DBMS > Apache Drill vs. Apache IoTDB vs. Google Cloud Datastore vs. IRONdb

System Properties Comparison Apache Drill vs. Apache IoTDB vs. Google Cloud Datastore vs. IRONdb

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Primary database model

Document store
Relational DBMS

Time Series DBMS

Document store

Time Series DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

drill.apache.org

iotdb.apache.org

cloud.google.com/datastore

www.circonus.com/solutions/time-series-database/

Technical documentation

drill.apache.org/docs

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

cloud.google.com/datastore/docs

docs.circonus.com/irondb/category/getting-started

Developer

Apache Software Foundation

Google

Circonus LLC.

Initial release

2012

2018

2008

2017

Current release

1.20.3, January 2023

1.1.0, April 2023

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 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 and C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM (>= 1.8)

hosted

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

text, numeric, histograms

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 SELECT statement is SQL:2003 compliant

SQL-like query language

SQL-like query language (GQL)

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
Native API

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

HTTP API

Supported programming languages

C++

C
C#
C++
Go
Java
Python
Scala

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Server-side scripts

Stored procedures

user defined functions

yes

using Google App Engine

yes, in Lua

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Automatic, metric affinity per node

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

Multi-source replication using Paxos

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Strong Consistency with Raft

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.

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

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

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

yes

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

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