DBMS > Sadas Engine vs. SAP Adaptive Server vs. Spark SQL vs. SurrealDB

System Properties Comparison Sadas Engine vs. SAP Adaptive Server vs. Spark SQL vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Document store
Graph DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

www.sadasengine.com

www.sap.com/products/technology-platform/sybase-ase.html

spark.apache.org/sql

surrealdb.com

Technical documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

help.sap.com/docs/SAP_ASE

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

surrealdb.com/docs

Developer

SADAS s.r.l.

SAP, Sybase

Apache Software Foundation

SurrealDB Ltd

Initial release

2006

1987

2014

2022

Current release

8.0

16.0

3.5.0 ( 2.13), September 2023

v1.5.0, May 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache 2.0

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C and C++

Scala

Rust

Server operating systems

AIX
Linux
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol

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

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

Java
Python
R
Scala

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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