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Is there any difference between ReduceByKey and CombineByKey when it comes to performance in Spark. Any help on this is appreciated.
Reduce by key internally calls combineBykey. Hence the basic way of task execution is same for both.
The choice of CombineByKey over reduceBykey is when the input Type and output Type is not expected to be the same. So combineByKey will have a extra overhead of converting one type to another .
If the type conversion is omitted there is no difference at all .
Please follow the following links
http://bytepadding.com/big-data/spark/reducebykey-vs-combinebykey
http://bytepadding.com/big-data/spark/groupby-vs-reducebykey
http://bytepadding.com/big-data/spark/combine-by-key-to-find-max
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I want to store massive amounts of data, specifically the amount of text equivalent to a book. How can I go about this? Is there a type of data storage that makes this process faster/easier (aka is fit) for this type of operation?
There are limits, but not that much. A single database can have (with default configurations) over a billion tables and each table can be 32TB in size.
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In Postgresql, the hstore and json datatypes seem to have very similar use cases. When would you choose to use one vs. the other? Initial thoughts:
You can nest with json; you can't with hstore
Functions for parsing json won't be available until 9.3
The json type is just a string. There are no built in functions to parse it. The only thing to be gained when using it is the validity checking.
Edit for those downvoting: This was written when 9.3 still didn't exist.It is correct for 9.2. Also the question was different. Check the edit history.
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What speed optimization techniques do antiviruses use today to scan a file, provided they have to check for all the signatures + the behavioral scan?
I'm not a antivirus programmer, but I think the scan engine scans through a file searching for known pattern inside. The greater number of patterns it can identify, the longer it will take to scan.
Optimization maybe similar to database optimization, with patterns indexing.
Identification Methods
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I want to select certain amount of data from one table. Based on those data, I want to check another two tables and insert into 2 tables.
So I want to iterate the resulted data. Which way is better(faster) and reasonable using DataReader or DataTable?
Thanks in advance
RedsDevils
You end up creating a reader to fill the table. The reverse isn't true, So I would stick with the dataReader.
-Josh
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How can I convert unstructured data into structured data? For example email contacts, from an unstructured text, to a structured format.
Are there any algorithms to do this?
There's no generic algorithm to "take unstructured data and convert it to structured data", no. It's highly dependent on what the possible range of input is, and what the desired structure is, and what conversions need to be applied, etc.
The class of problem is called "parsing": you need to construct a parser for the specific inputs you expect, and use that parser to generate structure from what it discovers about the input you get.
Your programming language will likely have parsing libraries available to assist with constructing a specific parser.