How did I build a Facebook Messenger chatbot for Tableau’s ‘VizOfTheDay’ feed

What is Viz Of The Day

It’s a Tableau Public product used mainly for promoting Tableau as a brand.

https://public.tableau.com/s/gallery

What is Facebook Messenger chatbot

https://developers.facebook.com/blog/post/2016/04/12/bots-for-messenger/

What can my bot do?

  • Users can query VizOfTheDay on any date through either a hardcoded format, or natural language (which will be processed by wit.ai). If the bot doesn’t find that or cannot recognize the message, it will return a random one for users.
  • Users can subscribe to VizOfTheDay, so that when a new viz is published on Tableau Public, users will be notified on Messenger App by a push notification. Of course, they can also unsubscribe it.

How does the system look like

  • Backend running on a single AWS EC2
  • ELB as gateway, to bind a domain name for https
  • Using DynamoDB for storage
  • Calling Facebook Messenger API to create UI
  • Taking advantages of wit.ai for some natural language processing

Modules and their Responsibilities:

  • FeedProcessor
  • periodically retrieves VizOfTheDay from a Tableau Public endpoint
  • serves queries of viz on a specified date (can be today or any date before)
  • Controller
  • receives, processes, and interprets requests from Facebook Messenger
  • distributes interpretted requests to corresponding services
  • Response Service
  • Takes responsibilities of replying requests
  • Supports different kind of templates, from pure text, pure thumbnails, to a combination of text thumbnails, and multiple tabs
  • User Service
  • Stores and serves queries on users’ meta data (id, first and last name, etc) and subscription options (subscribed or not)
  • Data is cached in memory for faster access to properties like first name and subscription option
  • Data is persisted in DynamoDB
  • Profile Querying Service
  • Takes a user id and query its meta data against Facebook Graph API
  • Push Notifier Service
  • Runs daily
  • Once detects a new viz from FeedProcessor, push it to all subscribed users by getting users from User Service
Facebook Messenger API
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AWS ELB
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AWS EC2
|
|       for interpretation
Controller ----------- wit.ai
|                                         |
|                                         |
------------------------|
enqueue id | |
| |
| |
DynamoDB -- User Service ------ Response Service
| | |
| | |
Profile Querying Serivce | |
| |
Push Notifier |
| |
| |
| |
Feed Processor----------

Some Design Tradeoffs

Whether to cache users in memory

  • Pros: faster queries, better performance
  • Concerns:
  • Can the data fit in memory?
    Yes, and it’s based on an estimation of user numbers * data size.

E.g. let’s assume the number of uses is 100,000, which is already much more than enough. For each single user, the metadata and its size are
– user id – long, 64 bit = 8 byte
– user names – string, assume it’s of 20 chars = 20 * 16 bits = 40 byte
– total size = 100,000 * (40 + 8) byte ~ 100,000 * 50 byte = 5,000,000 byte = 5 mb

Single machine or multiple

  • A single machine is enough, based on estimation of memory size, network IO, disk, etc
  • Developing a monolith code running on a single box is really agile
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