Abstract / Summary

Create a stream that returns the next available integer or a given id when requested.

Note

The full problem description, including helpful examples, can be found on LeetCode.

This problem is ranked as

and has an acceptance rate of about 83% (100k+ submissions).

Typical patterns to solve this problem:

• Array
• Data Stream
• Design
• Hash Table

Every single line of code was written by me, 100% from scratch, between August 2023 and August 2024.
However, two years later I set up this website and used various AI tools to generate most of the walkthrough / explanations below.

See this dedicated page for more details.

Overview

The provided OrderedStream class implements a stream that receives elements out of order and yields them in increasing order of their identifiers. The constructor receives the total number of expected elements, while the insert method places a new (idKey, value) pair into an internal priority queue and then emits any consecutive values that become available. The design relies on a min-heap to keep the smallest pending identifier at the top, enabling efficient detection of the next expected element.

Core Components

Initialization

def __init__(self, n: int):
    self.next = 1
    self.data = []  # min-heap

The constructor sets self.next to the identifier that the stream expects next, starting at 1. An empty list self.data is prepared to serve as a min-heap; the heap will store tuples of the form (idKey, value). The parameter n is accepted but not stored, indicating that the class trusts the caller to respect the total count.

Insertion and Emission

def insert(self, idKey: int, value: str) -> List[str]:
    heappush(self.data, (idKey, value))

    while self.data and self.data[0][0] == self.next:
        self.next += 1
        yield heappop(self.data)[1]

The insert method performs three logical actions:

1. Heap insertion - heappush adds the incoming pair to the heap, preserving the heap invariant. This guarantees that the smallest identifier among all pending elements is always accessible at self.data[0].

2. Consecutive check loop - The while condition examines two aspects: the heap is non-empty, and the smallest identifier matches the current expectation (self.next). If both hold, the loop body executes.

3. Yielding values - Inside the loop, self.next is incremented to reflect that the current identifier has been satisfied. heappop removes the smallest element from the heap, and its associated value is yielded to the caller. The loop repeats as long as the next expected identifier is present at the heap’s root, allowing the method to emit a contiguous block of values in a single call.

Edge-Case Handling

The implementation gracefully manages several boundary situations without explicit conditional branches:

• Insertion of the expected identifier first - When idKey equals self.next, the heap’s root immediately satisfies the loop condition, causing the value to be yielded right away. The increment of self.next prepares the stream for the following identifier.

• Out-of-order arrivals - If an element arrives with an identifier larger than self.next, it is stored in the heap. The loop condition fails because the smallest identifier in the heap does not match self.next, so no value is yielded. Later arrivals that fill the gap will trigger the loop, releasing all pending consecutive values.

• Duplicate identifiers - Although the code does not explicitly guard against duplicates, the heap will contain both entries. When the first occurrence is popped, self.next advances, and the second duplicate will sit at the heap’s root with an identifier that no longer matches self.next. Consequently, it will remain in the heap indefinitely, effectively being ignored by the emission logic. This behavior ensures that only the first occurrence influences the output order.

• Exhaustion of the stream - When all identifiers up to n have been yielded, self.next will be n+1. Subsequent insertions (if any) will never satisfy the loop condition because the expected identifier is beyond the defined range, so no further values are emitted.

Performance Optimizations

The design leverages a min-heap to keep insertion and extraction operations efficient. By using heappush and heappop, each insertion and each removal of the smallest element runs in logarithmic time relative to the number of pending items. This choice avoids linear scans that would be required if a simple list were used, especially when the stream receives many out-of-order elements.

The while loop processes consecutive identifiers in a single pass. As soon as the smallest pending identifier matches the expectation, the loop extracts it and immediately checks the next smallest. This approach minimizes the number of heap operations: a contiguous block of ready elements results in a series of heappop calls without intervening heap rebuilds, because each pop maintains the heap property automatically.

Yielding values directly from the method rather than accumulating them in an intermediate collection reduces memory overhead. The generator semantics allow the caller to consume each value as it becomes available, which is advantageous when the stream is large or when downstream processing can begin before the entire set of elements is received.

The use of a single integer self.next to track the next required identifier eliminates the need for auxiliary data structures such as hash sets or boolean arrays to record which identifiers have arrived. This compact state representation contributes to lower constant-factor overhead during each insertion.

Concluding Remarks

The OrderedStream class presents a concise implementation that combines a min-heap with a simple counter to reorder out-of-order inputs efficiently. The code handles the primary edge cases of early arrivals, gaps, and duplicate identifiers through its loop condition and heap ordering. Performance is optimized by relying on heap operations that scale logarithmically and by emitting values lazily via a generator, thereby keeping both time and space usage modest for typical streaming workloads.

Imagine a toy robot that organizes special blocks for a giant puzzle. The robot has a tiny shelf where it keeps blocks that arrive out of order. At the very beginning, the robot is waiting for the block labeled number one to show up.

When a new block arrives, the robot places it on its hidden shelf. It sorts all the blocks so the one with the smallest number is always right at the front. The robot then checks that front block to see if it is the specific number it is waiting for right now. If the number matches, the robot takes that block off the shelf and sets it aside to show it is ready. Then, it changes the number it is looking for to the next one in line. It keeps checking the shelf over and over again to see if the next block it needs is already waiting there. If it finds the next one, it takes that one out too and keeps going until it reaches a block that has not arrived yet.

Several LLMs ranked my code quality on a scale from 1 (very poor) to 10 (outstanding):

not analyzed yet

I wrote a basic crawler to find other solutions to that problem.
Obviously, I did that after I solved the problem myself.

In general, AlgoMonster and WalkCCC solutions are high-quality and always worth a look.
I haven't checked most of the other solutions, but they might be helpful as well.

And of course, the LeetCode forum is always a good place (or even the best) to look for alternative approaches.

(click on a column header to sort the table)

	author
author	solutions
www	AlgoMonster	Python	C++		Java					TS
	WalkCCC	Python	C++		Java
	leetcode.ca	Python	C++		Java	Go			Rust	TS
	Progiez	Python	C++		Java
github	black-shadows		C++
	chandansgowda	Python
	charles-wangkai				Java
	DimitrisJim							C
	everthis			JS
	fsq		C++
	halfrost					Go
	JoshCrozier			JS
	Krazune		C++
	lkwq007	Python
	muhammadfarhankt					Go
	suxrobGM						C#
	trung-hn	Python
	varunu28				Java
	YuriSpiridonov	Python
AlgoMonster	C++, Python, Java, TypeScript
WalkCCC	C++, Python, Java
leetcode.ca	C++, Go, Java, Python, Rust, TypeScript
Progiez	C++, Java, Python
black-shadows	C++, C++
chandansgowda	Python
charles-wangkai	Java
DimitrisJim	C
everthis	JavaScript
fsq	C++
halfrost	Go
JoshCrozier	JavaScript
Krazune	C++
lkwq007	Python
muhammadfarhankt	Go
suxrobGM	C#
trung-hn	Python
varunu28	Java
YuriSpiridonov	Python

There are a few extra pages giving you some insights into my setup:

• Tech Stack - my development environment and tools
• AI - how AI helped me create this website (NOT solving the problems)
• Progress - progression over time
• Quality - code quality