DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. LeanXcale vs. Splunk vs. VelocityDB

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. LeanXcale vs. Splunk vs. VelocityDB

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.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Analytics Platform for Big Data

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Document store

Time Series DBMS

Key-value store
Relational DBMS

Search engine

Graph DBMS
Object oriented DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

cloud.google.com/datastore

Technical documentation

cloud.google.com/datastore/docs

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

docs.splunk.com/Documentation/Splunk

velocitydb.com/UserGuide

Developer

Google

Community supported by Red Hat

LeanXcale

Splunk Inc.

VelocityDB Inc

Initial release

2008

2014

2015

2003

2011

Current release

7.x

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

commercial

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

Linux
OS X
Solaris
Windows

Any that supports .NET

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

through Apache Derby

Splunk Search Processing Language for search commands

APIs and other access methods

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

HTTP REST

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP REST

.Net

Supported programming languages

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

Go
Java
Python
Ruby

C
Java
Scala

C#
Java
JavaScript
PHP
Python
Ruby

.Net

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

yes

Callbacks are triggered when data changes

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

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

Access rights for users and roles

Based on Windows Authentication

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More resources

DB-Engines blog posts

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