How to Solve Design an Ordered Stream

To solve this problem, focus on designing an ordered stream system where values are returned based on increasing ID order. The stream allows values to be inserted at any time, and the output is given in chunks after each insertion. The critical part of the solution is maintaining the next expected ID and efficiently handling insertions with hash lookups and array scanning.

Problem Statement

You are given a stream of n (idKey, value) pairs arriving in an arbitrary order. Each pair contains an integer idKey between 1 and n and a string value. The idKey values are unique, and no pair repeats. Your task is to design a stream that returns the values in increasing order of their IDs, with each insertion returning a chunk of values. The concatenation of all chunks must result in a sorted list of values based on their IDs.

Implement the OrderedStream class with the following operations: the constructor OrderedStream(n) initializes the stream with a capacity of n, and insert(id, value) inserts a value at the given id and returns all the values from the stream whose IDs are greater than or equal to the current id, in order. The function insert is called exactly n times, once for each value.

Examples

Example 1

Input: See original problem statement.

Output: See original problem statement.

Input ["OrderedStream", "insert", "insert", "insert", "insert", "insert"] [[5], [3, "ccccc"], [1, "aaaaa"], [2, "bbbbb"], [5, "eeeee"], [4, "ddddd"]] Output [null, [], ["aaaaa"], ["bbbbb", "ccccc"], [], ["ddddd", "eeeee"]]

Explanation // Note that the values ordered by ID is ["aaaaa", "bbbbb", "ccccc", "ddddd", "eeeee"]. OrderedStream os = new OrderedStream(5); os.insert(3, "ccccc"); // Inserts (3, "ccccc"), returns []. os.insert(1, "aaaaa"); // Inserts (1, "aaaaa"), returns ["aaaaa"]. os.insert(2, "bbbbb"); // Inserts (2, "bbbbb"), returns ["bbbbb", "ccccc"]. os.insert(5, "eeeee"); // Inserts (5, "eeeee"), returns []. os.insert(4, "ddddd"); // Inserts (4, "ddddd"), returns ["ddddd", "eeeee"]. // Concatentating all the chunks returned: // [] + ["aaaaa"] + ["bbbbb", "ccccc"] + [] + ["ddddd", "eeeee"] = ["aaaaa", "bbbbb", "ccccc", "ddddd", "eeeee"] // The resulting order is the same as the order above.

Constraints

• 1 <= n <= 1000
• 1 <= id <= n
• value.length == 5
• value consists only of lowercase letters.
• Each call to insert will have a unique id.
• Exactly n calls will be made to insert.

Solution Approach

Track the Next Expected ID

Maintain a pointer for the next ID that should be outputted. When an insertion occurs, check if the inserted id matches the next expected ID. If it does, output the values sequentially until there are gaps in the IDs, which are skipped until the next valid id arrives.

Efficient Chunk Retrieval

Use a list or array to store values at corresponding indices based on their IDs. This allows for quick access and efficient chunk retrieval after each insertion, without needing to sort or scan the entire stream.

Hash Map or Array for Fast Lookups

Incorporate a hash map or array to store and access values based on their IDs. This helps quickly identify the correct value to return when the next expected ID is encountered, optimizing the solution for both time and space complexity.

Complexity Analysis

The time complexity depends on the approach chosen. If using a list for storage, insertions can occur in constant time, but retrieving the chunks requires linear scanning based on the current ID. If using a hash map, retrieval can be quicker, but it may require additional space. The space complexity is O(n) due to storing n values for the stream.

What Interviewers Usually Probe

• Candidates should demonstrate an understanding of managing ordered data streams efficiently.
• Expect familiarity with data structures like arrays or hash maps for optimal performance.
• Look for solutions that efficiently handle insertions and output without unnecessary sorting.

Common Pitfalls or Variants

Common pitfalls

• Failure to track the next expected ID, leading to incorrect chunk retrieval.
• Inefficiently sorting or scanning the stream with every insertion, which can be avoided by using direct access structures.
• Not handling gaps in the stream correctly, leading to skipped values or incorrect output.

Follow-up variants

• What if the stream were designed to return sorted values by a different key (e.g., value rather than id)?
• How would the solution change if we had multiple concurrent streams with interleaved insertions?
• What if the stream had to handle updates or deletions to values after insertion?

How GhostInterview Helps

• GhostInterview will walk you through constructing efficient stream handling algorithms using data structures like arrays and hash maps.
• You can practice managing ordered data and implementing efficient insertion retrieval strategies.
• The platform's hints focus on optimizing time complexity by emphasizing direct index access rather than expensive sorting operations.

Topic Pages

• Array
• Hash Table
• Design
• Data Stream

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FAQ

How can I solve the Design an Ordered Stream problem efficiently?

Focus on tracking the next expected ID and using an efficient data structure like an array or hash map for lookups and chunk retrieval.

What data structures are ideal for the Design an Ordered Stream problem?

Arrays or hash maps are best for storing values by ID and efficiently retrieving chunks in the correct order.

What are common mistakes when solving Design an Ordered Stream?

Common mistakes include failing to track the next expected ID and inefficiently sorting the stream after every insertion.

What is the time complexity of the Design an Ordered Stream problem?

The time complexity depends on the chosen approach, with O(1) insertion time and O(n) retrieval time in most cases.

How does GhostInterview help with the Design an Ordered Stream problem?

GhostInterview offers targeted hints on tracking IDs and efficiently managing ordered data streams using array scanning and hash lookup techniques.