This article walks through a step‑by‑step troubleshooting process for JVM Out‑Of‑Memory (OOM) incidents, covering symptom identification, monitoring tools like top, jstat, and jmap, root‑cause analysis, and actionable recommendations to prevent future memory leaks.
The article details how repeatedly calling FileSystem.get(uri, conf, user) created distinct UserGroupInformation objects, inflating the static FileSystem cache and causing a heap‑memory leak that triggered an Out‑Of‑Memory error, and explains that using the two‑argument get method or explicitly closing instances resolves the issue.
The article describes a production OOM incident caused by downloading a 2.4 GB file, walks through GC log and heap‑dump analysis using MAT, identifies the problematic use of FileUtils.readFileToByteArray, and presents streaming and external storage solutions to prevent memory exhaustion.
This case study examines a recurring out‑of‑memory issue in an Elasticsearch 6.8 cluster that spikes during low‑traffic night hours, analyzes GC logs and heap dumps, and details how adjusting G1 GC parameters resolved the problem and stabilized performance.
This article examines a recurring Spark batch task failure caused by OutOfMemory errors and data skew, details the investigation steps—including increasing executor memory, raising parallelism, and analyzing shuffle metrics—and proposes solutions such as data validation, filtering oversized keys, and memory adjustments.
This article analyzes why Apache POI runs out of memory when writing hundreds of thousands of rows to an Excel file, examines the underlying HSSFWorkbook implementation, and demonstrates how switching to the streaming SXSSFWorkbook API provides a stable, low‑memory solution for large‑scale Excel generation.
The article analyzes a production outage caused by misuse of ExecutorCompletionService, explains why missing take/poll leads to memory leaks, demonstrates correct and incorrect Java code examples, compares ExecutorService with ExecutorCompletionService, and provides practical guidelines to avoid similar OOM problems in backend services.
After upgrading Spark from 3.0.1 to 3.2.1 an ETL job began failing with OutOfMemory errors; this article examines the root causes, including AQE‑related metric accumulation, skipped stages, and stage‑metric growth, and presents a debugging process and a code‑level fix to mitigate memory pressure.
This article explains the causes of Java out‑of‑memory problems, introduces essential diagnostic commands and memory‑analysis tools, and walks through a real‑world case study that shows how to locate, analyze, and fix a memory leak caused by unreleased OSS client connections.
This article explores the distinction between OutOfMemory errors and memory leaks in Java, examines how improper use of static fields, unclosed streams, incorrect equals/hashCode implementations, and ThreadLocal can cause leaks, and provides practical solutions and tools such as JVisualVM to detect and prevent these issues.
This article explains how different Android versions handle image memory, clarifies screen size, density, and resolution concepts, shows how folder placement and bitmap formats affect RAM usage, and provides practical Glide configuration to prevent out‑of‑memory crashes.
This article explores the distinction between OutOfMemory errors and memory leaks in Java, explains how improper use of static fields, unclosed streams, incorrect equals/hashCode implementations, and ThreadLocal can lead to leaks, and provides practical solutions and tools such as JVisualVM to detect and prevent them.
This article explains the meaning of Java OOM errors, presents a real‑world case where an Oracle database lock caused a slow memory leak leading to OOM, details the lock types and modes, provides SQL to identify and kill offending sessions, and offers best‑practice tips to avoid lock‑related performance issues.
This article details the DRS team's step‑by‑step investigation and optimization of a massive MySQL migration involving 860,000 tables, addressing timeout, OOM, and performance bottlenecks to reduce total migration time from over 19 hours to under 10 hours.
This article examines Spark's memory management and the shuffle process, identifies the components that consume the most memory during shuffle write and read, analyzes common OOM scenarios such as task concurrency and data skew, and offers configuration tips to prevent out‑of‑memory failures.
A production Java service was unexpectedly killed due to out‑of‑memory conditions caused by oversized JVM heap settings and a thread‑pool that retained large log objects, and the article walks through the investigation, temporary fixes, deeper profiling, and the final remediation steps.
