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I need a server with a lot of RAM, around ~1TB, for a GIS DB, that would be not written to hard disk, because the data is irrelevant after a few seconds. So I do not need a lot of disk space; I wish to hold all data in memory. The write data would be 1% of INSERT'S and 99% of UPDATE's. Write/Read ratio would be 20/1. I have to choose to rent a dedicated server or rent an Amazon service. I'm wondering: how to calculate the price of Amazon services with traffic ~100TB/month.
I think you might reconsider your configuration, using SSD disk, a really great CPU, and a lot of ram, but 1To of ram is way too much, most system will not handle it. After it's for the price ! It's really expensive, that kind of configuration is 15000$++ a month at OVH for example. So I thinks if you have that kind of problem, the better is to directly call Amazon and ask them for the best configuration and negociate the price.
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My employer runs a Hadoop cluster, and as our data is rarely larger than 1GB, I have found that Hadoop is rarely needed to meet the needs of our office (this isn't big data), but my employer seems to want to be able to say we're using our Hadoop cluster, so we're actively seeking out data that needs analysis using our big fancy tool.
I've seen some reports saying that anything less than 5tb shouldn't utilize hadoop. What's the magic size where Hadoop becomes a practical solution to data analysis?
There isn't something like magic size. Hadoop is not only about the amount of data, it include resources and processing "cost". It's not the same process one image that could require a lot of memory and CPU than parse a text file. And haoop is being used for both.
To justify the use of hadoop you need to answer the follow questions:
Is your process able to run in one machine and complete the work on time ?
How fast is your data growing?
It's not the same read one time by day 5TB to generate a report than to read 1GB ten times by second from a customer facing API. But if you haven't facing these kind of problems before, very probably that you don't require use hadoop to process your 1GB :)
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I have developed an app which talks to a sql database on a server. I have just arranged with a server company to host my sql database and in setting it up have asked how many public ip's do i need.
The application will be used by 10/20 companies each having approx 10-20 ipads / android tablets. There will also be a website they can log onto to again look at the data on the server.
How many public ip's would i require, or what factors do i need to consider when deciding.
I should add if you haven't already worked it out, know nothing about servers.
The number of ip address wouldn't matter that much if you don't need them. When will you need them?
When you want to add an extra layer of separation between companies (each its own ip)
More ip addresses means more DIFFERENT connections (so failover)
More ip addresses means more SIMULTANEOUS connections (so more possible users)
You want to have extra maintenance ;)
Just to mention a few
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I want know which one is good to follow whether auto vacuum or vacuum manually. Right now we are following manually in cron jobs, but sometimes it gets struck to vaccum on particular tables. so we are thinking about the auto vacuum. does it give good performance to production server? please suggest. Lot of thanks in advance.
Performance tuning is a difficult subject. Auto vacuum works in most cases very well. In certain cases however "manual" vacuum using cron might work better because for instance you know the database has nothing to do at night while during the day the vacuum might be to disruptive.
A good book on postgres performance and tuning is PostgreSQL 9.0 High performance
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Hej,
I recently read a lot of papers from Lamport, Fischer, Lynch, Brewer to get a feeling for their perspective of distributed systems.
I was wondering, what are current open distributed computing research questions/topics? Many areas from databases, communication, fault-tolerance, number crunching, etc. seem to be tackled and in quite solid hands.
What do you think are new areas, maybe someone did think of in the past but rendered it impossible and now it becomes possible? A topic like graph algorithms/databases/analysis?
I would also appreciate if anyone can give a some hints of must-read papers about distributed systems. They can also be a more "sci-fi" to just stay inspired.
Something I have had a big interest in is the potential for using cloud computing / distributed systems to run 3D software, such as you could set up essentially a virtual production studio "in the cloud", as it stands now the cloud providers offer only very basic rudimentary graphics support as their hardware is not equipped with anything approaching high end graphics cards..
I think in the future this type of platform could be also used for online games and things of that nature, such as to take away the need for local computing power, with the increase in broadband speeds (some places in the U.S. now have access to fiberoptic lines with 50+ MB per second) this is becoming an increasing possibility in the near future.
I don't play computer games myself and just used to do some 3D design / animation work but I look at it more from a business perspective and think that this has a lot of potential as, for example, someone with just a basic notebook laptop could eventually be able to use a remote connection to a distributed computing network to play a CPU intensive game (likely through a subscription based set up as this obviously would be taxing on the company providing such a game service as they would be providing all the computing power).
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This isn't a platform specific question - rather I'm interested in the general platform independent areas of computer science that are particularly relevant to mobile applications development.
For example, things like compression techniques, distributed synchronisation algorithims etc.. what theoretical concepts have you found relevant, useful or enabling when building mobile apps?
Human-computer interaction is an important consideration, when you consider that mobile devices have all sorts of inputs that a "normal" computer would not - such as touch screens (with multi-touch), one or more microphones, camera(s), etc...
Taken from embedded software development is the habit to handle scarce resources such as CPU load and battery life.
My 2 cents: Augmented reality, NFC (RFID)
process calculi
I don't understand why "All of computer science" isn't relevant.
(even things large large scale computing is relevant: you can't have
a small device in your hands that does really complicated stuff
on large scale unless there's a big engine someplace else).
Derecursivation (turning recursive code into an iterative loop) came handy once because some systems try to limit the default available stack size.
Pagination (how the OS splits heap memory into "page" units) is useful to understand when deciding the size of temporary buffers.
The notion of context: context-awareness and/or context-orientation
And also mobile ad-hoc network