DBMS > Google Cloud Datastore vs. HugeGraph vs. Neo4j vs. Redis vs. RocksDB

System Properties Comparison Google Cloud Datastore vs. HugeGraph vs. Neo4j vs. Redis vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A fast-speed and highly-scalable Graph DBMS

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

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.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store

Graph DBMS

Key-value store

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

Key-value store

Secondary database models

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	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	44.89
Rank	#21	Overall
	#1	Graph DBMS

Score	155.94
Rank	#6	Overall
	#1	Key-value stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

cloud.google.com/datastore

github.com/hugegraph
hugegraph.apache.org

neo4j.com

redis.com
redis.io

rocksdb.org

Technical documentation

cloud.google.com/datastore/docs

hugegraph.apache.org/docs

neo4j.com/docs

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

github.com/facebook/rocksdb/wiki

Developer

Google

Baidu

Neo4j, Inc.

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Facebook, Inc.

Initial release

2008

2018

2007

2009

2013

Current release

0.9

5.20, May 2024

7.2.5, May 2024

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version3, commercial licenses available

Open Source

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

Open Source

BSD

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.

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

Implementation language

Java

Java, Scala

C++

Server operating systems

hosted

Linux
macOS
Unix

Linux

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

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux

Data scheme

schema-free

yes

schema-free and schema-optional

schema-free

Typing

predefined data types such as float or date

yes, details here

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.

Secondary indexes

yes

also supports composite index and range index

yes

pluggable indexing subsystem, by default Apache Lucene

yes

with RediSearch module

SQL

Support of SQL

SQL-like query language (GQL)

with RediSQL module

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

C++ API
Java API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Groovy
Java
Python

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

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

asynchronous Gremlin script jobs

yes

User defined Procedures and Functions

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

Triggers

Callbacks using the Google Apps Engine

yes

via event handler

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

yes using Neo4j Fabric

Sharding

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

depending on used storage backend, e.g. Cassandra and HBase

Causal Clustering using Raft protocol

available in in Enterprise Version only

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

via hugegraph-spark

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

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

via ReferenceProperties or Ancestor paths

yes

edges in graph

yes

Relationships in graphs

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Atomic execution of command blocks and scripts and optimistic locking

yes

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users, roles and permissions

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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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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