18 Meta JavaScript Frontend Developer Interview Questions & Answers

I would first copy the original items so I never overwrite a value before using it. Then, for every original index i, I place copy[i] at items[newIndexes[i]]. This directly follows the destination mapping and takes O(n) time with O(n) auxiliary space. For the O(1)-space follow-up, I swap items while also swapping entries in newIndexes until every newIndexes[i] equals i. That also takes O(n) time, uses O(1) auxiliary space, and mutates newIndexes.

We have two owned arrays of the same length. Each value in newIndexes tells us where the item from the same original position must go. For example, the original item at index 0 must move to index 1 because newIndexes[0] is 1. We must change items itself, preserve the exact elements and object references, and produce ['F','A','E','D','C','B']. The simple solution first copies items so later writes cannot destroy original values that are still needed.

1. Can I rely on newIndexes always being a valid permutation of 0 through n - 1?
2. For the O(1)-space follow-up, is it acceptable to mutate newIndexes?

The input is items plus a parallel array called newIndexes. For every original index i, the original items[i] must finish at newIndexes[i]. With items = ['A','B','C','D','E','F'] and newIndexes = [1,5,4,3,2,0], A goes to index 1, B goes to 5, C goes to 4, D stays at 3, E goes to 2, and F goes to 0. The required final items array is ['F','A','E','D','C','B']. Tests ignore the function's return value, so the important result is the mutation of items.

First, make copy = items.slice(). The copy keeps every original array entry available. Then process i from 0 to n - 1. At each index, write copy[i] into items[newIndexes[i]]. The central invariant is that copy never changes, so copy[i] is always the original entry from index i. This also preserves object identity because slice() makes a shallow copy. It copies references instead of cloning the objects.

Start with copy = ['A','B','C','D','E','F']. At i = 0, newIndexes[0] is 1, so put 'A' at items[1]. At i = 1, newIndexes[1] is 5, so put 'B' at items[5]. At i = 2, newIndexes[2] is 4, so put 'C' at items[4]. At i = 3, newIndexes[3] is 3, so put 'D' at items[3]. At i = 4, newIndexes[4] is 2, so put 'E' at items[2]. At i = 5, newIndexes[5] is 0, so put 'F' at items[0]. Now items is ['F','A','E','D','C','B'].

Because newIndexes is owned by the function, we can use it as mutable state. For each i, while newIndexes[i] !== i, let j = newIndexes[i]. Swap items[i] with items[j]. Then swap newIndexes[i] with newIndexes[j]. The item and its destination entry move together. When newIndexes[i] becomes i, the item at index i is in its final position. For the example, index 0 first swaps with 1, then with 5. Index 2 then swaps with 4. Index 3 is already fixed. The final items array is ['F','A','E','D','C','B'], and newIndexes becomes [0,1,2,3,4,5]. This follow-up uses O(1) auxiliary space, but it mutates newIndexes.

For the copy method, copy[i] always means the original entry from index i, and we write it directly to its required destination newIndexes[i]. Since newIndexes is a permutation, every destination appears exactly once. For the O(1) method, items and their destination entries are swapped together. When newIndexes[i] equals i, the entry currently at i belongs there. Each swap makes permanent progress toward the identity permutation, so the process finishes with every entry at its required destination.

The first function creates a shallow copy with slice(), then performs one loop and assigns items[newIndexes[i]] = copy[i]. It does not need to return the array because the required result is the mutation of items. The follow-up function uses a for loop with an inner while loop. It swaps both arrays in parallel until every destination entry equals its own index. The inputs are guaranteed valid, so no extra validation is required.

Both methods take O(n) total time. The copy method needs O(n) auxiliary space because the copied array grows with n. The swap-based follow-up uses O(1) auxiliary space because it only keeps a few variables, but it mutates newIndexes. Empty and one-element arrays require no meaningful movement. An identity permutation already has every item in place. Duplicate values and duplicate object references work correctly because both methods move array entries by position and preserve the original references.

The key idea is that newIndexes is a destination mapping. Original entry i must move to newIndexes[i]. The simple solution keeps an unchanged shallow copy of items. Its invariant is that copy[i] always refers to the original entry from index i, so writing items[newIndexes[i]] = copy[i] cannot destroy information that will be needed later. The O(1)-auxiliary-space follow-up uses the permutation itself as mutable state. It swaps an item and its destination entry together until newIndexes becomes the identity permutation. This removes the copied array, but the tradeoff is that newIndexes is mutated.

Let n be items.length. The copy-based solution takes O(n) time. Creating items.slice() takes O(n), and the placement loop takes another O(n), which is still O(n) overall. It uses O(n) auxiliary space for the copied array. The O(1)-space follow-up also takes O(n) total time. Even though it has a while loop inside a for loop, each swap makes permanent progress in the permutation, so there are only O(n) swaps in total. It uses O(1) auxiliary space because it stores only a few variables. Its tradeoff is that newIndexes is mutated.

A common mistake is writing items[i] directly to items[newIndexes[i]] without first protecting the original entries. An earlier write can destroy a value that is still needed. Another mistake is reversing the mapping and treating newIndexes[i] as a source index instead of a destination index. Deep-cloning objects is also wrong because it can break element identity. In the O(1)-space follow-up, swapping items without swapping the matching newIndexes entries breaks the invariant. It is also incorrect to claim that the O(1) follow-up leaves newIndexes unchanged.

State the mapping before coding: "The original entry at index i must go to destination newIndexes[i]." Then explain why the copy protects the first solution from overwriting needed values. For the follow-up, say clearly that O(1) auxiliary space is possible because the owned newIndexes array is allowed to be mutated while the permutation is swapped into identity order.

I would give each NodeStore instance its own expando property name using an incrementing store ID. When set is called, I attach a small record directly to that exact DOM node. The record keeps a presence flag and the stored value, so undefined is still a valid value. get reads the record, and has checks whether the expando exists. Different store instances use different expando names, so they do not share entries. Each operation is O(1), and retained space is O(k) for k stored nodes.

The goal is to store a value for an exact DOM node without using Map. The value can even be undefined, so we cannot use the returned value alone to decide whether a node is present. The diagram solves this by giving every NodeStore instance a different property name. The store places a small record under that property directly on each node. This keeps node identity naturally. Reading and checking the record is direct, and separate stores use different property names, so their entries stay separate.

1. Can I assume every key passed to the methods is a valid DOM Node?
2. Is it acceptable to attach a non-enumerable private property to each DOM node?
3. Should a value of undefined still count as an existing association?

The key is the DOM node object itself. set(node, value) must create or overwrite the association for that exact node. get(node) returns the stored value. It returns undefined when the node has no association. has(node) must still return true when the stored value itself is undefined. Separate NodeStore objects must not see each other's entries.

An expando is simply an extra JavaScript property placed on an object. A counter starts at 1. The first NodeStore gets the property name "__nodeStore_1", the second gets "__nodeStore_2", and so on. This is the central isolation rule. A record written by one store is under a different property from records written by another store.

For set(node, value), the store reads its own expando key. For the valid DOM-node inputs required by the problem, if that property is not already on the node, it creates a non-enumerable property with a record like { has: true, value: value }. If the record already exists, set overwrites its value and keeps has true. The node object itself therefore carries the store entry.

Create one NodeStore. Its expando is "__nodeStore_1". After store.set(a, 3), node a has { has: true, value: 3 } under that property. After store.set(b, 'x'), node b has { has: true, value: 'x' }. store.has(a) is true. store.has(b) is true. store.get(a) returns 3. store.get(b) returns 'x'.

The diagram then shows the important undefined case. store.set(b, undefined) updates b's existing record to { has: true, value: undefined }. store.get(b) now returns undefined, but store.has(b) remains true. A node such as document.body that was never set has no expando for this store, so has returns false.

The invariant is simple: a node belongs to this store exactly when it has this store's own expando property. Its record contains the current value. Because each NodeStore gets a different counter-generated expando key, two stores do not read each other's records. Because membership is checked from property existence rather than from the stored value, an explicit undefined value is different from an absent entry.

The constructor creates the instance's unique expando name. set rejects a falsy key defensively, then uses Object.prototype.hasOwnProperty.call to test whether the expando is already present. Object.defineProperty creates the first record as non-enumerable. Later calls update the existing record. get returns undefined for a falsy key, then uses the same own-property test and returns node[key].value when present. has returns false for a falsy key and otherwise performs the same own-property check. Valid DOM nodes are the only required inputs, so those defensive checks do not change the stated contract.

set, get, and has each perform a constant number of direct object-property operations, so their normal operation cost is O(1). If this store has entries on k distinct nodes, retained space is O(k). Each of those DOM nodes carries one small record for this store. Removing a node from the document does not itself remove that JavaScript property. The record remains as long as the node object itself remains reachable. An explicitly stored undefined value remains present, resetting a node overwrites its value without losing membership, and different stores remain isolated.

The key insight is to use the DOM node object as the physical place where the entry is stored. Each NodeStore instance receives a distinct expando property name from an incrementing counter. That property is the store-specific namespace on every node. The invariant is: a node is present in a particular store exactly when that node owns that store's expando property. The property contains { has: true, value: ... }. This makes exact node identity automatic, lets has distinguish an absent entry from a stored undefined value, and keeps different NodeStore instances isolated because their expando names are different.

set, get, and has each do only a fixed number of object-property checks or updates, so each operation is O(1) in the representation shown. If the store has been used with k distinct DOM nodes, retained space is O(k). Each stored node keeps one small record under this store's expando property. Removing a node from the DOM does not automatically delete that property, so the record remains while the node object itself remains reachable.

A common mistake is implementing has(node) as get(node) !== undefined. That fails when undefined is a valid stored value. Another mistake is using one shared expando name for every NodeStore instance, which makes stores share entries. Using a random name also does not give the deterministic isolation shown in the diagram, so this solution uses an incrementing store ID. Candidates may also use node.hasOwnProperty directly instead of Object.prototype.hasOwnProperty.call. Finally, removing a node from the document does not automatically remove the expando record from that JavaScript object.

State the invariant early: a node is present in this store exactly when it owns this store instance's expando property. Then use the undefined example to show why has must check membership separately from get's returned value.

I would scan the Roman numeral from left to right and keep a running total. I use a fixed lookup table for the seven Roman symbols. At each index, I compare the current symbol value with the next one. If the current value is smaller, I subtract it. Otherwise, I add it. This correctly handles subtractive pairs such as CM and XC. For MCMXCIX, the result is 1999. The time complexity is O(n), and the auxiliary space is O(1).

The input is one valid uppercase Roman numeral in standard form. We need to return the integer represented by that text without changing the input. Each Roman symbol has a fixed number value. We move from left to right. Usually we add the current value. The special case is when the next symbol has a larger value. Then the current value is part of a subtractive pair, so we subtract it instead. This direct rule matches the required conversion and needs only a running total and a fixed symbol table.

1. Can I assume the input is always a valid uppercase Roman numeral in standard form?
2. Should I return only the integer and leave the original string unchanged?

The input is a valid uppercase Roman numeral such as MCMXCIX. The allowed symbols are I, V, X, L, C, D, and M. Their values are 1, 5, 10, 50, 100, 500, and 1000. We return one integer. For the diagram example, MCMXCIX must return 1999. We do not mutate or normalize the input. Invalid Roman strings are outside the stated contract.

I keep a fixed lookup table from each Roman symbol to its value. I process the string from left to right. At every index, I read the current value and the next value. If the current value is smaller than the next value, I subtract the current value from the total. Otherwise, I add it. The central invariant is that after each index, the running total contains the correct contribution of every symbol processed so far.

The symbol table is I=1, V=5, X=10, L=50, C=100, D=500, and M=1000. I set total to 0. The loop starts at index 0. When there is no next symbol, the code uses 0 as the next value. That makes the final Roman symbol get added because its value is greater than or equal to 0.

At index 0, M is 1000 and the next symbol C is 100. Since 1000 is not smaller than 100, add 1000. Total becomes 1000.

At index 1, C is 100 and the next symbol M is 1000. Since 100 < 1000, subtract 100. Total becomes 900.

At index 2, M is 1000 and the next symbol X is 10. Since 1000 is not smaller than 10, add 1000. Total becomes 1900.

At index 3, X is 10 and the next symbol C is 100. Since 10 < 100, subtract 10. Total becomes 1890.

At index 4, C is 100 and the next symbol I is 1. Since 100 is not smaller than 1, add 100. Total becomes 1990.

At index 5, I is 1 and the next symbol X is 10. Since 1 < 10, subtract 1. Total becomes 1989.

At index 6, X is 10 and there is no next Roman symbol, so next is treated as 0. Since 10 is not smaller than 0, add 10. The final total becomes 1999.

In a valid standard Roman numeral, a smaller value before a larger value is subtracted. Otherwise, the value is added. The algorithm applies exactly that rule at every position. Because each symbol's contribution is decided using only itself and the next symbol, the running total stays correct after every processed index. After the final symbol, all contributions have been included, so the total is the correct integer.

The JavaScript object stores the seven fixed symbol values. The loop reads each character from left to right. It gets the current value and, when available, the next value. The comparison decides whether to subtract or add the current value. Finally, the function returns total. With n equal to text.length, the loop takes O(n) time. The symbol table always has seven entries, so auxiliary space is O(1). A single symbol such as M returns 1000. The valid example MMMCDXX returns 3420. The last symbol is handled by treating the missing next value as 0.

The key idea is to decide the contribution of each Roman symbol by comparing it with the symbol immediately after it. A fixed lookup table gives each symbol its numeric value. If current < next, the current value is subtractive, so we subtract it. Otherwise, we add it. The invariant is that after processing index i, the running total contains the correct contribution of every symbol from index 0 through i. This works because the input is guaranteed to be a valid Roman numeral in standard form.

Let n be the number of characters in the Roman numeral. We visit each character once, so the time complexity is O(n). The lookup table always contains exactly seven Roman symbols, regardless of input length. The algorithm also keeps only a few numeric variables. Therefore, the auxiliary space complexity is O(1).

1. Always adding every symbol. That fails on subtractive pairs such as CM, XC, and IX.
2. Subtracting the larger symbol instead of the smaller current symbol when current < next.
3. Comparing Roman characters directly instead of comparing their numeric values from the lookup table.
4. Mishandling the last character. In this solution, the missing next value is treated as 0, so the final symbol is added.
5. Claiming that the fixed seven-entry symbol table uses O(n) space. Its size never grows with the input, so auxiliary space is O(1).

While coding, say the rule before writing the loop: "If the current value is smaller than the next value, subtract it. Otherwise, add it." Then trace one subtractive pair such as C before M from MCMXCIX. This makes the code and correctness argument easy to follow.

I would save the original browser timer functions, then wrap setTimeout and clearTimeout. I keep every still-pending timeout ID in a Set. When a timeout starts, I remove its ID before running its callback. An individual clear also removes the ID. clearAllTimeout loops through the Set, cancels every pending timeout with the original clearTimeout, then empties the Set. Set operations are O(1) on average. clearAllTimeout takes O(n) time and the tracking Set uses O(n) auxiliary space.

The goal is to add one operation that can cancel every timeout that is still waiting. Normal setTimeout and clearTimeout calls must continue to work in the usual way. I save the original browser functions and replace the globals with small wrappers. A Set remembers only timeout IDs that are still waiting. When a timeout starts or is individually cleared, its ID is removed. This keeps the stored state accurate. clearAllTimeout can then cancel exactly the timers that are still pending.

1. Should clearAllTimeout cancel only timeouts that are still pending when it is called? Yes, that is the required behavior here.
2. Should timers created after clearAllTimeout work normally? Yes. They should be tracked as new pending timers.
3. Should repeated calls to clearAllTimeout be harmless when nothing is pending? Yes.

We replace window.setTimeout and window.clearTimeout with wrappers while keeping their normal way of being called. setTimeout must still return the browser timeout ID. clearTimeout must still delegate to the original browser clearTimeout. The new clearAllTimeout function must cancel every timeout that is still pending at the moment it is called.

I use a Set named pending. Each entry is one browser timeout ID. The central invariant is simple: pending contains only timeout IDs whose callbacks have not started and that have not been individually cleared. A Set fits because we only need to add IDs, delete IDs, and iterate over the remaining IDs.

The setTimeout wrapper calls the saved original setTimeout. It adds the returned ID to pending and returns that same ID to the caller. When the timeout starts firing, the internal callback deletes its own ID from pending before running the user's callback. The clearTimeout wrapper also deletes the ID from pending and then calls the saved original clearTimeout. Deleting an ID that is not present is harmless.

At t = 0, setTimeout(func1, 10000) returns id1, so pending is [id1]. Then setTimeout(func2, 10000) returns id2, so pending is [id1, id2]. Then setTimeout(func3, 10000) returns id3, so pending is [id1, id2, id3]. Immediately afterward, clearAllTimeout() runs. It calls the original clearTimeout for id1, id2, and id3, then clears pending. The Set is now empty. When 10,000 ms passes, func1, func2, and func3 do not run. A timeout scheduled later gets a new ID, is added to pending, and works normally.

The Set represents the timers that are still pending. A timer is added when it is scheduled. It is removed when its callback starts or when it is individually cleared. Therefore, when clearAllTimeout iterates over pending, every stored ID represents a timeout that still needs cancellation. After those IDs are cancelled and the Set is emptied, none of those cancelled callbacks can run.

The code first saves bound references to the original browser timer functions. It then creates pending as a Set. The setTimeout wrapper schedules an internal callback. That callback deletes its own timeout ID before calling the user's function. The returned ID is added to pending and returned unchanged. The clearTimeout wrapper removes the ID from tracking and delegates to the original clearTimeout. clearAllTimeout iterates through pending, calls the original clearTimeout for every ID, and then calls pending.clear().

Adding and deleting IDs in a JavaScript Set are O(1) on average. Each normal setTimeout or clearTimeout wrapper therefore has O(1) expected tracking work. If n timeouts are pending, clearAllTimeout takes O(n) time because it visits each pending ID once. The Set can hold up to n pending IDs, so auxiliary space is O(n). Repeated clearing is safe. Individually cleared or already-fired timers are no longer tracked. New timers scheduled after clearing work normally.

The key idea is to maintain an exact registry of timers that are still pending. The registry is a Set of browser timeout IDs. Its invariant is: an ID is in pending only while that timeout has not started its callback and has not been individually cleared. The setTimeout wrapper adds each new ID. The wrapped callback removes its own ID before user code runs. The clearTimeout wrapper also removes the ID. Because pending always describes the current pending timers, clearAllTimeout can iterate through it, cancel every stored ID with the original clearTimeout, and then empty the Set.

JavaScript Set add and delete operations are O(1) on average. Therefore, the extra tracking work in each wrapped setTimeout or clearTimeout call is O(1) expected time. If n timeouts are still pending, clearAllTimeout takes O(n) time because it cancels each pending ID once. The Set can store up to n pending timeout IDs, so the auxiliary space is O(n).

A common mistake is keeping timeout IDs after their callbacks have started. That leaves stale references in the tracking Set. Another mistake is removing the ID only after user callback code finishes instead of before it starts. Candidates may also forget to remove IDs when clearTimeout is called individually. Another common error is overwriting window.setTimeout or window.clearTimeout before saving the original functions, which can cause the wrappers to call themselves recursively. Finally, clearAllTimeout should clear only the current pending timers and must not stop future timeouts from working normally.

State the invariant early: the Set contains exactly the timeout IDs that are still pending. Then explain each wrapper by showing how it keeps that invariant true. This makes the correctness of clearAllTimeout easy to justify.

I would keep one boolean flag for the current test. it resets that flag to true, runs the whole test function, and prints one final result. expect(actual).toBe(expected) checks that both values are finite numbers and then uses strict equality. A failed expectation only changes the flag to false, so later expectations still run. If the test function throws, the test also fails. With m expectations, the time is O(m) and the auxiliary space is O(1).

This problem asks us to build a very small test tool. A test has a name and a function that contains one or more checks. Every check must run. A check passes only when two finite numbers are exactly equal. If any check fails, the whole test fails. We wait until the test function finishes before printing anything. Then we print exactly one line. It is a success line only when every check passed. Otherwise, it is a failure line. A thrown error also makes the test fail.

1. Should a thrown error inside testFn make the test fail? The diagram treats it as a failure.
2. Should NaN, Infinity, and -Infinity fail because the contract only accepts finite numbers? The diagram says yes.
3. Are return values from it, expect, and toBe important? The problem says they are not tested.

it(name, testFn) receives a test name and a zero-argument synchronous function. Inside that function, the code can call expect(actual).toBe(expected) several times. Each actual and expected value is a finite number under the stated contract. After all checks finish, it prints exactly one line. It prints Success: <name> when every check passes. Otherwise, it prints Failure: <name>.

The implementation uses _allPassed as the current test state. At the start of every call to it, _allPassed becomes true. This means the test is considered successful until a failed check or thrown error proves otherwise. _currentName stores the current test name so the final message can use it.

The central invariant is simple: _allPassed is true only if no failure has been seen so far in the current test. Once it becomes false, it stays false for the rest of that test.

it calls testFn() inside try...catch. The important part is that toBe does not throw when a comparison fails. It only changes _allPassed to false. This lets later expectations continue running. If testFn itself throws an exception, the catch block also changes _allPassed to false. Only after testFn finishes or throws does it print one final line.

The diagram uses it('should fail on last check', () => { expect(1).toBe(1); expect(2).toBe(2); expect(3).toBe(4); }).

At the start, _allPassed is true. The first check compares 1 === 1, so it passes and the flag stays true. The second check compares 2 === 2, so it also passes and the flag stays true. The third check compares 3 === 4, which is false. toBe changes _allPassed to false. The test function then finishes. No more checks remain. it sees that _allPassed is false and prints exactly Failure: should fail on last check.

The passing example follows the same flow. 1 === 1, 2 === 2, and 3 === 3 all pass. The flag stays true, so the final output is Success: should be an equal number.

Every expectation is executed because a failed comparison only updates the shared result flag. It does not stop the test function. The flag starts as true and can only move from true to false. Therefore, after the test function finishes, the flag tells us whether every expectation passed. Reporting happens once, after execution, so multiple passing checks followed by one failed check still produce only one failure line.

it stores the test name, resets _allPassed, and executes testFn. The catch block marks the test as failed if the test function throws. After execution, it uses the flag to choose the exact success or failure message.

expect(actual) returns an object with a toBe(expected) method. toBe first checks both values with Number.isFinite. If either value is not finite, it marks the test as failed and returns from that matcher call. Otherwise, it uses strict inequality, !==, to detect a failed strict-equality comparison. It never prints its own result.

If a test contains m expectations, each expectation does constant work, so the time is O(m). The implementation keeps only constant-size state for the active test, so the auxiliary space is O(1) with respect to m.

Relevant edge cases shown in the diagram are zero expectations, negative numbers, decimal numbers, NaN, Infinity, -Infinity, large finite numbers, and an exception thrown inside testFn. Zero expectations produce success because no failure occurs. NaN, Infinity, and -Infinity fail the finite-number check.

The key idea is to separate checking from reporting. Each toBe only updates one boolean result flag. It does not print and it does not stop the test. it owns the lifetime of that flag. It resets the flag before the test starts and reads it only after the test finishes. The invariant is that _allPassed stays true exactly while no failed expectation or thrown error has been seen. Once it becomes false, later successful checks cannot change it back. This directly supports the requirement to run every expectation and still print exactly one final line.

Let m be the number of toBe calls in one test. Each call checks finiteness and strict equality in constant time, so the total time is O(m). The implementation does not keep any collection that grows with m. It stores only constant-size state for the active test, so the auxiliary space is O(1).

A common mistake is printing inside toBe, which can produce several output lines instead of one final line. Another mistake is throwing on the first failed comparison, which stops later expectations from running. Resetting the pass flag inside expect can erase an earlier failure. Using loose equality instead of strict equality is also wrong. Candidates may also forget the finite-number check for NaN, Infinity, and -Infinity, or forget to reset the state at the start of each it call.

Explain the invariant before writing code: the test starts as passing, any failed expectation can change the flag to false, and nothing changes it back to true until the next it call. Then explain that reporting happens exactly once after testFn finishes.

I would use setInterval at about 60 FPS, but I would not assume each callback arrives exactly every 16 milliseconds. I record the start time with performance.now(), calculate progress from real elapsed time, clamp it to 1, and move the element by progress times distance while keeping its existing transform. At completion, I write the exact final distance and clear the interval. Each callback is O(1), and the solution uses O(1) auxiliary space.

The function moves a connected browser element horizontally by a requested number of CSS pixels over a requested amount of time. The timer may run late, so the movement must be based on real elapsed time instead of counting timer callbacks. We also keep the element's existing transform. The movement must approach the requested distance monotonically, finish at the exact final displacement, and stop updating afterward. In the verified example, the element starts at horizontal offset 0px and moves 120px over about 1000ms.

1. Should an existing transform on the element remain while the horizontal displacement is added?
2. Should a negative distance move the element in the opposite horizontal direction?
3. Is performance.now() the expected clock for calculating elapsed browser time?

The inputs are a connected HTMLElement, a positive finite duration in milliseconds, and a finite distance in CSS pixels. The function's return value does not matter because the test harness observes the element's rendered horizontal offset. For the verified example, duration is 1000ms and distance is 120px. The element begins at horizontal offset 0px and must finish exactly 120px from that starting position.

First, save the element's current computed transform. This lets the animation add its horizontal displacement without throwing away an existing transform. Record the start time with performance.now(). The interval runs about every 16ms, which is approximately 60 callbacks per second. At the beginning, elapsed time is 0ms, progress is 0, and displacement is 0px.

On every interval callback, read performance.now() again. Compute elapsed = now - start. Then compute progress = Math.min(elapsed / duration, 1). This clamps progress to the range from 0 to 1. Next compute displacement = progress * distance. Because progress moves monotonically toward 1, the displacement moves monotonically toward the requested distance.

At 0ms, progress is 0.00 and displacement is 0px. At 250ms, progress is 0.25 and displacement is 30px. At 500ms, progress is 0.50 and displacement is 60px. At 750ms, progress is 0.75 and displacement is 90px. When a callback runs at or after 1000ms, progress is clamped to 1.00 and the displacement is 120px. The code writes the exact 120px final displacement and clears the interval, so there are no later updates.

Real elapsed time is the source of truth. A late interval callback therefore catches up automatically instead of pretending that only one 16ms step passed. Clamping progress to 1 prevents the animation displacement from going past the requested distance. Multiplying progress by distance makes the displacement move toward the correct final value. The element's existing transform is retained. At completion, the code explicitly writes distance itself, so the final displacement is exact before the interval is cleared.

The implementation reads the current computed transform and converts the special value 'none' into an empty base transform. It records the start time and starts setInterval with a 16ms delay. Each callback calculates elapsed time, clamped progress, and the current displacement. It prepends translateX(displacement) to the saved base transform. When progress reaches 1, it writes translateX(distance) exactly and calls clearInterval. A delayed or throttled callback may reduce the number of visible intermediate updates, but the next callback still uses the correct elapsed time.

The key insight is to use real elapsed time as the animation clock. setInterval only tells the program when it has an opportunity to update the element. It must not be treated as perfectly regular timing. Save the start time, then calculate progress as Math.min((performance.now() - start) / duration, 1) on each callback. The central invariant is that progress remains in [0, 1] and moves monotonically toward 1. Therefore displacement = progress * distance moves monotonically toward distance. The saved base transform is retained, and the interval is cleared after the exact final displacement is written.

Each interval callback performs a constant amount of work, so one callback is O(1). With an approximately 16ms interval, the normal number of callbacks is roughly duration / 16, although browsers can delay or throttle callbacks. The function stores only a few numbers, a timer id, and a saved transform string. Auxiliary space is O(1).

A common mistake is counting interval callbacks and assuming every callback represents exactly 16ms. That causes wrong positions when the browser delays callbacks. Another mistake is forgetting to clamp progress to 1, which can overshoot the requested distance. Candidates may also forget to write the exact final distance before clearing the interval. Replacing the transform without retaining the existing transform can remove styling the element already had. Finally, distance === 0 means no movement, while a negative distance moves in the opposite horizontal direction.

Explain that setInterval is only the update trigger and performance.now() is the clock. Then write progress = Math.min(elapsed / duration, 1). This single formula makes delayed callbacks, monotonic movement, and exact completion easy to explain.

I would store the producer on the Observable and create independent stopped state for every subscribe call. I normalize either a function observer or an observer object, then pass the producer a safe subscriber with next, error, and complete methods. next forwards values only while active. error and complete stop the subscription before forwarding once. unsubscribe also stops it. Each notification takes O(1) time, and each subscription uses O(1) auxiliary space.

The Observable stores a function that produces values. Each call to subscribe starts that producer again for a new and independent subscriber. A subscriber can receive normal values, an error, or a completion signal. Values must stay in producer order. Error or completion can be delivered only once. After error, completion, or unsubscribe, later notifications are ignored. The solution uses one isStopped flag for each subscription. This keeps every subscriber independent and makes the lifecycle easy to control.

1. Should a function passed to subscribe be treated as only the next callback?
2. Should missing next, error, or complete callbacks simply be ignored?
3. Are producer teardown return values outside this basic contract, as stated in the question?

The constructor receives the producer function and saves it. The producer does not run yet. Each later subscribe call starts it for that new subscription.

When subscribe(observer) runs, I normalize the observer first. If it is a function, I treat that function as next. If it is an object, its next, error, and complete callbacks are optional.

I then create isStopped = false. This variable belongs only to this subscription. Another subscription gets a different isStopped variable.

The producer receives a safe subscriber with next, error, and complete methods.

next(value) first checks isStopped. If the subscription is active, it forwards the value to observer.next when that callback exists.

error(error) also checks isStopped. If active, it first changes isStopped to true. It then calls observer.error when provided. This makes error terminal and prevents later notifications.

complete() uses the same rule. It changes isStopped to true before calling observer.complete. Completion therefore happens at most once.

The producer calls next(1), next(2), then later next(3), next(4), and complete().

Subscriber X stays active. It receives 1, 2, 3, 4, then complete.

Subscriber Y is a separate subscription. It receives 1 and 2, then unsubscribe() is called. Its isStopped flag becomes true. Its later next(3), next(4), and complete() notifications are ignored.

Subscriber X is not affected because its state is independent.

The central invariant is that callbacks are forwarded only while the current subscription has isStopped equal to false. error, complete, and unsubscribe change that state to true. After that transition, next, error, and complete all do nothing. Because subscribe creates fresh state each time, one subscriber cannot stop another subscriber.

The constructor validates and stores the producer. subscribe normalizes the observer and creates isStopped = false. It builds guarded next, error, and complete functions. It then executes the producer with that safe subscriber. A synchronous exception thrown by the producer is passed through the safe error path. Finally, subscribe returns an object with unsubscribe(), which sets isStopped to true. Producer teardown return values are intentionally outside this basic implementation.

Each next, error, or complete call does constant work, so notification dispatch is O(1). Each subscription keeps a fixed amount of state, so auxiliary space is O(1) per subscription. A function observer is treated as next only. Missing callbacks are safely ignored. Repeated unsubscribe calls are harmless. Terminal notifications are delivered at most once, and multiple subscriptions remain independent.

The key idea is to give every subscription its own small lifecycle state. The state is one boolean named isStopped. While isStopped is false, next values may be forwarded. The first error, completion, or unsubscribe changes isStopped to true. After that, every later next, error, or complete call is ignored. The central invariant is: the safe subscriber forwards observer callbacks only while its own subscription is active. Because subscribe creates fresh state and a fresh safe subscriber each time, multiple subscriptions to the same Observable remain independent.

Each next, error, or complete notification performs a state check and at most one callback, so it takes O(1) time per notification. subscribe creates only a fixed set of variables and wrapper functions. The auxiliary space is therefore O(1) per subscription. These bounds describe the Observable wrapper itself. They do not include extra work or memory used inside the producer.

A common mistake is using one isStopped flag for the whole Observable instead of one flag per subscription. Then one subscriber could incorrectly stop another. Another mistake is forwarding next after error, complete, or unsubscribe. It is also important to set isStopped before calling the error or complete callback so a terminal state is already established. Candidates may incorrectly require every observer callback even though the callbacks are optional. Another mistake is adding producer teardown behavior even though teardown return values are outside this basic contract.

Explain the per-subscription isStopped flag first. Then show that next works only while it is false, while error, complete, and unsubscribe all move that one subscription into the stopped state.

I would make the editor API framework-neutral and keep a versioned JSON document as the source of truth, not raw HTML. The editor owns a tree with stable node IDs and an anchor/focus selection. It exposes commands for bold, italic, lists, blockquotes, text insertion, undo, and redo, plus read-only queries. It supports controlled ownership with value and onChange, or uncontrolled ownership with defaultValue. Invalid values are rejected without changing state. Pasted HTML is sanitized and mapped into the document model. The trade-off is more validation and migration work, but the API becomes predictable and extensible.

This question asks us to design the public contract for a rich-text editor. The editor should work without depending on React, Vue, or another framework. I would keep content in a versioned JSON document. The document contains blocks, text nodes, formatting marks, and stable IDs. The editor also keeps a selection. Commands change editor state. Queries only read state. Events tell the consumer what changed. Validation rejects bad values before they replace good state. Raw HTML is not the required source of truth.

• When an extension introduces a new node or mark, should older documents be migrated automatically or rejected?
• For pasted HTML, how much unsupported formatting should be removed versus mapped into supported nodes and marks?

The editor value is a versioned JSON document.

The root contains block nodes. The shown block types include paragraphs, lists, list items, and blockquotes. Text lives in text nodes. Inline formatting is stored as marks such as bold and italic.

Every node has a stable id. The ID is separate from the node position. If the same logical node survives an edit, its ID should stay stable. This helps selection mapping, history, collaboration, and diffing.

The selection is a range with anchor and focus. Each point uses a path into the tree plus an offset. A collapsed selection has the same anchor and focus. A non-collapsed range covers content between two different points.

createEditor(options) returns an editor instance.

The API supports controlled ownership with value and onChange. The consumer stores the current value in that mode.

It also supports uncontrolled ownership with defaultValue. The editor stores its own current value in that mode.

The initial value is a JSON document. It is not raw HTML.

The instance exposes getValue() and setValue(value, source?). It also exposes selection and state queries such as getSelection(), isReadOnly(), isDisabled(), and isFocused().

Commands may change editor state.

Inline commands include toggleBold() and toggleItalic(). Block commands include toggleList(type) and toggleBlockquote(). Editing commands include insertText(text), insertParagraph(), deleteSelection(), undo(), redo(), and clearHistory().

Queries do not change state. The diagram shows getSelection(), isCollapsed(), getFormat(), getBlockType(at?), canUndo(), canRedo(), and getDocument().

The consumer should not edit tree objects directly. A command goes through the editor. The editor validates the action, builds a document transaction, updates the document and selection, and emits the supported events.

A collapsed selection changes formatting for future input.

For example, toggleBold() at the caret does not rewrite existing text. Text typed after the command becomes bold.

A range selection behaves differently. Calling toggleItalic() adds or removes the italic mark on the selected text.

Block commands operate on block boundaries. If a selection covers several blocks, the command applies to every fully or partially covered block.

These rules make formatting predictable for callers.

The editor exposes onChange, onSelectionChange, onFocus, onBlur, onPaste, onUndo, onRedo, and onError.

The diagram models onChange with the new value, change information, and a source. onSelectionChange carries the new selection.

Events flow from the editor to the consumer. A controlled consumer stores value and selection and updates visible UI such as the content area and toolbar.

Focus and selection remain separate concepts. The editor can report each one independently.

The API supports toJSON() and fromJSON(value).

Validation checks the incoming document before it becomes editor state. It checks the schema, required fields, and allowed nodes and marks.

If setValue() receives an invalid document, the editor rejects it and keeps the old value. The error is reported instead of partly applying bad state.

Replacing the value while focused has a defined rule. The editor maps the current selection into the new document when possible. If mapping fails, the selection moves to the start.

The JSON value remains the source of truth. HTML paste is sanitized and mapped into supported blocks, text, and marks.

The consumer may optionally persist or share the JSON value. The diagram shows LocalStorage, IndexedDB, or a backend API as application choices. Persistence is outside the core editor contract.

readOnly blocks edits but still allows selection.

disabled blocks editing, focus, and selection interaction.

Undo and redo belong to editor history. The diagram treats a command as a history item and allows batching for complex operations.

Plain inserted text becomes text nodes. Pasted HTML is sanitized before it is mapped into the supported document model.

This prevents pasted markup from becoming a second source of truth.

The document carries a version.

Extensions may add nodes, marks, commands, paste rules, or serializers. The public API follows semantic versioning.

Breaking API changes belong in major versions. Schema versioning and migration help older stored documents remain usable.

The core editor stays framework-neutral. React, Vue, Svelte, or another UI system can use adapters around the same editor contract. The document and command API do not depend on those adapters.

The diagram initializes the editor with createEditor({ defaultValue: initialValue, onChange, onSelectionChange }) and then calls editor.focus().

The shown command sequence is:

1. insertText("Hello")
2. toggleBold()
3. insertText("world")
4. insertParagraph()
5. toggleBlockquote()
6. insertText("Be concise.")

The first text node contains Hello without marks. The collapsed toggleBold() changes the active mark for future input. The next inserted text, world, therefore has the bold mark. A new block is then created, changed into a blockquote, and filled with Be concise..

The resulting document shown in the diagram contains a paragraph with Hello and bold world, followed by a blockquote containing Be concise..

For emitted events, I would preserve the concrete sequence exactly as the approved diagram defines it: onChange(value1, delta1, 'api'), onChange(value2, delta2, 'api'), onChange(value3, delta3, 'api'), onChange(value4, delta4, 'api'), onSelectionChange(sel1), and onFocus(). I would not infer additional callbacks beyond the explicit example contract.

A structured JSON tree requires more work than storing HTML. The editor must validate nodes, preserve stable IDs, map selections, and migrate older document versions.

The benefit is predictable behavior. Commands work against one known model. Consumers do not need to parse arbitrary browser HTML to understand document state.

Controlled mode gives the application full ownership, but it requires careful state synchronization. Uncontrolled mode is simpler, but the editor owns more state.

Extensions add flexibility. They also increase schema, migration, testing, and compatibility work.

The cost depends on how much content a command touches. Formatting a small range should inspect only that range. A block command may update several selected blocks. Validation and serialization may inspect much more of the document, so they should not run unnecessarily. Stable IDs, selection mapping, and undo history use extra memory, but they make editing behavior easier to reason about. Controlled mode adds coordination between application state and editor state. Extensions improve flexibility, but every new node or mark increases validation, migration, testing, and long-term compatibility work.