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DeWu Technology
DeWu Technology
Feb 9, 2026 · Big Data

How to Build a Production‑Ready Flink ClickHouse Sink with Dynamic Sharding, Batch‑by‑Size, and Robust Retry

This article presents a production‑grade Flink ClickHouse sink that solves common pain points such as lack of size‑based batching, static table schemas, and distributed‑table latency by introducing data‑size batching, dynamic table routing, local‑table writes, load‑balanced node discovery, back‑pressure queues, dual‑trigger flush, and recursive retry with node exclusion, all integrated with Flink checkpoint semantics for at‑least‑once guarantees.

BatchingCheckpointClickHouse
0 likes · 25 min read
How to Build a Production‑Ready Flink ClickHouse Sink with Dynamic Sharding, Batch‑by‑Size, and Robust Retry
Sohu Tech Products
Sohu Tech Products
Dec 17, 2025 · Artificial Intelligence

How We Cut Vision Transformer Inference Latency from 53 ms to 8 ms

Facing 53.64 ms per‑image latency in a Flask‑served Vision Transformer classifier, we iteratively optimized the pipeline—switching to ONNX Runtime, leveraging TensorRT, replacing Pillow with OpenCV, eliminating URL downloads, and finally batching requests—reducing average server‑side processing to 8.34 ms, a 6.4× speedup.

BatchingFlaskONNX
0 likes · 28 min read
How We Cut Vision Transformer Inference Latency from 53 ms to 8 ms
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 3, 2025 · Big Data

Kafka High‑Throughput Tricks: Sequential Writes, Zero‑Copy, Partitioning

The article explains how Kafka achieves high throughput by writing messages sequentially to disk, leveraging OS page cache and zero‑copy system calls, using partitioned topics for parallelism, batching and compressing records on both producer and broker sides, and employing asynchronous replication with configurable persistence strategies.

BatchingHigh ThroughputKafka
0 likes · 4 min read
Kafka High‑Throughput Tricks: Sequential Writes, Zero‑Copy, Partitioning
Data Party THU
Data Party THU
Nov 2, 2025 · Operations

How to Maximize vLLM Throughput: Batch Size, Quantization, and Monitoring Tips

This guide explains how to unleash vLLM’s full potential by optimizing batch size, leveraging 4‑bit quantization, tuning concurrency parameters, planning capacity with token‑per‑second metrics, and implementing robust monitoring to balance latency, cost, and scalability in production deployments.

BatchingLLM servingcapacity planning
0 likes · 10 min read
How to Maximize vLLM Throughput: Batch Size, Quantization, and Monitoring Tips
Architect Chen
Architect Chen
Sep 10, 2025 · Big Data

How Kafka Achieves Million‑Message Throughput: Sequential Writes, Page Cache, Batching & Zero‑Copy

The article explains how Kafka attains high‑throughput performance by using sequential disk writes, leveraging the OS page cache, employing producer and consumer batching with configurable parameters, and utilizing zero‑copy sendfile to minimize CPU and memory overhead, enabling stable million‑message per second rates.

BatchingBig DataHigh Throughput
0 likes · 5 min read
How Kafka Achieves Million‑Message Throughput: Sequential Writes, Page Cache, Batching & Zero‑Copy
Tencent Cloud Middleware
Tencent Cloud Middleware
Aug 1, 2025 · Backend Development

Mastering TDMQ CKafka Production: Configurations, Partitioning, and Reliable Messaging

This guide explains how to efficiently produce messages with TDMQ CKafka, covering topic creation, partition sizing, retry policies, asynchronous sending, key/value handling, batch tuning, sticky partitioning, idempotence, ACK settings, code examples, and strategies to avoid data skew and ensure high‑throughput, low‑latency messaging.

BatchingCKafkaIdempotence
0 likes · 18 min read
Mastering TDMQ CKafka Production: Configurations, Partitioning, and Reliable Messaging
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Jul 7, 2025 · Big Data

How Kafka Achieves Billion-Message Throughput: Sequential Disk Writes, Page Cache, and Zero‑Copy

This article explains how Kafka sustains massive traffic by writing logs sequentially to disk, leveraging the operating system’s page cache for fast in‑memory writes, employing zero‑copy techniques like sendfile to avoid user‑space copying, and batching messages to reduce network overhead, thereby delivering high‑throughput, low‑latency streaming.

BatchingHigh ThroughputKafka
0 likes · 4 min read
How Kafka Achieves Billion-Message Throughput: Sequential Disk Writes, Page Cache, and Zero‑Copy
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
May 22, 2025 · Big Data

Optimizing Kafka Producer for High Throughput: Batching, Asynchronous Sending, Compression, and Concurrency

This article details practical techniques for boosting Kafka producer throughput, covering batch sending optimization, linger.ms tuning, asynchronous send handling, compression options, and concurrent sending strategies, while providing code examples and performance considerations for high‑throughput streaming applications.

AsynchronousBatchingHigh Throughput
0 likes · 4 min read
Optimizing Kafka Producer for High Throughput: Batching, Asynchronous Sending, Compression, and Concurrency
ITPUB
ITPUB
Dec 10, 2022 · Backend Development

Unlock Kafka’s Speed: Deep Dive into Performance Optimizations

This article explores Kafka's performance secrets by examining network, disk, and algorithmic factors, detailing sequential writes, zero‑copy techniques, page‑cache usage, Reactor‑based networking, batching, compression, partition concurrency, and file‑structure optimizations, and provides practical guidance for high‑throughput deployments.

BatchingDisk I/OReactor Model
0 likes · 18 min read
Unlock Kafka’s Speed: Deep Dive into Performance Optimizations
Taobao Frontend Technology
Taobao Frontend Technology
Sep 28, 2021 · Backend Development

How DataLoader Solves the GraphQL N+1 Problem: Deep Dive into Batch & Cache Mechanics

This article explains the GraphQL N+1 performance issue, demonstrates how DataLoader batches and caches database calls to eliminate redundant queries, walks through its core TypeScript implementation—including batch scheduling, cache handling, and load methods—while providing practical examples and integration tips for real‑world GraphQL servers.

BatchingDataLoaderGraphQL
0 likes · 28 min read
How DataLoader Solves the GraphQL N+1 Problem: Deep Dive into Batch & Cache Mechanics
Tencent IMWeb Frontend Team
Tencent IMWeb Frontend Team
Sep 7, 2018 · Frontend Development

Unveiling React’s setState: How It Works Internally and Common Pitfalls

This article explains the fundamentals and deep internals of React’s setState method, covering basic usage, asynchronous behavior, state‑update pitfalls, functional updates, and the underlying mechanisms such as enqueueSetState, enqueueUpdate, batchingStrategy, and transaction processing, illustrated with code snippets and diagrams.

BatchingReactState Management
0 likes · 7 min read
Unveiling React’s setState: How It Works Internally and Common Pitfalls