README.md

[TOC]

introduction

everything is a function

Attributes also take a functional syntax, so that when the source code is normalized by the compiler

test.people

becomes

people(test)

and

test.people(@.age.$gte(30))

becomes

people(test $gte(age(@) 30))

and either form is acceptable as source.

Functional closures (e.g. for use with $filter, $map, $reduce, and $fun below) can be expressed in three ways:

1. simply pass a function (i.e., not anonymously)

coll.$filter(foo)

2. declare 0 or more arguments between square braces [] followed by the function body

coll.$filter([item idx]
  idx.$mod(2).$when(item.foo)
)

3. same as 2, but use @ argument aliases instead of specifying the arguments

coll.$filter(
  @1.$mod(2).$when(@.foo)
)

$so-much.$fun($sum)
$so-much.$fun([arg1 arg2] $sum(arg1 arg2))
$so-much.$fun($sum(@ @1 @2 @3))
test.buildings.$map(@.marketValue.$times(@.appreciationRate)).$reduce($sum)

find with $filter

A simple find looks like this

test.people

or to return only those documents that have a name

test.people.$filter(name)

calling a collection as a function is an alias for $filter

test.people(name)

test.people([person]
  person.age.gte(30)
)

test.people(@.age.gte(30))

$map joins with composition

Example: get all the people who are known to be children. Since we have no metadata labeling people as children, we have to infer this from the parents' children property:

test.people(children).$map(children).$cat

or simply

test.people.$map(children).$cat

The $cat here is necessary because we don't want a list of lists, just a single list of children, so we concatenate the lists returned by each parent.children at the end.

However, these queries only return references to the children (i.e., foreign keys), so to dereference the children

test.people.$map([person]
  person.children.$map([child]
    test.people([p] child.$eq(p.name)).$first
  )
).$cat

which can get pretty unwieldy. The solution to this problem is composition. So we define $join as a function (with arity overloading)

$join.$fun(

  [self coll]
  self.$join(coll coll.$name)

  [self coll from]
  self.$join(coll from _id)

  [self coll from to]
  self.from.$map(
    coll(@.$eq(@@.to)).$first
  )

)

so that we can join two collections more easily.

test.people.$map(
  @.$join(test.people children name)
).$cat

Compare to SQL:

SELECT * FROM test.person
WHERE person.name in (
  SELECT c.person_name FROM test.person p, test.child c
  WHERE p.name=c.person_name
)

Compare to Mongodb (using the nodejs native client [not isolated]):

test.collection("people").find({
  children: { $not: { $size: 0 } }
}).toArray(function(err, docs) {
  var childList = docs.reduce(function(p1, p2) {
    return p2.children.concat(p1.children || p1);
  });
  test.collection("people").find({
    name: { $in: childList }
  }).toArray(callbackDefinedElsewhere);
});

Compare using new FDB DocumentLayer $deref extension:

db.people.find({
  children: {
    $exists: true,
    $not: {$size: 0}
  }
}, {
  children: {              // overwrite
    $deref: {
      $coll: "test.people",
      $from: {
        $each: "children"
      },
      $to: "name"           // defaults to "_id"
    }
  }
})

projection and aggregation

$map with $reduce and $count

Say you have a list of buildings and you want to get the sum of their market value

test.buildings.$map(marketValue).$reduce($sum)

Or how about the number of buildings having 5 or more tenants?

test.buildings(@.tenants.$count.$gte(5)).$count

Now that we know the building count, get the tenants in each building

test.buildings(@.tenants.$count.$gte(5)).$map(
  @.$join(test.people tenants name)
)

$map with $assoc and $let

From the previous example, instead of just the tenants, get the buildings with the tenants, and an easily accessible tenant count for each building

test.buildings(@.tenants.$count.$gte(5)).$map(
  @.$assoc(tenants @.$join(test.people tenants name)).$assoc(tenantCount @.tenants.$count)
)

One problem with this is that the tenant count is requested (and calculated?) twice for each filtered building. So use $let to bind the result to a symbol to be used anywhere in the body.

test.buildings.$map(
  $let([c @.tenants.$count]
    c.$when(@.$assoc(tenants @.$join(test.people tenants name)).$assoc(tenantCount c))
  )
)(@)

pattern matching

E.g., a simple pattern matching query using SPARQL:

SELECT ?friend ?item WHERE {
  ?person x:knows ?friend .
  ?friend x:transaction ?transaction .
  ?transaction x:item ?item .
}

Get a preconstructed projection instead of a table you have to reconstruct in application code

test.people.$map([person]
  person.$join(test.people knows name)(transactions).$map([friend]
    friend.$merge({
      items: friend.join(test.transactions).$map([transaction]
        transaction.$join(test.catalog items)
      ).$apply($cat)
    })
  )
)

indexes

create an index

db.coll.$index(index-name key1 key2 key3 ... )

test.buildings.$index("buildings-by-age-and-market-value" age marketValue)

explicitly query with an index

test.buildings.$index("buildings-by-age-and-market-value")([building] building.age.$eq(30))

test.people.$index("people-by-name")(@.name.$eq("Tom"))

comments

; this is a comment
;; so is this

ontologies rather than schemata