DBMS > Google BigQuery vs. Hawkular Metrics vs. HyperSQL vs. Warp 10

System Properties Comparison Google BigQuery vs. Hawkular Metrics vs. HyperSQL vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

www.hawkular.org

hsqldb.org

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

hsqldb.org/web/hsqlDocsFrame.html

www.warp10.io/content/02_Getting_started

Developer

Google

Community supported by Red Hat

SenX

Initial release

2010

2014

2001

2015

Current release

2.7.2, June 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

based on BSD license

Open Source

Apache License 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

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP REST

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, SQL

yes

WarpScript

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

based on HBase

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

ACID

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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