DBMS > Brytlyt vs. GBase vs. Google Cloud Firestore vs. JanusGraph vs. TimesTen

System Properties Comparison Brytlyt vs. GBase vs. Google Cloud Firestore vs. JanusGraph vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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

In-Memory RDBMS compatible to Oracle

Primary database model

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

brytlyt.io

www.gbase.cn

firebase.google.com/products/firestore

janusgraph.org

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

Technical documentation

docs.brytlyt.io

firebase.google.com/docs/firestore

docs.janusgraph.org

docs.oracle.com/database/timesten-18.1

Developer

Brytlyt

General Data Technology Co., Ltd.

Google

Linux Foundation; originally developed as Titan by Aurelius

Oracle, TimesTen Performance Software, HP

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

Initial release

2016

2004

2017

1998

Current release

5.0, August 2023

GBase 8a, GBase 8s, GBase 8c

0.6.3, February 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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, C++ and CUDA

C, Java, Python

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
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

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

Standard with numerous extensions

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
C API
JDBC
ODBC

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

Clojure
Java
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

yes, Firebase Rules & Cloud Functions

yes

PL/SQL

Triggers

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication

yes

Multi-source replication
Source-replica replication

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

Immediate Consistency

Eventual Consistency
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

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

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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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More resources

DB-Engines blog posts

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12 December 2019, Paul Andlinger

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