DBMS > AlaSQL vs. Google BigQuery vs. LevelDB vs. PouchDB vs. VoltDB

System Properties Comparison AlaSQL vs. Google BigQuery vs. LevelDB vs. PouchDB vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

JavaScript DBMS with an API inspired by CouchDB

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Relational DBMS

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	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

alasql.org

cloud.google.com/bigquery

github.com/google/leveldb

pouchdb.com

www.voltdb.com

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

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

pouchdb.com/guides

docs.voltdb.com

Developer

Andrey Gershun & Mathias R. Wulff

Google

Apache Software Foundation

VoltDB Inc.

Initial release

2014

2010

2011

2012

2010

Current release

1.23, February 2021

7.1.1, June 2019

11.3, April 2022

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

BSD

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

C++

JavaScript

Java, C++

Server operating systems

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

hosted

Illumos
Linux
OS X
Windows

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

Linux
OS X

for development

Data scheme

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

yes

via views

yes

SQL

Support of SQL

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

yes

only a subset of SQL 99

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

JavaScript

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

View functions in JavaScript

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

with automatic compression on writes

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

Snapshots and command logging

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

Google Cloud Identity & Access Management (IAM)

Users and roles with access to stored procedures

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