DBMS > HBase vs. HugeGraph vs. Microsoft Access vs. RocksDB vs. Sphinx

System Properties Comparison HBase vs. HugeGraph vs. Microsoft Access vs. RocksDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

A fast-speed and highly-scalable Graph DBMS

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

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

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

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

hbase.apache.org

github.com/hugegraph
hugegraph.apache.org

www.microsoft.com/en-us/microsoft-365/access

rocksdb.org

sphinxsearch.com

Technical documentation

hbase.apache.org/book.html

hugegraph.apache.org/docs

developer.microsoft.com/en-us/access

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Baidu

Microsoft

Facebook, Inc.

Sphinx Technologies Inc.

Initial release

2008

2018

1992

2013

2001

Current release

2.3.4, January 2021

0.9

1902 (16.0.11328.20222), March 2019

8.11.4, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

Open Source

BSD

Open Source

GPL version 2, commercial licence available

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

Java

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
macOS
Unix

Windows

Not a real database server, but making use of DLLs

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP API
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DAO
ODBC
OLE DB

C++ API
Java API

Proprietary protocol

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

Groovy
Java
Python

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

yes

Coprocessors in Java

asynchronous Gremlin script jobs

yes

since Access 2010 using the ACE-engine

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

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

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

but no files for transaction logging

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users, roles and permissions

a simple user-level security was built in till version Access 2003

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