DBMS > Apache Druid vs. Google BigQuery vs. Hawkular Metrics vs. SingleStore

System Properties Comparison Apache Druid vs. Google BigQuery vs. Hawkular Metrics vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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.

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	2.67
Rank	#107	Overall
	#53	Relational DBMS
	#7	Time Series DBMS

Score	36.00
Rank	#24	Overall
	#15	Relational DBMS

Score	0.01
Rank	#329	Overall
	#30	Time Series DBMS

Score	5.51
Rank	#65	Overall
	#37	Relational DBMS

Website

druid.apache.org

cloud.google.com/bigquery

www.hawkular.org

www.singlestore.com

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

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

docs.singlestore.com

Developer

Apache Software Foundation and contributors

Google

Community supported by Red Hat

SingleStore Inc.

Initial release

2012

2010

2014

2013

Current release

0.20.0, October 2020

7.1.11, October 2020

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

commercial

free developer edition available

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.

SingleStore Managed Service: The world's fastest, most capable cloud database for operational analytics, machine learning, & AI. Available instantly with multi-cloud and hybrid-cloud capabilities.

Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

hosted

Linux
OS X
Windows

Linux

64 bit version required

Data scheme

yes

schema-less columns are supported

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

SQL

Support of SQL

SQL for querying

yes

yes, but no triggers and foreign keys

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

HTTP REST

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

based on Cassandra

Source-replica replication

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 only within each node

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Only isolation level: READ COMMITED

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access privileges (owner, writer, reader) for whole datasets, not for individual tables

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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