DBMS > Apache IoTDB vs. DolphinDB vs. Google Cloud Spanner vs. OushuDB vs. searchxml

System Properties Comparison Apache IoTDB vs. DolphinDB vs. Google Cloud Spanner vs. OushuDB vs. searchxml

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

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Time Series DBMS

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

iotdb.apache.org

www.dolphindb.com

cloud.google.com/spanner

www.oushu.com/product/oushuDB

www.searchxml.net/category/products

Technical documentation

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

docs.dolphindb.cn/en/help200/index.html

cloud.google.com/spanner/docs

www.oushu.com/documentation

www.searchxml.net/support/handouts

Developer

Apache Software Foundation

DolphinDB, Inc

Google

Oushu

informationpartners gmbh

Initial release

2018

2017

2015

Current release

1.1.0, April 2023

v2.00.4, January 2022

4.0.1, August 2020

1.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community version available

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++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

hosted

Linux

Windows

Data scheme

yes

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

Query statements complying to ANSI 2011

Full-featured ANSI SQL support

APIs and other access methods

JDBC
Native API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Go
Java
JavaScript (Node.js)
Python

C
C++

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

Sharding

yes

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

yes

Multi-source replication with 3 replicas for regional instances.

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

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

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

yes

ACID

Strict serializable isolation

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

Administrators, Users, Groups

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

Kerberos, SSL and role based access

Domain, group and role-based access control at the document level and for application services

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