DBMS > BigObject vs. Ehcache vs. Google Cloud Firestore vs. JanusGraph vs. Microsoft SQL Server

System Properties Comparison BigObject vs. Ehcache vs. Google Cloud Firestore vs. JanusGraph vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

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.

A Graph DBMS optimized for distributed clusters

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

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

bigobject.io

www.ehcache.org

firebase.google.com/products/firestore

janusgraph.org

www.microsoft.com/en-us/sql-server

Technical documentation

docs.bigobject.io

www.ehcache.org/documentation

firebase.google.com/docs/firestore

docs.janusgraph.org

learn.microsoft.com/en-US/sql/sql-server

Developer

BigObject, Inc.

Terracotta Inc, owned by Software AG

Google

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Initial release

2015

2009

2017

1989

Current release

3.10.0, March 2022

0.6.3, February 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

hosted

Linux
OS X
Unix
Windows

Linux
Windows

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JCache

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

Java

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

Clojure
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

Lua

yes, Firebase Rules & Cloud Functions

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Cache Event Listeners

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Sharding

yes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

Multi-source replication

yes

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Relationships in graphs

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

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

yes

In-memory capabilities

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

yes

User concepts

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.

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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