DBMS > BoltDB vs. Couchbase vs. Oracle Coherence vs. RocksDB vs. Vertica

System Properties Comparison BoltDB vs. Couchbase vs. Oracle Coherence vs. RocksDB vs. Vertica

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

Oracles in-memory data grid solution

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

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
Time Series DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

github.com/boltdb/bolt

www.couchbase.com

www.oracle.com/java/coherence

rocksdb.org

www.vertica.com

Technical documentation

docs.couchbase.com

docs.oracle.com/en/middleware/standalone/coherence

github.com/facebook/rocksdb/wiki

vertica.com/documentation

Developer

Couchbase, Inc.

Oracle

Facebook, Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2013

2011

2007

2013

2005

Current release

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

14.1, August 2023

8.11.4, April 2024

12.0.3, January 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

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

commercial

Open Source

BSD

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

All OS with a Java VM

Linux

Data scheme

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

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

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

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

JCache
JPA
RESTful HTTP API

C++ API
Java API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
C++
Java

C
C++
Go
Java
Perl
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

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

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via the TAP protocol

yes

Live Events

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

horizontal partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

including cross data center replication
Source-replica replication

yes, with selectable consistency level

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

configurable

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

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

authentification to access the cache via certificates or http basic authentication

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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