DBMS > Amazon Redshift vs. Apache IoTDB vs. Rockset vs. ScyllaDB vs. SpaceTime

System Properties Comparison Amazon Redshift vs. Apache IoTDB vs. Rockset vs. ScyllaDB vs. SpaceTime

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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 scalable, reliable search and analytics service in the cloud, built on RocksDB

Cassandra and DynamoDB compatible wide column store

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

Primary database model

Secondary database models

Relational DBMS
Search engine

Key-value store

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

aws.amazon.com/redshift

iotdb.apache.org

rockset.com

www.scylladb.com

www.mireo.com/spacetime

Technical documentation

docs.aws.amazon.com/redshift

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

docs.rockset.com

docs.scylladb.com

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Rockset

ScyllaDB

Mireo

Initial release

2012

2018

2019

2015

2020

Current release

1.1.0, April 2023

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Open Source (AGPL), commercial license available

commercial

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.

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

restricted

yes

all fields are automatically indexed

yes

cluster global secondary indices

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

Read-only SQL queries, including JOINs

SQL-like DML and DDL statements (CQL)

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
ODBC

JDBC
Native API

HTTP REST

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

Go
Java
JavaScript (Node.js)
Python

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

yes, Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Automatic sharding

Sharding

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

selectable replication factor

Representation of geographical distribution of servers is possible

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

in-memory tables

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Access rights for users and organizations can be defined via Rockset console

Access rights for users can be defined per object

yes

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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