DBMS > Amazon Neptune vs. Apache Druid vs. Prometheus vs. RocksDB vs. Valentina Server

System Properties Comparison Amazon Neptune vs. Apache Druid vs. Prometheus vs. RocksDB vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

Open-source Time Series DBMS and monitoring system

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Object-relational database and reports server

Primary database model

Graph DBMS
RDF store

Relational DBMS
Time Series DBMS

Time Series DBMS

Key-value store

Relational DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

aws.amazon.com/neptune

Technical documentation

aws.amazon.com/neptune/developer-resources

druid.apache.org/docs/latest/design

prometheus.io/docs

github.com/facebook/rocksdb/wiki

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Amazon

Apache Software Foundation and contributors

Facebook, Inc.

Paradigma Software

Initial release

2017

2012

2015

2013

1999

Current release

29.0.1, April 2024

9.2.1, May 2024

5.7.5

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

BSD

commercial

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++

Server operating systems

hosted

Linux
OS X
Unix

Linux
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

C++ API
Java API

ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

manual/auto, time-based

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes, via HDFS, S3 or other storage engines

yes

by Federation

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

fine grained access rights according to SQL-standard

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