DBMS > Google Cloud Spanner vs. JanusGraph vs. Lovefield vs. MonetDB vs. MySQL

System Properties Comparison Google Cloud Spanner vs. JanusGraph vs. Lovefield vs. MonetDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Embeddable relational database for web apps written in pure JavaScript

A relational database management system that stores data in columns

Widely used open source RDBMS

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

cloud.google.com/spanner

janusgraph.org

google.github.io/lovefield

www.monetdb.org

www.mysql.com

Technical documentation

cloud.google.com/spanner/docs

docs.janusgraph.org

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

www.monetdb.org/Documentation

dev.mysql.com/doc

Developer

Google

Linux Foundation; originally developed as Titan by Aurelius

Google

MonetDB BV

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2017

2014

2004

1995

Current release

0.6.3, February 2023

2.1.12, February 2017

Dec2023 (11.49), December 2023

8.3.0, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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

JavaScript

C and C++

Server operating systems

hosted

Linux
OS X
Unix
Windows

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

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like query language

via JavaScript builder pattern

yes

SQL 2003 with some extensions

yes

with proprietary extensions

APIs and other access methods

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

Clojure
Java
Python

JavaScript

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

yes

proprietary syntax

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

Sharding via remote tables

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

yes

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Relationships in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Strict serializable isolation

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

using MemoryDB

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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