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

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

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

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.

Primary database model

Time Series DBMS

Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

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

Website

www.mireo.com/spacetime

Technical documentation

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

phoenix.apache.org

cratedb.com/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Crate

Snowflake Computing Inc.

Mireo

Initial release

2018

2014

2013

2014

2020

Current release

1.1.0, April 2023

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

5.8.1, August 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

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

SQL-like query language

yes

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
Native API

JDBC

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

CLI Client
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

user defined functions (Javascript)

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

yes

Fixed-grid hypercubes

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

Configurable replication on table/partition-level

yes

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

yes

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

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