DBMS > Apache IoTDB vs. EXASOL vs. Google BigQuery vs. Manticore Search vs. RDF4J

System Properties Comparison Apache IoTDB vs. EXASOL vs. Google BigQuery vs. Manticore Search vs. RDF4J

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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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

Multi-storage database for search, including full-text search.

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.23
Rank	#317	Overall
	#21	Search engines

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Website

iotdb.apache.org

www.exasol.com

cloud.google.com/bigquery

manticoresearch.com

rdf4j.org

Technical documentation

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

www.exasol.com/resources

cloud.google.com/bigquery/docs

manual.manticoresearch.com

rdf4j.org/documentation

Developer

Apache Software Foundation

Exasol

Google

Manticore Software

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2018

2000

2010

2017

2004

Current release

1.1.0, April 2023

6.0, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2

Open Source

Eclipse Distribution License (EDL), v1.0.

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++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

FreeBSD
Linux
macOS
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

Fixed schema

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
Native API

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

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 based on Galera library

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

in-memory storage is supported as well

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

Access rights for users, groups and roles according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

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