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Embedded Event Processing in Clojure

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EEP, Embedded Event Processing in Clojure

EEP is a Clojure library for embedded event processing. It combines a lightweight generic event handling system, and with multiple windowed stream operations.

eep-clj is heavily influenced by other EEP projects:

Project Maturity

EEP is a very young and experimental project, but it's been used in production for some time and is fairly stable bug-wise. The API may change significantly in the upcoming months, so use it at your own discretion.

Maven Artifacts

Most Recent Release

With Leiningen:

[clojurewerkz/eep "1.0.0-alpha4"]

With Maven:


Documentation & Examples


In order to create an emitter, use clojurewerkz.eep.emitter/create function:

(ns my-eep-ns
  (:require clojurewerkz.eep.emitter :refer :all))

(def emitter (create :dispatcher-type :ring-buffer))

You can register even handlers on an emitter by using handler helper functions. For example, in order to calculate sums for even and odd numbers, you can first define a splitter and then two aggregators, one for given and one for odd ones:

(defsplitter emitter :entrypoint (fn [i] (if (even? i) :even :odd)))

(defaggregator emitter :even f 0)
(defaggregator emitter :odd f 0)

Here, :entrypoint, :even and :odd are event types, unique event identifiers.

In order to push data to emitter, use clojurewerkz.eep.emitter/notify, which takes an emitter, event type and payload:

(notify emitter :entrypoint 1)
(notify emitter :entrypoint 2)
(notify emitter :entrypoint 3)

Core Concepts

  • Emitter is responsible for handler registration and event routing. It holds everything together.

  • Events are dispatched by user code. An event is an arbitrary tuple of user-defined structure. There's no validation provided for it.

  • Event Type is a unique event type identifier, used for routing. It can be a number, a symbol, a keyword, a string or anything else. All the events coming into Emitter have a type.

  • Handler is a function and optional state attached to it. The function acts as a callback, executed whenever an event is matched on the type. The same handler can be used for multiple event types, but an event type can only have one handler at most.

Handler types

handler is a processing unit that may have state or be stateless. Each handler is attached to emitter with a type, which uniquely identifies it within an emitter. You can only attach a single handler for any given type. However, you can attach single Handler at to multiple types.

Examples of handlers are:

filter receives events of a certain type, and forwards ones for which filter-fn returns true to one or more other handlers:

;; Filters events going through the stream, allowing only even ones
;; to go through
(deffilter emitter :entrypoint even? :summarizer)

splitter receives events of a certain type, and dispatches them to type returned by predicate function. For example, you can split stream of integers to even and odd ones and process them down the pipeline differently.

;; Splits event stream to two parts, routing even events with :even
;; type and odd ones with :odd.
(defsplitter emitter :entrypoint (fn [i] (if (even? i) :even :odd)))

transformer defines a transformer that gets typed tuples, applies transformation function to each one of them and forwards them to one or more other handlers. It's similar to applying map to elements of a list, except for function is applied to stream of data.

;; Transforms event stream by multiplying each event to 2
(deftransformer emitter :entrypoint (partial * 2) :summarizer)

aggregator is initialized with initial value, then gets events of a certain type and aggregates state by applying aggregate function to current state and an incoming event. It's similar to reduce function in Clojure, except for it's applied to the stream of data.

;; Accumulates sum of numbers in the stream
(defaggregator emitter :even (fn [acc i] (+ acc i) 0))

multicast receives events of a certain type and broadcasts them to several handlers with different types. For example, whenever an alert is received, you may want to send notifications via email, IRC, Jabber and append event to the log file.

;; Redistributes incoming events, routing them by three given types
(defmulticast emitter :entrypoint [:summarizer1 :summarizer2 :summarizer3])

;; It's also possible to attach additional multicast entries. this will
;; append :summarizer4 for to be re-disributed whenever events are
;; coming to :entrypoint
(defmulticast emitter :entrypoint [:summarizer4])

observer receives events of a certain type and runs function (potentially with side-effects) on each one of them.

(defobserver emitter :countdown (fn [_] (.countDown latch)))

buffer receives events of a certain type and stores them in a circular buffer with given capacity. As soon as capacity is reached, it distributes them to several other handlers.

;; Holds most recent 20 values
(defbuffer emitter :load-times-last-20-events 20)

rollup acts in a manner similar to buffer, except for it's time-bound but not capacity-bound, so whenever a time period is reached, it dispatches all the events to several other handlers.

;; Aggregates events for 100 milliseconds and emits them to :summarizer
;; as soon as timespan elapsed
(defrollup emitter :entrypoint 100 :summarizer)

Handlers may be stateful and stateless. filter, splitter, transformer, multicast and observer are stateless. On the other hand, aggregator, buffer and rollup are stateful.

Toplogy DSL

There's a DSL that threads emitter through all handler declarations, in order to greate aggregation topologies in a more concise and obvious way:

(build-topology (create)
                :entrypoint (defsplitter (fn [i] (if (even? i) :even :odd)) [:even :odd])
                :even (defaggregator f 0)
                :odd  (defaggregator f 0))

Topology visualisation

You can also visualise your topology by calling clojurewerkz.eep.visualization/visualise-graph and giving it an emitter. You'll get an image like this one:

Topology Visualisation Example


Windows and buffers are an essential part of event processing. We've added the most important implementations of windowed oprations, such as sliding, tumbling, monotonic and timed windows to EEP to allow you to use them within topologies.

Sliding window

Sliding windows have a fixed a-priori known size.

Example: Sliding window of size 2 computing sum of values.

    t0     t1      (emit)   t2             (emit)       tN
  +---+  +---+---+          -...-...-
  | 1 |  | 2 | 1 |   <3>    : x : x :
  +---+  +---+---+          _...+---+---+               ...
             | 2 |              | 2 | 3 |    <5>
             +---+              +---+---+
                                    | 4 |

Useful to hold last size elements.

Tumbling window

Tumbling windows (here) have a fixed a-priori known size.

Example: Tumbling window of size 2 computing sum of values.

    t0     t1      (emit)    t2            t3         (emit)    t4
  +---+  +---+---+         -...-...-
  | 1 |  | 2 | 1 |   <3>   : x : x :
  +---+  +---+---+         -...+---+---+   +---+---+            ...
                                   | 3 |   | 4 | 3 |    <7>
                                   +---+   +---+---+

Useful to accumulate size elements and aggreagate on overflow.

Monotonic window

Makes a clock tick on every call. Whenever clock is elapsed, emits to aggregator.

In essence, it's an alternative implementation of tumbling-window that allows to use custom emission control rather than having a buffer overflow check.

Useful for cases when emission should be controlled by arbitrary function, possibly unrelated to window contents.

Timed window

A simple timed window, that runs on wall clock. Receives events and stores them until clock is elapsed, emits for aggregation after that.

In essence, it's an alternative implementation of tumbling-window or monotonic-window that allows wall clock control.

Useful for accumulating events for time-bound events processing, accumulates events for a certain period of time (for example, 1 minute), and aggregates them.


Whenever you create an emitter, you may notice that one of your cores is 100% busy. You should not worry about it, since all dispatchers use a tight loop for dispatch, without sleeping, therefore not yielding control back to OS, so OS defines that as 100% processor load.

Supported Clojure Versions

EEP requires Clojure 1.4+.


EEP uses Leiningen 2. Make sure you have it installed and then run tests against all supported Clojure versions using

lein2 all test

Then create a branch and make your changes on it. Once you are done with your changes and all tests pass, submit a pull request on Github.


Copyright © 2013 Michael Klishin, Alex P

Distributed under the Eclipse Public License, the same as Clojure.

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