DBMS > Atos Standard Common Repository vs. Heroic vs. InfluxDB vs. JanusGraph vs. RocksDB

System Properties Comparison Atos Standard Common Repository vs. Heroic vs. InfluxDB vs. JanusGraph vs. RocksDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

DBMS for storing time series, events and metrics

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Key-value store

Time Series DBMS

Graph DBMS

Key-value store

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	22.12
Rank	#28	Overall
	#1	Time Series DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	2.84
Rank	#97	Overall
	#16	Key-value stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

github.com/spotify/heroic

www.influxdata.com/products/influxdb-overview

janusgraph.org

rocksdb.org

Technical documentation

spotify.github.io/heroic

docs.influxdata.com/influxdb

docs.janusgraph.org

github.com/facebook/rocksdb/wiki

Developer

Atos Convergence Creators

Spotify

Linux Foundation; originally developed as Titan by Aurelius

Facebook, Inc.

Initial release

2016

2014

2013

2017

2013

Current release

1703

2.7.6, April 2024

1.0.0, October 2023

9.4.0, June 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache 2.0

Open Source

BSD

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++

Server operating systems

Linux

Linux
OS X

through Homebrew

Linux
OS X
Unix
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

Numeric data and Strings

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.

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

LDAP

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

HTTP API
JSON over UDP

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

C++ API
Java API

Supported programming languages

All languages with LDAP bindings

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Clojure
Java
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

in enterprise version only

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

in enterprise version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Atomic execution of specific operations

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Depending on used storage engine

yes

User concepts

Access control

LDAP bind authentication

simple rights management via user accounts

User authentification and security via Rexster Graph Server

More information provided by the system vendor

Specific characteristics

InfluxData is the creator of InfluxDB , the open source time series database. It...
» more

Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
» more

Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more

Key customers

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more

Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more

Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
» more

News

Real-Time Visualization for IIoT Data
24 September 2024

Deploying InfluxDB and Telegraf to Monitor Kubernetes
17 September 2024

Telegraf 1.32 Release Notes
13 September 2024

An Introductory Guide to Cloud Security for IIoT
12 September 2024

Building Real-Time Android Apps with InfluxDB Cloud: Data Logging, Querying, and Visualization
10 September 2024

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

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

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

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

show all

Recent citations in the news

Run and manage open source InfluxDB databases with Amazon Timestream
14 March 2024, AWS Blog

InfluxData avoids ’AI magic beans’ in InfluxDB time series database update for enterprises
4 September 2024, VentureBeat

InfluxData makes performance, storage improvements to InfluxDB 3.0
4 September 2024, InfoWorld

InfluxData's Latest Updates Optimize Time Series Data for Better Performance, Scale and Management
19 September 2024, Integration Developers

InfluxData Brings Higher Performance and New Features to InfluxDB 3.0 to Power Massive Time Series Workloads at Scale
4 September 2024, Business Wire

provided by Google News

Simple Deployment of a Graph Database: JanusGraph
12 October 2020, Towards Data Science

Database Deep Dives: JanusGraph
8 August 2019, ibm.com

JanusGraph Picks Up Where TitanDB Left Off
13 January 2017, Datanami

Nordstrom Builds Flexible Backend Ops with Kubernetes, Spark and JanusGraph
3 October 2019, The New Stack

Compose for JanusGraph arrives on Bluemix
15 September 2017, ibm.com

provided by Google News

AMD EPYC vs. Intel Xeon Cascadelake With Facebook's RocksDB Database
17 October 2019, Phoronix

Meta’s Velox Means Database Performance Is Not Subject To Interpretation
31 August 2022, The Next Platform

Facebook’s MyRocks Truly Rocks!
21 September 2020, Open Source For You

Did Rockset Just Solve Real-Time Analytics?
25 August 2021, Datanami

Power your Kafka Streams application with Amazon MSK and AWS Fargate
10 August 2021, AWS Blog

provided by Google News

Share this page