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Your data fits in RAM

yourdatafitsinram.com

21–30 of 230 posts

Re: Your data fits in RAM

#21
post #2

Inspired by https://twitter.com/garybernhardt/status/600783770925420546

Can someone explain in a bit more detail what this is about? Is the 'joke' that running data computation in RAM is faster than what? From disk?

The subtext is that running a fancy distributed system is more exciting and beneficial for ones resume than simply buying a massive bloody server and putting postgres on it, and that people are making tech decisions on this basis.

Re: Your data fits in RAM

#22

Earlier quoted context omitted.

Can someone explain in a bit more detail what this is about? Is the 'joke' that running data computation in RAM is faster than what? From disk?

There is no point deploying a heavy, complex (and usually pretty slow due to the overheads involved) distributed database, when you could just buy a server with xTB ram, load any sql database on it, and run your queries in a fraction of the time. If your data is so large that it can't fit in the RAM of a single machine, then distributed databases make more sense (since loading data off disk is very slow, modulo SSD).

For some problems SQL would already be way too much overhead.

Re: Your data fits in RAM

#23
post #17

Yes! As someone who frequently runs memory-intensive algorithms on large(ish) datasets, I have a hard time explaining to many technical people that moving from a single server to a cluster increases complexity and cost by an incredible amount. It affects key decisions like algorithm and language, and generally requires a lot of tweaking. When a problem becomes big enough, moving to a cluster is absolutely the right d…

Complexity, sure. But cost? I thought a single 1 TB RAM server is more expensive than 10x 100 GB RAM servers. And many people don't want to deal with physical hardware. Dealing with physical hardware increases operational complexity too. They want to rent a virtual/cloud server. Which provider allows you to rent a virtual server with 1 TB RAM?

10 servers with 100G will use a lot more power and will require distribution of your algorithm right along with your dataset, so instead of 10 server you will probably end up with a pretty high multiple of 10.

Re: Your data fits in RAM

#24
post #17

Yes! As someone who frequently runs memory-intensive algorithms on large(ish) datasets, I have a hard time explaining to many technical people that moving from a single server to a cluster increases complexity and cost by an incredible amount. It affects key decisions like algorithm and language, and generally requires a lot of tweaking. When a problem becomes big enough, moving to a cluster is absolutely the right d…

Complexity, sure. But cost? I thought a single 1 TB RAM server is more expensive than 10x 100 GB RAM servers. And many people don't want to deal with physical hardware. Dealing with physical hardware increases operational complexity too. They want to rent a virtual/cloud server. Which provider allows you to rent a virtual server with 1 TB RAM?

Cost follows complexity, even if it's not always immediately obvious.

A 1TB RAM server is more expensive than 10x 100GB RAM servers, but the hardware cost is often small compared to the business and technical cost of getting a solution to scale across a cluster.

Of course, generalizations are always dangerous—the take-home point here is perhaps that before going to a cluster because “that's the way big data is handled,” it's a good idea to do a proper cost-benefit analysis.

Re: Your data fits in RAM

#25
post #14

Earlier quoted context omitted.

Can someone explain in a bit more detail what this is about? Is the 'joke' that running data computation in RAM is faster than what? From disk?

Right, RAM an order of magnitude faster than disk, so calculations will be performed very quickly. Big data usually implies clusters of servers because the data won't fit on one server (even on the disk).

Big Data usually implies 'big dollars', not necessarily a large amount of data. Simply use some in-efficient algorithm and a datastore with sufficient overhead and you're in Big Data territory.

Re-do the same thing using an optimal algorithm operating on a compact datastructure and you make it look easy, fast and cheap. Of course you're not going to make nearly as much money.

Re: Your data fits in RAM

#26
post #2

Inspired by https://twitter.com/garybernhardt/status/600783770925420546

Can someone explain in a bit more detail what this is about? Is the 'joke' that running data computation in RAM is faster than what? From disk?

See also: https://www.chrisstucchio.com/blog/2013/hadoop_hatred.html

Re: Your data fits in RAM

#27

Earlier quoted context omitted.

Complexity, sure. But cost? I thought a single 1 TB RAM server is more expensive than 10x 100 GB RAM servers. And many people don't want to deal with physical hardware. Dealing with physical hardware increases operational complexity too. They want to rent a virtual/cloud server. Which provider allows you to rent a virtual server with 1 TB RAM?

10 servers with 100G will use a lot more power and will require distribution of your algorithm right along with your dataset, so instead of 10 server you will probably end up with a pretty high multiple of 10.

Isn't this what the cloud is for?

Surely you just rent an instance of the computing power you need for an hour or two then upload the data, a script and wait.

Re: Your data fits in RAM

#28
post #17

Yes! As someone who frequently runs memory-intensive algorithms on large(ish) datasets, I have a hard time explaining to many technical people that moving from a single server to a cluster increases complexity and cost by an incredible amount. It affects key decisions like algorithm and language, and generally requires a lot of tweaking. When a problem becomes big enough, moving to a cluster is absolutely the right d…

Complexity, sure. But cost? I thought a single 1 TB RAM server is more expensive than 10x 100 GB RAM servers. And many people don't want to deal with physical hardware. Dealing with physical hardware increases operational complexity too. They want to rent a virtual/cloud server. Which provider allows you to rent a virtual server with 1 TB RAM?

Apart from what other commenters already said about the cost of software complexity, is there a variant of Amdahl's law that could be used here? 10x 100 GB servers working on a problem together will probably never be 10x faster than 1x 100 GB server. Perhaps just the increase in the order of magnitude of the distance information needs to travel is already sufficient to set some higher bounds... So you may need to buy more than 10 of them to match 1x 1 TB server.

Re: Your data fits in RAM

#29
post #17

Yes! As someone who frequently runs memory-intensive algorithms on large(ish) datasets, I have a hard time explaining to many technical people that moving from a single server to a cluster increases complexity and cost by an incredible amount. It affects key decisions like algorithm and language, and generally requires a lot of tweaking. When a problem becomes big enough, moving to a cluster is absolutely the right d…

Complexity, sure. But cost? I thought a single 1 TB RAM server is more expensive than 10x 100 GB RAM servers. And many people don't want to deal with physical hardware. Dealing with physical hardware increases operational complexity too. They want to rent a virtual/cloud server. Which provider allows you to rent a virtual server with 1 TB RAM?

> Complexity, sure. But cost? I thought a single 1 TB RAM server is more expensive than 10x 100 GB RAM servers.

Sure, but in the latter case you'd also have to pay for the manpower to build a cluster solution out of a formerly simple application. And people are usually more expensive than servers.

Re: Your data fits in RAM

#30
Looks like 1.5TB RAM with 15 cores costs $50K. But it shouldn't be just about RAM. The problems I'm working on requires 250 cores on similar amount of data. If there was an option to get say 150 cores with 2TB RAM, things would fly for sure.
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