DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. Prometheus
System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. Prometheus
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|Editorial information provided by DB-Engines|
|Name||Google Cloud Datastore Xexclude from comparison||Hawkular Metrics Xexclude from comparison||Prometheus 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.||Open-source TimeSeries DBMS and monitoring system|
|Primary database model||Document store||Time Series DBMS||Time Series DBMS|
|Developer||Community supported by Red Hat|
|License Commercial or Open Source||commercial||Open Source Apache 2.0||Open Source Apache 2.0|
|Cloud-based only Only available as a cloud service||yes||no||no|
|DBaaS offerings (sponsored links) Database as a Service|
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems||hosted||Linux|
|Typing predefined data types such as float or date||yes, details here||yes||Numeric data only|
|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||no Import of XML data possible|
|SQL Support of SQL||SQL-like query language (GQL)||no||no|
|APIs and other access methods||gRPC (using protocol buffers) API|
RESTful HTTP/JSON API
|HTTP REST||RESTful HTTP/JSON API|
|Supported programming languages||.Net|
|Server-side scripts Stored procedures||using Google App Engine||no||no|
|Triggers||Callbacks using the Google Apps Engine||yes via Hawkular Alerting||no|
|Partitioning methods Methods for storing different data on different nodes||Sharding||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 by Federation|
|MapReduce Offers an API for user-defined Map/Reduce methods||yes using Google Cloud Dataflow||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
|Foreign keys Referential integrity||yes via ReferenceProperties or Ancestor paths||no||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||no|
|Concurrency Support for concurrent manipulation of data||yes||yes||yes|
|Durability Support for making data persistent||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|
|User concepts Access control||Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)||no||no|
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|Google Cloud Datastore||Hawkular Metrics||Prometheus|
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