Bentobox Software Engineer Interview Guide

The 60-second brief

Candidates describe Bentobox as a maker of software for the restaurant industry, with products that range from restaurant management systems to online ordering platforms. The Software Engineer role covers building and maintaining applications of that kind, working with product management and design, taking part in code reviews, and contributing to the architecture of the systems. The stack candidates see listed spans JavaScript or Node.js, Python or Ruby, React or Angular, REST APIs, and SQL or NoSQL databases.

This guide is for engineers preparing for the Bentobox Software Engineer loop, especially web and full-stack developers. It covers the three reported stages, the twenty reported questions grouped as technical discussion, coding, system design and behavioral, and a seven-day plan that points at them. It does not describe how any interviewer will behave, only what to prepare.

Candidates report three stages over roughly 3-5 weeks: a phone screen with a recruiter, one or more technical interviews, and an onsite made of several rounds that includes hands-on exercises.

Focus areas

• API Design
• React
• Data Modeling

Overview

The work candidates describe is web software for restaurants: management systems and online ordering. The reported responsibilities are building and maintaining applications, working alongside product management and design, reviewing teammates' code, and contributing to system architecture. For preparation, that means you should be ready to talk about a full request path, from a React screen through a REST API to a SQL or NoSQL table, and to explain the decisions you made at each layer.

The question mix is broad rather than deep in one area. Across 11 reported loops, the topics candidates list are API design, React, data modeling, pair programming and frontend development. The reported questions add classic coding problems (reversing a linked list, balanced parentheses, longest substring without repeating characters, a dynamic programming problem, and explaining complexity), technical discussion (REST versus GraphQL, MVC, maintainable code, database design, API performance), system design (a URL shortening service, a real-time chat application, API design considerations, distributed data consistency, performance bottlenecks) and behavioral questions about projects, conflict, motivation, team culture and prioritisation.

Plan around three skills. First, write correct code in your strongest language while narrating, because the coding and pair-programming topics reward fluency more than trivia. Second, build a short, concrete answer for each discussion question from your own projects, so database design, API performance and maintainability do not turn into definitions. Third, have six behavioral stories outlined. The nice-to-have items in the role (AWS or Azure, Docker and Kubernetes, DevOps practice) are worth a basic vocabulary but should not displace the core topics.

The Bentobox interview process

Candidates describe three stages, and they report that the exact structure varies by team, so confirm the shape with your recruiter.

Phone Screen

Candidates describe the first stage as a call with a recruiter about your background and fit for the role. Use it to make your experience legible against the stack the role lists: JavaScript or Node.js, Python or Ruby, React, SQL or NoSQL databases and Git. Ask how the later stages are arranged for the team you are applying to, since the structure is reported to vary by team.

What to demonstrate

• Your background as you describe it to a recruiter
• Fit for the Software Engineer role and its listed stack

How to prepare

• Write a short career walkthrough that ends on the stack the role lists, and decide which of JavaScript, Node.js, Python, Ruby, React, SQL and NoSQL you can say you have shipped in.
• Read about Bentobox's restaurant management and online ordering products so you can say what part of that work interests you.
• Have logistics ready (location, start timing) and a short list of questions about how the technical and onsite stages are organised for your team.

Technical Interviews

Candidates report one or more interviews that combine coding assessments with discussions of your previous experience. The coding questions candidates report are reversing a linked list, balanced parentheses, a dynamic programming problem and the longest substring without repeating characters, each followed by a complexity explanation. The experience discussions touch maintainable code, database design and API performance. Practise both modes, because the same stage asks you to code and to talk about past work.

What to demonstrate

• Working code for algorithm problems, which a coding assessment inherently tests
• Your ability to state time and space complexity for your own solution
• How you describe previous projects, including database design and API performance

How to prepare

• Solve the reported coding problems in your strongest language, run them against empty, single-element and repeated-character inputs, and say the complexity out loud.
• Prepare one real example each for maintainable code, database design and API optimisation, with the decision you made and its measured effect.
• Practise coding while narrating, since pair programming is among the topics candidates report.

Onsite Interviews

Candidates report an onsite made of multiple rounds that cover technical skills and cultural fit, with hands-on exercises that resemble real work. Given the reported topic mix, the exercises are most likely to touch API design, React and data modeling, and the system design questions candidates report (URL shortener, real-time chat, distributed consistency) are good rehearsal for the technical rounds. Keep behavioral stories ready for the cultural-fit rounds.

What to demonstrate

• Technical skills in hands-on exercises
• Cultural fit, as the onsite is described
• Working with others on a problem, since collaboration is a reported theme

How to prepare

• Build a small React component with state, list rendering and a data fetch with loading and error states, then explain each choice.
• Sketch a REST API and its tables for an ordering-style resource: endpoints, pagination, error responses, keys and indexes.
• Rehearse the system design questions with a repeatable structure: requirements, data model, API, scaling, failure modes.
• Have the six behavioral stories outlined and rehearsed aloud.

Where candidates lose points

Losing the rest of the list while reversing a linked list, or skipping the empty and one-node cases

Save the next node before you reassign the pointer, then trace an empty list, a single node and a two-node list by hand before you say you are done. Offer the recursive version only after the iterative one works.

Solving longest substring without repeating characters with nested loops, or moving the window start backwards

State the brute-force cost, then use a sliding window with a map of last-seen index and move the start with max(start, last_seen + 1). Say the time is linear and the space is bounded by the character set.

Answering REST versus GraphQL, MVC or API performance as a list of definitions

Anchor each answer to a scenario from your own work: over-fetching on a nested ordering screen, caching differences, error shapes, or an N+1 query you removed. Name the trade-off and when you would choose the other side.

Telling behavioral stories as what the team did, never naming your own decision

For each of the six reported behavioral prompts, outline the situation, your decision, the evidence and the result, and mark the sentence where your personal choice appears.

Listing JavaScript, React, SQL, Docker and AWS on your CV and then having no concrete decision to discuss in a hands-on exercise

For every technology you list, prepare one specific thing you built or fixed with it. Remove or downplay any item you could not discuss for two minutes of follow-up questions.

Questions & practice

Write a function that reverses a linked list.

Medium · Coding / Algorithms

Approach

1. Iterative version with three pointers: prev = null, curr = head. In the loop: next = curr.next; curr.next = prev; prev = curr; curr = next. Return prev. O(n) time, O(1) extra space.
2. Recursive version: reverse the rest of the list, then set head.next.next = head and head.next = null. It is O(n) time but also O(n) call-stack space, and long lists can hit recursion limits (Python's default is about 1000).
3. Edge cases to state and test: empty list, a single node, two nodes. Make sure the old head ends with next = null, otherwise you create a cycle.
4. Test it by building a list from an array, reversing it, converting it back to an array and comparing the result.

Follow-up

• Reverse only the nodes between positions left and right in one pass.
• Reverse the list in groups of k nodes.
• Check whether a linked list is a palindrome using O(1) extra space.

How would you implement a function to check for balanced parentheses?

Medium · Coding / Algorithms

Approach

1. Use a stack. Push every opener. On a closer, fail if the stack is empty or its top is not the matching opener (keep a map such as ')' -> '('), otherwise pop. At the end the string is balanced only if the stack is empty. O(n) time, O(n) space in the worst case.
2. With only one bracket type a counter is enough: add 1 for '(', subtract 1 for ')', and fail if it ever goes negative. That is O(1) space. Explain why counters break with mixed types, for example '([)]'.
3. Edge cases: the empty string (balanced), a string starting with a closer, leftover openers like '((', and other characters (ignore or reject them, depending on the spec).
4. A quick early exit: an odd count of bracket characters can never be balanced.

Follow-up

• Return the minimum number of removals needed to make the string valid.
• Find the length of the longest valid parentheses substring.
• Generate every balanced string of n pairs. How many are there?

Solve a problem using dynamic programming.

Medium · Coding / Algorithms

Approach

1. Say why DP fits: optimal substructure plus overlapping subproblems. Then define the state in words before writing code, for example dp[a] = the fewest coins that make amount a.
2. Write the recurrence, base cases and evaluation order. For coin change: dp[0] = 0, dp[a] = min(dp[a - c] + 1) over coins c <= a, unreachable amounts stay at infinity, and you return -1 for them. O(amount x coins) time, O(amount) space.
3. Choose top-down memoization (closer to the recursion, computes only the states it needs) or a bottom-up table (no recursion-depth risk, easier to compress) and say why.
4. Compress space when a state depends only on recent ones: house robber needs two variables, and LCS needs two rows, so O(min(m, n)).
5. Check the result against a brute-force recursion on small inputs and trace a tiny case by hand.

Follow-up

• Reconstruct the actual choices, not just the optimal value.
• Count the number of ways to make the amount instead of the minimum. Why does the loop order decide between combinations and permutations?
• Convert your memoized solution to bottom-up and compare their space.

Explain the time and space complexity of your solution.

Medium · Coding / Algorithms

Approach

1. Define your variables first (n = string length, k = alphabet size). Then count work: nested loops multiply. Solve recursive solutions with a recurrence, for example T(n) = 2T(n/2) + O(n) gives O(n log n) by the master theorem.
2. Count hidden costs in library calls: list.pop(0) is O(n) (use a deque), membership in a list is O(n) against O(1) average for a set, sorting is O(n log n), and string slicing copies.
3. Separate auxiliary space from output space, and count the recursion stack. A hash map over characters is bounded by O(min(n, k)).
4. State worst, average and amortized cases correctly: hash lookups are O(1) on average but O(n) in the worst case, and dynamic-array append is amortized O(1).
5. For two-pointer or sliding-window code, argue it is O(n) because each pointer only moves forward and at most n times in total.

Follow-up

• Can this be done faster? What is the lower bound?
• Which term dominates in practice at n = 10^6?
• How would you trade memory for time here, or the reverse?

Write a program that finds the longest substring without repeating characters.

Medium · Coding / Algorithms

Approach

1. Brute force first: check every substring for repeats, O(n^3), or O(n^2) with a set per start index. Then move to a sliding window.
2. Sliding window with a map from character to its last index. For each right index: if s[right] is in the map and its index >= left, set left = lastIndex + 1. Update the map and best = max(best, right - left + 1). O(n) time, O(min(n, k)) space.
3. The >= left check matters. On 'abba', the stale index of 'a' would otherwise move left backwards and overcount.
4. Test cases: empty string -> 0, 'bbbbb' -> 1, 'pwwkew' -> 3 ('wke'), 'abcabcbb' -> 3. Track the start index if the substring itself must be returned.
5. Character handling: in JavaScript, strings are UTF-16, so iterate with for...of to step by code point if emoji or other astral characters are possible.

Follow-up

• Find the longest substring with at most k distinct characters.
• How would you adapt this to a stream of characters you cannot store?
• Return every longest substring, not just the length.

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

• A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
• Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

• A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
• How do you honour an erasure request while resource_revision.actor_user_id still references this table?

What is the difference between REST and GraphQL?

Medium · Technical / Domain Questions

Approach

1. REST exposes resources at URLs and uses HTTP verbs and status codes. GraphQL exposes one typed schema, usually at a single POST endpoint, and the client chooses which fields it gets back.
2. GraphQL solves over-fetching and under-fetching: one query can collect an order, its items and the customer, where REST might need several round trips or a custom endpoint.
3. Caching is the main cost. REST GETs work with Cache-Control, ETags and CDNs out of the box. GraphQL POSTs need persisted queries or a client cache such as Apollo's normalized store.
4. GraphQL resolvers cause N+1 queries unless you batch them, for example with DataLoader. A public GraphQL API also needs query depth or cost limits so one nested query cannot overload the database.
5. Versioning: REST often puts a version in the path or a header. GraphQL schemas change additively and retire fields with @deprecated. Pick GraphQL when many clients need different shapes; pick REST for simple CRUD, public APIs and heavily cached reads.

Follow-up

• How would you stop a deeply nested GraphQL query from overloading the database?
• How do you cache GraphQL responses at the edge?
• Where does per-field authorization live in a GraphQL server?

Explain MVC architecture and its benefits.

Medium · Technical / Domain Questions

Approach

1. Define the three parts. The Model holds data and business rules (validation, persistence). The View renders a representation (HTML or JSON). The Controller takes the request, calls the model and chooses the view.
2. Walk through one request: the router sends POST /orders to a controller action, the controller asks the Order model to validate and save, then renders a view or redirects. Name a framework you know, such as Rails, or Django, which calls the pattern MTV.
3. Benefits: separation of concerns, models you can unit test without HTTP, several views (web page, JSON API) over the same model, and people working on different layers in parallel.
4. Pitfalls: fat controllers and logic leaking into templates. When a workflow spans several models, move it into service objects or domain modules and keep controllers thin.
5. Contrast with the frontend: React is mostly a view layer built from components with one-way data flow. State lives in components or a store, not in a classic controller.

Follow-up

• Where does business logic go when it spans several models?
• How does MVC compare with how a React app organizes state?
• How would you test each layer separately?

How do you ensure your code is maintainable?

Medium · Technical / Domain Questions

Approach

1. Lead with concrete habits: small functions with one job, names that say intent, clear module boundaries, and no abstraction before a pattern repeats (the rule of three).
2. Tests as a safety net: unit tests on business rules, integration tests at the database and API boundaries, all run in CI on every pull request so refactoring is safe.
3. Automate style and type checks: formatters and linters (Prettier/ESLint, RuboCop, Black/Ruff) plus type checking (TypeScript, mypy) enforced in CI, so review time goes to design instead of formatting.
4. Small, reviewable pull requests. Comments explain why, not what. Short decision records or READMEs for non-obvious choices.
5. Back it with one real example, such as a module you refactored, and what changed afterwards (fewer bugs, faster changes, easier onboarding).

Follow-up

• How do you trade maintainability against a hard deadline?
• How do you start improving a legacy module that has no tests?
• When would you deliberately not add an abstraction?

What is your approach to optimizing API performance?

Medium · Technical / Domain Questions

Approach

1. Measure before changing anything: p50, p95 and p99 latency per endpoint from APM or tracing, then split the time between database, downstream calls, serialization and the network.
2. Database first, since that is the usual cause. Fix N+1 queries with eager loading or batching (Rails includes, Django select_related/prefetch_related). Add indexes that match the WHERE and ORDER BY columns and confirm them with EXPLAIN ANALYZE. Use keyset (cursor) pagination instead of large OFFSETs.
3. Cache at the right layer: HTTP caching with ETag/Cache-Control on GETs, a CDN for public content, and Redis for computed results with a TTL and invalidation on write.
4. Shrink the work per request: return only the fields needed, compress (gzip or brotli), reuse connections through pooling and keep-alive, run independent downstream calls in parallel with timeouts, and move slow side effects (email, images) to a queue, returning 202 where that fits.
5. Prove it with a load test that compares percentiles before and after, and add a latency alert so the gain does not quietly regress.

Follow-up

• The p50 is fine but the p99 is bad on one endpoint. What do you check?
• How do you invalidate a cached response when the underlying data changes?
• How would you stop a performance regression from shipping again?

How would you design a URL shortening service?

Medium · System Design / Architecture

Approach

1. Scope it: create a short link, redirect, optional custom alias and expiry, click counts. State the assumption that redirects far outnumber creates, so the read path drives the design.
2. Key generation: base62-encode a unique ID from a counter or ID allocator (each server leases a block of IDs), or use random 7-character keys with a uniqueness check on insert. 62^7 is about 3.5 trillion keys. Truncated URL hashes need collision handling.
3. Storage and the read path: a table or key-value store keyed by the code with a unique constraint, a Redis cache in front for hot codes, and expired codes returning 404 or 410.
4. Pick the redirect status deliberately: browsers cache a 301, which cuts load but hides repeat clicks; a 302 or 307 sends every click through the service for analytics.
5. Write click events to a queue and aggregate them asynchronously so analytics never slow the redirect. Rate-limit creation and screen destination URLs for abuse.

Follow-up

• How do you guarantee unique codes across several app servers without a single bottleneck?
• Would you return a 301 or a 302, and what does each cost you?
• One link suddenly gets millions of clicks. What changes?

Describe the architecture you would use for a real-time chat application.

Medium · System Design / Architecture

Approach

1. Transport: WebSockets for persistent two-way connections, with long polling as a fallback. Gateway servers hold the connections, and a presence map in Redis records which gateway each online user is on.
2. Send path: client -> gateway -> chat service, which assigns a per-conversation sequence number, persists the message, and publishes it through pub/sub (Redis or Kafka) to the gateways holding the recipients, then acks the sender.
3. Delivery semantics: at-least-once delivery with a client-generated message ID so retries are deduplicated, ordering by sequence within a conversation, and delivered/read receipts as separate events.
4. Offline users: the message stays stored and a push notification goes out. On reconnect the client fetches everything after its last seen sequence number.
5. Storage and scale: partition messages by conversation ID with time-ordered IDs (Cassandra, or partitioned Postgres). Use a load balancer that supports WebSocket upgrades. Big group channels need fan-out handled differently from one-to-one chats.

Follow-up

• Two people send at the same moment. How do all clients agree on the order?
• A gateway server crashes. What happens to its users' connections and in-flight messages?
• How does fan-out change for a channel with thousands of members?

What considerations do you take into account when designing an API?

Medium · System Design / Architecture

Approach

1. Model resources as nouns and use HTTP methods correctly: GET is safe and idempotent, PUT and DELETE are idempotent, POST is not. Return precise status codes such as 201, 400, 404, 409 and 429.
2. Make retries safe for non-idempotent writes: the client sends an Idempotency-Key header, and the server stores the key with the first response and replays it, so a timed-out order is never created twice.
3. Collections: cursor-based pagination for data that changes underneath the reader, explicit filter and sort parameters, and one consistent error body (code, message, field details).
4. Evolution: make additive changes, publish a deprecation policy, and keep a versioning scheme (path or header) for breaking changes. Cover authentication, per-resource authorization and rate limiting.
5. Document the contract in OpenAPI, add contract tests, and log request IDs so failures can be traced.

Follow-up

• How would you ship a breaking change without breaking existing clients?
• Offset or cursor pagination: when does each fail?
• A client retries after a timeout. How does the server know it already did the work?

Discuss how you would handle data consistency in a distributed system.

Medium · System Design / Architecture

Approach

1. Start from each operation's requirement. Decide which operations need strong (linearizable) consistency and where eventual consistency is acceptable. Use CAP and PACELC: during a partition you choose consistency or availability, and otherwise you trade latency for consistency.
2. Keep invariants inside one database transaction where possible. Across services, avoid two-phase commit (it blocks when the coordinator fails) and use a saga with compensating actions.
3. Use a transactional outbox: write the event to an outbox table in the same transaction as the state change, then relay it to the broker, so the database and the events cannot diverge.
4. Assume at-least-once delivery. Consumers must be idempotent (deduplicate on an event ID), and per-key ordering comes from partitioning, for example by order ID in Kafka.
5. Detect conflicts with optimistic concurrency (UPDATE ... WHERE version = ?). In quorum systems, R + W > N makes read and write sets overlap. Run reconciliation jobs to catch drift.

Follow-up

• Walk through a saga for placing an order where the payment step fails.
• What exact failure does the outbox pattern prevent?
• How would you find and repair records that are already inconsistent?

How do you approach performance bottlenecks in large-scale applications?

Medium · System Design / Architecture

Approach

1. Define the symptom and a baseline (p99 latency, throughput, error rate) and reproduce it. Use distributed tracing to find which span dominates, and the USE method (utilization, saturation, errors) on CPU, memory, disk, network and connection pools.
2. Profile the layer you found: slow query logs and EXPLAIN ANALYZE for the database, CPU profilers and flame graphs for app code. On the frontend, use DevTools performance traces, bundle size and the React Profiler to find unnecessary re-renders.
3. Fix the largest cost first, since Amdahl's law caps the gain at the fraction you speed up. Typical fixes: indexes, removing N+1 queries, caching, connection pool sizing, async offloading, read replicas, and scaling a stateless tier horizontally.
4. If latency rises under load while CPU stays low, look at waiting: lock contention, pool exhaustion, I/O waits or a slow downstream call.
5. Verify with a load test (k6, Locust) comparing percentiles before and after, then add alerts so the bottleneck cannot come back unnoticed.

Follow-up

• Latency climbs under load but CPU is at 30%. What do you check next?
• How do you choose between adding a cache and optimizing the query?
• A React page feels slow to users. Where do you start?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

1. Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
2. Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
3. Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
4. Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

• The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
• Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

The plan follows the reported stages: coding fundamentals first, then API, data and architecture topics from the reported topic mix, then behavioral stories and a full run-through. Each day names the reported questions it practises.

Day 1 — Map the three stages and inventory your stack

• Write out the reported sequence: phone screen with a recruiter, technical interviews, onsite interviews with hands-on exercises.
• List the stack the role mentions (JavaScript or Node.js, Python or Ruby, React or Angular, SQL or NoSQL, REST, Git) and mark each one you can discuss in depth, shallowly, or not at all.
• List six projects from your history you could talk about, with one decision you made in each.

Deliverable: A one-page loop map, a marked stack inventory, and a list of six projects with one decision each.

Day 2 — Linked lists, stacks and sliding windows

• Implement reversing a linked list iteratively, then recursively, and test with empty, one-node and two-node lists.
• Implement the balanced parentheses check with a stack for round, square and curly brackets, including a closing bracket with nothing open.
• Implement longest substring without repeating characters with a last-seen map, and write the time and space cost of each of the three solutions in one line.

Deliverable: Three working solutions in your strongest language, each with a one-line time and space statement and a list of the edge cases you tested.

Day 3 — Dynamic programming and complexity analysis

• Pick one dynamic programming problem (coin change or longest common subsequence work well) and write the recursive solution, then add memoisation.
• Convert it to bottom-up, then reduce the space if the recurrence allows.
• For each version write the state, the transition and the complexity, and say them aloud as you would in an interview.

Deliverable: One dynamic programming problem in three forms (recursive, memoised, bottom-up) with a state-transition-complexity summary.

Day 4 — API design, REST versus GraphQL and API performance

• Design REST endpoints for an ordering-style resource: routes, pagination, error responses, and how a retried create is kept from duplicating.
• Write a REST versus GraphQL comparison with one scenario where each wins, covering over-fetching, caching and error handling.
• Build a ranked checklist for slow endpoints: measure first, then N+1 queries, missing indexes, caching, payload size and pagination.

Deliverable: A one-page API contract, a REST versus GraphQL comparison with two scenarios, and a ranked API performance checklist.

Day 5 — Data modeling and database experience

• Model menus, orders and order lines as tables with keys, constraints and the indexes your main queries need.

Deliverable: A schema sketch with indexes and the query each index serves, plus a written outline of your database design and management story.

Day 6 — Architecture, maintainability and React

• Explain MVC with a concrete mapping onto a React and Node application, and list what it buys you and where it gets in the way.
• Write your answer on keeping code maintainable: tests, review habits, naming, module boundaries, and one refactor you can describe.
• Design a URL shortening service and a real-time chat application on paper, then your approach to data consistency and bottlenecks in a distributed system.
• Build a small React component with state, a list and a data fetch with loading and error states.

Deliverable: An MVC explanation, a maintainability answer with a real example, two design sketches, and one working React component.

Day 7 — Behavioral stories and a full run-through

• Outline six stories covering a challenging project, a team conflict, what motivates you, a contribution to team culture, prioritising multiple projects, and your database experience.
• Rehearse each story aloud and mark the sentence where your own decision appears.
• Do a full mock: one coding problem, one design question and two behavioral prompts, back to back.

Deliverable: Six story outlines with the personal-decision sentence marked, and a note on what slipped during the mock run.

Stories to rehearse for Bentobox

Candidates report discussions of previous experience in the technical interviews and cultural-fit conversations in the onsite rounds. The prompts below are the behavioral ones reported for this role. Prepare a specific story for each, built around a decision you made, what you measured, and what changed afterwards.

Describe your experience with database design and management.

Medium · Technical / Domain Questions

Approach

1. Choose a database you designed or owned and open with its workload: the main entities, the balance of reads and writes, and the queries that had to be fast. An ordering system with orders, line items and menu items is a fair shape to borrow, but use your own system.
2. Walk through schema decisions with reasons: primary and foreign keys, where you normalised and where you denormalised on purpose, which rules you pushed into the database (unique, not null, check constraints), and why you picked SQL or NoSQL for that data.
3. Tie each index to a specific query. Say how you found slow queries (slow-query log, EXPLAIN ANALYZE) and what changed after you added, reordered or dropped an index.
4. Cover management, not just design: how you shipped a change to a live table (add a nullable column, backfill in batches, switch reads, then tighten the constraint), plus backups, migrations kept in version control and connection pooling.
5. Close with one thing you would redo, such as a column type, a missing constraint or an over-indexed table. A real flaw shows ownership; a list of tools you touched does not.

Follow-up

• When would you have chosen a NoSQL store for that data instead?
• How did you run that migration without blocking writes?
• Which index turned out to be unused or harmful, and how did you find out?

Describe a challenging project you worked on and how you overcame obstacles.

Medium · Behavioral / Leadership

Approach

1. Pick a project where the obstacle was specific, such as a failing integration, a performance wall or a scope change, and where you made the key decision rather than watching it happen.
2. Structure the answer: situation and goal in two sentences, the obstacle and what made it hard, the options you weighed, the one you chose and why, then the result.
3. Give an outcome you can defend with a number or a plain before-and-after: latency, error rate, a release date met, a support queue cleared.
4. Use "I" for your own decisions and name collaborators for theirs, so the listener can tell what you contributed.
5. End with what you now do differently. The mistake that sinks this answer is a long tour of the architecture with the obstacle and your decision squeezed into the last sentence.

Follow-up

• What was the alternative you rejected, and what would have happened with it?
• Who disagreed with your approach, and how did you handle that?
• What would you do differently if you ran the project again?

How do you handle conflicts within a team?

Medium · Behavioral / Leadership

Approach

1. Choose a real disagreement about a technical approach or a priority, with a teammate, a product partner or a reviewer. A code review disagreement fits well, since code review is part of the role.
2. Show you understood the other position first: what you asked, and which constraint or risk they were protecting.
3. Describe how you moved from opinion to evidence: a small prototype, a benchmark, a written comparison of options, or deferring to the person who owned the decision. State the decision rule you agreed on.
4. Report the outcome honestly, including a case where you were overruled and committed to the decision, and say what the working relationship looked like afterwards.
5. Avoid two failures: a story where the other person was simply wrong and you won, and the claim that you have never had a conflict.

Follow-up

• What if they still disagreed after you showed the data?
• How would you handle the same disagreement with a more senior engineer?
• What did you change about how you give feedback afterwards?

What motivates you as a Software Engineer?

Medium · Behavioral / Leadership

Approach

1. Answer with a concrete moment, not a slogan: a time you shipped something and saw it change what a user could do or what a teammate no longer had to do by hand.
2. Name two or three drivers that are true for you, such as owning a feature from API to UI, removing a recurring pain point, or learning an unfamiliar area, and back each with one sentence of evidence from your history.
3. Connect the answer to the work honestly. Candidates describe restaurant management and online ordering products, software that staff and diners use directly; if building things people use is what drives you, say so with your own example. Candidates also report a prompt phrased as "Motivation Through Customer Impact", so keep one customer-facing story ready.
4. Avoid "I love solving problems" with nothing behind it, and avoid drivers that would not survive a normal week of bug fixes and reviews.

Follow-up

• Tell me about a project where that motivation was missing. What did you do?
• What part of this role would motivate you least?
• What would make you leave a role you had accepted?

Give an example of how you’ve contributed to a positive team culture.

Medium · Behavioral / Leadership

Approach

1. Choose an action you started that lasted: a code review checklist, a pairing rotation, an onboarding document for new hires, blameless retrospectives after incidents, or a norm of writing tests with bug fixes.
2. Open with the problem it addressed, such as slow reviews, the same mistakes repeating, or new hires stuck for days, so the action has a reason.
3. Explain how you got others to adopt it: you asked for input, started small, and adjusted after objections instead of announcing a rule.
4. Give evidence it worked: faster review turnaround, fewer repeat incidents, new engineers shipping earlier, or teammates still following it after you stopped pushing.
5. Avoid social-event answers and vague claims of being friendly. A practice that only you ever followed is a weak example.

Follow-up

• How did you handle a teammate who ignored the practice?
• How did you know it was working?
• How do you give critical feedback in a code review without damaging trust?

How do you prioritize your tasks when managing multiple projects?

Medium · Behavioral / Leadership

Approach

1. State your method concretely: list everything, estimate effort, rank by impact and deadline, note what blocks other people, and re-rank when something changes.
2. Use a real case with two or three competing items. Say what you did first, what you deferred, what you dropped, and why.
3. Show the communication step: you told the people who lost out, with a revised date, instead of letting work slip silently.
4. Name a mechanism you rely on, such as a written backlog, a limit on work in progress, or reserved time for review requests and interruptions.
5. Avoid "I work longer hours" and "everything is a priority"; neither shows a decision.

Follow-up

• Two product managers each say their task is urgent. What do you do?
• A production bug lands while you are mid-feature. How do you decide?
• When did you get prioritisation wrong, and what changed?