DBMS > Heroic vs. NSDb vs. Postgres-XL vs. Sadas Engine

System Properties Comparison Heroic vs. NSDb vs. Postgres-XL vs. Sadas Engine

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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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

github.com/spotify/heroic

nsdb.io

www.postgres-xl.org

www.sadasengine.com

Technical documentation

spotify.github.io/heroic

nsdb.io/Architecture

www.postgres-xl.org/documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Spotify

SADAS s.r.l.

Initial release

2014

2017

2014

since 2012, originally named StormDB

2006

Current release

10 R1, October 2018

8.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Mozilla public license

commercial

free trial version 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.

Implementation language

Java

Java, Scala

C++

Server operating systems

Linux
macOS

AIX
Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

distributed, parallel query execution

yes

APIs and other access methods

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

gRPC
HTTP REST
WebSocket

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

JDBC
ODBC
Proprietary protocol

Supported programming languages

Java
Scala

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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Recent citations in the news

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

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