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Latest from Xiaohongshu Tech REDtech

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Xiaohongshu Tech REDtech

Dec 19, 2024 · Databases

Data Consistency Verification Practices and Implementation at Xiaohongshu

Xiaohongshu built a lock‑free, non‑disruptive data‑consistency verification tool that automatically selects optimal methods, handles heterogeneous sources and dynamic changes, performs full and incremental checks via chunked checksums or row‑by‑row comparison, quickly isolates mismatches, and supports automatic remediation, ensuring reliable migrations and sharding.

Data ConsistencyDatabase MigrationMySQL

0 likes · 16 min read

Data Consistency Verification Practices and Implementation at Xiaohongshu

Xiaohongshu Tech REDtech

Dec 5, 2024 · Big Data

Interview with Jianchen: Journey from Open Source Contributor to Data Engineer at Xiaohongshu

In this interview, Xiaohongshu data engineer Jianchen recounts his evolution from a computer‑science student discovering open‑source through MIT6.824 to contributing to SOFAJRaft and Apache RocketMQ, detailing his OSPP projects, the decision to join Xiaohongshu, and his work on a cloud‑native Kafka engine that cut storage and compute usage by half.

Apache RocketMQCareer DevelopmentKafka

0 likes · 11 min read

Interview with Jianchen: Journey from Open Source Contributor to Data Engineer at Xiaohongshu

Xiaohongshu Tech REDtech

Nov 7, 2024 · Artificial Intelligence

RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search

RTAMS‑GANNS, the award‑winning real‑time adaptive multi‑stream GPU system for online approximate nearest neighbor search, eliminates costly memory allocations and serial execution by using a dynamic memory‑block insertion algorithm and separate CUDA streams, cutting latency by 40‑80% and reliably serving over 100 million daily users in production.

GPUMulti‑StreamVector Insertion

0 likes · 19 min read

RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search

Xiaohongshu Tech REDtech

Oct 23, 2024 · Backend Development

Pulsar vs RocketMQ: Architecture, Cost Benefits, and Migration Strategy for Xiaohongshu Online Messaging

Xiaohongshu replaced its RocketMQ‑based online messaging platform with Apache Pulsar, achieving up to 48% total cost reduction, 43% higher CPU utilization, 30% resource savings, and a latency drop from 20.2 ms to 5.7 ms through cloud‑native, elastic scaling and a phased migration strategy.

Apache PulsarMessage QueueRocketMQ

0 likes · 12 min read

Pulsar vs RocketMQ: Architecture, Cost Benefits, and Migration Strategy for Xiaohongshu Online Messaging

Xiaohongshu Tech REDtech

Oct 11, 2024 · Artificial Intelligence

Harmonized Speculative Sampling (HASS): Aligning Training and Decoding for Efficient Large Language Model Inference

HASS aligns training and decoding contexts and objectives for speculative sampling, using harmonized objective distillation and multi-step context alignment, achieving 2.81–4.05× speedup and 8%–20% improvement over EAGLE‑2 while preserving generation quality in real-world deployments at Xiaohongshu.

AIHASSInference Acceleration

0 likes · 11 min read

Harmonized Speculative Sampling (HASS): Aligning Training and Decoding for Efficient Large Language Model Inference

Xiaohongshu Tech REDtech

Oct 9, 2024 · Operations

AIOps Implementation at Xiaohongshu: Fault Localization and Intelligent Operations

Xiaohongshu’s AIOps initiative builds a four‑layer framework that leverages machine‑learning‑driven anomaly detection, causal analysis, and trace‑based fault localization to automatically identify root‑cause services in micro‑service environments, achieving over 80 % accuracy across 1000 daily diagnoses while guiding future enhancements in change correlation and automated remediation.

DevOpsFault LocalizationIntelligent Operations

0 likes · 28 min read

AIOps Implementation at Xiaohongshu: Fault Localization and Intelligent Operations

Xiaohongshu Tech REDtech

Sep 23, 2024 · Artificial Intelligence

AlignRec: A Joint Training Framework for Aligning Multimodal Representations with Personalized Recommendation

AlignRec is a joint‑training framework that synchronizes multimodal encoders with personalized recommendation models through a staged alignment strategy and three specialized loss functions, preserving both content and ID signals, and achieving state‑of‑the‑art performance on multiple datasets while releasing superior Amazon multimodal features.

AIevaluation metricsjoint training

0 likes · 11 min read

AlignRec: A Joint Training Framework for Aligning Multimodal Representations with Personalized Recommendation

Xiaohongshu Tech REDtech

Sep 19, 2024 · Artificial Intelligence

Target-Driven Distillation (TDD): A Multi‑Goal Distillation Method for Accelerating Diffusion Models

Target‑Driven Distillation (TDD) is a multi‑goal distillation method that flexibly selects short‑range target steps and decouples guidance during training, enabling 4‑to‑8‑step diffusion generation that preserves high‑resolution detail, works with LoRA, ControlNet, InstantID, and outperforms existing consistency distillation techniques in speed and quality.

AI accelerationDiffusion ModelsDistillation

0 likes · 9 min read

Target-Driven Distillation (TDD): A Multi‑Goal Distillation Method for Accelerating Diffusion Models

Xiaohongshu Tech REDtech

Sep 9, 2024 · Cloud Native

Applying eBPF for Cloud‑Native Observability and Continuous Profiling

By deploying eBPF agents as DaemonSets that hook kernel network and performance events, the Xiaohongshu observability team extended cloud‑native monitoring from the application to the kernel, delivering real‑time traffic analysis and low‑overhead continuous profiling for C++ services, aggregating data into centralized collectors for dashboards, flame‑graphs, and rapid root‑cause diagnosis.

KubernetesPerformance MonitoringeBPF

0 likes · 37 min read

Applying eBPF for Cloud‑Native Observability and Continuous Profiling

Xiaohongshu Tech REDtech

Sep 2, 2024 · Artificial Intelligence

How AIGC Transforms Advertising Material Creation on Xiaohongshu

This article analyzes how large‑model AIGC reshapes the production, evaluation, and deployment of advertising creatives on Xiaohongshu, detailing the business motivations, technical pipeline, controllable generation, reward‑model filtering, and experimental results that balance commercial efficiency with community tone.

AIGCAdvertisingControllable Generation

0 likes · 14 min read

How AIGC Transforms Advertising Material Creation on Xiaohongshu