DBMS > Apache IoTDB vs. Bangdb vs. BaseX vs. Google Cloud Datastore

System Properties Comparison Apache IoTDB vs. Bangdb vs. BaseX vs. Google Cloud Datastore

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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

Converged and high performance database for device data, events, time series, document and graph

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Primary database model

Time Series DBMS

Document store
Graph DBMS
Time Series DBMS

Native XML DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

iotdb.apache.org

bangdb.com

basex.org

cloud.google.com/datastore

Technical documentation

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

docs.bangdb.com

docs.basex.org

cloud.google.com/datastore/docs

Developer

Apache Software Foundation

Sachin Sinha, BangDB

BaseX GmbH

Google

Initial release

2018

2012

2007

2008

Current release

1.1.0, April 2023

BangDB 2.0, October 2021

10.7, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

Open Source

BSD license

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, C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

XQuery supports types

yes, details here

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

SQL-like query language (GQL)

APIs and other access methods

JDBC
Native API

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Java
Python

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

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

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

yes, Notifications (with Streaming only)

yes

via events

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

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

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

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

selectable replication factor, Knob for CAP (enterprise version only)

none

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Tunable consistency, set CAP knob accordingly

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.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

User concepts

Access control

yes

yes (enterprise version only)

Users with fine-grained authorization concept on 4 levels

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

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