DBMS > Datomic vs. Google Cloud Datastore vs. HyperSQL
System Properties Comparison Datomic vs. Google Cloud Datastore vs. HyperSQL
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|Editorial information provided by DB-Engines|
|Name||Datomic Xexclude from comparison||Google Cloud Datastore Xexclude from comparison||HyperSQL also known as HSQLDB Xexclude from comparison|
|Description||Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability||Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform||Multithreaded, transactional RDBMS written in Java also known as HSQLDB|
|Primary database model||Relational DBMS||Document store||Relational DBMS|
|Current release||1.0.6362, January 2022||2.7.1, October 2022|
|License Commercial or Open Source||commercial limited edition free||commercial||Open Source based on BSD license|
|Cloud-based only Only available as a cloud service||no||yes||no|
|DBaaS offerings (sponsored links) Database as a Service|
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|Implementation language||Java, Clojure||Java|
|Server operating systems||All OS with a Java VM||hosted||All OS with a Java VM Embedded (into Java applications) and Client-Server operating modes|
|Typing predefined data types such as float or date||yes||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.||no||no||no|
|SQL Support of SQL||no||SQL-like query language (GQL)||yes|
|APIs and other access methods||RESTful HTTP API||gRPC (using protocol buffers) API|
RESTful HTTP/JSON API
|HTTP API JDBC via HTTP|
|Supported programming languages||Clojure|
|All languages supporting JDBC/ODBC|
|Server-side scripts Stored procedures||yes Transaction Functions||using Google App Engine||Java, SQL|
|Triggers||By using transaction functions||Callbacks using the Google Apps Engine||yes|
|Partitioning methods Methods for storing different data on different nodes||none But extensive use of caching in the application peers||Sharding||none|
|Replication methods Methods for redundantly storing data on multiple nodes||none But extensive use of caching in the application peers||Multi-source replication using Paxos||none|
|MapReduce Offers an API for user-defined Map/Reduce methods||no||yes using Google Cloud Dataflow||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency||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.||Immediate Consistency|
|Foreign keys Referential integrity||no||yes via ReferenceProperties or Ancestor paths||yes|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||ACID||ACID Serializable Isolation within Transactions, Read Committed outside of Transactions||ACID|
|Concurrency Support for concurrent manipulation of data||yes||yes||yes|
|Durability Support for making data persistent||yes using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)||yes||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||yes recommended only for testing and development||no||yes|
|User concepts Access control||no||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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|Datomic||Google Cloud Datastore||HyperSQL also known as HSQLDB|
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