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

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

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Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

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

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

Key-value store

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	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

etcd.io
github.com/etcd-io/etcd

github.com/spotify/heroic

www.postgres-xl.org

www.sadasengine.com

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

spotify.github.io/heroic

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

since 2012, originally named StormDB

2006

Current release

3.4, August 2019

10 R1, October 2018

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 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

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux
macOS

AIX
Linux
Windows

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

APIs and other access methods

gRPC
JSON over HTTP

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

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

JDBC
ODBC
Proprietary protocol

Supported programming languages

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

.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, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate 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

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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