I am going to convert a text file in the SQLite db form; I am concerned about these points because giving any effort to write code for it:
Will both text file or its corresponding sqlite db be of same size?
SQLite would take less space than text file?
Or text file db is the one with lowest space?
"Hardware is cheap" - I'd strongly recommend not worrying about size differences, which will likely be insignificant anyway, and instead pick whichever solution best meets the rest of your needs. A text file can work just fine for simple projects, but a database has many more features that can help you organize, backup, and query your data much more efficiently and robustly.
For a more in-depth look at the pros and cons of both options, check out: database vs. flat files
Some things to keep in mind:
(NOTE about this answer: Files here references to internal/external storage, not SharedPrefs)
SQL:
Databases have overheads, which does take up size
If the database or a table goes corrupt, all data is lost(how bad this is depends on your app. Losing several thousand pictures: bad. Losing deletion log: not very bad)
Databases can be compressed(see this)
You can split up data into different tables, if you have issues with ID(or whatever way you identify row X), meaning one database can have several tables for each object where object X have identification conflicts with object Y. That basically means you can keep everything in one file, and still avoid conflicts with names. (Read more at the bottom of the answer)
Files:
Every single file has to be defined as its own separate file, which takes up space(name of the file)
You cannot store all attributes in one file without having to set up an advanced reader that determines the different types of data. If you don't do that, and have one file for each attribute, you will use a lot of space.
Reading thousands of lines can go slow, especially if you have several(say 100+) very big files
The OS uses space for each file, excluding the content. The name of the file for an instance, that takes up space. But something to keep in mind is you can keep all the data of an app in a single file. If you have an app where objects of two different types may have naming issues, you create a new database.
Naming conflicts
Say you have two objects, object X and Y.
Scenario 1'
Object X stores two variables. The file names are(x and y are in this case coordinates):
x.txt
y.txt
But in a later version, object Y comes in with the same two files.
So you have to assign an ID to object X and Y:
0-x.txt
0-y.txt
Every file uses 3 chars(7 total, including extension) on the name alone. This grows bigger the more complex the setup is. See scenario 2
But saving in the database, you get the row with ID 0 and find column X or Y.
You do not have to worry about the file name.
Further, if every object saves a lot of files, the reference to load or save each file will take up a lot of space. And that affects your APK file, and slowly pushes you up towards the 50 MB limit(google play limit).
You can create universal methods, but you can do the same with SQL and save space in the APK file. But compared to text files, SQL does save some space in terms of name.
Note though, that if you save 2-3 files(just to take a number) those few bytes going to names aren't going to matter
It is when you start saving hundreds of files, long names to avoid naming conflicts, that is when SQL saves you space. And if the table gets too big, you can compress it. You can zip text files to maybe save some space, but with one-liner files, there is not much to save.
Scenario 2
Object X and Y has three children each.
Every child has 3 variables it saves to the file system. If there was only one object with 3 children, it could have saved it like this
[id][variable name].txt
But because there is another parent with 3 children(of the same type, and they save the same files) the object's children who get saved last are the ones that stay saved. The first 3 get overwritten.
So you have to add the parent ID:
[parent ID][child ID][variable name].txt
And keep in mind, these examples are focused on a few objects. The amount of space saved is low, but when you save hundreds, if not thousands of files, that is when you start to save space.
Now, if you create a table, you can store your main objects(X and Y in this case). Then, you can either create the first table in a way that makes it recognisable whether the object is the parent or child, or you can create a second table. The second table have two ID values; One to identify the parent and one to identify the child. So if you want to find all the children of object 436, you simply write this query:
SELECT * FROM childrentable WHERE `parent_id`='436'
And that will give you all the attributes for all the children with object 436 as its parent.
And everything is stored in the Cursor when returned.
If you were to do the same with a file, this line(where Saver is the file saving and loading class):
Saver.load("0-436-file_name", context);
It is, of course, possible to use a for-loop to cycle the children ID(the 0 at the start), but you would also have to save how many children there are: You cannot get the files as easily, so you have to store values about thee amount of objects and the objects children.
This meaning you have to save more values in more files to be able to get the files you saved in the first place. And this is a really hard way to do things. A database would help you not have to write files to keep track of how many files you saved. The database would return [x] results on each query. So if object 436 has no children, SQL returns 0 rows. But in files, you would have to save 0 as the amount of children. Guessing file names lead to I/O exceptions.
I would expect the text file to be smaller as it has no overhead: all the things a Database gives you have a cost in terms of space.
It sounds like space is the only thing that matters to you, and that you expect to change the contents of the text file often (you call it a 'text file db'). Please note that there is no such thing as a 'text file db'. Reading and writing to it will be very slow compared to a proper db (such as SQLite). Adding different record types (Tables in a db) will complicate your like and I wouldn't want to try to maintain any sort of referential links between record types in a text file.
Hope that helps -
Related
As far as I can tell, it is not really possible to "update" a single portion of a file. One must overwrite the entire thing or simply append. A database, however, usually has update functionality. How would one design a database to not append - because that causes tombstones - but rather update?
Files can be overwritten it just can be a bit of a tedious process. You will have to know the beginning index of whatever you want to update and set the file pointer to that index in the file before starting to write to that file.
Databases are easier to update because they are a combination of many data structures (Linked lists, Trees, Heaps, etc.) that all contain specific data and can be iterated through. For these data structures you just need to know which node in the structure you need to update and navigate to it and overwrite the data.
I have a large blob (azure) file with 10k json objects in a single array. This does not perform because of its size. As I look to re-architect it, I can either create multiple files with a single array in each of 500-1000 objects or I could keep the one file, but burst the single array into an array of arrays-- maybe 10 arrays of 1000 objects each.
For simplicity, I'd rather break into multiple files. However, I thought this was worth asking the question and seeing if there was something to be learned in the answers.
I would think this depends strongly on your use-case. The multiple files or multiple arrays you create will partition your data somehow: will the partitions be used mostly together or mostly separate? I.e. will there be a lot of cases in which you only read one or a small number of the partitions?
If the answer is "yes, I will usually only care about a small number of partitions" then creating multiple files will save you having to deal with most of your data on most of your calls. If the answer is "no, I will usually need either 1.) all/most of my data or 2.) data from all/most of my partitions" then you probably want to keep one file just to avoid having to open many files every time.
I'll add: in this latter case, it may well turn out that the file structure (one array vs an array-of-arrays) doesn't change things very much, since a full scan is a full scan is a full scan etc. If that's the case, then you may need to start thinking about how to move to the prior case where you partition your data so that your calls fall neatly within few partitions, or how to move to a different data format.
I'm working on a piece of software that stores files in a file system, as well as references to those files in a database. Querying the uploaded files can thus be done in the database without having to access the file system. From what I've read in other posts, most people say it's better to use a file system for file storage rather then storing binary data directly in a database as BLOB.
So now I'm trying to understand the best way to set this up so that both the database a file system stay in sync and I don't end up with references to files that don't exist, or files taking up space in the file system that aren't referenced. Here are a couple options that I'm considering.
Option 1: Add File Reference First
//Adds a reference to a file in the database
database.AddFileRef("newfile.txt");
//Stores the file in the file system
fileStorage.SaveFile("newfile.txt",dataStream);
This option would be problematic because the reference to the file is added before the actual file, so another user may end up trying to download a file before it is actually stored in the system. Although, since the reference to the the file is created before hand the primary key value could be used when storing the file.
Option 2: Store File First
//Stores the file
fileStorage.SaveFile("newfile.txt",dataStream);
//Adds a reference to the file in the database
//fails if reference file does not existing in file system
database.AddFileRef("newfile.txt");
This option is better, but would make it possible for someone to upload a file to the system that is never referenced. Although this could be remedied with a "Purge" or "CleanUpFileSystem" function that deletes any unreferenced files. This option also wouldn't allow the file to be stored using the primary key value from the database.
Option 3: Pending Status
//Adds a pending file reference to database
//pending files would be ignored by others
database.AddFileRef("newfile.txt");
//Stores the file, fails if there is no
//matching pending file reference in the database
fileStorage.SaveFile("newfile.txt",dataStream); database
//marks the file reference as committed after file is uploaded
database.CommitFileRef("newfile.txt");
This option allows the primary key to be created before the file is uploaded, but also prevents other users from obtaining a reference to a file before it is uploaded. Although, it would be possible for a file to never be uploaded, and a file reference to be stuck pending. Yet, it would also be fairly trivial to purge pending references from the database.
I'm leaning toward option 2, because it's simple, and I don't have to worry about users trying to request files before they are uploaded. Storage is cheap, so it's not the end of the world if I end up with some unreferenced files taking up space. But this also seems like a common problem, and I'd like to hear how others have solved it or other considerations I should be making.
I want to propose another option. Make the filename always equal to the hash of its contents. Then you can safely write any content at all times provided that you do it before you add a reference to it elsewhere.
As contents never change there is never a synchronization problem.
This gives you deduplication for free. Deletes become harder though. I recommend a nightly garbage collection process.
What is the real use of the database? If it's just a list of files, I don't think you need it at all, and not having it saves you the hassle of synchronising.
If you are convinced you need it, then options 1 and 2 are completely identical from a technical point of view - the 2 resources can be out of sync and you need a regular process to consolidate them again. So here you should choose the options that suits the application best.
Option 3 has no advantage whatsoever, but uses more resources.
Note that using hashes, as suggested by usr, bears a theoretical risk of collision. And you'd also need a periodical consolidation process, as for options 1 and 2.
Another questions is how you deal with partial uploads and uploads in progress. Here option 2 could be of use, but you could also use a second "flag" file that is created before the upload starts, and deleted when the upload is done. This would help you determine which uploads have been aborted.
To remedy the drawback you mentioned of option 1 I use something like fileStorage.FileExists("newfile.txt"); and filter out the result for which it returns a negative.
In Python lingo:
import os
op = os.path
filter(lambda ref: op.exists(ref.path()), database.AllRefs())
I'm trying to implement an SQLite-based database that can store the full structure of a 100GB folder with a complex substructure (expecting 50-100K files). The main aim of the DB would be to get rapid queries on various aspects of this folder (total size, size of any folder, history of a folder and all it's contents, etc).
However, I realized that finding all the files inside a folder, including all of it's sub-folders is not possible without recursive queries if I just make a "file" table with just a parent_directory field. I consider this as one of the most important features I want in my code, so I have considered two schema options for this as shown in the figure below.
In schema 1, I store all the file names in one table and directory names in another table. They both have a "parentdir" item, but also have a text (apparently text/blob are the same in sqlite) field called "FullPath" that will save the entire path from the root to the particular file/directory (like /etc/abc/def/wow/longpath/test.txt). I'm not assuming a maximum subfolder limit so this could theoretically be a field that allows up to 30K characters. My idea is that then if I want all the files or directories belonging to any parent I just query the fullpath of the parent on this field and get the fileIDs
In schema 2, I store only filenames, fileIDs and DirNames, DirIDs in the directories and files tables, respectively. But in a third table called "Ancestors", I store for each file a set of entries for each directory that is it's ancestor (so in the above example, test.txt will have 5 entries, pointing to the DirIDs of the folders etc,abc,def,wow and longpath respectively). Then if I want the full contents of any folder I just look for the DirID in this table and get all the fileIDs.
I can see that in schema 1 the main limit might be full-text search of variable length text column and in schema 2 the main limit being that I might have to add a ton of entries for files that are buried deep within 100 directories or something.
What would be the best of these solutions? Is there any better solution that I did not think of?
Your first schema will work just fine.
When you put an index on the FullPath column, use either the case-sensitive BETWEEN operator for queries, or use LIKE with either COLLATE NOCASE on the index or with PRAGMA case_sensitive_like.
Please note that this schema also stores all parents, but the IDs (the names) are all concatenated into one value.
Renaming a directory would require updating all its subtree entries, but you mention history, so it's possible that old entries should stay the same.
Your second schema is essentially the Closure Table mentioned in Dan D's comment.
Take care to not forget the entries for depth 0.
This will store lots of data, but being IDs, the values should not be too large.
(You don't actually need RelationshipID, do you?)
Another choice for storing trees is the nested set model, or the similar nested interval model.
The nested set model allows to retrieve subtrees by querying for an interval, but updates are hairy.
The nested interval model uses fractions, which are not a native data type and therefore cannot be indexed.
I'd estimate that the first alternative would be easiest to use.
I should also be no slower than the others if lookups are properly indexed.
My personal favourite is the visitation number approach, which I think would be especially useful for you since it makes it pretty easy to run aggregate queries against a record and its descendants.
We have a large number of documents and metadata (xml files) associated with these documents. What is the best way to organize them?
Currently we have created a directory hierarchy:
/repository/category/date(when they were loaded into our db)/document_number.pdf and .xml
We use the path as a unique identifier for the document in our system.
Having a flat structure doesn't seem to a good option. Also using the path as an id helps to keep our data independent from our database/application logic, so we can reload them easily in case of failure, and all documents will maintain their old ids.
Yet, it introduces some limitations. for example we can't move the files once they've been placed in this structure, also it takes work to put them this way.
What is the best practice? How websites such as Scribd deal with this problem?
Your approach does not seem unreasonable, but might suffer if you get more than a few thousand documents added within a single day (file systems tend not to cope well with very large numbers of files in a directory).
Storing the .xml document beside the .pdf seems a bit odd - If it's really metadata about the document, should it not be in the database (which it sounds like you already have) where it can be easily queries and indexed etc?
When storing very large numbers of files I've usually taken the file's key (say, a URL), hashed it, and then stored it X levels deep in directories based on the first characters of the hash...
Say you started with the key 'How to organize a large number of objects'. The md5 hash for that is 0a74d5fb3da8648126ec106623761ac5 so you might store it at...
base_dir/0/a/7/4/http___stackoverflow.com_questions_2734454_how-to-organize-a-large-number-of-objects
...or something like that which you can easily find again given the key you started with.
This kind of approach has one advantage over your date one in that it can be scaled to suit very large numbers of documents (even per day) without any one directory becoming too large, but on the other hand, it's less intuitive to someone having to manually find a particular file.