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Transparent, non-invasive RPC by clojure and for clojure
Clojure

README.md

slacker

slacker

slacker is a simple RPC framework designed for Clojure and created by Clojure.

Build Status Dependency Status

Features

  • Fast network infrastructure
  • Fast serialization based on Kryo or Nippy (Text based serialization is also supported)
  • Transparent and non-invasive API. Remote calls is just like local calls with slacker.
  • Extensible server with interceptor framework.
  • Flexible cluster with Zookeeper (moved to slacker-cluster)

Examples

A pair of example server/client can be found under "examples", you can run the examples by lein run-example-server and lein run-example-client. The example client will print out various outputs from the server as well as a RuntimeException: "Expected exception." This exception is part of the example - not a genuine error in the slacker source code.

Usage

Leiningen

latest version on clojars

Basic Usage

Slacker will expose all your public functions under a given namespace.

(ns slapi)
(defn timestamp
  "return server time in milliseconds"
  []
  (System/currentTimeMillis))

;; ...more functions

To expose slapi from port 2104, use:

(use 'slacker.server)
(start-slacker-server [(the-ns 'slapi)] 2104)

Multiple namespaces can be exposed by appending them to the vector

You can also add option :threads 50 to configure the size of server thread pool.

On the client side, You can use defn-remote to create facade one by one. Remember to add remote namespace here as facade name, slapi/timestamp, eg. Otherwise, the name of current namespace will be treated as remote namespace.

(use 'slacker.client)
(def sc (slackerc "localhost:2104"))
(defn-remote sc slapi/timestamp)
(timestamp)

Also the use-remote function is convenience for importing all functions under a remote namespace. (Note that use-remote uses inspection calls to fetch remote functions, so network is required.)

(use-remote 'sc 'slapi)
(timestamp)

By checking the metadata of timestamp, you can get some useful information:

(slacker-meta timestamp)
=> {:slacker-remote-name "timestamp", :slacker-remote-fn true,
:slacker-client #<SlackerClient
slacker.client.common.SlackerClient@575752>, :slacker-remote-ns
"slapi" :arglists ([]), :name timestamp
:doc "return server time in milliseconds"}

Advanced Usage

Options in defn-remote

You can specify the remote function name when there are conflicts in current namespace.

(defn-remote sc remote-time
  :remote-ns "slapi"
  :remote-name "timestamp")

If you add an :async? flag to defn-remote, then the facade will be asynchronous which returns a promise when you call it. You should deref it by yourself to get the return value.

(defn-remote sc slapi/timestamp :async? true)
@(timestamp)

You can also assign a callback (fn [error result]) for an asynchronous facade.

(defn-remote sc slapi/timestamp :callback #(println %2))
(timestamp)

The callback accepts to arguments

  • error
  • result

You need to check (nil? error) because reading the result. Also note that doing blocking tasks in callback function could ruin system performance.

Serialiation

As of 0.12, slacker is still using carbonite (my fork) by default to generate a binary format of data for exchange. I will switch to Peter Taoussanis's nippy in next release.

Serializing custom types

(deprecated since 0.12) By default, most clojure data types are registered in carbonite. (As kryo requires you to register a class before you can serialize its instances.) However, you may have additional types to transport between client and server. To add your own types, you should register custom serializers on both server side and client side. Run this before you start server or client:

(use '[slacker.serialization])
(register-serializers {Class Serializer)

Carbonite has some detailed docs on how to create your own serializers.

JSON Serialization

Other than binary format, you can also use JSON for serialization. JSON is a text based format which is more friendly to human beings. It may be useful for debugging, or communicating with external applications.

Configure slacker client to use JSON:

(def sc (slackerc "localhost:2104" :content-type :json))

One thing you should note is the representation of keyword in JSON. Keywords and strings are both encoded as JSON string in transport. But while decoding, all map keys will be decoded to keyword, and all other strings will be decoded to clojure string. This may lead to inconsistency of your clojure data structure between server and client. Try to avoid this by carefully design your data structure or just using carbonite(default and recommended).

EDN Serialization

From slacker 0.4, clojure pr/read is supported. You can just set content-type as :clj. clojure pr/read has full support on clojure data structures and also easy for debugging. However, it's much slower that carbonite so you'd better not use it if you have critical performance requirements.

Nippy Serialization

Slacker 0.12 has a preview of nippy serialization. Set the content-type as :nippy to use it. Nippy has excellent support for custom types, you can find detailed information on its page.

Slacker is using nippy custom type code 92 for serializing stacktrace elements in debug mode. You'd better to avoid this code in your project. I was talking with Peter about a namespaced custom type coded to avoid this conflict.

Server interceptors

To add custom functions on server, you can define custom interceptors before or after function called.

(definterceptor logging-interceptor
   :before (fn [req] (println (str "calling: " (:fname req))) req))

(start-slacker-server (the-ns 'slapi) 2104
                      :interceptors (interceptors logging-interceptor))

For more information about using interceptors and creating your own interceptors, query the wiki page.

Slacker on HTTP

From 0.4, slacker can be configured to run on HTTP protocol. To enable HTTP transport, just add a :http option to your slacker server:

(start-slacker-server ...
                      :http 4104)

The HTTP url pattern is http://localhost:4104/namespace/function-name.format. Arguments are encoded in format, and posted to server via HTTP body. If you have multiple arguments, you should put them into a clojure vector (for clj format) or javascript array (for json format).

See a curl example:

$ curl -d "[5]" http://localhost:4104/slapi/rand-ints.clj
(38945142 1413770549 1361247669 1899499977 1281637740)

Note that you can only use json or clj as format. Because HTTP is a text based protocol, so carb won't be supported.

Slacker as a Ring App

You can also use slacker as a ring app with slacker.server/slacker-ring-app. The ring app is fully compatible with ring spec. So it could be deployed on any ring adapter.

(use 'slacker.server)
(use 'ring.adapter.jetty)

(run-jetty (slacker-ring-app (the-ns 'slapi))  {:port 8080})

The url pattern of this ring app is same as slacker's built-in http module.

Access Control List

Slacker 0.7 supports IP based access control list (ACL). Consult wiki page for the ACL rule DSL.

Custom client on function call

One issue with previous version of slacker is you have to define a remote function with a slacker client, then call this function with that client always. This is inflexible.

From 0.10.3, we added a macro with-slackerc to isolate local function facade and a specific client. You can call the function with any slacker client.

;; conn0 and conn1 are two slacker clients

(defn-remote conn0 timestamp)

;; call the function with conn0
(timestamp)

;; call the function with conn1
(with-slackerc conn1
  (timestamp))

Note that you have ensure that the function you call is also available to the client. Otherwise, there will be a not-found exception raised.

API Documentation

API docs

Performance

To test performance, just start an example server with lein run -m slacker.example.server.

Then run the performance test script: lein exec -p scripts/performance-test.clj 200000 50. This will run 200,000 calls with 50 threads.

Tested on my working desktop (DELL optiplex 760, Intel(R) Core(TM)2 Duo CPU E7400 @ 2.80GHz, 8G memory), without any special JVM optimization. 200,000 calls with 50 threads is completed in 21923.806054 msecs, which means slacker could handle more than 9000 calls per second on this machine.

Change logs

See wiki page

Contributors

License

Copyright (C) 2011-2014 Sun Ning

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

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