DBMS > Heroic vs. JanusGraph vs. Postgres-XL vs. SingleStore

System Properties Comparison Heroic vs. JanusGraph vs. Postgres-XL vs. SingleStore

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

A Graph DBMS optimized for distributed clusters

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

github.com/spotify/heroic

janusgraph.org

www.postgres-xl.org

www.singlestore.com

Technical documentation

spotify.github.io/heroic

docs.janusgraph.org

www.postgres-xl.org/documentation

docs.singlestore.com

Developer

Spotify

Linux Foundation; originally developed as Titan by Aurelius

SingleStore Inc.

Initial release

2014

2017

2014

since 2012, originally named StormDB

2013

Current release

0.6.3, February 2023

10 R1, October 2018

8.5, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Mozilla public license

commercial

free developer edition available

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Java

C++, Go

Server operating systems

Linux
OS X
Unix
Windows

Linux
macOS

Linux

64 bit version required

Data scheme

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

but no triggers and foreign keys

APIs and other access methods

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

Clojure
Java
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

horizontal partitioning

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

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

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

In-memory capabilities

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

yes

User concepts

Access control

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more

Competitive advantages

SingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more

Typical application scenarios

Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more

Key customers

IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more

Market metrics

Customers in various industries worldwide including US and International Industry...
» more

Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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

Turbocharge Your Application Development Using WebAssembly With SingleStoreDB
17 October 2022, Akmal Chaudhri, SingleStore (sponsor)

Cloud-Based Analytics With SingleStoreDB
9 June 2022, Akmal Chaudhri, SingleStore (sponsor)

SingleStore: The Increasing Momentum of Multi-Model Database Systems
14 February 2022, Akmal Chaudhri, SingleStore (sponsor)

show all

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

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

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

From graph db to graph embedding. In 7 simple steps. | by Andy Greatorex
30 July 2020, Towards Data Science

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

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

provided by Google News

SingleStore CEO sees little future for purpose-built vector databases
24 January 2024, VentureBeat

SingleStore Announces Real-time Data Platform to Further Accelerate AI, Analytics and Application Development
24 January 2024, Business Wire

Building a Modern Database: Nikita Shamgunov on Postgres and Beyond
18 April 2024, Madrona Venture Group

SingleStore adds indexed vector search to Pro Max release for faster AI work – Blocks and Files
29 January 2024, Blocks & Files

SingleStore, Snowflake Help Customers Build Enterprise-Ready Generative AI Apps
3 January 2024, Acceleration Economy

provided by Google News

Share this page