Cognitect Joins Nubank!

We are thrilled to announce that Cognitect is joining the Nubank family of companies. This is the next step in a long relationship, and opens new opportunities for Clojure and Datomic worldwide. Please read the full story over on the Cognitect Blog.

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Datomic Analytics (Preview)

Today's releases of Datomic Cloud and Datomic On-Prem preview a major new feature: analytics support.
With analytics support, your data scientists, analysts, and operations people can directly access Datomic using the languages and tools they already know (e.g. SQL, Python, R, Tableau, Metabase, Superset and more), without you having to do any ETL.
The analytics metaschema specifies a mapping from Datomic entities and attributes to dynamic SQL tables and columns, as shown in the example below:
In Cloud, the bastion server has been renamed to access gateway, as it now also supports analytics. Analytics support is automatic when you select an EC2 instance size that supports it, and costs nothing beyond the cost of the EC2 instance.
In On-Prem, analytics support is available via the bin/presto script in the distribution.
To learn more about the analytics preview:
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Tuples and Database Predicates

Today's releases of Datomic Cloud and Datomic On-Prem include two major new features: tuples and  database predicates.

Tuples are a new compound data type, small vectors as values. You can use tuples to create multi-attribute unique keys on domain entities. You can also use tuples to optimize queries that otherwise would have to join two or more high-population attributes.

  • If you declare a composite tuple, Datomic will automatically populate the tuple from existing attributes. 
  • You can also define your own homogeneous or heterogeneous tuple types that you populate as you see fit.
Database predicates are functions and attribute lists that constrain the values accepted by transactions.
  • Attribute predicates are declared in schema and constrain the values taken on by a single attribute, in all contexts.  For example, you could limit a test/grade attribute to the range 0-100.
  • Entity specs comprise lists of required attributes and/or predicates of the entity and the post-transaction db. You can use entity specs to ensure properties across different attributes of an entity or even across entities.  For example, you might enforce that a game player's score/lowest must be less than or equal to their score/highest.  You must explicitly call for entity specs in transactions in which you want them to apply.
To learn more about tuples, database predicates, and other enhancements:


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Return Maps

Most Datomic queries return tuples, but sometimes you just want maps.  Today's release of the Datomic Cloud client library adds return maps to Datomic datalog.  For example, the following query uses the new :keys clause to request maps with :artist and :release keys:

[:find ?artist-name ?release-name
:keys artist release
:where [?release :release/name ?release-name]
[?release :release/artists ?artist]
[?artist :artist/name ?artist-name]]

Running against the mbrainz-sample database, this query returns:

#{{:artist "George Jones" :release "With Love"}
{:artist "Shocking Blue" :release "Hello Darkness / Pickin' Tomatoes"}
{:artist "Junipher Greene" :release "Friendship"}
...}

To try return maps, you can


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