DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. Heroic vs. IBM Db2 Event Store

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. Heroic vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store

Time Series DBMS

Event Store
Time Series DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Website

cloud.google.com/datastore

www.hawkular.org

github.com/spotify/heroic

www.ibm.com/products/db2-event-store

Technical documentation

cloud.google.com/datastore/docs

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

spotify.github.io/heroic

www.ibm.com/docs/en/db2-event-store

Developer

Google

Community supported by Red Hat

Spotify

IBM

Initial release

2008

2014

2017

Current release

2.0

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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.

Implementation language

Java

C and C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

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

via Elasticsearch

SQL

Support of SQL

SQL-like query language (GQL)

yes

through the embedded Spark runtime

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP REST

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Go
Java
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

yes

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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