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People always use Netflix for a positive example of a tech stack. Every time I read their articles, I just ask myself if this kind of complexity is really need

People always use Netflix for a positive example of a tech stack. Every time I read their articles, I just ask myself if this kind of complexity is really needed.

exdsq 5y ago
I’d be interested on the actual impact for business. I use Netflix to watch things I plan to watch or filter by genre. Would it be that much worse using a simple review system over an ML system? I don’t think so.
- tomnipotent 5y ago
  > I don’t think so. I do. The baseline comparison is against a human-curated experience (traditional programming) or metadata-driven rankings (most popular, recently added). Given the number of customers and number of titles, it absolutely stands that noticeable improvements can be had by optimizing the display of content using methods that statistically demonstrate those gains.
  - dmitriid 5y ago
    The problem now is that Netflix doesn't have a large catalog. So all their fancy ML and algorithms can only suggest the same handful of Netflix movies and shows on repeat. With a handful of 10-year old movies they could get licenses to thrown into the mix.
    - Ntrails 5y ago
      This is definitely my experience. When your recommendations for 3 genres have 3 of the same films in the initial set... it's indicative of either a small set of options or a really shitty recommendation system
      - kif 5y ago
        It aligns with my experience as well. I keep getting recommendations for shows I have already disliked. If it's due to a low number of options, and not due to their recommendation system, then surely they don't need such a complex recommendation system after all.
        dmitriid 5y ago
        They used to have a large catalog. But then almost everyone pulled out to start competing services. So now it's 90% Netflix films/series, 7% old movies, 3% recent-ish movies.
        exdsq 5y ago
        Yep! Hence why I imagine a review & like/dislike system wouldn't be an order of magnitude worse than their ML system but without having to pay millions in ML salaries, complicate the pipelines, and have a crazy number of microservices. I doubt the total business value for Netflix can truly be greater than the tech investment.
orf 5y ago
Tell us how you handle 20 trillion events per day? That’s 230 million a second.
- jiggawatts 5y ago
  By not collecting that much data. Netflix is a streaming platform. So... stream the movie. That's it. Collect just enough data for performance analysis and for keeping track of where the user stopped watching. That's mere millions of data points per day, not trillions. Like... why do they need real time machine learning at that rate? No, really: Why!? Would it be catastrophic to their business if they ran ML as batch jobs? Do their recommendations need to change second-to-second? Another crazy quote: "At that time, Netflix had ~500 microservices, generating more than 10PB data every day in the ecosystem." Wat? That's a 170 MB of logs pre customer per day! Most of those customers might watch an episode of a TV show per day, or watch one movie per day. Call it 2 hours of usage per day? These guys were blasting out 1.4MB of logs per minute per active user while essentially doing nothing much more than streaming big binary blocks of data from a CDN! In my mind that's the crux of the issue. The architecture astronauts have gone amok, solving problems that shouldn't exist... The argument / excuse is: "Many product features, such as personalized recommendation, search, etc., can benefit from fresher data to improve user experiences, leading to higher user retention, engagement, etc." My experience with NetFlix is that their recommendations are garbage and getting worse over time. Exabytes of data won't solve this.
  - nvarsj 5y ago
    I feel like the majority of celebrity tech companies are like this. They attract a certain kind of person who gets easily bored and needs interesting things to work on to stick around. There is also internal pressure to justify promotions by building things with "large impact". And CV padding to move onto the next big thing. So you end up with lots of large, complex, engineering projects with virtually 0 or most likely negative actual business impact, to keep all the smart kids happy. And these beget more projects to deal with the complexity. It's pretty much Parkinson's Law applied to tech.
    - 5y ago
irjustin 5y ago
> Every time I read their articles Who is "their"? Is this Netflix or a startup 'copying'/emulating Netflix? If their == Netflix, then I absolutely say it is necessary. If it's not, then I am on board with you.
- stevemk14ebr 5y ago
  it's clear their == netflix.
geodel 5y ago
The way I see it is Netflix is giant Enterprise Java shop. So most of their solutions are overwrought, complicated, full of heavily abstracted Java frameworks. Now Netflix being cool company has given respectability to this stuff. If another boring enterprise would have done it they would be blasted for their crappiness
- cbg0 5y ago
  They're being blasted by the comment that you're replying to.
  - geodel 5y ago
    Yeah, CIOs, industry analyst, enterprise architects are going to read this comment and learn that Netflix has undesirable and complicated IT architecture.

People always use Netflix for a positive example of a tech stack. Every time I read their articles, I just ask myself if this kind of complexity is really needed.

exdsq 5y ago

I’d be interested on the actual impact for business. I use Netflix to watch things I plan to watch or filter by genre. Would it be that much worse using a simple review system over an ML system? I don’t think so.

tomnipotent 5y ago
> I don’t think so. I do. The baseline comparison is against a human-curated experience (traditional programming) or metadata-driven rankings (most popular, recently added). Given the number of customers and number of titles, it absolutely stands that noticeable improvements can be had by optimizing the display of content using methods that statistically demonstrate those gains.
- dmitriid 5y ago
  The problem now is that Netflix doesn't have a large catalog. So all their fancy ML and algorithms can only suggest the same handful of Netflix movies and shows on repeat. With a handful of 10-year old movies they could get licenses to thrown into the mix.
  - Ntrails 5y ago
    This is definitely my experience. When your recommendations for 3 genres have 3 of the same films in the initial set... it's indicative of either a small set of options or a really shitty recommendation system
    - kif 5y ago
      It aligns with my experience as well. I keep getting recommendations for shows I have already disliked. If it's due to a low number of options, and not due to their recommendation system, then surely they don't need such a complex recommendation system after all.
      - dmitriid 5y ago
        They used to have a large catalog. But then almost everyone pulled out to start competing services. So now it's 90% Netflix films/series, 7% old movies, 3% recent-ish movies.
        exdsq 5y ago
        Yep! Hence why I imagine a review & like/dislike system wouldn't be an order of magnitude worse than their ML system but without having to pay millions in ML salaries, complicate the pipelines, and have a crazy number of microservices. I doubt the total business value for Netflix can truly be greater than the tech investment.

orf 5y ago

Tell us how you handle 20 trillion events per day? That’s 230 million a second.

jiggawatts 5y ago
By not collecting that much data. Netflix is a streaming platform. So... stream the movie. That's it. Collect just enough data for performance analysis and for keeping track of where the user stopped watching. That's mere millions of data points per day, not trillions. Like... why do they need real time machine learning at that rate? No, really: Why!? Would it be catastrophic to their business if they ran ML as batch jobs? Do their recommendations need to change second-to-second? Another crazy quote: "At that time, Netflix had ~500 microservices, generating more than 10PB data every day in the ecosystem." Wat? That's a 170 MB of logs pre customer per day! Most of those customers might watch an episode of a TV show per day, or watch one movie per day. Call it 2 hours of usage per day? These guys were blasting out 1.4MB of logs per minute per active user while essentially doing nothing much more than streaming big binary blocks of data from a CDN! In my mind that's the crux of the issue. The architecture astronauts have gone amok, solving problems that shouldn't exist... The argument / excuse is: "Many product features, such as personalized recommendation, search, etc., can benefit from fresher data to improve user experiences, leading to higher user retention, engagement, etc." My experience with NetFlix is that their recommendations are garbage and getting worse over time. Exabytes of data won't solve this.
- nvarsj 5y ago
  I feel like the majority of celebrity tech companies are like this. They attract a certain kind of person who gets easily bored and needs interesting things to work on to stick around. There is also internal pressure to justify promotions by building things with "large impact". And CV padding to move onto the next big thing. So you end up with lots of large, complex, engineering projects with virtually 0 or most likely negative actual business impact, to keep all the smart kids happy. And these beget more projects to deal with the complexity. It's pretty much Parkinson's Law applied to tech.
  - 5y ago

irjustin 5y ago

> Every time I read their articles Who is "their"? Is this Netflix or a startup 'copying'/emulating Netflix? If their == Netflix, then I absolutely say it is necessary. If it's not, then I am on board with you.

stevemk14ebr 5y ago
it's clear their == netflix.

geodel 5y ago

The way I see it is Netflix is giant Enterprise Java shop. So most of their solutions are overwrought, complicated, full of heavily abstracted Java frameworks. Now Netflix being cool company has given respectability to this stuff. If another boring enterprise would have done it they would be blasted for their crappiness

cbg0 5y ago
They're being blasted by the comment that you're replying to.
- geodel 5y ago
  Yeah, CIOs, industry analyst, enterprise architects are going to read this comment and learn that Netflix has undesirable and complicated IT architecture.