DBMS > Google BigQuery vs. Graph Engine vs. mSQL vs. NSDb

System Properties Comparison Google BigQuery vs. Graph Engine vs. mSQL vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

hughestech.com.au/products/msql

nsdb.io

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

nsdb.io/Architecture

Developer

Google

Microsoft

Hughes Technologies

Initial release

2010

1994

2017

Current release

4.4, October 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

free licenses can be provided

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

.NET and C

Java, Scala

Server operating systems

hosted

.NET

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

C
C++
Delphi
Java
Perl
PHP
Tcl

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Sharding

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

Immediate Consistency

none

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Using Apache Lucene

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)

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