DBMS > AlaSQL vs. Atos Standard Common Repository vs. Google BigQuery vs. Rockset

System Properties Comparison AlaSQL vs. Atos Standard Common Repository vs. Google BigQuery vs. Rockset

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

JavaScript DBMS library

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Large scale data warehouse service with append-only tables

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

Primary database model

Document store
Relational DBMS

Document store
Key-value store

Relational DBMS

Document store

Secondary database models

Relational DBMS
Search engine

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

alasql.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

rockset.com

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

docs.rockset.com

Developer

Andrey Gershun & Mathias R. Wulff

Atos Convergence Creators

Google

Rockset

Initial release

2014

2016

2010

2019

Current release

1703

License

Commercial or Open Source

Open Source

MIT-License

commercial

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

JavaScript

Java

C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux

hosted

Data scheme

schema-free

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

Read-only SQL queries, including JOINs

APIs and other access methods

JavaScript API

LDAP

RESTful HTTP/JSON API

HTTP REST

Supported programming languages

JavaScript

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

none

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

Atomic execution of specific operations

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

LDAP bind authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and organizations can be defined via Rockset console

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