DBMS > Amazon Redshift vs. BoltDB vs. FeatureBase vs. Heroic vs. Hyprcubd

System Properties Comparison Amazon Redshift vs. BoltDB vs. FeatureBase vs. Heroic vs. Hyprcubd

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Serverless Time Series DBMS

Primary database model

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Website

aws.amazon.com/redshift

github.com/boltdb/bolt

www.featurebase.com

github.com/spotify/heroic

hyprcubd.com (offline)

Technical documentation

docs.aws.amazon.com/redshift

docs.featurebase.com

spotify.github.io/heroic

Developer

Amazon (based on PostgreSQL)

Molecula and Pilosa Open Source Contributors

Spotify

Hyprcubd, Inc.

Initial release

2012

2013

2017

2014

Current release

2022, May 2022

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

Open Source

Apache 2.0

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.

Implementation language

Java

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

hosted

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

restricted

yes

via Elasticsearch

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL queries

SQL-like query language

APIs and other access methods

JDBC
ODBC

gRPC
JDBC
Kafka Connector
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

gRPC (https)

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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

fine grained access rights according to SQL-standard

token access

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