DBMS > Apache Druid vs. Couchbase vs. EventStoreDB vs. HEAVY.AI vs. Lovefield

System Properties Comparison Apache Druid vs. Couchbase vs. EventStoreDB vs. HEAVY.AI vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS
Time Series DBMS

Document store

Event 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

Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

druid.apache.org

www.couchbase.com

www.eventstore.com

github.com/heavyai/heavydb
www.heavy.ai

google.github.io/lovefield

Technical documentation

druid.apache.org/docs/latest/design

docs.couchbase.com

developers.eventstore.com

docs.heavy.ai

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

Developer

Apache Software Foundation and contributors

Couchbase, Inc.

Event Store Limited

HEAVY.AI, Inc.

Google

Initial release

2012

2011

2012

2016

2014

Current release

29.0.1, April 2024

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

21.2, February 2021

5.10, January 2022

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

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

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C, C++, Go and Erlang

C++ and CUDA

JavaScript

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux
Windows

Linux

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

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

schema-less columns are supported

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

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

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

JDBC
ODBC
Thrift
Vega

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Server-side scripts

Stored procedures

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

Triggers

yes

via the TAP protocol

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Automatic Sharding

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

fine grained access rights according to SQL-standard

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