Clojure library that makes remote data access code elegant and efficient at the same time
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README.md

Muse

Build Status

Add to your project (note weird muse2 artefact group id as Clojars now denies shadowing of Maven Central artifacts):

[muse2/muse "0.4.4"]

Latest pre-release version if you want to play with the latest features:

[muse2/muse "0.4.5-alpha4"]

What's That?

Muse is a Clojure library that works hard to make your relationship with remote data simple & enjoyable. We believe that concurrent code can be elegant and efficient at the same time.

Oftentimes, your business logic relies on remote data that you need to fetch from different sources: databases, caches, web services or 3rd party APIs, and you can't mess things up. Muse helps you to keep your business logic clear of low-level details while performing efficiently:

  • batch multiple requests to the same data source
  • request data from multiple data sources concurrently
  • cache previous requests

Having all this gives you the ability to access remote data sources in a concise and consistent way, while the library handles batching and overlapping requests to multiple data sources behind the scenes.

Heavily inspired by:

  • Haxl - Haskell library, Facebook, open-sourced
  • Stitch - Scala library, Twitter, not open-sourced

Talks:

  • "Reinventing Haxl: Efficient, Concurrent and Concise Data Access" at EuroClojure 2015: Video, Slides

Content

  1. The Idea
  2. Usage
  3. Quick Start
  4. Manifold
  5. Pull API
  6. Errors Handling
  7. Misc
  8. Examples
  9. How Does It Work?
  10. Roadmap
  11. Known Restrictions
  12. License
  13. Contribute

The Idea

A core problem of many systems is balancing expressiveness against performance.

(defn num-common-friends [x y]
  (count (set/intersection (friends-of x) (friends-of y))))

Here, (friends-of x) and (friends-of y) are independent, and you want it to be fetched concurrently in a single batch. Furthermore, if x and y refer to the same person, you don't want to redundantly re-fetch their friend list.

Muse allows your data fetches to be implicitly concurrent:

(defn num-common-friends [x y]
  (run! (fmap count (fmap set/intersection (friends-of x) (friends-of y)))))

Mapping over lists will also run concurrently:

(defn friends-of-friends [id]
  (run! (->> id
             friends-of
             (traverse friends-of)
             (fmap (partial apply set/union)))))

You can also use monad interface with cats library:

(defn get-post [id]
  (run! (m/mlet [post (fetch-post id)
                 author (fetch-user (:author-id post))]
          (m/return (assoc post :author author)))))

Usage

Attention! API is subject to change

Include the following to your lein project.clj dependencies:

[muse2/muse "0.4.4"]

or experimental alpha build, if you're brave enough:

[muse2/muse "0.4.5-alpha4"]

All functions are located in muse.core:

(require '[muse.core :as muse])

If you need to use manifold-based version, please do the following:

(require '[muse.deferred :as muse])

Quickstart

Simple helper to emulate async request to the remote source with unpredictable response latency:

(require '[clojure.core.async :refer [go <!! <! timeout]])

(defn remote-req [id result]
  (let [wait (rand 1000)]
    (println "-->" id ".." wait)
    (go
     (<! (timeout wait))
     (println "<--" id)
     result)))

Define data source (list of friends by given user id):

(require '[muse.core :refer :all])

(defrecord FriendsOf [id]
  DataSource
  (fetch [_] (remote-req id (set (range id)))))

Run simplest scenario:

core> (FriendsOf. 10)
#core.FriendsOf{:id 10}
core> (run! (FriendsOf. 10)) ;; returns a channel
#<ManyToManyChannel clojure.core.async.impl.channels.ManyToManyChannel@1aeaa839>
core> (<!! (run! (FriendsOf. 10)))
--> 10 .. 342.97080768100585
<-- 10
#{0 7 1 4 6 3 2 9 5 8}
core> (run!! (FriendsOf. 10)) ;; blocks until done
--> 10 .. 834.4564727277141
<-- 10
#{0 7 1 4 6 3 2 9 5 8}

There is nothing special about it (yet), let's do something more interesting:

core> (fmap count (FriendsOf. 10))
#<MuseMap (clojure.core$count@1b932280 core.FriendsOf[10])>
core> (run!! (fmap count (FriendsOf. 10)))
--> 10 .. 844.5086574753595
<-- 10
10
core> (fmap inc (fmap count (FriendsOf. 3)))
#<MuseMap (clojure.core$comp$fn__4192@4275ef0b core.FriendsOf[3])>
core> (run!! (fmap inc (fmap count (FriendsOf. 3))))
--> 3 .. 334.5374146247876
<-- 3
4

Let's imagine we have another data source: users' activity score by given user id.

(defrecord ActivityScore [id]
  DataSource
  (fetch [_] (remote-req id (inc id))))

Nested data fetches (you can see 2 levels of execution):

(defn first-friend-activity []
  (->> (FriendsOf. 10)
       (fmap sort)
       (fmap first)
       (flat-map #(ActivityScore. %))))

core> (run!! (first-friend-activity))
--> 10 .. 576.5833162596521
<-- 10
--> 0 .. 275.28637368204966
<-- 0
1

And now a few amazing facts.

(require '[clojure.set :refer [intersection]])

(defn num-common-friends [x y]
  (fmap count (fmap intersection (FriendsOf. x) (FriendsOf. y))))
  1. muse automatically runs fetches concurrently:
core> (run!! (num-common-friends 3 4))
--> 3 .. 374.6445696819365
--> 4 .. 162.1603407048976
<-- 4
<-- 3
3
  1. muse detects duplicated requests and caches results to avoid redundant work:
core> (run!! (num-common-friends 5 5))
--> 5 .. 781.2024344113081
<-- 5
5
  1. seq operations will also run concurrently:
(defn friends-of-friends [id]
  (->> (FriendsOf. id)
       (traverse #(FriendsOf. %))
       (fmap (partial apply set/union))))

core> (run!! (friends-of-friends 5))
--> 5 .. 942.2654519658018
<-- 5
--> 0 .. 429.0184498546441
--> 1 .. 316.54859989009765
--> 4 .. 365.7622736084006
--> 3 .. 752.5111238688877
--> 2 .. 618.4316806897967
<-- 1
<-- 4
<-- 0
<-- 2
<-- 3
#{0 1 3 2}
  1. you can implement BatchedSource protocol to tell muse how to batch requests:
(defrecord FriendsOf [id]
  DataSource
  (fetch [_] (remote-req id (set (range id))))

  BatchedSource
  (fetch-multi [this others]
    (let [ids (cons id (map :id (cons this others)))]
      (->> ids
           (map #(vector %1 (set (range %1))))
           (into {})
           (remote-req ids)))))

core> (run!! (frieds-of-friends 5))
--> 5 .. 13.055500150089605
<-- 5
--> (0 1 4 3 2) .. 436.6121922156462
<-- (0 1 4 3 2)
#{0 1 3 2}

A few notes on BatchedSource protocol as it might be kinda tricky from the first glance:

  • fetch-multi excepts first node as a first argument (usually, this) and all others as a second argument (usually, others)
  • you have an option to return either map id -> resource (make sure it's the same id you would return from resource-id) or a seq of resources preserving the order of identifiers given your as an argument (AST runner would double check that the size of your output is actually equal to the size of input params)

Manifold

core.async is a decent abstraction for working with async code, but it's not flexible enough to cover all cases. muse provides a separate namespace muse.deferred that gives you ability to define resources in terms of manifold.deferred. Just use import aliasing and you code will look the same. See the following:

(require '[muse.deferred :as muse])
(require '[manifold.deferred :as d])

(defrecord Numeric [n]
  muse/DataSource
  ;; note that fetch returns a deferred value instead of a channel
  (fetch [_] (d/future (* 2 n)))

  muse/LabeledSource
  (resource-id [_] n))

(muse/run! (muse/fmap inc (Numeric. 21)))
user=> << 43 >>

(muse/run!! (muse/fmap inc (Numeric. 21)))
user=> 43

Read more about manifold library here. Please note, that muse does not allow to mix different execution strategies in a single AST. In case you mess channels and deferred in your code, you have explitely convert them into a single source of truth before passing them to muse.

Pull API

Pull API is an extension build on top of Muse API as a higher level layer to help you to simplify data sources definitions and provide you with even more flexible way to optimize fetches when actual data usage is not defined in advance (yep, waving to GraghQL and friends right now).

Find more in documentation.

Errors Handling

To help you to deal with failures and to avoid messing up with exceptions, muse provides you with FetchFailure protocol that gives you ability to mark any fetch as failed and short-circuit all subsequent fetches. Easiest way to use it is muse/failure helper, that works the same way as muse/value does, except it means that something went very wrong and there's no need to proceed with the AST traversal.

If you already have a notion of an error in your code (like custom Either or Maybe) you can easily tell muse what values should be treated as errors. Quick example:

(extend-type clojure.lang.APersistentMap
  proto/FetchFailure
  (fetch-failed? [this] (contains? this :error))
  (failure-meta [this] this))

This tells us that any map with a key :error represents failure that should stop muse runner immediatly. Meaning, the following code

(defrecord MapWithError [reason]
  muse/DataSource
  (fetch [_] (d/success-deferred {:error reason}))
  muse/LabeledSource
  (resource-id [_] reason))

(muse/run!! (MapWithError. "Boom :(")))

throws an clojure.lang.ExceptionInfo with appropriate information on which node have failed.

Misc

If you come from Haskell you will probably like shortcuts:

core> (<$> inc (<$> count (FriendsOf. 3)))
#<MuseMap (clojure.core$comp$fn__4192@6f2c4a58 core.FriendsOf[3])>
core> (run!! (<$> inc (<$> count (FriendsOf. 3))))
4

Custom response cache id:

(defrecord Timeline [username]
  DataSource
  (fetch [_] (remote-req username (str username "'s timeline ")))

  LabeledSource
  (resource-id [_] username))

core> (fmap count (Timeline. "@kachayev"))
#<MuseMap (clojure.core$count@1b932280 core.Timeline[@kachayev])>
euroclojure.core> (run!! (fmap count (Timeline. "@kachayev")))
--> @kachayev .. 326.7199583652264
<-- @kachayev
20
core> (run!! (fmap str (Timeline. "@kachayev") (Timeline. "@kachayev")))
--> @kachayev .. 809.035607308747
<-- @kachayev
"@kachayev's timeline @kachayev's timeline "

Find more examples in test directory and check muse-examples repo.

ClojureScript

Muse can be used from ClojureScript code with few minor differences:

  • run!! macro isn't provided (as we don't have blocking experience)
  • all data sources should implement namespaced version of LabeledSource protocol (return pair [resource-name id])

Cats

MuseAST monad is compatible with cats library, so you can use mlet/return interface as well as fmap & bind functions provided by cats.core:

(require '[muse.core :refer :all])
(require '[clojure.core.async :refer [go <!!]])
(require '[cats.core :as m])

(defrecord Post [id]
  DataSource
  (fetch [_] (remote-req id {:id id :author-id (inc id) :title "Muse"})))

(defrecord User [id]
  DataSource
  (fetch [_] (remote-req id {:id id :name "Alexey"})))

(defn get-post [id]
  (run! (m/mlet [post (Post. id)
                 user (User. (:author-id post))]
                (m/return (assoc post :author user)))))

core> (<!! (get-post 10))
--> 10 .. 254.02115766996968
<-- 10
--> 11 .. 80.1692964764319
<-- 11
{:author {:id 11, :name "Alexey"}, :id 10, :author-id 11, :title "Muse"}

Real-World Data Sources

HTTP calls:

(require '[muse.core :refer :all])
(require '[org.httpkit.client :as http])
(require '[clojure.core.async :refer [chan put!]])

(defn async-get [url]
  (let [c (chan 1)] (http/get url (fn [res] (put! c res))) c))

(defrecord Gist [id]
  DataSource
  (fetch [_] (async-get (str "https://gist.github.com/" id))))

(defn gist-size [{:keys [headers]}]
  (get headers "Content-Size"))

(run!! (fmap gist-size (Gist. "21e7fe149bc5ae0bd878")))

(defn gist [id] (fmap gist-size (Gist. id)))

;; will fetch 2 gists concurrently
(run!! (fmap compare (gist "21e7fe149bc5ae0bd878") (gist "b5887f66e2985a21a466")))

SQL databases (see more detailed example here: "Solving the N+1 Selects Problem with Muse"):

(require '[clojure.string :as s])
(require '[clojure.core.async :as async :refer [<! go]])
(require '[muse.core :refer :all])
(require '[postgres.async :refer :all])

(def posts-sql "select id, user, title, text from posts limit $1")
(def user-sql "select id, name from users where id = $1")

(defrecord Posts [limit]
  DataSource
  (fetch [_]
    (async/map :rows [(execute! db [posts-sql limit])]))

  LabeledSource
  (resource-id [_] limit))

(defrecord User [id]
  DataSource
  (fetch [_]
    (async/map :rows [(execute! db [user-sql id])]))

  BatchedSource
  (fetch-multi [this others]
    (let [all-ids (cons id (map :id (cons this others)))
          query (str "select id, name from users where id IN (" (s/join "," all-ids) ")")]
      (go
        (let [{:keys [rows]} (<! (execute! db [query]))]
          (into {} (map (fn [{:keys [id] :as row}] [id row]) rows)))))))

(defn attach-author [{:keys [user] :as post}]
  (fmap #(assoc post :user %) (User. user)))

(defn fetch-posts [limit]
  (traverse attach-author (Posts. limit)))

;; will execute 2 SQL queries instead of 11
(run!! (fetch-posts 10))

You can do the same tricks with Redis.

How Does It Work?

  • You define data sources that you want to work with using DataSource protocol (describe how fetch should be executed).

  • You declare what do you want to do with the result of each data source fetch. Yeah, right, your data source is a functor now.

  • You build an AST of all operations placing data source fetching points as leaves using muse low-level building blocks (value/fmap/flat-map) and higher-level API (collect/traverse/etc). Read more about free monads approach.

  • muse implicitly rebuilds AST to work with tree levels instead of separate leaves that gives ability to batch requests and run independent fetches concurrently.

  • muse/run! is an interpreter that reduces AST level by level until the whole computation is finished (it returns a core.async channel that you can read from).

TODO & Ideas

(any support is very welcome)

  • catch & propagate exceptions
  • a simple way to deal with timeouts and other concurrency pitfalls
  • debuggability with nice visualization for AST & fetching (attaching special meta variables to each AST node during the execution)
  • build node-relations delarative notation on top of low-level API to describe your data - done with a new Pull API
  • clean up code, generative tests coverage

Still thinking about:

  • make manifold-first implementation as it's more stable than core.async one
  • move ClojureScript support to a separate library, as it becomes problematic to keep both in sync, due to the difference in the underlying concurrency handling

Known Restrictions

  • assumes your operations with data sources are "side-effects free", so you don't really care about the order of fetches
  • yes, you need enough memory to store the whole data fetched during a single run! call (in case it's impossible you should probably look into other ways to solve your problem, i.e. data stream libraries)

License

Release under the MIT license. See LICENSE for the full license.

Contribute

  • Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
  • Fork the repository on Github & fork master to feature-* branch to start making your changes.
  • Write a test which shows that the bug was fixed or that the feature works as expected.

or simply...

  • Use it.
  • Enjoy it.
  • Spread the word.

Thanks

Thanks go to Simon Marlow for creating/leading Haxl project (and talking about it). And to Facebook for open-sourcing it.