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I mean if you're doing data science the data is not always organized and of course you would want multi-processing. 1 TB of memory is like 5 grand from a quick

I mean if you're doing data science the data is not always organized and of course you would want multi-processing. 1 TB of memory is like 5 grand from a quick Google search then you probably need specialized motherboards.

coldtea 2y ago
>I mean if you're doing data science the data is not always organized and of course you would want multi-processing Not necessarily - I might not want it or need it. It's a few TB, it can be on a fast HD, on an even faster SSD, or even in memory. I can crunch them quite fast even with basic linear scripts/tools. And organized could just mean some massaging or just having them in csv format. This is already the same rushed notions about "needing this" and "must have that" that the OP describes people jumping to, that leads them to suggest huge setups, distributed processing, multi-machine infrastructure, for use cases and data sizes that could fit on a single server with redundancy and be done it. DHH has often written about this for their Basecamp needs (scalling vertically where others scale horizontally having worked for them for most of their operation), there's also this classic post: https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html https://adamdrake.com/command-line-tools-can-be-235x-faster-... >1 TB of memory is like 5 grand from a quick Google search then you probably need specialized motherboards. Not that specialized, I've work with server deployments (HP) with 1, 1.5 and 2TB RAM (and > 100 cores), it's trivial to get. And 5 or even 30 grand would still be cheaper (and more effective and simpler) than the "big data" setups some of those candidates have in mind.
- the_real_cher 2y ago
  Yeah I agree about over engineering. Im just trying to understand the parent to my original comment. How would running awk for analysis on 6TB of data work quickly and efficiently? They say it would go into memory but its not clear to me how that would work as would still have paging and thrashing issues if the data didnt have often used sections of the data. am I overthinking it and they were they just referring to buying a big ass Ram machine?
  - coldtea 2y ago
    >How would running awk for analysis on 6TB of data work quickly and efficiently? In that 6TB is not that huge of an amount That's their total dataset, and there's no "real time" requirement. They can start a batch process, process the data, and be done with it. Here's an example of someone using awk (read further down for the relevant section): https://livefreeordichotomize.com/posts/2019-06-04-using-awk-and-r-to-parse-25tb/ https://livefreeordichotomize.com/posts/2019-06-04-using-awk... "I was now able to process a whole 5 terabyte batch in just a few hours." >They say it would go into memory but its not clear to me how that would work as would still have paging and thrashing issues if the data didnt have often used sections of the data There's no need to have paging and thrashing issues if you can fit all (or even most) of your data in memory. And you can always also split, process, and aggregate partial results. >am I overthinking it and they were they just referring to buying a big ass Ram machine? Yeah, they said one can buy a machine with several TB of memory.

coldtea 2y ago

>I mean if you're doing data science the data is not always organized and of course you would want multi-processing Not necessarily - I might not want it or need it. It's a few TB, it can be on a fast HD, on an even faster SSD, or even in memory. I can crunch them quite fast even with basic linear scripts/tools. And organized could just mean some massaging or just having them in csv format. This is already the same rushed notions about "needing this" and "must have that" that the OP describes people jumping to, that leads them to suggest huge setups, distributed processing, multi-machine infrastructure, for use cases and data sizes that could fit on a single server with redundancy and be done it. DHH has often written about this for their Basecamp needs (scalling vertically where others scale horizontally having worked for them for most of their operation), there's also this classic post: https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html https://adamdrake.com/command-line-tools-can-be-235x-faster-... >1 TB of memory is like 5 grand from a quick Google search then you probably need specialized motherboards. Not that specialized, I've work with server deployments (HP) with 1, 1.5 and 2TB RAM (and > 100 cores), it's trivial to get. And 5 or even 30 grand would still be cheaper (and more effective and simpler) than the "big data" setups some of those candidates have in mind.

the_real_cher 2y ago
Yeah I agree about over engineering. Im just trying to understand the parent to my original comment. How would running awk for analysis on 6TB of data work quickly and efficiently? They say it would go into memory but its not clear to me how that would work as would still have paging and thrashing issues if the data didnt have often used sections of the data. am I overthinking it and they were they just referring to buying a big ass Ram machine?
- coldtea 2y ago
  >How would running awk for analysis on 6TB of data work quickly and efficiently? In that 6TB is not that huge of an amount That's their total dataset, and there's no "real time" requirement. They can start a batch process, process the data, and be done with it. Here's an example of someone using awk (read further down for the relevant section): https://livefreeordichotomize.com/posts/2019-06-04-using-awk-and-r-to-parse-25tb/ https://livefreeordichotomize.com/posts/2019-06-04-using-awk... "I was now able to process a whole 5 terabyte batch in just a few hours." >They say it would go into memory but its not clear to me how that would work as would still have paging and thrashing issues if the data didnt have often used sections of the data There's no need to have paging and thrashing issues if you can fit all (or even most) of your data in memory. And you can always also split, process, and aggregate partial results. >am I overthinking it and they were they just referring to buying a big ass Ram machine? Yeah, they said one can buy a machine with several TB of memory.