DBMS > AlaSQL vs. Google BigQuery vs. Graph Engine vs. HugeGraph vs. jBASE

System Properties Comparison AlaSQL vs. Google BigQuery vs. Graph Engine vs. HugeGraph vs. jBASE

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A fast-speed and highly-scalable Graph DBMS

A robust multi-value DBMS comprising development tools and middleware

Primary database model

Document store
Relational DBMS

Relational DBMS

Graph DBMS
Key-value store

Graph DBMS

Multivalue DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Website

alasql.org

cloud.google.com/bigquery

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Andrey Gershun & Mathias R. Wulff

Google

Microsoft

Baidu

Rocket Software (formerly Zumasys)

Initial release

2014

2010

2018

1991

Current release

0.9

5.7

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

MIT License

Open Source

Apache Version 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

JavaScript

.NET and C

Java

Server operating systems

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

hosted

.NET

Linux
macOS
Unix

AIX
Linux
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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

also supports composite index and range index

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

Embedded SQL for jBASE in BASIC

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

Groovy
Java
Python

.Net
Basic
Jabbascript
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

asynchronous Gremlin script jobs

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Access rights can be defined down to the item level

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