Fuite mémoire Spring Boot en production — Guide de diagnostic

 Une fuite mémoire en production peut faire tomber votre application sans avertissement.

Dans Spring Boot, elle se manifeste souvent par :

  • Mémoire heap qui augmente constamment

  • Full GC fréquents

  • API de plus en plus lentes

  • OutOfMemoryError

  • Redémarrage de conteneurs (Kubernetes)

Ce guide explique comment détecter, analyser et corriger une fuite mémoire.

Cette architecture est couramment utilisée dans les environnements microservices modernes.

👉 environnements microservices modernes

📌 Symptômes d’une fuite mémoire

Signes typiques :

  • La mémoire JVM ne redescend jamais

  • Les pauses GC deviennent longues

  • L’application ralentit avec le temps

  • La charge normale provoque un crash

🖼️ Exemple : mémoire qui augmente

Si le graphique monte en escalier → fuite probable.

🧠 Étape 1 — Confirmer la fuite

Une forte mémoire n’est pas toujours une fuite.

Vérifier :

  • La mémoire baisse-t-elle après GC ?

  • Le niveau se stabilise-t-il ?

  • Seulement pendant un pic de trafic ?

Activer logs GC :

-XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:gc.log

Si la mémoire ne redescend jamais après Full GC → fuite confirmée.

🧠 Étape 2 — Générer un heap dump

Quand la mémoire est élevée :

jmap -dump:live,format=b,file=heap.hprof <PID>

Ou automatiquement :

-XX:+HeapDumpOnOutOfMemoryError

🖼️ Analyse heap dump

Ouvrir le fichier .hprof avec Eclipse MAT.

Vérifier :

  • Dominator Tree

  • Retained Heap

  • Objets dominants

🧠 Étape 3 — Causes fréquentes

1️⃣ Collections statiques

private static List<String> cache = new ArrayList<>();

Accumulation infinie → fuite.

2️⃣ Cache mal configuré

Utiliser cache limité :

Caffeine.newBuilder()
    .maximumSize(1000)
    .build();

3️⃣ Mauvaise utilisation ThreadLocal

ThreadLocal<User> context = new ThreadLocal<>();

Toujours nettoyer :

context.remove();

4️⃣ Ressources non fermées

  • Streams

  • Connexions DB

  • WebClient

Utiliser try-with-resources.

5️⃣ Sessions HTTP volumineuses

Ne pas stocker objets lourds en session.

🧠 Étape 4 — Analyser objets retenus

Dans MAT :

  • Identifier objet dominant

  • Vérifier GC Roots

  • Suivre chaîne de références

Souvent :

ConcurrentHashMap
ArrayList
Session utilisateur

🖼️ Dominator Tree

🧠 Étape 5 — Surveillance production

Utiliser :

  • Prometheus

  • Grafana

  • Micrometer

  • JVM metrics

Surveiller :

  • Heap used

  • GC pause

  • Allocation rate

🔐 Bonnes pratiques prévention

✔ Pas de collections statiques modifiables
✔ Cache avec limite
✔ Nettoyer ThreadLocal
✔ Fermer ressources
✔ Pas d’objets lourds en session
✔ Monitoring continu

🧰 Outils utiles

  • VisualVM

  • Eclipse MAT

  • JProfiler

  • YourKit

  • /actuator/metrics

📚 Articles liés

🎯 Conclusion

Une fuite mémoire a toujours une cause identifiable.

Processus correct :

  1. Confirmer

  2. Dump mémoire

  3. Analyser

  4. Corriger

  5. Surveiller

Avec les bons outils, la majorité des fuites sont corrigées rapidement.

💼 Support professionnel disponible

Si vous rencontrez des problèmes sur des projets réels liés au développement backend d’entreprise ou à l’automatisation des workflows, je propose des services de conseil payants, de débogage en production, de support projet et de formations ciblées.

Les technologies couvertes incluent Java, Spring Boot, PL/SQL, Azure, CMS, ainsi que l’automatisation des workflows (jBPM, Camunda BPM, RHPAM), DMN/Drools.

📧 Contact: ishikhanirankari@gmail.com | info@realtechnologiesindia.com

🌐 Website: IT Trainings | Digital lectern | Digital rostrum | Digital metal podium     


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