DBMS > FeatureBase vs. InfinityDB vs. OpenTSDB vs. Stardog

System Properties Comparison FeatureBase vs. InfinityDB vs. OpenTSDB vs. Stardog

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

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.

A Java embedded Key-Value Store which extends the Java Map interface

Scalable Time Series DBMS based on HBase

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

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

Score	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

Technical documentation

docs.featurebase.com

boilerbay.com/infinitydb/manual

opentsdb.net/docs/build/html/index.html

docs.stardog.com

Developer

Molecula and Pilosa Open Source Contributors

Boiler Bay Inc.

currently maintained by Yahoo and other contributors

Stardog-Union

Initial release

2017

2002

2011

2010

Current release

2022, May 2022

4.0

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

LGPL

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Server operating systems

Linux
macOS

All OS with a Java VM

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

numeric data for metrics, strings for tags

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/export of XML data possible

Secondary indexes

manual creation possible, using inversions based on multi-value capability

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL queries

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP API
Telnet API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Java
Python

Java

Erlang
Go
Java
Python
R
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

based on HBase

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

based on HBase

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

yes

relationships in graphs

Transaction concepts

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

yes

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Access rights for users and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

show all

Recent citations in the news

Get Your Infrastructure Ready for Real-Time Analytics
8 March 2022, Built In

Funding wrap: H.O. Maycotte's Molecula raises $10M, rebrands; UT snags $15M to lead regional innovation hub
12 September 2022, The Business Journals

The 10 Coolest Big Data Tools Of 2021
7 December 2021, CRN

Pilosa: A Scalable High Performance Bitmap Database Index
17 June 2019, hackernoon.com

32 Data and Analytics Startups That Will Go Big, According to VCs
28 September 2021, Business Insider

provided by Google News

Comparing Different Time-Series Databases
10 February 2022, hackernoon.com

Brain Monitoring with Kafka, OpenTSDB, and Grafana
5 August 2016, KDnuggets

MapR to help admins peer into dense Hadoop clusters
28 June 2016, SiliconANGLE News

A real-time processing revival – O'Reilly
1 April 2015, O'Reilly Media

MakeMyTrip travels forward in time using the power of open source
16 May 2017, Open Source For You

provided by Google News

Share this page