DBMS > Apache Phoenix vs. CrateDB vs. Snowflake vs. SpaceTime vs. StarRocks

System Properties Comparison Apache Phoenix vs. CrateDB vs. Snowflake vs. SpaceTime vs. StarRocks

Editorial information provided by DB-Engines

Name

Description

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

Distributed Database based on Lucene

Cloud-based data warehousing service for structured and semi-structured data

SpaceTime is a spatio-temporal DBMS with a focus on performance.

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.90
Rank	#125	Overall
	#59	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	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Score	0.95
Rank	#193	Overall
	#89	Relational DBMS

Website

phoenix.apache.org

cratedb.com

www.snowflake.com

www.mireo.com/spacetime

www.starrocks.io

Technical documentation

phoenix.apache.org

cratedb.com/docs

docs.snowflake.net/manuals/index.html

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Apache Software Foundation

Crate

Snowflake Computing Inc.

Mireo

The Linux Foundation

since Feb 2023

Initial release

2014

2013

2014

2020

Current release

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

5.8.1, August 2024

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

C++, Java

Server operating systems

Linux
Unix
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

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

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

A subset of ANSI SQL is implemented

yes

APIs and other access methods

JDBC

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

CLI Client
JDBC
ODBC

RESTful HTTP API

JDBC
MySQL protocol

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.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

JavaScript (Node.js)
Python

C#
C++
Python

Java

Server-side scripts

Stored procedures

user defined functions

user defined functions (Javascript)

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Fixed-grid hypercubes

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Configurable replication on table/partition-level

yes

Real-time block device replication (DRBD)

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

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

rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

Role based access control and fine grained access rights

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