Netflix Backend Developer Interview Preparation Guide - Junior Level (1-2 Years)
Netflix's backend developer interview process for junior-level candidates evaluates hands-on coding proficiency, foundational system design understanding, and alignment with Netflix's 'Freedom & Responsibility' culture. The process spans 4-6 weeks and includes an initial recruiter screening, a technical phone screen focusing on coding fundamentals, followed by 5 onsite interview rounds covering system design, coding problems, behavioral assessment, production incident management, and cultural fit. Netflix emphasizes building scalable microservices that handle millions of concurrent users, with particular focus on real-world problem-solving, observability, and owning features end-to-end.
Interview Rounds
Recruiter Screening
What to Expect
Initial conversation with a Netflix recruiter to assess background fit, motivation, and logistics. The recruiter will review your resume, discuss your backend engineering experience, verify your understanding of the role and Netflix's business, and answer initial questions. This is a conversational round designed to ensure mutual fit before investing recruiter and engineering time. You'll discuss your previous projects, why you're interested in Netflix, and confirm you meet baseline technical requirements for a junior-level backend position.
Tips & Advice
Be genuine and specific about why Netflix interests you—reference actual Netflix technical challenges or products you've researched. Clearly articulate your backend engineering background: specific technologies you've worked with (e.g., 'Built Node.js microservices handling 10K QPS' or 'Optimized PostgreSQL queries reducing latency by 40%'). Prepare 2-3 concrete project examples that showcase end-to-end ownership. Ask informed questions about Netflix's tech stack, incident response culture, and how junior engineers grow. Keep responses concise; this round is not deep technical assessment. Confirm your availability for the full interview loop and be transparent about any scheduling constraints.
Focus Topics
Questions About Netflix Culture, Tech Stack, and Growth
Prepare thoughtful questions about Netflix's backend infrastructure (microservices architecture, caching strategies, incident response process), how junior engineers grow, team structure, or technical initiatives of interest.
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Production Experience and Incident Awareness
Describe your experience deploying code to production, monitoring system health, and responding to issues. Have at least one example of an outage or bug you helped resolve—how you detected it, debugged it, and improved processes to prevent recurrence.
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Communication and Learning Ability
Show ability to explain technical concepts clearly to both technical and non-technical audiences. Describe a time you learned a new technology quickly, collaborated with teammates to solve a hard problem, or received feedback that improved your work.
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Motivation for Netflix and Understanding of Role
Explain why Netflix specifically appeals to you. Reference Netflix's business (streaming infrastructure, personalization at scale, or specific challenges like content delivery), their 'Freedom & Responsibility' culture, or technical initiatives you admire. Connect to the specific backend role.
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End-to-End Project Ownership
Prepare 2-3 examples where you owned a feature or system from conception through production. Include: what you built, technical decisions made, challenges encountered, how you measured success, and impact. Examples should span API design, database work, or infrastructure tasks.
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Professional Background and Backend Experience
Articulate your 1-2 years of backend engineering experience, technologies mastered (Node.js, Python, Java, databases), and specific contributions to projects. Be prepared to discuss scalability challenges you've solved and systems you've built or improved.
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Technical Phone Screen - Coding
What to Expect
A 60-minute technical interview conducted remotely with a Netflix backend engineer. You'll solve 1-2 algorithmic coding problems in a shared editor (e.g., CoderPad), focusing on correctness, code clarity, and problem-solving approach. Problems are typically medium difficulty and often reflect real backend scenarios (e.g., parsing, data manipulation, graph traversal). The interviewer evaluates your coding fundamentals, ability to write clean and efficient code, and communication during problem-solving. This round is a filter to ensure you meet baseline coding competency before investing onsite interview time.
Tips & Advice
Start by clarifying the problem: ask about input/output formats, edge cases, and constraints (e.g., data size, time limits). Think aloud so the interviewer follows your reasoning. Write pseudocode or outline your approach before diving into implementation. Test your solution mentally with examples, including edge cases (empty input, single element, duplicates). Write clean, readable code with meaningful variable names—Netflix values maintainability. Don't optimize prematurely; aim for correct solutions first, then discuss optimization. If you get stuck, ask clarifying questions or explain your thought process to get hints. Time management is critical: if you can't solve the first problem fully, communicate your approach clearly, then move to the second problem rather than spending 45+ minutes on one. Treat this like real backend code you'd deploy—handle errors, avoid hardcoding, and consider testing.
Focus Topics
Debugging and Edge Case Handling
Proactively identify edge cases: empty inputs, single elements, duplicates, negative numbers, null values, or boundary conditions. Walk through your code with examples. Debug systematically by checking variable states and identifying where assumptions break.
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Time and Space Complexity Analysis
Understand Big-O notation thoroughly. Be able to analyze the time and space complexity of your solutions and discuss optimizations. Know common complexities: O(1), O(log n), O(n), O(n log n), O(n²), and when to apply each.
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Backend-Specific Languages
Be proficient in at least one language Netflix uses: Node.js (JavaScript), Python, or Java. Understand language-specific syntax, standard libraries, and common patterns. Be comfortable switching between languages if you know multiple.
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Code Quality and Communication
Write readable code with clear variable names, proper error handling, and comments for complex logic. Communicate your approach before coding. Explain trade-offs (e.g., space vs. time complexity). Test edge cases verbally. Ask clarifying questions to eliminate ambiguity.
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Real-World Coding Scenarios
Practice problems related to backend concerns: parsing JSON/CSV, rate limiting counters, batch processing, dependency resolution, event log aggregation, deduplication in streams, and idempotency checking. Study problems from Stripe, Amazon, and Uber's coding rounds.
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Core Data Structures and Algorithms
Master arrays, hash maps, linked lists, stacks, queues, trees (binary search trees, tries), and graphs. Understand search and sort algorithms (binary search, merge sort, quicksort). Know when to apply each structure for optimal performance. Be comfortable implementing these from scratch.
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System Design Interview - Onsite
What to Expect
A 60-minute onsite interview where you design a backend system for a realistic Netflix-related or general scenario (e.g., designing a rate limiter, notification system, or ad frequency capping system). The interviewer presents an open-ended problem with intentionally vague requirements. You'll gather requirements, propose a high-level architecture, design APIs and database schemas, discuss scalability, and analyze trade-offs. For junior level, the focus is on foundational design thinking and understanding real-world constraints rather than deep distributed systems expertise. You'll draw diagrams, discuss component interactions, and defend your design choices. The interviewer will ask follow-up questions to explore your understanding of databases, caching, APIs, and deployment.
Tips & Advice
Start by clarifying requirements: ask about scale (users, QPS, data volume), availability needs, and business constraints. Don't assume; confirm what 'scale' means (Netflix has hundreds of millions of users, so clarify if you're designing for all or a subset). Propose a simple design first, then iterate. Use familiar technologies and justify choices (e.g., 'PostgreSQL for transactional consistency' or 'Redis for caching because it's fast and supports atomic operations'). Draw clearly: boxes for services, arrows for communication, and labels for technologies. Discuss trade-offs explicitly: availability vs. consistency, latency vs. cost, complexity vs. performance. For junior level, you're not expected to design globally distributed systems, but you should understand single-region scaling, caching strategies, database optimization, and basic API design. Be honest about unknowns; saying 'I'd need to research how Kafka handles exactly-once semantics' is fine. Avoid over-engineering; junior-level designs should be comprehensive but not absurdly complex. Prepare to discuss monitoring, logging, and operational concerns—Netflix emphasizes observability.
Focus Topics
Observability and Monitoring
Discuss logging, metrics, and alerting: structured logs (JSON with correlation IDs), key metrics (latency, error rate, throughput), and alerting thresholds. Understand how you'd detect and debug production issues. Mention monitoring for database performance, API latency, and service health.
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High-Level Architecture Patterns
Understand common patterns: microservices (independent services with clear interfaces), pub/sub for asynchronous communication, fan-out (distributing work), eventual consistency, and idempotency. Know trade-offs between synchronous and asynchronous processing.
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Caching Strategies
Understand when to cache (high-read, low-write data), where to cache (client, CDN, application layer, Redis), and cache invalidation challenges. Discuss eviction policies (LRU, LFU), cache-warming, and managing cache consistency. Know cache-aside, write-through, and write-behind patterns.
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Scalability and Load Handling
Discuss horizontal scaling (adding more servers vs. vertical scaling). Understand load balancing, stateless services (enabling scaling), database replication, and sharding. Address bottlenecks systematically: compute, memory, database connections, or network. Know when each becomes limiting.
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Trade-Offs and Design Justification
Articulate trade-offs explicitly: consistency vs. availability (CAP theorem), latency vs. throughput, simplicity vs. performance, and cost vs. capability. Justify your choices: 'I chose PostgreSQL over MongoDB because the data is highly relational and we need ACID transactions.' Discuss when and why you'd revisit decisions.
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Database Design and Selection
Choose appropriate databases: relational (PostgreSQL) for transactional, consistent data; NoSQL (MongoDB, DynamoDB) for flexible schemas and horizontal scalability. Design schemas considering normalization, indexing, and query patterns. Understand read replicas, sharding, and eventual consistency trade-offs. Know query optimization basics.
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Requirement Gathering and Scope Definition
Ask clarifying questions about functional requirements (features), non-functional requirements (scale, latency, consistency), and constraints (tech stack, budget, time). Estimate scale: DAU, QPS, data size, growth rate. Define success metrics before designing.
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API Design and Protocols
Design RESTful APIs with proper resource modeling, HTTP status codes, pagination, error responses, and idempotency for mutation endpoints. Understand when to use REST, gRPC (internal service communication with strict schemas), or GraphQL (diverse clients with varied data needs). Include rate limiting and authentication considerations.
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Coding Interview - Onsite
What to Expect
A 60-minute onsite coding interview, typically with a different Netflix engineer than the phone screen. You'll solve 1-2 medium-difficulty algorithmic problems, similar in scope and style to the phone screen but often slightly harder. The focus remains on clean code, problem-solving ability, and communication. Unlike the phone screen, this round might involve more complex problems (e.g., graph algorithms, dynamic programming) or backend-specific scenarios (e.g., implementing a concurrent data structure, designing a retry mechanism). You'll use a shared editor and whiteboard. The interviewer assesses whether you consistently write solid code under pressure and think through edge cases and testing.
Tips & Advice
This round tests consistency: you've demonstrated coding ability in the phone screen, so the onsite verifies that performance wasn't a fluke. Approach these problems the same way: clarify, plan, code, test. Push yourself slightly harder than the phone screen—these problems might involve backtracking, graph traversal, or moderate DP. Explain your approach and ask for hints if stuck; Netflix interviewers expect some struggle and value how you handle it. Write testable code with error handling. Don't overthink optimization; correct > optimal. Use the full 60 minutes productively—finish one problem cleanly rather than leaving two half-solved. This interview is slightly harder than the phone screen, so expect depth. After solving the main problem, be ready for follow-up questions like 'How would you test this?' or 'What if the data doesn't fit in memory?' Prepare to discuss scaling your solution.
Focus Topics
Problem-Solving Under Pressure
Develop strategies for tackling unknown problems: break problems into subproblems, identify patterns (is this a graph problem? a DP problem?), and test edge cases thoroughly. Stay calm when stuck; think aloud and ask for guidance. Time management: solve one problem well rather than two partially.
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Concurrent and Distributed Algorithm Concepts
Understand thread-safe data structures, race conditions, mutual exclusion, and atomic operations. Practice problems on implementing thread-safe queues, counters, or cache systems. Understand eventual consistency and consistency models in distributed context.
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Dynamic Programming Fundamentals
Understand DP concepts: optimal substructure, overlapping subproblems, memoization, and tabulation. Solve classic problems: longest increasing subsequence, longest common subsequence, knapsack, coin change. Be comfortable identifying DP opportunities and implementing solutions efficiently.
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Practical Backend Problem-Solving
Solve backend-specific coding problems: implementing a rate limiter, designing a cache eviction strategy, processing logs, detecting anomalies, or handling data deduplication. These problems blend algorithms with real system constraints.
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Advanced Data Structures
Master heaps, hash tables with collision handling, balanced trees (AVL, Red-Black), tries, and graphs. Understand when each is optimal and how to implement them. Know standard graph representations (adjacency list, matrix) and traversal algorithms (BFS, DFS).
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Graph and Tree Algorithms
Understand DFS, BFS, Dijkstra's algorithm, topological sort, and cycle detection. Be comfortable solving problems involving path-finding, dependency resolution, and network traversal. Practice problems on LeetCode or similar platforms.
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Production Incident and Debugging Interview
What to Expect
A 45-60 minute onsite behavioral and technical interview focused on real-world incident response and debugging. The interviewer (often a senior engineer) will ask you to describe a production incident you experienced, debugged, or learned from. You'll walk through what broke, how you detected the issue, your debugging approach, root cause analysis, and preventive measures implemented. The interviewer may also present a hypothetical production scenario and ask how you'd troubleshoot it. This round assesses your operational mindset, ability to handle pressure, learning from failure, and understanding of observability. Netflix heavily emphasizes this because every engineer is on-call and owns their code in production.
Tips & Advice
Prepare 1-2 strong incident stories using the STAR method: Situation (what was the system, your role), Task (what went wrong), Action (how you responded), Result (outcome and lessons). Choose incidents where you had meaningful involvement—debugging, mitigation, or prevention. Include quantifiable impact if possible: 'Reduced latency from 500ms to 100ms' or 'Prevented 2 hours of downtime.' Walk through your debugging methodology: how did you identify the problem? What tools did you use (logs, metrics, distributed tracing)? What was your hypothesis? How did you validate it? For prevention, describe concrete steps taken: code changes, monitoring improvements, runbooks, or process improvements. Be honest about what you didn't know initially; learning is valuable. If you don't have a production incident, describe a complex bug you debugged thoroughly or a system you built and later had to optimize based on real-world performance data. Show that you think beyond code: understanding operations, monitoring, alerting, and deployment processes matters. Netflix values engineers who take ownership of failures and drive systemic improvements. Avoid blame; focus on what you learned and how you improved the system.
Focus Topics
Resilience and Stress Management
Show how you stayed calm under pressure, managed your stress, and focused on systematic problem-solving. Discuss a time when you were stuck and how you broke through (asking for help, taking a break, approaching from a different angle). Avoid panic or blame language in your story.
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Communication and Collaboration During Incidents
Describe how you communicated during the incident: keeping stakeholders informed, updating status, explaining findings to teammates, and coordinating with on-call engineers if it was a team effort. Show you think beyond solo debugging; incidents often require collaboration.
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System Observability and Monitoring Design
Discuss how you instrument systems: structured logging (JSON logs with correlation IDs for tracing requests), metrics (latency, error rate, business metrics), and alerts (thresholds, actionable alert messages). Explain how good observability would have caught your incident faster. Share examples of metrics you'd add to prevent recurrence.
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Learning and Prevention
Discuss concrete steps taken after the incident: code changes to fix the root cause, monitoring improvements to catch similar issues, runbooks or playbooks for faster response, architectural changes to prevent recurrence, or process improvements (better testing, code review practices). Show you don't just fix the immediate issue; you address systemic causes.
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Production Incident Response and Triage
Demonstrate experience detecting, diagnosing, and mitigating production issues. Walk through your process: how you identified the problem (alerts, customer reports, monitoring), assessed severity, gathered data (logs, metrics), formed hypotheses, and tested them. Discuss communication during incidents (status updates, coordination) and when to escalate.
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Debugging and Root Cause Analysis
Articulate your debugging methodology: reading logs with context, checking metrics and trends, understanding system state at time of failure, isolating variables, and forming and testing hypotheses. Describe tools you've used: log aggregation, monitoring dashboards, distributed tracing, profilers. Explain how you identified the root cause (not symptoms).
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Behavioral and Culture Fit Interview
What to Expect
A 30-45 minute onsite interview with a Netflix engineer (often from a different team) focused on behavioral fit and alignment with Netflix's culture, particularly 'Freedom & Responsibility.' The interviewer will ask about your collaboration, how you handle ambiguity and setbacks, your approach to learning, and your values. You'll discuss specific examples of working on teams, dealing with feedback, contributing to decision-making, and taking initiative. This round assesses cultural alignment and interpersonal skills beyond coding ability. Netflix values owners who thrive in autonomy, adapt to change, and drive results while supporting teammates.
Tips & Advice
Research Netflix's culture: 'Freedom & Responsibility' means Netflix gives engineers autonomy to make decisions and expects them to take ownership of outcomes. Prepare examples that showcase autonomy, initiative, learning, and accountability. Use the STAR method for behavioral questions. For a junior-level candidate, focus on demonstrating growth mindset, willingness to learn from senior engineers, solid collaboration, and drive—not on independent decision-making authority (you don't have that yet). Describe a time you took initiative on a small project, learned a new skill, or improved a process within your scope. Show respect for experienced engineers and eagerness to develop. Discuss feedback positively: 'I received feedback that I should slow down and think through edge cases before coding. I implemented a habit of pseudocoding first, and it improved my code quality.' Netflix values learning and growth; demonstrating coachability is crucial at junior level. Be authentic; Netflix hires people who fit the culture because it matters for long-term success. If you're uncertain about your fit (e.g., you prefer structured processes), acknowledge it honestly. Prepare questions about Netflix's culture, how teams make decisions, and how junior engineers grow into leaders.
Focus Topics
Receiving and Acting on Feedback
Describe feedback you received and how you responded. A strong answer shows humility, effort to improve, and follow-through. For example: 'My code reviews were slow; feedback was that I needed to give more constructive, actionable comments. I adopted a template and spent more time on reviews. My team appreciated it.' Show you value growth over ego.
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Alignment with Netflix's Technical Values
Netflix values pragmatism, simplicity, and data-driven decisions. Discuss how you make technical decisions: do you choose technologies based on job fit, or do you default to familiar tools? Have you A/B tested changes or measured impact before and after? Show you think about effectiveness, not just elegance.
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Taking Initiative and Proposing Improvements
Share an example where you identified a problem and proposed or implemented a solution without being asked. For junior level, this could be a small optimization, a helpful documentation update, or a process improvement. Show you don't just execute tasks; you think about how to improve systems.
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Handling Ambiguity and Ownership
Describe a situation with unclear requirements or goals. How did you break down the ambiguity? Did you ask clarifying questions, propose an approach, or iterate based on feedback? Show you don't need hand-holding; you can propose solutions, gather feedback, and move forward.
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Learning and Growth Mindset
Demonstrate willingness to learn: a technology you mastered, a skill you developed, feedback that improved your work, or a mistake that taught you something. Show you view challenges as opportunities. At junior level, emphasize eagerness to grow and specific examples of rapid learning.
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Teamwork and Collaboration
Describe how you collaborate with teammates: code reviews, design discussions, sharing knowledge, helping teammates unblock. Share an example where you had a disagreement and how you resolved it. Show you can both contribute ideas and respect others' expertise. At junior level, demonstrate good communication and willingness to learn from seniors.
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Netflix's Freedom & Responsibility Culture
Understand Netflix's core principle: engineers have freedom to make decisions but own the consequences. This means you're expected to propose solutions, make trade-off decisions, and take accountability. For junior level, this translates to owning features within your scope, proposing improvements, and not waiting for permission to suggest ideas.
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Frequently Asked Backend Developer Interview Questions
Sample Answer
#include <cstddef>
#include <utility>
#include <stdexcept>
template<typename T>
class Vector {
T* data_;
std::size_t size_;
std::size_t cap_;
public:
Vector(): data_(nullptr), size_(0), cap_(0) {}
~Vector(){ delete [] data_; }
std::size_t size() const noexcept { return size_; }
std::size_t capacity() const noexcept { return cap_; }
T& operator[](std::size_t i){ return data_[i]; }
const T& operator[](std::size_t i) const{ return data_[i]; }
void push_back(const T& v){
if(size_ == cap_){
std::size_t newcap = cap_ ? cap_*2 : 1;
T* newdata = new T[newcap]; // may throw
for(std::size_t i=0;i<size_;++i) newdata[i] = std::move(data_[i]);
delete [] data_;
data_ = newdata;
cap_ = newcap;
}
data_[size_++] = v;
}
void pop_back(){
if(size_==0) throw std::out_of_range("pop from empty");
--size_;
data_[size_].~T(); // optional for non-trivial T
}
};Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
# compute Grundy up to limit or until periodicity found
def compute_grundy(S, special_fn, limit=10**6, detect_window=5000):
grundy = [0]
seen_patterns = {}
for n in range(1, limit+1):
moves = [n - a for a in S if n - a >= 0] + special_fn(n)
reachable = {grundy[m] for m in moves}
g = 0
while g in reachable:
g += 1
grundy.append(g)
# periodicity detection: capture tail pattern of last detect_window values
if n > detect_window*2:
tail = tuple(grundy[n-detect_window+1:n+1])
if tail in seen_patterns:
prev_n = seen_patterns[tail]
period = n - prev_n
offset = prev_n - detect_window + 1
return grundy, (offset, period)
seen_patterns[tail] = n
return grundy, None
# example special_fn for moves that depend on n (returns list of resulting indices)
def special_fn_example(n):
# e.g., if n is even you can remove n//2
if n % 2 == 0 and n//2 >= 0:
return [n//2]
return []
# usage
S = {1,3,4}
grundy, pat = compute_grundy(S, special_fn_example, limit=200000)
def first_wins(n):
if pat and n > pat[0]:
offset, period = pat
idx = offset + ((n - offset) % period)
return grundy[idx] != 0
return grundy[n] != 0Sample Answer
# Compute prefix-function (pi): longest proper prefix which is also suffix for each prefix
def prefix_function(p):
m = len(p)
pi = [0] * m
j = 0
for i in range(1, m):
while j > 0 and p[i] != p[j]:
j = pi[j-1]
if p[i] == p[j]:
j += 1
pi[i] = j
return pi
# Find all occurrences of pattern p in text t, return start indices
def kmp_search(t, p):
if not p:
return list(range(len(t)+1))
pi = prefix_function(p)
res = []
j = 0
for i, ch in enumerate(t):
while j > 0 and ch != p[j]:
j = pi[j-1]
if ch == p[j]:
j += 1
if j == len(p):
res.append(i - len(p) + 1)
j = pi[j-1]
return res
# Example
text = "ababcababcab"
pattern = "ababc"
print(kmp_search(text, pattern)) # [0, 5]Sample Answer
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