DBMS > Apache IoTDB vs. Google Cloud Bigtable vs. Oracle vs. TinkerGraph

System Properties Comparison Apache IoTDB vs. Google Cloud Bigtable vs. Oracle vs. TinkerGraph

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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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Widely used RDBMS

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Time Series DBMS

Key-value store
Wide column store

Relational DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Website

iotdb.apache.org

cloud.google.com/bigtable

www.oracle.com/database

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

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

cloud.google.com/bigtable/docs

docs.oracle.com/en/database

Developer

Apache Software Foundation

Google

Oracle

Initial release

2018

2015

1980

2009

Current release

1.1.0, April 2023

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache 2.0

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++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

TinkerPop 3

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Groovy
Java

Server-side scripts

Stored procedures

yes

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Sharding, horizontal partitioning

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

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Atomic single-row operations

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

yes

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

fine grained access rights according to SQL-standard

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