DBMS > Apache IoTDB vs. Apache Pinot vs. Graph Engine vs. HBase vs. RavenDB

System Properties Comparison Apache IoTDB vs. Apache Pinot vs. Graph Engine vs. HBase vs. RavenDB

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Wide-column store based on Apache Hadoop and on concepts of BigTable

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS
Key-value store

Wide column store

Document store

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

Technical documentation

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

docs.pinot.apache.org

www.graphengine.io/docs/manual

hbase.apache.org/book.html

ravendb.net/docs

Developer

Apache Software Foundation

Apache Software Foundation and contributors

Microsoft

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Hibernating Rhinos

Initial release

2018

2015

2010

2008

2010

Current release

1.1.0, April 2023

1.0.0, September 2023

2.3.4, January 2021

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache version 2

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

.NET and C

Java

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java JDK11 or higher

.NET

Linux
Unix
Windows

using Cygwin

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

SQL-like query language (RQL)

APIs and other access methods

JDBC
Native API

JDBC

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
Python

C#
C++
F#
Visual Basic

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

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
Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Authorization levels configured per client per database

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