DBMS > Apache Druid vs. Newts vs. Oracle NoSQL vs. PlanetScale

System Properties Comparison Apache Druid vs. Newts vs. Oracle NoSQL vs. PlanetScale

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Time Series DBMS based on Cassandra

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	1.09
Rank	#178	Overall
	#82	Relational DBMS

Website

druid.apache.org

opennms.github.io/newts

www.oracle.com/database/nosql/technologies/nosql

planetscale.com

Technical documentation

druid.apache.org/docs/latest/design

github.com/OpenNMS/newts/wiki

docs.oracle.com/en/database/other-databases/nosql-database/index.html

planetscale.com/docs

Developer

Apache Software Foundation and contributors

OpenNMS Group

Oracle

PlanetScale

Initial release

2012

2014

2011

2020

Current release

30.0.0, June 2024

24.1, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux
Solaris SPARC/x86

Docker
Linux
macOS

Data scheme

yes

schema-less columns are supported

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP REST
Java API

RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Java

C
C#
Go
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

based on Cassandra

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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.

yes

off heap cache

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

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