5 ms·

Aside from being pretty cool, what's the use case for this?

ajkjk 12y ago
It's cool precisely because it's useful! Have an expensive computation task? Just dump it onto the cloud instead of running it locally. This is probably the most streamlined way to do this that I've ever seen.
- ossreality 12y ago
  How are people so naive? I'm always curious what people really are thinking when they see stuff like this and assume it's magical and works for all scale before even trying to imagine how it might conceivably work.
- ajkjk 12y ago
  Re: the dead comment below me. There's nothing mystical about this. The source code is 4 files, which I read. It sends your function to Lambda, and that's cool, and the syntax is really elegant. Enough that I could totally see throwing something together using this to solve some complex problems without really thinking about it. I don't think this is all the way there, but I really like the idea of programming with APIs like this being as easy to use as language libraries.
- capisce 12y ago
  If you have an expensive computation task and you're paying for computing time, do you really want to be using JavaScript for the purpose?
  - ajkjk 12y ago
    Probably not, but this isn't purporting to be the best way to do it. It's just a cool thing you can do in your JS code. Especially nice for spinning something up with zero overhead. Maybe not optimal for production apps. Maybe good if you're constrained on server resources but less so on budget. Maybe good if you're still on the Lambda free tier pricing.
  - atonse 12y ago
    Actually, one of their examples was to generate image thumbnails, where they import the ImageMagick native libraries to do the heavy lifting. Essentially, this can help offset the need for managing extra servers for those kinds of tasks.
kevan 12y ago
A concrete example: video upload and processing. Your frontend handles uploads, then you can offload conversion to AWS while you continue to handle other requests.
- nostrademons 12y ago
  Seems like a poor example - if you're building a product, you can easily afford the couple hours it takes to set up an EC2 image and autoscaling, dump a work item in SQS, and pick it up on an EC2 spot instance. And if you're doing video processing, you really really want to use a more efficient language than Javascript (like C) to handle the video processing. Combine the two of these and you'll get roughly a 100x cost saving over dumping a JS function into Amazon Lambda. I see this being most useful if you have a one-off analytic you need to write against some big data in S3 or RDS. For one-off scripts dealing with the raw AWS APIs is just useless overhead, and the expense of running the script will be negligible.
  - emidln 12y ago
    Having done this a few times from scratch for various companies, there a ton of moving pieces for almost any processing pipeline. Being able to scale that pipeline without writing the ops code to make it happen is actually magical. I'm not saying everyone should jump out and use this, but it takes a lot of work to: (a) measure each of the points of your service (b) deploy your code in an automated manner (c) deploy your monitoring in an automated manner (d) make sure your code is under supervision (e) setup alerting on the monitoring (f) scale up / down and within price constraints as needed (g) repeat this for all supporting services (queue, db, etc) (h) write your actual application code The potential to handle certain classes of problems via SQS/SNS/S3 pipelines is pretty alluring. You still have to do configuration, but the bet is that the configuration necessary for the SQS/SNS/S3/Lambda pipeline is far lower than that necessary to setup random autoscaling Celery, Resque, or random JMS/AMQP system on top of Ubuntu with Chef/Puppet/whatever.
    - fat0wl 12y ago
      Cool! As someone with more experience than me in this, would you mind responding quickly to these points? I will give my personal opinions but if you can trump with more info that would be cool too. :) 1. I agree that JMS sounds like a hassle but is that really necessary? I would think that you can batch process data on an EC2 instance, then pick it up in your local code directly using AWS APIs... not sure. 2. I am not so familiar with the Lambda system but I'm also not sure how it would scale db as necessary (item "g" in your list) thus overall processing time would still be bottlenecked by other resources (database IO, for example), no? I agree with your points but in all these cloud-compute scenarios I always wonder "Are we trying to reach a theoretical limit of fastest-possible computation, or just reach some reasonable saturation point close to the natural bottlenecks/throttles of our system integrations?". 3. Having been burned a few times now by over-optimizing when considering cloud I would probably now first consider just picking a slightly oversized EC2 instance and throwing some high-performing code onto it (Java, C++). Dynamic languages + auto-scalable resources (though I'm talking about web hosting in particular now) seems to drain clients wallets more than anything. At this point I'd actually recommend anyone with new web infrastructure to just buy a static instance and write optimized Java rather than trying their hand at auto-scaling Ruby/Python/Node. Do you notice a similar issue with your clients regarding code optimization vs. auto-scaling?
panarky 12y ago
Each event is processed individually so thousands of functions can run in parallel and performance remains consistently high regardless of the frequency of events. http://aws.amazon.com/lambda/ http://aws.amazon.com/lambda/ Now we're talking. If you can carve your application into small, independent tasks, Lambda can run thousands of them in parallel. This could be cost-effective if you have a large amount of data stored in small chunks in S3, and you need to query it or transform it sporadically. So instead of keeping terabytes of logs or transactions in Hadoop or Spark on hundreds of machines, keep the data in 100 MB chunks in S3. Then map-reduce with Lambda. Set up one web server to track chunks and tasks, have each Lambda instance get tasks from the server. You could effectively spin up thousands of cores for a few minutes and only pay for the minutes you use.
- z3t4 12y ago
  It would be interesting to work on Amazon and decide how many off-line CPU's you should have ready to "spin up" if needed ... Also considering Moore's law.
- jerf 12y ago
  It probably won't be suitable for map-reduce. Buried in the FAQ is a statement that a "Lambda" function (scare-quoting because darn it, that name has been taken for longer than anybody working on it has been alive and is still in active use... grrrr... I'd like to see their trademark on that denied) can only run for up to a minute, with the initial default set to 3 seconds ("How long can a Lambda function execute?"). It's suitable for flinging a map-reduce job in response to some event, but I wouldn't try to jam a map-reduce job into those constraints. I mean, sure, yeah, theoretically possible, but really the wrong way to do it. If you're doing a task that even takes a second or two in Lambda you're coming perilously close to being less than an order of magnitude from a hard cutoff, which isn't a great plan in the public cloud. You really ought to be targeting getting in and out of Lambda much faster than that, and anything that needs to be longer being a process triggered in another more permanent instance.
  - panarky 12y ago
    In the preview release, you can set the timeout value to anything between 1 second and 60 seconds. If you don't specify a timeout value, the default is 3 seconds. I can stream a 100 MB chunk from S3 and map it concurrently as it streams in 10 to 15 seconds. Sixty seconds is more than enough time to process a chunk. The bigger issue is that during the preview, Lambda is limited to 25 concurrent functions. If Amazon delivers a product where "the same code that works for one request a day also works for a thousand requests a second[1]," then you might be able to analyze hundreds of gigabytes of data in a few seconds, spin up no servers, and only pay for the few seconds that you use. 500gb = 5000 chunks of 100mb each. 1000 concurrent tasks each running 10 seconds could process 500gb in 50 seconds. You would use 5000 Lambda requests out of your free monthly allotment of 1,000,000. You'd also consume 5000 * 0.1gb * 10 seconds = 5000 gb-sec of your free monthly allotment of 400,000. S3 transfer is free within the same region, and S3 requests cost $0.004 per 10000 GETs, or $0.002 for this query. Even after you exhaust the free Lambda allotment, processing 500gb would cost $0.000000208 * 100 * 5000 or about 10 cents. Scaling this up, querying 10 terabytes would take about 20 minutes to execute, cost $2 for the query, and about $300 per month for storage. For sporadic workloads it might be more responsive and much cheaper than spinning up a fleet of machines for Hadoop or Spark. [1] http://www.allthingsdistributed.com/2014/11/aws-lambda.html http://www.allthingsdistributed.com/2014/11/aws-lambda.html

ajkjk 12y ago

It's cool precisely because it's useful! Have an expensive computation task? Just dump it onto the cloud instead of running it locally. This is probably the most streamlined way to do this that I've ever seen.

ossreality 12y ago
How are people so naive? I'm always curious what people really are thinking when they see stuff like this and assume it's magical and works for all scale before even trying to imagine how it might conceivably work.
ajkjk 12y ago
Re: the dead comment below me. There's nothing mystical about this. The source code is 4 files, which I read. It sends your function to Lambda, and that's cool, and the syntax is really elegant. Enough that I could totally see throwing something together using this to solve some complex problems without really thinking about it. I don't think this is all the way there, but I really like the idea of programming with APIs like this being as easy to use as language libraries.
capisce 12y ago
If you have an expensive computation task and you're paying for computing time, do you really want to be using JavaScript for the purpose?
- ajkjk 12y ago
  Probably not, but this isn't purporting to be the best way to do it. It's just a cool thing you can do in your JS code. Especially nice for spinning something up with zero overhead. Maybe not optimal for production apps. Maybe good if you're constrained on server resources but less so on budget. Maybe good if you're still on the Lambda free tier pricing.
- atonse 12y ago
  Actually, one of their examples was to generate image thumbnails, where they import the ImageMagick native libraries to do the heavy lifting. Essentially, this can help offset the need for managing extra servers for those kinds of tasks.

kevan 12y ago

A concrete example: video upload and processing. Your frontend handles uploads, then you can offload conversion to AWS while you continue to handle other requests.

nostrademons 12y ago
Seems like a poor example - if you're building a product, you can easily afford the couple hours it takes to set up an EC2 image and autoscaling, dump a work item in SQS, and pick it up on an EC2 spot instance. And if you're doing video processing, you really really want to use a more efficient language than Javascript (like C) to handle the video processing. Combine the two of these and you'll get roughly a 100x cost saving over dumping a JS function into Amazon Lambda. I see this being most useful if you have a one-off analytic you need to write against some big data in S3 or RDS. For one-off scripts dealing with the raw AWS APIs is just useless overhead, and the expense of running the script will be negligible.
- emidln 12y ago
  Having done this a few times from scratch for various companies, there a ton of moving pieces for almost any processing pipeline. Being able to scale that pipeline without writing the ops code to make it happen is actually magical. I'm not saying everyone should jump out and use this, but it takes a lot of work to: (a) measure each of the points of your service (b) deploy your code in an automated manner (c) deploy your monitoring in an automated manner (d) make sure your code is under supervision (e) setup alerting on the monitoring (f) scale up / down and within price constraints as needed (g) repeat this for all supporting services (queue, db, etc) (h) write your actual application code The potential to handle certain classes of problems via SQS/SNS/S3 pipelines is pretty alluring. You still have to do configuration, but the bet is that the configuration necessary for the SQS/SNS/S3/Lambda pipeline is far lower than that necessary to setup random autoscaling Celery, Resque, or random JMS/AMQP system on top of Ubuntu with Chef/Puppet/whatever.
  - fat0wl 12y ago
    Cool! As someone with more experience than me in this, would you mind responding quickly to these points? I will give my personal opinions but if you can trump with more info that would be cool too. :) 1. I agree that JMS sounds like a hassle but is that really necessary? I would think that you can batch process data on an EC2 instance, then pick it up in your local code directly using AWS APIs... not sure. 2. I am not so familiar with the Lambda system but I'm also not sure how it would scale db as necessary (item "g" in your list) thus overall processing time would still be bottlenecked by other resources (database IO, for example), no? I agree with your points but in all these cloud-compute scenarios I always wonder "Are we trying to reach a theoretical limit of fastest-possible computation, or just reach some reasonable saturation point close to the natural bottlenecks/throttles of our system integrations?". 3. Having been burned a few times now by over-optimizing when considering cloud I would probably now first consider just picking a slightly oversized EC2 instance and throwing some high-performing code onto it (Java, C++). Dynamic languages + auto-scalable resources (though I'm talking about web hosting in particular now) seems to drain clients wallets more than anything. At this point I'd actually recommend anyone with new web infrastructure to just buy a static instance and write optimized Java rather than trying their hand at auto-scaling Ruby/Python/Node. Do you notice a similar issue with your clients regarding code optimization vs. auto-scaling?

panarky 12y ago

Each event is processed individually so thousands of functions can run in parallel and performance remains consistently high regardless of the frequency of events. http://aws.amazon.com/lambda/ http://aws.amazon.com/lambda/ Now we're talking. If you can carve your application into small, independent tasks, Lambda can run thousands of them in parallel. This could be cost-effective if you have a large amount of data stored in small chunks in S3, and you need to query it or transform it sporadically. So instead of keeping terabytes of logs or transactions in Hadoop or Spark on hundreds of machines, keep the data in 100 MB chunks in S3. Then map-reduce with Lambda. Set up one web server to track chunks and tasks, have each Lambda instance get tasks from the server. You could effectively spin up thousands of cores for a few minutes and only pay for the minutes you use.

z3t4 12y ago
It would be interesting to work on Amazon and decide how many off-line CPU's you should have ready to "spin up" if needed ... Also considering Moore's law.
jerf 12y ago
It probably won't be suitable for map-reduce. Buried in the FAQ is a statement that a "Lambda" function (scare-quoting because darn it, that name has been taken for longer than anybody working on it has been alive and is still in active use... grrrr... I'd like to see their trademark on that denied) can only run for up to a minute, with the initial default set to 3 seconds ("How long can a Lambda function execute?"). It's suitable for flinging a map-reduce job in response to some event, but I wouldn't try to jam a map-reduce job into those constraints. I mean, sure, yeah, theoretically possible, but really the wrong way to do it. If you're doing a task that even takes a second or two in Lambda you're coming perilously close to being less than an order of magnitude from a hard cutoff, which isn't a great plan in the public cloud. You really ought to be targeting getting in and out of Lambda much faster than that, and anything that needs to be longer being a process triggered in another more permanent instance.
- panarky 12y ago
  In the preview release, you can set the timeout value to anything between 1 second and 60 seconds. If you don't specify a timeout value, the default is 3 seconds. I can stream a 100 MB chunk from S3 and map it concurrently as it streams in 10 to 15 seconds. Sixty seconds is more than enough time to process a chunk. The bigger issue is that during the preview, Lambda is limited to 25 concurrent functions. If Amazon delivers a product where "the same code that works for one request a day also works for a thousand requests a second[1]," then you might be able to analyze hundreds of gigabytes of data in a few seconds, spin up no servers, and only pay for the few seconds that you use. 500gb = 5000 chunks of 100mb each. 1000 concurrent tasks each running 10 seconds could process 500gb in 50 seconds. You would use 5000 Lambda requests out of your free monthly allotment of 1,000,000. You'd also consume 5000 * 0.1gb * 10 seconds = 5000 gb-sec of your free monthly allotment of 400,000. S3 transfer is free within the same region, and S3 requests cost $0.004 per 10000 GETs, or $0.002 for this query. Even after you exhaust the free Lambda allotment, processing 500gb would cost $0.000000208 * 100 * 5000 or about 10 cents. Scaling this up, querying 10 terabytes would take about 20 minutes to execute, cost $2 for the query, and about $300 per month for storage. For sporadic workloads it might be more responsive and much cheaper than spinning up a fleet of machines for Hadoop or Spark. [1] http://www.allthingsdistributed.com/2014/11/aws-lambda.html http://www.allthingsdistributed.com/2014/11/aws-lambda.html