Blog
Technical exploration and thoughts · 655 articles
Kafka Components: Producer, Broker, Consumer Full Flow
Deep dive into Kafka's three core components: Producer partitioning strategy and ACK mechanism, Broker Leader/Follower architecture, Consumer Group partition assignment and offset management.
Kafka Installation: From ZooKeeper to KRaft Evolution
Introduction to Kafka 2.x vs 3.x core differences, detailed cluster installation steps, ZooKeeper configuration, Broker parameter settings, and how KRaft mode replaces ZooKeeper dependency.
ClickHouse Concepts & Basics | Why Fast? Columnar + Vecto...
For high-concurrency, low-latency OLAP scenarios, this article explains ClickHouse's underlying advantages (columnar+compression+vectorized, MergeTree family),...
ClickHouse Single Machine + Cluster Node Deployment Guide...
Official recommended keyring + signed-by installation of ClickHouse on Ubuntu, start with systemd and self-check; provides single machine minimum example...
Flink CEP Practice: 24 Hours ≥5 Transactions & 10 Minutes...
Flink CEP (Complex Event Processing) complex event processing mechanism, combined with actual cases to deeply explain its application principles and practical...
Flink SQL Quick Start | Table API + SQL in 3 Minutes with...
Engineering perspective to quickly run Flink SQL: Provides modern dependencies (no longer using blink planner), minimum runnable example (MRE), Table API and...
Flink CEP Deep Dive: Complex Event Processing Complete Guide
Flink CEP is the core component for real-time analysis of complex event streams in Flink, providing a complete pattern matching framework, supporting...
Flink CEP Timeout Event Extraction: Complete Guide with M...
Flink CEP timeout event extraction is a key step in stream processing, used to capture partial matching events that exceed the window time (within) during pattern matching, enabling timeout order detection.
Redis High Availability: Master-Slave Replication & Sentinel
Deep dive into Redis high availability: master-slave replication, Sentinel automatic failover, and distributed lock design with Docker deployment examples.
Kafka Architecture: High-Throughput Distributed Messaging
Systematic introduction to Kafka core architecture: Topic/Partition/Replica model, ISR mechanism, zero-copy optimization, message format and typical use cases.
Flink StateBackend Deep Dive: Memory, Fs, RocksDB & Opera...
ManagedOperatorState is used to manage non-keyed state, achieving state consistency when operators recover from faults or scale. Developers can use ManagedOperatorState by implementing Checkpointed...
Flink Parallelism Deep Dive: From Concepts to Best Practices
In Flink, Parallelism is the core parameter measuring task concurrent processing capability, determining the number of tasks that can run simultaneously for...
Flink Broadcast State: Dynamic Logic Updates in Real-time...
Broadcast State is an important mechanism in Apache Flink that supports dynamic logic updates in streaming applications, widely used in real-time risk control,...
Flink State Backend: Memory, Fs, RocksDB & Performance Di...
State Storage (State Backend) is the core mechanism for implementing stateful stream computing in Flink, determining data reliability, performance and fault...
Flink Parallelism Setting Priority: Principles, Configura...
A Flink program consists of multiple Operators (Source, Transformation, Sink). An Operator is executed by multiple parallel Tasks (threads), and the number of...
Flink State: Keyed State, Operator State & KeyGroups Work...
Based on whether intermediate state is needed, Flink computation can be divided into stateful and stateless: Stateless computation like Map, Filter, FlatMap...
Redis Cache Problems: Penetration, Breakdown, Avalanche, ...
Systematic overview of the five most common Redis cache problems in high-concurrency scenarios: cache penetration, cache breakdown, cache avalanche, hot key, and big key. Analyzes the root cause of...
Redis Distributed Lock: Optimistic Lock, WATCH and SETNX ...
Redis optimistic lock in practice: WATCH/MULTI/EXEC mechanism explained, Lua scripts for atomic operations, SETNX+EXPIRE distributed lock from basics to Redisson, with complete Java code examples.
Flink Time Semantics: EventTime, ProcessingTime, Ingestio...
Watermark is a special marker used to tell Flink the progress of events in the data stream. Simply put, Watermark is the 'current time' estimated by Flink in...
Flink Watermark Complete Guide: Event Time Window, Out-of...
Flink's Watermark mechanism is one of the most core concepts in event time window computation, used for handling out-of-order events and ensuring accurate...