DBMS > Heroic vs. Hyprcubd vs. openGauss vs. RavenDB

System Properties Comparison Heroic vs. Hyprcubd vs. openGauss vs. RavenDB

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Editorial information provided by DB-Engines

Name

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

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Time Series DBMS

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

github.com/spotify/heroic

hyprcubd.com (offline)

gitee.com/opengauss
opengauss.org

ravendb.net

Technical documentation

spotify.github.io/heroic

docs.opengauss.org/en
gitee.com/opengauss/docs

ravendb.net/docs

Developer

Spotify

Hyprcubd, Inc.

Huawei and openGauss community

Hibernating Rhinos

Initial release

2014

2019

2010

Current release

3.0, March 2022

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

AGPL version 3, 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

Java

C, C++, Java

Server operating systems

hosted

Linux

Linux
macOS
Raspberry Pi
Windows

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

ANSI SQL 2011

SQL-like query language (RQL)

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

gRPC (https)

JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

C
C++
Java

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

token access

Access rights for users, groups and roles according to SQL-standard

Authorization levels configured per client per database

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More resources

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