DBMS > Apache IoTDB vs. Dgraph vs. Dragonfly vs. EsgynDB vs. Google Cloud Bigtable

System Properties Comparison Apache IoTDB vs. Dgraph vs. Dragonfly vs. EsgynDB vs. Google Cloud Bigtable

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Distributed and scalable native Graph DBMS

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Primary database model

Key-value store
Wide column store

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

Website

iotdb.apache.org

dgraph.io

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.esgyn.cn

cloud.google.com/bigtable

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

dgraph.io/docs

www.dragonflydb.io/docs

cloud.google.com/bigtable/docs

Developer

Apache Software Foundation

Dgraph Labs, Inc.

DragonflyDB team and community contributors

Esgyn

Google

Initial release

2018

2016

2023

2015

Current release

1.1.0, April 2023

1.0, March 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

BSL 1.1

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

C++, Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux

hosted

Data scheme

yes

schema-free

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

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

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

All languages supporting JDBC/ODBC/ADO.Net

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

Server-side scripts

Stored procedures

yes

Lua

Java Stored Procedures

Triggers

yes

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Synchronous replication via Raft

Source-replica replication

Multi-source replication between multi datacenters

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Immediate Consistency

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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.

yes

User concepts

Access control

yes

Planned for future releases

Password-based authentication

fine grained access rights according to SQL-standard

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

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