Apple Python Developer Interview Questions & Answers

I scan the expression from left to right. I build one number at a time and apply the previous operator when I reach the next operator. I keep completed additive terms in total and the current term in last_term. Multiplication and division update last_term immediately, so precedence works without a stack. Division truncates toward zero. Finally, I return total + last_term. The solution takes O(n) time and O(1) auxiliary space.

The input is a valid arithmetic-expression string containing nonnegative integers, spaces, and the operators +, -, *, and /. The output is the evaluated integer result. The main idea is to keep the newest term separate from the completed total. This lets multiplication and division change that term before addition or subtraction is finalized.

1. What input sizes, value ranges, and edge cases should the solution handle?
2. What output should be returned for empty, invalid, or duplicate input?
3. Should I prioritize execution time or memory use, and may I use the standard library?

The function receives one expression string. Spaces are ignored. The expression has no parentheses. Multiplication and division have higher precedence than addition and subtraction. Division truncates toward zero. The diagram assumes valid input, so the function does not validate malformed expressions.

For the example, the input is "3 + 2*2 - 8/4". The returned result is 5.

I use four variables.

• total stores additive terms that are already complete.
• last_term stores the newest term. It can still change if the next operation is multiplication or division.
• current_number stores the number currently being read.
• operator stores the previous operator that must be applied to current_number.

The invariant is that total stores completed additive terms, while last_term stores the current term after any multiplication or division updates. Therefore, total + last_term equals the value of the processed part of the expression.

I start with total = 0, last_term = 0, current_number = 0, and operator = '+'.

I scan the expression from left to right. A digit updates current_number with current_number * 10 + int(ch). This builds multi-digit numbers. A space is skipped.

The code appends a final '+' sentinel. A sentinel is an extra operator used only to make the loop commit the last number.

When I reach an operator, I apply the previous operator to current_number.

For '+', I add the old last_term to total and start a new positive last_term.

For '-', I add the old last_term to total and start a new negative last_term.

For '*', I multiply last_term by current_number immediately.

For '/', I divide last_term by current_number and truncate toward zero. The implementation divides the absolute values with // and then restores the sign. This avoids floating-point conversion.

Then I save the new operator and reset current_number to 0.

Start with total = 0 and last_term = 0.

Read 3 and reach '+'. The previous operator is '+'. Before the update, the state is total = 0, last_term = 0, current_number = 3. The code performs total += 0 and last_term = 3. The state becomes total = 0, last_term = 3.

Read 2 and reach '*'. The previous operator is '+'. Before the update, the state is 0, 3, 2. The code performs total += 3 and last_term = 2. The state becomes 3, 2.

Read the next 2 and reach '-'. The previous operator is '*'. The code performs last_term = 2 * 2 = 4. The state becomes 3, 4.

Read 8 and reach '/'. The previous operator is '-'. The code performs total += 4 and last_term = -8. The state becomes 7, -8.

Read 4 and reach the sentinel at the end. The previous operator is '/'. The diagram shows int(-8 / 4) = -2. The integer-only implementation gets the same result by calculating abs(-8) // 4 = 2 and restoring the negative sign. The state becomes 7, -2.

Finally, the function returns total + last_term = 7 + (-2) = 5.

Addition and subtraction finalize the previous term by moving it into total. Multiplication and division update only last_term. This keeps higher-precedence work inside the current term before that term is added to total.

At every operator boundary, total + last_term equals the value of the processed prefix. After the sentinel commits the final number, this value equals the whole expression.

The loop processes each character at most once, so the time complexity is O(n), where n is the string length. The algorithm uses only a fixed number of variables, so the auxiliary space complexity is O(1).

Relevant cases are spaces, multi-digit numbers such as 14-3/2, chains such as 2*3*4, subtraction that creates a negative last_term, and an expression containing one number such as 42.

The key idea is to separate completed additive terms from the newest term. total stores terms that can no longer change. last_term stores the current term, which may still be multiplied or divided. When the previous operator is + or -, the old last_term is moved into total and a new signed term begins. When the operator is * or /, only last_term changes. This preserves precedence without a stack. The invariant is that total + last_term equals the value of the processed prefix.

Let n be the number of characters in the expression. The algorithm scans from left to right and processes each character at most once, so the time complexity is O(n). It stores only total, last_term, current_number, operator, quotient, and the loop character. The amount of extra memory does not grow with n, so the auxiliary space complexity is O(1).

A common mistake is adding every number directly into total, which loses multiplication and division precedence. Another mistake is applying the new operator instead of the previous operator at an operator boundary. Candidates may forget to process the final number, so the sentinel is important. Using a // b directly when a is negative is wrong because // floors instead of truncating toward zero. Other mistakes are not resetting current_number and not skipping spaces.

State the invariant before coding: total contains completed additive terms, while last_term contains the current term that multiplication or division may still change. Then connect every operator case to that invariant.

I would use a trie with the fixed order wmi, model, year, and engine. Each record follows one path. Exact values use normal edges, "*" is a wildcard edge, and missing values use "∅". During lookup, I follow every matching exact or fallback edge and track a score of exact and null matches. The highest score wins. A tie is ambiguous. Insertion is O(d). Lookup is typically O(d), worst case O(2^d), and the stored trie uses O(n·d) space.

The input is a vehicle query with the dimensions wmi, model, year, and engine. Stored records may contain exact values, the wildcard "*", or a missing value represented by "∅". The goal is to return the single most specific matching record. A trie fits this problem because each level represents one dimension, while exact, wildcard, and null edges capture the matching rules.

1. What input sizes, value ranges, and edge cases should the solution handle?
2. What output should be returned for empty, invalid, or duplicate input?
3. Should I prioritize execution time or memory use, and may I use the standard library?

Every record and query uses the fixed dimension order [wmi, model, year, engine].

An exact stored value matches only the same query value. The wildcard "*" matches any query value. The null token "∅" matches only when the query field is also missing.

The specificity priority is exact > null > wildcard.

The lookup returns the unique most specific Record. It returns None when no complete record matches. It reports ambiguity when different records share the same best specificity score.

Each trie level represents one dimension. A complete root-to-leaf path represents one stored record. The leaf stores the Record object.

The four example paths are:

A: 1HG → Civic → 2020 → * B: 1HG → Civic → 2020 → 2.0L C: 1HG → * → 2020 → * D: 1HG → Civic → ∅ → *

Before insertion, exact values remain exact, "*" remains a wildcard, and None becomes "∅".

The example query is [1HG, Civic, 2020, 2.0L].

The lookup starts with a stack containing the root node, dimension index 0, zero exact matches, and zero null matches.

The invariant is: every active path matches all query dimensions processed so far.

At WMI, the query value is 1HG. The lookup takes the exact 1HG edge. Records A, B, C, and D are still reachable. The score prefix is (1,0).

At model, the query value is Civic. The lookup follows both the exact Civic edge and the wildcard "*" edge. The exact branch keeps A, B, and D with score (2,0). The wildcard branch keeps C with score (1,0).

At year, the query value is 2020. The Civic branch follows exact 2020. Record D is removed because its stored "∅" does not match a provided year. The wildcard-model branch also follows exact 2020. A and B now have score (3,0). C has score (2,0).

At engine, the query value is 2.0L. The exact 2.0L edge reaches B with score (4,0). Wildcard edges reach A with score (3,0) and C with score (2,0).

The lookup compares the terminal matches. B has the highest score, so it returns record B.

The algorithm adds a path to the stack only when its next edge can match the current query field. Therefore, the active-path invariant remains true after every dimension.

Every terminal record reached by the traversal is a valid match for the full query. The score tuple stores exact matches first and null matches second. Python compares tuples from left to right, so more exact matches always win. When exact counts tie, more null matches win. Wildcards add no score.

This implements exact > null > wildcard. If different records have the same best score, neither is more specific, so the result is ambiguous.

The Node class stores child edges in a dictionary and records at terminal leaves. insert() normalizes the four values and creates one trie path with setdefault().

lookup() normalizes the query and uses a stack to explore every valid matching branch. Each stack entry stores the current node, the next dimension index, the exact-match count, and the null-match count.

A wildcard child is always a valid fallback. A null child is considered only when the query field is missing. Otherwise, the matching exact child is considered.

At each terminal node, the code compares the path score with the best score found so far. It replaces a lower score and collects records with an equal best score.

Let d be the number of dimensions and n be the number of stored records. Here d is four.

Insertion takes O(d) time per record. A typical lookup takes O(d) when few fallback branches survive. In the worst case, an exact or null branch and a wildcard branch may both survive at many levels, producing O(2^d) reachable paths.

The stored trie uses O(n·d) space. The temporary lookup stack can contain up to O(2^d) active states in the generalized case.

Important edge cases are a missing query field, no terminal match, wildcard fallback, and multiple best records with the same score.

The key idea is to organize records by one fixed dimension order. Each trie edge stores an exact token, the wildcard "*", or the null token "∅". During lookup, the algorithm keeps every branch that can still match the query. It also carries a specificity score of (exact_matches, null_matches). The invariant is that every active path matches all processed query dimensions. Exact matches increase the first score value. Null matches increase the second. Wildcards increase neither. Tuple comparison therefore implements exact > null > wildcard. The unique terminal record with the highest score is returned. A shared best score is reported as ambiguity.

Let d be the number of dimensions and n be the number of records. Insertion takes O(d) time for each record because it processes every dimension once. A typical lookup takes O(d) time when only a small number of fallback branches remain active. The worst case is O(2^d) because an exact or null branch and a wildcard branch may both survive at several levels. Python dictionary lookup and insertion are O(1) on average. The stored trie uses O(n·d) space. The temporary traversal stack can use O(2^d) space in the generalized worst case.

A common mistake is treating a stored null as a wildcard. The null token "∅" must match only a missing query field. Another mistake is following every child instead of only valid exact, null, and wildcard edges. Candidates may return the first terminal record without comparing specificity scores. They may also ignore equal best scores and choose an arbitrary record instead of reporting ambiguity. Another error is comparing only the exact count and forgetting that null matches must outrank wildcard matches. Finally, do not claim that every lookup is guaranteed O(d), because fallback branches can create O(2^d) work.

State the three matching rules before writing code: exact matches the same value, null matches only a missing query field, and wildcard matches anything. Then define the score tuple (exact_matches, null_matches). This makes the traversal, priority, and ambiguity rules easier to explain.

I project the input into query, key, and value tensors. Then I split the model dimension into multiple heads and compute scaled dot-product attention inside each head. I apply the optional mask before a numerically stable softmax. Next, I multiply the attention weights by the values, merge the heads, and apply the output projection. The output shape is (B, T, d_model). The full time cost is O(B * T * d_model^2 + B * T^2 * d_model), and attention memory is O(B * H * T^2).

The input tensor has shape (B, T, d_model). Here, B is the batch size, T is the sequence length, and d_model is the feature width. The solution projects the input into query, key, and value tensors. It splits their channels across H heads. Each head calculates attention independently. The head outputs are then joined and projected back to the original feature width.

1. What input sizes, value ranges, and edge cases should the solution handle?
2. What output should be returned for empty, invalid, or duplicate input?
3. Should I prioritize execution time or memory use, and may I use the standard library?

The input X has shape (B, T, d_model).

The projection matrices W_Q, W_K, W_V, and W_O each have shape (d_model, d_model) in this implementation.

The number of heads is H. Each head uses:

head_dim = d_model // H

Therefore, d_model must be divisible by H.

The output Y has shape (B, T, d_model).

First, calculate:

Q = X @ W_Q

K = X @ W_K

V = X @ W_V

Each result has shape (B, T, d_model).

Next, reshape each tensor to (B, T, H, head_dim). Then transpose it to (B, H, T, head_dim).

This layout lets NumPy calculate every head in parallel.

For each head, every query token is compared with every key token:

scores = Q @ K^T / sqrt(head_dim)

The score tensor has shape (B, H, T, T).

The division by sqrt(head_dim) keeps dot products from becoming too large. Without this scaling, softmax can become too sharp.

If a Boolean mask is supplied, True keeps a position and False blocks it. Blocked scores are replaced with -1e9 before softmax. Their final probabilities become close to zero.

Before exponentiation, subtract the largest value in each score row:

scores = scores - scores.max(axis=-1, keepdims=True)

This does not change the softmax result. It reduces the chance of overflow.

Then calculate:

weights = exp(scores) / sum(exp(scores))

Softmax runs across the key dimension. Each row of attention weights sums to approximately 1.

Use B = 1, T = 3, d_model = 4, H = 2, and head_dim = 2.

The input is:

X = [[1, 0, 1, 0], [0, 1, 1, 1], [1, 1, 0, 1]]

For this example, W_Q = W_K = W_V = W_O = I_4. Therefore, Q = K = V = X, and the output projection does not change the merged context.

Head 1 receives:

[[1, 0], [0, 1], [1, 1]]

Its score matrix is approximately:

[[0.707, 0.000, 0.707], [0.000, 0.707, 0.707], [0.707, 0.707, 1.414]]

Its attention weights are approximately:

[[0.401, 0.198, 0.401], [0.198, 0.401, 0.401], [0.248, 0.248, 0.503]]

Its output is approximately:

[[0.802, 0.599], [0.599, 0.802], [0.752, 0.752]]

Head 2 receives:

[[1, 0], [1, 1], [0, 1]]

Its score matrix is approximately:

[[0.707, 0.707, 0.000], [0.707, 1.414, 0.707], [0.000, 0.707, 0.707]]

Its attention weights are approximately:

[[0.401, 0.401, 0.198], [0.248, 0.503, 0.248], [0.198, 0.401, 0.401]]

Its output is approximately:

[[0.802, 0.599], [0.752, 0.752], [0.599, 0.802]]

Concatenating the two head outputs gives:

[[0.802, 0.599, 0.802, 0.599], [0.599, 0.802, 0.752, 0.752], [0.752, 0.752, 0.599, 0.802]]

Because W_O = I_4, this is also the final output Y.

Inside each head, softmax creates one probability distribution over the key positions for every query position. The weights in each row sum to approximately 1.

Multiplying those weights by V produces a valid weighted combination of the value vectors. Each head performs this calculation independently.

Concatenating all head outputs restores d_model channels. Applying W_O keeps the required final shape (B, T, d_model).

The dense query, key, value, and output projections cost O(B * T * d_model^2) time.

The score calculation and weighted-value calculation cost O(B * H * T^2 * head_dim), which simplifies to O(B * T^2 * d_model).

The full time complexity is therefore O(B * T * d_model^2 + B * T^2 * d_model).

The attention scores and weights need O(B * H * T^2) memory. The projected tensors and merged context also need O(B * T * d_model) memory.

Important edge cases are an invalid head count, padding or causal masks, long sequences with quadratic attention cost, and numerical overflow if softmax is not stabilized.

The key idea is to perform scaled dot-product attention in several smaller channel groups called heads. The algorithm projects X into Q, K, and V, then reshapes them from (B, T, d_model) to (B, H, T, head_dim). Every query compares itself with all keys in the same head. The central invariant is that each softmax row forms a probability distribution over key positions. Therefore, each output row is a valid weighted combination of value vectors. The head outputs are concatenated and projected with W_O, which restores the required shape (B, T, d_model).

Let B be the batch size, T the sequence length, H the number of heads, and d_model the model width. Each head has head_dim = d_model / H. The query-key scores and the weighted-value calculation cost O(B * H * T^2 * head_dim), which is O(B * T^2 * d_model). The four dense projections cost O(B * T * d_model^2). The full time cost is O(B * T * d_model^2 + B * T^2 * d_model). The attention score and weight tensors use O(B * H * T^2) extra memory. Projected and context tensors also use O(B * T * d_model) memory.

Common mistakes include forgetting that d_model must be divisible by the number of heads, reshaping or transposing the wrong axes, and normalizing across the wrong dimension. Softmax must run across the key dimension. Another mistake is forgetting the sqrt(head_dim) scaling. The mask must be applied before softmax, not after it. Candidates may also call exp directly without subtracting the row maximum, which can cause overflow. Finally, all heads must be merged before applying W_O, and the full complexity must include both attention and projection costs.

Say the tensor shape after every major operation. The most important checkpoints are (B, H, T, head_dim) after splitting, (B, H, T, T) for the scores, and (B, T, d_model) after merging. This makes transpose and reshape errors easy to catch.

At a high level, I would enforce rate limits at the API gateway before requests reach the backend. The gateway identifies each caller using an API key, user ID, OAuth client ID, or IP fallback. It loads the caller’s per-plan policy and checks a token bucket in Redis. Allowed requests reach the backend and return with RateLimit-Limit, RateLimit-Remaining, and RateLimit-Reset headers. When no token remains, the gateway returns 429 with Retry-After. This protects the service, but the gateway and Redis become important dependencies.

The goal is to protect a public API from overload and abusive traffic. The main challenge is limiting each caller while still allowing normal bursts. I would explain the design by following the request and response paths.

1. Which clients and core use cases must the API support?
2. What authentication, authorization, and data-validation rules should I assume?
3. What scale, error handling, idempotency, and versioning requirements matter?

At a high level, the API client sends an HTTPS API request. The request first reaches the API Gateway / Rate Limiting Layer. The gateway applies the limit before the backend performs business work.

This gives every public client one consistent enforcement point. It also keeps rate-limit logic outside the Backend API Service. The backend can focus on processing allowed requests.

The gateway authenticates or identifies the caller first. The Identity Key may be an API key or user ID. It may also be an OAuth client ID. An IP address is used as a fallback identity.

The selected identity determines the caller’s policy and counter. This prevents unrelated callers from sharing the same usage record. A stronger identity is better than IP when one is available.

The Abuse / WAF Rules also support this stage. They detect patterns such as botnets, spikes, and malicious IPs. They send blocking decisions to the gateway.

The gateway fetches the rule from the Rate Limit Policy Store. The store contains per-plan limits and burst capacity. Different plans can therefore receive different request allowances.

Burst capacity allows a short traffic spike. It avoids rejecting a caller after one small burst. The policy store owns the rule definition. It does not own the live request counter.

Keeping policy separate makes plan changes easier. It also keeps policy management separate from request enforcement.

The gateway checks the Counter Store in Redis. Redis stores token bucket counters and their TTL values. TTL means an unused counter can expire automatically.

A token bucket represents the caller’s current allowance. If a token is available, the gateway uses one token. The request is then allowed to continue. Short bursts are accepted until the bucket becomes empty.

The shared Redis store keeps counts consistent across gateway instances. The downside is that Redis becomes part of request admission. The diagram therefore uses a safe failure decision. For abuse-sensitive traffic, the gateway can fail closed. That means it rejects the request instead of risking overload.

When the request is allowed, the gateway forwards it. The Backend API Service performs the requested business operation. The backend sends its API response back to the gateway. The gateway then returns the response to the API client.

A successful response uses a 200 or another 2xx status. It includes RateLimit-Limit, which shows the configured limit. It includes RateLimit-Remaining, which shows remaining capacity. It also includes RateLimit-Reset, which helps the client plan later calls.

These stable headers improve compatibility between clients and the service. Clients can use the same response contract as policies change.

If the bucket is empty, the gateway rejects the request early. The Backend API Service is not called. The gateway returns 429 Too Many Requests. It also returns Retry-After guidance.

The client should wait before trying again. It should back off instead of retrying immediately. This prevents repeated retries from creating more load.

The gateway owns this error response. The backend does not generate it for rejected requests.

The gateway sends rate-limit events to Observability. These events include 429 responses and latency information. The Counter Store also sends usage metrics.

Observability collects logs, metrics, alerts, and dashboards. It helps operators detect abuse and capacity problems. It is not part of the business response path.

The main benefit is centralized and consistent protection. The main downside is added gateway and Redis dependency. We accept this trade-off because protecting the backend is critical.

At a high level, I would separate event ingestion from event reading. Producers send events through the API Gateway to the Ingest API. The Parse and Validate Service checks required fields, loads the requested schema, prevents duplicate writes, stores the parsed event, and updates the random-access index. Consumers use the Read API to find events by event ID, stream offset, or cursor. The gateway handles JWT authentication and authorization. The main trade-off is extra coordination during writes, but indexed reads become much faster.

The goal is to ingest events safely and serve them later through fast random-access reads. The main challenge is keeping validation, ordering, indexing, and schema versions consistent. I would explain the write flow first, followed by the read flow.

1. Which clients and core use cases must the API support?
2. What authentication, authorization, and data-validation rules should I assume?
3. What scale, error handling, idempotency, and versioning requirements matter?

I would separate the system into an ingest path and a read path. Both paths enter through the API Gateway plus AuthN and AuthZ. Authentication proves the caller's identity. Authorization decides whether that caller may perform the operation.

The producer sends write requests through the gateway. The gateway routes approved writes to the Ingest API. The consumer sends read requests through the same gateway. The gateway routes approved reads to the Read API.

This creates one clear security boundary for both APIs.

The producer calls POST /v1/streams/{stream_id}/events over HTTPS. The request carries a JWT and an Idempotency-Key header. The idempotency key helps prevent duplicate writes.

The body contains source_event_id, schema_version, occurred_at, and payload. The source_event_id identifies the event at the producer. The schema_version identifies the expected payload format. The occurred_at field records when the event happened.

After authorization, the Ingest API sends the request to the Parse and Validate Service.

The Parse and Validate Service checks required fields and parses the payload. It sends a schema lookup to the Schema Registry and Compatibility Rules component. The registry returns the schema and compatibility information.

The service also sends a dedupe-key check to the Idempotency Store. The store returns whether the request is new or already processed. For a duplicate request, it can return the prior result.

The visible ingest errors are 400, 401, 409, and 422. A 400 or 422 response represents invalid request data. A 401 response represents missing or invalid authentication. A 409 response represents a duplicate or conflicting request. Each response returns through the gateway to the producer.

For a valid new request, the service assigns an event_id and stream_offset. The event_id is the service's canonical event identifier. The stream_offset gives the event an ordered position in its stream.

The service writes the parsed event to the Parsed Event Store. It also updates the Random-Access Index Store. The index supports lookup by event ID, offset, and timestamp.

The service sends ingest metrics and audit records to Observability and Audit Logs. Invalid events follow the rejection path into the Failure Log.

On success, the gateway returns 201 Created. The response contains event_id, stream_offset, and schema_version.

A consumer can request one event using GET /v1/streams/{stream_id}/events/{event_id}. It can also request a page using GET /v1/streams/{stream_id}/events?offset=&limit=&cursor=.

After authorization, the gateway sends the request to the Read API. The Read API first asks the Random-Access Index Store to locate matching events. The index returns event locations or pointers to the Read API.

The Read API then requests the parsed events from the Parsed Event Store. The store returns the stored event data. This two-step flow avoids scanning the full event store.

The limit value controls the page size. The cursor identifies where the next page should continue. A successful response returns 200 OK with events[], next_cursor, and schema_version. An invalid cursor returns 400. A missing event returns 404.

For older event versions, the Read API checks the Schema Registry. It requests an upcast when the reader expects a newer schema. Upcasting converts an older event into a reader-compatible representation. The registry returns that compatible representation to the Read API.

The Read API also sends read metrics and access logs to Observability and Audit Logs.

The main benefit is fast lookup without scanning every stored event. The downside is that each accepted write updates two data structures. The Parsed Event Store and Random-Access Index Store must remain consistent. This adds write-path complexity, but it improves read performance.

The design separates writes from reads because each path has different work. The write path validates fields, checks the schema, prevents duplicates, stores the event, and updates the index. The read path uses the index before loading full event data. The benefit is faster random access. The downside is more write coordination. Idempotency reduces duplicate writes, but it adds another lookup and another store to maintain. Cursor pagination works well for long streams, but invalid cursors need clear errors. Versioned schemas protect older data. Upcasting helps newer readers understand older events, but conversion rules must be maintained. We accept these costs because the system needs ordered storage, safe ingestion, and efficient lookup.

At a high level, I would build a read-heavy key-value API that keeps several versions of each key and gives every version its own TTL. Clients send HTTPS JSON requests through the API Gateway. The gateway handles JWT authentication and authorization, rate limiting, validation, and routing. It forwards requests over mTLS to the KV Service Cluster. The cluster supports PUT, GET, GET_AT, DELETE, and LIST operations. It stores versions in distributed shards and returns JSON through the gateway. Strong consistency protects each key. Background workers remove expired versions and compact tombstones. The trade-off is higher storage use for faster historical reads.

The API stores changing values and supports reads at different times. The main challenge is combining fast reads, TTL expiry, and safe writes. I would explain the design by following the diagram from client to storage.

1. Which clients and core use cases must the API support?
2. What authentication, authorization, and data-validation rules should I assume?
3. What scale, error handling, idempotency, and versioning requirements matter?

I would begin with the API boundary. The client sends an HTTPS JSON request to the API Gateway. The gateway checks authentication and authorization using a JWT. A JWT is a signed token that identifies the caller. The gateway also applies rate limits, validates the request, and routes it.

The gateway forwards the accepted request to the KV Service Cluster over mTLS. mTLS encrypts the connection and lets both systems verify each other. The response follows the reverse path. The cluster returns JSON to the gateway. The gateway then returns an HTTPS JSON response to the client.

PUT /v1/kv/{key} creates a new version. Its body contains value and ttl_seconds. The service records a write timestamp and sets expire_at to the current time plus the TTL. Previous versions remain stored because writes are append-only.

GET /v1/kv/{key} returns the latest valid version. A version is valid when the current time is earlier than expire_at.

GET_AT /v1/kv/{key}?at=timestamp performs a historical read. It returns the latest version where write_ts is at or before the requested timestamp and that timestamp is before expire_at.

DELETE /v1/kv/{key} creates a tombstone version. A tombstone is a marker that says the key was deleted. It hides the earlier versions from normal reads.

LIST /v1/kv?prefix=xxx&cursor=yyy returns keys matching a prefix. The cursor supports optional pagination, so the service does not return every result at once.

The KV Service Cluster handles reads and writes. It owns versioning, TTL checks during reads, and concurrency control. It sends read and write operations to the distributed storage layer.

The design partitions keys across shards. The diagram uses Hash(key) modulo N to select the shard. Each shard stores key versions sorted by timestamp. It also stores each value, expire_at, and other metadata. This structure supports fast latest reads and historical lookups.

TTL is enforced during every read. This means expired data becomes invisible immediately, even before physical deletion. GET returns 404 when the key is missing or expired. GET_AT also returns 404 when no version was valid at the requested time.

The KV Service Cluster sends expiry tasks to the TTL Expiry Worker. The worker scans data, deletes expired versions, and emits expiry events or metrics. The Compaction Worker later removes old tombstones and optimizes storage. These jobs run asynchronously. They are not part of the synchronous client response path.

I would use strong consistency per key. Writes for one key are linearizable. This means every accepted write appears in one clear order.

The API may support optimistic concurrency using an if-match version or timestamp. The client sends the version it expects to update. The service compares it with the current version. A match allows the write. A mismatch can return 409 Conflict. This prevents one writer from silently replacing another writer's newer value.

Different keys can still scale across separate shards because they do not share the same write order.

The API returns 400 for invalid input. It returns 401 when authentication is missing or invalid. It returns 403 when the caller is authenticated but not allowed. It returns 404 when a key is missing or expired. It may return 409 when a conditional write conflicts with the current version.

The main benefit is fast latest and historical reads with clear expiry behavior. The downside is additional storage for old versions, indexes, tombstones, and background cleanup. We accept this cost because time-aware reads are a core requirement.

The API is simple for callers, but the storage model adds internal work. PUT always creates a new version. The benefit is safe history and easy time-aware reads. The downside is higher storage use. GET is fast because versions are ordered by timestamp. GET_AT uses the same ordering to find the latest version valid at a past time. TTL is checked during reads, so expired data disappears immediately. Background workers can delete it later. This is reliable, but it needs expiry scanning and compaction. Strong consistency per key prevents confusing write order. Optional if-match checks prevent lost updates, but callers may receive 409 and retry. Sharding improves scale across many keys. The downside is that changing the shard count requires careful data movement.

At a high level, I would expose a versioned create-or-update API at /v1/resources. The client first obtains a JWT, then sends a POST, PUT, or PATCH request over HTTPS. The request includes JSON, an Idempotency-Key, and optional If-Match. The Resource Service authenticates and authorizes the caller, validates the contract, checks duplicate retries, and verifies the ETag before updating. It returns 201 Created, 200 OK, or a standard JSON error. The trade-off is extra storage and processing for safer writes.

The goal is to create or update a resource without duplicate writes or lost updates. The main challenge is handling retries, concurrent clients, invalid input, and older API clients. I would explain the design by following the request path in the diagram.

1. Which clients and core use cases must the API support?
2. What authentication, authorization, and data-validation rules should I assume?
3. What scale, error handling, idempotency, and versioning requirements matter?

I would start with the versioned endpoint /v1/resources.

The client uses POST to create a resource. It uses PUT or PATCH to update one. All communication uses HTTPS.

The request contains a JSON body and Content-Type: application/json. It also includes Authorization: Bearer <JWT> and an Idempotency-Key.

An update may include If-Match. This header carries the ETag or version the client expects.

The /v1 contract remains stable. New fields should be optional and additive. Existing field meanings should not change inside version one.

The client first obtains a JWT from the Identity Provider or Auth Server.

The identity system authenticates the client and issues a signed token. The token contains scopes, roles, or other claims.

The client sends the JWT with the resource request. The Resource Service validates the token and checks the caller's scopes, roles, and permissions.

Authentication proves the caller's identity. Authorization decides whether that caller may create or update the resource.

An invalid token returns 401 Unauthorized. A valid caller without permission returns 403 Forbidden.

The Resource Service checks the request with the Validation and Contract Compatibility component.

This component checks required fields, the JSON schema, and business rules. It also checks that the request remains compatible with /v1.

A malformed request returns 400 Bad Request. A request with invalid field values can return 422 Unprocessable Entity.

If the contract is valid, the service continues to the idempotency check. Otherwise, it returns a standard error response.

The Resource Service looks up the Idempotency-Key in the Idempotency Store.

The store keeps the key, a request hash, the response status, the response body, and an expiry time.

If the key is not found, the service continues with the write. After completion, it stores the result for later retries.

If the same key and request appear again, the service returns the stored response. It does not perform another create or update.

This is useful when the client loses the first response because of a network failure. The client can retry safely without creating duplicate data.

The Resource Service reads the current resource and its version or ETag from the Resource Store.

For an update, it compares that value with If-Match. This is optimistic concurrency control, which prevents stale clients from overwriting newer data.

When the values match, the service applies its business logic. It writes the resource and stores a new version or ETag.

When the values do not match, the service rejects the update. It returns 409 Conflict or 412 Precondition Failed.

The database also uses unique constraints and indexes to protect stored resource data.

A successful create returns 201 Created. The response includes the resource JSON, an ETag, and a Location header.

A successful update returns 200 OK. It includes the updated resource and its new ETag.

Errors use application/problem+json. The body includes code, message, details, requestId, timestamp, and path.

Unexpected failures return 500 Internal Server Error. Temporary service failures return 503 Service Unavailable.

The Resource Service separately emits structured logs, audit events, metrics, and traces. The requestId connects those records to the original request. Observability helps investigation, but it does not own the business response.