DBMS > AgensGraph vs. BigObject vs. Datomic vs. Google BigQuery vs. HugeGraph

System Properties Comparison AgensGraph vs. BigObject vs. Datomic vs. Google BigQuery vs. HugeGraph

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Analytic DBMS for real-time computations and queries

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Graph DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

bitnine.net/agensgraph

bigobject.io

www.datomic.com

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

Technical documentation

bitnine.net/documentation

docs.bigobject.io

docs.datomic.com

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

Developer

Bitnine Global Inc.

BigObject, Inc.

Cognitect

Google

Baidu

Initial release

2016

2015

2012

2010

2018

Current release

2.1, December 2018

1.0.6735, June 2023

0.9

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free community edition available

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

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

Java, Clojure

Java

Server operating systems

Linux
OS X
Windows

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
macOS
Unix

Data scheme

depending on used data model

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.

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

Cypher Query Language
JDBC

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

C
Java
JavaScript
Python

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

Server-side scripts

Stored procedures

yes

Lua

yes

Transaction Functions

user defined functions

in JavaScript

asynchronous Gremlin script jobs

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

But extensive use of caching in the application peers

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

edges in graph

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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 external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

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