DBMS > AnzoGraph DB vs. GBase vs. InfluxDB vs. Lovefield vs. Snowflake

System Properties Comparison AnzoGraph DB vs. GBase vs. InfluxDB vs. Lovefield 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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

DBMS for storing time series, events and metrics

Embeddable relational database for web apps written in pure JavaScript

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

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.gbase.cn

www.influxdata.com/products/influxdb-overview

google.github.io/lovefield

www.snowflake.com

Technical documentation

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

docs.influxdata.com/influxdb

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

General Data Technology Co., Ltd.

Google

Snowflake Computing Inc.

Initial release

2018

2004

2013

2014

Current release

2.3, January 2021

GBase 8a, GBase 8s, GBase 8c

2.7.6, April 2024

2.1.12, February 2017

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, Java, Python

JavaScript

Server operating systems

Linux

Linux
OS X

through Homebrew

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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.

Standard with numerous extensions

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

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

ADO.NET
C API
JDBC
ODBC

HTTP API
JSON over UDP

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Triggers

yes

Using read-only observers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

in enterprise version only

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

selectable replication factor

in enterprise version only

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

Depending on used storage engine

yes

using MemoryDB

User concepts

Access control

Access rights for users and roles

yes

simple rights management via user accounts

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

More information provided by the system vendor

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