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ime.datetime.now()), <span class="hljs-string">'price'</span>: price, <span class="hljs-string">'item'</span>: item, <span class="hljs-string">'discount'</span>: discount }</pre></div><p id="7bae">In our case, the output is:</p><div id="477f"><pre>{ 'date': '<span class="hljs-number">2018-05-13</span> 13:37:21.<span class="hljs-number">414342</span>', 'price': '169.99', 'item': 'Dyson AM08 Bladeless Pedestal Fan | White/Silver | Refurbished', 'discount': '399.99 | 57% off' }</pre></div><h1 id="8e42">Step 2: Saving to S3</h1><p id="f971">To save our result to S3, we use <a href="https://boto3.readthedocs.io/en/latest/index.html">Boto 3</a>, the AWS SDK for Python. First we get a reference to S3. Then we create an <code>object</code> with given <code>bucket</code> and <code>file_name</code>(the bucket was created beforehand though this can be done programmatically). Finally, we convert our data to <code>json</code> and write our <code>data</code> into the object.</p><div id="acf2"><pre><span class="hljs-keyword">import</span> boto3 <span class="hljs-keyword">import</span> json</pre></div><div id="34fd"><pre>def <span class="hljs-built_in">save_file_to_s3</span>(bucket, file_name, data): s3 = boto3.<span class="hljs-built_in">resource</span>(<span class="hljs-string">'s3'</span>) obj = s3.<span class="hljs-built_in">Object</span>(bucket, file_name) obj.<span class="hljs-built_in">put</span>(Body=json.<span class="hljs-built_in">dumps</span>(data))</pre></div><p id="1bfc">We will package this utility function in the handler file where we house the actual function Lambda will call. More on this in the following steps.</p><p id="5919">Incidentally, if saving tabular data as in our example, we might choose a database instead of S3. We illustrate S3 here as it is also a good choice for documents, we are also frequently scraped items.</p><h1 id="dd59">Step 3: The Handler Function</h1><p id="6f6c">A Lambda function needs a <a href="https://docs.aws.amazon.com/lambda/latest/dg/python-programming-model-handler-types.html">handler function</a>, which is the function Lambda will execute when it gets called. We will put the handler function, along with our Python dependencies, in a sub-directory called <code>ebay_deal_scraper</code>. This will allow us to separate the files which will be part of the Lambda package, and ancillary project files such as the Serverless config file discussed in later steps.</p><p id="2abf">We name our handler function <code>scrape</code> and give it the signature required by Lambda. We don’t use the <code>event</code> or <code>context</code> parameters, but if you needed to pass data into your Lambda function, they are what you would use.</p><div id="2ee8"><pre><span class="hljs-title">def</span> scrape(event, context): <span class="hljs-class"><span class="hljs-keyword">data</span> = deal_scrape()</span> file_name = f<span class="hljs-string">"deals-{data['date']}"</span> save_file_to_s3('ebay_daily_deals', file_name, <span class="hljs-class"><span class="hljs-keyword">data</span>)</span></pre></div><p id="c7da">Our handler calls our <code>deal_scrape()</code> function, then writes the returned data to S3 under a file name based on the date.</p><h1 id="6b1f">Step 4: Packaging our Function</h1><p id="8815">Our custom code is ready to go, but Lambda also requires you include your dependencies in the package you upload to AWS. In our case this means <code>pip installing</code> our Python packages locally. In the <code>ebay_deal_scrapper</code> directory, we run:</p><div id="bc42"><pre>pip3 <span class="hljs-keyword">install </span>requests <span class="hljs-keyword">bs4 </span>-t .</pre></div><p id="2ef0">(if you have any problems, see this <a href="https://stackoverflow.com/questions/24257803/distutilsoptionerror-must-supply-either-home-or-prefix-exec-prefix-not-both">Stack Overflow issue</a>)</p><p id="8621">This will install the <code>requests</code> and <code>beatiful soup</code> packages in our directory. Lamdba has<code>boto3</code> pre-installed, so you don’t need to include it.</p><p id="bea3">Incidentally, including dependencies can be quite hairy if you require platform-dependent C/C++ libraries like <a href="https://www.boost.org/">Boost</a>, and may require you to use Docker to bundle everything together under the Amazon Linux OS that Lambda requires. But that’s the topic of another blog post.</p><p id="8188">When you have dependencies, you probably want to use a zip file to package everything up. We give ours the generic name of <code>package.zip</code>:</p><div id="f3ce"><pre><span class="hljs-built_in">zip</span> -r package.<span class="hljs-built_in">zip</span> *</pre></div><h1 id="b305">Step 5: Deploying to Lambda</h1><p id="6f52">We will use the Serverless framework to deploy to AWS. Serverless offers a set of command line tools which make it very easy to deploy to the major serverless cloud providers including AWS. You can install it using <code>npm</code>:</p><p id="8fc7"><code>npm install -g serverless</code></p><p id="110d">You specify Serverless deployment instructions in a file called <code>serverless.yml</code>. If you want to generate a boilerplate file, you can run:</p><div id="4295"><pre>serverless <span class="hljs-built_in">create</span> <span class="hljs-comment">--template aws-python</span></pre></div><p id="d62a">This will also create a boilerplate<code>handler.py</code> file, but all we need there is the function which we specify i

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n the <code>serverless.yml</code> config file under the <code>functions</code> section.</p><div id="7831"><pre><span class="hljs-symbol">service:</span> ebay-deal-scraper <span class="hljs-symbol"> provider:</span> <span class="hljs-symbol"> name:</span> aws <span class="hljs-symbol"> runtime:</span> python3<span class="hljs-number">.6</span> <span class="hljs-symbol"> package:</span> <span class="hljs-symbol"> artifact:</span> ebay_deal_scraper/package.zip <span class="hljs-symbol"> functions:</span> <span class="hljs-symbol"> ebay_scrape:</span> <span class="hljs-symbol"> handler:</span> handler.scrape</pre></div><p id="8b75">The <code>provider</code> section tells Serverless we’re deploying to AWS and that we’re using <code>python 3.6</code>.</p><p id="5fec">The <code>package</code> section is where we specify the zip file we created in the last step.</p><p id="5313">We are ready to deploy!</p><p id="273c">From the top directory of your project run:</p><div id="d1ed"><pre><span class="hljs-attribute">serverless deploy</span></pre></div><p id="c024">If you have multiple AWS profiles (such as work and personal), you can specify a profile:</p><div id="981f"><pre>serverless <span class="hljs-keyword">deploy</span> <span class="hljs-params">--aws-profile</span> profile_i_want_to_use</pre></div><p id="eed1">If all goes well, you should get the following output:</p><div id="4f5e"><pre>➜ ebay<span class="hljs-params">-deals</span><span class="hljs-params">-scrape</span> git:(master) ✗ sd -<span class="hljs-params">-aws</span><span class="hljs-params">-profile</span> michael Serverless: Packaging service<span class="hljs-params">...</span> Serverless: Uploading CloudFormation file <span class="hljs-keyword">to</span> S3<span class="hljs-params">...</span> Serverless: Uploading artifacts<span class="hljs-params">...</span> Serverless: Validating template<span class="hljs-params">...</span> Serverless: Updating <span class="hljs-built_in">Stack</span><span class="hljs-params">...</span> Serverless: Checking <span class="hljs-built_in">Stack</span> update progress<span class="hljs-params">...</span> <span class="hljs-params">...</span><span class="hljs-params">...</span><span class="hljs-params">...</span> Serverless: <span class="hljs-built_in">Stack</span> update finished<span class="hljs-params">...</span> Service Information service: ebay<span class="hljs-params">-deal</span><span class="hljs-params">-scraper</span> stage: dev region: us<span class="hljs-params">-east</span><span class="hljs-number">-1</span> <span class="hljs-built_in">stack</span>: ebay<span class="hljs-params">-deal</span><span class="hljs-params">-scraper</span><span class="hljs-params">-dev</span> api keys: <span class="hljs-literal">None</span> endpoints: <span class="hljs-literal">None</span> functions: ebay_scrape: ebay<span class="hljs-params">-deal</span><span class="hljs-params">-scraper</span><span class="hljs-params">-dev</span><span class="hljs-params">-ebay_scrape</span> Serverless: Removing old service versions<span class="hljs-params">...</span></pre></div><p id="d320">You can now try to invoke your function with:</p><div id="b911"><pre>serverless<span class="hljs-built_in"> invoke </span>-f ebay_scrape <span class="hljs-comment"># --aws_profile profile_name </span></pre></div><p id="4385">This will result in an AccessDenied error. To fix this, you need to add permission to your Lambda function’s role (this gets created together with the function).</p><h1 id="da14">Step 6: Giving Lambda S3 Privileges</h1><p id="b13d">Go to the Roles page in the IAM section of the AWS dashboard. Find the role for your Lambda function, in our case <code>ebay-deal-scraper-dev-us-east-1-lambdaRole</code> and add a policy which allows it to access S3. <code>AmazonS3FullAccess</code> will work for our demo, though in production you may want to create a new policy which is more restrictive.</p><p id="e0d1">Once your function is deployed you should also be able to view and test it from the AWS console.</p><figure id="dc68"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*A1uCWEB2Hb-YS7lMEiXCww.png"><figcaption></figcaption></figure><h1 id="6080">Step 7: Scheduling the Lambda Function Using CloudWatch</h1><p id="8cd3">Final step! Go to the CloudWatch Management Page and click the<code>Rules</code> tab. Under Event Source, select <code>Schedule</code> and fill in a cron expression. We set ours to run every day at 6PM GMT, or afternoon in US Eastern Time. Next, in the <code>Targets</code> section, choose <code>Lambda function</code> in the select and then your Lambda function from the list in the <code>Function</code> select. You’re done!</p><figure id="1595"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*amgaVqAlsAl0pQdYaAP_PA.png"><figcaption></figcaption></figure><p id="eb37">That’s the whirlwind tour of scraping the serverless way. I hope this gives a taste of the technologies involved, though each of them could be the subject of many, many blog posts.</p><p id="f860">As far as scraping goes, with the rise of big data/machine learning, data acquisition is becoming more and more important. And if, for instance, you wanted to do something like feed your machine learning model with data to <a href="https://www.dataquest.io/blog/machine-learning-tutorial/">make price predictions for AirBnB</a>, scraping might be your only option. The serverless architecture exemplified here offers an efficient way to do this.</p></article></body>

Step 68: Put Everything Under Version Control~ Diomidis Spinellis

This is the 68th Step towards gaining the Programming Enlightenment series. If you didn’t learn the 67th Step, read it.

Atlassian git flow

What is Version Control?

It is a component of Software Configuration Management which tracks the changes to documents or changes in the software code. Also, known as revision control or source control.

What are the different types of Version Control System?

The list of different VCS are:

  • Local Data Model: In this system, all developers must use the same file system. Example; Revision Control System (RCS), Source Code Control System (SCCS).
Local version Control
  • Client-Server Model: In this system, all developers use a single shared repository. Example; CVS (Concurrent Versions System) , SVN (Subversion) etc. Also, know as centralized VCS.
Centralized VCS
  • Distributed Model: In this system, each developer works directly with his or her own local repository, and changes are shared between repositories as a separate step. Example; Git, Mercurial, Fossil etc.
Distributed VCS

Why use Version Control?

We use Version Control because:

  • Collaboration: Allows developers to concurrently modify the same set of files with branching and merge conflict management tools.
  • Storing Versions: We can store the version of the software releases without much work.
  • Restoring Previous Versions: We can restore older version if we messed something.
  • History of Changes: With VCS we can track why certain changes were made in the past.
  • Backup: Using distributed VCS every local repository acts as a backup.

How to use Version Control?

Git is one of the most popular VCS. They have been used by great clients like github, bitbucket, gitlab etc. Great tutorial on how to use git are:

TL;DR Let’s Git this Party Started.

Go to the series.

Go to 67th Step

Go to the 69th Step

References:

Git
Software Development
Java
Android
Programming
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