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Twitter's attitude is laughable. Yes, you have a decent amount of traffic but you're also only dealing with ~160 uncompressed bytes (plus whatever overhead) per

Twitter's attitude is laughable. Yes, you have a decent amount of traffic but you're also only dealing with ~160 uncompressed bytes (plus whatever overhead) per event. The hurdles you've overcame aren't that particularly amazing nor challenging.

achompas 14y ago
Yes, you have a decent amount of traffic but you're also only dealing with ~160 uncompressed bytes (plus whatever overhead) per event. The hurdles you've overcame aren't that particularly amazing nor challenging. >42M uniques last month.[0] Are you really going to assert Twitter hasn't dealt with amazing or challenging hurdles in getting this far? [0] http://siteanalytics.compete.com/twitter.com/ http://siteanalytics.compete.com/twitter.com/ EDIT: this ignores that twitter.com is not the only Twitter client--they served 15B (!!) requests/day (!!!) as of a year ago. Not to mention metadata, instrumentation for services, logging, DB backups, and managing configuration of all of those distributed resources. Are we still talking about the ease of 160B? http://www.readwriteweb.com/hack/2011/07/twitter-serves-more-api-calls.php http://www.readwriteweb.com/hack/2011/07/twitter-serves-more...
- timaelliott 14y ago
  Yes. In 2008/2009, another engineer and myself built an ad-platform that received around 500M impressions per day, 5M clicks per day. And it wasn't just recording a tweet or publishing out to followers. We took the user input query, had to do some keyword/relevancy targeting, geofiltering, matching to advertisers and deliver back a large result set of adverts. All within 100ms. Our platform was also apache, mod_php, memcached, mysql and rabbitmq. So definitely not the most optimal of platforms by any means. We had two colos with ~20 servers (dell r410s) at each facility. Twitter just recently announced 400M tweets/day. I'm not trying to brag about my experiences, because looking back now we made numerous amateur mistakes, but just showing that Twitter's "scale" is a joke compared to everyday challenges at any large internet ad network.
  - shadowfiend 14y ago
    You understand that 400M tweets a day is the number of tweets posted to their system, right? That speaks not at all to the consumption of those tweets, which is the metric you're using for your ad platform. Additionally, they don't just deal with 160 characters, because again, somehow you're still talking about data being posted, and not data being consumed. Data is consumed off their site via polling APIs, streaming APIs, and a website, all of which are pushing those 400M tweets a day out to plenty of consumers. They may not have as ridiculous a scale as they act like they do. But let's be clear: it is nowhere near as trivial as you make it out to be, either. Armchair quarterbacking is always easy, because you aren't exposed to the complexity that arises when you've spent a few months and years hitting the corner cases of the problem you're commenting on.
  - chewxy 14y ago
    Add to that, the challenges of sub-60ms RTB. All the fun!
  - marcusf 14y ago
    So you had 500m reads on a relatively static data set + 5m writes on an unrelated log? Sounds like a fun problem, but I agree I doesn't sound like rocket science. On the other hand, it also doesn't sound like Twitter, having 400m writes per day, and 400*x million reads on that very dynamic data set. Just seems that's a slightly harder problem.
    - chewxy 14y ago
      Adserving is not really static. Cachebusters are named so for good reason. Nowadays ad server developers are clever enough to separate click tracking and impression tracking (the non-Enterprise version of OpenX still deserves a lot of ಠ_ಠ though). In an RTB environment, there is an additional constraint of having to serve up your ad (or decision) within 60ms (Google ADX sets a hard limit of 80ms), and the fastest best bid wins. I don't think that's a less hard problem compared to Twitter, especially at high volumes. You can't just say "scale sideward!". That said, the first link was totally misleading. I was actually quite shocked to see that Twitter only had 42M uniques per month, because a typical ad network does a lot more EDIT: ah.. 15B requests/day makes more sense. Wtf is with the wrong stats?
taligent 14y ago
Decent amount of traffic ? Sorry but the only thing laughable is that comment.
InclinedPlane 14y ago
What does content size matter? The challenge is that every single page of content except for each individual tweet is utterly unique for every user. That defeats the vast majority of straightforward caching implementations. You can't cache fully rendered pages ever because the chance that one random timeline view at a given time will be identical to any other view (even by the same person at a different time) is pretty much as close to zero as possible. Every view is dynamically generated content from up to several hundred or thousand different streams of data and needs to be put in order and have all of the per-user metadata set correctly. Once you start looking into the actual mathematical constraints of the problem of twitter you realize that it's a scaling nightmare. Hundreds of millions of updates per day and tens of thousands of views per second (billions per day). There's only a few people in the world who have the right to look down on stats like that.
- burningout 14y ago
  Again, as the parent poster also posted, I think you have never worked on large data. Twitter is like a big mailbox, only that every mail only has 160 bytes. This has been solved 10 years ago.
  - achompas 14y ago
    Wait, see my comment below. Twitter received 15B (yes, B) API calls/day last July. How does that compare to your typical email client? I don't want to argue that Twitter is astoundingly hard, but serving ~170K requests/sec can't really be that trivial, even if they're 160 bytes (they're not, since Twitter sends metadata, logs those messages, tracks service metrics, etc. for those messages)
  - jasonwatkinspdx 14y ago
    If you don't understand that the request distribution matters more than payload size, you aren't even seeing the problems. I encourage you to analyze infrastructure for a twitter style app using inbox duplication. Once you model this against hardware costs you'll learn something about how utterly expensive write amplification is in a hot data set that must be backed by ram due to availability requirements.
jasonwatkinspdx 14y ago
Your assumptions are laughable. Tweets have considerable metadata that pushes them far beyond 160 bytes. Fragmenting this into a secondary object is counterproductive due to the constant factor of 2 requests vs one larger payload. Someone who's been around the block a few times understands that it's difficult to make pronouncements without informed observation. That you are not willing to extend twitter's engineering staff the benefit of the doubt considering your lack of visibility into their measurements speaks loudly.

achompas 14y ago

Yes, you have a decent amount of traffic but you're also only dealing with ~160 uncompressed bytes (plus whatever overhead) per event. The hurdles you've overcame aren't that particularly amazing nor challenging. >42M uniques last month.[0] Are you really going to assert Twitter hasn't dealt with amazing or challenging hurdles in getting this far? [0] http://siteanalytics.compete.com/twitter.com/ http://siteanalytics.compete.com/twitter.com/ EDIT: this ignores that twitter.com is not the only Twitter client--they served 15B (!!) requests/day (!!!) as of a year ago. Not to mention metadata, instrumentation for services, logging, DB backups, and managing configuration of all of those distributed resources. Are we still talking about the ease of 160B? http://www.readwriteweb.com/hack/2011/07/twitter-serves-more-api-calls.php http://www.readwriteweb.com/hack/2011/07/twitter-serves-more...

timaelliott 14y ago
Yes. In 2008/2009, another engineer and myself built an ad-platform that received around 500M impressions per day, 5M clicks per day. And it wasn't just recording a tweet or publishing out to followers. We took the user input query, had to do some keyword/relevancy targeting, geofiltering, matching to advertisers and deliver back a large result set of adverts. All within 100ms. Our platform was also apache, mod_php, memcached, mysql and rabbitmq. So definitely not the most optimal of platforms by any means. We had two colos with ~20 servers (dell r410s) at each facility. Twitter just recently announced 400M tweets/day. I'm not trying to brag about my experiences, because looking back now we made numerous amateur mistakes, but just showing that Twitter's "scale" is a joke compared to everyday challenges at any large internet ad network.
- shadowfiend 14y ago
  You understand that 400M tweets a day is the number of tweets posted to their system, right? That speaks not at all to the consumption of those tweets, which is the metric you're using for your ad platform. Additionally, they don't just deal with 160 characters, because again, somehow you're still talking about data being posted, and not data being consumed. Data is consumed off their site via polling APIs, streaming APIs, and a website, all of which are pushing those 400M tweets a day out to plenty of consumers. They may not have as ridiculous a scale as they act like they do. But let's be clear: it is nowhere near as trivial as you make it out to be, either. Armchair quarterbacking is always easy, because you aren't exposed to the complexity that arises when you've spent a few months and years hitting the corner cases of the problem you're commenting on.
- chewxy 14y ago
  Add to that, the challenges of sub-60ms RTB. All the fun!
- marcusf 14y ago
  So you had 500m reads on a relatively static data set + 5m writes on an unrelated log? Sounds like a fun problem, but I agree I doesn't sound like rocket science. On the other hand, it also doesn't sound like Twitter, having 400m writes per day, and 400*x million reads on that very dynamic data set. Just seems that's a slightly harder problem.
  - chewxy 14y ago
    Adserving is not really static. Cachebusters are named so for good reason. Nowadays ad server developers are clever enough to separate click tracking and impression tracking (the non-Enterprise version of OpenX still deserves a lot of ಠ_ಠ though). In an RTB environment, there is an additional constraint of having to serve up your ad (or decision) within 60ms (Google ADX sets a hard limit of 80ms), and the fastest best bid wins. I don't think that's a less hard problem compared to Twitter, especially at high volumes. You can't just say "scale sideward!". That said, the first link was totally misleading. I was actually quite shocked to see that Twitter only had 42M uniques per month, because a typical ad network does a lot more EDIT: ah.. 15B requests/day makes more sense. Wtf is with the wrong stats?

taligent 14y ago

Decent amount of traffic ? Sorry but the only thing laughable is that comment.

InclinedPlane 14y ago

What does content size matter? The challenge is that every single page of content except for each individual tweet is utterly unique for every user. That defeats the vast majority of straightforward caching implementations. You can't cache fully rendered pages ever because the chance that one random timeline view at a given time will be identical to any other view (even by the same person at a different time) is pretty much as close to zero as possible. Every view is dynamically generated content from up to several hundred or thousand different streams of data and needs to be put in order and have all of the per-user metadata set correctly. Once you start looking into the actual mathematical constraints of the problem of twitter you realize that it's a scaling nightmare. Hundreds of millions of updates per day and tens of thousands of views per second (billions per day). There's only a few people in the world who have the right to look down on stats like that.

burningout 14y ago
Again, as the parent poster also posted, I think you have never worked on large data. Twitter is like a big mailbox, only that every mail only has 160 bytes. This has been solved 10 years ago.
- achompas 14y ago
  Wait, see my comment below. Twitter received 15B (yes, B) API calls/day last July. How does that compare to your typical email client? I don't want to argue that Twitter is astoundingly hard, but serving ~170K requests/sec can't really be that trivial, even if they're 160 bytes (they're not, since Twitter sends metadata, logs those messages, tracks service metrics, etc. for those messages)
- jasonwatkinspdx 14y ago
  If you don't understand that the request distribution matters more than payload size, you aren't even seeing the problems. I encourage you to analyze infrastructure for a twitter style app using inbox duplication. Once you model this against hardware costs you'll learn something about how utterly expensive write amplification is in a hot data set that must be backed by ram due to availability requirements.

jasonwatkinspdx 14y ago

Your assumptions are laughable. Tweets have considerable metadata that pushes them far beyond 160 bytes. Fragmenting this into a secondary object is counterproductive due to the constant factor of 2 requests vs one larger payload. Someone who's been around the block a few times understands that it's difficult to make pronouncements without informed observation. That you are not willing to extend twitter's engineering staff the benefit of the doubt considering your lack of visibility into their measurements speaks loudly.