DBMS > Amazon Neptune vs. BigchainDB vs. Hive vs. RocksDB vs. Solr

System Properties Comparison Amazon Neptune vs. BigchainDB vs. Hive vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Spatial 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	0.79
Rank	#212	Overall
	#36	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

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

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/neptune

Technical documentation

aws.amazon.com/neptune/developer-resources

bigchaindb.readthedocs.io/en/latest

cwiki.apache.org/confluence/display/Hive/Home

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Amazon

Apache Software Foundation

initially developed by Facebook

Facebook, Inc.

Apache Software Foundation

Initial release

2017

2016

2012

2013

2006

Current release

3.1.3, April 2022

8.11.4, April 2024

9.5.0, February 2024

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

Apache Version 2

Open Source

BSD

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Java

C++

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

Solr Parallel SQL Interface

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

CLI Client
RESTful HTTP API

JDBC
ODBC
Thrift

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

C++
Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

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.

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes,with MongoDB ord RethinkDB

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)

yes

Access rights for users, groups and roles

yes

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