DBMS > Hypertable vs. NSDb vs. RavenDB vs. ReductStore vs. Splunk

System Properties Comparison Hypertable vs. NSDb vs. RavenDB vs. ReductStore vs. Splunk

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

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

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.

Analytics Platform for Big Data

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	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Website

nsdb.io

ravendb.net

github.com/reductstore
www.reduct.store

www.splunk.com

Technical documentation

nsdb.io/Architecture

ravendb.net/docs

www.reduct.store/docs

docs.splunk.com/Documentation/Splunk

Developer

Hypertable Inc.

Hibernating Rhinos

ReductStore LLC

Splunk Inc.

Initial release

2009

2017

2010

2023

2003

Current release

0.9.8.11, March 2016

5.4, July 2022

1.9, March 2024

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

Open Source

Business Source License 1.1

commercial

Limited free edition and free developer edition available

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

C++

Java, Scala

C++, Rust

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Docker
Linux
macOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

Secondary indexes

restricted

only exact value or prefix value scans

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

Splunk Search Processing Language for search commands

APIs and other access methods

C++ API
Thrift

gRPC
HTTP REST
WebSocket

.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

HTTP REST

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

Java
Scala

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

C++
JavaScript (Node.js)
Python
Rust

C#
Java
JavaScript
PHP
Python
Ruby

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

selectable replication factor on file system level

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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.

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID, Cluster-wide transaction available

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

Authorization levels configured per client per database

Access rights for users and roles

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Related products and services

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More resources

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