10 System Design Concepts That Took Me From Junior Dev to Senior Engineer

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After bombing my first three system design interviews at top tech companies, I made a decision: I would master system design properly, not just memorize answers.

What followed was 18 months of deep study, failed attempts, successful interviews, and real production experience. The result? I went from a developer who dreaded system design questions to one who genuinely enjoys them.

The turning point was mastering 10 foundational concepts that underpin every system design decision. These aren't just interview topics—they're the principles that separate engineers who build systems that survive production from those who build systems that survive demos.

In this article, I'm sharing those 10 concepts with the depth and clarity I wish someone had given me years ago.

Why System Design Knowledge Makes or Breaks Your Career

Before diving in, let me be direct about the stakes:

In interviews: FAANG and top tech companies use system design rounds to filter for senior-level thinking. Without these concepts, you won't pass. With them, you'll stand out.

In production: These concepts determine whether your system handles 10 users or 10 million users. Whether it survives a server failure or goes down for 12 hours. Whether your team ships features confidently or lives in fear of deployments.

In salary: Senior engineers earn 2-3x junior salaries largely because they understand these concepts. They're the difference between an $80k and a $180k+ career trajectory.

Quick resources: If you're preparing for system design interviews, these platforms are the best I've found:

• ByteByteGo — Best visual system design content
• Design Guru — Best interactive courses
• Exponent — Best mock interviews
• Educative — Best text-based learning
• Codemia — Best for hands-on practice
• BugFree.ai — Best AI-powered interview prep
• System Design School — Best structured curriculum
• Udemy — Best affordable option

Now let's dive into the 10 concepts.

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The 10 System Design Concepts Every Developer Must Know

1. Scalability — Handle growth without breaking
2. Availability — Stay operational when things go wrong
3. Reliability — Deliver consistent, accurate results
4. Fault Tolerance — Survive failures gracefully
5. Caching Strategies — Deliver speed at scale
6. Load Balancing — Distribute work intelligently
7. Security — Protect data and systems
8. Scalable Data Management — Handle growing data effectively
9. Design Patterns — Apply proven solutions to common problems
10. Performance Optimization — Build systems users love

Let's explore each in depth.

1. Scalability

Scalability is the ability of a system to handle increasing amounts of work—more users, more data, more requests—without significant performance degradation.

Think of it this way: your application works great with 100 users. Scalability determines whether it still works great with 100,000 users, 10 million users, or 1 billion users.

Why It Matters

Without scalability planning, growth kills systems. I've seen startups succeed themselves into failure—a viral moment brings 100x traffic and takes down the entire application. Scalability ensures success doesn't become your biggest problem.

Real-world impact:

• Netflix streams to 230+ million subscribers simultaneously
• Amazon processes 66,000 orders per second on Prime Day
• WhatsApp handles 100 billion messages daily

None of this is possible without deliberate scalability design.

Vertical vs Horizontal Scaling

Vertical Scaling (Scale Up): Add more power to existing machines—bigger CPU, more RAM, faster storage. Simple but has limits and creates single points of failure.

Horizontal Scaling (Scale Out): Add more machines to distribute the load. More complex but theoretically unlimited and more resilient.

The key techniques for horizontal scalability:

• Load balancing — distribute requests across servers
• Database sharding — split data across multiple databases
• Partitioning — divide workloads logically
• Distributed processing — process data across multiple nodes

How to achieve it:
Caching, asynchronous processing, parallel processing, and distributed databases are the primary tools. But achieving real scalability requires architectural decisions from day one—retrofitting scalability into an unscalable architecture is painfully expensive.

Image credit: ByteByteGo

My experience: The hardest scalability lesson I learned was that you can't retrofit it. By the time you need to scale, it's too late to redesign. Build with scalability in mind from day one.

Interview tip: When asked about scaling, always clarify whether the bottleneck is compute, storage, or network. Each requires different solutions. ByteByteGo has excellent visual breakdowns of scaling patterns that are perfect for interview preparation.

Learn more:

• System Design School — Structured scalability curriculum
• Codemia — Hands-on scalability design exercises

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2. Availability

Availability is the percentage of time a system remains operational and accessible to users. It's typically expressed in "nines":

• 99% availability = 87.6 hours of downtime per year (unacceptable for most)
• 99.9% availability = 8.76 hours of downtime per year (acceptable for many)
• 99.99% availability = 52.6 minutes of downtime per year (high availability)
• 99.999% availability = 5.26 minutes of downtime per year (mission-critical)

Why It Matters

Downtime is expensive—in ways beyond just lost revenue:

• Amazon loses ~$220,000 per minute during outages
• 88% of customers are less likely to return after a bad experience
• One major outage can permanently damage brand trust

For mission-critical systems—banking, healthcare, emergency services—downtime can be catastrophic.

How to Achieve High Availability

The core principle: eliminate single points of failure. If any single component failing takes down your system, you don't have high availability.

Key strategies:

Redundancy — Run multiple instances of every critical component. If one fails, others continue serving traffic.

Load balancing — Distribute traffic so no single server is a bottleneck or single point of failure.

Replication — Keep copies of your data in multiple locations. Database replication ensures data availability even when primary fails.

Failover mechanisms — Automatically switch to backup systems when primary systems fail, ideally without user-visible interruption.

Health monitoring — Continuously monitor system health and automatically remove unhealthy instances from rotation.

My experience: The most common availability mistake I've seen is treating the database as an afterthought. Engineers build redundant application servers but run a single database instance. Your system is only as available as its least available component.

Interview tip: When discussing availability, mention the CAP theorem—you cannot simultaneously guarantee consistency, availability, and partition tolerance. Different systems make different trade-offs. Practice explaining this on Exponent with real mock interviews.

Learn more:

• Design Guru — Grokking availability patterns interactively
• BugFree.ai — AI-powered availability question practice

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3. Reliability

Reliability is the consistency and dependability of a system in delivering expected results correctly over time. An available system is running; a reliable system is running AND giving you correct answers.

A system can be available (running) but unreliable (producing wrong results). Both matter.

Reliability vs. Availability

These are related but distinct:

• Availability: Is the system running?
• Reliability: Is the system producing correct results?

A system that's up 100% of the time but gives wrong answers half the time is available but unreliable.

Key Metrics

Mean Time Between Failures (MTBF): Average time the system operates before failing. Higher is better.

Failure Rate (FR): How often failures occur over time. Lower is better.

Mean Time To Recovery (MTTR): How quickly the system recovers from failure. Lower is better.

How to Build Reliable Systems

Redundancy — Multiple components performing the same function. When one fails, others maintain correct operation.

Error detection and correction — Checksums, validation logic, and error correction codes catch data corruption before it propagates.

Robust error handling — Every failure path should be anticipated and handled gracefully.

Comprehensive testing — Unit tests, integration tests, chaos engineering, and load testing catch reliability issues before production.

Circuit breakers — Detect when downstream services are failing and stop sending requests, preventing cascade failures.

My experience: The most underrated reliability practice is chaos engineering—deliberately introducing failures to verify your system handles them correctly. Netflix's Chaos Monkey is famous for this. If you don't break your system yourself, production will break it for you.

Interview tip: Distinguish between reliability and availability clearly in interviews—most candidates conflate them. This distinction signals advanced understanding. Educative has excellent courses on building reliable distributed systems.

Learn more:

• ByteByteGo — Visual reliability pattern breakdowns
• Udemy System Design Masterclass — Comprehensive reliability coverage

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4. Fault Tolerance

Fault tolerance is the ability to continue functioning correctly even when components fail. Where reliability focuses on preventing failures, fault tolerance focuses on surviving them.

The key distinction: a fault-tolerant system doesn't just detect failures—it continues operating through them.

Why It's Different From Availability

• Availability: The system stays up (might degrade)
• Fault Tolerance: The system stays up AND continues functioning correctly despite faults

Fault tolerance is a higher bar than simple availability.

Core Fault Tolerance Techniques

Replication: Maintain multiple copies of data and services across different locations. If one fails, others immediately take over without data loss.

Checkpointing: Periodically save system state so that if failure occurs, the system can resume from the last good state rather than starting over.

Graceful degradation: When components fail, reduce functionality rather than complete failure. Netflix still works if recommendations fail—it just shows generic content.

Retry logic with exponential backoff: Automatically retry failed operations with increasing delays to handle transient failures.

Bulkhead pattern: Isolate different parts of the system so failures don't cascade. Like a ship's bulkheads—one flooded section doesn't sink the ship.

My experience: The biggest fault tolerance mistake is assuming failures won't happen. They always do. Design for the failure case first, then optimize for the happy path.

Interview tip: Discuss how you handle partial failures—when some components work and others don't. This demonstrates sophisticated understanding beyond basic redundancy. Practice these scenarios on Codemia with real design challenges.

Learn more:

• Exponent — Mock interviews covering fault tolerance scenarios
• System Design School — Structured fault tolerance curriculum

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5. Caching Strategies

Caching stores frequently accessed data in fast, temporary storage so it can be retrieved quickly without hitting slower data sources repeatedly.

The impact is dramatic: a database query might take 100ms; the same data from cache takes <1ms. At scale, this difference determines whether your system survives traffic spikes.

Why It's Critical

Without caching, every request hits your database. At 10,000 requests per second, that's 10,000 database queries per second—most databases can't handle that load for complex queries.

With caching, 95% of requests might be served from cache, reducing database load by 20x.

The 7 Essential Caching Strategies

1. Full Caching
Cache the entire dataset. Best for small, frequently accessed, slow-changing datasets. Fast but memory-intensive.

2. Partial Caching
Cache only the most frequently accessed subset of data. Best when full caching isn't feasible due to dataset size.

3. Time-Based Expiration (TTL)
Cache data for a fixed duration, then refresh from the source. Best for data with predictable staleness tolerance.

4. LRU (Least Recently Used) Eviction
Evict the least recently accessed data when cache is full. Best when recent access patterns predict future access.

5. LFU (Least Frequently Used) Eviction
Evict the least frequently accessed data when cache is full. Best when access frequency is a better predictor than recency.

6. Write-Through vs Write-Behind

• Write-through: Write to cache AND database simultaneously. Strong consistency, higher write latency.
• Write-behind: Write to cache immediately, database asynchronously. Lower write latency, risk of data loss.

7. Distributed Caching
Cache spread across multiple nodes. Essential for distributed systems. Technologies: Redis Cluster, Memcached, Hazelcast.

Choosing the Right Strategy

The right caching strategy depends on:

• Data size — Can it fit in memory?
• Access patterns — Is access uniform or heavily skewed?
• Data volatility — How often does the data change?
• Consistency requirements — Can you tolerate stale data?
• Write patterns — Read-heavy vs write-heavy workloads

My experience: The most impactful caching win I've seen was caching user session data in Redis. Database load dropped 70% overnight. Start with caching database query results—it's the highest-impact, lowest-effort optimization in most systems.

Interview tip: Always discuss cache invalidation. It's notoriously difficult and demonstrates advanced understanding. "There are only two hard things in computer science: cache invalidation and naming things." — Phil Karlton. BugFree.ai has great AI-generated practice problems specifically on caching scenarios.

Learn more:

• ByteByteGo — Deep visual dives into caching patterns
• Design Guru — Interactive caching strategy exercises

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6. Load Balancing

Load balancing distributes incoming traffic across multiple servers to ensure no single server is overwhelmed. It's the traffic cop of distributed systems.

Without load balancing: all traffic hits one server → server overloads → system fails.

With load balancing: traffic distributed across 10 servers → each handles 10% of load → system handles 10x more traffic.

Load Balancing Algorithms

Round Robin
Requests distributed sequentially: server 1, server 2, server 3, server 1...

• Best for: Servers with similar capacity and request complexity
• Simple, predictable

Least Connections
New requests go to the server with fewest active connections.

• Best for: Long-lived connections (WebSocket, streaming)
• More intelligent than round robin

Source IP Affinity (Sticky Sessions)
Requests from same client IP go to same server.

• Best for: Applications with server-side session state
• Maintains session consistency

Weighted Round Robin
Servers assigned weights based on capacity. Higher-weight servers get more traffic.

• Best for: Heterogeneous server environments
• Enables gradual rollouts and canary deployments

Adaptive Load Balancing
Dynamically adjusts distribution based on real-time server health and performance.

• Best for: Production systems needing optimal resource utilization
• Most sophisticated, requires monitoring infrastructure

Load Balancer vs API Gateway

A common confusion in interviews—these are different:

Image credit: DesignGuru.io

• Load Balancer: Distributes traffic. Simple routing decisions.
• API Gateway: Load balances PLUS authentication, rate limiting, SSL termination, request transformation, analytics. Learn more about API Gateway vs Load Balancer here.

My experience: The biggest load balancing mistake is forgetting about the database. You can load balance application servers all day, but if they all hit one database, you've just moved the bottleneck.

Interview tip: Discuss Layer 4 (transport layer) vs Layer 7 (application layer) load balancing. Layer 7 can make smarter routing decisions but has higher overhead. Practice explaining this difference on Exponent with mock interviewers.

Learn more:

• Codemia — Load balancing design challenges
• Udemy System Design Masterclass — Comprehensive load balancing coverage

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7. Security

Security in system design means building protection against unauthorized access, data breaches, and malicious attacks directly into your architecture—not bolting it on afterwards.

Security isn't a feature you add; it's a property you design for from day one.

Why It's Non-Negotiable

The consequences of security failures are severe:

• Average data breach costs $4.45 million (IBM 2023)
• Regulatory fines (GDPR: up to 4% of global revenue)
• Reputational damage that can be existential
• Legal liability for customer data exposure

The 9 Security Principles Every System Must Address

1. Authentication
Verify identity before granting access. Modern approaches: OAuth 2.0, JWT tokens, multi-factor authentication, passwordless.

2. Authorization
Control what authenticated users can do. Role-based access control (RBAC), attribute-based access control (ABAC).

3. Encryption

• In transit: TLS/HTTPS for all network communication
• At rest: Encrypt sensitive data in databases and file systems
• Key management: Secure storage and rotation of encryption keys

4. Input Validation
Never trust user input. Validate and sanitize all inputs to prevent:

• SQL injection
• Cross-site scripting (XSS)
• Cross-site request forgery (CSRF)
• Command injection

5. Principle of Least Privilege
Users, services, and processes should have only the minimum permissions necessary. Limits blast radius when compromise occurs.

6. Defense in Depth
Multiple layers of security: firewalls, WAF, IDS/IPS, application security, data encryption. No single layer is sufficient.

7. Secure Communication
HTTPS everywhere. No exceptions. Internal service communication should also be encrypted.

8. Auditing and Logging
Log all security-relevant events. Enables detection, forensics, and compliance. Store logs securely and immutably.

9. Patching and Updates
Maintain current security patches across all dependencies. Most breaches exploit known vulnerabilities with available patches.

My experience: The most common security mistake I've seen is treating security as a final step. "We'll add security before launch." By then, insecure patterns are baked throughout the codebase. Design security in from the first line of code.

Interview tip: Discuss the OWASP Top 10—the most critical web application security risks. Knowing these signals real security understanding. BugFree.ai has AI-powered practice for security-focused system design questions.

Learn more:

• Educative — Security in distributed systems courses
• System Design School — Security-focused system design curriculum

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8. Scalable Data Management

Scalable data management is the ability to handle growing data volumes—terabytes to petabytes—while maintaining performance, reliability, and cost efficiency.

As systems grow, data management becomes the hardest scaling challenge. Application servers scale horizontally easily; data doesn't.

Why It's the Hardest Scaling Problem

Data has properties that make scaling difficult:

• State — Unlike stateless application servers, data must be persistent
• Consistency — Multiple copies of data must stay synchronized
• Size — Data grows indefinitely; compute can scale elastically
• Compliance — Data has regulatory requirements (GDPR, HIPAA)

10 Core Scalable Data Management Techniques

1. Data Partitioning (Sharding)
Split datasets across multiple databases based on a shard key. Each shard handles a subset of data. Read more about database sharding here.

2. Distributed Database Systems
Databases designed for horizontal scaling: Cassandra, DynamoDB, MongoDB, CockroachDB. Trade consistency for scale.

3. Data Replication
Maintain multiple copies across nodes. Provides fault tolerance and read scaling. Synchronous (strong consistency) vs asynchronous (eventual consistency) replication.

4. Caching and In-Memory Storage
Redis, Memcached for hot data. Dramatically reduces database load and improves response times.

5. Indexing and Query Optimization
Proper indexes are the single highest-impact database optimization. A query without an index is a full table scan—catastrophic at scale.

6. Data Compression
Reduce storage costs and improve I/O performance. Column-oriented databases (Parquet, ORC) are particularly effective.

7. Data Archiving and Purging
Move old, infrequently accessed data to cold storage. Keeps operational databases lean and fast.

8. Scalable Processing Frameworks
Apache Spark, Flink, Hadoop for large-scale data processing. Distribute computation across clusters.

9. Cloud-Based Data Management
Amazon S3, RDS, DynamoDB, Google Bigtable. Managed services handle operational complexity and scale automatically.

10. Monitoring and Scalability Testing
Regular load testing and performance monitoring catch data scaling issues before production discovers them.

My experience: The most expensive data decision is choosing the wrong database early. Relational databases are great for many workloads but terrible for others. Take time to understand your data access patterns before choosing a database technology.

Interview tip: When discussing data management at scale, always address the CAP theorem trade-offs. Show you understand that scalable data systems require explicit consistency vs. availability choices. Codemia has hands-on data management design problems that mirror real interview questions.

Learn more:

• ByteByteGo — Visual deep dives into data management patterns
• Udemy System Design Masterclass — Scalable data architecture coverage

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9. Design Patterns

Design patterns are proven, reusable solutions to commonly occurring design problems. They're not code you copy—they're templates for solving classes of problems you'll encounter repeatedly.

The famous Gang of Four book (1994) catalogued 23 foundational patterns. Decades later, they remain essential knowledge.

The Four Categories

1. Creational Patterns
How objects are created. Abstract the instantiation process.

• Singleton — One instance globally
• Factory Method — Delegate object creation to subclasses
• Abstract Factory — Create families of related objects
• Builder — Construct complex objects step by step
• Prototype — Clone existing objects

2. Structural Patterns
How classes and objects compose to form larger structures.

• Adapter — Make incompatible interfaces work together
• Bridge — Separate abstraction from implementation
• Composite — Treat individual objects and compositions uniformly
• Decorator — Add behavior without changing the class
• Facade — Simplify a complex subsystem

3. Behavioral Patterns
How objects communicate and distribute responsibility.

• Observer — Notify dependents when state changes
• Strategy — Encapsulate algorithms and make them interchangeable
• Command — Encapsulate requests as objects
• Iterator — Sequential access without exposing underlying structure
• Template Method — Define skeleton of algorithm, defer steps to subclasses

4. Architectural Patterns
High-level system organization strategies.

• MVC/MVVM — Separate concerns in UI applications
• Microservices — Build applications as small, independent services
• Event-Driven — Communicate through events
• CQRS — Separate read and write operations
• Layered Architecture — Organize code in horizontal layers

Essential Microservice Patterns

In recent articles, I've covered critical microservice patterns in depth:

• Event Sourcing — Store state changes as events
• CQRS — Separate command and query models
• Database Per Service — Each service owns its data
• Circuit Breaker — Prevent cascade failures
• API Gateway — Single entry point for clients

My experience: The most valuable design pattern knowledge is knowing WHEN NOT to use a pattern. Overengineering with patterns is as harmful as not knowing them. Start simple, apply patterns when the problem they solve actually exists.

Interview tip: Don't just name patterns—explain the problem they solve and the trade-offs they introduce. This signals real understanding vs pattern memorization. Design Guru has excellent pattern-focused system design courses with interactive exercises.

Learn more:

• Educative — Design patterns in distributed systems
• ByteByteGo — Microservice patterns visual guides

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10. Performance Optimization

Performance is the speed, responsiveness, and efficiency with which a system processes requests and delivers results. It directly determines user experience—slow systems lose users.

Why Performance Is Non-Negotiable

The data is unambiguous:

• 100ms delay costs Amazon 1% in sales
• 1 second delay reduces conversions by 7%
• 53% of mobile users abandon pages taking more than 3 seconds
• Google uses page speed as a search ranking factor

Performance isn't just about user experience—it's directly tied to revenue.

Key Performance Dimensions

Response Time: How long to process a single request. Measured in milliseconds. Affects individual user experience.

Throughput: How many requests per second the system handles. Affects system capacity.

Resource Utilization: How efficiently CPU, memory, network, and disk are used. Affects cost.

Latency: Time for data to travel from source to destination. Affects distributed systems especially.

Performance Optimization Strategies

Algorithm and Data Structure Selection
The foundation. An O(n²) algorithm will kill performance at scale regardless of hardware. Choose the right algorithm first.

Database Optimization

• Indexes on query columns (dramatic impact)
• Query optimization and explain plans
• Connection pooling
• Read replicas for read-heavy workloads
• Denormalization where appropriate

Caching
Multiple levels: application cache, database query cache, CDN for static assets, browser caching.

Asynchronous Processing
Move slow operations (email sending, image processing, report generation) to background jobs. Don't make users wait for things they don't need to wait for.

Code Optimization

• Profile before optimizing (don't guess at bottlenecks)
• Optimize the critical path
• Minimize unnecessary computation
• Use efficient data structures

Infrastructure Optimization

• Geographic distribution (CDN, edge computing)
• Right-sizing compute resources
• Network optimization

My experience: The most impactful performance optimization is almost always database-related. Before touching application code, always check query performance, missing indexes, and N+1 query problems. 80% of the performance issues I've encountered were database problems.

Interview tip: Discuss the importance of measuring before optimizing. "Premature optimization is the root of all evil." — Donald Knuth. Always profile first, then optimize the actual bottleneck. BugFree.ai generates realistic performance-focused interview questions with AI feedback on your answers.

Learn more:

• ByteByteGo — Performance optimization visual guides
• Codemia — Performance design challenges
• Udemy System Design Masterclass — End-to-end performance optimization coverage
• System Design School — Architectural patterns curriculum

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The System Design Interview Framework

When you understand these 10 concepts deeply, you can structure any system design interview answer:

Step 1: Clarify requirements (2-3 minutes)

• Scale: users, requests per second, data volume
• Availability requirements: 99.9% vs 99.999%
• Consistency requirements: strong vs eventual

Step 2: Capacity estimation (2-3 minutes)

• Traffic: reads/writes per second
• Storage: data volume and growth rate
• Bandwidth: data transfer requirements

Step 3: High-level design (10-12 minutes)

• Core components: clients, servers, databases, caches
• Data flow between components
• Key design decisions and trade-offs

Step 4: Deep dive (10-15 minutes)

• Scale the bottlenecks
• Handle failures and edge cases
• Discuss trade-offs explicitly

Step 5: Wrap up (2-3 minutes)

• Summarize key decisions
• Identify what you'd improve with more time
• Discuss monitoring and operational concerns

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Best Resources to Master These Concepts

After extensive research and personal experience, here are the best platforms to master all 10 concepts:

For books:

• Designing Data-Intensive Applications (Martin Kleppmann) — The bible of distributed systems
• System Design Interview Volumes 1 & 2 (Alex Xu) — Best interview prep books
• Clean Architecture (Robert C. Martin) — Foundational architecture principles

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Conclusion

These 10 concepts aren't just interview topics—they're the vocabulary and mental models that separate engineers who build systems that scale from those who build systems that struggle.

Master these 10 concepts, and you'll be equipped to design, discuss, and build systems that handle real-world demands—in interviews and in production.

Which concept do you find most challenging? Drop a comment—I'd love to help.

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