DBMS > Apache IoTDB vs. Apache Pinot vs. RocksDB vs. SAP HANA

System Properties Comparison Apache IoTDB vs. Apache Pinot vs. RocksDB vs. SAP HANA

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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

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.35
Rank	#274	Overall
	#128	Relational DBMS

Score	2.84
Rank	#97	Overall
	#16	Key-value stores

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

iotdb.apache.org

pinot.apache.org

rocksdb.org

www.sap.com/products/hana.html

Technical documentation

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

docs.pinot.apache.org

github.com/facebook/rocksdb/wiki

help.sap.com/hana

Developer

Apache Software Foundation

Apache Software Foundation and contributors

Facebook, Inc.

SAP

Initial release

2018

2015

2013

2010

Current release

1.1.0, April 2023

1.0.0, September 2023

9.4.0, June 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java JDK11 or higher

Linux

Appliance or cloud-service

Data scheme

yes

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JDBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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

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