DBMS > Rockset vs. Spark SQL vs. SQLite vs. SQream DB

System Properties Comparison Rockset vs. Spark SQL vs. SQLite vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

A scalable, reliable search and analytics service in the cloud, built on RocksDB

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

Widely used embeddable, in-process RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Document store

Relational DBMS

Secondary database models

Relational DBMS
Search engine

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

Technical documentation

docs.rockset.com

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

www.sqlite.org/docs.html

docs.sqream.com

Developer

Rockset

Apache Software Foundation

Dwayne Richard Hipp

SQream Technologies

Initial release

2019

2014

2000

2017

Current release

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Public Domain

commercial

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

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux
OS X
Windows

server-less

Linux

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

dynamic typing

yes

not rigid because of 'dynamic typing' concept.

yes, ANSI Standard SQL Types

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.

ingestion from XML files supported

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

Read-only SQL queries, including JOINs

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

HTTP REST

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.Net
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
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++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes, utilizing Spark Core

none

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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 rights for users and organizations can be defined via Rockset console

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