Clojure Data Structures Tutorial

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In this tutorial, you'll learn Clojure data structures easily with code examples. We'll go through Clojure's main collections, data structure usage patterns, and thought processes for using Clojure's powerful collection library including how to use collections like vectors, hashmaps, and sets, and common patterns like tuple and entity.

Table of Contents

Data structures

Now you'll see each collection and get some details of its operation. I won't get into any implementation details. These are just the things you need to know to use them effectively.


Vectors are very common in Clojure, and for good reason. They strike a nice balance between speed and expressivity. They are useful for:


Usually, you'll create a Vector using the literal syntax:

[1 2 3 4]

You can also use the function vector, which takes any number of arguments and constructs a Vector and puts the arguments into it.

Finally, you can convert any collection to a Vector by calling vec on it.


Literal Vectors are read in by the reader, which constructs a Vector containing the expressions inside of it. When that Vector is evaluated, a new Vector is created with all of its elements evaluated.

For example,

[1 :x (+ 2 2)]

Is read in just like you read it above. Then it is evaluated. That means evaluating each of the arguments and putting them into a new Vector.

(eval '[1 :x (+ 2 2)]) ;=>

[(eval 1) (eval :x) (eval '(+ 2 2))] ;=>

[1 :x 4]

Function semantics

Vectors can be called like functions by putting them at the beginning of an s-expression.

(def v [:a :b :c])

(v 0)

What does this do? It does a lookup by index. Essentially, (v 0) is equivalent to (get v 0).


HashMaps are the workhorses of Clojure. Essentially, they map keys to values. They are used mostly to represent Entities, but they can serve many different purposes. They are useful for:


We normally create HashMaps with the literal syntax:

{:a 1
 :b 2
 :c 3}

There are a couple of rules to remember. It is an error to have duplicate keys in a literal HashMap. They all have to be unique. You cannot write this:

{:a 1
 :a 2} ;=> throws clojure.lang.LispReader$ReadException: Duplicate Key: :a

Note that there are two chances for errors: at read time and at eval time. The unevaluated keys have to be unique, as do the evaluated keys.

(def x :a)

{:a 1
 x  2} ;=> throws java.lang.IllegalArgumentException: Duplicate key: :a

We talk about how HashMaps are evaluated in an upcoming section.

You can also create HashMaps using the hash-map function. It takes alternating keys and values and adds them to an empty HashMap.

(hash-map :a 1 :b 2) ;=> {:b 2 :a 1}

hash-map does not care about duplicates.

Another common pattern is to add key-value pairs to an empty HashMap. You can create key-value pairs with Vectors.

(into {} ;; add to empty HashMap
  [[:a 1] [:b 2] [:c 3]]) ;; a sequence of key-value pairs
                          ;=> {:b 2 :c 3 :a 1}

Other operations

  • keys will return a seq of the keys, and
  • vals will return a seq of the values
  • merge will combine the keys and values of two or more HashMaps
  • select-keys lets you remove unknown or unwanted keys from a Map by saying which keys to keep

See the docs for more HashMap operations.


Like all Clojure expressions, literal HashMaps have an evaluation semantic. There are two phases, read and eval.

During the read phase, a HashMap is created with the key and value expressions. These are checked for uniqueness. That means that the following is illegal:

{(rand) :a
 (rand) :b} ;=> throws clojure.lang.LispReader$ReadException: Duplicate key: (rand)

Even though two calls to (rand) are unlikely to return the same value, the expressions are the same, so they will fail during the read phase.

Then all keys and values are evaluated and put into a new HashMap. Again, there is a check for uniqueness.

(eval '{10 :ten (+ 1 1) :two (* 7 2) :fourteen}) ;=>

{(eval 10)       (eval :ten)
 (eval '(+ 1 1)) (eval :two)
 (eval '(* 7 2)) (eval :fourteen)} ;=>

{10 :ten 2 :two 14 :fourteen}

Function semantics

HashMaps can be called as functions by putting them at the beginning of an s-expression. Doing so will look up the key provided as an argument.

(def numbers {:zero 0 :one 1 :two 2})

(numbers :two)

Essentially, (numbers :two) is equivalent to (get numbers :two).

Sorted Map

Sorted Maps are cool, but I'll be honest: I can't remember ever using one. They're kind of like a library: there's a card catolog so you can quickly find a book, and also the books are shelved in order (based on the Dewey Decimal system, their key).

Sorted Maps are cool for:


There's no literal syntax for creating Sorted Maps. And to confuse things a little bit more, they look like regular Maps when you print them. But Clojure does provide a couple of functions for construction:

(sorted-map :b 43 :a 100 :c 4) ;=> {:a 100 :b 43 :c 4} ;; notice the order

You can also make a Sorted Map that takes a custom comparator function:

(sorted-map-by (comparator <) 0 :a 1 :b 2 :c) ;=> {0 :a 1 :b 2 :c}
(sorted-map-by (comparator >) 0 :a 1 :b 2 :c) ;=> {2 :c 1 :b 0 :a}

Other operations

Besides maintaining the order of keys, Sorted Maps have all of the same operations as HashMaps.

Function semantics

You can use Sorted Maps as a function, which does a lookup:

(def students (sorted-map 123 {:name "Eric"} 332 {:name "Mary"} ...))

(students 332) ;=> {:name "Mary"}


What if you are coding up a cat university. Each cat has a meow id number. And you're taking attendance. Since a classroom full of cats is notoriously hard to count, you're probably going to count the same one twice. Why not just capture all of the meow ids you can, and ignore duplicates? That's why sets are cool.

Sets are awesome for:


Sets have a literal syntax.

#{1 2 3} ;=> #{1 3 2}

You have similar issues with duplicates in a literal as you do with Hash Maps. It is illegal to have duplicates in a literal Set.

#{1 1} ;=> clojure.lang.LispReader$ReaderException: Duplicate key: 1

You can create a new set with the built-in hash-set:

(hash-set 1 2 3) ;=> #{1 3 2}

You can also convert any collection to a Set by using . . . set. Note that it's tolerant of duplicates.

(set [1 2 3 1]) ;=> #{1 2 3}

Evaluation semantics

Evaluating a Set means creating a new Set with all of the elements evaluated.

(eval '#{1 (+ 2 2) (* 9 3)}) ;=>

#{(eval 1) (eval '(+ 2 2)) (eval '(* 9 3))} ;=>

#{1 4 27}

Note that this means there are two ways to have duplicates in a literal: during the read and during the eval.

;; during read
#{(rand) (rand)} ;=> clojure.lang.LispReader$ReaderException: Duplicate key: (rand)

;; during eval
(def x 1)
#{1 x} ;=> java.lang.IllegalArgumentException: Duplicate key: 1

Other operations

There is a whole suite of operations for doing mathematical set operations and relational algebra with Sets. Check out my guide to clojure.set.

For more information, see the documentation.

Function call semantics

Sets can be called just like functions by putting them in the first position in an s-expression. It performs a lookup. Essentially, it's like calling get.

(#{1 2 3} 3)  ;=> 3
(#{1 2 3} 10) ;=> nil

Sorted Set

Sorted Sets are like Sets. The only difference is that you get ordered sequential access. The order you get out of a Sorted Set is determined by either their natural order (numerical order for numbers, alphabetical for Strings, keywords, and symbols, etc.), or, if you want, you can give it your own ordering function.

Sorted Sets are neat for:


There is no literal syntax for Sorted Sets. You can make one with sorted-set.

(sorted-set 7 3 1 2 3 1) ;=> #{1 2 3 7}

Note that they look like regular Sets when they are printed.

Function call semantics

Sorted Sets, like regular Sets, will do a lookup when used in a function call. ((sorted-set 1 2 3) x) is equivalent to (get (sorted-set 1 2 3) x).


Lists are most commonly used in Clojure to represent code. However, they are often used interchangeably with seqs. However, they are their own type (clojure.lang.IPersistentList). You can tell if you have a List (instead of a seq) by calling list?.

(list? '(1 2 3))      ;=> true
(list? (seq [1 2 3])) ;=> false

They are not used as much as Vectors, because Vectors have several important advantages.


Clojure has no literal representation for Lists. If you write a List out in your program, it is interpreted as code. At first glance, it appears that quoting the List is a good solution. However, if you quote a List, the elements of the List won't be evaluated. That means that a quoted List is only useful for Lists of literals.

'(1 2 3) ;=> (1 2 3) ;; ok!
'(1 (+ 1 1) 3) ;=> (1 (+ 1 1) 3) ;; perhaps not what you want

Usually, if you want a List, you build one with the list function.

(list 1 2 3)       ;=> (1 2 3)
(list 1 (+ 1 1) 3) ;=> (1 2 3)

Evaluation semantics

When you evaluate a List, the List is treated as an s-expression. This is one of the big reasons why Vectors are preferred in Clojure for representing data.


Okay, I'm going to be honest: I can't remember ever using a Queue in Clojure. But there are times where I should have. I'm open to learn!

Queues are cool for:


How do you create a Queue? Well, it's actually the hardest one to make. There's no literal syntax and there's no function to make one. You have to start with an empty one and build it up with conj.

(def queue clojure.lang.PersistentQueue/EMPTY)

(conj queue 1 2 3) ;=> (1 2 3)

Other operations

  • peek shows you the first element
  • conj adds to the end
  • pop removes the first element

Access patterns

In Clojure, we organize collections by how they are accessed. Those different access patterns define how we use the collections. When we are modeling a domain, we ask how we will want to access the information in the domain. Then we pick the data structures that match the access patterns we want.

I'm going to go through many common access patterns. Along the way, we'll learn about how to use each collection from each of those different patterns. Then we'll go over some examples of how to analyze a domain problem along those perspectives. And finally, we'll have a reference to all of the collections at the end.

The goal is to have a framework for choosing and using collections that is systematic and reduces the cognitive burden of programming. With time, it will become intuitive.

So the questions we ask of our domain:

  • How will we access information?
  • What information will we have?
  • What information will we want?

The answers to these questions will guide you to implementing your solution. Usually, once you've asked the right questions, the right data structure becomes super obvious.

And this question about access, it has to do with speed. Well, more specifically, speed at scale. Different data structures have different access speeds. For example, finding the last element of a large linked list is slow, while getting the first element is fast, no matter how large the list is. So we will say that we access elements of a linked list from the front, because that is fast.

That doesn't mean that you can't get the last item, and in certain rare circumstances you might do a slow access just once, but you'd never choose the linked list over other collections if you had to access the last item frequently. So we will simplify and say "we don't access the last item of a linked list".

By looking at our collections this way, we'll be able to answer the questions and choose the right collection to implement your domain solution.

Sequential access

Let's say we want to print out ten strings in sequence. Meaning, we print them out in a given order. We can do this like this:

(doseq [s strings]
  (println s))

Given that we know we want those strings printed in order, what type should strings be? Well, we have two main choices: Lists and Vectors.

Lists and Vectors both maintain the order of their elements. They're based on the order things were added. However, note that Vectors add to the end of the sequence and Lists add to the beginning. The important thing is that the order is stable (it doesn't change when you add new elements) and can be arbitrary (you get to decide which order).

Another less common option is the Queue (clojure.lang.PersistentQueue). The Queue maintains the order of elements (adding to the end), as well as letting you pop them off from the beginning.

Two other options are the Sorted Map and the Sorted Set. They maintain an order, but it's not the order you add them in. The order is defined by a comparison function which you can provide. So if you want the order to be alphabetical order, you can do that.

If you don't care about order, any old collection will do. HashMaps and Sets do not maintain order. Note that HashMaps and Sets do seem to maintain the order while they are small. But that's just an implementation detail and once they grow big enough, the order is lost.

Some questions to ask of your domain

  • Do you need to keep things in order?
  • Do you want to remember the order you added things in?
  • Or do you want an order maintained by resorting? (Like keeping them alphabetical)
  • Where do you want new items to go?

List - Maintains the order you add items in. Adds to the front.

Vector - Maintains the order you add items in. Adds to the back.

Queue - Maintains the order you add items in. Adds to the back.

Sorted Map - Keeps items sorted by a key.

Sorted Set - Keeps items sorted by a key.

HashMap - Does not maintain order.

Set - Does not maintain order.


We add elements with the conj function. We can get a sequence from any collection with seq. In the sequence functions, seq is called for you.


Let's say you need to make a TODO List. You want to put new TODO items at the bottom. The bottom of the list means at the back of the sequence. We have two requirements: maintain order and add to the end. We go through our list of collections above and find Vector and Queue. Both will work. But we'll choose Vector because it's more common to work with.

(def todos (atom [])) ;; use a vector

(defn add-todo! [item]
  (swap! todos conj item))

(add-todo! "Buy kitten")
(add-todo! "Buy cat food")
(add-todo! "Feed kitten")

(doseq [item @todos]
  (prn item))

New items are lower in the list.

What if we want to add to the top? We go through our list of collections and we see that List will do that.

(def todos (atom ())) ;; use a list

(defn add-todo! [item]
  (swap! todos conj item))

(add-todo! "Buy kitten")
(add-todo! "Buy cat food")
(add-todo! "Feed kitten")

(doseq [item @todos]
  (prn item))

Besides that first line changing, the rest of the code is the same. This is important. Clojure was explicitly designed this way. For a large portion of access patterns, you can simply swap out the collection type and get different behavior.

What if we like to keep our TODOs in alphabetical order? We can use a Sorted Set.

(def todos (atom (sorted-set))) ;; use a sorted set

(defn add-todo! [item]
  (swap! todos conj item))

(add-todo! "Buy kitten")
(add-todo! "Buy cat food")
(add-todo! "Feed kitten")

(doseq [item @todos]
  (prn item))

Again, we change the behavior by changing the collections. But we're unsatisfied with alphabetical order. It feels organized, but maybe alphabetical order is wrong for our domain. Let's give each tasks a priority, and tell the sorted set that we want to compare the items by priority.

(defn priority-order [a b]
  (compare (:priority a) (:priority b)))

(def todos (atom (sorted-set-by priority-order))) ;; use a sorted set

(defn add-todo! [item]
  (swap! todos conj item))

(add-todo! {:priority 1 :name "Take nap"})
(add-todo! {:priority 4 :name "Clean kitchen"})
(add-todo! {:priority 2 :name "Eat lunch"})

(doseq [item @todos]
  (prn item))

Notice that again, we changed the collection to get different behavior. It's so important I need to bring it up again and again.

Now, what if we don't need order at all? What if we are just in "capture mode", where we are brainstorming all of the things we might want to do, but we don't really know the order yet? It's just a bag of things. We look through the list of collections above. Both Sets and HashMaps don't maintain order. But we don't really have keys (just values). So HashMaps are out. We can use a Set.

(def todos (atom #{})) ;; set

(defn add-todo! [item]
  (swap! todos conj item))

(doseq [letter "ABCDEFGHIJKL"] ;; add some letters
  (add-todo! (str letter)))

(println (apply str @todos))

I've added a bunch of letters so that the order is evident.

There's three things to bring up. First thing is that with only a few items, the Set looks like it's maintaining order. Try it with three items. That has to do with an implementation detail of Sets that they actually do maintain order when they're small. But you can't rely on it. When do they switch over to not maintaining order? That's an implementation detail. Sets do not guarantee order—ever.

The second thing is that these are often terrible for UIs. Imagine every time you added an item, the whole thing was reordered arbitrarily. People expect their UIs to be more stable. Just keep that in mind. It's often better to order them arbitrarily.

The third thing is that Sets don't remember duplicates. That's another access pattern, so let's look at it now.

Remember duplicates

When you're learning things, and storing that information, do you need to remember if you've seen it twice? For instance, when you're making an inventory of your books, you probably want to know if you have two copies of something. However, when you're making a list of books you've read, you probably won't need to write down that you've read some books twice.

Some questions to ask of your domain

  • Do I need to remember duplicates?

Vector, List, Queue - Remember duplicates.

HashMap (and Sorted Map) - Do not remember duplicates, using only the key for equality.

Set (and Sorted Set) - Do not remember duplicates.


Let's say you need to count every visit to your website and keep track of ips to know visits per ip. We need to remember two visits from the same ip. We could use a collection that remembers duplicates. We scan through the list above and find Vectors.

(def visits (atom []))

(defn record-visit! [ip]
  (swap! visits conj ip))

However, what if we don't want to remember the visits, we want to remember the visitors? We want to forget the duplicates. We can use a Set.

(def visitors (atom #{}))

(defn record-visitor! [ip]
  (swap! visits conj ip))

Lookup by key

If you've got a bunch of friends, you probably have their phone numbers in your phone. How do you look up their number? Well, if you were to do it with pen and paper, you'd probably have a card per friend. The card would have their name at the top and their phone number somewhere on the card. You store all of your cards in alphabetical order by name. When you need to find their phone number, you quickly look up their name and read their number.

It's a very common access pattern. The name is the key and the phone number is the value. HashMaps allow you to lookup a value given a key. The keys and values can be any type, including collections.

Now, Vectors also let you look up a value by key. The key is an integer between 0 and the length of the list. It's also called an index. Vectors let you get any element out of them very quickly, regardless of the size of the Vector. You just have to know where it is in the Vector.

Some questions to ask of your domain

  • Do you need to look up one value using another value (called the key)?
  • What is the type of key?

HashMap (and Sorted Map) - Arbitrary key and value types.

Vector - Arbitrary key and value types, but you'll never find any values for keys that aren't positive integers and a valid index. You should really use Vectors only for positive integer keys.

Set (and Sorted Set) - Lookup the value in the Set that matches equal to the given value. It's like Sets are HashMaps with keys mapped to themselves.


The name of the operation to look up a value based on the key in Clojure is get.


Let's make our rolodex:

(def rolodex {"Eric" "504-543-0093"
              "Jane" "303-221-3333"
              "Joe" "222-323-2222"})

(get rolodex "Jane")

Associate a key and value

Now, being able to look up values based on keys is very useful. But how do we store a new key and value? That's what Associate is all about. It's the "file away" action, as opposed to looking stuff up.

Associative data structures in Clojure are HashMaps and Vectors. You can associate any key to any value in a HashMap. If the key already exists, the old value will be replaced. Vectors are similar, but just like with Lookup, you have to use an integer as the key. That will let you replace any existing element in the Vector with a new value.

Some questions to ask of your domain

  • Do you need to store values based on a key?
  • What is the type of key?

HashMap (and Sorted Map) - Arbitrary key and value types.

Vector - Values are any type, but keys are non-negative integers. You can re-associate any key (aka index) that already exists in the Vector, as well as one past the end of the Vector.

;; replace existing value
(assoc [:a :b :c] 2 :x) ;=> [:a :b :x]

;; assoc one past the end
(assoc [:a :b :c] 3 :x) ;=> [:a :b :c :x]

;; can't skip numbers
(assoc [:a :b :c] 5 :x) ;=> throws IndexOutOfBoundsException


We add new key-value pairs or replace existing values with assoc.

(assoc {} :greeting "Hello, World!")

If you need to modify an existing value, you can use the function called update.

(def meals {:breakfast []})

(update meals :breakfast conj :eggs)

It's roughly equivalent to:

(assoc meals :breakfast (conj (get meals :breakfast) :eggs))

** Examples **

Well, a couple of sections ago, we wanted to record web page visits, so we wrote down every ip address into a Vector, including duplicates. It worked, but it's not the best way to get that job done. A better way is to think of the problem as an Accumulator Index. We want to associate a count of visits to each ip, which means we'll need something from our list above. We don't care about the order, so let's choose a HashMap.

(def visits (atom {}))

(defn record-visit! [ip]
  (swap! visits update ip (fnil inc 0)))

(record-visit! "")
(record-visit! "")
(record-visit! "")
(record-visit! "")

The fnil lets you give a default value if the key isn't found.

Dissociate a key and value

Well, once you put something in your HashMap, you might want to get rid of it out of the data structure also based on the key. HashMaps are the only data structure that you can remove stuff from by key.

Some questions to ask of your domain

  • Do I need to remove key-value pairs?

HashMap (or Sorted Map) - Can remove key-value pairs given the keys.


The name of the operation is dissoc.


Let's say we're generating a report of visitors. We want to exclude all localhost visits from the hashmap of visits. The localhost ip address is We can remove it from the HashMap before we generate the report from it.

(def visits (atom {"" 102
                   "" 80
                   "" 1008}))

(dissoc @visits "")


To know how many items are in a collection, you can call the count operation. count typically is very fast on collections, but it’s overloaded to work on some things that are not fast. For instance, it works on iterators and lazy sequences. In both of those cases, it will have to go through all of the items to count up how many elements there are. So the lazy sequence will become completely realized. And infinite sequences and infinite iterators will never end. They'll keep counting forever.

Some questions to ask of your domain

  • Do I need to know the count of items?

Vector, Set (or Sorted Set), List - Returns the count of items.

HashMap (or Sorted Map) - Return the count of key-value pairs.

Lazy seqs - Realizes the entire seq, which can be slow. Infinite lazy lists will run forever.


Call count on the collection.


How many visitors did we get? We can use count on our HashMap to figure it out.

(def visits (atom {"" 102
                   "" 80
                   "" 1008}))

(count @visits)

Equality comparison

All collections have their own version of equality checks. You can use the = function to compare two values, including collections. For collections to be equal, their items must be equal.

But the story doesn’t stop there. Clojure divides collections into Equality Partitions. If two collections are in different equality partitions, they are never equal—for example, a vector is not equal to any hashmap. But if they are in the same partition, then the partition’s comparison rules kick in.

The sequential equality partition compares two collections, item-by-item, in order. The first item of each collection have to be equal. And the second items have to be equal. And the third items, etc. Basically, the sequences have to have the same items, in the same order.

The collections that fall in the sequential equality partition are vectors and lists. That means that (= [1 2 3] '(1 2 3)) returns true. They're equal, even though they're different data structures.

The map equality partition compares two collections of key-value pairs. The same key-value pairs have to exist in both hashmaps, regardless of order. All of the map types belong to this equality partition.

The set equality partition compares two collections of values. The same values have to exist in both sets, but the order doesn’t matter. All of the set types belong to this equality partition.

Some questions to ask of your domain

  • Do I need to compare it as equal to other values?
  • What kinds of values am I comparing?
  • How do I want equality to be computed?

Vector, List, Queue - Compare equality by comparing items by equality in order.

HashMap (and Sorted Map) - Compare equality by comparing all key-value pairs by equality, without order.

Set (and Sorted Set) - Compare equality by comparing all elements by equality, without order.


Use the = function to compare two or more values. Its opposite is not=.


(when (= [1 2 3] '(1 2 3))
  (println "Sequences are equal with same elements."))

(when (not= [1 2 3] [3 2 1])
  (println "Sequences are unequal with different order."))

(when (= {:a 1 :b 2} {:b 2 :a 1})
  (println "Maps are equal if they have same keys and values."))

(when (not= {:a 1 :b 2} {:a 1 :b 3})
  (println "Maps are unequal with different values."))

(when (= #{1 2 3} #{3 2 1})
  (println "Sets with same values are equal"))

Removing an item from a Set

If you’ve got a Set, you can remove items very quickly with disj (short for disjoin, the opposite of conjoin). The reason it’s only defined for Sets is that to remove something from a Vector or a List, you’ve got to go through the sequence one item at a time and find the thing first, then figure out how to remove it (which is probably not fast either). Only Sets let you remove something quickly without looking at every item. (Note: to remove a key/value pair from a HashMap, use dissoc.)

Some questions to ask of your domain

  • Do I need to remove a value, knowing only the value?

Set (or Sorted Set) - Remove an element given that element.


Use disj to remove an element.


If we have a Set of people who have RSVP'd to the Star Wars Christmas Party, but we want to remove Darth Vader from the guest list:

(def guest-list #{"Leia" "Han" "Luke" "Chewie" "Ackbar" "Darth Vader"})

(disj guest-list "Darth Vader")

Split a sequence

Sometimes you are interested in quickly creating a subsequence from a longer sequence. Lists can’t do this. To make a subsequence from a List, you have to walk down the sequence, making a copy—so slow! But Vectors let you create a subsequence quickly. You can tell it the start and end index, and it creates a new one based on the old one.

Some questions to ask of your domain

  • Do I need to split a sequence into consecutive pieces?

Vector - Create a subvector from a given vector and start and end indices.


Use the function subvec to create a subvector.


Let's say we want to do a binary search on an ordered Vector.

(defn binary-search
  "Return the index of the element, or nil if not found"
  ([vec el]
   (binary-search vec el 0))
  ([vec el offset]
   (let [middle (quot (count vec) 2)
         c (compare (get vec middle) el)]
       (empty? vec)

       (zero? c)
       (+ middle offset)

       (pos? c)
       (recur (subvec vec 0 middle) el offset)

       (neg? c)
       (recur (subvec vec (inc middle)) el (+ middle offset 1))))))

(binary-search [:a :b :c :d :e] :d)

Containment check

Sometimes you have a value and you want to know if that value is in your collection. Now, you can imagine going to each item, in turn, and checking if it’s equal to the value you’re looking for. How slow would that be?

Sets to the rescue! Sets will tell you right away if the value is in there, regardless of how big the set is.

Now, I’m going to give you a secret that trips people up when they’re first starting in Clojure. Vectors and HashMaps also can check for containment. However!!! Here’s the secret: the containment check is only for the keys, because that’s the only one that can be fast. HashMaps will check if the value you have is a key inside of the HashMap. And Vectors will check if your value is a valid index into that Vector. Basically, is it an integer and is it non-zero and is it smaller than the length of the Vector.

Some questions to ask of your domain

  • Do you need to know if a value is in a collection?
  • Do you need to know if a key is in a HashMap?
  • Do you need to know if an index is within the range of a Vector?

Vector - Reports true for a non-negative integer, smaller than the length of the Vector.

HashMap (or Sorted Map) - Reports true if the given value is a key in the Map.

Set (or Sorted Set) - Reports true if the given value is in the Set.


contains? is the function for checking containment.


;; not that useful, I'll admit, but you can do it!
(def breakfast [:eggs :juice :toast :coffee :bacon])

;; check if there's a 7th element
(contains? breakfast 6)
(def meals {:breakfast [:eggs :juice :toast :coffee :bacon]
            :lunch [:sandwich]
            :dinner [:soup :salad :pasta :chicken]})

(contains? meals :snack)
(def fridge #{:milk :eggs :carrots :tomato})

(contains? fridge :eggs)

FIFO (first-in, first-out, like a queue)

If you want to put values into a data structure and pull them out in the same order you put them in, you’ll want a FIFO data structure. FIFO data structures necessarily are sequential, but they guarantee being able to quickly add to the end AND removing from the beginning, which is kind of special. Clojure provides a Queue implementation.

Queues are great for producer/consumer patterns. A producer adds values to the Queue, and the consumer can grab the oldest one still on the Queue and use it.

Some questions to ask of your domain

  • Do you need to consume values in the same order they are produced?

Queue - Add values to the end and remove values from the beginning.


Use conj to add values to a Queue, peek to get the next value, and pop to remove the next value.


Let's keep a queue of tasks to do.

;; for ClojureScript:
(def tasks (atom cljs.core.PersistentQueue/EMPTY))
;; for Clojure:
;; (def tasks (atom clojure.lang.PersistentQueue/EMPTY))

(defn add-task! [task]
  (swap! tasks conj task))

(defn take-task! []
  (let [[old new] (swap-vals! tasks pop)] ;; new in Clojure 1.9
    (peek old)))

LIFO (last-in, first-out, like a stack)

Now, sometimes you want to keep track of the most recent value you add to the data structure. There’s a thing called a LIFO data structure. When you take something out, it’s the last thing you put in. This is like a stack of papers, where you make a note and put it right on the top. Then when you go through them, the last note you wrote is on the top—it’s a stack. Stacks are very useful. They provide fast adding and removal from the same end. In Clojure, we use Lists, because they have fast adding to the beginning (with cons) and fast removal (with rest).

But Clojure provides more idiomatic functions for stacks that work with Lists and Vectors.

Some questions to ask of your domain

  • Do you need to consume the newest values before the oldest?

Vector - Add values to the end and remove them from the end.

List - Add values to the beginning and remove them from the beginning.


To add items, use conj, to get the most recent item, use peek, and to remove the most recent item, use pop.


Let's keep track of stuff we want to work on. We're very recency focused. The last thing we add is the one we think is most important.

(def todos (atom []))

(defn add-todo! [task]
  (swap! todos conj task))

(defn get-todo! []
  (let [[old new] (swap-vals! todos pop)] ;; new in Clojure 1.9
    (peek old)))

Usage Patterns

When you’re reading someone else’s code, you're going to have to take this into account. They may have made all of these decisions when they wrote it. (And they may have done it incorrectly.) But, luckily, it turns out that there are some very common patterns that most usage actually falls into. If you know this handful of usage patterns, wow, you’ll be very well on your way to mastery. Use these patterns to keep your code readable.


It’s very common in software to be modeling the data you know about an entity. For instance, all of the information about a person—their name, address, height, date of birth, etc. It's like you filled out a form with all of the information. The names of the fields are the same for everybody, and the values are different for each person.

In Clojure, we would put this data into a HashMap. The form field labels are the keys and the personal data is the values. It might look something like this:

{:name “Eric Normand”
 :address “123 Main St.”
 :height 1.6
 :date-of-birth #inst “1981-07-18”}

The keys are typically keywords and the values are whatever type is appropriate for that particular bit of data. In other words, the value types are heterogeneous. Whenever you want the value for a given key, you can just pull it out with get ((get person :name)), or, if you’re brave, you can just use the keyword directly in function position: (:name person).

To change someone’s information, or to add new information, use assoc:

(assoc person :name “John Smith”)

To remove some information, use dissoc:

(dissoc person :name)

Variant Entity

Very often, you’ll use the Entity Pattern above but then kind of lose track of what Maps belong to which kinds of entities. They may have similar sets of attributes. Is that an employee or a client? Is it a debit or a credit?

If you find that you’re passing different kinds of entities through similar functions, and each type of entity needs different treatment, you’ll want a convenient way to determine what kind of entity it is. Just add a key-value pair that indicates the variant.

{:relationship :client
 :name “Eric Normand”
 :address “123 Main St.”
 :height 1.6
 :date-of-birth #inst “1981-07-18”}

{:relationship :employee
 :name “Jane Smith”
 :address “532 Oak St.”
 :height 1.2
 :date-of-birth #inst “1954-02-01”}

In the code above, we're using the :relationship keyword to distinguish clients from employees. We can call that the variant's identifier. You can use whatever key you like.

Code smell

Watch out for generic words like :type being used as the variant's identifier. Generic words imply that there isn't enough coherence between the different variants.

For example, if you're modeling different shapes (triangle, square, circle, etc.), those are all very related. But they're probably not at all related to login methods (cookie, Basic Auth, JSON Web Token, OAuth2, etc.). To use the same word (:type) to distinguish between them will be confusing.

{:type :circle
 :radius 15}

{:type :cookie
 :session-id "23332"}

Do these really belong together? It's hard to tell that they don't.

Instead of a generic word, use a more specific word. Often the word that describes the category is best. It's clear and doesn't confuse Entities from different categories.

{:shape :circle
 :radius 15}

{:login-method :cookie
 :session-id "23332"}

End Code smell


Note that it’s kind of a bad practice to switch on the type of entity when they’re totally different. You might think it’s totally convenient to have the same function handle different kinds of entities. For instance, your coffee shop software tracks inventory and processes payments for orders. So you could have an entity for payments that looks like this:

{:order-id 123
 :items [{:item :coffee :price 3}]
 :total 3
 :payment :cash}

And then while you’re counting the inventory, as you measure each item, you create entities that look like this:

{:item :dark-roast
 :quantity 4
 :unit :kg}

And then as a clever programmer, you think to yourself that both of them need to be saved to the database. You’ll create a fn called save that tries to figure out what it’s got and then saves it to the right spot.

However, this is a mistake. You shouldn't use the same save function for both entities. Careful analysis of the code might show that the code paths of each entity type never cross. At each point, you know what you’ve got—until you pass it to this save function, which has to figure out again what you’ve got. That's why I'm calling it an antipattern.

Let’s look at the two workflows. Fulfilling an order might look like this:

  • Save order (unpaid)
  • Provide goods (coffee, etc.)
  • Accept payment
  • Validate payment
  • Process payment
  • Save order (paid)

And then doing an inventory check might look like this:

  • For each item on shelves
    • Count quantity of item
    • Save inventory check

The two processes both use the term “save”, and they may both go to the same place (the database), so you may be tempted to “abstract” the differences out. However, the two share very little in common. The “save inventory check” belongs to an entirely different process from the “save order”. Each process is distinct. Data flowing through the order process will never be confused with data from the inventory process. So we should consider the two operations to be distinct and keep them distinct. Just create separate functions, like this:

(defn save-inventory-count [inventory-count]

(defn save-payment [payment]

You’ll save headaches in the long run if you don’t use the Variant Entity pattern. In this case, don’t add a :type key. However, some people do use this antipattern, so you should be aware of it.

End Antipattern

If that's an antipattern, then what is the real pattern? Here's how the Variant Entity pattern should be applied.

Our coffee shop accepts cash, credit cards, debit cards, checks, and gift cards. We need to account for those different types of payments, and each type of payment has a different set of information associated with it.

Cash requires no information. It’s just cold cash.

{:payment-method :cash}

Credit cards require us to get the name on the card, the card number, expiration date, and card code. Debit is similar.

{:payment-method :credit ;; or :debit
 :name “Jane Doe”
 :number “12345...”
 :expiration “11/19”
 :code “333”}

Checks require the routing number, account number, check number, and date.

{:payment-method :check
 :routing “123...”
 :account “00333.”
 :check-number “445”
 :date “3/4/2019”}

And finally, we use an internal gift card system:

{:payment-method :gift-card
 :card-id “333244...”}

Notice that in this payment method case, as compared to the payment/inventory case, these are just different ways to fulfill the same purpose. You could call them different variants of paying your system should handle. Each case will have a divergent branch that will quickly converge back into the main workflow.

The steps of the workflow are:

  • Record purchase
  • Provide goods (coffee, etc)
  • Accept payment
  • Validate payment
    • Cash: check security features of bills
    • Credit card/debit card: authorize
    • Check: check amount, signature, and that account is not banned from using checks
    • Gift card: check remaining balance
  • Process payment
    • Cash: Put cash in drawer and give change
    • Credit/debit: finalize payment
    • Check: put check in check inbox to be deposited during next trip to bank
    • Gift card: debit total
  • Mark purchase as paid

There are 6 steps in this workflow, and four of them are the same regardless of payment type. Two depend on the payment type. This is a valid case for using a Variant Entity. In this example, the variant’s identifier is the :payment-method.

Also notice that we don’t have to use the generic word :type. We can be much more concrete about the different cases because they are all similar enough to fall into a common category. They are payment methods. This specificity is a good sign that a Variant Entity pattern is appropriate.


After we finish our inventory, we’ll want to sum things up. We want a way to quickly ask “do I have enough of product X to last the day or do I need to find a replacement?” The index in the back of a book tells us which pages you can find a topic. So, given a topic, we get a set of pages. Similarly, we want an index from item to quantity. Given an item, we get a quantity.

Let's build one:

{:dark-roast  {:quantity 4 :unit :kg}
 :light-roast {:quantity 2 :unit :kg}
 :milk        {:quantity 3 :unit :gallon}}

We can note a few things about this pattern:

  • We used a Map again
  • The keys are identifiers of the same type
  • The values are all the same type (or structure)

It’s just like in the phone number index we created before. In that example, the keys were all the same type (people’s names) and the values were all the same type (phone numbers).

Accumulator Index

A very common type of Index is called the Accumulator Index. It's primarily used to accumulate values under keys. While normal Indexes associate a key with a value, an Accumulator Index accumulate values over time.

We usually use update to update the value

Let's build one. Let's say you want to count how many times you eat each kind of food during the week. Here we're accumulating the count of each food into a HashMap.

(def food-log [:egg :toast :milk :chicken :egg :carrot :orange :milk])

(reduce (fn [idx food]
          (update idx food (fnil inc 0)))
  {} food-log)

We could also accumulate into a collection. Let's separate out even numbers from odd numbers we've seen.

(def numbers [3 4 4 3 2 1 2 3 4 5 6 5 4 3 2 4 3 6 7 8 6 44 6 6])

(reduce (fn [idx n]
          (update idx
            (if (even? n) :even :odd)
            (fnil conj [])
  {} numbers)

Dispatch table

What happens if you need to run different code depending on a value? Maybe you need to program your kitchen bot to fill orders at your coffee shop. You could have a big case statement:

(defn prepare [item]
  (case item
    :coffee (brew-coffee)
    :tea    (make-tea)
    :bagel  (prepare-bagel)

That will work. But how do you add new items? You have to modify the code. Instead, let's take a look at the structure of this conditional. Given a keyword, we call a function. It sounds like looking up a value given a key! And to do that, we know we need a HashMap.

(def prep-routines {:coffee brew-coffee
                    :tea    make-tea
                    :bagel  prepare-bagel})

(defn prepare [item]
  (let [f (get prep-routines item)] ;; look up prep-routine
    (f)))                           ;; then call it

What we've done is created a dispatch table. We can easily add new items to the HashMap. We could even make it dynamic by wrapping it in an atom.

Conversion Table

Sometimes we have a fixed number of values that convert into a fixed number of other values. Here's an example: I need to convert HTTP Methods into CRUD (Create Read Update Delete) operations. I could write a function to do it. Or I could store them as data in a conversion table, like this:

(def op-table {:post   :create
               :get    :read
               :put    :update
               :delete :delete})

Then I can either do a get to convert, or simply call op-table like a function:

(op-table :get) ;=> :read


Tuples are very convenient when you’ve got a few pieces of data that you need to keep grouped together, but going all the way to an Entity pattern, with named keys, seems like overkill. Tuples are easy in Clojure. You use a Vector and put values in it, where the position of the value in the Vector tells you what that value means.

There’s a great example in Clojure’s core library: re-find.

(re-find #”aa(b+)(c+)” “aabbccc”) => [“aabbccc” “bb” “ccc”])

Notice the return value is a Vector. The first thing that’s returned is the whole match, meaning the maximum string matched by the regex. After that, it’s the first group (inside parenthesis) match. Then the second group match. Notice that the meaning of those elements is given by its position.

It’s a cool pattern, but it’s got disadvantages compared to Entities. First of all, when the tuple gets longer, it gets harder to figure out what each value means. Check this out:

Code smell: long tuples

[“Eric” “Normand” “443-2222” “23 Jones St” “” nil]

Just imagine trying to remember all of the positions as it gets longer. Is that nil in the right place? The longer it gets, the harder it is to notice if it’s right. If it’s longer than 3 items, you should probably use an Entity.

End Code smell

Code smell: tuples that escape local context

On a related note, if you saw this example in the wild, would you be able to figure out what all of the values mean? For instance, what does that nil mean? Entities give names to each value, so Entities are much more self-sufficient and human readable. If you’re using using this Tuple locally, where it will never slip out to another library, it’s probably okay. But if this data is going to be persisted somewhere else, or be part of a public API, Entities are probably better.

For example, you should use the return value of re-find pretty close to where you call re-find. If you passed it along to something else, would that something else know how to interpret it? Probably not. Here's a good test: if you took the tuple and emailed it to your friend, would they know what it meant?

End Code smell

Finally, Tuples aren’t very future-proof. How do you add new values? You have to add them to the end—making them longer. Tuples couple code together through the order of the elements. If the order where the Tuple is defined changes, all code the accesses the values by index has to change. Entities don't have this problem because you refer to values by key.

Variant Tuple

Just like you can make a Variant Entity, you can make a Variant Tuple. All of the warnings above still apply. However, Tuples are still useful (when they're short and used in local scopes), and the Variant kind might be just what you’re looking for.

Just like regular Tuples, Variant Tuples are Vectors. The difference is you reserve the first element to identify the Variant (aka case), you’re dealing with.

For example, if you want to represent different ways to find a file, you could use this system:

[:url “”]
[:disk “/home/eric/my-file.txt”]
[:paper    5    3      12]
        ;; ^row ^shelf ^box

The first elements of the Tuples represent the kind of thing we’ve got. Then we follow that with the data relevant for that type. To figure out what type of thing you’ve got, it’s easy. Just get the first element and switch on that.


Let's say you want to compare one value to many other values. Are any of them equal? For instance, you're working at a giant conglomerate that makes monkey hats, and there are 317 different vice presidents, and just for fun, their names are hard coded into the system. You need to write a function to test whether someone is a vice president.

You could do this:

(defn vp? [name]
  (or (= name "John Jacobsson")
      (= name "Linda Laurens")
      (= name "June James")
      (= name "Fred Franklin")

Now, that sucks, for many reasons. First of all, it's a lot of code! Second, what happens if the name matches none of them? You still had to go through and compare the name to all 317 of them. So slow! It's linear, in fact, no better than using a list. What we really want to do is check if the name is in a collection really quickly. So luckily, we have the perfect tool for that: Sets.

(def vice-presidents #{"John Jacobsson"
                       "Linda Laurens"
                       "June James"
                       "Fred Franklin"

(defn vp? [name]
  (contains? vice-presidents name))

This will be much faster. Checking for containment is a very fast operation.

Some people will even use just the Set directly as a function. Check out this one:

(filter vice-presidents meeting-attendance)

And many people will do it inline:

(defn vowel? [letter]
  (#{a e i o u} letter))

Note that this only works because all of the values are truthy. It won't work if for some reason you are storing nil or false.

(#{false} false) ;=> false
(#{nil} nil)     ;=> nil

Notice these return falsey even when the values are contained inside.

Immutability, Persistence, Transients

Clojure's collections are immutable. They don't have any way to modify them after they are constructed.

Clojure follows a copy-on-write discipline. That means every time you want to change something (like add a new key-value pair to a HashMap), you actually create a modified copy.

But with all of that copying, isn't it slow?

Well, it is slower than modifying a collection directly. For instance, if you make a modified copy of Clojure's HashMap, it is way slower than modifying a Java HashMap. However, it's faster than making a copy of a Java HashMap. Clojure's copy-on-write discipline is faster than it would be in Java.

How does it do that?

Clojure's collections are persistent. That doesn't mean they're written to disk. It means they share common structure. HashMaps are implemented as a tree, with a root node, branche nodes, and leaf nodes. Imagine a HashMap with 1,000 key-value pairs in it. If you add one more key-value pair, most of the nodes in the tree are still correct. So the modified HashMap will just reuse those. The vast majority of the memory used by the original HashMap is also used by the modified copy.

All of this means that copy-on-write can be relatively cheap. Clojure can have fast, immutable data structures. And we can take advantage of them, mostly without concern for their performance.

However, sometimes we do need a part of our code to be faster. And sometimes we're making many modifications to a collection, causing many copies to get made, just to be thrown away as they're modified again. Clojure has a solution to this. It's called transients.

Transients are a way to avoid making intermediate copies of a data structure. Your code gets faster because it isn't producing as much garbarge. It does so basically by making a mutable copy, modifying that copy, then turning it back into an immutable thing.

Here's some code that makes a lot of modifications to a Set.

;; add one million numbers to a set
(time (reduce conj #{} (range 1000000)))
"Elapsed time: 1456.145802 msecs"

We can make this faster by using transients:

(time (persistent! (reduce conj! (transient #{}) (range 1000000))))
"Elapsed time: 428.903734 msecs"

The answers are the same, but the transient version is three times faster.

Here's how you do it:

  1. Call transient on your collection. This creates a transient copy.
  2. Use the same operations you normally would, but using the ! variant, which is made for working with transients.
  3. At the end, convert it back to an immutable collection with persistent!.

Transients should only be used locally, where you have total trust in the code. Never let a transient collection slip out of your control.

Usage in an atom

Clojure's data structures are immutable. And Clojure does not provide any mutable variables. We need to model changing state somehow. This need is especially pronounced when using a Queue. If you have producers adding values to the Queue, and consumers taking values, they obviously need to modify something.

The best way to do that is to use an atom. Atoms are detailed in my concurrency guide. But I'll give a brief overview of how you can use an atom with a Queue to achieve the producer/consumer pattern.

(def queue (atom clojure.lang.PersistentQueue/EMPTY))

(defn enqueue! ;; mutation, so let's use a !
  "Add a value to the end of the queue."
  (swap! queue conj value)
  nil) ;; we want to return nil

(defn dequeue!
  "Remove and return the first item from the queue."
  (let [[old new] (swap-vals! queue pop)] ;; pop removes the first
    (peek old))) ;; return the first

Note that we're using swap-vals!, which is new in Clojure 1.9. Older versions of Clojure made this more awkward. I recommend this swap-vals! version. This kind of use case is the exact reason it was added.

However, just for completeness, here is an implementation that will work with Clojure versions before 1.9.

;; we use a Tuple to store the previous first value and the Queue.
(def queue (atom [nil clojure.lang.PersistentQueue/EMPTY]))

(defn enqueue! ;; mutation, so let's use a !
  "Add a value to the end of the queue."
  (swap! queue update 1 conj value) ;; add a value to the queue (index 1)
  nil) ;; return nil

(defn dequeue!
  "Remove and return the first item from the queue."
  (let [[val] (swap! queue (fn [[_ queue]]
                             [(peek queue) (pop queue)]))]

It's not so bad, but a little more to deal with on your own.

When no one collection fits

Okay, we get it. We're supposed to look for the holes, figure out their properties, then look for collections that fill those holes because they have complementary properties. That's all good, but what happens if you can't find one?

Like, for instance, what if I need both a fast lookup by key and I need to remember duplicates and rememeber the order I add them in? There's no data structure for that!!

So what do we do?

The way I solve that problem is by finding two collections that I can combine to get all of the properties I want.

In this case, I can use a HashMap to get the lookup by key, and a Vector to remember duplicates and order. I'll call it a hybrid collection.

Here's a simple implementation of my hybrid:

(def empty-hybrid {:map {}
                   :vec []})

;; how we add new elements
(defn hybrid-assoc [coll key value]
  (-> coll
    (update :map assoc key value) ;; store in the map
    (update :vec conj key)))      ;; remember the key

(defn hybrid-get [coll key]
  (get-in coll [:map key]))

;; to get a sequence, we look up the values for each key in the vector
(defn hybrid-seq [coll]
  (seq (map (:map coll) (:vec coll))))

If you want, you could even deftype a new type to make this official. I'll leave that up to you, but look at this implementation of a new Map type for guidance.

Vectors vs Lists in Clojure syntax

Someone once asked me what the difference is in Clojure when you use Lists for syntax versus when you use Vectors. For instance, in a let form, you use parens around the whole thing, but square brackets around the bindings:

(let [a 1] ;; square
 ...)      ;; round

You've also got function definitions:

(defn myfn [arg1 arg2] ;; square
  ...)                 ;; round

I believe it's a conscious design decision. Lists (parens) are used to denote s-expressions. They're either special forms, macro calls, or function calls. But inside of special forms and macros, Clojure uses Vectors to group things. You get this nice alternation of round and square brackets that makes it clearer what to pay attention to.

Laziness and sequences

Clojure's sequence functions are lazy. That means they won't evaluate all of the sequence at once. Instead, they can evaluate only part of the sequence, saving the rest for later. And that later may never come, so you save lots of computation. They also let you represent some other interesting things like infinite sequences (e.g., all prime numbers) and things yet to happen (e.g., the rest of the lines of the file that is still being downloaded).

In order to support laziness, all of the sequence functions (map, filter, take, etc.) are lazy. It's not the best design ever. There are lots of weird gotchas you have to be aware of. I go over them in a course on lazy sequences I recorded. Whether you like them or not, they are present in Clojure (and in your face). You can't ignore them, so you should make friends with them.

Collections vs sequences

When I first started learning Clojure, I got a weird feeling that argument orders were not consistent across similar functions. Here’s what I mean:

(map f coll)    ;; collection comes last
(update coll f) ;; collection comes first

Those kinds of inconsistencies can really bum you out, especially when you’re learning and things aren’t cemented in your memory.

However, I eventually learned, by talking to people, that these things actually are consistent. But it requires thinking a little different.

What’s the key?

The key is to mentally divide them into collection operations and sequence operations.

Sequence operations will coerce their argument to a sequence and return a particular type. These are your map, filter, take, drop, group-by, frequencies, etc. You’ll notice that all sequence operations take the sequence last.

Collection operations take a certain type of collection and return the same type. So if you do an update with a map, it returns a new map. If you do update with a vector, it returns a new vector. These operations are update, dissoc, assoc, etc.

This division between sequences and collections is why you see a weird thing in Clojure: (cons value sequence) vs (conj collection value). The arguments appear reversed. Now it makes sense. cons operates on sequences and conj works on collections in general.

And now that we know the key, we can see that some stuff lines up really well. If you’re using threading macros, the -> (thread first) macro works really well with collection operations, while the ->> (thread last) macro works better with sequence operations.

Also, the order of arguments for the function that swap! takes works out really well for collection functions. Here’s an example:

(swap! some-atom-with-a-map update-in [:a :b :c] inc)

swap! puts the collection as the first argument, so it’s perfect for update-in. Now look at what it does for a sequence operation:

(swap! some-atom-with-a-sequence (fn [s] (map inc s)))

You have to wrap the call to map in a function to switch the argument order. That’s way less convenient and also way less common.

How to choose the right data structure

Every program has to deal with two separate layers of semantics—otherwise known as meaning.

1. Language semantics

The programming language has its own semantics, which is how everything the language gives you works. The collections come with built-in semantics—rules governing how they work and how you can use them. These are fixed by the language itself, and you have very little control over these semantics. They form the building material out of which you build your program.

I like to think of the Clojure collections like Lego bricks. They fit together in certain limited and fixed ways. Yet it is those limitations that mean they fit together to build a huge variety of things.

What we've got to do is learn those pieces and how they work. Then we'll be able to build whatever we want out of them.

2. Domain semantics

The second kind of semantics is governed by your domain. If you're writing an accounting system, you have to follow the rules of accounting. Your job as a programmer is to understand those rules and implement them in the language.

Once you understand the thing you're trying to implement, it's all about finding the right language semantics to implement it. Think about it this way: if you need to build a wall, you'll reach for the rectangular Lego bricks. If you need to build a high tower, you'll reach for tall, vertical pieces. We've all built with blocks before. We've played with them so much, there's a lot that is intuitive.

We want to develop that same intuition for building with Clojure's data structures. As an experienced Clojure programmer, I have to say that Clojure's building blocks are really nice. The problem is, when you first start programming in Clojure, it's like someone handed you a big box of Legos and asked you to build a scale model of Rivendell.

Chances are, you're not ready for that. But what are you missing?

What you need is to learn to see the properties of the data structures and how they can fit together. That's what this guide is about.

But that won't get you all the way there. You'll also get some help in the form of usage patterns. These are common ways of using the data structures that help you build out the medium- to large-scale structures.

Those usage patterns have been proven by professional Clojure programmers. They're idiomatic uses of the data structures that you'll find in code at most companies and in open source projects.

So how should we look at the Clojure collections?

Well, when we look at a Lego brick's particular purpose, we can look at it in different ways. How tall is it? How wide? How long? Where can it attach to other bricks? There are all sorts of factors you could use to describe the properties of the brick.

And those are exactly the questions you ask when you've got a gap in a wall you'd like to fill. What size is the gap? Where can you connect a piece? They're the same questions! If, in the hole in your wall, you connect on the top, you'll look for a piece that connects on the bottom.

Likewise, we'll ask questions of our domain solution and try to find data structures that have appropriate answers. If our domain needs to keep things in order, we look for a data structure that maintains order, like a List or Vector.

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