DBMS > Google BigQuery vs. Hazelcast vs. SwayDB vs. Tkrzw

System Properties Comparison Google BigQuery vs. Hazelcast vs. SwayDB vs. Tkrzw

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

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	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

cloud.google.com/bigquery

hazelcast.com

swaydb.simer.au

dbmx.net/tkrzw

Technical documentation

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

Developer

Google

Hazelcast

Simer Plaha

Mikio Hirabayashi

Initial release

2010

2008

2018

2020

Current release

5.3.6, November 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

GNU Affero GPL V3.0

Open Source

Apache Version 2.0

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

Scala

C++

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
Kotlin
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

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

Foreign keys

Referential integrity

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

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

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

using specific database classes

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.