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Decreased logging to warn. more docs.
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@@ -50,21 +50,31 @@ But using JSpark you can totally reverse this trend. Build websites that run FAS | |
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How(Getting started with npm) | ||
============================= | ||
To add a distributed job cue to anny node app simply: | ||
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npm i --save js-spark | ||
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Look for **Usage with npm**. | ||
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How(Getting started) | ||
==================== | ||
Prerequisites: install `Node.js`, then: | ||
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git clone [email protected]:syzer/JS-Spark.git && cd $_ | ||
npm install | ||
Then run: | ||
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node index & | ||
node client | ||
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Start on your machine and see how the clients do all calculation. | ||
Or: | ||
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npm start | ||
After that you may see how the clients do all calculation. | ||
and all heavy lifting. | ||
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wait for clients to do all heavy lifting | ||
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Running with UI | ||
--------------- | ||
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@@ -100,8 +110,8 @@ jsSpark([20, 30, 40, 50]) | |
}) | ||
``` | ||
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Usage | ||
===== | ||
Usage(Examples) | ||
=============== | ||
Client side heavy CPU computation(MapReduce) | ||
-------------------------------------------- | ||
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@@ -117,6 +127,7 @@ task = jsSpark([20, 30, 40, 50]) | |
.run(); | ||
``` | ||
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Distributed version of lodash/underscore | ||
---------------------------------------- | ||
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@@ -142,6 +153,9 @@ jsSpark(_.range(10)) | |
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Multiple retry and clients elections | ||
------------------------------------ | ||
If you run calculations via unknown clients is better to recalculate | ||
same tasks on different clients: | ||
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```JavaScript | ||
jsSpark(_.range(10)) | ||
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"name": "js-spark", | ||
"description": "Distributed calculation / data processing system. Run computation/jobs on 1000+ cores", | ||
"author": "syzer <[email protected]>", | ||
"version": "0.3.1", | ||
"version": "0.3.2", | ||
"dependencies": { | ||
"bluebird": "^2.3.4", | ||
"body-parser": "^1.5.0", | ||
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