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iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 28, 2023 · Operations

Distributed System Log Printing Optimization and Performance Evaluation

The study evaluates log4j2 and logback performance, recommends asynchronous logback for high‑concurrency workloads, demonstrates latency reductions in a production service, and introduces a TraceContext‑based flag to share logging state across micro‑services, cutting daily log volume by ~80 % and easing distributed system overhead.

Performance Testinglog optimizationlog4j2
0 likes · 16 min read
Distributed System Log Printing Optimization and Performance Evaluation
MaGe Linux Operations
MaGe Linux Operations
Jan 15, 2023 · Operations

How to Slim Down Your Application Logs by Up to 80%

This article explains why oversized logs hurt system performance, then presents a step‑by‑step methodology—including printing only necessary logs, merging duplicate entries, and simplifying payloads—illustrated with real Java code and a concrete case study that reduces daily log volume from 5 GB to under 1 GB.

Operationsdebugjava
0 likes · 8 min read
How to Slim Down Your Application Logs by Up to 80%
Architecture Digest
Architecture Digest
Dec 31, 2022 · Operations

Log Size Reduction Techniques: Methodology and Case Study

This article explains why excessive INFO‑level logs can cause performance problems, presents three practical strategies—printing only necessary logs, merging log entries, and simplifying log content with code examples—and demonstrates their impact through a real‑world Java bean pipeline case that cuts daily log volume from about 5 GB to under 1 GB.

Operationsjavalog optimization
0 likes · 7 min read
Log Size Reduction Techniques: Methodology and Case Study
Architecture Digest
Architecture Digest
Aug 8, 2022 · Operations

Log Shrinking Techniques and Case Study for Reducing Log Size

This article explains why oversized logs hurt system performance, presents three practical log‑shrinking strategies—printing only necessary logs, merging duplicate entries, and simplifying content—illustrates them with Java code snippets, and evaluates their impact through a real‑world case that cuts daily log volume from 5 GB to under 1 GB.

BackendOperationslog optimization
0 likes · 7 min read
Log Shrinking Techniques and Case Study for Reducing Log Size
dbaplus Community
dbaplus Community
Aug 7, 2022 · Operations

How to Slim Down Application Logs: Practical Techniques and Real‑World Case Study

Developers often flood systems with INFO logs, causing massive files that strain operations; this article outlines practical log‑slimming strategies—printing only essential logs, merging entries, using abbreviations, and context‑aware level switches—illustrated with a concrete case that reduced daily log volume from 5 GB to under 1 GB.

Code Refactoringbackend operationsjava logging
0 likes · 7 min read
How to Slim Down Application Logs: Practical Techniques and Real‑World Case Study
Selected Java Interview Questions
Selected Java Interview Questions
Jul 23, 2022 · Backend Development

Log Reduction Techniques for Backend Systems

This article discusses practical methods for reducing log volume in backend applications, including printing only necessary logs, merging log entries, simplifying messages, and applying these techniques in a real-world Java case to shrink daily log size from several gigabytes to under one gigabyte while preserving debugging capability.

BackendInFOdebug
0 likes · 7 min read
Log Reduction Techniques for Backend Systems
Programmer DD
Programmer DD
Jul 22, 2022 · Operations

How to Shrink Log Files: Cut 5GB Daily Logs to Under 1GB with Proven Techniques

This article explains practical methods for reducing oversized log files—such as printing only essential logs, merging entries, and simplifying messages—illustrated with code examples and a real‑world case study that lowered daily log volume from 5 GB to under 1 GB while preserving debugging capability.

Operationslog optimization
0 likes · 8 min read
How to Shrink Log Files: Cut 5GB Daily Logs to Under 1GB with Proven Techniques
JD Tech
JD Tech
Nov 29, 2018 · Big Data

JD.com’s Big Data System Upgrade for the 11.11 Shopping Festival: Multi‑Region Scheduler, Intelligent Storage, Containerized Streaming, and Blockchain Traceability

The article details JD.com’s large‑scale big‑data system overhaul before the 11.11 shopping festival, highlighting a multi‑region Hydra Scheduler, intelligent storage policies, full containerization of the streaming platform, enhanced log reporting, and blockchain‑based traceability that together dramatically improve performance, stability, and user experience.

Distributed SchedulingIntelligent StorageSupply Chain
0 likes · 7 min read
JD.com’s Big Data System Upgrade for the 11.11 Shopping Festival: Multi‑Region Scheduler, Intelligent Storage, Containerized Streaming, and Blockchain Traceability