DBMS > Amazon Neptune vs. Datomic vs. HEAVY.AI vs. IRONdb vs. RocksDB

System Properties Comparison Amazon Neptune vs. Datomic vs. HEAVY.AI vs. IRONdb vs. RocksDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Fast, reliable graph database built for the cloud

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

aws.amazon.com/neptune

www.datomic.com

github.com/heavyai/heavydb
www.heavy.ai

www.circonus.com/solutions/time-series-database/

rocksdb.org

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.datomic.com

docs.heavy.ai

docs.circonus.com/irondb/category/getting-started

github.com/facebook/rocksdb/wiki

Developer

Amazon

Cognitect

HEAVY.AI, Inc.

Circonus LLC.

Facebook, Inc.

Initial release

2017

2012

2016

2017

2013

Current release

1.0.7075, December 2023

5.10, January 2022

V0.10.20, January 2018

9.2.1, May 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

BSD

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, Clojure

C++ and CUDA

C and C++

C++

Server operating systems

hosted

All OS with a Java VM

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP API

C++ API
Java API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Clojure
Java

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes, in Lua

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Round robin

Automatic, metric affinity per node

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.

none

But extensive use of caching in the application peers

Multi-source replication

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Relationships in graphs

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

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