DBMS > HEAVY.AI vs. KairosDB vs. Neo4j vs. Oracle Berkeley DB vs. Redis

System Properties Comparison HEAVY.AI vs. KairosDB vs. Neo4j vs. Oracle Berkeley DB vs. Redis

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed Time Series DBMS based on Cassandra or H2

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

Widely used in-process key-value store

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Secondary database models

Spatial DBMS

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	44.46
Rank	#23	Overall
	#1	Graph DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	157.80
Rank	#6	Overall
	#1	Key-value stores

Website

github.com/heavyai/heavydb
www.heavy.ai

github.com/kairosdb/kairosdb

neo4j.com

www.oracle.com/database/technologies/related/berkeleydb.html

redis.com
redis.io

Technical documentation

docs.heavy.ai

kairosdb.github.io

neo4j.com/docs

docs.oracle.com/cd/E17076_05/html/index.html

docs.redis.com/latest/index.html
redis.io/docs

Developer

HEAVY.AI, Inc.

Neo4j, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Initial release

2016

2013

2007

1994

2009

Current release

5.10, January 2022

1.2.2, November 2018

5.19, April 2024

18.1.40, May 2020

7.2.4, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

GPL version3, commercial licenses available

Open Source

commercial license available

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

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.

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

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

C++ and CUDA

Java

Java, Scala

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

Linux
OS X
Windows

Linux

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

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Data scheme

yes

schema-free

schema-free and schema-optional

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

only with the Berkeley DB XML edition

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

yes

with RediSearch module

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

with RediSQL module

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Graphite protocol
HTTP REST
Telnet API

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Java
JavaScript

Node.js
PHP
Python

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

User defined Procedures and Functions

Lua; Redis Functions coming in Redis 7 (slides and Github)

Triggers

yes

via event handler

yes

only for the SQL API

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

based on Cassandra

yes using Neo4j Fabric

none

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

Causal Clustering using Raft protocol

available in in Enterprise Version only

Source-replica replication

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

MapReduce

Offers an API for user-defined Map/Reduce methods

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

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

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Over 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
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Market metrics

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Licensing and pricing models

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