DBMS > AnzoGraph DB vs. Apache Kylin vs. Apache Pinot vs. Faircom DB vs. GeoMesa

System Properties Comparison AnzoGraph DB vs. Apache Kylin vs. Apache Pinot vs. Faircom DB vs. GeoMesa

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

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store
Relational DBMS

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	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Website

cambridgesemantics.com/anzograph

kylin.apache.org

pinot.apache.org

www.faircom.com/products/faircom-db

www.geomesa.org

Technical documentation

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

kylin.apache.org/docs

docs.pinot.apache.org

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.geomesa.org/documentation/stable/user/index.html

Developer

Cambridge Semantics

Apache Software Foundation, originally contributed from eBay Inc

Apache Software Foundation and contributors

FairCom Corporation

CCRi and others

Initial release

2018

2015

1979

2014

Current release

2.3, January 2021

3.1.0, July 2020

1.0.0, September 2023

V12, November 2020

5.0.0, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

ANSI C, C++

Scala

Server operating systems

Linux

All OS with a Java JDK11 or higher

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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.

Secondary indexes

yes

SQL

Support of SQL

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

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

yes, ANSI SQL with proprietary extensions

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

JDBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Supported programming languages

C++
Java
Python

Go
Java
Python

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

.Net, JavaScript, C/C++

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

depending on storage layer

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

depending on storage layer

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

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

yes

depending on storage layer

User concepts

Access control

Access rights for users and roles

Fine grained access rights according to SQL-standard with additional protections for files

yes

depending on the DBMS used for storage

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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