Learn Algolia, Nginx and Twit

Today’s users expect great things from their search experiences — speed, relevance, and usability, to name a few. Algolia provides developers with out-of-the-box and customizable search experiences that are a joy to build.

This introductory course on the Algolia search solution focuses on the user-facing part of search. Using the Algolia InstantSearch.js front-end library, we’ll see how to build an engaging search experience in no time. (28 minutes, to be exact.) 

**Prerequisites:** 
You don’t need previous experience with search to follow this course — we’ll use an example index with an e-commerce data set preconfigured. We will work mainly in JavaScript, so basic JS knowledge is expected.

If you want to play with your own index settings and data set, you can [create a free Algolia account here](https://www.algolia.com/users/sign_up/hacker/?utm_medium=link&utm_source=egghead&utm_campaign=education), but this is not required to follow along with this first course. 

Algolia's full suite APIs enable teams to develop unique search and discovery experiences for their customers across all platforms and devices. Type a query. Click a link. Open an app. Everything your customers do sends a signal about what they want. Algolia decodes that intent to deliver the right content - instantly.

algolia

Getting Started with Algolia InstantSearch.js

In this course we’ll learn how to create Twitter bots. We’ll first learn how to interface with the Twitter API using Twit.js, which allows us to post statuses, search tweets, and interact with followers. We’ll create several example bots that show the diversity of possibilities, using powerful tools such as RiTa.js, Google’s Cloud Vision API, and Tracery. We’ll learn how to tweet data from Google Spreadsheets, and to compose and tweet a song for an audio bot. We’ll also learn how to host our bot logic on Heroku and Docker. 

Twitter API Client for node

Supports both the REST and Streaming API.

twit

Create your own twitter bots

We’ll get a real-time stream of tweets, and learn how to filter a stream by keyword, location, and user.

Use the Twitter Stream API with Twit.js

NGINX is a free, open-source, high-performance HTTP server and reverse proxy, as well as an IMAP/POP3 proxy server. 

nginx

Learn about the concepts of managing data within Docker volumes, including all of the commands to create and manage volumes. We'll demonstrate how to mount a Docker volume to one or many containers by creating and mounting a volume to an Nginx proxy, and then verify the output with curl. You'll also see how changes to files within volumes of one container propagate to other containers with the same volume mounted, and learn various commands to inspect Docker volumes.

Manage Data within Docker Volumes

In this lesson, our bot will tweet hello to the Twitter world! We'll do this by using Twit's POST method to post our status to the statuses/update endpoint.

Post Statuses with Twit.js

In this lesson, we’ll create multiple functions to request, download, and save photos and data from NASA's API, and then have our bot upload these photos to Twitter and post them along with their descriptions. We'll also learn how to tweet videos using a video from NASA’s space archives.

Tweet Media Files with Twit.js

We’ll learn how your bot can get its list of followers, follow people, and look up friendships. We'll use Twit's GET method to get our followers at the followers/list endpoint, to get the users we follow at the friends/ids and friends/list endpoints, and to look up our friendships with the friendships/lookup endpoint. We'll also use Twit's POST method to follow someone at the friendships/create endpoint, and to send messages by posting to the direct_messages/new endpoint. 

Work with Users in Twit.js

We’ll learn the basics of interacting with tweets, including retweeting, deleting, favoriting, and replying to tweets. We'll get our home timeline by using Twit's GET method to access the `statuses/home_timeline` endpoint, including the count parameter, which lets us get back a certain number of tweets. We'll also pass it a callback. We'll learn how to cycle through the data (the tweets) we get back and see what information is included. We'll learn how to retweet statuses by posting to `statuses/retweet` and including the tweet id. We can unretweet by posting to `statuses/unretweet` with the same tweet id. We'll also learn how to like a tweet by posting to `favorites/create` with a tweet id, and unlike a tweet by posting to `favorites/destroy` with a tweet id. We'll also learn how to reply to a tweet by posting to `statuses/update`, with a status that includes the handle of the user we're replying to and `in_reply_to_status_id` parameter, which is the id of the tweet we're replying to. We'll learn how to delete a tweet by posting to `statuses/destroy` with the tweet's id. 

Interact with Tweets with Twit.js 

We’ll learn how to search tweets with the Twitter Search API, using Twit's GET method to get the search/tweets endpoint. We'll use the query and count parameters to get the search term(s) and number of tweets that we want. We'll learn how to get exact phrases, multiple words, one of several words, emoticons, hashtags, photos/videos, urls, and how to remove words from our results. We'll learn how to implement the safe filter and how to filter by media, website, or date. We'll learn how to get recent results, popular results, results by location, and results by language. 

The search API returns for relevance, and not completeness. If you want all the tweets from a search term, you should use the stream API (which we'll go over in the next lesson). 

Use the Twitter Search API with Twit.js

We’ll go over the basic Twitter bot setup, including how to start a project from scratch, install necessary packages, and store our private keys in environment variables. 

Set Up a Basic Twitter Bot with Twit.js

Learn how to setup an Nginx proxy server with Docker that sits in front of a Node.js app. You can use a proxy to control the flow of frontend requests hitting your app, as well as to achieve better performance and scalability. We'll create a sample Node.js app, configure a default Nginx configuration file for proxying web requests, build both Node.js and Nginx Docker images, and learn how to link everything up and get it running with Docker specifics.

Setup an Nginx Proxy for a Node.js App with Docker

Node.js apps built with Docker cannot scale horizontally by themselves. In this lesson we will spawn out multiple Node.js processes/containers, and move the incoming request handler to an Nginx proxy to load balance and properly scale our Node.js app.

Scale Docker Horizontally with Nginx Load Balancing

This will get you started on how to setup the Javascript API client of Algolia search solution. To follow along the video having a free Algolia account already setup will make things easier (but is not required). Once the client is setup we will test it by pushing a couple of records to an index.  

Set up the Algolia JavaScript Client

We’ll load a simple JSON file containing 500 actors records from disk and push it to our Algolia index using the Javascript API client.

Push JSON Data to an Algolia Index from the JavaScript Client

We’ll learn how to push a small amount of data from a local MySQL database to an Algolia index. The first step is to connect to the DB using the `mysql` package, then select all records from the DB and finally push them to Algolia using the JS API client.

Push MySQL Data to an Algolia Index from the JS Client

Using the Lodash library, we’ll see how to split large amounts of data from a local MySQL database into smaller chunks and push them to an Algolia index.  

Push Chunks of Large MySQL Data to an Algolia Index with Lodash

Before playing with Instantsearch widgets and build a rich search experience, the fundamental step is to understand what the library will be dealing with. In this lesson we we’ll cover the anatomy of the JSON search API response send back by Algolia for each search query and how it can be used.

Anatomy of an Algolia Search API Response

We now have a good understanding of the API Response structure, we could go playing with instantsearch JS library right away, that said this Library is building on top of the JS API Client. To get a better grasp on how it works at a lower level, let’s play first with raw hits straight from the JS API client, we’ll take care of better UI with Instantsearch JS right after that.

Leverage the Algolia JS API Client to Display Raw Hits from the API Response

The first step to implement Algolia on the front end using Instantsearch : importing and setting up the library with your credentials. This is what will allow the library to initiate the connection to Algolia servers and the first step to complete before adding some useful widgets.

Set up the Instantsearch JS Library

In this lesson we’ll build a search experience reduced to it’s 2 essential components : a search input and the displayed results. As simple as it is, to build this from scratch would require a fair amount of work. Between listening to the input, making the api call, parsing and displaying the result... To build this using Algolia’s Instantsearch library requires a few lines of code with simply two widgets: the Searchbox and the Hits widget.

Search an Algolia Index with Instantsearch JS searchbox and hits widgets

Displaying barebones Hits won’t catch your user’s attention. The UI is key to provide a good search experience and hits are the meat of this experience so they need extra attention. By using Mustache templating we’ll see how to display rich results to your users. 