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StringBuilder used with PadLeft/Right OutOfMemoryException

All, I have the following Append which I am performing when I am producing a single line for a fixed text file
formattedLine.Append(this.reversePadding ?
strData.PadLeft(this.maximumLength) :
strData.PadRight(this.maximumLength));
This particular exception happens on the PadLeft() where this.maximumLength = 1,073,741,823 [a field length of an NVARCHAR(MAX) gathered from SQL Server]. formattedLine = "101102AA-1" at the time of exception so why is this happening. I should have a maximum allowed length of 2,147,483,647?
I am wondering if https://stackoverflow.com/a/1769472/626442 be the answer here - however, I am managing any memory with the appropriate Dispose() calls on any disposable objects and using block where possible.
Note. This fixed text export is being done on a background thread.
Thanks for your time.

This particular exception happens on the PadLeft() where this.maximumLength = 1,073,741,823
Right. So you're trying to create a string with over a billion characters in.
That's not going to work, and I very much doubt that it's what you really want to do.
Note that each char in .NET is two bytes, and also strings in .NET are null-terminated... and have some other fields beyond the data (the length, for one). That means you'd need at least 2147483652 bytes + object overhead, which pushes you over the 2GB-per-object limit.
If you're running on a 64-bit version of Windows, in .NET 4.5, there's a special app.config setting of <gcAllowVeryLargeObjects> that allows arrays bigger than 2GB. However, I don't believe that will change your particular use case:
Using this element in your application configuration file enables arrays that are larger than 2 GB in size, but does not change other limits on object size or array size:
The maximum number of elements in an array is UInt32MaxValue.
The maximum index in any single dimension is 2,147,483,591 (0x7FFFFFC7) for byte arrays and arrays of single-byte structures, and 2,146,435,071 (0X7FEFFFFF) for other types.
The maximum size for strings and other non-array objects is unchanged.
What would you want to do with such a string after creating it, anyway?

In order to allocate memory for this operation, the OS must find contiguous memory that is large enough to perform the operation.
Memory fragmentation can cause that to be impossible, especially when using a 32-bit .NET implementation.

I think there might be a better approach to what you are trying to accomplish. Presumably, this StringBuilder is going to be written to a file (that's what it sounds like from your description), and apparently, you are also potentially dealing with large (huge) database records.
You might consider a streaming approach, that wont require allocating such a huge block of memory.
To accomplish this you might investigate the following:
The SqlDataReader class exposes a GetChars() method, that allows you to read a chunk of a single large record.
Then, instead of using a StringBuilder, perhaps using a StreamWriter ( or some other TextWriter derived class) to write each chunk to the output.
This will only require having one buffer-full of the record in your application's memory space at one time. Good luck!

Data in a condensed format

I need a library which would help me to save and query data in a condensed format (a mini DSL in essence) here's a sample of what I want:
Update 1 - Please note, figures in the samples above are made small just to make is easier to follow the logic, the real figures are limited with c# long type capacity, ex:
1,18,28,29,39,18456789,18456790,18456792,184567896.
Sample Raw Data set: 1,2,3,8,11,12,13,14
Condensed Sample Data set:
1..3,8,11..14
What would be absolute nice to have is to be able to present 1,2,4,5,6,7,8,9,10 as 1..10-3.
Querying Sample Data set:
Query 1 (get range):
1..5 -> 1..3
Query 2 (check if the value exists)
?2 -> true
Query 3 (get multiple ranges and scalar values):
1..5,11..12,14 -> 1..3,11..12,14
I don't want to develop it from scratch and would highly prefer to use something which already exists.

Here are some ideas I've had over the days since I read your question. I can't be sure any of them really apply to your use case but I hope you'll find something useful here.
Storing your data compressed
Steps you can take to reduce the amount of space your numbers take up on disk:
If your values are between 1 and ~10M, don't use a long, use a uint. (4 bytes per number.)
Actually, don't use a uint. Store your numbers 7 bits to a byte, with the remaining bit used to say "there are more bytes in this number". (Then 1-127 will fit in 1 byte, 128-~16k in 2 bytes, ~16k-~2M in 3 bytes, ~2M-~270M in 4 bytes.)
This should reduce your storage from 8 bytes per number (if you were originally storing them as longs) to, say, on average 3 bytes. Also, if you end up needing bigger numbers, the variable-byte storage will be able to hold them.
Then I can think of a couple of ways to reduce it further, given you know the numbers are always increasing and may contain lots of runs. Which works best for you only you can know by trying it on your actual data.
For each of your actual numbers, store two numbers: the number itself, followed by the number of numbers contiguous after it (e.g. 2,3,4,5,6 => 2,4). You'll have to store lone numbers as e.g. 8,0 so will increase storage for those, but if your data has lots of runs (especially long ones) this should reduce storage on average. You could further store "single gaps" in runs as e.g. 1,2,3,5,6,7 => 1,6,4 (unambiguous as 4 is too small to be the start of the next run) but this will make processing more complex and won't save much space so I wouldn't bother.
Or, rather than storing the numbers themselves, store the deltas (so 3,4,5,7,8,9 => 3,1,1,2,1,1. This will reduce the number of bytes used for storing larger numbers (e.g. 15000,15005 (4 bytes) => 15000,5 (3 bytes)). Further, if the data contains a lot of runs (e.g. lots of 1 bytes), it will then compress (e.g. zip) nicely.
Handling in code
I'd simply advise you to write a couple of methods that stream a file from disk into an IEnumerable<uint> (or ulong if you end up with bigger numbers), and do the reverse, while handling whatever you've implemented from the above.
If you do this in a lazy fashion - using yield return to return the numbers as you read them from disk and calculate them, and streaming numbers to disk rather than holding them in memory and returning them at once, you can keep your memory usage down whatever the size of the stored data.
(I think, but I'm not sure, that even the GZipStream and other compression streams will let you stream your data without having it all in memory.)
Querying
If you're comparing two of your big data sets, I wouldn't advise using LINQ's Intersect method as it requires reading one of the sources completely into memory. However, as you know both sequences are increasing, you can write a similar method that needs only hold an enumerator for each sequence.
If you're querying one of your data sets against a user-input, small list of numbers, you can happily use LINQ's Intersect method as it is currently implemented, as it only needs the second sequence to be entirely in memory.

I'm not aware of any off-the-shelf library that does quite what you want, but I'm not sure you need one.
I suggest you consider using the existing BitArray class. If, as your example suggests, you're interested in compressing sets of small integers then a single BitArray with, say 256 bits, could represent any set of integers in the range [0..255]. Of course, if your typical set has only 5 integers in it then this approach would actually expand your storage requirements; you'll have to figure out the right size of such arrays from your own knowledge of your sets.
I'd suggest also looking at your data as sets of integers, so your example 1,2,3,8,11,12,13,14 would be represented by setting on the corresponding bits in a BitArray. Your query operations then reduce to intersection between a test BitArray and your data BitArray.
Incidentally, I think your example 2, which transforms 2 -> true, would be better staying in the domain of functions that map sets of integers to sets of integers, ie it should transform 2 -> 2. If you want to, write a different method which returns a boolean.
I guess you'd need to write code to pack integers into BitArrays and to unpack BitArrays into integers, but that's part of the cost of compression.

How to print line numbers for textbox in c#

This is going to be a long post. I would like to have suggestions if any on the procedure I am following. I want the best method to print line numbers next to each CRLF-terminated-line in a richtextbox. I am using C# with .NET. I have tried using ListView but it is inefficient when number of lines grow. I have been successful in using Graphics in custom control to print the line numbers and so far I am happy with the performance.
But as the number of lines grow to 50K to 100K the scrolling is affected badly. I have overridden WndProc method and handling all the messages to call the line-number printing only when required. (Overriding OnContentsResized and OnVScroll make redundant calls to the printing method).
Now the line number printing is fine when number of lines is small say upto 10K (with which I am fine as it is rare need to edit a file with 10000 lines) but I want to remove the limitation.
Few Observations
Number of lines displayed in the richtexbox is constant +-1. So, the performance difference should be due to large text and not because I am using Graphics painting.
Painting line numbers for large text is slower when compared to small files
Now the Pseudo Code
FIRST_LINE_NUMBER = _textBox.GetFirstVisibleLineNumber();
LAST_LINE_NUMBER = _textBox.GetLastVisibleLineNUmber();
for(loop_from_first_to_last_line_number)
{
Y = _textBox.GetYPositionOfLineNumber(current_line_number);
graphics_paint_line_number(current_line_number, Y);
}
I am using GetCharIndexFromPosition and loop through the RichTextBox.Lines to find the line number in both the functions which get the line numbers. To get Y position I am using GetPositionFromCharIndex to get the Point struct.
All the above RichTextBox methods seem to be of O(n), which eats up the performance. (Correct me if I am wrong.)
I have decided to use a binary-tree to store the line numbers to improve the search perfomance when searching for line number by char index. I have an idea of getting a data-structure which takes O(n) construction time, O(nlgn) worst-case-update, and O(lgn) search.
Is this approach worth the effort?
Is there any other approach to solve the problem? If required I am ready to write the control from scratch, I just want it to be light-weight and fast.

Before deciding on the best way forward, we need to make sure we understand the bottleneck.
First of all, it is important to know how RichTextbox (which I assume you are using as you mentioned it) handles the large files. So I would recommend to remove all line printing stuff and see how it performs with large text. If it is poor, there is your problem.
Second step would be to put some profiling statements or just use a profiler (one comes with the VS 2010) to find the bottleneck. It might turn out to be the method for finding the line number, or something else.
At this point, I would only suggest more investigation. If you have finished the investigation and have more info, update your question and I will get back to you accordingly.

Space-efficient in-memory structure for sorted text supporting prefix searches

I have a problem: I need space-efficient lookup of file-system data based of file path prefix. Prefix searching of sorted text, in other words. Use a trie, you say, and I thought the same thing. Trouble is, tries are not space-efficient enough, not without other tricks.
I have a fair amount of data:
about 450M in a plain-text Unix-format listing on disk
about 8 million lines
gzip default compresses to 31M
bzip2 default compresses to 21M
I don't want to be eating anywhere close to 450M in memory. At this point I'd be happy to be using somewhere around 100M, since there's lots of redundancy in the form of prefixes.
I'm using C# for this job, and a straightforward implementation of a trie will still require one leaf node for every line in the file. Given that every leaf node will require some kind of reference to the final chunk of text (32 bits, say an index into an array of string data to minimize string duplication), and CLR object overhead is 8 bytes (verified using windbg / SOS), I'll be spending >96,000,000 bytes in structural overhead with no text storage at all.
Let's look at some of the statistical attributes of the data. When stuffed in a trie:
total unique "chunks" of text about 1.1 million
total unique chunks about 16M on disk in a text file
average chunk length is 5.5 characters, max 136
when not taking into account duplicates, about 52 million characters total in chunks
Internal trie nodes average about 6.5 children with a max of 44
about 1.8M interior nodes.
Excess rates of leaf creation is about 15%, excess interior node creation is 22% - by excess creation, I mean leaves and interior nodes created during trie construction but not in the final trie as a proportion of the final number of nodes of each type.
Here's a heap analysis from SOS, indicating where the most memory is getting used:
[MT ]--[Count]----[ Size]-[Class ]
03563150 11 1584 System.Collections.Hashtable+bucket[]
03561630 24 4636 System.Char[]
03563470 8 6000 System.Byte[]
00193558 425 74788 Free
00984ac8 14457 462624 MiniList`1+<GetEnumerator>d__0[[StringTrie+Node]]
03562b9c 6 11573372 System.Int32[]
*009835a0 1456066 23297056 StringTrie+InteriorNode
035576dc 1 46292000 Dictionary`2+Entry[[String],[Int32]][]
*035341d0 1456085 69730164 System.Object[]
*03560a00 1747257 80435032 System.String
*00983a54 8052746 96632952 StringTrie+LeafNode
The Dictionary<string,int> is being used to map string chunks to indexes into a List<string>, and can be discarded after trie construction, though GC doesn't seem to be removing it (a couple of explicit collections were done before this dump) - !gcroot in SOS doesn't indicate any roots, but I anticipate that a later GC would free it.
MiniList<T> is a replacement for List<T> using a precisely-sized (i.e. linear growth, O(n^2) addition performance) T[] to avoid space wastage; it's a value type and is used by InteriorNode to track children. This T[] is added to the System.Object[] pile.
So, if I tot up the "interesting" items (marked with *), I get about 270M, which is better than raw text on disk, but still not close enough to my goal. I figured that .NET object overhead was too much, and created a new "slim" trie, using just value-type arrays to store data:
class SlimTrie
{
byte[] _stringData; // UTF8-encoded, 7-bit-encoded-length prefixed string data
// indexed by _interiorChildIndex[n].._interiorChildIndex[n]+_interiorChildCount[n]
// Indexes interior_node_index if negative (bitwise complement),
// leaf_node_group if positive.
int[] _interiorChildren;
// The interior_node_index group - all arrays use same index.
byte[] _interiorChildCount;
int[] _interiorChildIndex; // indexes _interiorChildren
int[] _interiorChunk; // indexes _stringData
// The leaf_node_index group.
int[] _leafNodes; // indexes _stringData
// ...
}
This structure has brought down the amount of data to 139M, and is still an efficiently traversable trie for read-only operations. And because it's so simple, I can trivially save it to disk and restore it to avoid the cost of recreating the trie every time.
So, any suggestions for more efficient structures for prefix search than trie? Alternative approaches I should consider?

Since there are only 1.1 million chunks, you can index a chunk using 24 bits instead of 32 bits and save space there.
You could also compress the chunks. Perhaps Huffman coding is a good choice. I would also try the following strategy: instead of using a character as a symbol to encode, you should encode character transitions. So instead of looking at the probability of a character appearing, look at the probability of the transition in a Markov chain where the state is the current character.

You can find a scientific paper connected to your problem here (citation of the authors: "Experiments show that our index supports fast queries within a space occupancy that is close to the one achievable by compressing the string dictionary via gzip, bzip or ppmdi." - but unfortunately the paper is payment only). I'm not sure how difficult these ideas are to implement. The authors of this paper have a website where you can find also implementations (under "Index Collection") of various compressed index algorithms.
If you want to go on with your approach, make sure to check out the websites about Crit-bit trees and Radix tree.

Off-the-wall idea: Instead of a trie a hash table. You'd have in memory just the hash and the string data, perhaps compressed.
Or can you afford one page read? Only hash and file position in memory, retrieve the "page" with lines matching that hash, presumably small number of ordered lines, hence very quick to search in the event of collisions.

Reading a large file into a Dictionary

I have a 1GB file containing pairs of string and long.
What's the best way of reading it into a Dictionary, and how much memory would you say it requires?
File has 62 million rows.
I've managed to read it using 5.5GB of ram.
Say 22 bytes overhead per Dictionary entry, that's 1.5GB.
long is 8 bytes, that's 500MB.
Average string length is 15 chars, each char 2 bytes, that's 2GB.
Total is about 4GB, where does the extra 1.5 GB go to?
The initial Dictionary allocation takes 256MB.
I've noticed that each 10 million rows I read, consume about 580MB, which fits quite nicely with the above calculation, but somewhere around the 6000th line, memory usage grows from 260MB to 1.7GB, that's my missing 1.5GB, where does it go?
Thanks.

It's important to understand what's happening when you populate a Hashtable. (The Dictionary uses a Hashtable as its underlying data structure.)
When you create a new Hashtable, .NET makes an array containing 11 buckets, which are linked lists of dictionary entries. When you add an entry, its key gets hashed, the hash code gets mapped on to one of the 11 buckets, and the entry (key + value + hash code) gets appended to the linked list.
At a certain point (and this depends on the load factor used when the Hashtable is first constructed), the Hashtable determines, during an Add operation, that it's encountering too many collisions, and that the initial 11 buckets aren't enough. So it creates a new array of buckets that's twice the size of the old one (not exactly; the number of buckets is always prime), and then populates the new table from the old one.
So there are two things that come into play in terms of memory utilization.
The first is that, every so often, the Hashtable needs to use twice as much memory as it's presently using, so that it can copy the table during resizing. So if you've got a Hashtable that's using 1.8GB of memory and it needs to be resized, it's briefly going to need to use 3.6GB, and, well, now you have a problem.
The second is that every hash table entry has about 12 bytes of overhead: pointers to the key, the value, and the next entry in the list, plus the hash code. For most uses, that overhead is insignificant, but if you're building a Hashtable with 100 million entries in it, well, that's about 1.2GB of overhead.
You can overcome the first problem by using the overload of the Dictionary's constructor that lets you provide an initial capacity. If you specify a capacity big enough to hold all of the entries you're going to be added, the Hashtable won't need to be rebuilt while you're populating it. There's pretty much nothing you can do about the second.

Everyone here seems to be in agreement that the best way to handle this is to read only a portion of the file into memory at a time. Speed, of course, is determined by which portion is in memory and what parts must be read from disk when a particular piece of information is needed.
There is a simple method to handle deciding what's the best parts to keep in memory:
Put the data into a database.
A real one, like MSSQL Express, or MySql or Oracle XE (all are free).
Databases cache the most commonly used information, so it's just like reading from memory. And they give you a single access method for in-memory or on-disk data.

Maybe you can convert that 1 GB file into a SQLite database with two columns key and value. Then create an index on key column. After that you can query that database to get the values of the keys you provided.

Thinking about this, I'm wondering why you'd need to do it... (I know, I know... I shouldn't wonder why, but hear me out...)
The main problem is that there is a huge amount of data that needs to be presumably accessed quickly... The question is, will it essentially be random access, or is there some pattern that can be exploited to predict accesses?
In any case, I would implement this as a sliding cache. E.g. I would load as much as feasibly possible into memory to start with (with the selection of what to load based as much on my expected access pattern as possible) and then keep track of accesses to elements by time last accessed.
If I hit something that wasn't in the cache, then it would be loaded and replace the oldest item in the cache.
This would result in the most commonly used stuff being accessible in memory, but would incur additional work for cache misses.
In any case, without knowing a little more about the problem, this is merely a 'general solution'.
It may be that just keeping it in a local instance of a sql db would be sufficient :)

You'll need to specify the file format, but if it's just something like name=value, I'd do:
Dictionary<string,long> dictionary = new Dictionary<string,long>();
using (TextReader reader = File.OpenText(filename))
{
string line;
while ((line = reader.ReadLine()) != null)
{
string[] bits = line.Split('=');
// Error checking would go here
long value = long.Parse(bits[1]);
dictionary[bits[0]] = value;
}
}
Now, if that doesn't work we'll need to know more about the file - how many lines are there, etc?
Are you using 64 bit Windows? (If not, you won't be able to use more than 3GB per process anyway, IIRC.)
The amount of memory required will depend on the length of the strings, number of entries etc.

I am not familiar with C#, but if you're having memory problems you might need to roll your own memory container for this task.
Since you want to store it in a dict, I assume you need it for fast lookup?
You have not clarified which one should be the key, though.
Let's hope you want to use the long values for keys. Then try this:
Allocate a buffer that's as big as the file. Read the file into that buffer.
Then create a dictionary with the long values (32 bit values, I guess?) as keys, with their values being a 32 bit value as well.
Now browse the data in the buffer like this:
Find the next key-value pair. Calculate the offset of its value in the buffer. Now add this information to the dictionary, with the long as the key and the offset as its value.
That way, you end up with a dictionary which might take maybe 10-20 bytes per record, and one larger buffer which holds all your text data.
At least with C++, this would be a rather memory-efficient way, I think.

Can you convert the 1G file into a more efficient indexed format, but leave it as a file on disk? Then you can access it as needed and do efficient lookups.
Perhaps you can memory map the contents of this (more efficient format) file, then have minimum ram usage and demand-loading, which may be a good trade-off between accessing the file directly on disc all the time and loading the whole thing into a big byte array.

Loading a 1 GB file in memory at once doesn't sound like a good idea to me. I'd virtualize the access to the file by loading it in smaller chunks only when the specific chunk is needed. Of course, it'll be slower than having the whole file in memory, but 1 GB is a real mastodon...

Don't read 1GB of file into the memory even though you got 8 GB of physical RAM, you can still have so many problems. -based on personal experience-
I don't know what you need to do but find a workaround and read partially and process. If it doesn't work you then consider using a database.

If you choose to use a database, you might be better served by a dbm-style tool, like Berkeley DB for .NET. They are specifically designed to represent disk-based hashtables.
Alternatively you may roll your own solution using some database techniques.
Suppose your original data file looks like this (dots indicate that string lengths vary):
[key2][value2...][key1][value1..][key3][value3....]
Split it into index file and values file.
Values file:
[value1..][value2...][value3....]
Index file:
[key1][value1-offset]
[key2][value2-offset]
[key3][value3-offset]
Records in index file are fixed-size key->value-offset pairs and are ordered by key.
Strings in values file are also ordered by key.
To get a value for key(N) you would binary-search for key(N) record in index, then read string from values file starting at value(N)-offset and ending before value(N+1)-offset.
Index file can be read into in-memory array of structs (less overhead and much more predictable memory consumption than Dictionary), or you can do the search directly on disk.