DBMS > Google BigQuery vs. Hazelcast vs. Linter vs. PouchDB vs. searchxml

System Properties Comparison Google BigQuery vs. Hazelcast vs. Linter vs. PouchDB vs. searchxml

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

RDBMS for high security requirements

JavaScript DBMS with an API inspired by CouchDB

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Native XML DBMS
Search engine

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

cloud.google.com/bigquery

hazelcast.com

linter.ru

pouchdb.com

www.searchxml.net/category/products

Technical documentation

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

pouchdb.com/guides

www.searchxml.net/support/handouts

Developer

Google

Hazelcast

relex.ru

Apache Software Foundation

informationpartners gmbh

Initial release

2010

2008

1990

2012

2015

Current release

5.3.6, November 2023

7.1.1, June 2019

1.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

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

Java

C and C++

JavaScript

C++

Server operating systems

hosted

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

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

Windows

Data scheme

yes

schema-free

yes

schema-free

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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

HTTP REST

only for PouchDB Server
JavaScript API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

JavaScript

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

View functions in JavaScript

yes

on the application server

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

one or two-phase-commit; repeatable reads; read commited

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Google Cloud Identity & Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

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