Introduction

In this chapter, you will learn more about expressing complex logic in a functional language like Daml. Specifically, you’ll learn about:

Function signatures and functions
Advanced control flow (if...else, folds, recursion, when)

There is a project template daml-intro-functional-101 for this chapter, containing the code snippets from this section.

The Haskell connection

The previous chapters of this introduction to Daml have mostly covered the structure of templates, and their connection to the Daml Ledger Model. The logic of what happens within the do blocks of choices has been kept relatively simple. In this chapter, we will dive deeper into Daml’s expression language, the part that allows you to write logic inside those do blocks. But we can only scratch the surface here. Daml borrows a lot of its language from Haskell. If you want to dive deeper, or learn about specific aspects of the language you can refer to standard literature on Haskell. Some recommendations:

When comparing Daml to Haskell it’s worth noting:

Haskell is a lazy language, which allows you to write things like head [1..], meaning “take the first element of an infinite list”. Daml by contrast is strict. Expressions are fully evaluated, which means it is not possible to work with infinite data structures.
Daml has a with syntax for records and a dot syntax for record field access, neither of which is present in Haskell. However, Daml supports Haskell’s curly brace record notation.
Daml has a number of Haskell compiler extensions active by default.
Daml doesn’t support all features of Haskell’s type system. For example, there are no existential types or GADTs.
Actions are called Monads in Haskell.

Functions

In data you learnt about one half of Daml’s type system: Data types. It’s now time to learn about the other, which are Function types. Function types in Daml can be spotted by looking for -> which can be read as “maps to”. For example, the function signature Int -> Int maps an integer to another integer. There are many such functions, but one would be:

increment : Int -> Int
increment n = n + 1

You can see here that the function declaration and the function definitions are separate. The declaration can be omitted in cases where the type can be inferred by the compiler, but for top-level functions (ie ones at the same level as templates, directly under a module), it’s often a good idea to include them for readability. In the case of increment it could have been omitted. Similarly, we could define a function add without a declaration:

add n m = n + m

If you do this, and wonder what type the compiler has inferred, you can hover over the function name in the IDE:

add
  : Additive a
  => a -> a -> a

Defined at /tmp/daml-intro-9/daml/Main.daml:20:1
add n m = n + m

What you see here is a slightly more complex signature:

add : Additive a => a -> a -> a

There are two interesting things going on here:

We have more than one ->.
We have a type parameter a with a constraint Additive a.

Function application

Let’s start by looking at the right hand part a -> a -> a. The -> is right associative, meaning a -> a -> a is equivalent to a -> (a -> a). Using the “maps to” way of reading ->, we get “a maps to a function that maps a to a”. And this is indeed what happens. We can define a different version of increment by partially applying add:

increment2 = add 1

If you try this out in your IDE, you’ll see that the compiler infers type Int -> Int again. It can do so because of the literal 1 : Int. So if we have a function f : a -> b -> c -> d and a value valA : a, we get f valA : b -> c -> d, i.e. we can apply the function argument by argument. If we also had valB : b, we would have f valA valB : c -> d. What this tells you is that function application is left associative: f valA valB == (f valA) valB.

Infix functions

Now add is clearly just an alias for +, but what is +? + is just a function. It’s only special because it starts with a symbol. Functions that start with a symbol are infix by default which means they can be written between two arguments. That’s why we can write 1 + 2 rather than + 1 2. The rules for converting between normal and infix functions are simple. Wrap an infix function in parentheses to use it as a normal function (i.e. prefix), and wrap a normal function in backticks to make it infix:

three = 1 `add` 2

With that knowledge, we could have defined add more succinctly as the alias that it is:

add2 : Additive a => a -> a -> a
add2 = (+)

If we want to partially apply an infix operation we can also do that as follows:

increment3 = (1 +)
double = (* 2)

Associativity and Precedence

When dealing with multiple infix operators, precedence determines how the Daml compiler should parse an expression. For example, for the expression x + y * z, because \* has a higher precedence than +, the expression is parsed as x + (y * z) instead of (x + y) * z. When dealing with infix operators with the same precedence, associativity determines how the Daml compiler should parse an expression. For example, because + and - are left-associative, the expression x + y - z is parsed as (x + y) - z instead of x + (y - z). For built-in operators this has been predefined, for user-defined operators, it must be user-defined. See the Daml reference on Fixity, Associativity and Precedence for details.

Type constraints

The Additive a => part of the signature of add is a type constraint on the type parameter a. Additive here is a typeclass. You already met typeclasses like Eq and Show earlier. The Additive typeclass says that you can add a thing, i.e. there is a function (+) : a -> a -> a. Now the way to read the full signature of add is “Given that a has an instance for the Additive typeclass, a maps to a function which maps a to a”. Typeclasses in Daml are a bit like interfaces in other languages. To be able to add two things using the + function, those things need to “expose” (have an instance for) the Additive interface (typeclass). Unlike interfaces, typeclasses can have multiple type parameters. A good example, which also demonstrates the use of multiple constraints at the same time, is the signature of the exercise function:

exercise : (Template t, Choice t c r) => ContractId t -> c -> Update r

Let’s turn this into prose: Given that t is the type of a template, and that t has a choice c with return type r, the exercise function maps a ContractId for a contract of type t to a function that takes the choice arguments of type c and returns an Update resulting in type r. That’s quite a mouthful, and does require one to know what meaning the typeclass Choice gives to parameters t c and r, but in many cases, that’s obvious from the context or names of typeclasses and variables. Using single letters, while common, is not mandatory. The above may be made a little bit clearer by expanding the type parameter names, at the cost of making the code a bit longer:

exercise : (Template template, Choice template choice result) =>
             ContractId template -> choice -> Update result

Pattern matching in arguments

You met pattern matching earlier, using case expressions which is one way of pattern matching. However, it can also be convenient to do the pattern matching at the level of function arguments. Think about implementing the function uncurry:

uncurry : (a -> b -> c) -> (a, b) -> c

uncurry takes a function with two arguments (or more, since c could be a function), and turns it into a function from a 2-tuple to c. Here are three ways of implementing it, using tuple accessors, case pattern matching, and function pattern matching:

uncurry1 f t = f t._1 t._2

uncurry2 f t = case t of
  (x, y) -> f x y

uncurry f (x, y) = f x y

Any pattern matching you can do in case you can also do at the function level, and the compiler helpfully warns you if you did not cover all cases, which is called “non-exhaustive”.

fromSome : Optional a -> a
fromSome (Some x) = x

The above will give you a warning:

warning:
  Pattern match(es) are non-exhaustive
  In an equation for ‘fromSome’: Patterns not matched: None

A function that does not cover all its cases, like fromSome here, is called a partial function. fromSome None will cause a runtime error. We can use function level pattern matching together with a feature called Record Wildcards to write the function issueAsset:

issueAsset : Asset -> Script (ContractId Asset)
issueAsset asset@(Asset with ..) = do
  assetHolders <- queryFilter @AssetHolder issuer
    (\ah -> (ah.issuer == issuer) && (ah.owner == owner))

  case assetHolders of
    (ahCid, _)::_ -> submit asset.issuer do
      exerciseCmd ahCid Issue_Asset with ..
    [] -> abort ("No AssetHolder found for " <> show asset)

The .. in the pattern match here means bind all fields from the given record to local variables, so we have local variables issuer, owner, etc. The .. in the second to last line means fill all fields of the new record using local variables of the matching names, in this case (per the definition of Issue_Asset), symbol and quantity, taken from the asset argument to the function. In other words, this is equivalent to:

exerciseCmd ahCid Issue_Asset with symbol = asset.symbol, quantity = asset.quantity

because the notation asset@(Asset with ..) binds asset to the entire record, while also binding all of the fields of asset to local variables.

Functions everywhere

You have probably already guessed it: Anywhere you can put a value in Daml you can also put a function. Even inside data types:

data Predicate a = Predicate with
  test : a -> Bool

More often it makes sense to define functions locally, inside a let clause or similar. Good examples of this are the validate and transfer functions defined locally in the Trade_Settle choice of the model from dependencies:

let
          validate (asset, assetCid) = do
            fetchedAsset <- fetch assetCid
            assertMsg
              "Asset mismatch"
              (asset == fetchedAsset with
                observers = asset.observers)

        mapA_ validate (zip baseAssets baseAssetCids)
        mapA_ validate (zip quoteAssets quoteAssetCids)

        let
          transfer (assetCid, approvalCid) = do
            exercise approvalCid TransferApproval_Transfer with assetCid

        transferredBaseCids <- mapA transfer (zip baseAssetCids baseApprovalCids)
        transferredQuoteCids <- mapA transfer (zip quoteAssetCids quoteApprovalCids)

You can see that the function signature is inferred from the context here. If you look closely (or hover over the function in the IDE), you’ll see that it has signature

validate : (HasFetch r, Eq r, HasField "observers" r a) => (r, ContractId r) -> Update ()

Bear in mind that functions are not serializable, so you can’t use them inside template arguments, as choice inputs, or as choice outputs. They also don’t have instances of the Eq or Show typeclasses which one would commonly want on data types.

The mapA and mapA_ functions loop through the lists of assets and approvals, and apply the functions validate and transfer to each element of those lists, performing the resulting Update action in the process. We’ll look at that more closely under loops below.

Lambdas

Daml supports inline functions, called “lambda”s. They are defined using the (\x y z -> ...) syntax. For example, a lambda version of increment would be (\n -> n + 1).

Control flow

In this section, we will cover branches and loops, and look at a few common patterns of how to translate procedural code into functional code.

Branches

Until compose the only real kind of control flow introduced has been case, which is a powerful tool for branching.

If-else expression

constraints also showed a seemingly self-explanatory if ... else expression, but didn’t explain it further. Let’s implement the function boolToInt : Bool -> Int which in typical fashion maps True to 1 and False to 0. Here is an implementation using case:

boolToInt b = case b of
  True -> 1
  False -> 0

If you write this function in the IDE, you’ll get a warning from the linter:

Suggestion: Use if
Found:
case b of
    True -> 1
    False -> 0
Perhaps:
if b then 1 else 0

The linter knows the equivalence and suggests a better implementation:

boolToInt2 b = if b
  then 1
  else 0

In short: if ... else expressions are equivalent to case expressions, but can be easier to read.

Control flow as expressions

case and if ... else expressions really are control flow in the sense that they short-circuit:

doError t = case t of
  "True" -> True
  "False" -> False
  _ -> error ("Not a Bool: " <> t)

This function behaves as you would expect: the error only gets evaluated if an invalid text is passed in. This is different from functions, where all arguments are evaluated immediately:

ifelse b t e = if b then t else e
boom = ifelse True 1 (error "Boom")

In the above, boom is an error. While providing proper control flow, case and if ... else expressions do result in a value when evaluated. You can actually see that in the function definitions above. Since each of the functions is defined just as a case or if ... else expression, the value of the evaluated function is just the value of the case or if ... else expression. Values have a type: the if ... else expression in boolToInt2 has type Int as that is what the function returns; similarly, the case expression in doError has type Bool. To be able to give such expressions an unambiguous type, each branch needs to have the same type. The below function does not compile as one branch tries to return an Int and the other a Text:

typeError b = if b
  then 1
  else "a"

If we need functions that can return two (or more) types of things we need to encode that in the return type. For two possibilities, it’s common to use the Either type:

intOrText : Bool -> Either Int Text
intOrText b = if b
  then Left 1
  else Right "a"

When you have more than two possible types (and sometimes even just for two types), it can be clearer to define your own variant type to wrap all possibilities.

Branches in actions

The most common case where this becomes important is inside do blocks. Say we want to create a contract of one type in one case, and of another type in another case. Let’s say we have two template types and want to write a function that creates an S if a condition is met, and a T otherwise.

template T
  with
    p : Party
  where
    signatory p

template S
  with
    p : Party
  where
    signatory p

It would be tempting to write a simple if ... else, but it won’t typecheck if each branch returns a different type:

typeError b p = if b
  then create T with p
  else create S with p

We have two options:

Use the Either trick from above.
Get rid of the return types.

ifThenSElseT1 b p = if b
  then do
    cid <- create S with p
    return (Left cid)
  else do
    cid <- create T with p
    return (Right cid)

ifThenSElseT2 b p = if b
  then do
    create S with p
    return ()
  else do
    create T with p
    return ()

The latter is so common that there is a utility function in DA.Action to get rid of the return type: void : Functor f => f a -> f ().

ifThenSElseT3 b p = if b
  then void (create S with p)
  else void (create T with p)

void also helps express control flow of the type “Create a T only if a condition is met.

conditionalS b p = if b
  then void (create S with p)
  else return ()

Note that we still need the else clause of the same type (). This pattern is so common, it’s encapsulated in the standard library function DA.Action.when : (Applicative f) => Bool -> f () -> f ().

conditionalS2 b p = when b (void (create S with p))

Despite when looking like a simple function, the compiler does some magic so that it short-circuits evaluation just like if ... else and case. The following noop function is a no-op (i.e. “does nothing”), not an error as one might otherwise expect:

noop : Update () = when False (error "Foo")

With case, if ... else, void and when, you can express all branching. However, one additional feature you may want to learn is guards. They are not covered here, but can help avoid deeply nested if ... else blocks. Here’s just one example. The Haskell sources at the beginning of the chapter cover this topic in more depth.

tellSize : Int -> Text
tellSize d
  | d < 0 = "Negative"
  | d == 0 = "Zero"
  | d == 1 = "Non-Zero"
  | d < 10 = "Small"
  | d < 100 = "Big"
  | d < 1000 = "Huge"
  | otherwise = "Enormous"

Loops

Other than branching, the most common form of control flow is looping. Looping is usually used to iteratively modify some state. We’ll use JavaScript in this section to illustrate the procedural way of doing things.

function sum(intArr) {
  var result = 0;
  intArr.forEach (i => {
    result += i;
  });
  return result;
}

A more general loop looks like this:

function whileF(init, cont, step, finalize) {
  var state = init();
  while (cont(state)) {
    state = step(state);
  }
  return finalize(state);
}

In both cases, state is being mutated: result in the former, state in the latter. Values in Daml are immutable, so it needs to work differently. In Daml we will do this with folds and recursion.

Folds

Folds correspond to looping with an explicit iterator: for and forEach loops in procedural languages. The most common iterator is a list, as is the case in the sum function above. For such cases, Daml has the foldl function. The l stands for “left” and means the list is processed from the left. There is also a corresponding foldr which processes from the right.

foldl : (b -> a -> b) -> b -> [a] -> b

Let’s give the type parameters semantic names. b is the state, a is an item. foldls first argument is a function which takes a state and an item, and returns a new state. That’s the equivalent of the inner block of the forEach. It then takes a state, which is the initial state, and a list of items, which is the iterator. The result is again a state. The sum function above can be translated to Daml almost instantly with those correspondences in mind:

sum ints = foldl (+) 0 ints

If we wanted to be more verbose, we could replace (+) with a lambda (\result i -> result + i) which makes the correspondence to result += i from the JavaScript clearer. Almost all loops with explicit iterators can be translated to folds, though we have to take a bit of care with performance when it comes to translating for loops:

function sumArrs(arr1, arr2) {
  var l = min (arr1.length, arr2.length);
  var result = new int[l];
  for(var i = 0; i < l; i++) {
    result[i] = arr1[i] + arr2[i];
  }
  return result;
}

Translating the for into a forEach is easy if you can get your hands on an array containing values [0..(l-1)]. And that’s how you do it in Daml, using ranges. [0..(l-1)] is shorthand for enumFromTo 0 (l-1), which returns the list you’d expect. Daml also has an operator (!!) : [a] -> Int -> a which returns an element in a list. You may now be tempted to write sumArrs like this:

sumArrs : [Int] -> [Int] -> [Int]
sumArrs arr1 arr2 =
  let l = min (length arr1) (length arr2)
      sumAtI i = (arr1 !! i) + (arr2 !! i)
   in foldl (\state i -> (sumAtI i) :: state) [] [1..(l-1)]

Unfortunately, that’s not a very good approach. Lists in Daml are linked lists, which makes access using (!!) too slow for this kind of iteration. A better approach in Daml is to get rid of the i altogether and instead merge the lists first using the zip function, and then iterate over the “zipped” up lists:

sumArrs2 arr1 arr2 = foldl (\state (x, y) -> (x + y) :: state) [] (zip arr1 arr2)

zip : [a] -> [b] -> [(a, b)] takes two lists, and merges them into a single list where the first element is the 2-tuple containing the first element of the two input lists, and so on. It drops any left-over elements of the longer list, thus making the min logic unnecessary.

Maps

In effect, the lambda passed to foldl only “wants” to act on a single element of the (zipped-up) input list, but still has to manage the concatenation of the whole state. Acting on each element separately is a common-enough pattern that there is a specialized function for it: map : (a -> b) -> [a] -> [b]. Using it, we can rewrite sumArr to:

sumArrs3 arr1 arr2 = map (\(x, y) -> (x + y)) (zip arr1 arr2)

As a rule, use map if the result has the same shape as the input and you don’t need to carry state from one iteration to the next. Use folds if you need to accumulate state in any way.

Recursion

If there is no explicit iterator, you can use recursion. Let’s try to write a function that reverses a list, for example. We want to avoid (!!) so there is no sensible iterator here. Instead, we use recursion:

reverseWorker rev rem = case rem of
  [] -> rev
  x::xs -> reverseWorker (x::rev) xs
reverse xs = reverseWorker [] xs

You may be tempted to make reverseWorker a local definition inside reverse, but Daml only supports recursion for top-level functions so the recursive part recurseWorker has to be its own top-level function.

Folds and maps in action contexts

The folds and map function above are pure in the sense introduced earlier: The functions used to map or process items have no side effects. If you have looked at the multi-package models, you’ll have noticed mapA, mapA_, and forA, which seem to serve a similar role but within Actions . A good example is the mapA call in the testMultiTrade script:

let rels =
        [ Relationship chfbank alice
        , Relationship chfbank bob
        , Relationship gbpbank alice
        , Relationship gbpbank bob
        ]
  [chfha, chfhb, gbpha, gbphb] <- mapA setupRelationship rels

Here we have a list of relationships (type [Relationship]) and a function setupRelationship : Relationship -> Script (ContractId AssetHolder). We want the AssetHolder contracts for those relationships, i.e. something of type [ContractId AssetHolder]. Using the map function almost gets us there, but map setupRelationship rels would have type [Update (ContractId AssetHolder)]. This is a list of Update actions, each resulting in a ContractId AssetHolder. What we need is an Update action resulting in a [ContractId AssetHolder]. The list and Update are nested the wrong way around for our purposes. Intuitively, it’s clear how to fix this: we want the compound action consisting of performing each of the actions in the list in turn. There’s a function for that: sequence : : Applicative m => [m a] -> m [a]. It implements that intuition and allows us to “take the Update out of the list”, so to speak. So we could write sequence (map setupRelationship rels). This is so common that it’s encapsulated in the mapA function, a possible implementation of which is

mapA f xs = sequence (map f xs)

The A in mapA stands for “Action”, and you’ll find that many functions that have something to do with “looping” have an A equivalent. The most fundamental of all of these is foldlA : Action m => (b -> a -> m b) -> b -> [a] -> m b, a left fold with side effects. Here the inner function has a side-effect indicated by the m so the end result m b also has a side effect: the sum of all the side effects of the inner function. To improve your familiarity with these concepts, try implementing foldlA in terms of foldl, as well as sequence and mapA in terms of foldlA. Here is one set of possible implementations:

foldlA2 fn init xs =
  let
    work accA x = do
      acc <- accA
      fn acc x
   in foldl work (pure init) xs

mapA2 fn [] = pure []
mapA2 fn (x :: xs) = do
  y <- fn x
  ys <- mapA2 fn xs
  return (y :: ys)

sequence2 [] = pure []
sequence2 (x :: xs) = do
  y <- x
  ys <- sequence2 xs
  return (y :: ys)

forA is just mapA with its arguments reversed. This is useful for readability if the list of items is already in a variable, but the function is a lengthy lambda.

[usdCid, chfCid] <- forA [usdCid, chfCid] (\cid -> submit alice do
    exerciseCmd cid SetObservers with
      newObservers = [bob]
    )

Lastly, you’ll have noticed that in some cases we used mapA_, not mapA. The underscore indicates that the result is not used, so mapA_ fn xs fn == void (mapA fn xs). The Daml Linter will alert you if you could use mapA_ instead of mapA, and similarly for forA_.

Next up

You now know the basics of functions and control flow, both in pure and Action contexts. The multi-package example shows just how much can be done with just the tools you have encountered here, but there are many more tools at your disposal in the Daml standard library, which provides functions and typeclasses for many common circumstances.

This section was copied from existing reviewed documentation. Source: docs-website:docs/replicated/daml/3.4/sdk/tutorials/smart-contracts/data.rst Reviewers: Skip this section. Remove markers after final approval.

Data types

In contracts, you learnt about contract templates, which specify the types of contracts that can be created on the ledger, and what data those contracts hold in their arguments. In daml-scripts, you learnt about the script view in Daml Studio, which displays the current ledger state. It shows one table per template, with one row per contract of that type and one column per field in the arguments. This actually provides a useful way of thinking about templates: like tables in databases. Templates specify a data schema for the ledger:

each template corresponds to a table
each field in the with block of a template corresponds to a column in that table
each contract of that type corresponds to a table row

In this section, you’ll learn how to create rich data schemas for your ledger. Specifically you’ll learn about:

Daml’s built-in and native data types
Record types
Derivation of standard properties
Variants
Data manipulation
Contract manipulation

After this section, you should be able to use a Daml ledger as a simple database where individual parties can write, read, and delete complex data.

Remember that you can load all the code for this section into a folder called intro-data by running dpm new intro-data --template daml-intro-data

Native types

You have already encountered a few native Daml types: Party in contracts, and Text and ContractId in daml-scripts. Here are those native types and more:

Party Stores the identity of an entity that is able to act on the ledger, in the sense that they can sign contracts and submit transactions. In general, Party is opaque.
Text Stores a unicode character string like "Alice".
ContractId a Stores a reference to a contract of type a.
Int Stores signed 64-bit integers. For example, -123.
Decimal Stores fixed-point number with 28 digits before and 10 digits after the decimal point. For example, 0.0000000001 or -9999999999999999999999999999.9999999999.
Bool Stores True or False.
Date Stores a date.
Time Stores absolute UTC time.
RelTime Stores a difference in time.

The below script instantiates each one of these types, manipulates it where appropriate, and tests the result:

-- Code from: daml/daml-intro-data/daml/Native.daml
-- [Include actual code example here]

Despite its simplicity, there are quite a few things to note in this script:

The import statements at the top import two packages from the Daml standard library, which contain all the date and time related functions we use here as well as the functions used in Daml Scripts. More on packages, imports and the standard library later.
Most of the variables are declared inside a let block. That’s because the script do block expects script actions like submit or allocateParty. An integer like 123 is not an action, it’s a pure expression, something we can evaluate without any ledger. You can think of the let as turning variable declaration into an action.
Most variables do not have annotations to say what type they are. That’s because Daml is very good at inferring types. The compiler knows that 123 is an Int, so if you declare my_int = 123, it can infer that my_int is also an Int. This means you don’t have to write the type annotation my_int : Int = 123. However, if the type is ambiguous so that the compiler can’t infer it, you do have to add a type annotation. This is the case for 0.001 which could be any Numeric n. Here we specify 0.001 : Decimal which is a synonym for Numeric 10. You can always choose to add type annotations to aid readability.
The assert function is an action that takes a boolean value and succeeds with True and fails with False. Try putting assert False somewhere in a script and see what happens to the script result.

With templates and these native types, it’s already possible to write a schema akin to a table in a relational database. Below, Token is extended into a simple CashBalance, administered by a party in the role of an accountant:

-- Code from: daml/daml-intro-data/daml/Native.daml
-- [Include actual code example here]

Assemble types

There’s quite a lot of information on the CashBalance above and it would be nice to be able to give that data more structure. Fortunately, Daml’s type system has a number of ways to assemble these native types into much more expressive structures.

Tuples

A common task is to group values in a generic way. Take, for example, a key-value pair with a Text key and an Int value. In Daml, you could use a two-tuple of type (Text, Int) to do so. If you wanted to express a coordinate in three dimensions, you could group three Decimal values using a three-tuple (Decimal, Decimal, Decimal):

-- Code from: daml/daml-intro-data/daml/Tuple.daml
-- [Include actual code example here]

You can access the data in the tuples using:

functions fst, snd, fst3, snd3, thd3
a dot-syntax with field names _1, _2, _3, etc.

Daml supports tuples with up to 20 elements, but accessor functions like fst are only included for 2- and 3-tuples.

Lists

Lists in Daml take a single type parameter defining the type of thing in the list. So you can have a list of integers [Int] or a list of strings [Text], but not a list mixing integers and strings. That’s because Daml is statically and strongly typed. When you get an element out of a list, the compiler needs to know what type that element has. The below script instantiates a few lists of integers and demonstrates the most important list functions.

-- Code from: daml/daml-intro-data/daml/List.daml
-- [Include actual code example here]

Note the type annotation on empty : [Int] = []. It’s necessary because [] is ambiguous. It could be a list of integers or of strings, but the compiler needs to know which it is.

Records

You can think of records as named tuples with named fields. Declare them using the data keyword: data T = C with, where T is the type name and C is the data constructor. In practice, it’s a good idea to always use the same name for type and data constructor:

-- Code from: daml/daml-intro-data/daml/Record.daml
-- [Include actual code example here]

You’ll notice that the syntax to declare records is very similar to the syntax used to declare templates. That’s no accident because a template is really just a special record. When you write template Token with, one of the things that happens in the background is that this becomes a data Token = Token with. In the assert statements above, we always compared values of in-built types. If you wrote assert (my_record == my_record) in the script, you may be surprised to get an error message No instance for (Eq MyRecord) arising from a use of ‘==’. Equality in Daml is always value equality and we haven’t written a function to check value equality for MyRecord values. But don’t worry, you don’t have to implement this rather obvious function yourself. The compiler is smart enough to do it for you, if you use deriving (Eq):

-- Code from: daml/daml-intro-data/daml/Record.daml
-- [Include actual code example here]

Eq is what is called a typeclass. You can think of a typeclass as being like an interface in other languages: it is the mechanism by which you can define a set of functions (for example, == and /= in the case of Eq) to work on multiple types, with a specific implementation for each type they can apply to. There are some other typeclasses that the compiler can derive automatically. Most prominently, Show to get access to the function show (equivalent to toString in many languages) and Ord, which gives access to comparison operators <, >, <=, >=. It’s a good idea to always derive Eq and Show using deriving (Eq, Show). The record types created using template T with do this automatically, and the native types have appropriate typeclass instances. For example, Int derives Eq, Show, and Ord. As another example, ContractId a derives Eq and Show. Records can give the data on CashBalance a bit more structure:

-- Code from: daml/daml-intro-data/daml/Record.daml
-- [Include actual code example here]

If you look at the resulting script view, you’ll see that this still gives rise to one table. The records are expanded out into columns using dot notation.

Variants and pattern matching

Suppose now that you also wanted to keep track of cash in hand. Cash in hand doesn’t have a bank, but you can’t just leave bank empty. Daml doesn’t have an equivalent to null. Variants can express that cash can either be in hand or at a bank:

-- Code from: daml/daml-intro-data/daml/Variants.daml
-- [Include actual code example here]

The way to read the declaration of Location is “A Location either has value InHand OR has a value InAccount a where a is of type Account”. This is quite an explicit way to say that there may or may not be an Account associated with a CashBalance and gives both cases suggestive names. Another option is to use the built-in Optional type. The None value of type Optional a is the closest Daml has to a null value:

-- Code from: daml/daml-intro-data/daml/Variants.daml
-- [Include actual code example here]

Variant types where none of the data constructors take a parameter are called enums:

-- Code from: daml/daml-intro-data/daml/Variants.daml
-- [Include actual code example here]

To access the data in variants, you need to distinguish the different possible cases. For example, you can no longer access the account number of a Location directly, because if it is InHand, there may be no account number. To do this, you can use pattern matching and either throw errors or return compatible types for all cases:

-- Code from: daml/daml-intro-data/daml/Variants.daml
-- [Include actual code example here]

Manipulate data

You’ve got all the ingredients to build rich types expressing the data you want to be able to write to the ledger, and you have seen how to create new values and read fields from values. But how do you manipulate values once created? All data in Daml is immutable, meaning once a value is created, it will never change. Rather than changing values, you create new values based on old ones with some changes applied:

-- Code from: daml/daml-intro-data/daml/Record.daml
-- [Include actual code example here]

changed_record and better_changed_record are each a copy of eq_record with the field my_int changed. better_changed_record shows the recommended way to change fields on a record. The syntax is almost the same as for a new record, but the record name is replaced with the old value: eq_record with instead of EqRecord with. The with block no longer needs to give values to all fields of EqRecord. Any missing fields are taken from eq_record. Throughout the script, eq_record never changes. The expression "Zero" :: eq_record.my_list doesn’t change the list in-place, but creates a new list, which is eq_record.my_list with an extra element in the beginning.

Manipulate contracts

Daml’s type system allows you to store structured data within Daml contracts. Like records and other data structures, contracts are immutable. They can only be created and archived. If you need to change a contract, you must archive the original contract and create a new one with the updated data.

-- Code from: daml/daml-intro-data/daml/ContractManipulation.daml
-- [Include actual code example here]

The script above uses archiveCmd and createCmd to modify a contract of type Account in the same transaction. This process creates a new contract, and a new, distinct contract ID, as shown by newAccountCid =/= accountCid When you modify a contract, by archiving it and creating a new one, you generate a new contract ID. That makes contract IDs unstable, and it can cause stale references.

-- Code from: daml/daml-intro-data/daml/ContractManipulation.daml
-- [Include actual code example here]

In the script above, the ContractId in balance.account still refers to the archived contract. Consequently, querying the balance.account fails and returns None.

Next up

You can now define data schemas for the ledger, read, write, and delete data from the ledger. In choices you’ll learn how to define data transformations and give other parties the right to manipulate data in restricted ways.

Standard library

For an overview of the Prelude, important types from the standard library, and how to search the library, see The Daml Standard Library.

Get Started

Module 1: Understanding Canton

Module 2: Canton for Ethereum Developers

Module 3: Daml Smart Contracts

Module 4: Full-Stack Application Development

Module 5: Testing & Deployment

Module 6: Smart Contract Upgrades

Module 7: Production Best Practices

Deep Dives

Tooling

App Rewards

Troubleshooting

Reference

Documentation Index

​Introduction

​The Haskell connection

​Functions

​Function application

​Infix functions

​Associativity and Precedence

​Type constraints

​Pattern matching in arguments

​Functions everywhere

​Lambdas

​Control flow

​Branches

​If-else expression

​Control flow as expressions

​Branches in actions

​Loops

​Folds

​Maps

​Recursion

​Folds and maps in action contexts

​Next up

​Data types

​Native types

​Assemble types

​Tuples

​Lists

​Records

​Variants and pattern matching

​Manipulate data

​Manipulate contracts

​Next up

​Standard library

Introduction

The Haskell connection

Functions

Function application

Infix functions

Associativity and Precedence

Type constraints

Pattern matching in arguments

Functions everywhere

Lambdas

Control flow

Branches

If-else expression

Control flow as expressions

Branches in actions

Loops

Folds

Maps

Recursion

Folds and maps in action contexts

Next up

Data types

Native types

Assemble types

Tuples

Lists

Records

Variants and pattern matching

Manipulate data

Manipulate contracts

Next up

Standard library