DBMS > Amazon Redshift vs. etcd vs. LevelDB vs. Spark SQL

System Properties Comparison Amazon Redshift vs. etcd vs. LevelDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A distributed reliable key-value store

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

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

github.com/google/leveldb

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

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

github.com/google/leveldb/blob/main/doc/index.md

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

Developer

Amazon (based on PostgreSQL)

Google

Apache Software Foundation

Initial release

2012

2011

2014

Current release

3.4, August 2019

1.23, February 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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

hosted

FreeBSD
Linux
Windows

experimental

Illumos
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

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.

Secondary indexes

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

gRPC
JSON over HTTP

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

informational only, not enforced by the system

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

fine grained access rights according to SQL-standard

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