I recently put up a post on my blog about some of the new concurrent collections in .NET 4.0, and I noticed that a lot of people were being sent by Google to those posts who were only searching for System.Collections. I figured that maybe people could use a similar overview of the collections available to them in the System.Collections.Generic namespace, since it seems to me that no one uses anything other than List and Dictionary. So, in this post, I am going to take a look at a few of those collections, and explain exactly why you would want to use them.

Keep in mind as you read through this list that you shouldn’t just start switching out collection types in code that you already have working. If something is working and performing properly for you, it is almost always better to take the easier route until you have proof that the easy approach does not work for you.

### System.Collections.Generic.List<T>

The first collection that we are going to look at is List<T>. Like I said before, this collection seems to be the fallback, and with good reason. It is unsorted (but supports sorting), items can be added, removed, or inserted at a specific index. It also has indexed access, meaning that items can be accessed directly using a numeric index. You can add Ranges, perform binary searches, perform sequential searches, built in sorting, get the count of items, check for items within it, pass a delegate to it to perform actions, etc… It really is the Swiss Army knife of collections.

In computer science we know that there are always trade-offs. Nothing comes for free, and in order to have the ability to perform operations like indexed lookups, we need to have some kind of data structure which will support this ability. Because of this, List<T> is internally implemented as a simple array. And as we add more and more items to the list, if the array fills up, then an entirely new array is allocated and the items in the current array are copied over into the new array, and the new item is added:

The default length of the array inside of a List<T> is 4, and it only doubles in size each time it fills up (so it goes 4, 8, 16, 32….), so if you know that you have a really large number of items to pass to the list, then you should probably consider using the constructor that allows you to tell the List<T> what size to start off at. It will really keep you from having to copy a lot of data around.

Most of the operations are really straight forward, and looking at the operations below, you can probably see why List<T> is the generic fallback for any list of unknown length, it provides good performance across a very wide variety of operations.

Examples of most common operations:

var list = new List<string>(); list.Add("one"); list.Add("two"); list.Add("three"); list.AddRange(new[] {"four", "five", "six"}); //insert item at index 2 list.Insert(2, "seven"); //removes first occurrence of "seven" list.Remove("seven"); //returns true //removes all occurrences of "seven" list.RemoveAll(s => s == "seven"); //checks is list contains two list.Contains("two"); //returns true //finds the first item which matches predicate string value = list.Find(s => s == "three"); //finds all items which match predicate List<string> values = list.FindAll(s => s == "three"); //gets a range of the list List<string> range = list.GetRange(2, 3); //in-place sort of list list.Sort(); //performs a binary search of a sorted list to find index of item int index = list.BinarySearch("three"); // must sort first! //removes all elements list.Clear();

Bottom Line: | |

Add | Very Fast – O(1) |

Inserts | Average – O(N) |

Lookup | Average – O(N) |

Sorted Lookup | Fast – O(log N) – must sort first |

Delete | Average – O(N) |

Versatility | High |

### System.Collections.Generic.SortedList<TKey,TValue>

SortedList is very similar to List, with one obvious difference, it is sorted. In order to add an item, you have to provide a sort key and a value, which are both stored. A binary search is performed across the list in order to find the right place to insert the item, and then the item is inserted into the array. In order to do this, the entire array has to be shifted in order to make room for the new item:

Due to this, inserting items into the SortedList is an expensive operation. But because the list is maintained as sorted, lookups are very cheap. Because you need both a key and a value, you must provide these on most operations. Since lookups are so cheap, if you need a list which will have heavy lookups with a lower number of inserts, then the SortedList may be a good option for you.

Example of most common operations:

var sortedList = new SortedList<int, string>(); sortedList.Add(1, "one"); sortedList.Add(2, "two"); sortedList.Add(3, "three"); sortedList.Add(4, "four"); // returns true sortedList.ContainsKey(1); // returns true sortedList.ContainsValue("two"); // get index of key sortedList.IndexOfKey(1); // get index of value sortedList.IndexOfValue("two"); // remove by key sortedList.Remove(3); // remove by index sortedList.RemoveAt(2); // lookup by key string value = sortedList[2]; // get value by index string value = sortedList.Values[2]; // get key by index int key = sortedList.Keys[2]; string value; if (sortedList.TryGetValue(4, out value)) //do something foreach (int key in sortedList.Keys) { Console.WriteLine(key); } foreach (string value in sortedList.Values) { Console.WriteLine(value); } //removes all elements sortedList.Clear();

Bottom Line: | |

Add | Average – O(N) |

Inserts | Average – O(N) |

Lookup | Average – O(N) |

Sorted Lookup | Fast – O(log N) -presorted |

Delete | Average – O(N) |

Versatility | Medium |

### System.Collections.Generic.Dictionary<TKey,TValue>

The next collection that we are going to look at is the Dictionary. The Dictionary class is an implementation of a dictionary data structure (sometimes referred to as an associative array or a map). Its operations look very similar to a SortedList, since it takes both a key and a value, but in reality they have very different performance characteristics.

The reason for this is that while a SortedList performs an insertions and lookups using a binary search, the Dictionary class actually uses a hash table as its backing store. This means that the data stored inside of the dictionary isn’t sorted, it is looked up by a hash value (meaning that the keys must also be unique). This allows for individual items to be looked up extremely quickly by hashing it, and then looking up its hash inside the hash table:

Because of this, finding a range of values would require you to iterate over all of the values in the dictionary and look for your values. It also means that the data stored in the Dictionary, when iterated over, comes back in an non-determinant order (currently this appears to be in inserted order, but don’t rely on that because it could change at any time) and not in a sorted order.

Example of most common operations:

var dictionary = new Dictionary<int, string>(); dictionary.Add(1, "one"); dictionary.Add(2, "two"); dictionary.Add(3, "three"); dictionary.Add(4, "four"); // returns true dictionary.ContainsKey(1); // returns true dictionary.ContainsValue("two"); // remove by key dictionary.Remove(3); // lookup by key string indexedValue = dictionary[4]; string value; if (dictionary.TryGetValue(4, out value)) //do something foreach (int key in dictionary.Keys) { } foreach (string value in dictionary.Values) { } //removes all elements dictionary.Clear();

Bottom Line: | |

Add | Average – O(1) |

Inserts | Not applicable, data isn’t in any order |

Lookup | Average – O(N) |

Indexed Lookup | Fast – O(1) |

Delete | Fast – O(1) |

Versatility | Low |

### System.Collections.Generic.SortedDictionary<TKey,TValue>

In all likelihood, you have probably never used a SortedDictionary. You might not even know what in the world a SortedDictionary is, or why it would be any different from a SortedList. You might also be thinking that it doesn’t really make any sense to have a sorted dictionary since the idea of a dictionary is to provide indexed lookup of items. So where does this leave the SortedDictionary?

Well, the SortedDictionary is actually quite a different beast. Internally it is implemented as a Red-black tree. Red-black trees are a much more complicated data structure than what we have looked at so far, so I’ll just give you a link to the Wikipedia page on Red-black trees. What is important to know about Red-black trees is that they have very good worst-case operations.

The biggest difference between this and a SortedList is that the SortedDictionary doesn’t allow indexed access. It does however have faster add, insertion, and deletion times. Also, because of the tree structure in which the data is stored, the SortedDictionary uses more memory than a SortedList.

Example of most common operations:

var sortedDictionary = new SortedDictionary<int, string>(); sortedDictionary.Add(1, "one"); sortedDictionary.Add(2, "two"); sortedDictionary.Add(3, "three"); sortedDictionary.Add(4, "four"); // returns true sortedDictionary.ContainsKey(1); // returns true sortedDictionary.ContainsValue("two"); // remove by key sortedDictionary.Remove(3); // lookup by key string value = sortedDictionary[2]; string value; if (sortedDictionary.TryGetValue(4, out value)) //do something // sorted by key foreach (int key in sortedDictionary.Keys) { Console.WriteLine(key); } // sorted by key foreach (string value in sortedDictionary.Values) { Console.WriteLine(value); } //removes all elements sortedDictionary.Clear();

Bottom Line: | |

Add | Fast – O(log N) |

Inserts | Fast – O(log N) |

Lookup | Not Applicable |

Sorted Lookup | Fast – O(log N) |

Delete | Fast – O(log N) |

Versatility | Low |

### System.Collections.Generic.LinkedList<T>

The LinkedList class is an implementation of a doubly linked list data structure. A doubly linked list is a fairly simple data structure consisting of a bunch of nodes with pointers to the previous and next node:

The .Net Framework implementation also point the next and previous pointers on the first and last item to each other. Because of the way that this data structure works, it makes it very cheap to add and insert items, assuming that you are already in the place in the list where the data needs to be inserted. In order to insert a node, the pointers merely need to be redirected:

Also, since pointers only exist between nodes, there is no indexed access and it in order to find items in the list, each item needs to be iterated through.

The LinkedList may be a simple data structure, but it is an excellent data structure if you have a list which has a heavy amount of insertion and deletion within the list. It is especially useful if you are inserting ranges of data into the middle of a list.

Example of most common operations:

var linkedList = new LinkedList<string>(); // add node to front of list linkedList.AddFirst("one"); // add node to end of list var nodeThree = linkedList.AddLast("three"); // add node before given node linkedList.AddBefore(nodeThree, "two"); // add node after given node linkedList.AddAfter(nodeThree, "four"); // iterate list printing one, two, three, four foreach (string value in linkedList) { Console.WriteLine(value); } // find first node with value "two" var nodeTwo = linkedList.Find("two"); // find last node with value "three" nodeThree = linkedList.FindLast("three"); // get first node in list var firstNode = linkedList.First; // get last node in list var lastNode = linkedList.Last; // remove the first node with value "two" linkedList.Remove("two"); // remove first node in list linkedList.RemoveFirst(); // remove last node in list linkedList.RemoveLast(); // remove all items from list linkedList.Clear();

Bottom Line: | |

Add | Very Fast – O(1) |

Inserts | Very Fast – O(1) |

Lookup | Slow – O(N) |

Sorted Lookup | Not Applicable |

Delete | Very Fast – O(1) |

Versatility | Low |

### System.Collections.Generic.SortedSet<T>

SortedSet is new to .NET 4.0. It is actually the backing store for the SortedDictionary data type, but unlike the SortedDictionary there is only values and no keys. Also, the SortedSet exposes some set operations unlike the SortedDictionary. Since it is the backing store for the SortedDictionary it has the same performance characteristics.

The SortedSet represents a set abstract data structure. A set is a collection that contains unordered unique values. It is an abstract data structure because a set does not imply any particular implementation. This implementation uses a Red-black tree, which is a sorted data structure, in order to maintain the rules of the set. While technically a set does not have any order, this is a sorted set, and therefore if you iterate over all of the items within the set they will be in sorted order.

The SortedSet is best used when you are storing a unique set of values which need to be sorted and which might require ranged lookups, or other set based operations (intersects, unions, etc…).

Example of most common operations:

var sortedSet1 = new SortedSet<string>(); sortedSet1.Add("one"); sortedSet1.Add("two"); sortedSet1.Add("three"); sortedSet1.Add("four"); var sortedSet2 = new SortedSet<string>(); sortedSet2.Add("two"); sortedSet2.Add("four"); sortedSet1.Contains("three"); // sortedSet1 now contains one, three sortedSet1.ExceptWith(sortedSet2); // sortedSet1 now contains two, four sortedSet1.IntersectWith(sortedSet2); // a subset is a set whose elements are all in // another set sortedSet1.IsSubsetOf(sortedSet2); // a superset is a set which contains all elements // in another set sortedSet1.IsSupersetOf(sortedSet2); // a proper subset is a subset which is not equal sortedSet1.IsProperSubsetOf(sortedSet2); // a proper superset is a superset which is not equal sortedSet1.IsProperSupersetOf(sortedSet2); // returns bool if both sets contain any of the same items sortedSet1.Overlaps(sortedSet2); // removes the element with value "two" sortedSet1.Remove("two"); // removes all elements that match the predicate sortedSet1.RemoveWhere(v => v == "three"); // returns true if both sets are identical sortedSet1.SetEquals(sortedSet2); // sortedSet1 now contains one, three // only items which are in one or the other, not both sortedSet1.SymmetricExceptWith(sortedSet2); // sortedSet1 now contains one, two, three, four // all items in both sets sortedSet1.UnionWith(sortedSet2); // returns all items between the given values SortedSet<string> result = sortedSet1.GetViewBetween("two", "four"); // elements are in sorted order foreach (string value in sortedSet1) { } // remove all items in both sets sortedSet1.Clear();

Bottom Line: | |

Add | Fast – O(log N) |

Inserts | Fast – O(log N) |

Lookup | Not Applicable |

Sorted Lookup | Fast – O(log N) |

Delete | Fast – O(log N) |

Versatility | Medium |

### System.Collections.Generic.HashSet<T>

HashSet is another set data structure that uses hashes in order to maintain a unique set of items. Internally the items are stored in an array, which requires expansion when the array fills up. The hash table size is increased to each subsequence prime number in size as the buckets fill up (this is done because some hash functions will only evenly hash values if the number of available buckets is prime).

Because hashes are used as a backing store, the data is unsorted, which means that finding ranges is an O(N) operation since all data would need to be searched through. At the same time, hashes are very fast with insertions and lookups and so performing set operations such as unions, intersects, etc… could be much faster on a HashSet than a SortedSet. Keep in mind that inserting can cause the array to be expanded, which would cause the operation to be much more expensive.

Since HashSet and SortedSet both implement ISet, the only real reason to use SortedSet is if you have data that you might need to look up in ranges or if you have data which cannot be easily hashed. If neither of those applies to your situation, then the HashSet will probably be the faster of the two sets to use, and it will use less memory.

Example of most common operations:

var hashSet1 = new HashSet<string>(); hashSet1.Add("one"); hashSet1.Add("two"); hashSet1.Add("three"); hashSet1.Add("four"); var hashSet2 = new HashSet<string>(); hashSet2.Add("two"); hashSet2.Add("four"); hashSet1.Contains("three"); // sortedSet1 now contains one, three hashSet1.ExceptWith(hashSet2); // sortedSet1 now contains two, four hashSet1.IntersectWith(hashSet2); // a subset is a set whose elements are all in // another set hashSet1.IsSubsetOf(hashSet2); // a superset is a set which contains all elements // in another set hashSet1.IsSupersetOf(hashSet2); // a proper subset is a subset which is not equal hashSet1.IsProperSubsetOf(hashSet2); // a proper superset is a superset which is not equal hashSet1.IsProperSupersetOf(hashSet2); // returns bool if both sets contain any of the same items hashSet1.Overlaps(hashSet2); // removes the element with value "two" hashSet1.Remove("two"); // removes all elements that match the predicate hashSet1.RemoveWhere(v => v == "three"); // returns true if both sets are identical hashSet1.SetEquals(hashSet2); // sortedSet1 now contains one, three // only items which are in one or the other, not both hashSet1.SymmetricExceptWith(hashSet2); // sortedSet1 now contains one, two, three, four // all items in both sets hashSet1.UnionWith(hashSet2); // elements are no in any guaranteed order foreach (string value in hashSet1) { } // remove all items in both sets hashSet1.Clear();

Bottom Line: | |

Add | Very Fast – O(1) |

Inserts | Not Applicable |

Lookup | Average – O(N) |

Indexed Lookup | Very Fast – O(1) |

Delete | Very Fast – O(1) |

Versatility | Low |

### System.Collections.Generic.Queue<T>

The Queue is a special purpose data structure which implements a queue data structure. A queue is a FIFO (First In First Out) collection with two main operations: Enqueue and Dequeue. Items are placed into one end of the queue, and then are taken off the other end:

The Queue is really only useful if you need the exact semantics that the queue data structure provides. One reason might be to place work items into the queue and then be able to take the off and process them in order. If you need this kind of functionality, then the Queue is for you. In .NET 4.0 a ConcurrentQueue was also implemented which allows for items to be enqueued and dequeued on multiple threads.

Example of most common operations:

var queue = new Queue<string>(); // pushes an item on the end of the queue queue.Enqueue("one"); queue.Enqueue("two"); queue.Enqueue("three"); queue.Enqueue("four"); // returns true queue.Contains("four"); // takes the item off the front of the queue string item = queue.Dequeue(); // gets the item off the front of the queue // without removing it string peekItem = queue.Peek(); // remove all items from the queue queue.Clear();

Bottom Line: | |

Add | Very Fast – O(1) |

Inserts | Not Applicable |

Lookup | Average – O(N) |

Sorted Lookup | Not Applicable |

Delete | Very Fast – O(1) – Only from end. |

Versatility | Very Low |

### System.Collections.Generic.Stack<T>

The Stack is another special purpose data structure which implements a stack data structure. A stack is a LIFO (Last In First Out) collection with two operations: Push and Pop. Items are placed on the top of the queue and then are taken off the top:

Again, the Stack is a special purpose data structure that has applicability only within a subset of problems. In .NET 4.0 a ConcurrentStack was implemented which allows you to push and pop items on multiple threads without locking.

Example of most common operations:

var stack = new Stack<string>(); // puts an item on the top of the stack stack.Push("one"); stack.Push("two"); stack.Push("three"); stack.Push("four"); // removes an item from the top of the stack string item = stack.Pop(); // gets the item on the top of the stack // without removing it string peekedItem = stack.Peek(); // returns true if item is in stack stack.Contains("two"); // removes all items from the stack stack.Clear();

Bottom Line: | |

Add | Very Fast – O(1) |

Inserts | Not Applicable |

Lookup | Average – O(N) |

Sorted Lookup | Not Applicable |

Delete | Very Fast – O(1) – Only from end. |

Versatility | Very Low |

### Summary

In addition to the collections that we looked at here, in .NET 4.0 they have also added a new namespace called System.Collections.Concurrent which contains a many thread safe collections for you to use, such as ConcurrentQueue, ConcurrentBag, ConcurrentStack, and BlockingCollection. If you want to find even more useful collections, I recommend that you go check out Power Collections for .NET. I hope you enjoyed this overview of the classes within the System.Collections.Generic namespace.