DBMS > BigObject vs. GigaSpaces vs. Hawkular Metrics vs. NSDb vs. OpenEdge

System Properties Comparison BigObject vs. GigaSpaces vs. Hawkular Metrics vs. NSDb vs. OpenEdge

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

Application development environment with integrated database management system

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Object oriented DBMS

Values are user defined objects

Time Series DBMS

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	0.13
Rank	#342	Overall
	#150	Relational DBMS

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	3.75
Rank	#88	Overall
	#47	Relational DBMS

Website

www.progress.com/openedge

Technical documentation

docs.bigobject.io

docs.gigaspaces.com/latest/landing.html

www.hawkular.org/hawkular-metrics/docs/user-guide

nsdb.io/Architecture

documentation.progress.com/output/ua/OpenEdge_latest

Developer

BigObject, Inc.

Gigaspaces Technologies

Community supported by Red Hat

Progress Software Corporation

Initial release

2015

2000

2014

2017

1984

Current release

15.5, September 2020

OpenEdge 12.2, March 2020

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

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++, .Net

Java

Java, Scala

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
macOS
Solaris
Windows

Linux
OS X
Windows

Linux
macOS

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-99 for query and DML statements

SQL-like query language

yes

close to SQL 92

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

HTTP REST

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

Go
Java
Python
Ruby

Java
Scala

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

Lua

yes

Triggers

yes, event driven architecture

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

selectable replication factor

based on Cassandra

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

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

Role-based access control

Users and groups

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