DBMS > etcd vs. gStore vs. Postgres-XL vs. Sadas Engine vs. Spark SQL

System Properties Comparison etcd vs. gStore vs. Postgres-XL vs. Sadas Engine vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

A native Graph DBMS to store and maintain very large RDF datasets.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Key-value store

Graph DBMS
RDF store

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	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

Technical documentation

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

en.gstore.cn/#/enDocs

www.postgres-xl.org/documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

SADAS s.r.l.

Apache Software Foundation

Initial release

2016

2014

since 2012, originally named StormDB

2006

2014

Current release

3.4, August 2019

1.2, November 2023

10 R1, October 2018

8.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Mozilla public license

commercial

free trial version available

Open Source

Apache 2.0

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

C++

Scala

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux

Linux
macOS

AIX
Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

schema-free and OWL/RDFS-schema support

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

SQL

Support of SQL

yes

distributed, parallel query execution

yes

SQL-like DML and DDL statements

APIs and other access methods

gRPC
JSON over HTTP

HTTP API
SPARQL 1.1

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

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Supported programming languages

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

C++
Java
JavaScript (Node.js)
PHP
Python

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

Users, roles and permissions, Role-Based Access Control (RBAC) supported

fine grained access rights according to SQL-standard

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

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

Recent citations in the news

etcd: the Not-so-Secret Sauce in Google's Kubernetes and Pivotal's Cloud Foundry
31 May 2024, Data Center Knowledge

Monitor Amazon EKS Control Plane metrics using AWS Open Source monitoring services | Amazon Web Services
12 October 2023, AWS Blog

6 Cool Kubernetes Operators and How to Use Them
22 January 2024, hackernoon.com

ETCD directives don't go well with RBI's stellar reputation
14 April 2024, Business Standard

Killing a market, softly: How an RBI communique could end India's thriving ETCD market
7 April 2024, The Economic Times

provided by Google News

Performance Insights from Sigma Rule Detections in Spark Streaming
1 June 2024, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

The Future of Spark Technology: Igniting Tomorrow!
25 April 2024, Simplilearn

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

Share this page