DBMS > AnzoGraph DB vs. BigchainDB vs. GridGain vs. RocksDB vs. Solr

System Properties Comparison AnzoGraph DB vs. BigchainDB vs. GridGain vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

GridGain is an in-memory computing platform, built on Apache Ignite

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
RDF store

Document store

Key-value store
Relational DBMS

Key-value store

Search engine

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

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

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cambridgesemantics.com/anzograph

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

bigchaindb.readthedocs.io/en/latest

www.gridgain.com/docs/index.html

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Cambridge Semantics

GridGain Systems, Inc.

Facebook, Inc.

Apache Software Foundation

Initial release

2018

2016

2007

2013

2006

Current release

2.3, January 2021

GridGain 8.5.1

9.2.1, May 2024

9.6.1, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL v3

commercial

Open Source

BSD

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Java, C++, .Net

C++

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux

All OS with a Java VM

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

Data scheme

Schema-free and OWL/RDFS-schema support

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

SPARQL and SPARQL* as primary query language. Cypher preview.

ANSI-99 for query and DML statements, subset of DDL

Solr Parallel SQL Interface

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

CLI Client
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

Go
Haskell
Java
JavaScript
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

user defined functions and aggregates

yes (compute grid and cache interceptors can be used instead)

Java plugins

Triggers

yes (cache interceptors and events)

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes (compute grid and hadoop accelerator)

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed 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

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

yes

Security Hooks for custom implementations

yes

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