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Getting started with FQL, FaunaDB’s native query language - part 2

#fauna #serverless #jamstack

Author: Pier Bover
Date: July 10, 2020

Welcome back, fellow space developer! In part 1 of this five-part series we got our first look at FQL and some fundamental FaunaDB concepts. Today we're going to take a more in-depth look into FaunaDB's indexes.

In this article:

  • Recap
  • What can indexes do?
  • Indexing across multiple collections
  • Sorting results
  • Filtering results
  • Enforcing unique values
  • Combining multiple indexes
  • Index bindings

Recap

We briefly introduced indexes in the previous article, but here's a recap of the FQL commands we learned.

First, we created a simple index to be able to retrieve all our pilots from the Pilots collection:

CreateIndex({
  name: "all_Pilots",
  source: Collection("Pilots")
})
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Then, we retrieved a list of references:

Paginate(
  Match(
    Index("all_Pilots")
  )
)
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Finally, we learned how to use Map, Lambda, and Var to retrieve a list of documents:

Map(
  Paginate(Match(Index("all_Pilots"))),
  Lambda('pilotRef', Get(Var('pilotRef')))
)
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What can indexes do?

So far, we've seen that indexes allow you to retrieve all the documents in a collection, but indexes are much more powerful than that.

With indexes you can:

  • Enforce unique constraints
  • Sort and filter results
  • Create computed values from document data

Indexes vs SQL views

If you're coming from the relational world, it can make sense to think about indexes similar to views on a relational database. Views are stored queries that can retrieve data from multiple tables, calculate computed data, join tables to create virtual entities, filter, etc. In a way, FaunaDB's indexes perform similar functions, as we will explore in this article.

Indexing across multiple collections

Until now, our indexes have been created on documents from a single collection, but you can configure an index to include documents from multiple collections.

There are many reasons why you might want to do that. Maybe, when designing your database, you'd like to group some collections under a single virtual collection, so to speak. In the relational world, combining database entities under a single entity is known as polymorphism.

To test this, let's create a new collection to store our land vehicles:

CreateCollection({name: "Speeders"})
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Now, with this index, you'd be able to retrieve all the vehicles in the database:

CreateIndex({
  name: "all_Vehicles",
  source: [
    Collection("Spaceships"),
    Collection("Speeders")
  ]
})
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When indexing multiple collections, keep in mind that the indexed fields need to be of the same type (string, number, etc) across collections. In the rest of the examples, we'll use indexes with a single collection for simplicity's sake.

Sorting results

Indexes also allow us to sort results. Let's create a new index to get all our pilots sorted by their name:

CreateIndex({
  name: "all_Pilots_sorted_by_name",
  source: Collection("Pilots"),
  values: [
    { field: ["data", "name"] },
    { field: ["ref"] }
  ]
})
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Here, we're using a values object which defines the output values for the index.

In this case, we are defining two output values:

  • ["data", "name"] a path referring to the name property of the document.
  • ["ref"] another path which will output a reference to the matched document. In a moment, we'll see why we need this.

When using a values object, FaunaDB will always sort the results in ascending order by default:

Paginate(Match(Index("all_Pilots_sorted_by_name")))

// Results:

{
  "data": [
    [
      "Buck Rogers",
      Ref(Collection("Pilots"), "266359371696439826")
    ],
    [
      "Flash Gordon",
      Ref(Collection("Pilots"), "266350546751848978")
    ],
    [
      "Jean-Luc Picard",
      Ref(Collection("Pilots"), "266359447111074322")
    ],
    // etc...
  ]
}
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As you can see, FaunaDB will output two values per result as defined in the values object of the index, and these results are now ordered by those values.

Reverse order

If we wanted to get the pilots sorted by their name in descending order, we'd need a new index with the reverse setting:

CreateIndex({
  name: "all_Pilots_sorted_by_name_desc",
  source: Collection("Pilots"),
  values: [
    { field: ["data", "name"], reverse: true},
    { field: ["ref"] }
  ]
})
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Getting documents from sorting results

You can add as many output values as needed without any performance penalty, but we might need to get a document from these types of results:

["Buck Rogers", Ref(Collection("Pilots"), "266359371696439826")]
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So how do we actually get documents?

One option would be using the Select function like we learned in the previous article:

Map(
  Paginate(Match(Index("all_Pilots_sorted_by_name"))),
  Lambda("pilotResult", Get(Select([1], Var("pilotResult"))))
)
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Since FaunaDB uses zero-based arrays, the trick here is selecting the reference in the second item with [1], then using Get to return a document.

Another option would be to simply configure our Lambda to expect an array with two values:

Map(
  Paginate(Match(Index("all_Pilots_sorted_by_name"))),
  Lambda(["name", "pilotRef"], Get(Var("pilotRef")))
)
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In both cases, we'd get the same result:

{
  "data": [
    {
      "ref": Ref(Collection("Pilots"), "266359371696439826"),
      "ts": 1590278941740000,
      "data": {
        "name": "Buck Rogers"
      }
    },
    {
      "ref": Ref(Collection("Pilots"), "266350546751848978"),
      "ts": 1590270525630000,
      "data": {
        "name": "Flash Gordon"
      }
    },
    {
      "ref": Ref(Collection("Pilots"), "266359447111074322"),
      "ts": 1590279013675000,
      "data": {
        "name": "Jean-Luc Picard"
      }
    }
    // etc...
  ]
}
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Filtering results

Another useful feature of indexes is being able to search and filter results.

To test this, let's create a Planets collection:

CreateCollection({name: "Planets"})
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Then, create some planets with three different types: TERRESTRIAL, GAS, and ICE:

Create(Collection("Planets"),
  {
    data: {
      name: "Mercury",
      type: "TERRESTRIAL"
    }
  }
)

Create(Collection("Planets"),
  {
    data: {
      name: "Saturn",
      type: "GAS"
    }
  }
)

// etc..
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Finally, let's create an index to filter our planets by type:

CreateIndex({
  name: "all_Planets_by_type",
  source: Collection("Planets"),
  terms: [
    { field: ["data", "type"]}
  ]
})
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As we saw earlier, the terms object is used as the search input for the index, whereas the values object defines which data the index will return. With this index, the values object is not defined, so the index will return the ref by default.

In this case, we're telling FaunaDB that the search term will use a field of the document found at the path ["data", "type"].

We can now query our index by passing a parameter to Match:

Map(
  Paginate(Match(Index("all_Planets_by_type"), "GAS")),
  Lambda("planetRef", Get(Var("planetRef")))
)

// Result:

{
  "data": [
    {
      "ref": Ref(Collection("Planets"), "267081152090604051"),
      "ts": 1590967285200000,
      "data": {
        "name": "Jupiter",
        "type": "GAS"
      }
    },
    {
      "ref": Ref(Collection("Planets"), "267081181884842515"),
      "ts": 1590967313610000,
      "data": {
        "name": "Saturn",
        "type": "GAS"
      }
    }
  ]
}
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Filtering on an array value

If we wanted to match an item inside an array instead of filtering on a single string, we would only need to pass the term FaunaDB needs to search inside the array.

To test this, let's add some colors to our ships:

Update(
  Ref(Collection("Spaceships"), "266356873589948946"),
  {
    data: {
      colors: ["RED","YELLOW"]
    }
  }
)

// etc...
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If we now wanted to filter ships based on a single color, we could create this index which uses the colors array as a filtering term:

CreateIndex({
  name: "all_Spaceships_by_color",
  source: Collection("Spaceships"),
  terms: [
    { field: ["data","colors"]}
  ]
})
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And then query it:

Map(
  Paginate(Match(Index("all_Spaceships_by_color"), "WHITE")),
  Lambda("shipRef", Let({
    shipDoc: Get(Var("shipRef"))
  },{
    name: Select(["data","name"], Var("shipDoc")),
    colors: Select(["data","colors"], Var("shipDoc"))
  }))
)

// Result:

{
  data: [
    {
      name: "Explorer IV",
      colors: ["BLUE", "WHITE", "RED"]
    },
    {
      name: "Navigator",
      colors: ["WHITE", "GREY"]
    },
    {
      name: "Le Super Spaceship",
      colors: ["PINK", "MAGENTA", "WHITE"]
    }
  ]
}
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FaunaDB is smart enough to understand that if the field used in the terms object is an array, then it should search for an item inside that array instead of an exact match on the full array.

About full text search

At this time, it's only possible to filter documents using indexes with exact matches. This feature is on Fauna's roadmap, but no official timeframes have been provided yet.

It's certainly possible to solve this in other ways. FQL has a number of functions that will allow you to implement full text search.

Let's create an index first to get all planets:

CreateIndex({
  name: "all_Planets",
  source: Collection("Planets")
})
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Now, we could combine Filter, ContainsStr, and LowerCase to make a case insensitive search of the string "ur" on the planets' names:

Map(
  Filter(
    Paginate(Match(Index("all_Planets"))),
    Lambda("planetRef",
      ContainsStr(
        LowerCase(Select(["data","name"],Get(Var("planetRef")))),
        "ur"
      )
    )
  ),
  Lambda("planetRef", Get(Var("planetRef")))
)

// Result:

{
  data: [
    {
      ref: Ref(Collection("Planets"), "267081079730471443"),
      ts: 1590977548370000,
      data: {
        name: "Mercury",
        type: "TERRESTRIAL",
        color: "GREY"
      }
    },
    {
      ref: Ref(Collection("Planets"), "267081181884842515"),
      ts: 1590977684790000,
      data: {
        name: "Saturn",
        type: "GAS",
        color: "YELLOW"
      }
    },
    {
      ref: Ref(Collection("Planets"), "267081222719537683"),
      ts: 1590977359690000,
      data: {
        name: "Uranus",
        type: "ICE",
        color: "BLUE"
      }
    }
  ]
}
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Sorting and filtering at the same time

You can certainly do both at the same time by combining terms and values in the same index:

CreateIndex({
  name: "all_Planets_by_type_sorted_by_name",
  source: Collection("Planets"),
  terms: [
    { field: ["data", "type"]}
  ],
  values: [
    { field: ["data", "name"]},
    { field: ["ref"] }
  ]
})
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And then:

Map(
  Paginate(Match(Index("all_Planets_by_type_sorted_by_name"), "TERRESTRIAL")),
  Lambda("planetResult", Get(Select([1], Var("planetResult"))))
)

// Result:

{
  "data": [
    {
      "ref": Ref(Collection("Planets"), "267081091831038483"),
      "ts": 1590967227710000,
      "data": {
        "name": "Earth",
        "type": "TERRESTRIAL"
      }
    },
    {
      "ref": Ref(Collection("Planets"), "267081096484618771"),
      "ts": 1590967232165000,
      "data": {
        "name": "Mars",
        "type": "TERRESTRIAL"
      }
    },
    // etc ...
  ]
}
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Enforcing unique values

Another important function of indexes, besides retrieving documents, is enforcing a unique constraint on the documents that can be created.

For example, to add a unique code to our spaceships:

CreateIndex({
  name: "all_Spaceships_by_code",
  source: Collection("Spaceships"),
  terms: [
    {field: ["data", "code"]}
  ],
  unique: true
})
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This index accomplishes two purposes:

  • We're configuring it to accept a filtering term with the terms object.
  • We're ensuring the defined terms are unique across the documents matched by this index by using unique: true.

We're using a single term here for simplicity's sake, but you could create a unique constraint over multiple terms much like you'd do in SQL by creating constraints over multiple columns.

Let's test this by creating a new spaceship:

Create(
  Collection("Spaceships"),
  {
    data: {
      name: "Rocinante",
      code: "ROCINANTE"
    }
  }
)

// Result:

{
  "ref": Ref(Collection("Spaceships"), "267072793181422099"),
  "ts": 1590959313500000,
  "data": {
    "name": "Rocinante",
    "code": "ROCINANTE"
  }
}
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So far so good. Let's create another one with the same code:

Create(
  Collection("Spaceships"),
  {
    data: {
      name: "Rocinante 2",
      code: "ROCINANTE"
    }
  }
)

// Result:

error: instance not unique
document is not unique.
position: ["create"]
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As expected, FaunaDB throws an error since there is already a ship with the ROCINANTE code.

Quick tip: when using unique constraints, we know in advance that an index can only return a single document. So, instead of using Paginate, we can simply use Get to get a single document:

Get(Match(Index("all_Spaceships_by_code"), 'ROCINANTE'))

// Result:

{
  "ref": Ref(Collection("Spaceships"), "267072793181422099"),
  "ts": 1591022503995000,
  "data": {
    "name": "Rocinante",
    "code": "ROCINANTE"
  }
}
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Combining multiple indexes

FQL has a number of functions that allow you to combine results from indexes and other sources in different ways:

  • Union will add the results from all indexes.
  • Intersection will return the results that are similar from each index and discard the rest.
  • Difference will return the results that are unique in the first index and discard the rest.

Alt Text

To be able to test these, let's add some colors to our planets (please excuse any scientific inaccuracies).

// Earth

Update(Ref(Collection("Planets"), "267081091831038483"),
  {data: {color: "BLUE"}}
)

// Etc...
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Let's also create a new index:

CreateIndex({
  name: "all_Planets_by_color",
  source: Collection("Planets"),
  terms: [
    { field: ["data", "color"]}
  ]
})
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OR filtering with Union

Union will combine whatever results each index returns. We're just using two indexes here, but you could use any number of indexes.

"Hey Fauna, get me the planets that are of type GAS or are YELLOW"

Map(
  Paginate(
    Union(
      Match(Index("all_Planets_by_type"), "GAS"),
      Match(Index("all_Planets_by_color"), "YELLOW")
    )
  ),
  Lambda("planetRef", Get(Var("planetRef")))
)

// Result:

{
  "data": [
    {
      "ref": Ref(Collection("Planets"), "267081152090604051"),
      "ts": 1590977605890000,
      "data": {
        "name": "Jupiter",
        "type": "GAS",
        "color": "BROWN"
      }
    },
    {
      "ref": Ref(Collection("Planets"), "267081181884842515"),
      "ts": 1590977684790000,
      "data": {
        "name": "Saturn",
        "type": "GAS",
        "color": "YELLOW"
      }
    }
  ]
}
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As you can see, Union will skip duplicates since Saturn being a gas giant appears in the results of both indexes.

AND filtering with Intersection

Intersection will return only the results that are the same in all indexes. Again, you could use any number of indexes.

"Hey Fauna, get me the planets that are of type TERRESTRIAL and are BLUE"

Map(
  Paginate(
    Intersection(
      Match(Index("all_Planets_by_type"), "TERRESTRIAL"),
      Match(Index("all_Planets_by_color"), "BLUE")
    )
  ),
  Lambda("planetRef", Get(Var("planetRef")))
)

// Result:

{
  "data": [
    {
      "ref": Ref(Collection("Planets"), "267081091831038483"),
      "ts": 1590977345595000,
      "data": {
        "name": "Earth",
        "type": "TERRESTRIAL",
        "color": "BLUE"
      }
    }
  ]
}
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NOT filtering with Difference

Difference will compare the first index you provide with the rest of the indexes, and return the results that exist only in the first index.

"Hey Fauna, get me the planets that are TERRESTRIAL but are not BLUE nor RED"

Map(
  Paginate(
    Difference(
      Match(Index("all_Planets_by_type"), "TERRESTRIAL"),
      Match(Index("all_Planets_by_color"), "BLUE"),
      Match(Index("all_Planets_by_color"), "RED")
    )
  ),
  Lambda("planetRef", Get(Var("planetRef")))
)

// Result:

{
  "data": [
    {
      "ref": Ref(Collection("Planets"), "267081079730471443"),
      "ts": 1590977548370000,
      "data": {
        "name": "Mercury",
        "type": "TERRESTRIAL",
        "color": "GREY"
      }
    },
    {
      "ref": Ref(Collection("Planets"), "267081085891904019"),
      "ts": 1590977561660000,
      "data": {
        "name": "Venus",
        "type": "TERRESTRIAL",
        "color": "GREY"
      }
    }
  ]
}
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Index bindings

With index bindings, it's possible to create pre-computed values based on some document data, using pretty much any FQL expression.

These values are calculated beforehand, which makes retrieving these values super efficient as the operation consumes little CPU. The downside is that these computed values consume storage space. Before deciding to use a binding at scale, you should consider if the performance boost is worth the storage cost for your use case.

Let's see a couple examples on how to use index bindings.

Remember our spaceship from the previous article?

{
  "name": "Voyager",
  "pilot": Ref(Collection("Pilots"), "266350546751848978"),
  "type": "Rocket",
  "fuelType": "Plasma",
  "actualFuelTons": 7,
  "maxFuelTons": 10,
  "maxCargoTons": 25,
  "maxPassengers": 5,
  "maxRangeLightyears": 10,
  "celestialPosition": {
    "x": 2234,
    "y": 3453,
    "z": 9805
  },
  "code": "VOYAGER"
}
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So here comes our boss, the fleet admiral. He has 100 ships in the dock that need refueling and wants to know which ships could be filled faster so that he can fill them first and empty the dock as fast as possible.

Easy, right? To do that, we'd only need to sort our ships by pendingFuelTons.

But Pier, pendingFuelTons is not in the document! We're doomed!

Don't panic my friend, we have the perfect tool to solve this problem.

Index bindings allow you to create computed values dynamically based on the data of the document. In this case, we could just calculate the value pendingFuelTons by subtracting actualFuelTons from maxFuelTons.

So let's create our index:

CreateIndex({
  name: "all_Spaceships_by_pendingFuelTons",
  source: {
    collection: Collection("Spaceships"),
    fields: {
      pendingFuelTons: Query(
        Lambda("shipDoc",
          Subtract(
            Select(["data","maxFuelTons"], Var("shipDoc")),
            Select(["data","actualFuelTons"], Var("shipDoc"))
          )
        )
      )
    }
  },
  values: [
    { binding: "pendingFuelTons"},
    { field: ["data", "name"]}
  ]
})
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The only new FQL function we're using here is Subtract, which simply subtracts the second number from the first.

So let's query our new index:

Paginate(Match(Index("all_Spaceships_by_pendingFuelTons")))

// Result:

{
  "data": [
    [
      3,
      "Explorer IV"
    ],
    [
      3,
      "Voyager"
    ],
    [
      10,
      "Navigator"
    ],
    [
      18,
      "Destroyer"
    ]
    // etc...
  ]
}
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As you can see, FaunaDB is sorting first by the new computed value pendingFuelTons and then by the ship name.

Cool!

Filtering by the first letter

Let's create another example. What if we wanted to get all the planets that started with the letter M? Our planet documents do not have a firstLetter property, but we can solve this with bindings too.

We can create a new index with a binding for the first letter of the name and add a terms object to be able to filter the documents by firstLetter:

CreateIndex({
  name: "all_Planets_by_firstLetter",
  source: {
    collection: Collection("Planets"),
    fields: {
      firstLetter: Query(
        Lambda("planetDoc",
          SubString(Select(["data", "name"], Var("planetDoc")), 0, 1)
        )
      )
    }
  },
  terms: [
    { binding: "firstLetter"}
  ]
})
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As you can see in the terms object, we're now telling FaunaDB that the value we want to use for filtering is an index binding instead of a document field.

Great, so let's query the index as usual and pass the letter M:

Map(
  Paginate(Match(Index("all_Planets_by_firstLetter"), "M")),
  Lambda("planetDoc", Get(Var("planetDoc")))
)

// Result:


{
  "data": [
    {
      "ref": Ref(Collection("Planets"), "267081079730471443"),
      "ts": 1590977548370000,
      "data": {
        "name": "Mercury",
        "type": "TERRESTRIAL",
        "color": "GREY"
      }
    },
    {
      "ref": Ref(Collection("Planets"), "267081096484618771"),
      "ts": 1590977464930000,
      "data": {
        "name": "Mars",
        "type": "TERRESTRIAL",
        "color": "RED"
      }
    }
  ]
}
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Easy, right?

These bindings are very powerful. We can access all the FQL commands available to produce computed values.

Filtering by any letter

As a final example, let's see how we could check if an array produced by a binding includes a search term.

Quick note: The NGram function is currently undocumented, but will be officially supported in a future release. You can check more details here.

CreateIndex({
  name: "filter_Spaceships_by_letter",
  source: {
    collection: Collection("Spaceships"),
    fields: {
      nameLetters: Query(
        Lambda("shipDoc",
          NGram(Select(["data","name"], Var("shipDoc")),1,1)
        )
      )
    }
  },
  terms: [
    { binding: "nameLetters"}
  ]
})
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And query it:

Map(
  Paginate(Match(Index("filter_Spaceships_by_letter"), "V")),
  Lambda("shipRef", Let({
    shipDoc: Get(Var("shipRef"))
  },{
    name: Select(["data","name"], Var("shipDoc"))
  }))
)

// Result:

{
  data: [
    {
      name: "Voyager"
    },
    {
      name: "Explorer IV"
    }
  ]
}
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This works because the NGram function produces an array of letters which can be queried by the index.

NGram("FaunaDB",1,1)

// Result:

["F", "a", "u", "n", "a", "D", "B"]
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Or:

NGram("FaunaDB",2,3)

// Result:

["Fa", "Fau", "au", "aun", "un", "una", "na", "naD", "aD", "aDB", "DB"]
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You can create all sorts of binding values. For example, you could extract the day of the week from a timestamp using DayOfWeek to get all events that happened on a Friday.

Binding and unique constraints

If you're wondering, yes, you can use unique constraints over bindings too.

Imagine we wanted to have keycards with ids for accessing our ships. We know that pilots have a history of forgetting their keycard ids, so these ids should be memorable and obvious. What if we create them based on the ships' names? And, since keycards would only be available for a single ship, these ids should be unique.

CreateIndex({
  name: "all_Keycards",
  source: {
    collection: Collection("Spaceships"),
    fields: {
      keyCardId: Query(
        Lambda("shipDoc",
          UpperCase(
            ReplaceStr(Select(["data", "name"], Var("shipDoc")), " ", "_")
          )
        )
      )
    }
  },
  values: [
    { binding: "keyCardId"}
  ],
  unique: true
})
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If we query this index, you'll see how it all makes sense:

Paginate(Match(Index("all_Keycards")))

// Result:

{
  data: [
    "DESTROYER",
    "EXPLORER_IV",
    "LE_SUPER_SPACESHIP",
    "NAVIGATOR",
    "ROCINANTE",
    "VOYAGER"
  ]
}
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If we now try to create a new ship by using a name we've already used, we get an error. The all_Keycards index will prevent two keycards from having the same keyCardId, even if we have no unique constraints on the names of the spaceships themselves:

Create(
  Collection("Spaceships"),
  {
    data: {
      name: "Le Super Spaceship"
    }
  }
)

// Result:

error: instance not unique
document is not unique.
position: ["create"]
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Conclusion

So that's it for today. Hopefully you learned something valuable!

In part 3 of this series, we will continue our space adventure by learning how to model data in FaunaDB.

If you have any questions don't hesitate to hit me up on Twitter: @pierb

Oldest comments (1)

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yaldram profile image
Arsalan Ahmed Yaldram •

Thanks for the detailed explanation. Eagerly waiting for article 3.

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