DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. Postgres-XL vs. Sequoiadb

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. Postgres-XL vs. Sequoiadb

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Editorial information provided by DB-Engines
Name	Google Cloud Datastore  Xexclude from comparison	Hawkular Metrics  Xexclude from comparison	Postgres-XL  Xexclude from comparison	Sequoiadb  Xexclude from comparison
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.	Based on PostgreSQL enhanced with MPP and write-scale-out cluster features	NewSQL database with distributed OLTP and SQL
Primary database model	Document store	Time Series DBMS	Relational DBMS	Document store Relational DBMS
Secondary database models			Document store Spatial 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 0.53 Rank #254   Overall #117   Relational DBMS	Score 0.50 Rank #258   Overall #41   Document stores #120   Relational DBMS
Website	cloud.google.com/­datastore	www.hawkular.org	www.postgres-xl.org	www.sequoiadb.com
Technical documentation	cloud.google.com/­datastore/­docs	www.hawkular.org/­hawkular-metrics/­docs/­user-guide	www.postgres-xl.org/­documentation	www.sequoiadb.com/­en/­index.php?m=Files&a=index
Developer	Google	Community supported by Red Hat		Sequoiadb Ltd.
Initial release	2008	2014	2014 since 2012, originally named StormDB	2013
Current release			10 R1, October 2018
License Commercial or Open Source	commercial	Open Source Apache 2.0	Open Source Mozilla public license	Open Source Server: AGPL; Client: Apache V2
Cloud-based only Only available as a cloud service	yes	no	no	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language		Java	C	C++
Server operating systems	hosted	Linux OS X Windows	Linux macOS	Linux
Data scheme	schema-free	schema-free	yes	schema-free
Typing predefined data types such as float or date	yes, details here	yes	yes	yes oid, date, timestamp, binary, regex
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.	no	no	yes XML type, but no XML query functionality	no
Secondary indexes	yes	no	yes	yes
SQL Support of SQL	SQL-like query language (GQL)	no	yes distributed, parallel query execution	SQL-like query language
APIs and other access methods	gRPC (using protocol buffers) API RESTful HTTP/JSON API	HTTP REST	ADO.NET JDBC native C library ODBC streaming API for large objects	proprietary protocol using JSON
Supported programming languages	.Net Go Java JavaScript (Node.js) PHP Python Ruby	Go Java Python Ruby	.Net C C++ Delphi Erlang Java JavaScript (Node.js) Perl PHP Python Tcl	.Net C++ Java PHP Python
Server-side scripts Stored procedures	using Google App Engine	no	user defined functions	JavaScript
Triggers	Callbacks using the Google Apps Engine	yes via Hawkular Alerting	yes	no
Partitioning methods Methods for storing different data on different nodes	Sharding	Sharding based on Cassandra	horizontal partitioning	Sharding
Replication methods Methods for redundantly storing data on multiple nodes	Multi-source replication using Paxos	selectable replication factor based on Cassandra		Source-replica replication
MapReduce Offers an API for user-defined Map/Reduce methods	yes using Google Cloud Dataflow	no	no	no
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
Foreign keys Referential integrity	yes via ReferenceProperties or Ancestor paths	no	yes	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	ACID Serializable Isolation within Transactions, Read Committed outside of Transactions	no	ACID MVCC	Document is locked during a transaction
Concurrency Support for concurrent manipulation of data	yes	yes	yes	yes
Durability Support for making data persistent	yes	yes	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	no	no	no	no
User concepts Access control	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	no	fine grained access rights according to SQL-standard	simple password-based access control
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More resources
	Google Cloud Datastore	Hawkular Metrics	Postgres-XL	Sequoiadb
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