DBMS > Blazegraph vs. Couchbase vs. InfluxDB vs. Lovefield vs. YottaDB

System Properties Comparison Blazegraph vs. Couchbase vs. InfluxDB vs. Lovefield vs. YottaDB

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

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

DBMS for storing time series, events and metrics

Embeddable relational database for web apps written in pure JavaScript

A fast and solid embedded Key-value store

Primary database model

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

with GEO package

Relational DBMS

using the Octo plugin

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

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

blazegraph.com

www.couchbase.com

www.influxdata.com/products/influxdb-overview

google.github.io/lovefield

yottadb.com

Technical documentation

wiki.blazegraph.com

docs.couchbase.com

docs.influxdata.com/influxdb

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

yottadb.com/resources/documentation

Developer

Blazegraph

Couchbase, Inc.

Google

YottaDB, LLC

Initial release

2006

2011

2013

2014

2001

Current release

2.1.5, March 2019

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

2.7.6, April 2024

2.1.12, February 2017

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

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

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache 2.0

Open Source

AGPL 3.0

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

JavaScript

Server operating systems

Linux
OS X
Windows

Linux
OS X

through Homebrew

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

tested with Chrome, Firefox, IE, Safari

Docker
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

Numeric data and Strings

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

SPARQL is used as query language

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

SQL-like query language

via JavaScript builder pattern

by using the Octo plugin

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

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

HTTP API
JSON over UDP

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

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

Automatic Sharding

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

in enterprise version only

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

Transaction concepts

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

ACID

optimistic locking

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

Depending on used storage engine

yes

using MemoryDB

yes

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

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

simple rights management via user accounts

Users and groups based on OS-security mechanisms

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