DBMS > Amazon Redshift vs. LevelDB vs. Spark SQL vs. SwayDB vs. TerminusDB

System Properties Comparison Amazon Redshift vs. LevelDB vs. Spark SQL vs. SwayDB vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store
RDF store

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Trend Chart

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

aws.amazon.com/redshift

github.com/google/leveldb

spark.apache.org/sql

swaydb.simer.au

terminusdb.com

Technical documentation

docs.aws.amazon.com/redshift

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

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

terminusdb.github.io/terminusdb/#

Developer

Amazon (based on PostgreSQL)

Google

Apache Software Foundation

Simer Plaha

DataChemist Ltd.

Initial release

2012

2011

2014

2018

Current release

1.23, February 2021

3.5.0 ( 2.13), September 2023

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache 2.0

Open Source

GNU Affero GPL V3.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Scala

Prolog, Rust

Server operating systems

hosted

Illumos
Linux
OS X
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

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

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Python
R
Scala

Java
Kotlin
Scala

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Journaling Streams

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

yes

Transaction concepts

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

ACID

Atomic execution of operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

in-memory journaling

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

Role-based access control

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