DBMS > Fujitsu Enterprise Postgres vs. HugeGraph vs. Hyprcubd vs. Microsoft SQL Server vs. Sphinx

System Properties Comparison Fujitsu Enterprise Postgres vs. HugeGraph vs. Hyprcubd vs. Microsoft SQL Server vs. Sphinx

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

A fast-speed and highly-scalable Graph DBMS

Serverless Time Series DBMS

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.postgresql.fastware.com

github.com/hugegraph
hugegraph.apache.org

hyprcubd.com (offline)

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

sphinxsearch.com

Technical documentation

www.postgresql.fastware.com/product-manuals

hugegraph.apache.org/docs

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

sphinxsearch.com/docs

Developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Baidu

Hyprcubd, Inc.

Microsoft

Sphinx Technologies Inc.

Initial release

2018

1989

2001

Current release

Fujitsu Enterprise Postgres 14, January 2022

0.9

SQL Server 2022, November 2022

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

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
Windows

Linux
macOS
Unix

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, 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

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language

yes

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC (https)

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

Proprietary protocol

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Groovy
Java
Python

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

asynchronous Gremlin script jobs

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

yes

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

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

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

yes, but depending on the SQL-Server Edition

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Users, roles and permissions

token access

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

100% compatible with community PostgreSQL
» more

Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
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Licensing and pricing models

Core based licensing
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