7 ms·

$2,000 each for 128GB LRDIMMs, 48 of those will be $100,000 and then you'll need another $20,000 to buy the rest of the server it goes in.

belltaco 7y ago
Finally a machine that can run Chrome.
- dijit 7y ago
  Can still “only” have 6000 tabs. (Someone did it recently with 1.5TiB of ram on a Mac Pro. Then Linus from Linus tech tips did it on Windows with 2TiB) https://youtu.be/ldDTgp5irKA https://youtu.be/ldDTgp5irKA
deleted 7y ago
[deleted]
wmf 7y ago
If the result is faster than a $250K Hadoop cluster then you're still ahead.
- trhway 7y ago
  the way things today, the Hadoop cluster will be just a bit faster on that thing. 25 years ago i was suggesting clients to upgrade from 4MB to 6-8MB as it was improving their experience with our business software, these days i've already suggested a couple of customers to upgrade from 6TB and 8TB respectively ... as it would improve their experience with our business software. What's funny is that customer experience with business software back then was better than today.
  - Aperocky 7y ago
    Everything is way too much bloat now
- malux85 7y ago
  Yeah, except Hadoop provides redundancy, easier backups, turnkey solutions for governance and compliance and easier scaling I’m no fan of distributed systems that sit idle at 2% utilisation when a single node would do : BUT, reducing it down to “cost” and “does it fit in RAM” is way too reductive
  - FridgeSeal 7y ago
    And reducing it to a single machine means I have an order of magnitude less time spent on setting it up, maintaining it, adapting all my code, debugging, etc. These days I’m firmly of the opinion that if you can make it run on a single machine, you absolutely should, and you don’t get more machines until you can prove you can fully utilise one machine.
    - malux85 7y ago
      Waiting for a machine to be “fully utilised” before scaling just shows your lack of experience at systems engineering. Do you know how quickly disks fail if you force them at 100% utilisation, 24/7? Then what happens when this system dies? How much downtime do you have because you have to replace then hardware then get your hundreds of gigabyte dataset back in RAM and hot again? I’ve worked as a lead developer at companies where I’ve been personally responsible for hundreds of thousands of machines, and running a node to 100% and THEN thinking about scaling is short sighted and stupid
      - FridgeSeal 7y ago
        I don't mean "let your production systems spool up to point where you're maxing out a single machine" - that would be exceedingly silly. I mean "when you've proven that the application you've written can fully, or near fully utilise the available power on a single machine, and that when running production-grade workloads, actually does so, then you may scale to additional machines. What this means is not getting a 9-node spark cluster to push a few hundred gb of data from S3 to a database because "it took too long to run in python" because it's a single threaded, non-async, non-performance tuned.
- threeseed 7y ago
  But that Hadoop cluster will also work just fine if the data set does not fit in RAM. And I've never met a data set that didn't expand over time. And with Spark you get a robust, scalable and industry standard way of distributing work amongst the nodes. Also did you know that Hadoop is open source. So that $250K is purely for hardware.
kube-system 7y ago
if you can get by with 4TB, it's less than $30/hr on AWS
- objektif 7y ago
  What is? Anything I touch on AWS is 5k a month. /s
  - andyjpb 7y ago
    $30/hr is between $20,000 and $22,000 per month depending on the length of the month. You don't have to run it full time tho'...
PeterisP 7y ago
If that allows you to simplify your architecture so that stuff just runs on a single machine instead of needing to develop, debug and maintain a distributed solution, then you save much more money in engineer salaries than this.

belltaco 7y ago

Finally a machine that can run Chrome.

dijit 7y ago
Can still “only” have 6000 tabs. (Someone did it recently with 1.5TiB of ram on a Mac Pro. Then Linus from Linus tech tips did it on Windows with 2TiB) https://youtu.be/ldDTgp5irKA https://youtu.be/ldDTgp5irKA

deleted 7y ago

[deleted]

wmf 7y ago

If the result is faster than a $250K Hadoop cluster then you're still ahead.

trhway 7y ago
the way things today, the Hadoop cluster will be just a bit faster on that thing. 25 years ago i was suggesting clients to upgrade from 4MB to 6-8MB as it was improving their experience with our business software, these days i've already suggested a couple of customers to upgrade from 6TB and 8TB respectively ... as it would improve their experience with our business software. What's funny is that customer experience with business software back then was better than today.
- Aperocky 7y ago
  Everything is way too much bloat now
malux85 7y ago
Yeah, except Hadoop provides redundancy, easier backups, turnkey solutions for governance and compliance and easier scaling I’m no fan of distributed systems that sit idle at 2% utilisation when a single node would do : BUT, reducing it down to “cost” and “does it fit in RAM” is way too reductive
- FridgeSeal 7y ago
  And reducing it to a single machine means I have an order of magnitude less time spent on setting it up, maintaining it, adapting all my code, debugging, etc. These days I’m firmly of the opinion that if you can make it run on a single machine, you absolutely should, and you don’t get more machines until you can prove you can fully utilise one machine.
  - malux85 7y ago
    Waiting for a machine to be “fully utilised” before scaling just shows your lack of experience at systems engineering. Do you know how quickly disks fail if you force them at 100% utilisation, 24/7? Then what happens when this system dies? How much downtime do you have because you have to replace then hardware then get your hundreds of gigabyte dataset back in RAM and hot again? I’ve worked as a lead developer at companies where I’ve been personally responsible for hundreds of thousands of machines, and running a node to 100% and THEN thinking about scaling is short sighted and stupid
    - FridgeSeal 7y ago
      I don't mean "let your production systems spool up to point where you're maxing out a single machine" - that would be exceedingly silly. I mean "when you've proven that the application you've written can fully, or near fully utilise the available power on a single machine, and that when running production-grade workloads, actually does so, then you may scale to additional machines. What this means is not getting a 9-node spark cluster to push a few hundred gb of data from S3 to a database because "it took too long to run in python" because it's a single threaded, non-async, non-performance tuned.
threeseed 7y ago
But that Hadoop cluster will also work just fine if the data set does not fit in RAM. And I've never met a data set that didn't expand over time. And with Spark you get a robust, scalable and industry standard way of distributing work amongst the nodes. Also did you know that Hadoop is open source. So that $250K is purely for hardware.

kube-system 7y ago

if you can get by with 4TB, it's less than $30/hr on AWS

objektif 7y ago
What is? Anything I touch on AWS is 5k a month. /s
- andyjpb 7y ago
  $30/hr is between $20,000 and $22,000 per month depending on the length of the month. You don't have to run it full time tho'...

PeterisP 7y ago

If that allows you to simplify your architecture so that stuff just runs on a single machine instead of needing to develop, debug and maintain a distributed solution, then you save much more money in engineer salaries than this.