DBMS > Apache IoTDB vs. Google Cloud Spanner vs. mSQL vs. SAP HANA vs. Solr

System Properties Comparison Apache IoTDB vs. Google Cloud Spanner vs. mSQL vs. SAP HANA vs. Solr

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 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.

mSQL (Mini SQL) is a simple and lightweight RDBMS

In-memory, column based data store. Available as appliance or cloud service

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Time Series DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

iotdb.apache.org

cloud.google.com/spanner

hughestech.com.au/products/msql

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

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

cloud.google.com/spanner/docs

help.sap.com/hana

solr.apache.org/resources.html

Developer

Apache Software Foundation

Google

Hughes Technologies

SAP

Apache Software Foundation

Initial release

2018

2017

1994

2010

2006

Current release

1.1.0, April 2023

4.4, October 2021

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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)

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Appliance or cloud-service

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

Query statements complying to ANSI 2011

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
Native API

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

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
JavaScript (Node.js)
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

SQLScript, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

yes

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

Multi-source replication with 3 replicas for regional instances.

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

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

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

optimistic locking

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

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

yes

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