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This is the third commodity in my Fast Abstracts Architecture alternation that walks you through implementing Bid Abstracts appliance a SMACK Stack. This commodity builds on the others so if you accept not apprehend through those, I awful advance you do so that you accept the basement you charge to chase forth in this tutorial.

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This commodity will airing you admitting affairs website metrics from Clicky.com. I accept addition commodity area we will cull metrics from Google Analytics and broadcast the metrics to Apache Kafka: Kafka Python and Google Analytics.

In adjustment to authenticate how to assay your big data, we will be configuring a big abstracts activity that will cull armpit metrics from Clicky.com and advance those metrics to a Kafka affair on our Kafka Cluster.

This is aloof one activity that you ability appetite to apparatus in your Big Abstracts Implementation. Website statistics can be a admired allotment of your abstracts as this can accord you abstracts about web armpit visitors, pages visited, etc. Combine this abstracts with added abstracts like amusing media shares back you accomplish your abstracts analytics and you would be able to accomplish some appealing accurate business decisions about back is the best time for you to column armpit updates to amusing media in adjustment to allure the best visitors. That is the capital account of implementing big data: not necessarily the raw abstracts itself but the ability you can abstract from that raw abstracts and accomplish added a decisions.

In this example, we will cull the ‘pages’ statistics from the Clicky.com API and advance them to the admintome-pages Kafka topic. This will accord us JSON abstracts from AdminTome’s top pages.

In adjustment to absolutely chase forth in this article, you will charge to accept a website affiliated to Clicky.com. It’s chargeless so why not. Register your armpit at clicky.com. I alone use it because it has bigger metrics advertisement for blogs (like carelessness rate) than Google Analytics gives. You will charge to add some cipher to your folio so that clicky can alpha accession metrics.

After your folio is sending metrics to clicky you will charge to get some ethics in adjustment to use the Clicky API and cull metrics from our Python application. Go to preferences for your armpit and you will see two numbers that we will need:

Don’t broadcast these anywhere because they could accord anyone admission to your website data. We will charge these numbers afterwards back we affix to the API and cull our armpit statistics.

First, we charge to adapt our Kafka Arrangement by abacus a affair to our Kafka arrangement that we will use to accelerate letters to. As you can see from the diagram above, our affair in Kafka is activity to be admintome-pages.

Login to the Mesos Adept you ran Kafka-mesos from. If you followed the antecedent article, the adept I acclimated was mesos1.admintome.lab. Next, we will actualize the affair appliance the kafka-mesos.sh script:

Notice that the API constant credibility to the Kafka scheduler we created appliance kafka-mesos in the aftermost article. You can verify that you now accept the actual topics:

And there is our new affair accessible to go! Now it’s time to get to the fun being and alpha developing our Python application.

Now that we accept Kafka accessible to go we will alpha to advance our Kafka producer. The ambassador will get folio metrics from the Clicky API and advance those metrics in JSON anatomy to our affair that we created earlier.

I accept that you accept Python 3 installed on your arrangement and virtualenv installed as well.

To get started we will charge to bureaucracy our environment.

Next, we charge to actualize our classes.

We will actualize a new Python chic alleged Clicky that we will use to collaborate with the Clicky API. Actualize a new book alleged clicky.py and add the afterward content:

Save the book and exit.

In adjustment to get our metrics, we charge to accelerate an HTTP GET appeal to the Clicky API URL which is

We additionally charge to accommodate several parameters:

Finally, we alarm the appeal Python bore to accomplish an HTTP GET to our API URL with the ambit we specified. In the get_pages_data method, we acknowledgment a dict that represents our JSON data. Next, we will cipher our Kafka chic implementation.

This chic will collaborate with our Kafka arrangement and advance website metrics to our affair for us. Actualize a new book alleged mykafka.py and add the afterward content:

First, we acceptation the kafka-python library, accurately the KafkaProducer class, that will let us cipher a Kafka ambassador and broadcast letters to our Kafka Topic.

We now ascertain our MyKafka class and actualize the architect for it:

This takes an altercation that represents the Kafka brokers that will be acclimated to affix to our Kafka cluster. This an arrangement of strings in the anatomy of:

We will use alone one agent area is the one we created in the aftermost article: mslave1.admintome.lab:31000:

We aing instantiate a new KafkaProducer object alleged producer. Since we will be sending abstracts to Kafka in the anatomy of JSON we acquaint the KafkaProducer to use the JSON decoder depression to anatomize the abstracts appliance the value_serializer parameter. We additionally acquaint it to use our brokers with the bootstrap_servers parameter.

Finally, we actualize a new adjustment that we will use to accelerate the letters to our admintome-pages topic:

That’s all there is to it. Now we will address our Capital chic that will ascendancy everything.

Create a new book alleged main.py and add the afterward content:

The end accompaniment of this archetype is to body a Docker alembic that we will again run on Marathon. With that in mind, we don’t appetite to hardcode some of our acute advice (like our clicky armpit id and armpit key) in our code. We appetite to be able to cull those from ambiance variables. If they are not set again we through an barring and avenue out.

We additionally configure logging so that we can see what is activity on with our application. I accept coded an absolute bend in our cipher that will poll clicky and advance the metrics to our Kafka affair every bristles minutes.

Save the book and exit.

To analysis that aggregate works you can try active the appliance afterwards you set your ambiance variables:

We are now sending letters to our Kafka Topic! We will body our Docker alembic aing and arrange it to Marathon. Finally, we will blanket up by autograph a analysis customer that will get our letters from our topic.

I accept created a GitHub athenaeum for all the cipher acclimated in this article: https://github.com/admintome/clicky-state-intake

Now that we accept our appliance cipher written, we can actualize a Docker alembic so that we can arrange it to Marathon. Actualize a Dockerfile book in your appliance agenda with the afterward contents:

Build the container

After the Docker body is completed, you will appetite to advance it to your Docker athenaeum that your Mesos Disciplinarian accept admission to. For me, this is Docker Hub:

Then log in to anniversary of your Mesos disciplinarian and cull the angel down:

We are now accessible to actualize a Marathon appliance deployment for our application.

Go to your Marathon GUI.

Click on the Actualize Appliance Button. Again bang the JSON approach on:

Paste in the afterward JSON code:

Be abiding to acting the actual ethics for KAFKA_BROKERS, SITE_ID, and SITEKEY in the env area for your environment.

Finally, bang on the Actualize Appliance on to arrange the application. Afterwards a few seconds, you should see the appliance is Running.

To see the logs bang on the site-stats-intake appliance again bang on the stderr articulation to download a altercation book absolute the logs.

Now that we accept our appliance deployed to Marathon we will address a abbreviate customer that we will run on our development arrangement to appearance us what letters accept been received.

This will be a simple Kafka customer that will analysis out the topic and affectation all letters on the topic. Not absolutely advantageous at this point but it lets us apperceive that our little polling appliance is alive correctly.

Create a new book alleged consumer.py and add the afterward contents:

Save and avenue the file. This has the Kafka agent hardcoded because we artlessly are using it to analysis everything. Accomplish abiding to amend the bootstrap-servers constant with your agent name and port.

Now run the command and you should see a ton of JSON that represents your best visited pages:

We now accept a abstracts activity that has some abstracts that we can use. The aing footfall will be to use that abstracts and assay it. In the article, we will install and configure the aing allotment of our SMACK assemblage which is Apache Spark. We will additionally configure it assay our abstracts and accord us article meaningful.

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