DBMS > Google BigQuery vs. Hawkular Metrics vs. Ignite vs. LokiJS vs. MarkLogic

System Properties Comparison Google BigQuery vs. Hawkular Metrics vs. Ignite vs. LokiJS vs. MarkLogic

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.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

In-memory JavaScript DBMS

Operational and transactional Enterprise NoSQL database

Primary database model

Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

Document store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

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	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.43
Rank	#264	Overall
	#42	Document stores

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Website

cloud.google.com/bigquery

www.hawkular.org

ignite.apache.org

github.com/techfort/LokiJS

www.marklogic.com

Technical documentation

cloud.google.com/bigquery/docs

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

apacheignite.readme.io/docs

techfort.github.io/LokiJS

docs.marklogic.com

Developer

Google

Community supported by Red Hat

Apache Software Foundation

MarkLogic Corp.

Initial release

2010

2014

2015

2014

2001

Current release

Apache Ignite 2.6

11.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

commercial

restricted free version is 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.

Implementation language

Java

C++, Java, .Net

JavaScript

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

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

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Schema can be enforced

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

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

yes

SQL92

APIs and other access methods

RESTful HTTP/JSON API

HTTP REST

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JavaScript API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

JavaScript

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

View functions in JavaScript

yes

via XQuery or JavaScript

Triggers

yes

via Hawkular Alerting

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes (replicated cache)

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

none

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

ACID

atomic operations within a single collection possible

ACID

can act as a resource manager in an XA/JTA transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, with Range Indexes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Role-based access control at the document and subdocument levels

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