so I am aware of .plist and archives and SQLLite as methods to store persistent data within the app, however, I have been stuck for a while trying to figure out how to store data from a user's daily activity so it may be displayed in a graph. For example, if a user walks 1 mile one day and 3 the next, how would one go about knowing when its one day and when it's the next?
THe best I can come up with is to store the current date, then the next time the user performs an action, check if the stored date is the same as the current date and if it's not, then save the data for a different day.
Has anyone got any better ideas or is there a standard way to deal with this issue?
I would use a SQLLite DB with a timestamp column indicating when the entry has been written. With NSDateComponents you can figure out later which day the entry has been added.
When you create an index over the timestamp column your queries will be pretty fast, what's kind of perfect for drawing graphs...
Using NSDate is a perfectly fine way. You'll probably need the dates for the graph anyways, or?
SQLLite is still a great method for that. Just don't treat it like a flat file.
SQLLite supports time stamping. Each time you enter the data you can add that stamp to the field. Running a simple SQL query will give you your data in a format you can use for a graph. You can also query the database to see what the current data is or how long it has been since data was last entered.
Read this page for a few hints:
http://www.sqlite.org/lang_datefunc.html
If you are asking about the right data structure for this, just store your data and timestamp in an array. Next time when you update, check the timestamp of last object in the array and see if the data has changed. If yes, add a new entry.
To persist this, just save this array into a PList. Since it's not a lot of data PList won't be a bad idea.
Related
I have a table that loads in data from a webservice that returns a bunch of JSON data. I load more data when the user scrolls down as the DB I am querying holds quite a bit of data.
The question I have is - will it be feasible to implement the right side alphabetical listing on such a table and how could this be done? It is definitely possible if I load in ALL the data and then sort them locally, populate the index and cache the data for every other time. But what if this is going to be 10K rows of data or more. Maybe load this data on application first launch is one option.
So in terms of performance and usability, does anyone have any recommendations of what is possible to do?
I don't think that you should download all data to make those indexes, it would decrease refreshing time and might cause memory problems.
But if you think that indexes could make a good difference than you can add some features to your server API. I would add either a different API call like get_indexes. Or even I would add POST parameter get_indexes which adds an array of indexes to any call which has this parameter set.
And you should be ready to handle cases when user taps on indexes without any downloaded data or when user just stresses out your app making fast index scrolling up and down.
First see how big the data download is. If the server can gzip the data, it may be surprisingly small - JSON zips very well because of the duplicated keys.
If it's too big, I would recommend modifying the server if possible to let you specify a starting letter. That way, if the user hits the "W" in the index you should be able to request all items that begin with "W".
It would also be helpful to get a total record count from the server so you can know how many rows are in the table ahead of time. I would also return a "loading..." string for each unknown row until the actual data comes down.
I am developing an iphone app which will fetch the data from CSV file as per the keyword entered in to the UITextFiled, eg. if user enters london than all the possible entries containing the same keyword should be listed down, I have tried CHCSVParser but i am still not able to conclude any result. Can anyone tell me is it even feasible??? and if yes than please help me through the initial steps.
Thanks.
If you can, then using a plist instead of csv will be much easier/flexible.
Maybe you can import your data in a .sqlite ressource file that contains all elements from your csv file.
Then for listing 15 000 elements or a subset of them in a tableview, the nsfetchedresultscontroller will help you. Initializing it with a fetch request will permits you to filter your elements based on one or more attribute name(s).
http://developer.apple.com/library/ios/#documentation/CoreData/Reference/NSFetchedResultsController_Class/Reference/Reference.html
Yeah, if you're going to repeatedly reference "random" elements of your CSV file, you should convert it to an SQLite DB (though that would be overkill if you're only referencing things once). For your app, as well as I can understand the description, a DB of some sort is most definitely warranted.
As to reading the CSV file, there are, no doubt, tools available, but it's probably just as easy to use NSString componentsSeparatedByString to parse each line.
I am working on a Website which is displaying all the apps from the App Store. I am getting AppStore data by their EPF Data Feeds through EPF Importer. In that database I get the pricing of each App for every store. There are dozen of rows in that set of data whose table structure is like:
application_price
The retail price of an application.
Name Key Description
export_date The date this application was exported, in milliseconds since the UNIX Epoch.
application_id Y Foreign key to the application table.
retail_price Retail price of the application, or null if the application is not available.
currency_code The ISO3A currency code.
storefront_id Y Foreign key to the storefront table.
This is the table I get now my problem is that I am not getting any way out that how I can calculate the price reduction of apps and the new free apps from this particular dataset. Can any one have idea how can I calculate it?
Any idea or answer will be highly appreciated.
I tried to store previous data and the current data and then tried to match it. Problem is the table is itself too large and comparing is causing JOIN operation which makes the query execution time to more than a hour which I cannot afford. there are approx 60, 000, 000 rows in the table
With these fields you can't directly determine price drops or new application. You'll have to insert these in your own database, and determine the differences from there. In a relational database like MySQL this isn't too complex:
To determine which applications are new, you can add your own column "first_seen", and then query your database to show all objects where the first_seen column is no longer then a day away.
To calculate price drops you'll have to calculate the difference between the retail_price of the current import, and the previous import.
Since you've edited your question, my edited answer:
It seems like you're having storage/performance issues, and you know what you want to achieve. To solve this you'll have to start measuring and debugging: with datasets this large you'll have to make sure you have the correct indexes. Profiling your queries should helping in finding out if they do.
And probably, your environment is "write once a day", and read "many times a minute". (I'm guessing you're creating a website). So you could speed up the frontend by processing the differences (price drops and new application) on import, rather than when displaying on the website.
If you still are unable to solve this, I suggest you open a more specific question, detailing your DBMS, queries, etc, so the real database administrators will be able to help you. 60 million rows are a lot, but with the correct indexes it should be no real trouble for a normal database system.
Compare the table with one you've downloaded the previous day, and note the differences.
Added:
For only 60 million items, and on a contemporary PC, you should be able to store a sorted array of the store id numbers and previous prices in memory, and do an array lookup faster than the data is arriving from the network feed. Mark any differences found and double-check them against the DB in post-processing.
Actually I also trying to play with these data, and I think best approach for you base on data from Apple.
You have 2 type of data : full and incremental (updated data daily). So within new data from incremental (not really big as full) you can compare only which record updated and insert them into another table to determine pricing has changed.
So you have a list of records (app, song, video...) updated daily with price has change, just get data from new table you created instead of compare or join them from various tables.
Cheers
I'm creating an iPhone app, which fetches information from a server every time it is started. However, i'm planning on using the fetched data of the last month/few months/year to calculate some averages.
I had been thinking about saving them to NSUserDefaults using dictionaries (associating a date with a value), but i just remembered there also exists something like core data. Seeing that i do not have any experience with core data, i don't know if it's better. If it wouldn't, i could save the
time i'd use learning it otherwise.
The data comes in in XML format, and i get several sets of the same response each time (for different locations on a map). The amount of sets can change, as the user can add more locations. I currently only save the raw data to the disk to load if the load fails next time it starts. However, i also want to save some specific values from that XML in a way that i can easily access it. What would be the best way to do this?
Edit: I actually also need to know how fast/efficient core data is. I'm currently passing around NSArrays with NSDictionaries for the sets of data during that session. For saving the data that last longer than the session core data is ideal, i found out that much (just need to find a nice way to associate an entity with a date), i just need some advice on the efficiency.
If you're going to be working with larger amounts of data, it's probably anyway better to give Core Data a try, it's after all not that complicated and there's a plenty of good tutorials where you can learn it. There are different settings for the storage type, you can either use a sqlite database or an xml file.
According to the guys from Apple, it should be fast and memory effective to use Core Data in contrast to self-made solutions, so it's a preferred way to go.
Core data would be easier to manipulate the data and query the data using predicates. Core Data supports dates so you can even find items in date ranges.
In my dictionary IPhone app I need to save an array of strings which actually contains about 125.000 distinct words; this transforms in aprox. 3.2Mb of data.
The first time I run the app I get this data from an SQLite db. As it takes ages for this query to run, I need to save the data somehow, to read it faster each time the app launches.
Until now I've tried serializing the array and write it to a file, and afterword I've tested if writing directly to NSUserDefaults to see if there's any speed gain but there's none. In both ways it takes about 7 seconds on the device to load the data. It seems that not reading from the file (or NSUserDefaults) actually takes all that time, but the deserialization does:
objectsForCharacters = [[NSKeyedUnarchiver unarchiveObjectWithData:data] retain];
Do you have any ideeas about how I could write this data structure somehow that I could read/put in memory it faster?
The UITableView is not really designed to handle 10s of thousands of records. If would take a long time for a user to find what they want.
It would be better to load a portion of the table, perhaps a few hundred rows, as the user enters data so that it appears they have all the records available to them (Perhaps providing a label which shows the number of records that they have got left in there filtered view.)
The SQLite db should be perfect for this job. Add an index to the words table and then select a limited number of rows from it to show the user some progress. Adding an index makes a big difference to the performance of the even this simple table.
For example, I created two tables in a sqlite db and populated them with around 80,000 words
#Create and populate the indexed table
create table words(word);
.import dictionary.txt words
create unique index on words_index on word DESC;
#Create and populate the unindexed table
create table unindexed_words(word);
.import dictionary.txt unindexed_words
Then I ran the following query and got the CPU Time taken for each query
.timer ON
select * from words where word like 'sn%' limit 5000;
...
>CPU Time: user 0.031250 sys 0.015625;
select * from unindex_words where word like 'sn%' limit 5000;
...
>CPU Time: user 0.062500 sys 0.0312
The results vary but the indexed version was consistently faster that the unindexed one.
With fast access to parts of the dictionary through an indexed table, you can bind the UITableView to the database using NSFecthedResultsController. This class takes care of fecthing records as required, caches results to improve performance and allows predicates to be easily specified.
An example of how to use the NSFetchedResultsController is included in the iPhone Developers Cookbook. See main.m
Just keep the strings in a file on the disk, and do the binary search directly in the file.
So: you say the file is 3.2mb. Suppose the format of the file is like this:
key DELIMITER value PAIRDELIMITER
where key is a string, and value is the value you want to associate. The DELIMITER and PAIRDELIMITER must be chosen as such that they don't occur in the value and key.
Furthermore, the file must be sorted on the key
With this file you can just do the binary search in the file itself.
Suppose one types a letter, you go to the half of the file, and search(forwards or backwards) to the first PAIRDELIMITER. Then check the key and see if you have to search upwards or downwards. And repeat untill you find the key you need,
I'm betting this will be fast enough.
Store your dictionary in Core Data and use NSFetchedResultsController to manage the display of these dictionary entries in your table view. Loading all 125,000 words into memory at once is a terrible idea, both performance- and memory-wise. Using the -setFetchBatchSize: method on your fetch request for loading the words for your table, you can limit NSFetchedResultsController to only handling the small subset of words that are visible at any given moment, plus a little buffer. As the user scrolls up and down the list of words, new batches of words are fetched in transparently.
A case like yours is exactly why this class (and Core Data) was added to iPhone OS 3.0.
Do you need to store/load all data at once?
Maybe you can just load the chunk of strings you need to display and load all other strings in the background.
Perhaps you can load data into memory in one thread and search from it in another? You may not get search results instantly, but having some searches feel snappier may be better than none at all, by waiting until all data are loaded.
Are some words searched more frequently or repeatedly than others? Perhaps you can cache frequently searched terms in a separate database or other store. Load it in a separate thread as a searchable store, while you are loading the main store.
As for a data structure solution, you might look into a suffix trie to search for substrings in linear time. This will probably increase your storage requirements, though, which may affect your ability to implement this with an iPhone's limited memory and disk storage capabilities.
I really don't think you're on the right path trying to load everything at once.
You've already determined that your bottleneck is the deserialization.
Regardless what the UI does, the user only sees a handful (literally) of search results at a time.
SQLlite already has a robust indexing mechanism, there is likely no need to re-invent that wheel with your own indexing, etc.
IMHO, you need to rethink how you are using UITableView. It only needs a few screenfuls of data at a time, and you should reuse cell objects as they scroll out of view rather than creating a ton of them to begin with.
So, use SQLlite's indexing and grab "TOP x" rows, where x is the right balance between giving the user some immediately-available rows to scroll through without spending too much time loading them. Set the table's scroll bar scaling using a separate SELECT COUNT(*) query, which only needs to be updated when the user types something different.
You can always go back and cache aggressively after you deserialize enough to get something up on-screen. A slight lag after the first flick or typing a letter is more acceptable than a 7-second delay just starting the app.
I have currently a somewhat similar coding problem with a large amount of searchable strings.
My solution is to store the prepared data in one large memory array, containing both the texttual data and offsets as links. Meaning I do not allocate objects for each item. This makes the data use less memory and also allows me to load & save it to a file without further processing.
Not sure if this is an option for you, since this is quite an obvious solution once you've realized that the object tree is causing the slowdown.
I use a large NSData memory block, then search through it. Well, there's more to it, it took me about two days to get it well optimized.
In your case I suspect you have a dictionary with a lot of words that have similar beginnings. You could prepare them on another computer in a format the both compacts the data and also facilitates fast lookup. As a first step, the words should be sorted. With that, you can already perform a binary search on them for a fast lookup. If you store it all in one large memory area, you can do the search quite fast, compared to how sqlite would search, I think.
Another way would be to see the words as a kind of tree: You have many thousands that begin with the same letter. So you divide your data accordingly: You have a sql table for each beginning letter of your set of words. that way, if you look up a word, you'd select one of the now-smaller tables depening on the first letter. This makes the amount that has to be searched already much smaller. and you can do this for the 2nd and 3rd letter as well, and you already could have quite a fast access.
Did this give you some ideas?
Well actually I figured it out myself in the end, but of course I thank you all for your quick and pertinent answers. To be concise I will just say that, the fact that Objective-C, just like any other object-based programming language, due to introspection and other objective requirements is significantly slower than procedural programming languages.
The solution was in fact to load all my data in a continuous chunk of memory using malloc (a char **) and search on-demand in it and transform to objects. This concluded in a .5 sec loading time (from file to memory) and resonable (should be read "fast") operations during execution. Thank you all again and if you have any questions I'm here for you. Thanks