DBMS > AnzoGraph DB vs. Apache Impala vs. JanusGraph vs. MySQL vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Apache Impala vs. JanusGraph vs. MySQL vs. Snowflake

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

Analytic DBMS for Hadoop

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Widely used open source RDBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

Document store
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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cambridgesemantics.com/anzograph

Technical documentation

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

impala.apache.org/impala-docs.html

docs.janusgraph.org

dev.mysql.com/doc

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Linux Foundation; originally developed as Titan by Aurelius

Oracle

since 2010, originally MySQL AB, then Sun

Snowflake Computing Inc.

Initial release

2018

2013

2017

1995

2014

Current release

2.3, January 2021

4.1.0, June 2022

0.6.3, February 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Java

C and C++

Server operating systems

Linux

Linux
OS X
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

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

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
Proprietary native API

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

All languages supporting JDBC/ODBC

Clojure
Java
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

yes

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

query execution via MapReduce

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Relationships in graphs

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

User authentification and security via Rexster Graph Server

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept, user roles and pluggable authentication

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