19GB isn a particularly large data set

Storing lots of rows with minor changes in the data

It would be easier to offer useful suggestions if you stated explicitly both what is your goal and in what Canada Goose Outlet way your current solution fails to meet the goal. 19GB isn a particularly large data set. Are you anticipating scaling up the number of users massively? Or is it that your report queries don execute sufficiently quickly? Or you want a solution that can hold many years worth of historical data? Also, what kind of reports are you running?The advantages of storing data in a relational database like MySQL are (1) support for multiple simultaneous updates, (2) declarative data integrity guarantees, canada goose uk outlet (3) flexible query language that can join tables, group rows, and compute arbitrary functions.If those aren features that you are taking advantage of, you could probably build more efficient data storage and reporting methods customized to your application. For example, storing the incoming data as flat text files would still allow you to perform computations on individual records or groups of records using any number of general purpose computation tools (python, perl, etc.). It looks like your data records in raw text are around 40 bytes of data per record. For 121 million rows canada goose clearance that would be around 5GB. Quite likely that data is highly compressible. Storing lines of text is more efficient that storing database rows, but it canada goose factory sale still is less compact than a binary representation would be for example. Also there a lot of repetition between rows. If you compressed old data files, using gzip or bzip2, the total amount of storage would probably be significantly less. It easy to decompress Canada Goose Online files on the fly for analysis as needed.If most of the reports you are computing are based on the entire historical data set, it might make more sense to load only summary data. For example, you might primarily be looking at daily averages, in which case you could compute those averages once per day and leave the rest of the raw data in compressed files.Or, if you are only always looking at reports for individual users (rather than across users), it might make more sense to split the data into files by user.The way to optimize storage is to build a tool that is specific to the task at hand. But canada goose coats on sale in order to do that you need clarity on what the specific task is. If flexibility is your primary goal, you may not have many options for optimizing. From some other suggestions I believe it canada goose clearance sale makes sense to build this data out into a separate table.I also need the last year of detailed data in order to build analytics (like how many http://www.canadagoosejacketoutlett.com degrees does the temperature drop in your house when the AC isn on and it 90 outside). I do not yet know all of the analytics I like to build so I need to keep a year of data so I can build new ones when I want.19GB isn a particularly large data setTo MySQL, maybe not. For me it is as this is the largest chunk of data I had the responsibility to care for. buy canada goose jacket If it turns out I can trivially store 100s of GB of data without really worrying about it, then good. I just not experienced enough to know that answer.Are you anticipating scaling up the number of canada goose users massively?I have 1,000 users right now canada goose coats and would like to be comfortable with my database up to 10,000 users. If I ever cheap Canada Goose hit canadian goose jacket 10k I worry about more buy canada goose jacket cheap then.Or is it that your report queries don execute sufficiently quickly?Reports are currently running very quickly on a fairly low end PC.Or you want a solution that can hold many years worth of historical data?It would be awesome if I could store 100% of the detailed records for all users, but there no huge benefit to that except canada goose outlet for maybe a handful of users. This is not a priority except in aggregate.Also, what kind of reports are you running?Best example is this screenshot of the Canada Goose sale application. I am most certainly taking advantage canada goose uk black friday of MySQL functionality and have no interest in going to flat file storage except for maybe archival.If changes are generally more rare (and the observed variables few) than the polling interval, this way can uk canada goose outlet save you a ton of traffic besides the storage.2) Many big data sites archive older detailled data. Usually if data is older than a specific time, it granularity requirement decreases. you might be interested in increasing the time Canada Goose Coats On Sale between values (and averaging) with age of the data.Also you could for older data just store canada goose black friday sale start/end/average values for any particular day, like stock markets handle historic data.There are many more ideas and you can freely elaborate them, but that Canada Goose Parka the fun part so I leave this to you : )Hah, I Canada Goose Jackets less surprised by the amount of data per user and more surprised by the number of users.

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