DBMS > FeatureBase vs. Heroic vs. RavenDB vs. ReductStore

System Properties Comparison FeatureBase vs. Heroic vs. RavenDB vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.featurebase.com

github.com/spotify/heroic

ravendb.net

github.com/reductstore
www.reduct.store

Technical documentation

docs.featurebase.com

spotify.github.io/heroic

ravendb.net/docs

www.reduct.store/docs

Developer

Molecula and Pilosa Open Source Contributors

Spotify

Hibernating Rhinos

ReductStore LLC

Initial release

2017

2014

2010

2023

Current release

2022, May 2022

5.4, July 2022

1.9, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL version 3, commercial license available

Open Source

Business Source License 1.1

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++, Rust

Server operating systems

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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 queries

SQL-like query language (RQL)

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

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

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

HTTP API

Supported programming languages

Java
Python

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

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

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

yes

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using Linux fsync

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

Authorization levels configured per client per database

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