DBMS > Apache Drill vs. Apache IoTDB vs. ClickHouse vs. Hazelcast vs. Hyprcubd

System Properties Comparison Apache Drill vs. Apache IoTDB vs. ClickHouse vs. Hazelcast vs. Hyprcubd

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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 high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

A widely adopted in-memory data grid

Serverless Time Series DBMS

Primary database model

Document store
Relational DBMS

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Website

hyprcubd.com (offline)

Technical documentation

drill.apache.org/docs

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

clickhouse.com/docs

hazelcast.org/imdg/docs

Developer

Apache Software Foundation

Clickhouse Inc.

Hazelcast

Hyprcubd, Inc.

Initial release

2012

2018

2016

2008

Current release

1.20.3, January 2023

1.1.0, April 2023

v24.4.1.2088-stable, May 2024

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

C++

Java

Server operating systems

Linux
OS X
Windows

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
macOS

All OS with a Java VM

hosted

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
Native API

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JCache
JPA
Memcached protocol
RESTful HTTP API

gRPC (https)

Supported programming languages

C++

C
C#
C++
Go
Java
Python
Scala

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

key based and custom

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

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

yes

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Role-based access control

token access

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