4 ms·

Your command-line tools may be 235x faster but they don’t have the same features

3 points by Samus_ 12y ago

dalke 12y ago
Adam Drake's essay concludes: > If you have a huge amount of data or really need distributed processing, then tools like Hadoop may be required, but more often than not these days I see Hadoop used where a traditional relational database or other solutions would be far better in terms of performance, cost of implementation, and ongoing maintenance. This new essay says "I agree with the points made but in the end I feel like it’s missing the point." It then argues that scalability, reliability, and performance are the missing parts. (Though only in the context of batch jobs and large data sets that don't fit on a single machine. While it mentions scaling down, the overhead of going through Hadoop is too much if you want to scale down to search 1K of data.) In the end, I think Adam Drake made exactly the point that this new essay says is missing. Several people in the HN thread (and earlier threads on the same topic) have commented about people using Hadoop for "large" data which wasn't large and wasn't ever going scale past a single machine[1]. Complaining against that misapplication of tools was the thrust of Drake essay. It wasn't a statement that it's better than Hadoop for all cases. [1] For the record, data in my field takes about 10 years to double. The same algorithms that took a small cluster 5 minutes to search in 1990 now take a second on a laptop.
- Samus_ 12y ago
  Hey there! I agree with the misuses but I don't think the reasons stated are the right ones. For instance, the fault-tolerance abilities of those frameworks is really important and it isn't mentioned, in some cases that may even offset the speed differences. The data growth you mention is also a relevant factor which is missing, in your case you have an argument against a tool that helps you scale, that's the right mindset because you're evaluating the use of such tool over time not on a single situation. As for the scaling if you go as low as 1k sure, Hadoop becomes useless but what if you had to scale from 500 machines to 100? if you bought a supercomputer that costs millions and three months later you're no longer using it then "scaling down" would be a great advantage. So in short I like the "cons" of the original and I agree with them but there are "pros" that should also be considered.
  - dalke 12y ago
    The original essay's point was to say that people over-weigh the pros, that there are cons, and that some of those cons have considerable real-world significance. It wasn't to say that there are no pros. It wasn't to provide a balanced analysis of all the pros and cons. It had a specific point, it made it, and it acknowledged that the point wasn't universal. This new essay misinterprets the point in order to set itself up as a more realistic viewpoint, when in fact it's arguing against a straw interpretation of the original essay. Yes, fault-tolerance can be really important. And sometimes not. Such a discussion is orthogonal the point of the original. A single node is quite reliable. There are many ways to get redundancy on top of that. For the same price as a Hadoop cluster, you could buy a machine with more reliable hardware, plus a hot backup, plus a load balancer. This may be all that's needed. If you need 100 machines then you are still in the realm where you "really need distributed processing", which was one of the times where the original essay said that Hadoop could be the right solution. If you have 100 machines, and the single node can go 200x faster, then you really only need 1 machine.
    - Samus_ 12y ago
      > The original essay's point was to say that people over-weigh the pros, that there are cons, and that some of those cons have considerable real-world significance. > > It wasn't to say that there are no pros. It wasn't to provide a balanced analysis of all the pros and cons. It had a specific point, it made it, and it acknowledged that the point wasn't universal. > > This new essay misinterprets the point in order to set itself up as a more realistic viewpoint, when in fact it's arguing against a straw interpretation of the original essay. I don't see the pros mentioned, at all. What I see is another "Hadoop is fast, Hadoop is slow" comparison based entirely on speed which on its own isn't either a "pro" or a "con" is just a factor among many. Without the complete picture we'll keep misinterpreting and arguing over this technology for the wrong reasons. > Yes, fault-tolerance can be really important. And sometimes not. Such a discussion is orthogonal the point of the original. A single node is quite reliable. There are many ways to get redundancy on top of that. For the same price as a Hadoop cluster, you could buy a machine with more reliable hardware, plus a hot backup, plus a load balancer. This may be all that's needed. Redundancy and fault-tolerance are not the same thing, you're also thinking in the context of web development and this is a different scenario. In web you can use redundancy to improve reliability because you're either serving the same content to many clients or performing independent computations to each of them; data processing frameworks are used to perform a single computation with several different inputs over many machines, replicating it may give you some safety regarding hardware failures but none for software issues and you'll be also wasting a lot of power thus limiting the range of applications. Big Data frameworks partition the input, they do not replicate it; they also keep track of the results and allow you to retry failed chunks, it's a different approach than those used on web. > If you need 100 machines then you are still in the realm where you "really need distributed processing", which was one of the times where the original essay said that Hadoop could be the right solution. > > If you have 100 machines, and the single node can go 200x faster, then you really only need 1 machine. Like I said the speed is just one factor, you can't determine the right solution based exclusively on it and that's what the original article missed, you're disregarding all the other advantages that a cluster and a Big Data framework provide. Also the very concept of "machine" can vary wildly, there's an infinite range of combinations of disk, ram and processor so what is "a machine" and how can you say when you need one, ten or 100? The real choice here is a single "powerful enough" machine vs. a set of "commodity" hardware, then you can compare prices, speed, scalability and reliability of each scenario in all the possible situations you expect to encounter and hopefully some unforeseen ones as well.

3 points by Samus_ 12y ago

dalke 12y ago

Adam Drake's essay concludes: > If you have a huge amount of data or really need distributed processing, then tools like Hadoop may be required, but more often than not these days I see Hadoop used where a traditional relational database or other solutions would be far better in terms of performance, cost of implementation, and ongoing maintenance. This new essay says "I agree with the points made but in the end I feel like it’s missing the point." It then argues that scalability, reliability, and performance are the missing parts. (Though only in the context of batch jobs and large data sets that don't fit on a single machine. While it mentions scaling down, the overhead of going through Hadoop is too much if you want to scale down to search 1K of data.) In the end, I think Adam Drake made exactly the point that this new essay says is missing. Several people in the HN thread (and earlier threads on the same topic) have commented about people using Hadoop for "large" data which wasn't large and wasn't ever going scale past a single machine[1]. Complaining against that misapplication of tools was the thrust of Drake essay. It wasn't a statement that it's better than Hadoop for all cases. [1] For the record, data in my field takes about 10 years to double. The same algorithms that took a small cluster 5 minutes to search in 1990 now take a second on a laptop.

Samus_ 12y ago
Hey there! I agree with the misuses but I don't think the reasons stated are the right ones. For instance, the fault-tolerance abilities of those frameworks is really important and it isn't mentioned, in some cases that may even offset the speed differences. The data growth you mention is also a relevant factor which is missing, in your case you have an argument against a tool that helps you scale, that's the right mindset because you're evaluating the use of such tool over time not on a single situation. As for the scaling if you go as low as 1k sure, Hadoop becomes useless but what if you had to scale from 500 machines to 100? if you bought a supercomputer that costs millions and three months later you're no longer using it then "scaling down" would be a great advantage. So in short I like the "cons" of the original and I agree with them but there are "pros" that should also be considered.
- dalke 12y ago
  The original essay's point was to say that people over-weigh the pros, that there are cons, and that some of those cons have considerable real-world significance. It wasn't to say that there are no pros. It wasn't to provide a balanced analysis of all the pros and cons. It had a specific point, it made it, and it acknowledged that the point wasn't universal. This new essay misinterprets the point in order to set itself up as a more realistic viewpoint, when in fact it's arguing against a straw interpretation of the original essay. Yes, fault-tolerance can be really important. And sometimes not. Such a discussion is orthogonal the point of the original. A single node is quite reliable. There are many ways to get redundancy on top of that. For the same price as a Hadoop cluster, you could buy a machine with more reliable hardware, plus a hot backup, plus a load balancer. This may be all that's needed. If you need 100 machines then you are still in the realm where you "really need distributed processing", which was one of the times where the original essay said that Hadoop could be the right solution. If you have 100 machines, and the single node can go 200x faster, then you really only need 1 machine.
  - Samus_ 12y ago
    > The original essay's point was to say that people over-weigh the pros, that there are cons, and that some of those cons have considerable real-world significance. > > It wasn't to say that there are no pros. It wasn't to provide a balanced analysis of all the pros and cons. It had a specific point, it made it, and it acknowledged that the point wasn't universal. > > This new essay misinterprets the point in order to set itself up as a more realistic viewpoint, when in fact it's arguing against a straw interpretation of the original essay. I don't see the pros mentioned, at all. What I see is another "Hadoop is fast, Hadoop is slow" comparison based entirely on speed which on its own isn't either a "pro" or a "con" is just a factor among many. Without the complete picture we'll keep misinterpreting and arguing over this technology for the wrong reasons. > Yes, fault-tolerance can be really important. And sometimes not. Such a discussion is orthogonal the point of the original. A single node is quite reliable. There are many ways to get redundancy on top of that. For the same price as a Hadoop cluster, you could buy a machine with more reliable hardware, plus a hot backup, plus a load balancer. This may be all that's needed. Redundancy and fault-tolerance are not the same thing, you're also thinking in the context of web development and this is a different scenario. In web you can use redundancy to improve reliability because you're either serving the same content to many clients or performing independent computations to each of them; data processing frameworks are used to perform a single computation with several different inputs over many machines, replicating it may give you some safety regarding hardware failures but none for software issues and you'll be also wasting a lot of power thus limiting the range of applications. Big Data frameworks partition the input, they do not replicate it; they also keep track of the results and allow you to retry failed chunks, it's a different approach than those used on web. > If you need 100 machines then you are still in the realm where you "really need distributed processing", which was one of the times where the original essay said that Hadoop could be the right solution. > > If you have 100 machines, and the single node can go 200x faster, then you really only need 1 machine. Like I said the speed is just one factor, you can't determine the right solution based exclusively on it and that's what the original article missed, you're disregarding all the other advantages that a cluster and a Big Data framework provide. Also the very concept of "machine" can vary wildly, there's an infinite range of combinations of disk, ram and processor so what is "a machine" and how can you say when you need one, ten or 100? The real choice here is a single "powerful enough" machine vs. a set of "commodity" hardware, then you can compare prices, speed, scalability and reliability of each scenario in all the possible situations you expect to encounter and hopefully some unforeseen ones as well.