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Difference between revisions of "MapReduce"

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<pre class=setup>
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{{TopTenTips}}
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#ENCODING
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<div style="min-height:25em">
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import io
 
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import sys
 
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-16')
 
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#MONGO
 
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from pymongo import MongoClient
 
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client = MongoClient()
 
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client.progzoo.authenticate('scott','tiger')
 
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db = client['progzoo']
 
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#PRETTY
 
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import pprint
 
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pp = pprint.PrettyPrinter(indent=4)
 
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</pre>
 
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==Page Under Construction==
 
 
==Introducing the MapReduce function==
 
==Introducing the MapReduce function==
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The MapReduce function is an aggregate function that consists of two functions: Map and Reduce. As the name would suggest, the map is always performed before the reduce.<br/><br/>
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The MapReduce function is an aggregate function that consists of two functions: Map and Reduce.
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The map function takes data and breaks it down into tuples (key/value pairs) for each element in the dataset<br/>
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The reduce function then takes the result of the map function and simply reduces it in to a smaller set of tuples by merging all values with the same key.<br/><br/>
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The map is always performed before the reduce.
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Map is used to deal with [https://en.wikipedia.org/wiki/Embarrassingly_parallel "embarassingly parallel problems"] where a task can be broken down into subtasks that can then be ran simultaneously without affecting each other. Instead of just processing elements one by one, all elements can all be dealt with at the same time in parallel. This allows for massively reduced processing times as well as large scalability across multiple servers, making it an attractive solution to handling [https://en.wikipedia.org/wiki/Big_data Big Data].<br/><br/>
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This is a feature more suited for the shell or a Node.JS implementation, as here we will need to use JavaScript code inside Pymongo. Also note that the Mongo shell version of this is mapReduce, whereas pymongo use map_reduce()<br/><br/>
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The map function examines every document in the collection and emits '''(key,value)''' pairs.
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For this example our MapReduce takes the form:<br/>
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<pre>db.<collection>.map_reduce(
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The map function takes no input however the current document can be accessed as '''this'''
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    <map function>,
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    <reduce function>,
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The reduce function has two inputs, for every distinct key emitted by map the reduce function is called with a list of the corresponding values.
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    <out collection>
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)
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==Population of each continent==
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</pre>
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<div class=q data-lang="mongo">
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<div class=q data-lang="py3">In this example we will be returning the population of all the continents.<br/>
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Here the map function emits the continent and the population for each country.
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<code>emit(k,v)</code> lets us pick the fields we want to turn into tuples, where k is the key and v is the value. Our keys will be the continents and our values will be the population<br/>
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In our reduce for each continent we add each population value to it's total.
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The reduce function uses the JavaScript function <code>Array.sum</code> to add the populations.
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Finally we specify that we want the result to be inline, so we can print it to screen.  
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<pre class="def"><nowiki>
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<pre class=def>
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db.world.mapReduce(
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from bson.code import Code
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  function () {emit(this.continent, this.population);},  
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pp.pprint(
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  function (k, v) { return Array.sum(v); },
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    db.world.map_reduce(
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  {out: {inline: 1}}
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        Code("function(){emit(this.continent, this.population)}"),  
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);</nowiki></pre>
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        Code("function(key, values){"
 
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                  "    var total = 0;"
 
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                  "    for (var i = 0; i < values.length; i++){"
 
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                  "        total += values[i];"
 
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                  "    }"
 
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                  "    return total;"
 
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                  "}"),
 
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        {"inline":1},
 
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    )
 
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)
 
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</pre>
 
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<div class=ans>
 
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from bson.code import Code;pp.pprint(db.world.map_reduce(Code("function(){emit(this.continent, this.population)}"), Code("function(key, values){var total = 0;for (var i = 0; i < values.length; i++){total += values[i];}return total;}"),{"inline":1}))
 
 
</div>
 
</div>
 +
 +
==Number of countries in each continent==
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<div class=q data-lang="mongo">
 +
Instead of sending populations you can send a list one 1s to the reduce function.
 +
 +
The reduce function will now create a count of the number of countries in each continent.
 +
<pre class="def"><nowiki>
 +
db.world.mapReduce(
 +
  function () {emit(this.continent, 1);},
 +
  function (k, v) { return Array.sum(v); },
 +
  {out: {inline: 1}}
 +
);</nowiki></pre>
 +
</div>
 +
 +
==Count only some countries==
 +
<div class=q data-lang="mongo">
 +
The map function does not need to emit once for every entry.
 +
 +
In this example we are only counting the countries that have a large population.
 +
<pre class="def"><nowiki>
 +
db.world.mapReduce(
 +
  function () {
 +
    if (this.population > 100000000)
 +
    {
 +
      emit(this.continent, 1);
 +
    }
 +
  },
 +
  function (k, v) { return Array.sum(v); },
 +
  {out: {"inline": 1}}
 +
);</nowiki></pre>
 +
</div>
 +
 +
==Examine the reduce function==
 +
<div class=q data-lang="mongo">
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<p class="strong">Examine the reduce function.</p>
 +
 +
Here we emit the continent and the name, and in the reduce function we <code>return v.join(',')</code> to see a comma separated list of the values in the list.
 +
<pre class="def"><nowiki>
 +
db.world.mapReduce(
 +
  function () {
 +
    if (this.population > 100000000) {
 +
      emit(this.continent, this.name);
 +
    }
 +
  },
 +
  function (k, v) { return v.join(','); },
 +
  {out: {"inline": 1}}
 +
);</nowiki></pre>
 +
</div>
 +
 +
==Reduce to a single value==
 +
<div class=q data-lang="mongo">
 +
If you emit the same key every time you will get exactly one result from your query.
 +
 +
Here we emit the value 1 as the key and 1 as the value. The reduce function sums those 1s to get a count of the total number of countries.
 +
<pre class="def"><nowiki>
 +
db.world.mapReduce(
 +
  function () {
 +
    emit(1, 1);
 +
  },
 +
  function (k, v) { return Array.sum(v); },
 +
  {out: {"inline": 1}}
 +
);</nowiki></pre>
 +
</div>
 +
 +
==Emit a name==
 +
<div class=q data-lang="mongo">
 +
You can use the list given in the reduce function.
 +
 +
Here we emit the key '''this.continent''' and the value '''this.name'''.
 +
The reduce function returns the first element of the collected list.
 +
<pre class="def"><nowiki>
 +
db.world.mapReduce(
 +
  function () {
 +
    emit(this.continent, this.name);
 +
  },
 +
  function (k, v) { return v[0]; },
 +
  {out: {"inline": 1}}
 +
);</nowiki></pre>
 
</div>
 
</div>

Latest revision as of 08:47, 26 June 2018

Top Tips

Introducing the MapReduce function

The MapReduce function is an aggregate function that consists of two functions: Map and Reduce.

The map is always performed before the reduce.

The map function examines every document in the collection and emits (key,value) pairs.

The map function takes no input however the current document can be accessed as this

The reduce function has two inputs, for every distinct key emitted by map the reduce function is called with a list of the corresponding values.

Population of each continent

Here the map function emits the continent and the population for each country.

The reduce function uses the JavaScript function Array.sum to add the populations. 

db.world.mapReduce(
  function () {emit(this.continent, this.population);}, 
  function (k, v) { return Array.sum(v); },
  {out: {inline: 1}}
);

Number of countries in each continent

Instead of sending populations you can send a list one 1s to the reduce function.

The reduce function will now create a count of the number of countries in each continent. 

db.world.mapReduce(
  function () {emit(this.continent, 1);}, 
  function (k, v) { return Array.sum(v); },
  {out: {inline: 1}}
);

Count only some countries

The map function does not need to emit once for every entry.

In this example we are only counting the countries that have a large population. 

db.world.mapReduce(
  function () {
    if (this.population > 100000000)
    {
      emit(this.continent, 1);
    }
  },
  function (k, v) { return Array.sum(v); },
  {out: {"inline": 1}}
);

Examine the reduce function

Examine the reduce function.

Here we emit the continent and the name, and in the reduce function we return v.join(',') to see a comma separated list of the values in the list. 

db.world.mapReduce(
  function () {
    if (this.population > 100000000) {
      emit(this.continent, this.name);
    }
  },
  function (k, v) { return v.join(','); },
  {out: {"inline": 1}}
);

Reduce to a single value

If you emit the same key every time you will get exactly one result from your query.

Here we emit the value 1 as the key and 1 as the value. The reduce function sums those 1s to get a count of the total number of countries. 

db.world.mapReduce(
  function () {
    emit(1, 1);
  },
  function (k, v) { return Array.sum(v); },
  {out: {"inline": 1}}
);

Emit a name

You can use the list given in the reduce function.

Here we emit the key this.continent and the value this.name. The reduce function returns the first element of the collected list. 

db.world.mapReduce(
  function () {
    emit(this.continent, this.name);
  },
  function (k, v) { return v[0]; },
  {out: {"inline": 1}}
);
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