DBMS > Hyprcubd vs. IBM Db2 Event Store vs. NSDb vs. OpenEdge vs. Oracle Berkeley DB

System Properties Comparison Hyprcubd vs. IBM Db2 Event Store vs. NSDb vs. OpenEdge vs. Oracle Berkeley DB

Editorial information provided by DB-Engines

Name

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

Description

Serverless Time Series DBMS

Distributed Event Store optimized for Internet of Things use cases

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Application development environment with integrated database management system

Widely used in-process key-value store

Primary database model

Time Series DBMS

Event Store
Time Series DBMS

Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

hyprcubd.com (offline)

www.ibm.com/products/db2-event-store

nsdb.io

www.progress.com/openedge

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

www.ibm.com/docs/en/db2-event-store

nsdb.io/Architecture

documentation.progress.com/output/ua/OpenEdge_latest

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Hyprcubd, Inc.

IBM

Progress Software Corporation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2017

1984

1994

Current release

2.0

OpenEdge 12.2, March 2020

18.1.40, May 2020

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache Version 2.0

commercial

Open Source

commercial license 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

C and C++

Java, Scala

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS

AIX
HP-UX
Linux
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

yes: int, bigint, decimal, 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

only with the Berkeley DB XML edition

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

through the embedded Spark runtime

SQL-like query language

yes

close to SQL 92

yes

SQL interfaced based on SQLite is available

APIs and other access methods

gRPC (https)

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

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

Java
Scala

Progress proprietary ABL (Advanced Business Language)

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

since Version 11.4

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

Using Apache Lucene

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

token access

fine grained access rights according to SQL-standard

Users and groups

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