DBMS > AlaSQL vs. Google BigQuery vs. Graph Engine vs. Postgres-XL vs. WakandaDB

System Properties Comparison AlaSQL vs. Google BigQuery vs. Graph Engine vs. Postgres-XL vs. WakandaDB

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store
Relational DBMS

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Object oriented DBMS

Secondary database models

Document store
Spatial 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	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

alasql.org

cloud.google.com/bigquery

www.graphengine.io

www.postgres-xl.org

wakanda.github.io

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

www.postgres-xl.org/documentation

wakanda.github.io/doc

Developer

Andrey Gershun & Mathias R. Wulff

Google

Microsoft

Wakanda SAS

Initial release

2014

2010

2014

since 2012, originally named StormDB

2012

Current release

10 R1, October 2018

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

MIT License

Open Source

Mozilla public license

Open Source

AGPLv3, extended commercial license available

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

C++, JavaScript

Server operating systems

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

hosted

.NET

Linux
macOS

Linux
OS X
Windows

Data scheme

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

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

yes

distributed, parallel query execution

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

RESTful HTTP API

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

RESTful HTTP API

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

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

MVCC

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)

fine grained access rights according to SQL-standard

yes

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