DBMS > Blazegraph vs. Couchbase vs. Google Cloud Firestore vs. LevelDB vs. TimesTen

System Properties Comparison Blazegraph vs. Couchbase vs. Google Cloud Firestore vs. LevelDB vs. TimesTen

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

In-Memory RDBMS compatible to Oracle

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

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

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

blazegraph.com

www.couchbase.com

firebase.google.com/products/firestore

github.com/google/leveldb

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

wiki.blazegraph.com

docs.couchbase.com

firebase.google.com/docs/firestore

github.com/google/leveldb/blob/main/doc/index.md

docs.oracle.com/database/timesten-18.1

Developer

Blazegraph

Couchbase, Inc.

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2006

2011

2017

2011

1998

Current release

2.1.5, March 2019

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

1.23, February 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

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

commercial

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++, Go and Erlang

C++

Server operating systems

Linux
OS X
Windows

hosted

Illumos
Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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

yes

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

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

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

yes, Firebase Rules & Cloud Functions

PL/SQL

Triggers

yes

via the TAP protocol

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

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

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

by means of logfiles and checkpoints

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

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

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

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