The article explains why adding Redis alone often remains slow, introduces a three‑level cache pyramid (L1 Caffeine, L2 Redis, L3 MySQL) built with Spring Boot 3, and shows how this design reduces request latency from 28 ms to 2 ms, cuts CPU usage by 35 % and achieves up to 14‑fold throughput improvement.
This guide explains how to boost Redis query per second (QPS) from 50,000 to 500,000 by applying horizontal sharding, read‑write separation with master‑slave replication, memory and data‑structure optimizations, and network‑level tuning, while providing concrete examples and configuration tips.
This article introduces JetCache, an open‑source distributed cache built on Spring and Redis, explains its core features and typical use cases, details the main API and annotation support, and provides a step‑by‑step SpringBoot integration guide with full code examples.
The article demonstrates how to combine Spring Boot 3, Caffeine local cache, and Redis into a three‑level cache pyramid, reducing API response time from 28 ms to 2 ms, cutting CPU usage by 35 %, and providing detailed configuration, code examples, performance benchmarks, and mitigation strategies for common high‑concurrency pitfalls.
This article details a Redis bucket‑based inventory deduction architecture that guarantees strong consistency while eliminating over‑sell and under‑sell, boosting hot‑spot deduction TPS and stability through a merge‑commit process and comprehensive module design.
After three months of preparation, a friend secured an offer from SF Express Technology with a base salary of 20k*15—well above the initially expected 15k—and the post shares the full set of interview questions that led to the success.
Faced with minutes‑long posting delays and exploding review costs in a fast‑growing social app, the team introduced 24‑hour shift staffing, a local blacklist stored in MySQL, an in‑memory Trie + Aho‑Corasick matcher, Redis‑driven hot updates and a machine‑audit fallback with a feedback loop, dramatically cutting latency, cost and false‑positives.
This article walks through building a Spring MVC interceptor that leverages Redis to enforce per‑IP request limits, explains configurable parameters, shows how to apply protection selectively via mapping rules or custom annotations, and discusses practical pitfalls such as sliding‑window logic, path‑parameter handling, and real‑IP detection.
This article explains how to build a lightweight, production‑grade idempotency middleware for Spring Boot 3 using AOP, a custom @Idempotent annotation, SpEL‑generated keys, and Redis atomic operations, covering architecture, core components, code examples, use cases, and performance characteristics.
This article examines three practical approaches—microservice isolation, Redis‑based whitelist, and gateway‑plus‑AOP—to prevent external exposure of internal APIs, compares their trade‑offs, and provides a complete SpringBoot implementation of the gateway‑AOP solution with code samples.
This article examines whether Redis can serve as a reliable message queue by exploring its List, Pub/Sub, and Stream data structures, comparing their features, limitations, and durability against professional brokers like Kafka and RabbitMQ, and offering practical usage guidelines.
This article breaks down Redis's nine data types, explains the underlying structures and typical scenarios for each, compares best‑practice usage, highlights interview focus points, and warns about common misconceptions to help developers choose the most efficient storage solution.
Redis achieves single‑machine million‑level QPS not merely because it stores data in memory, but through a tightly coordinated set of four core design principles—pure in‑memory operation, single‑threaded architecture, I/O multiplexing, and highly optimized data structures—that together deliver ultra‑low latency and massive throughput.
This guide explains why delayed messages are essential for distributed system stability, compares RabbitMQ's TTL+DLX and delayed‑message plugin, details RocketMQ's precise timing and delay‑level features, and offers custom Redis and time‑wheel solutions with practical Java code examples and deployment tips.
This guide compares four common distributed ID generation techniques—database auto‑increment, UUID, Twitter’s Snowflake, and Redis INCR—detailing their advantages, drawbacks, and ideal use‑cases, helping architects select the most suitable method for their system’s scalability and performance requirements.
The article explains that overwhelming request rates exceed database capacity, causing exponential latency, and presents six proven cache‑design techniques—including in‑process, Redis, multi‑level, cache‑aside, write‑through/write‑behind, and edge caching—to keep Spring Boot APIs stable, fast, and cost‑effective under high load.
This article examines whether Redis can serve as a reliable message queue by comparing its List, Pub/Sub, and Stream features against professional solutions like Kafka and RabbitMQ, covering usage patterns, code examples, performance trade‑offs, persistence, and handling of message loss and backlog.
This article explains how Redis master‑slave replication provides hot backup, read‑write separation, high availability, and horizontal scaling by detailing its three‑stage workflow, full and partial synchronization mechanisms, key configuration options, and practical analogies for clear understanding.
This guide explains how to achieve pagination and multi‑condition fuzzy queries in Redis by leveraging ZSet for ordered paging and Hash with HSCAN for pattern matching, detailing command usage, combination strategies, and performance optimizations such as temporary set expiration and data freshness handling.
This article walks through the evolution of Redis distributed locks—from basic SETNX mutual exclusion to atomic SET with expiration, Lua‑based safe unlocking, Redisson's WatchDog auto‑renewal, and the RedLock algorithm—highlighting pitfalls, best‑practice implementations, and interview‑style Q&A for robust production use.
Redis offers five core data types—String, Hash, List, Set, and ZSet—each acting as a high‑performance concurrent data structure that determines system throughput, latency, and stability; this guide explains their characteristics, optimal use‑cases, anti‑patterns, and practical code examples for robust architecture design.
This article dissects a production‑grade Go database access framework for microservices, covering unified interface abstraction, factory pattern for multi‑DB support, PostgreSQL array handling, read‑write splitting with load balancing, Redis cache protection, monitoring, and deployment considerations, with full code examples and open‑source links.
To reliably handle RocketMQ's at-least-once delivery semantics, this guide explains why duplicate consumption is inevitable, outlines three defensive layers—Redis‑based idempotency, database unique constraints, and state‑machine checks—provides production‑grade Java code, and details ACK/retry strategies and monitoring practices for robust systems.
This guide walks you through RedisInsight—a visual Redis GUI that supports clusters, SSL/TLS, and memory analysis—covering Linux installation, environment variable setup, service startup, Kubernetes deployment via YAML, and core usage such as browsing keys, executing commands, and monitoring performance.
This article explains how to protect Go services from overload by implementing HTTP request rate limiting using the standard token‑bucket limiter, per‑IP throttling with a map and mutex, and a distributed approach with Redis and Lua scripts, complete with practical code examples and middleware integration.
This guide explains how Redis implements distributed locks, outlines common pitfalls such as lock contention, zombie locks, and mismatched expiration times, and provides step‑by‑step solutions—including single‑node SET commands, Redlock high‑availability algorithm, Lua‑based safe release, and best‑practice recommendations for real‑world deployments.
This article walks through the strengths, typical use cases, and Python connection code for relational databases (MySQL, PostgreSQL, SQLite), NoSQL stores (MongoDB, Redis, Neo4j), cloud‑native options (DynamoDB), and emerging vector databases such as Milvus, helping you decide which fits your project.
This article breaks down the core concerns interviewers have for senior Go engineers—system stability, middleware pitfalls, Redis risk isolation, message‑queue buffering, distributed consistency, configuration management, ID design, and avalanche prevention—offering concrete insights and practical interview answers.
The article explains the delayed double‑delete cache‑invalidation pattern, its four major drawbacks in high‑concurrency environments, and presents four production‑grade alternatives—event‑driven binlog updates, distributed‑lock with versioning, write‑through proxy layers, and logical‑delete with async cleanup—used by leading Chinese internet firms to achieve reliable data consistency.
The article explains how Redisson implements a re‑entrant distributed lock using Redis hash structures and the atomic HINCRBY command to manage client identity, re‑entry counting, concurrency safety, and graceful release, providing a complete technical analysis with code, Lua scripts, and best‑practice guidelines.
Rudis v0.2.0 introduces a major architecture overhaul, upgraded Tokio runtime, shared‑nothing threading, master‑slave replication, transaction support, and a refactored KEYS command that boosts performance by up to 99.55% for large data sets.
Redis achieves sub‑millisecond response times by storing all data in RAM, using a single‑threaded event loop with I/O multiplexing (epoll/select/poll), and employing highly optimized data structures such as skip lists and hash tables that provide O(1) or O(log N) operations.
This article presents a systematic benchmark comparing ClickHouse, Doris, and Redis for IP‑range dimension lookups using Flink‑Kafka pipelines, detailing test design, result table schema, query interfaces, and performance results across varying data rates, concluding that Redis offers the fastest and most stable query latency.
This article explains why duplicate order requests occur, analyzes the idempotency challenges, and presents practical front‑end and Redis‑based solutions—including button disabling, SETNX locking, and token validation—to ensure only one successful order per user action.
This article explains the unique challenges of flash‑sale systems—massive, short‑lived traffic spikes—and presents practical backend optimizations such as front‑end request filtering, Redis‑based atomic counters, streaming queues, safe database updates, and unit‑level isolation for global deployments.
This step‑by‑step guide shows how to design, install, configure, start, and verify a Redis Sentinel high‑availability setup with one master, two replica nodes, and three sentinel monitors, including client connection examples and common troubleshooting tips.
This article explains why Redis cache penetration occurs and presents four practical mitigation techniques—using a Bloom filter, caching placeholder values, pre‑warming hot keys, and applying authentication with rate‑limiting—to protect backend performance and database stability.
Redis delivers millisecond‑level response and millions of operations per second by storing data entirely in memory, using a single‑threaded event‑driven model, efficient I/O multiplexing with epoll, and highly optimized data structures such as strings, hashes, lists, sets and sorted sets.
Redis has introduced a multithreaded query engine that dramatically reduces latency and increases throughput—up to 16×—for vector similarity searches, enabling vertical scaling and better support for real‑time RAG applications compared to traditional single‑threaded architectures and competing vector databases.
This article explains Redis master‑slave replication, covering its purpose for data redundancy, read/write separation, and high availability, and details the full‑synchronization and incremental‑synchronization processes with step‑by‑step diagrams.
This article outlines practical cache design principles, covering suitable scenarios, health metrics, common pitfalls like avalanche, breakdown and penetration, and concrete implementation rules for both local (Caffeine) and Redis caches to ensure microsecond‑level response and stable high‑traffic performance.
This article explains a flash‑sale system architecture from seven dimensions—including Nginx + CDN, routing with Redis, MQ clustering, business logic, read‑write‑separated databases, security controls, and page‑level optimizations—while providing concrete Nginx configs, Redis lock strategies, and database transaction tips to handle massive concurrent requests.
The article starts by highlighting Xiaomi's competitive software developer salaries, then provides a detailed Java interview guide covering reflection fundamentals, static vs dynamic proxies, SPI vs API, synchronization behavior, Redis caching strategies, and JWT authentication, complete with code examples and practical comparisons.
This guide introduces RedisInsight, a powerful Redis GUI, and provides step‑by‑step instructions for physical and Kubernetes installations, environment configuration, service startup, and basic usage including Redis setup and UI operations, all illustrated with code snippets and screenshots.
This article explains the principles and implementation details of common rate‑limiting algorithms—token bucket and sliding‑window counting—including their core concepts, key processes, Java code examples, and how to extend them to distributed scenarios with Redis Lua scripts.
This article explains why Redis can handle massive concurrent traffic by storing all data in RAM, using a single‑threaded event loop, leveraging Linux epoll for I/O multiplexing, and employing highly optimized in‑memory data structures that keep latency in the microsecond range.
This guide walks through installing RedisInsight on Linux, setting environment variables, launching the service, deploying it with Kubernetes, and using its GUI to monitor and manage Redis instances, complete with command examples and configuration details.
This article explains how to design a high‑concurrency ticket‑spike service that can handle millions of requests by using multi‑layer load balancing, Nginx weighted round‑robin, local stock caching, and atomic Redis operations, complete with Go code examples and performance testing.
This article explains when to store user objects in Redis as a JSON‑encoded String or as a field‑level Hash, comparing memory usage, atomic updates, network overhead, concurrency safety, scalability, and provides practical tips for large keys, hot keys, and Redis threading.
The article details Alibaba's 2024 campus backend salary bands, compares them with peers, outlines the interview process and project presentation tips, and provides in‑depth technical tutorials on Redis Lua scripts, cache consistency, slow‑query logging, MySQL optimization, Java reflection, WebSocket vs polling, SSE, TCP/UDP, and HTTP vs HTTPS.
This article explains Redis eviction policies, how the eviction process works, and practical solutions for cache penetration, breakdown, and avalanche, helping you select optimal strategies and mitigate high‑traffic cache failures.
This guide walks you through three practical Spring Boot solutions—database scheduled scans, message‑queue delayed queues, and Redis key‑expiration notifications—to automatically cancel unpaid orders after 30 minutes, complete with code samples, architecture diagrams, pros and cons, and best‑practice recommendations.
Learn how to design and implement a ready-to-use rule engine combined with a gray release system using Golang, Kafka, Redis, and CEL, complete with Docker‑compose deployment, edge execution, token‑bucket throttling, and webhook actions, plus full source code for a production‑grade marketing strategy platform.
Redis offers three persistence options—RDB snapshots, AOF command logging, and the hybrid RDB+AOF mode introduced in Redis 4.0—each with distinct workflows, configuration settings, advantages, and trade‑offs, and the article outlines their mechanisms, pros/cons, practical configuration tips, and deployment recommendations.
This article explains how Uber solves the "nearby driver" problem by employing the open‑source H3 hexagonal spatial index, hierarchical grids, Cassandra for persistent storage, and Redis caching to deliver fast, accurate, and scalable real‑time location services.
The article walks through a real‑world OOM incident in a high‑traffic hotel information service, detailing the root‑cause analysis of memory exhaustion, JVM heap configuration, GC behavior, heap‑dump inspection, and the concrete optimizations applied to prevent similar failures.
This article provides a comprehensive guide to Java concurrency and related concepts, covering synchronized lock upgrades, object vs class locks, lazy and double‑checked singleton implementations, ThreadLocal mechanics, reflection usage, annotation scopes, JVM class loading, and Redis cluster threading behavior.
Spring Boot 4.0, built on Spring Framework 7.0, introduces HTTP Service Clients, native API versioning, full JSpecify null‑safety, Java 25 support, upgraded dependencies, Gradle 9 compatibility, Redis static master‑replica configuration, and drops Undertow, with migration advice to move through 3.5 first.
Redis 6.0 adds a multithreaded network I/O model, client caching, ACL and RESP3, and explains why earlier versions were single‑threaded, how the new IO threads cooperate with the main thread, and the practical steps to enable and tune multithreading for better performance.
The article shares iFlytek's recent campus salary packages, then dives into a detailed Java interview guide covering Redis data types, key expiration handling, thread safety, ThreadLocal usage, MySQL covering and composite indexes, slow‑query analysis, and SQL‑injection prevention techniques.
This article details the three‑year evolution of a self‑built Redis service, covering its massive scale, architectural redesign, migration from LB to a custom DRedis SDK, same‑city active‑active near‑read support, Redis‑server version upgrades, instance specifications, proxy rate‑limiting, and extensive automation that together boost performance while cutting costs.
This article explains Redis master‑slave replication, detailing the full synchronization process that creates an RDB snapshot and streams buffered writes, as well as the incremental synchronization that resumes after a disconnection using PSYNC and the replication backlog.
This article walks through the design and implementation of a complete seckill (flash‑sale) system built on SpringBoot, MyBatis, MySQL, RabbitMQ and Redis, covering double MD5 password hashing, distributed sessions, unified exception handling, caching strategies, memory flags, pre‑decrement inventory, asynchronous order processing, oversell prevention, and rate limiting, with code snippets and UI screenshots.
This article compiles concise, high‑scoring answers to essential operations interview questions, covering security hardening, intrusion response, high‑availability architecture, disaster‑recovery design, Redis replication and clustering, Docker fundamentals and networking, Kubernetes components, monitoring, CI/CD pipelines, and the evolving role of DevOps.
A real‑world incident at JD Tech shows that a single 1.5 MB Redis cache key caused a full‑site outage during a high‑traffic event, and the article explains the underlying cache‑breakdown and bandwidth traps, then details three emergency mitigations and long‑term preventive practices.
This article explains Redis master‑slave replication, covering the roles of master and replica, the full‑copy synchronization process, the incremental sync mechanism, and the step‑by‑step workflow illustrated with diagrams to help engineers implement reliable high‑availability data stores.
This article explains Redis Pipeline’s core principle of batching commands to reduce network round‑trips, presents benchmark data showing up to 17‑fold speedups, details real‑world use cases such as cache warm‑up, heartbeat reporting, and high‑traffic events, and provides best‑practice guidelines on batch sizing, error handling, cluster constraints, and comparisons with transactions and Lua scripts.
ByteDance’s recent salary announcements show higher offers up to 40K, while the company’s rigorous backend interview covers Java heap OOM analysis, JVM tuning, thread‑pool configuration, Redis expiration strategies, MQ usage, network request flow, and algorithm challenges, providing a comprehensive technical deep‑dive for candidates.
This article provides a comprehensive guide to Redis Cluster, covering its hash‑slot sharding, master‑slave replication, automatic failover, smart client routing, deployment best practices, operational tips, and the limitations you must know before adopting it.
The article dissects a high‑traffic flash‑sale interview question—how to guarantee zero order loss at 100,000 TPS when the master MySQL instance fails—by explaining the underlying performance‑consistency conflict, the three skills interviewers assess, and presenting five concrete, code‑driven solutions ranging from MySQL parameter tuning to semi‑sync replication, local message tables, group replication, and Redis‑Kafka traffic shaping.
Shopee’s 2023 backend positions offer competitive salaries ranging from 23.5k to 32k in Shenzhen, and the article provides a comprehensive interview guide covering network models, TCP handshake, HTTP/HTTPS differences, MySQL isolation levels, foreign keys, slow query optimization, JWT authentication, RBAC, and Redis sorted sets.
This article walks through the motivation, design, implementation, performance tuning, deployment, and lessons learned when creating a Golang‑based offline Redis RDB parser that efficiently identifies large keys without impacting a live cluster.
JIMDB, a Redis‑based distributed cache, introduces the Big‑Hot Key concept and a multi‑layer proactive governance framework that dynamically identifies resource‑intensive keys, automatically mitigates them, and delivers up to an 80% performance gain while dramatically improving system stability.
JIMDB, a high‑performance Redis‑based distributed cache, introduces the “Big‑Hot Key” concept to dynamically identify keys that strain CPU or bandwidth, and implements a multi‑layer active governance framework—including real‑time detection, server‑side caching, circuit‑breaker, and client‑side consistency—to dramatically reduce resource consumption and boost throughput.
This article combines salary insights for ZTE offers, practical internship interview tips, a deep dive into Spring bean‑injection annotations, a side‑by‑side comparison of MySQL InnoDB and MyISAM, Redis usage patterns, and JVM OutOfMemoryError diagnostics to help you excel in Java backend interviews.
This article explains why a single Redis shard cannot handle extreme traffic, introduces multi‑level caching, and compares four practical methods—MQ synchronization, Redis Pub/Sub, version‑check, and automatic refresh—to keep local caches consistent under heavy load.
This comprehensive guide explains the causes of Redis cache penetration, breakdown, and avalanche, and provides production‑tested solutions such as Bloom filters, distributed locks, logical expiration, random TTL, cache pre‑warming, multi‑level caching, high‑availability deployment, monitoring, and backup strategies.
Redis uses multiple expiration and eviction mechanisms—including lazy deletion, periodic scanning, and memory eviction—to balance performance and memory usage, and this guide explains each strategy, shows how to configure them, and provides Java/Jedis code examples for setting TTLs, handling large objects, preventing cache avalanches, and monitoring stats.
The article compares storing user objects in Redis using plain SET with JSON versus using HASH fields, providing code demos, benchmark results, memory and concurrency analysis, and practical guidelines on when to choose each approach for optimal performance and safety.
To serve billions of users with real‑time timelines, modern social platforms combine push‑based delivery for regular users and pull‑based retrieval for high‑profile accounts, employing hot‑cold separation, Kafka fan‑out, Redis caching, and scalable storage strategies to balance write and read loads.
This comprehensive guide explains the three major Redis cache issues—penetration, breakdown, and avalanche—detailing their causes, impacts, and production‑ready solutions such as Bloom filters, distributed locks, logical expiration, random TTL, multi‑level caching, high‑availability setups, monitoring, backup, and best‑practice recommendations.
This article explains Redis cache penetration, its causes such as malicious attacks and faulty business logic, and presents four mitigation strategies—Bloom filters, caching empty values, rate limiting, and authentication with validation—to protect database performance and system stability.
This article explains four practical idempotency solutions—token tokens, database unique indexes, distributed locks, and request content digests—detailing their concepts, core keywords, and providing ready‑to‑copy Spring Boot code examples, along with implementation tips and a comparison table to help you choose the right approach for high‑concurrency APIs.
This article explains practical techniques to stop Redis cache penetration—including using Bloom filters to pre‑check keys, caching empty responses, applying request rate limiting, and adding authentication checks—while outlining each method's advantages and drawbacks.
Learn how to restrict API calls per user within a specific time window by creating a custom @RequestLimit annotation, an interceptor that checks Redis counters, and configuring Spring Boot's WebMvc to enforce rate limiting, with complete code examples and usage instructions.
The article explains why using the KEYS command in Redis is a blocking operation that can cripple production systems, demonstrates the safe, incremental SCAN approach, and proposes an index‑based architecture or replica scans for high‑frequency or offline key‑lookup scenarios.
Learn how to set up Redis Sentinel for high‑availability caching, covering prerequisites, anti‑patterns, detailed configuration of master, replicas and Sentinel nodes, firewall rules, monitoring, failover testing, troubleshooting, performance tuning, backup, rollback and best practices—all achievable within a 30‑minute deployment.
This guide details seven critical Redis hardening steps—including timeout, syslog, strong password, non‑root execution, strict file permissions, trusted bind addresses, and command renaming—to transform insecure default settings into a verifiable, production‑ready security baseline.
This article explains Redis cache eviction policies—including noeviction, allkeys‑lru, volatile‑lru, random, ttl, and lfu—detailing when to use each, how the eviction process works, and practical solutions for cache penetration, breakdown, and avalanche to keep your backend stable under high load.
This article explores why precise per‑minute API call statistics are essential for performance bottleneck detection, capacity planning, security alerts, billing, and troubleshooting, and presents five practical implementations—including fixed‑window counters, sliding windows, AOP‑based interception, Redis time‑series storage, and Micrometer‑Prometheus integration—along with their trade‑offs and capacity‑planning guidelines.
This guide walks you through installing RedisInsight, configuring its environment variables, deploying it on Kubernetes, and using its GUI to monitor Redis metrics, edit data, and analyze memory, complete with command‑line examples and visual screenshots.
The article presents a step‑by‑step, code‑driven design of a Spring Cloud SaaS multi‑tenant solution that balances resource sharing and strict data isolation by using a shared‑database, shared‑schema approach with tenant_id filtering, ThreadLocal context, MyBatis‑Plus interceptors, Redis key prefixing, Sa‑Token session segregation, and Spring Boot auto‑configuration.
The article shares recent JD backend salary data ranging from 24k to 32k RMB per month, explains the compensation structure, and provides a step‑by‑step interview preparation guide covering project presentation, JWT, Redis, thread pools, MySQL‑Elasticsearch sync, isolation levels and performance analysis.
Explore Redis cache eviction policies, understand how strategies like allkeys‑lru, volatile‑ttl, and noeviction work, and learn practical solutions for cache penetration, breakdown, and avalanche—including Bloom filters, mutex locks, and staggered expirations—to keep your backend resilient under high load.
This article explains how to build a JWT blacklist in Spring Boot 3, covering token fingerprinting, storage options using Caffeine or Redis, request interception, configuration, and sample login/logout endpoints to revoke tokens before they expire.
This article walks through a production Redis connection timeout incident, detailing how concurrent AOF persistence and RDB snapshots caused disk I/O blockage, the diagnostic steps taken, and the optimization measures implemented to eliminate the timeouts and improve performance.
During a Double‑11 promotion, an oversized Redis cache key caused a cascade of failures—cache miss, network bandwidth saturation, and a full‑blown cache avalanche—prompting the team to implement big‑key mitigation, compression, lock‑based cache back‑source, and monitoring measures to safeguard future deployments.
This article analyzes a production incident where Redis failed to retrieve keys at 2 AM, tracing the root cause to a short‑term memory write‑full condition caused by massive GET request bursts that overflowed client output buffers, and outlines both emergency fixes and long‑term mitigations.
This article presents a complete, step‑by‑step analysis of a high‑concurrency Redis counter component that supports up to 100 000 QPS, covering business pain points, architectural design, two core counting strategies, sharding, local batch optimization, code walkthroughs, and performance benchmark results.
The article introduces Microstrategy, a US‑based BI firm with a large Hangzhou R&D center, outlines its generous 9‑5‑style work environment, details the interview stages—including written test, technical and non‑technical rounds—and provides concrete advice on self‑introduction, project presentation, system design, Canal‑MySQL sync, XXL‑Job video transcoding, Redis, MongoDB, SQL injection, Java exceptions, OOP concepts, and interview puzzles.
