DBMS > Couchbase vs. Databricks vs. HBase vs. Lovefield vs. Sequoiadb

System Properties Comparison Couchbase vs. Databricks vs. HBase vs. Lovefield vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Embeddable relational database for web apps written in pure JavaScript

NewSQL database with distributed OLTP and SQL

Primary database model

Document store

Document store
Relational DBMS

Wide column store

Relational DBMS

Document store
Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

www.couchbase.com

www.databricks.com

hbase.apache.org

google.github.io/lovefield

www.sequoiadb.com

Technical documentation

docs.couchbase.com

docs.databricks.com

hbase.apache.org/book.html

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Couchbase, Inc.

Databricks

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Google

Sequoiadb Ltd.

Initial release

2011

2013

2008

2014

2013

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

2.3.4, January 2021

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

Java

JavaScript

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
Unix
Windows

using Cygwin

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

oid, date, timestamp, binary, regex

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

with Databricks SQL

SQL-like query language

via JavaScript builder pattern

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

proprietary protocol using JSON

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Python
R
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

user defined functions and aggregates

yes

Coprocessors in Java

JavaScript

Triggers

yes

via the TAP protocol

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Single row ACID (across millions of columns)

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Ephemeral buckets

yes

using MemoryDB

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

simple password-based access control

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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