DBMS > Apache Phoenix vs. ClickHouse vs. CrateDB vs. Hawkular Metrics

System Properties Comparison Apache Phoenix vs. ClickHouse vs. CrateDB vs. Hawkular Metrics

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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.

Distributed Database based on Lucene

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Time Series DBMS

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Website

Technical documentation

phoenix.apache.org

clickhouse.com/docs

cratedb.com/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Apache Software Foundation

Clickhouse Inc.

Crate

Community supported by Red Hat

Initial release

2014

2016

2013

2014

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

v24.6.2.17-stable, July 2024

5.8.1, August 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

Java

C++

Java

Server operating systems

Linux
Unix
Windows

FreeBSD
Linux
macOS

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

Secondary indexes

yes

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

yes, but no triggers and constraints, and PostgreSQL compatibility

APIs and other access methods

JDBC

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

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

HTTP REST

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
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
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions (Javascript)

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

Configurable replication on table/partition-level

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency
Read-after-write consistency on record level

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

ACID

unique row identifiers can be used for implementing an optimistic concurrency control strategy

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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.

rights management via user accounts

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