DBMS > AlaSQL vs. Google BigQuery vs. Microsoft SQL Server vs. VoltDB vs. Warp 10

System Properties Comparison AlaSQL vs. Google BigQuery vs. Microsoft SQL Server vs. VoltDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

alasql.org

cloud.google.com/bigquery

www.microsoft.com/en-us/sql-server

www.voltdb.com

www.warp10.io

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

learn.microsoft.com/en-US/sql/sql-server

docs.voltdb.com

www.warp10.io/content/02_Getting_started

Developer

Andrey Gershun & Mathias R. Wulff

Google

Microsoft

VoltDB Inc.

SenX

Initial release

2014

2010

1989

2010

2015

Current release

SQL Server 2022, November 2022

11.3, April 2022

License

Commercial or Open Source

Open Source

MIT-License

commercial

restricted free version is available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

JavaScript

C++

Java, C++

Java

Server operating systems

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

hosted

Linux
Windows

Linux
OS X

for development

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

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

yes

only a subset of SQL 99

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
JDBC
RESTful HTTP/JSON API

HTTP API
Jupyter
WebSocket

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

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

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

Snapshots and command logging

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

Users and roles with access to stored procedures

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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