DBMS > Heroic vs. Prometheus vs. TerminusDB vs. TimesTen

System Properties Comparison Heroic vs. Prometheus vs. TerminusDB vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

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

Open-source Time Series DBMS and monitoring system

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

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
RDF store

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	7.56
Rank	#49	Overall
	#3	Time Series DBMS

Score	0.19
Rank	#311	Overall
	#27	Graph DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

github.com/spotify/heroic

prometheus.io

terminusdb.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

spotify.github.io/heroic

prometheus.io/docs

terminusdb.github.io/terminusdb/#

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Spotify

DataChemist Ltd.

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2014

2015

2018

1998

Current release

11.0.0, January 2023

Release 22.1

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

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

Prolog, Rust

Server operating systems

Linux
Windows

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language (WOQL)

yes

APIs and other access methods

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

RESTful HTTP/JSON API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

JavaScript
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Graph Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by Federation

Journaling Streams

Multi-source replication
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

none

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

yes

by means of logfiles and checkpoints

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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