DBMS > Amazon DocumentDB vs. Solr vs. Spark SQL vs. SQLite vs. STSdb

System Properties Comparison Amazon DocumentDB vs. Solr vs. Spark SQL vs. SQLite vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

A widely used distributed, scalable search engine based on Apache Lucene

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

Widely used embeddable, in-process RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

aws.amazon.com/documentdb

solr.apache.org

spark.apache.org/sql

www.sqlite.org

github.com/STSSoft/STSdb4

Technical documentation

aws.amazon.com/documentdb/resources

solr.apache.org/resources.html

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

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Dwayne Richard Hipp

STS Soft SC

Initial release

2019

2006

2014

2000

2011

Current release

9.6.1, May 2024

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

server-less

Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

yes

primitive types and user defined types (classes)

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

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

Solr Parallel SQL Interface

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Java API
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.NET Client API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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#
Java

Server-side scripts

Stored procedures

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

optimistic locking

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 rights for users and roles

yes

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