03. Iterators and streaming¶

A collection can hold values that already exist. A stream produces values as a consumer asks for them.

Hara separates three related concepts:

A persistent collection is an immutable value.
A lazy Seq produces values on demand.
A raw iterator is a one-shot source that advances.

Learning goals¶

By the end of this lesson, you can:

Transform collections with map, filter, and reduce.
Explain why a lazy pipeline does not do all work immediately.
Build bounded streams from generators.
Create and consume a raw iterator.
Close an iterator when work stops early.
Choose between a persistent result, a Seq, and an iterator.

Transform every item with `map`¶

map applies one function to each source item:

(map inc [1 2 3])

The result is a lazy Seq. Its printed form can vary by runtime display, but its logical values are 2, 3, and 4.

Use a domain function when the operation has a domain meaning:

(defn line-length [line]
  (count line))

(map line-length ["one" "three" "seven"])

The mapping function receives one item and returns one item.

Keep selected items with `filter`¶

filter calls a predicate and keeps matching items:

(defn non-empty-line? [line]
  (not (empty? (str/trim line))))

(filter non-empty-line?
        ["first" "  " "third"])

A predicate answers a question. It does not transform the item.

Combine items with `reduce`¶

reduce carries an accumulator through the source:

(reduce + 0 [10 20 12])
; => 42

Count total characters:

(defn add-line-length [total line]
  (+ total (count line)))

(reduce add-line-length
        0
        ["one" "three" "seven"])
; => 13

Use reduce when many input items become one result.

Build a lazy pipeline¶

Nest operations from source to consumer:

(take 2
  (map str/trim
    (filter non-empty-line?
            [" first " " " " second " " third "])))

Read the pipeline from the inside:

Start with the vector.
Keep non-empty lines.
Trim each line.
Take two results.

The pipeline can stop after it has enough output.

The threading macro can express the same flow:

(->> [" first " " " " second " " third "]
     (filter non-empty-line?)
     (map str/trim)
     (take 2))

Choose the form that makes the data flow easiest to inspect.

Laziness delays work¶

A lazy operation creates a plan for producing values. A consumer causes that plan to advance.

(def numbers
  (map inc (range 0 1000000)))

The runtime does not need to create one million incremented values immediately.

A bounded consumer requests only part of the source:

(take 3 numbers)

This property supports large inputs and early termination.

Keep effects out of lazy mapping functions. An effect can occur later than the source form suggests.

Realize a result at a boundary¶

A UI model, encoded response, or saved result often needs a complete persistent collection.

Use mapv when the result must be an eager vector:

(mapv str/trim [" one " " two "])
; => ["one" "two"]

Use realize when you already have a lazy value and need its realized result:

(realize (take 3 numbers))

Do not realize a large source without a reason. Preserve streaming until the next boundary needs a complete value.

Create a raw iterator¶

Use iter to acquire an iterator:

(def line-iterator
  (iter ["one" "two" "three"]))

Check whether it has another item:

(iter-has? line-iterator)
; => true

Advance it:

(iter-next line-iterator)
; => "one"

(iter-next line-iterator)
; => "two"

The iterator has changed position. It does not restart when you read its Var again.

Raw iterators are one-shot¶

A persistent vector can create another iterator later. An existing iterator represents one traversal.

(def values [1 2 3])

(def first-pass (iter values))
(def second-pass (iter values))

The two iterators advance independently.

Do not store a partially consumed iterator where code expects a reusable collection value.

Transform iterators directly¶

The iter-* functions return raw iterator pipelines:

(def source
  (iter-range 0 100))

(def even-source
  (iter-filter even? source))

(def doubled-source
  (iter-map (fn [number] (* number 2))
            even-source))

Take a bounded iterator view:

(def first-five
  (iter-take 5 doubled-source))

Each iter-next request moves the pipeline forward by one output value.

Direct iterator control is useful for protocol adapters, large sources, and code that must manage resource lifetime.

Close an iterator¶

Close an iterator when the consumer stops before exhaustion:

(iter-close first-five)

Closing a wrapper closes its acquired source iterators.

Use cleanup paths when the source owns a file handle, socket, decoder, or other host resource.

A plain vector iterator has little to release, but the same discipline applies to resource-backed iterators.

Use `Seq` for normal lazy code¶

The ordinary functions map, filter, take, drop, mapcat, keep, cycle, and partition return lazy Seq values.

(take 3
  (map (fn [number] (* number number))
       (range 0 100)))

A Seq gives application code a stable lazy boundary. The raw iterator forms expose traversal mechanics.

Prefer ordinary collection functions unless you need one-shot control.

Transform constructors¶

Hara also supports a one-argument transform form:

(def trim-all
  (map str/trim))

(trim-all [" one " " two "])
; => ["one" "two"]

For an ordinary collection, the transform returns an eager collection result. Existing lazy sources remain lazy.

This is a Hara transform contract. It is not a transducer contract.

Stop when the answer is known¶

Use any? for a boolean existential result:

(any? empty? ["one" "" "three"])
; => true

Use every? when all items must match:

(every? string? ["one" "two"])
; => true

These consumers can stop as soon as the result is known.

Build the course stream¶

Create a line transformation:

(defn normalize-line [line]
  (str/to-lower (str/trim line)))

Build a lazy pipeline:

(defn normalized-lines [lines]
  (->> lines
       (filter non-empty-line?)
       (map normalize-line)))

Consume only the first three lines:

(take 3
  (normalized-lines
    [" Alpha " " " " Beta " " Gamma " " Delta "]))

Create a raw iterator version:

(defn normalized-line-iterator [lines]
  (iter-map normalize-line
    (iter-filter non-empty-line?
      (iter lines))))

Advance it manually and close it when you stop.

Streaming and state¶

Keep the stream transformation pure. Update the run atom at the consumer boundary:

(defn consume-line! [line]
  (swap! run add-line (count (str/encode line)))
  line)

Do not place this function inside a lazy map unless delayed state updates are intentional.

A safer design lets the consumer request one line, update state, then request the next line.

Practice loop¶

Predict the next iterator value.
Call iter-next once.
Check iter-has?.
Change the source data.
Explain whether the pipeline is reusable or one-shot.
Close the iterator before its source is exhausted.

Common mistakes¶

Treating a lazy plan as a completed result¶

A lazy pipeline may not have processed every source item yet.

Reusing a consumed iterator¶

Acquire a new iterator from a replayable source when you need another pass.

Performing hidden effects in `map`¶

Use map for transformations. Put state changes and I/O at a clear consumer boundary.

Forgetting to bound a large source¶

Use take, a predicate, or another stopping rule before realization.

Check yourself¶

You are ready for the next lesson when you can answer these questions:

What question does map answer?
Why can a lazy pipeline stop early?
What changes when iter-next runs?
Why is a raw iterator one-shot?
When should an iterator be closed?
When should you realize a lazy result?
Why should effects stay outside ordinary lazy transforms?

Continue with 04. Coroutines and promises.