DBMS > RavenDB vs. RocksDB vs. SAP Adaptive Server vs. Spark SQL

System Properties Comparison RavenDB vs. RocksDB vs. SAP Adaptive Server vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Open Source Operational and Transactional Enterprise NoSQL Document Database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Document store

Key-value store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

ravendb.net

rocksdb.org

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

spark.apache.org/sql

Technical documentation

ravendb.net/docs

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_ASE

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

Developer

Hibernating Rhinos

Facebook, Inc.

SAP, Sybase

Apache Software Foundation

Initial release

2010

2013

1987

2014

Current release

5.4, July 2022

8.11.4, April 2024

16.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

AGPL version 3, commercial license available

Open Source

BSD

commercial

Open Source

Apache 2.0

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

Server operating systems

Linux
macOS
Raspberry Pi
Windows

Linux

AIX
HP-UX
Linux
Solaris
Unix
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (RQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

C++ API
Java API

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

JDBC
ODBC

Supported programming languages

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Cobol
Java
Java
Perl
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID, Cluster-wide transaction available

yes

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

User concepts

Access control

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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