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c

molecule.input.InputMolecule_3

InputMolecule_3_13

abstract class InputMolecule_3_13[I1, I2, I3, A, B, C, D, E, F, G, H, I, J, K, L, M] extends InputMolecule_3[I1, I2, I3]

Source
InputMolecule_3.scala
Linear Supertypes
InputMolecule_3[I1, I2, I3], InputMolecule, MoleculeBase, AnyRef, Any
Type Hierarchy
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  2. Alphabetic
  3. By Inheritance
Inherited
  1. InputMolecule_3_13
  2. InputMolecule_3
  3. InputMolecule
  4. MoleculeBase
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. Protected

internal

  1. val _model: Model

    Internal Model representation of molecule.

    Internal Model representation of molecule.

    Molecule transforms custom boilerplate DSL constructs to Datomic queries in 3 steps:

    Custom DSL molecule --> Model --> Query --> Datomic query string

    Definition Classes
    InputMolecule_3_13 → MoleculeBase
  2. val _nestedQuery: Option[Query]

    Internal optional Query representation of nested molecule with added entity search for each level.

    Internal optional Query representation of nested molecule with added entity search for each level.

    Molecule transforms custom boilerplate DSL constructs to Datomic queries in 3 steps:

    Custom DSL molecule --> Model --> Query --> Datomic query string

    Definition Classes
    InputMolecule_3_13 → MoleculeBase
  3. val _query: Query

    Internal Query representation of molecule.

    Internal Query representation of molecule.

    Molecule transforms custom boilerplate DSL constructs to Datomic queries in 3 steps:

    Custom DSL molecule --> Model --> Query --> Datomic query string

    Definition Classes
    InputMolecule_3_13 → MoleculeBase
  4. val _rawNestedQuery: Option[Query]

    Internal un-optimized optional Query representation of nested molecule with added entity search for each level.

    Internal un-optimized optional Query representation of nested molecule with added entity search for each level.

    Molecule transforms custom boilerplate DSL constructs to Datomic queries in 3 steps:

    Custom DSL molecule --> Model --> Query --> Datomic query string

    Definition Classes
    InputMolecule_3_13 → MoleculeBase
  5. val _rawQuery: Query

    Internal un-optimized Query representation molecule.

    Internal un-optimized Query representation molecule.

    Molecule transforms custom boilerplate DSL constructs to Datomic queries in 3 steps:

    Custom DSL molecule --> Model --> Query --> Datomic query string

    Definition Classes
    InputMolecule_3_13 → MoleculeBase

Ungrouped

  1. abstract def apply(in1: Seq[I1], in2: Seq[I2], in3: Seq[I3])(implicit conn: Conn): Molecule13[A, B, C, D, E, F, G, H, I, J, K, L, M]

    Resolve input molecule by applying 3 groups of values, one for each of the 3 input attributes

    Resolve input molecule by applying 3 groups of values, one for each of the 3 input attributes

    // Sample data set
Person.name.profession.age.score insert List(
  ("Ann", "doctor", 37, 1.0),
  ("Ben", "teacher", 37, 1.0),
  ("Joe", "teacher", 32, 1.0),
  ("Liz", "teacher", 28, 2.0)
)

// Input molecule awaiting 3 inputs for `profession`, `age` and `score`
val profAgeScore = m(Person.name.profession_(?).age_(?).score_(?))

// Apply 3 Seq of values, each matching one of the input attributes
proOfAge(Seq("doctor"), Seq(37)).get === List("Ann")
proOfAge(Seq("doctor", "teacher"), Seq(37, 32)).get.sorted === List("Ann", "Ben", "Joe")

// Number of arguments in each Seq don't have to match but can be asymmetric
profAgeScore.apply(Seq("doctor"), Seq(37), Seq(1.0)).get === List("Ann")
profAgeScore.apply(Seq("doctor", "teacher"), Seq(37), Seq(1.0)).get.sorted === List("Ann", "Ben")
    in1

    Seq of values matching first input attribute (professions: Seq[String])

    in2

    Seq of values matching second input attribute (ages: Seq[Int])

    in3

    Seq of values matching third input attribute (scores: Seq[Double])

    conn

    Implicit Conn in scope

    returns

    Resolved molecule that can be queried

    Definition Classes
    InputMolecule_3_13 → InputMolecule_3
  2. abstract def apply(ins: Seq[(I1, I2, I3)])(implicit conn: Conn): Molecule13[A, B, C, D, E, F, G, H, I, J, K, L, M]

    Resolve input molecule by applying Seq of value triples

    Resolve input molecule by applying Seq of value triples

    // Sample data set
Person.name.profession.age.score insert List(
  ("Ann", "doctor", 37, 1.0),
  ("Ben", "teacher", 37, 1.0),
  ("Joe", "teacher", 32, 1.0),
  ("Liz", "teacher", 28, 2.0)
)

// Input molecule awaiting 3 inputs for `profession`, `age` and `score`
val profAgeScore = m(Person.name.profession_(?).age_(?).score_(?))

// Apply Seq of one or more value triples, each matching all 3 input attributes
profAgeScore.apply(Seq(("doctor", 37, 1.0))).get === List("Ann")
profAgeScore.apply(Seq(("doctor", 37, 1.0), ("teacher", 37, 1.0))).get.sorted === List("Ann", "Ben")
    ins

    Seq of value triples, each matching the 3 input attributes

    conn

    Implicit Conn in scope

    returns

    Resolved molecule that can be queried

    Definition Classes
    InputMolecule_3_13 → InputMolecule_3
  3. def apply(and: And3[I1, I2, I3])(implicit conn: Conn): Molecule13[A, B, C, D, E, F, G, H, I, J, K, L, M]

    Resolve input molecule by applying 3 groups of expressions, one for each of the 3 input attributes

    Resolve input molecule by applying 3 groups of expressions, one for each of the 3 input attributes

    // Sample data set
Person.name.profession.age.score insert List(
  ("Ann", "doctor", 37, 1.0),
  ("Ben", "teacher", 37, 1.0),
  ("Joe", "teacher", 32, 1.0),
  ("Liz", "teacher", 28, 2.0)
)

// Input molecule awaiting 3 inputs for `profession`, `age` and `score`
val profAgeScore = m(Person.name.profession_(?).age_(?).score_(?))

// Apply 3 expressions, one for each input attribute
// [profession-expression] and [age-expression] and [score-expression]
profAgeScore.apply("doctor" and 37 and 1.0).get === List("Ann")
profAgeScore.apply(("doctor" or "teacher") and 37 and 1.0).get.sorted === List("Ann", "Ben")
profAgeScore.apply(("doctor" or "teacher") and (37 or 32) and 1.0).get.sorted === List("Ann", "Ben", "Joe")
profAgeScore.apply(("doctor" or "teacher") and (37 or 32) and (1.0 or 2.0)).get.sorted === List("Ann", "Ben", "Joe")
    and

    First input expr and second input expr and third input expr

    conn

    Implicit Conn in scope

    returns

    Resolved molecule that can be queried

    Definition Classes
    InputMolecule_3_13 → InputMolecule_3
  4. def apply(or: Or3[I1, I2, I3])(implicit conn: Conn): Molecule13[A, B, C, D, E, F, G, H, I, J, K, L, M]

    Resolve input molecule by applying one or more triples of expressions, each matching the 3 input attributes

    Resolve input molecule by applying one or more triples of expressions, each matching the 3 input attributes

    // Sample data set
Person.name.profession.age.score insert List(
  ("Ann", "doctor", 37, 1.0),
  ("Ben", "teacher", 37, 1.0),
  ("Joe", "teacher", 32, 1.0),
  ("Liz", "teacher", 28, 2.0)
)

// Input molecule awaiting 3 inputs for `profession`, `age` and `score`
val profAgeScore = m(Person.name.profession_(?).age_(?).score_(?))

// Apply two or more triple expressions, each matching all 3 input attributes
// [profession/age/score-expression] or [profession/age/score-expression] or ...
profAgeScore.apply(("doctor" and 37 and 1.0) or ("teacher" and 32 and 1.0)).get.sorted === List("Ann", "Joe")
    or

    Two or more tuple3 expressions separated by or

    conn

    Implicit Conn in scope

    returns

    Resolved molecule that can be queried

    Definition Classes
    InputMolecule_3_13 → InputMolecule_3
  5. def apply(tpl: (I1, I2, I3), tpls: (I1, I2, I3)*)(implicit conn: Conn): Molecule13[A, B, C, D, E, F, G, H, I, J, K, L, M]

    Resolve input molecule by applying one or more value triples

    Resolve input molecule by applying one or more value triples

    // Sample data set
Person.name.profession.age.score insert List(
  ("Ann", "doctor", 37, 1.0),
  ("Ben", "teacher", 37, 1.0),
  ("Joe", "teacher", 32, 1.0),
  ("Liz", "teacher", 28, 2.0)
)

// Input molecule awaiting 3 inputs for `profession`, `age` and `score`
val profAgeScore = m(Person.name.profession_(?).age_(?).score_(?))

// Apply one or more value triples, each matching all 3 input attributes
profAgeScore.apply(("doctor", 37, 1.0)).get === List("Ann")
profAgeScore.apply(("doctor", 37, 1.0), ("teacher", 37, 1.0)).get.sorted === List("Ann", "Ben")
    tpl

    First triple of values matching the 3 input attributes

    tpls

    Optional more triples of values matching both input attributes

    conn

    Implicit Conn in scope

    returns

    Resolved molecule that can be queried

    Definition Classes
    InputMolecule_3_13 → InputMolecule_3
  6. def apply(i1: I1, i2: I2, i3: I3)(implicit conn: Conn): Molecule13[A, B, C, D, E, F, G, H, I, J, K, L, M]

    Resolve input molecule by applying 3 input values as individual args

    Resolve input molecule by applying 3 input values as individual args

    // Sample data set
Person.name.profession.age.score insert List(
  ("Ann", "doctor", 37, 1.0),
  ("Ben", "teacher", 37, 1.0),
  ("Joe", "teacher", 32, 1.0),
  ("Liz", "teacher", 28, 2.0)
)

// Input molecule awaiting 3 inputs for `profession`, `age` and `score`
val profAgeScore = m(Person.name.profession_(?).age_(?).score_(?))

// Apply 3 input values (1 triple)
profAgeScore.apply("doctor", 37, 1.0).get === List("Ann")
    i1

    Input value matching first input attribute (profession: String)

    i2

    Input value matching second input attribute (age: Int)

    i3

    Input value matching third input attribute (score: Double)

    conn

    Implicit Conn in scope

    returns

    Resolved molecule that can be queried

    Definition Classes
    InputMolecule_3_13 → InputMolecule_3