We Keep Coding

HashMap. Create entry (int, string[]) and push into value immediately

I want to get all files of the same size in buckets according, size is a key.
Default behaviour would override the value whenever you associate it with an existing key. I want to push a value to string[] array whenever the same value is met.
1556 - "1.txt" - Entry added to the dictionary, 1.txt put to the string[]
1556 - "7.txt" - 7.txt pushed to the string[] associated with 1556
My current thought is to enumerate once through all files and create entries with keys and empty arrays in the dictionary:
foreach(var file in directory){
map[file.length] = new string[]/List<string>();
then enumerate second time retrieving array associated with current key:
foreach(var file in directory){
map[file.length].push(file.name);
}
Are there any better ways to do this?

As far as I understood, you want entries to consist of fileSize as keys,
fileNames array as value.
In that case, I'd suggest to use just one loop, like so:
foreach(var file in directory)
{
if (!map.ContainsKey(file.Length))
{
map.Add(file.Length, new List<string>());
}
map[file.Length].Add(file.Name);
}
Edit: removed space for each file name.

What is the fastest way to populate a C# DataTable with data stored on columns?

I have a DataTable object that I need to fill based on data stored in a stream of columns - i.e. the stream initially contains the schema of the DataTable, and subsequently, values that should go into it organised by column.
At present, I'm taking the rather naive approach of
Create enough empty rows to hold all data values.
Fill those rows per cell.
The result is a per-cell iteration, which is not especially quick to say the least.
That is:
// Create rows first...
// Then populate...
foreach (var col in table.Columns.Cast<DataColumn>)
{
List<object> values = GetValuesfromStream(theStream);
// Actual method has some DBNull checking here, but should
// be immaterial to any solution.
for (var i=0; i<values.Count; i++)
table.Rows[i][col] = values[i];
}
My guess is the backing DataStorage items for each column aren't expanding as the rows are added, but as values are added to each column, but I'm far from certain. Any tips for loading this kind of data.
NB that loading all lists first and then reading in by row is probably not sensible - this approach is being taken in the first place to mitigate potential out of memory exceptions that tend to result when serializing huge DataTable objects, so grabbing a clone of the entire data grid and reading it in would probably just move the problem elsewhere. There's definitely enough memory for the original table and another column of values, but there probably isn't for two copies of the DataTable.

Whilst I haven't found a way to avoid iterating cells, as per the comments above, I've found that writing to DataRow items that have already been added to the table turns out to be a bad idea, and was responsible for the vast majority of the slowdown I observed.
The final approach I used ended up looking something like this:
List<DataRow> rows = null;
// Start population...
var cols = table.Columns.Cast<DataColumn>.Where(c => string.IsNullOrEmpty(c.Expression));
foreach (var col in cols)
{
List<object> values = GetValuesfromStream(theStream);
// Create rows first if required.
if (rows == null)
{
rows = new List<DataRow>();
for (var i=0; i<values.Count; i++)
rows.Add(table.NewRow());
}
// Actual method has some DBNull checking here, but should
// be immaterial to any solution.
for (var i=0; i<values.Count; i++)
rows[i][col] = values[i];
}
rows.ForEach(r => table.Rows.Add(r));
This approach addresses two problems:
If you try to add an empty DataRow to a table that has null-restrictions or similar, then you'll get an error. This approach ensures all the data is there before it's added, which should address most such issues (although I haven't had need to check how it works with auto-incrementing PK columns).
Where expressions are involved, these are evaluated when row state changes for a row that has been added to a table. Consequently, where before I had re-calculation of all expressions taking place every time a value was added to a cell (expensive and pointless), now all calculation takes place just once after all base data has been added.
There may of course be other complications with writing to a table that I've not yet encountered because the tables I am making use of don't use those features of the DataTable class/model. But for simple cases, this works well.

Abysmal performance with Dictionary that has a KeyValuePair as key (C# .NET)

In an app that I'm writing I have two potentially large sets of data I need to map against each other. One is a List returned from a web service and one is a DataTable. I need to take the ANSI (or ISO) number for each item in the list and find the row of the DataTable containing that ANSI number and then do stuff with it.
Since DataTable.Select is pretty slow and I would have to do that for each item in the List, I experimented with faster alternatives. Keep in mind that there is no database for the DataTable object. So I can't leverage any SQL capabilities or anything like that.
I thought the fastest way might be to create a dictionary with a KeyValuePair (A:Ansi number or I:Iso number) and use that as a key. The value would be the rest of the row. Creating that dictionary would obviously take a little processing time, but then I could leverage the extremely fast search times of the dictionary to find each row I need and then add the rows back to the table afterwards. So within the foreach loop going for the list I would only have a complexity of O(1) with the dictionary instead of O(n) or whatever DataTable.Select has.
To my surprise it turned out the dictionary was incredibly slow. I couldn't figure out why until I found out that using a string (just ANSI number) instead of a KeyValuePair increased the performance dramatically. I'm talking hundreds of times faster. How on Earth is that possible? Here is how I test:
I generate a List that simulates the output from the web service. I create a dictionary based on that list with a key (either string or KeyValuePair) and the DataRow as value. I go through a foreach loop for that list and search each item in that list in my dictionary and then assign a value to the DataRow that is returned. That's it.
If I use KeyValuePair as a key to access the dictionary it takes seconds for 1,000 items, if I modify the dictionary to take only a string as a key it takes milliseconds for 10,000 items. FYI: I designed the test so that there would always be hits, so all keys are always found.
Here is the block of code for which I'm measuring the time:
foreach(ProductList.Products item in pList.Output.Products)
{
//KeyValuePair<string, string> kv = new KeyValuePair<string, string>("A", item.Ansi);
DataRow row = dict[item.Ansi];
for (int i = 0; i < 10; i++)
{
row["Material"] = item.Material + "a"; //Do stuff just for debugging
}
hits++;
}
So how on Earth is it possible that the execution time suddenly becomes hundreds of times longer if I use a Dictionary(KeyValuePair,DataRow) instead of Dictionary(String,DataRow)?

KeyValuePair<TKey, TValue> doesn't implement the GetHashCode() method. This means that the only way to meaningfuly organize the dictionary is gone, and you're left with an inefficient linear search.
This shouldn't be surprising, since it's not what KeyValuePair<TKey, TValue> is designed for - it's an internal structure used by the dictionary, not a key. There's no requirement for .NET objects to be useful keys, and returning 0 from all GetHashCode() calls is perfectly valid.
If you don't want to use your own structures, use Tuple. But I would really just create my own structure for any kind of persistence, really.
As a side-note, DataTable.Select is actually pretty fast for what it's designed for - filtering data for output. It's not really designed for being called hundreds of times in a loop, though - the overhead dominates. This assumes that you have proper indices, of course. In your case, I think the indices are regenerated every time you call Select, which is a bit slow :)

You are probably getting a high number of hash collisions with key value pair. You can test with GetHashCode.
The link below is tuple but I highly suspect you have the same thing going on with key value pair. gethashcode-high-rate-of-duplicates I would mark as a duplicate but you many have something else going on.
In this link Microsoft recommends not using value types for key. GetHashCode for KVP is inherited from value type.

Simplifying complexity in for a table object structure

I have an object structure that is mimicking the properties of an excel table. So i have a table object containing properties such as title, header row object and body row objects. Within the header row and each body row object, i have a cell object containing info on each cell per row. I am looking for a more efficient way to store this table structure since in one of my uses for this object, i am printing its structure to screen. Currently, i am doing an O(n^2) complexity for printing each row for each cell:
foreach(var row in Table.Rows){
foreach(var cell in row.Cells){
Console.WriteLine(cell.ToString())
}
}
Is there a more efficient way of storing this structure to avoid the n^2? I ask this because this printing functionality exists in another n^2 loop. Basically i have a list of tables titles and a list of tables. I need to find those tables whose titles are in the title list. Then for each of those tables, i need to print their rows and the cells in each row. Can any part of this operation be optimized by using a different data structure for storage perhaps? Im not sure how exactly they work but i have heard of hashing and dictionary?
Thanks

Since you are looking for tables with specific titles, you could use a dictionary to store the tables by title
Dictionary<string,Table> tablesByTitle = new Dictionary<string,Table>();
tablesByTitle.Add(table.Title, table);
...
table = tablesByTitle["SomeTableTitle"];
This would make finding a table an O(1) operation. Finding n tables would be an O(n) operation.
Printing the tables then of cause depends on the number of rows and columns. There is nothing, which can change that.
UPDATE:
string tablesFromGuiElement = "Employees;Companies;Addresses";
string[] selectedTables = tablesFromGuiElement.Split(';');
foreach (string title in selectedTables) {
Table tbl = tablesByTitle[title];
PrintTable(tbl);
}

There isn't anything more efficient than an N^2 operation for outputting an NxN matrix of values. Worst-case, you will always be doing this.
Now, if instead of storing the values in a multidimensional collection that defines the graphical relationship of rows and columns, you put them in a one-dimensional collection and included the row-column information with each cell, then you would only need to iterate through the cells that had values. Worst-case is still N^2 for a table of N rows and N columns that is fully populated (the one-dimensional array, though linear to enumerate, will have N^2 items), but the best case would be that only one cell in that table is populated (or none are) which would be constant-time.

This answer applies to the, printing the table part, but the question was extended.
for the getting the table part, see the other answer.
No, there is not.
Unless perhaps your values follow some predictable distribution, then you could use a function of x and y and store no data at all, or maybe a seed and a function.
You could cache the print output in a string or StringBuider if you require it multiple times.
If there is enough data I guess you might apply some compression algorithm but I wouldn't say that was simpler or more efficient.

The right data structure to use for an Excel clone

Let say I'm working on an Excel clone in C#.
My grid is represented as follows:
private struct CellValue
{
private int column;
private int row;
private string text;
}
private List<CellValue> cellValues = new List<CellValue>();
Each time user add a text, I just package it as CellValue and add it into cellValues. Given a CellValue type, I can determine its row and column in O(1) time, which is great. However, given a column and a row, I need to loop through the entire cellValues to find which text is in that column and row, which is terribly slow. Also, given a text, I too need to loop through the entire thing. Is there any data structure where I can achive all 3 task in O(1) time?
Updated:
Looking through some of the answers, I don't think I had found one that I like. Can I:
Not keeping more than 2 copies of CellValue, in order to avoid sync-ing them. In C world I would have made nice use of pointers.
Rows and Columns can be dynamically added (Unlike Excel).

I would opt for a sparse array (a linked list of linked lists) to give maximum flexibility with minimum storage.
In this example, you have a linked list of rows with each element pointing to a linked list of cells in that row (you could reverse the cells and rows depending on your needs).
|
V
+-+ +---+ +---+
|1| -> |1.1| ----------> |1.3| -:
+-+ +---+ +---+
|
V
+-+ +---+
|7| ----------> |7.2| -:
+-+ +---+
|
=
Each row element has the row number in it and each cell element has a pointer to its row element, so that getting the row number from a cell is O(1).
Similarly, each cell element has its column number, making that O(1) as well.
There's no easy way to get O(1) for finding immediately the cell at a given row/column but a sparse array is as fast as it's going to get unless you pre-allocate information for every possible cell so that you can do index lookups on an array - this would be very wasteful in terms of storage.
One thing you could do is make one dimension non-sparse, such as making the columns the primary array (rather than linked list) and limiting them to 1,000 - this would make the column lookup indexed (fast), then a search on the sparse rows.
I don't think you can ever get O(1) for a text lookup simply because text can be duplicated in multiple cells (unlike row/column). I still believe the sparse array will be the fastest way to search for text, unless you maintain a sorted index of all text values in another array (again, that can make it faster but at the expense of copious amounts of memory).

I think you should use one of the indexed collections to make it work reasonably fast, the perfect one is the KeyedCollection
You need to create your own collection by extending this class. This way your object will still contain row and column (so you will not loose anything), but you will be able to search for them. Probably you will have to create a class encapsulating (row, column) and make it the key (so make it immutable and override equals and get hash code)

I'd create
Collection<Collection<CellValue>> rowCellValues = new Collection<Collection<CellValue>>();
and
Collection<Collection<CellValue>> columnCellValues = new Collection<Collection<CellValue>>();
The outer collection has one entry for each row or column, indexed by the row or column number, the inner collection has all the cells in that row or column. These collections should be populated as part of the process that creates new CellValue objects.
rowCellValues[newCellValue.Row].Add(newCellValue);
columnCellValues[newCellValue.Column].Add(newCellValue);

This smells of premature optimization.
That said, there's a few features of excel that are important in choosing a good structure.
First is that excel uses the cells in a moderately non-linear fashion. The process of resolving formulas involves traversing the spreadsheets in effectively random order. The structure will need a mechanism of easily looking up values of random keys cheaply, marking them dirty, resolved, or unresolvable due to circular reference. It will also need some way to know when there are no more unresolved cells left, so that it can stop working. Any solution that involves a linked list is probably sub-optimal for this, since they would require a linear scan to get those cells.
Another issue is that excel displays a range of cells at one time. This may seem trivial, and to a large extent it is, but It will certainly be ideal if the app can pull all of the data needed to draw a range of cells in one shot. part of this may be keeping track of the display height and width of the rows and columns, so that the display system can iterate over the range until the desired width and height of cells has been collected. The need to iterate in this manner may preclude the use of a hashing strategy for sparse storage of cells.
On top of that, there are some weaknesses of the representational model of spreadsheets that could be addressed much more effectively by taking a slightly different approach.
For example, column aggregates are sort of clunky. A column total is easy enough to implement in excel, but it has a sort of magic behavior that works most of the time but not all of the time. For instance, if you add a row into the aggregated area, further calculations on that aggregate may continue to work, or not, depending on how you added it. If you copy and insert a row (and replace the values) everything works fine, but if you cut and paste the cells one row down, things don't work out so well.

Given that the data is 2-dimensional, I would have a 2D array to hold it in.

Well, you could store them in three Dictionaries: two Dictionary<int,CellValue> objects for rows and columns, and one Dictionary<string,CellValue> for text. You'd have to keep all three carefully in sync though.
I'm not sure that I wouldn't just go with a big two-dimensional array though...

If it's an exact clone, then an array-backed list of CellValue[256] arrays. Excel has 256 columns, but a growable number of rows.

If rows and columns can be added "dynamically", then you shouldn't store the row/column as an numeric attribute of the cell, but rather as a reference to a row or column object.
Example:
private struct CellValue
{
private List<CellValue> _column;
private List<CellValue> _row;
private string text;
public List<CellValue> column {
get { return _column; }
set {
if(_column!=null) { _column.Remove(this); }
_column = value;
_column.Add(this);
}
}
public List<CellValue> row {
get { return _row; }
set {
if(_row!=null) { _row.Remove(this); }
_row = value;
_row.Add(this);
}
}
}
private List<List<CellValue>> MyRows = new List<List<CellValue>>;
private List<List<CellValue>> MyColumns = new List<List<CellValue>>;
Each Row and Column object is implemented as a List of the CellValue objects. These are unordered--the order of the cells in a particular Row does not correspond to the Column index, and vice-versa.
Each sheet has a List of Rows and a list of Columns, in order of the sheet (shown above as MyRows and MyColumns).
This will allow you to rearrange and insert new rows and columns without looping through and updating any cells.
Deleting a row should loop through the cells on the row and delete them from their respective columns before deleting the row itself. And vice-versa for columns.
To find a particular Row and Column, find the appropriate Row and Column objects, then find the CellValue that they contain in common.
Example:
public CellValue GetCell(int rowIndex, int colIndex) {
List<CellValue> row = MyRows[rowIndex];
List<CellValue> col = MyColumns[colIndex];
return row.Intersect(col)[0];
}
(I'm a little fuzzy on these Extension methods in .NET 3.5, but this should be in the ballpark.)

If I recall correctly, there was an article about how Visicalc did it, maybe in Byte Magazine in the early 80s. I believe it was a sparse array of some sort. But I think there were links both up-and-down and left-and-right, so that any given cell had a pointer to the cell above it (however many cells away that may be), below it, to the left of it, and to the right of it.