DBMS > Adabas vs. HugeGraph vs. MySQL vs. RocksDB vs. Sphinx

System Properties Comparison Adabas vs. HugeGraph vs. MySQL vs. RocksDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

A fast-speed and highly-scalable Graph DBMS

Widely used open source RDBMS

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Multivalue DBMS

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Key-value store

Search engine

Secondary database models

Document store
Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

github.com/hugegraph
hugegraph.apache.org

www.mysql.com

rocksdb.org

sphinxsearch.com

Technical documentation

hugegraph.apache.org/docs

dev.mysql.com/doc

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

Developer

Software AG

Baidu

Oracle

since 2010, originally MySQL AB, then Sun

Facebook, Inc.

Sphinx Technologies Inc.

Initial release

1971

2018

1995

2013

2001

Current release

0.9

8.4.0, April 2024

8.11.4, April 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

BSD

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
macOS
Unix

FreeBSD
Linux
OS X
Solaris
Windows

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
Proprietary native API

C++ API
Java API

Proprietary protocol

Supported programming languages

Natural

Groovy
Java
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

in Natural

asynchronous Gremlin script jobs

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

yes

depending on used storage backend, e.g. Cassandra and HBase

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

yes

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

Users, roles and permissions

Users with fine-grained authorization concept

no user groups or roles

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