DBMS > Apache Doris vs. Apache Druid vs. Google BigQuery vs. Neo4j

System Properties Comparison Apache Doris vs. Apache Druid vs. Google BigQuery vs. Neo4j

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Graph DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	44.46
Rank	#23	Overall
	#1	Graph DBMS

Website

doris.apache.org
github.com/apache/doris

druid.apache.org

cloud.google.com/bigquery

neo4j.com

Technical documentation

github.com/apache/doris/wiki

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

neo4j.com/docs

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation and contributors

Google

Neo4j, Inc.

Initial release

2017

2012

2010

2007

Current release

1.2.2, February 2023

29.0.1, April 2024

5.19, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache license v2

commercial

Open Source

GPL version3, commercial licenses available

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.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Java

Java, Scala

Server operating systems

Linux

Linux
OS X
Unix

hosted

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free and schema-optional

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.

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

SQL

Support of SQL

yes

SQL for querying

yes

APIs and other access methods

JDBC
MySQL client

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

Supported programming languages

Java

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

User defined Procedures and Functions

Triggers

yes

via event handler

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

manual/auto, time-based

none

yes using Neo4j Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, via HDFS, S3 or other storage engines

Causal Clustering using Raft protocol

available in in Enterprise Version only

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

More information provided by the system vendor

Specific characteristics

Neo4j delivers graph technology that has been battle tested for performance and scale...
» more

Competitive advantages

Neo4j is the market leader, graph database category creator, and the most widely...
» more

Typical application scenarios

Real-Time Recommendations Master Data Management Identity and Access Management Network...
» more

Key customers

Over 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
» more

Market metrics

Neo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more

Licensing and pricing models

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more

News

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4 May 2024

This Week in Neo4j: Nodes 2024, Data Modelling, Events, Knowledge Graphs and more
27 April 2024

GQL is Here: Your Cypher Queries in a GQL World
26 April 2024

GQL: The ISO Standard for Graphs Has Arrived
25 April 2024

What Is Retrieval-Augmented Generation (RAG)?
24 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.