DBMS > AgensGraph vs. BigchainDB vs. Microsoft Azure Cosmos DB vs. RocksDB vs. Solr

System Properties Comparison AgensGraph vs. BigchainDB vs. Microsoft Azure Cosmos DB vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

Globally distributed, horizontally scalable, multi-model database service

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

Graph DBMS
Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Search engine

Secondary database models

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

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

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

bitnine.net/agensgraph

www.bigchaindb.com

azure.microsoft.com/services/cosmos-db

rocksdb.org

solr.apache.org

Technical documentation

bitnine.net/documentation

bigchaindb.readthedocs.io/en/latest

learn.microsoft.com/azure/cosmos-db

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Bitnine Global Inc.

Microsoft

Facebook, Inc.

Apache Software Foundation

Initial release

2016

2014

2013

2006

Current release

2.1, December 2018

9.2.1, May 2024

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

AGPL v3

commercial

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

C++

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux

All OS with a Java VM

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

Data scheme

depending on used data model

schema-free

yes

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

Typing

predefined data types such as float or date

yes

JSON types

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 properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

Cypher Query Language
JDBC

CLI Client
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

C
Java
JavaScript
Python

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
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

JavaScript

Java plugins

Triggers

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

fine grained access rights according to SQL-standard

yes

Access rights can be defined down to the item level

yes

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