DBMS > etcd vs. Spark SQL vs. SQLite vs. YTsaurus

System Properties Comparison etcd vs. Spark SQL vs. SQLite vs. YTsaurus

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Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

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

Widely used embeddable, in-process RDBMS

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Key-value store

Relational DBMS

Document store
Key-value store

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

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.21
Rank	#324	Overall
	#45	Document stores
	#48	Key-value stores

Website

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

spark.apache.org/sql

www.sqlite.org

ytsaurus.tech

Technical documentation

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

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

www.sqlite.org/docs.html

ytsaurus.tech/docs/en

Developer

Apache Software Foundation

Dwayne Richard Hipp

Yandex

Initial release

2014

2000

2023

Current release

3.4, August 2019

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache License, Version 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

Scala

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux
OS X
Windows

server-less

Ubuntu

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

YQL, an SQL-based language, is supported

APIs and other access methods

gRPC
JSON over HTTP

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

none

Sharding

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

User concepts

Access control

Access Control Lists

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