DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. JanusGraph vs. Kdb vs. Snowflake

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. JanusGraph vs. Kdb vs. Snowflake

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 Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

High performance Time Series DBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store

Time Series DBMS

Graph DBMS

Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Relational 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	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cloud.google.com/datastore

Technical documentation

cloud.google.com/datastore/docs

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

docs.janusgraph.org

code.kx.com

docs.snowflake.net/manuals/index.html

Developer

Google

Community supported by Red Hat

Linux Foundation; originally developed as Titan by Aurelius

Kx Systems, a division of First Derivatives plc

Snowflake Computing Inc.

Initial release

2008

2014

2017

2000

kdb was released 2000, kdb+ in 2003

2014

Current release

0.6.3, February 2023

3.6, May 2018

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

free 32-bit version

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
Unix
Windows

Linux
OS X
Solaris
Windows

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

table attribute 'grouped'

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language (q)

yes

APIs and other access methods

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

HTTP REST

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

CLI Client
JDBC
ODBC

Supported programming languages

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

Go
Java
Python
Ruby

Clojure
Java
Python

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

yes

with views

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

via Faunus, a graph analytics engine

similar paradigm used for internal processing

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

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Relationships in graphs

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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)

User authentification and security via Rexster Graph Server

rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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