are both popular databases but have key differences that set them apart.

Data Modeling: Couchbase utilizes a flexible JSON data model that allows for dynamic schema updates, making it suitable for agile development. On the other hand, ObjectBox employs a more traditional object-oriented data model that provides strong data consistency and relationships.
Querying: While Couchbase supports queries using N1QL (SQL for JSON), ObjectBox offers a simpler query API that focuses on efficiency and speed by leveraging index-optimized query execution.
Scalability: Couchbase is designed for horizontal scalability with built-in sharding and replication capabilities to handle large volumes of data across multiple nodes. ObjectBox, on the other hand, provides a lightweight and embedded solution that is more suited for local storage or edge computing scenarios.
Supported Platforms: Couchbase provides support for a wide range of platforms including cloud services, mobile devices, and IoT devices. ObjectBox primarily targets mobile and IoT applications with a focus on performance and minimal resource consumption.
Data Sync: Couchbase offers robust data synchronization capabilities with features like bi-directional conflict resolution, making it ideal for applications requiring seamless offline and online data access. ObjectBox has a sync solution but primarily focuses on local database needs without as much emphasis on sync functionality.
Community and Support: Couchbase has a larger community and offers comprehensive documentation, tutorials, and support resources for developers. ObjectBox, being a newer player in the market, has a growing community and support but may have fewer resources compared to Couchbase.

In Summary, Couchbase excels in scalability and data synchronization, while ObjectBox focuses on efficiency in querying and working well in local storage or edge computing scenarios.