DBMS > Apache IoTDB vs. Dgraph vs. FoundationDB vs. Google BigQuery

System Properties Comparison Apache IoTDB vs. Dgraph vs. FoundationDB vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

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

Ordered key-value store. Core features are complimented by layers.

Large scale data warehouse service with append-only tables

Primary database model

Time Series DBMS

Graph DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	1.06
Rank	#185	Overall
	#31	Document stores
	#28	Key-value stores
	#85	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

iotdb.apache.org

dgraph.io

github.com/apple/foundationdb

cloud.google.com/bigquery

Technical documentation

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

dgraph.io/docs

apple.github.io/foundationdb

cloud.google.com/bigquery/docs

Developer

Apache Software Foundation

Dgraph Labs, Inc.

FoundationDB

Google

Initial release

2018

2016

2013

2010

Current release

1.1.0, April 2023

6.2.28, November 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

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++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

some layers support schemas

yes

Typing

predefined data types such as float or date

yes

some layers support typing

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

supported in specific SQL layer only

yes

APIs and other access methods

JDBC
Native API

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

RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

in SQL-layer only

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

Sharding

none

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

in SQL-layer only

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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

User concepts

Access control

yes

Planned for future releases

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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