DBMS > NSDb vs. OpenMLDB vs. OpenTSDB vs. OrigoDB vs. Spark SQL

System Properties Comparison NSDb vs. OpenMLDB vs. OpenTSDB vs. OrigoDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

An open-source machine learning database that provides a feature platform for training and inference

Scalable Time Series DBMS based on HBase

A fully ACID in-memory object graph database

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

nsdb.io/Architecture

openmldb.ai/docs/zh/main

opentsdb.net/docs/build/html/index.html

origodb.com/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

4 Paradigm Inc.

currently maintained by Yahoo and other contributors

Robert Friberg et al

Apache Software Foundation

Initial release

2017

2020

2011

2009

under the name LiveDB

2014

Current release

2024-2 February 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

LGPL

Open Source

Apache 2.0

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, Scala

C++, Java, Scala

Java

Scala

Server operating systems

Linux
macOS

Linux

Linux
Windows

Linux
OS X
Windows

Data scheme

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

yes

numeric data for metrics, strings for tags

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

gRPC
HTTP REST
WebSocket

JDBC
SQLAlchemy

HTTP API
Telnet API

.NET Client API
HTTP API
LINQ

JDBC
ODBC

Supported programming languages

Java
Scala

C++
Go
Java
Python
Scala

Erlang
Go
Java
Python
R
Ruby

.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

based on HBase

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

based on HBase

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Using Apache Lucene

yes

Write ahead log

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

fine grained access rights according to SQL-standard

Role based authorization

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

show all

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