DBMS > AlaSQL vs. Axibase vs. Microsoft SQL Server vs. OrientDB vs. Spark SQL

System Properties Comparison AlaSQL vs. Axibase vs. Microsoft SQL Server vs. OrientDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Microsofts flagship relational DBMS

Multi-model DBMS (Document, Graph, Key/Value)

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

alasql.org

axibase.com/docs/atsd/finance

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

orientdb.org

spark.apache.org/sql

Technical documentation

github.com/AlaSQL/alasql

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

www.orientdb.com/docs/last/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Andrey Gershun & Mathias R. Wulff

Axibase Corporation

Microsoft

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Initial release

2014

2013

1989

2010

2014

Current release

15585

SQL Server 2022, November 2022

3.2.29, March 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT-License

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

restricted free version is available

Open Source

Apache version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

JavaScript

Java

C++

Java

Scala

Server operating systems

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

Linux

Linux
Windows

All OS with a Java JDK (>= JDK 6)

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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.

SQL-like query language

yes

SQL-like query language, no joins

SQL-like DML and DDL statements

APIs and other access methods

JavaScript API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

JavaScript

Go
Java
PHP
Python
R
Ruby

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

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

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

yes

only for local storage and DOM-storage

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

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