DBMS > EJDB vs. GBase vs. Google Cloud Bigtable vs. TigerGraph

System Properties Comparison EJDB vs. GBase vs. Google Cloud Bigtable vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Document store

Relational DBMS

Key-value store
Wide column store

Graph DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

github.com/Softmotions/ejdb

www.gbase.cn

cloud.google.com/bigtable

www.tigergraph.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/bigtable/docs

docs.tigergraph.com

Developer

Softmotions

General Data Technology Co., Ltd.

Google

Initial release

2012

2004

2015

2017

Current release

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

GPLv2

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, Java, Python

C++

Server operating systems

server-less

Linux

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

Standard with numerous extensions

SQL-like query language (GSQL)

APIs and other access methods

in-process shared library

ADO.NET
C API
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Relationships in graphs

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

yes

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

Role-based access control

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