DBMS > Apache IoTDB vs. ArangoDB vs. Couchbase vs. Dgraph vs. Google Cloud Datastore

System Properties Comparison Apache IoTDB vs. ArangoDB vs. Couchbase vs. Dgraph vs. Google Cloud Datastore

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

Native multi-model DBMS for graph, document, key/value and search. All in one engine and accessible with one query language.

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Distributed and scalable native Graph DBMS

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

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Search engine

Document store

Graph DBMS

Document store

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	1.41
Rank	#151	Overall
	#14	Time Series DBMS

Score	3.09
Rank	#83	Overall
	#15	Document stores
	#5	Graph DBMS
	#11	Key-value stores
	#10	Search engines

Score	15.82
Rank	#36	Overall
	#5	Document stores

Score	1.34
Rank	#157	Overall
	#15	Graph DBMS

Score	3.98
Rank	#70	Overall
	#12	Document stores

Website

cloud.google.com/datastore

Technical documentation

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

docs.arangodb.com

docs.couchbase.com

dgraph.io/docs

cloud.google.com/datastore/docs

Developer

Apache Software Foundation

ArangoDB Inc.

Couchbase, Inc.

Dgraph Labs, Inc.

Google

Initial release

2018

2012

2011

2016

2008

Current release

1.1.0, April 2023

3.11.5, November 2023

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial license (Enterprise) available

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache 2.0

commercial

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.

Implementation language

Java

C++

C, C++, Go and Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

automatically recognizes schema within a collection

schema-free

Typing

predefined data types such as float or date

yes

string, double, boolean, list, hash

yes

yes, details here

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

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language (GQL)

APIs and other access methods

JDBC
Native API

AQL
Foxx Framework
Graph API (Gremlin)
GraphQL query language
HTTP API
Java & SpringData
JSON style queries
VelocyPack/VelocyStream

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Clojure
Elixir
Go
Java
JavaScript (Node.js)
PHP
Python
R
Rust

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

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

Server-side scripts

Stored procedures

yes

JavaScript

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

using Google App Engine

Triggers

yes

via the TAP protocol

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

since version 2.0

Automatic Sharding

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

Source-replica replication with configurable replication factor

Multi-source replication

including cross data center replication
Source-replica replication

Synchronous replication via Raft

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

can be done with stored procedures in JavaScript

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

configurable per collection or per write
Immediate Consistency
OneShard (highly available, fault-tolerant deployment mode with ACID semantics)

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

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.

Foreign keys

Referential integrity

yes

relationships in graphs

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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.

yes

Ephemeral buckets

User concepts

Access control

yes

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Planned for future releases

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

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