DBMS > Hive vs. Spark SQL vs. SQLite vs. SWC-DB vs. TinkerGraph

System Properties Comparison Hive vs. Spark SQL vs. SQLite vs. SWC-DB vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Widely used embeddable, in-process RDBMS

A high performance, scalable Wide Column DBMS

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Relational DBMS

Wide column store

Graph DBMS

Secondary database models

Time Series DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

hive.apache.org

spark.apache.org/sql

www.sqlite.org

github.com/kashirin-alex/swc-db
www.swcdb.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

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

www.sqlite.org/docs.html

Developer

Apache Software Foundation

initially developed by Facebook

Apache Software Foundation

Dwayne Richard Hipp

Alex Kashirin

Initial release

2012

2014

2000

2020

2009

Current release

3.1.3, April 2022

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

GPL V3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C++

Java

Server operating systems

All OS with a Java VM

Linux
OS X
Windows

server-less

Linux

Data scheme

yes

dynamic column types

schema-free

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

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Proprietary protocol
Thrift

TinkerPop 3

Supported programming languages

C++
Java
PHP
Python

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++

Groovy
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

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

optional

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 rights for users, groups and roles

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