DBMS > Dgraph vs. Google Cloud Bigtable vs. HarperDB vs. Hazelcast vs. MySQL

System Properties Comparison Dgraph vs. Google Cloud Bigtable vs. HarperDB vs. Hazelcast vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

A widely adopted in-memory data grid

Widely used open source RDBMS

Primary database model

Graph DBMS

Key-value store
Wide column store

Document store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

dgraph.io

cloud.google.com/bigtable

www.harperdb.io

hazelcast.com

www.mysql.com

Technical documentation

dgraph.io/docs

cloud.google.com/bigtable/docs

docs.harperdb.io/docs

hazelcast.org/imdg/docs

dev.mysql.com/doc

Developer

Dgraph Labs, Inc.

Google

HarperDB

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2015

2017

2008

1995

Current release

3.1, August 2021

5.3.6, November 2023

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Node.js

Java

C and C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

Event Listeners, Executor Services

yes

proprietary syntax

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

the nodes on which a table resides can be defined

yes

Replicated Map

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic single-row operations

Atomic execution of specific operations

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

yes, using LMDB

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

Planned for future releases

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

Access rights for users and roles

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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