DBMS > EsgynDB vs. Faircom DB vs. Google BigQuery vs. Lovefield vs. OrientDB

System Properties Comparison EsgynDB vs. Faircom DB vs. Google BigQuery vs. Lovefield vs. OrientDB

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Large scale data warehouse service with append-only tables

Embeddable relational database for web apps written in pure JavaScript

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Website

www.esgyn.cn

www.faircom.com/products/faircom-db

cloud.google.com/bigquery

google.github.io/lovefield

orientdb.org

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cloud.google.com/bigquery/docs

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

www.orientdb.com/docs/last/index.html

Developer

Esgyn

FairCom Corporation

Google

OrientDB LTD; CallidusCloud; SAP

Initial release

2015

1979

2010

2014

2010

Current release

V12, November 2020

2.1.12, February 2017

3.2.29, March 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

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

C++, Java

ANSI C, C++

JavaScript

Java

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

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

tested with Chrome, Firefox, IE, Safari

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema free, schema optional, schema required, partial schema,

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes

yes, ANSI SQL with proprietary extensions

yes

SQL-like query language

via JavaScript builder pattern

SQL-like query language, no joins

APIs and other access methods

ADO.NET
JDBC
ODBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

RESTful HTTP/JSON API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

Java, Javascript

Triggers

yes

Using read-only observers

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

Fine grained access rights according to SQL-standard with additional protections for files

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles; record level security configurable

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.