Top 100 Sample System Design Interview Questions and Answers

Top 100 Sample System Design Interview Questions and Answers
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1. How would you design a URL shortening service?

Answer: Create a mapping between long URLs and short alphanumeric strings. Store the mapping in a database. Use Base62 encoding for generating the short codes.

def encode(num):
    chars = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
    result = []
    while num:
        result.append(chars[num % 62])
        num //= 62
    return "".join(result[::-1])

Reference: Base62 encoding

2. How can you design a system to handle rate limiting?

Answer: Use the Token Bucket or Leaky Bucket algorithm. Redis can be employed to maintain bucket counts in a distributed environment.

import redis

redis_conn = redis.StrictRedis(host='localhost')
def rate_limiter(user_id, max_requests, time_period):
    return redis_conn.incr(user_id) <= max_requests

Reference: Redis rate limiting

3. How would you design a load balancer?

Answer: Load balancers distribute incoming traffic across servers. Strategies: Round Robin, Least Connections, or IP Hash. A simple Round Robin example:

servers = ["server1", "server2", "server3"]
def get_server(request_number):
    return servers[request_number % len(servers)]

Reference: Load balancing algorithms

4. How can you ensure data consistency in a distributed database?

Answer: Implement techniques like Two-Phase Commit, vector clocks, or merge resolutions.

def two_phase_commit():
    for node in nodes:
    for node in nodes:

Reference: Two-Phase Commit

5. How would you design a distributed cache?

Answer: Use consistent hashing to distribute cache entries across nodes. This minimizes re-distribution during node additions/removals.

def get_cache_node(key):
    hash_value = hash(key)
    return sorted_nodes[(bisect_left(sorted_nodes, hash_value) % len(sorted_nodes))]

Reference: Consistent hashing

6. How would you design a globally distributed file storage system?

Answer: Use a combination of replication and sharding. Metadata can be managed using a consensus system like Paxos or Raft.

def get_file_location(file_id):
    shard = file_id % TOTAL_SHARDS
    return shard_locations[shard]

Reference: Distributed file systems

7. How can you design an efficient logging system for a multi-server environment?

Answer: Use a centralized logging solution like ELK Stack (Elasticsearch, Logstash, Kibana) or Graylog.

logstash -f logstash.conf

Reference: ELK Stack

8. How would you handle versioning in a RESTful API?

Answer: There are multiple strategies: URI versioning, header versioning, or accept header versioning.

def resource_v1():
    return "This is version 1"

Reference: API versioning

9. How would you design an online multiplayer game backend?

Answer: Implement a state synchronization mechanism, use UDP for faster communication, and have dedicated game servers for different geographic locations.

import socket

sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.sendto(data, server_address)

Reference: UDP in Python

10. How do you ensure data integrity in a messaging system?

Answer: Implement checksums or CRCs for messages. On the receiver end, compute the checksum and compare.

import zlib

def compute_crc(data):
    return zlib.crc32(data)

Reference: CRC32 in Python

11. How would you design a scalable notification system?

Answer: Use a pub-sub model, where services publish notifications and users subscribe. Message brokers like RabbitMQ or Kafka can manage the messages.

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.basic_publish(exchange='', routing_key='notifications', body='New Notification')

Reference: RabbitMQ with Pika

12. How can you design a system to ensure zero downtime deployments?

Answer: Implement Blue-Green deployments or Canary releases. This allows switching between versions without affecting all users at once.

kubectl rollout status deployment/v2-deployment

Reference: Kubernetes Rollouts

13. How would you implement search functionality in a large database?

Answer: Use indexing and full-text search engines like Elasticsearch. This enhances search speed by avoiding table scans.

from elasticsearch import Elasticsearch

es = Elasticsearch()
response = es.search(index="my-index", body={"query": {"match": {'field': 'value'}}})

Reference: Elasticsearch-Py

14. How do you design a globally distributed authentication system?

Answer: Use OAuth2.0 or SSO (Single Sign-On) with token-based authentication. JWT (JSON Web Tokens) can carry user information.

import jwt

token = jwt.encode({"user_id": 123}, "SECRET_KEY", algorithm="HS256")

Reference: PyJWT

15. How would you handle data migration for a large-scale application?

Answer: Use ETL (Extract, Transform, Load) processes. Tools like Apache Nifi or Talend can help manage data flows.

nifi.sh start

Reference: Apache Nifi

16. How can you ensure real-time data synchronization between databases in different regions?

Answer: Use database replication techniques. Databases like MySQL offer Master-Slave replication for such purposes.

CHANGE MASTER TO MASTER_HOST='master_host_name', MASTER_USER='replication_user_name', MASTER_PASSWORD='replication_password';

Reference: MySQL Replication

17. How would you design a system to prevent abuse (e.g., repeated logins or DDoS attacks)?

Answer: Implement CAPTCHAs, rate limiting, and IP blacklisting. Analyze traffic patterns to detect anomalies.

if request_count > THRESHOLD:

Reference: Google reCAPTCHA

18. How do you design an e-commerce system with high availability?

Answer: Use a combination of CDN, database replication, distributed caching, and microservices architecture for modularity and scalability.

from flask import Flask
app = Flask(__name__)

def product_info():
    return "Product details"

Reference: Flask Microservices

19. How would you ensure data backup and recovery in a distributed system?

Answer: Implement regular backups, geographically distribute backup storage, and use databases that support point-in-time recovery.

aws s3 cp /data/backups s3://my-backup-bucket/

Reference: AWS S3 CLI

20. How can you optimize latency in a globally distributed application?

Answer: Use CDNs, optimize database queries, use edge locations for compute, and apply data sharding to minimize cross-region data transfers.

aws cloudfront create-distribution --origin-domain-name mywebsite.com

Reference: AWS CloudFront

21. How would you design a system for real-time analytics?

Answer: Use stream processing platforms like Apache Kafka or Apache Flink. Analyze incoming data streams in real-time.

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> source = builder.stream("analytics-input");

Reference: Kafka Streams

22. How can you handle multi-tenancy in a SaaS application?

Answer: Implement separate schemas for each tenant or use a shared schema with a Tenant ID. Ensure data isolation between tenants.

CREATE SCHEMA tenant1234;

Reference: Database Multi-tenancy

23. How would you ensure security for data at rest and in transit?

Answer: Use encryption. For data at rest, tools like TDE (Transparent Data Encryption) can be used. For data in transit, use protocols like TLS.

import ssl

ssl_sock = ssl.wrap_socket(sock, keyfile="server.key", certfile="server.crt")

Reference: Python SSL

24. How do you design a robust logging system that can handle varying log levels?

Answer: Use centralized logging solutions like ELK Stack and implement log levels like INFO, DEBUG, ERROR.

Logger logger = LoggerFactory.getLogger(App.class);
logger.error("This is an error message");

Reference: SLF4J

25. How would you design an API gateway?

Answer: An API gateway routes incoming requests to microservices. Tools like Kong or Amazon API Gateway can be used.

  - name: example-service
    url: http://example.com
    - name: example-route
      - /example

Reference: Kong

26. How do you handle schema evolution in a distributed database?

Answer: Use databases that support schema-less design or use tools like Apache Avro to serialize data with its schema.

Schema schema = new Schema.Parser().parse(new File("user.avsc"));

Reference: Apache Avro

27. How would you design a system to handle server health monitoring?

Answer: Use monitoring tools like Prometheus and Grafana. Create alerts based on health metrics.

  scrape_interval: 15s
  - job_name: 'prometheus'
    - targets: ['localhost:9090']

Reference: Prometheus

28. How can you ensure atomic transactions in a distributed system?

Answer: Implement distributed transaction protocols like Two-Phase Commit or Sagas.

UserTransaction userTransaction = ...;
// Operations...

Reference: JTA

29. How would you design a pub-sub system for real-time notifications?

Answer: Use messaging brokers like RabbitMQ or platforms like Google Pub/Sub. Implement topics for different message types.

from google.cloud import pubsub_v1

publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path("my-project", "my-topic")

Reference: Google Pub/Sub

30. How can you ensure high availability in a distributed system?

Answer: Implement replication, clustering, and failover strategies. Use load balancers to distribute traffic.

haproxy -f /etc/haproxy/haproxy.cfg

Reference: HAProxy

31. How would you design a globally distributed configuration system?

Answer: Use distributed configuration management systems like Apache ZooKeeper or etcd.

ZooKeeper zk = new ZooKeeper("localhost:2181", 3000, null);
byte[] data = zk.getData("/config/app", false, null);

Reference: Apache ZooKeeper

32. How can you handle A/B testing in a large-scale application?

Answer: Distribute incoming traffic to different versions using feature flags or load balancer configurations.

def get_version(user_id):
    return "A" if user_id % 2 == 0 else "B"

Reference: A/B Testing

33. How would you design a system for automatic scaling based on traffic?

Answer: Use orchestration platforms like Kubernetes. Configure Horizontal Pod Autoscalers based on CPU or custom metrics.

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
  name: my-hpa
    apiVersion: apps/v1
    kind: Deployment
    name: my-app

Reference: Kubernetes HPA

34. How can you design a system for processing and analyzing large streams of data in real-time?

Answer: Use stream processing frameworks like Apache Kafka Streams or Apache Flink.

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<String> dataStream = env.addSource(new FlinkKafkaConsumer<>("topic", new SimpleStringSchema(), properties));

Reference: Apache Flink

35. How would you handle database sharding in a microservices environment?

Answer: Shard data based on inherent domain boundaries. Each microservice should own its database shard.

CREATE DATABASE order_service_shard_01;
CREATE DATABASE inventory_service_shard_01;

Reference: Database Sharding

36. How can you design a content recommendation system, like YouTube’s?

Answer: Use collaborative filtering based on user behavior, content-based filtering, and hybrid methods.

from sklearn.metrics.pairwise import cosine_similarity

similarity_scores = cosine_similarity(content_features)

Reference: Content Recommendation

37. How would you design a system to handle scheduled tasks in a distributed environment?

Answer: Use distributed cron job systems like Apache Airflow or Kubernetes CronJobs.

apiVersion: batch/v1beta1
kind: CronJob
  name: my-cron-job
  schedule: "0 */3 * * *"

Reference: Kubernetes CronJobs

38. How can you ensure idempotency in distributed API requests?

Answer: Use unique transaction IDs and cache the results. Check cache before processing to avoid duplicate processing.

def process_request(transaction_id, data):
    if not cache.exists(transaction_id):
        result = process_data(data)
        cache.set(transaction_id, result)
    return cache.get(transaction_id)

Reference: API Idempotency

39. How would you design a distributed rate limiter?

Answer: Use Redis with sliding window logs or token buckets to handle rate limits across distributed systems.

import redis

r = redis.Redis(host='localhost')
r.incr("user_request_count", 1)

Reference: Distributed Rate Limiting

40. How can you ensure data consistency in microservices architecture?

Answer: Use distributed transactions, event-driven architectures, or eventual consistency with mechanisms like the Outbox Pattern.

public Message<String> handleOrder(Order order) {
    // Process and produce an event

Reference: Event-Driven Microservices

41. How would you design a system to manage secrets in a microservices environment?

Answer: Use centralized secret management solutions like HashiCorp Vault or AWS Secrets Manager.

vault write secret/my-service password=strongpassword

Reference: HashiCorp Vault

42. How can you achieve eventual consistency in a distributed e-commerce platform?

Answer: Use an event-driven approach, where operations publish events. Other services asynchronously consume and process these events.

def place_order(order_data):
    publish_event('order_placed', order_data)

Reference: Eventual Consistency

43. How would you design a chat system with millions of users?

Answer: Use WebSocket for real-time bidirectional communication. Distribute users across chat servers with load balancers and ensure data replication.

from websocket import create_connection

ws = create_connection("ws://chatserver.example.com/")
ws.send("Hello, Server!")

Reference: WebSocket

44. How can you ensure fast read operations in a data-heavy application?

Answer: Use caching mechanisms like Redis or Memcached. Denormalize data in databases and employ efficient indexing.

import redis

r = redis.Redis(host='localhost')
r.set('user:1234:name', 'Alice')

Reference: Redis

45. How would you design a distributed task queue?

Answer: Use systems like RabbitMQ or Apache Kafka. Distribute tasks across multiple workers to process them in parallel.

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.basic_publish(exchange='', routing_key='tasks', body='Task data')

Reference: RabbitMQ

46. How can you achieve high throughput in a financial transaction system?

Answer: Optimize database operations, shard databases, use in-memory databases for frequent operations, and employ efficient message queuing systems.

from kafka import KafkaProducer

producer = KafkaProducer(bootstrap_servers='localhost:9092')
producer.send('transactions', value='Transaction data')

Reference: Apache Kafka

47. How would you handle rolling updates in a microservices architecture?

Answer: Use container orchestration platforms like Kubernetes. Update services incrementally to ensure system availability.

apiVersion: apps/v1
kind: Deployment
  name: my-deployment
    type: RollingUpdate

Reference: Kubernetes Rolling Update

48. How can you ensure fault tolerance in a distributed data processing system?

Answer: Use distributed processing frameworks like Apache Spark. They can automatically handle node failures and job restarts.

from pyspark import SparkContext

sc = SparkContext("local", "FaultToleranceApp")
data = sc.parallelize([1, 2, 3, 4])

Reference: Apache Spark Fault Tolerance

49. How would you handle data deduplication in a distributed storage system?

Answer: Implement content-addressable storage, where data is indexed by its hash. This prevents storing duplicate content.

import hashlib

hash_object = hashlib.md5(b'Hello World')
hex_dig = hash_object.hexdigest()

Reference: Content-Addressable Storage

50. How can you optimize write-heavy workloads in a database system?

Answer: Use write-optimized databases like Cassandra or HBase. Partition data effectively and balance write operations across nodes.

from cassandra.cluster import Cluster

cluster = Cluster()
session = cluster.connect('mykeyspace')
session.execute("INSERT INTO mytable (id, data) VALUES (1234, 'data')")

Reference: Apache Cassandra

51. How would you design an automated alerting system?

Answer: Monitor system metrics using tools like Prometheus. Integrate with alerting tools like AlertManager or PagerDuty to send notifications based on predefined thresholds.

alert: HighMemoryUsage
expr: node_memory_MemTotal_bytes - node_memory_MemFree_bytes > 0.8 * node_memory_MemTotal_bytes
for: 5m

Reference: Prometheus Alerting

52. How can you ensure database backups in a distributed system?

Answer: Schedule regular database backups using tools like mysqldump or pg_dump. Store backups in distributed storage or cloud services for redundancy.

mysqldump -u username -p mydatabase > backup.sql

Reference: MySQL Backup

53. How would you prevent hotspots in a distributed cache?

Answer: Use consistent hashing to evenly distribute cache keys among cache nodes. This minimizes hotspots by uniformly distributing the data.

def get_cache_node(key):
    hash_val = consistent_hash(key)
    return nodes[hash_val % len(nodes)]

Reference: Consistent Hashing

54. How can you design a scalable reporting system for large datasets?

Answer: Use columnar storage databases like Redshift or BigQuery. Optimize queries using OLAP operations and materialized views.

CREATE MATERIALIZED VIEW mv_report AS SELECT column1, column2 FROM large_dataset;

Reference: Materialized Views

55. How would you design a system for logging and monitoring microservices?

Answer: Implement centralized logging using ELK Stack or Grafana Loki. Use distributed tracing tools like Jaeger or Zipkin.

docker run -d --name=jaeger -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 -p 5775:5775/udp -p 6831:6831/udp -p 6832:6832/udp -p 5778:5778 -p 16686:16686 -p 14268:14268 -p 9411:9411 jaegertracing/all-in-one:1.9

Reference: Jaeger Tracing

56. How can you manage distributed sessions in a microservices architecture?

Answer: Store session data in a distributed cache like Redis. Share the session store across microservices to maintain a consistent session state.

import redis

r = redis.Redis(host='localhost')
r.set('session:1234', 'session_data')

Reference: Redis

57. How would you design an image storage and processing system at scale?

Answer: Use distributed storage like Amazon S3 for storing images. Use a processing pipeline with tools like Apache Kafka and image processing libraries.

from PIL import Image

img = Image.open("image.jpg")
img.thumbnail((128, 128))

Reference: Pillow

58. How can you ensure fairness in a distributed job scheduling system?

Answer: Implement fair queuing or weighted fair queuing to give equal opportunities to different jobs or give priority based on weights.

job_queue = FairQueue()
job_queue.add_job(job1, weight=1)
job_queue.add_job(job2, weight=2)

Reference: Fair Queuing

59. How would you prevent data loss in a stream processing system?

Answer: Use exactly-once processing semantics. Tools like Apache Kafka can ensure messages aren’t lost or processed multiple times.

props.put("enable.idempotence", "true");
KafkaProducer<String, String> producer = new KafkaProducer<>(props);

Reference: Kafka Idempotent Producer

60. How can you optimize data transfer between microservices?

Answer: Use binary data formats like Protocol Buffers or Avro. They ensure efficient serialization and deserialization.

syntax = "proto3";
message Person {
  string name = 1;
  int32 age = 2;

Reference: Protocol Buffers

61. How would you design a distributed rate-limiting system?

Answer: Combine in-memory stores like Redis with algorithms like Token Bucket or Leaky Bucket. Distributed tokens ensure global rate limits.

import redis

def is_request_allowed(user_id):
    tokens = redis_conn.get(user_id)
    return tokens > 0

Reference: Distributed Rate Limiting

62. How can you achieve data deduplication in a distributed backup system?

Answer: Use chunk-based deduplication. Break data into chunks, hash each chunk, and store only if the hash is new.

import hashlib

chunk_hash = hashlib.sha256(chunk_data).hexdigest()

Reference: Data Deduplication Techniques

63. How would you ensure message ordering in a distributed messaging system?

Answer: Use sequence numbers or timestamps. Systems like Apache Kafka maintain order within partitions.

ProducerRecord<String, String> record = new ProducerRecord<>("topic", "key", "message");

Reference: Kafka Message Order

64. How can you handle data rollbacks in a distributed transaction system?

Answer: Implement the Saga pattern, where long-running transactions are split into multiple smaller, manageable transactions.

def book_trip():
    if error: cancel_flight()

Reference: Saga Pattern

65. How would you handle circuit breaking in a system with external service dependencies?

Answer: Use tools or libraries like Hystrix or Resilience4j. They allow systems to fail fast and provide fallbacks.

@HystrixCommand(fallbackMethod = "fallbackMethod")
public String dependentServiceCall() {
    return externalService.call();

Reference: Hystrix

66. How can you maintain data versioning in a distributed data storage?

Answer: Use systems that support built-in versioning like Amazon S3, or use strategies like event sourcing to keep track of changes.

s3.put_object(Bucket='mybucket', Key='myfile', VersionId='v1', Body=data)

Reference: S3 Versioning

67. How would you design an efficient autocomplete system?

Answer: Use Trie data structure or Prefix Hash Map. Index the dataset to retrieve possible words or phrases efficiently.

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end_of_word = False

Reference: Trie

68. How can you manage distributed locks in a system?

Answer: Use tools like ZooKeeper or Redis with RedLock algorithm to ensure mutual exclusion across distributed systems.

import redis

def acquire_lock(lock_id):
    return redis_conn.setnx(lock_id, 'LOCKED')

Reference: Redis Distributed Locks

69. How would you handle request retries in a system without causing a storm of requests?

Answer: Implement exponential backoff with jitter. This spreads out the retry attempts.

import time, random

def backoff(attempt):
    wait_time = (2 ** attempt) + random.uniform(0, 1)

Reference: Exponential Backoff

70. How can you handle large-scale data migrations without downtime?

Answer: Use the Strangler Fig pattern, shadow writes, or dual writes to the new system while reading from both old and new systems.

def write_data(data):

Reference: Strangler Fig Pattern

71. How would you design a low-latency system that requires global data access?

Answer: Use CDNs to cache frequently accessed data. Utilize geo-replication and read from the nearest data location.

from azure.cosmos import CosmosClient

client = CosmosClient(endpoint, key)
database = client.get_database_client(database_id)
container = database.get_container_client(container_id)

Reference: Azure CosmosDB

72. How can you ensure transactional consistency in a microservices architecture?

Answer: Use distributed transactions, compensating transactions, or event-driven approaches to ensure data consistency across services.

def process_order(order):
    if payment_successful(order):

Reference: Microservices Transactions

73. How would you design a load balancer for distributing traffic to a cluster of servers?

Answer: Use algorithms like Round Robin, Least Connections, or IP Hashing. Implement health checks for failover support.

http {
    upstream myapp {
        server server1;
        server server2;
    server {
        location / {
            proxy_pass http://myapp;

Reference: Nginx Load Balancing

74. How can you handle state in a stateless microservices architecture?

Answer: Store state externally in databases, caches, or centralized storage. Services retrieve state when needed.

def get_user_preferences(user_id):
    return database.fetch(user_id)

Reference: Stateful vs Stateless

75. How would you optimize a system handling high-frequency trading?

Answer: Use in-memory databases, optimized algorithms, and colocation to reduce latency. Ensure high-speed network connectivity.

// Pseudo code to represent fast trade processing
void process_trade(Trade trade) {

Reference: High-Frequency Trading

76. How can you design a distributed logging and monitoring system for microservices?

Answer: Use centralized logging solutions like the ELK Stack (Elasticsearch, Logstash, Kibana) or Fluentd. Integrate with monitoring tools like Grafana.

input {
  beats {
    port => 5044

Reference: ELK Stack

77. How would you handle slow consumers in a message-driven system?

Answer: Implement backpressure by pausing the producer or dropping messages. Use message priorities and TTL (time-to-live).

import pika


Reference: RabbitMQ Consumer Flow Control

78. How can you ensure data accuracy in a distributed analytics system?

Answer: Use end-to-end data validation, checksums, and reconciliation processes. Monitor for anomalies in data patterns.

def validate_data(data_chunk):
    return hash(data_chunk) == received_checksum

Reference: Data Validation

79. How would you handle versioning in a microservices-based system?

Answer: Implement semantic versioning, use API gateways, and support backward-compatible changes. Deprecate older versions with notice.

GET /api/v1/users
GET /api/v2/users

Reference: API Versioning

80. How can you implement centralized configuration in a distributed system?

Answer: Use tools like Apache ZooKeeper, Consul, or Spring Cloud Config to manage and distribute configurations across services.

private String myProperty;

Reference: Spring Cloud Config

81. How would you ensure data immutability in a distributed ledger system?

Answer: Use cryptographic hashing. Each block references the hash of the previous block, forming a chain that can’t be altered without changing subsequent blocks.

public String calculateHash() {
    return Sha256.apply(previousHash + timestamp + data);

Reference: Blockchain

82. How can you achieve zero-downtime deployments in a microservices environment?

Answer: Implement blue-green deployments or canary releases. Use load balancers or service meshes to control traffic routing.

kubectl set image deployment/my-app my-app=new-version

Reference: Kubernetes Rolling Updates

83. How would you handle GDPR compliance in a data storage system?

Answer: Implement data anonymization, right to erasure, and data export. Store user consent and process data based on it.

DELETE FROM users WHERE user_id = request.user_id;

Reference: GDPR Compliance

84. How can you design a fraud detection system in e-commerce platforms?

Answer: Use machine learning models to identify suspicious patterns. Collect and analyze user activities, transaction details, and behavioral data.

from sklearn.ensemble import IsolationForest

clf = IsolationForest().fit(train_data)
predictions = clf.predict(test_data)

Reference: Fraud Detection

85. How would you design a quota management system in API gateways?

Answer: Use token bucket or leaky bucket algorithms. Store and manage API usage data in fast databases like Redis.

if redis.get(api_key) < rate_limit:
    return rate_limit_exceeded_response

Reference: API Rate Limiting

86. How can you optimize storage in a time-series database?

Answer: Use data compression techniques, downsample older data, and use specialized time-series databases like InfluxDB.

influx -execute 'SELECT mean("value") FROM "cpu_load" WHERE time > now() - 1d GROUP BY time(10m)'

Reference: InfluxDB

87. How would you ensure end-to-end security in a microservices-based application?

Answer: Implement mutual TLS, use API gateways for edge security, manage secrets centrally, and ensure regular security audits.

openssl s_client -connect server:port -cert client.crt -key client.key

Reference: Mutual TLS

88. How can you design a distributed rate limiter with guaranteed fairness across multiple clients?

Answer: Use distributed token buckets stored in a system like Redis. Ensure even distribution of tokens among clients.

rate = 1000  # tokens per minute
allowance = redis.get_or_set(client_id, rate)

Reference: Token Bucket

89. How would you handle multi-tenancy in a cloud-native application?

Answer: Implement namespace-based isolation, use separate databases or schema-based multi-tenancy, and ensure resource quotas.

apiVersion: v1
kind: Namespace
  name: tenant-a

Reference: Kubernetes Namespaces

90. How can you prevent the cascading failure effect in a microservices architecture?

Answer: Implement circuit breakers, timeouts, and fallback strategies. Monitor services and detect anomalies.

@HystrixCommand(fallbackMethod = "fallback")
public String callService() {
    return externalService.call();

Reference: Circuit Breaker Pattern

91. How would you design a search system to handle multilingual content?

Answer: Use tools like Elasticsearch with language analyzers. This helps in tokenizing and indexing multilingual content efficiently.

PUT my_index
  "settings": {
    "analysis": {
      "analyzer": {
        "french_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["french_stemmer"]
      "filter": {
        "french_stemmer": {
          "type": "stemmer",
          "name": "light_french"

Reference: Elasticsearch Analyzers

92. How can you minimize data transfer costs between microservices in a cloud environment?

Answer: Keep frequently interacting microservices within the same region or data center. Use data compression techniques and optimized serialization formats.

import java.util.zip.DeflaterOutputStream;

OutputStream os = new DeflaterOutputStream(originalOutputStream);

Reference: Data Compression in Java

93. How would you ensure atomicity in a distributed job scheduling system?

Answer: Use two-phase commit or rely on distributed transaction coordinators. Design tasks to be idempotent.

if (prepare_all_services()) {
} else {

Reference: Two-Phase Commit

94. How can you implement a geo-distributed database system?

Answer: Use databases supporting multi-region replication like CockroachDB or Cassandra. Ensure data is partitioned based on geo-attributes.

cockroach start --locality=region=us-west --join=other_nodes

Reference: CockroachDB Locality

95. How would you design data replication in a multi-master database system?

Answer: Implement conflict resolution strategies, like vector clocks or last-writer-wins. Ensure each master can handle read and write operations.

def resolve_conflict(data1, data2):
    return max(data1, data2, key=lambda d: d.timestamp)

Reference: Conflict Resolution in Databases

96. How can you optimize a system for fast read operations with infrequent writes?

Answer: Use read-optimized databases like Amazon Redshift. Cache frequently accessed data using systems like Memcached or Redis.

import redis

cache = redis.StrictRedis(host='localhost')
data = cache.get_or_set('key', fetch_data_from_db)

Reference: Read-Optimized Databases

97. How would you handle schema changes in a distributed database?

Answer: Implement backward-compatible schema changes. Use versioned schemas and data migration scripts for major changes.


Reference: Database Schema Evolution

98. How can you design a low-latency API gateway?

Answer: Implement caching, request optimization, and load balancing. Use efficient encoding formats and compress responses.

server {
    listen 80;
    location / {
        proxy_pass http://backend;
        proxy_cache my_cache;

Reference: Nginx Caching

99. How would you ensure service discovery in a microservices architecture?

Answer: Use service registry tools like Consul, Eureka, or Kubernetes service discovery. Ensure dynamic registration and deregistration of services.

  "ID": "myservice_01",
  "Name": "myservice",
  "Address": "",
  "Port": 8080

Reference: Consul Service Discovery

100. How can you design an optimal data archiving strategy?

Answer: Implement tiered storage, moving older data to slower, cost-effective storage solutions. Use formats like Parquet or ORC for efficient compression.

aws s3api put-object --bucket mybucket --key archive/data.parquet --storage-class DEEP_ARCHIVE

Reference: AWS S3 Storage Classes