DBMS > Google Cloud Datastore vs. Lovefield vs. RDF4J vs. SwayDB vs. TerarkDB

System Properties Comparison Google Cloud Datastore vs. Lovefield vs. RDF4J vs. SwayDB vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Embeddable relational database for web apps written in pure JavaScript

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

cloud.google.com/datastore

google.github.io/lovefield

rdf4j.org

swaydb.simer.au

github.com/bytedance/terarkdb

Technical documentation

cloud.google.com/datastore/docs

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

rdf4j.org/documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Google

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Simer Plaha

ByteDance, originally Terark

Initial release

2008

2014

2004

2018

2016

Current release

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GNU Affero GPL V3.0

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

JavaScript

Java

Scala

C++

Server operating systems

hosted

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes, details here

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-like query language (GQL)

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript

Java
PHP
Python

Java
Kotlin
Scala

C++
Java

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Isolation support depends on the API used

Atomic execution of operations

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 storage is supported as well

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)

More information provided by the system vendor

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