A Cache maintains an internal state with a mapping from requests to Refs
that will contain the result of those requests when they are executed.
A Cache maintains an internal state with a mapping from requests to Refs
that will contain the result of those requests when they are executed. This
is used internally by the library to provide deduplication and caching of
requests.
A CompletedRequestMap is a universally quantified mapping from requests
of type Request[E, A] to results of type Either[E, A[ for all types E
and A.
A CompletedRequestMap is a universally quantified mapping from requests
of type Request[E, A] to results of type Either[E, A[ for all types E
and A. The guarantee is that for any request of type Request[E, A], if
there is a corresponding value in the map, that value is of type
Either[E, A]. This is used by the library to support data sources that
return different result types for different requests while guaranteeing that
results will be of the type requested.
A DataSource[R, A] is capable of executing requests of type A that
require an environment R.
A DataSource[R, A] is capable of executing requests of type A that
require an environment R.
Data sources must implement the method run which takes a collection of
requests and returns an effect with a CompletedRequestMap containing a
mapping from requests to results. Because run is parameterized on a
collection of requests rather than a single request, data sources have the
ability to introspect on all the requests being executed in parallel and
optimize the query.
Data sources will typically be parameterized on a subtype of Request[A],
though that is not strictly necessarily as long as the data source can map
the request type to a Request[A]. Data sources can then pattern match on
the collection of requests to determine the information requested, execute
the query, and place the results into the CompletedRequestsMap using
CompletedRequestMap.empty and CompletedRequestMap.insert. Data
sources must provide results for all requests received. Failure to do so
will cause a query to die with a QueryFailure when run.
A DataSourceFunction[R, R1] is a universally quantified function from
values of type DataSource[R, A] to values of type DataSource[R1, A] for
all types A.
A DataSourceFunction[R, R1] is a universally quantified function from
values of type DataSource[R, A] to values of type DataSource[R1, A] for
all types A. This is used internally by the library to describe functions
for transforming data sources that do not change the type of requests that a
data source is able to execute.
A Described[A] is a value of type A along with a string description of
that value.
A Described[A] is a value of type A along with a string description of
that value. The description may be used to generate a hash associated with
the value, so values that are equal should have the same description and
values that are not equal should have different descriptions.
QueryFailure keeps track of details relevant to query failures.
A Request[E, A] is a request from a data source for a value of type A
that may fail with an E.
A Request[E, A] is a request from a data source for a value of type A
that may fail with an E.
sealed trait UserRequest[+A] extends Request[Nothing, A] case object GetAllIds extends UserRequest[List[Int]] final case class GetNameById(id: Int) extends UserRequest[String]
A ZQuery[R, E, A] is a purely functional description of an effectual query
that may contain requests from one or more data sources, requires an
environment R, may fail with an E, and may succeed with an A.
A ZQuery[R, E, A] is a purely functional description of an effectual query
that may contain requests from one or more data sources, requires an
environment R, may fail with an E, and may succeed with an A. All
requests that do not need to be performed sequentially, as expressed by
flatMap or combinators derived from it, will automatically be batched,
allowing for aggressive data source specific optimizations. Requests will
also automatically be deduplicated and cached.
This allows for writing queries in a high level, compositional style, with confidence that they will automatically be optimized. For example, consider the following query from a user service.
val getAllUserIds: ZQuery[Any, Nothing, List[Int]] = ??? def getUserNameById(id: Int): ZQuery[Any, Nothing, String] = ??? for { userIds <- getAllUserIds userNames <- ZQuery.foreachPar(userIds)(getUserNameById) } yield userNames
This would normally require N + 1 queries, one for getAllUserIds and one
for each call to getUserNameById. In contrast, ZQuery will automatically
optimize this to two queries, one for userIds and one for userNames,
assuming an implementation of the user service that supports batching.
Based on "There is no Fork: an Abstraction for Efficient, Concurrent, and Concise Data Access" by Simon Marlow, Louis Brandy, Jonathan Coens, and Jon Purdy. http://simonmar.github.io/bib/papers/haxl-icfp14.pdf