DBMS > FeatureBase vs. Heroic vs. SWC-DB vs. TerminusDB vs. Yaacomo

System Properties Comparison FeatureBase vs. Heroic vs. SWC-DB vs. TerminusDB vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

A high performance, scalable Wide Column DBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Secondary database models

Time Series DBMS

Document store
RDF store

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

Score	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

www.featurebase.com

github.com/spotify/heroic

github.com/kashirin-alex/swc-db
www.swcdb.org

terminusdb.com

yaacomo.com

Technical documentation

docs.featurebase.com

spotify.github.io/heroic

terminusdb.github.io/terminusdb/#

Developer

Molecula and Pilosa Open Source Contributors

Spotify

Alex Kashirin

DataChemist Ltd.

Q2WEB GmbH

Initial release

2017

2014

2020

2018

2009

Current release

2022, May 2022

0.5, April 2021

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3

Open Source

GPL V3

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Prolog, Rust

Server operating systems

Linux
macOS

Linux

Android
Linux
Windows

Data scheme

yes

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL queries

SQL-like query language

SQL-like query language (WOQL)

yes

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

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

Proprietary protocol
Thrift

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

JDBC
ODBC

Supported programming languages

Java
Python

C++

JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Graph Partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Journaling Streams

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using Linux fsync

yes

in-memory journaling

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

Role-based access control

fine grained access rights according to SQL-standard

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