DBMS > Apache IoTDB vs. Apache Phoenix vs. Google BigQuery vs. Hive vs. OrigoDB

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. Google BigQuery vs. Hive vs. OrigoDB

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A fully ACID in-memory object graph database

Primary database model

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

iotdb.apache.org

phoenix.apache.org

cloud.google.com/bigquery

hive.apache.org

origodb.com

Technical documentation

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

phoenix.apache.org

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

origodb.com/docs

Developer

Apache Software Foundation

Google

Apache Software Foundation

initially developed by Facebook

Robert Friberg et al

Initial release

2018

2014

2010

2012

2009

under the name LiveDB

Current release

1.1.0, April 2023

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Open Source

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

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Unix
Windows

hosted

All OS with a Java VM

Linux
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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 be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Native API

JDBC

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

.NET Client API
HTTP API
LINQ

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

.Net

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

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
Source-replica replication

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

Role based authorization

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