DBMS > Apache Phoenix vs. ClickHouse vs. CrateDB vs. Dgraph vs. PostGIS

System Properties Comparison Apache Phoenix vs. ClickHouse vs. CrateDB vs. Dgraph vs. PostGIS

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

Distributed and scalable native Graph DBMS

Spatial extension of PostgreSQL

Primary database model

Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Graph DBMS

Spatial DBMS

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Website

Technical documentation

postgis.net/documentation

Developer

Apache Software Foundation

Clickhouse Inc.

Crate

Dgraph Labs, Inc.

Initial release

2014

2016

2013

2016

2005

Current release

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

v24.4.2.141-stable, June 2024

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL v2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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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

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

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

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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions (Javascript)

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

yes

based on PostgreSQL

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

Synchronous replication via Raft

yes

based on PostgreSQL

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

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

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

Planned for future releases

yes

based on PostgreSQL

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