Introduction
Many developers and DevOps engineers observe a common issue in production and staging environments: Docker container memory usage steadily increases over time and never decreases. Initially, the application runs fine, but after hours or days, the container starts consuming increasing amounts of memory. Eventually, this can lead to slow performance, container restarts, or even node-level outages.
In simple words, this usually happens because memory is being allocated but not properly released, or because Docker and the application handle memory differently than expected. This article explains the most common reasons why Docker container memory usage grows over time, how to identify the real cause, and what developers can do to fix it.
Containers Do Not Automatically Free Memory Back to the Host
One important thing to understand is that containers run inside a Linux kernel, and memory management follows Linux rules. Even if your application frees memory internally, the operating system may keep it reserved for future use rather than returning it to the host immediately.
This means memory usage shown by Docker or monitoring tools may look high even when the application is idle.
Example:
Application allocates memory → Application releases memory → OS keeps memory reserved
This behavior is normal and does not always indicate a real memory leak.
Application-Level Memory Leaks
One of the most common reasons for increasing container memory usage is an actual memory leak in the application code. This happens when objects are created but never released.
Common causes include:
Growing in-memory caches
Unclosed database connections
Event listeners not removed
Background jobs storing data indefinitely
Example in a Node.js app:
const cache = [];
setInterval(() => {
cache.push(new Array(100000).fill('data'));
}, 1000);
In this example, memory keeps growing because the data is never cleared.
Garbage Collection Behavior Can Be Misleading
Many modern languages such as Java, Node.js, and .NET use garbage collection. Garbage collectors do not immediately return memory to the operating system after cleaning unused objects.
Instead, they often keep memory reserved for reuse, which makes container memory usage appear high.
Example:
Objects deleted → Garbage collector runs → Memory reused internally → Not returned to OS
This is expected behavior, but it can confuse developers when monitoring container memory.
Missing or Incorrect Docker Memory Limits
If a container does not have memory limits defined, it can keep consuming memory until the host runs out.
Example of missing limits:
docker run my-app
Correct way with limits:
docker run --memory=512m --memory-swap=512m my-app
Without limits, Docker cannot enforce memory boundaries, making problems harder to detect early.
Caching Without Proper Eviction Policies
Many applications use caching to improve performance. However, if cache eviction rules are missing or incorrect, memory usage grows continuously.
Common problem areas include:
In-memory caches
Session storage
Large lookup tables
Example:
User requests → Data cached → Cache never cleared → Memory grows
Caches must always have size limits or time-based expiration.
File Descriptors and Buffer Leaks
Sometimes memory growth is caused by unclosed file handles, streams, or buffers. These resources consume memory indirectly.
Examples include:
Files opened but not closed
Network sockets left open
Large response buffers stored in memory
Example pattern:
Open file
Read data
Forget to close file
Over time, this increases memory and resource usage inside the container.
Native Libraries and OS-Level Memory Usage
Some applications rely on native libraries written in C or C++. These libraries manage memory outside the garbage-collected heap.
If native memory is not released properly, Docker memory usage increases even though the application heap looks normal.
Example:
Application heap stable → Native memory increases → Container memory grows
This is common in image processing, video processing, and machine learning workloads.
Long-Running Processes and Background Jobs
Containers running long-lived processes such as workers, schedulers, or stream processors often accumulate memory over time.
Reasons include:
Example:
Background job starts → Processes data → Keeps references → Memory slowly increases
Periodic cleanup or process restarts are often required.
Monitoring Tools Can Be Misleading
Docker reports memory usage based on cgroups, which includes:
Application memory
Cached memory
Buffer memory
This means the reported value is not always the same as actual used heap memory.
Example:
Docker memory usage = App memory + Cache + Buffers
Understanding this helps avoid false alarms.
How Developers Fix Increasing Docker Memory Usage
Developers and DevOps teams usually take a combination of steps:
Set strict memory limits on containers
Add proper cache eviction policies
Monitor heap and native memory separately
Fix application-level memory leaks
Use health checks and restarts for long-running containers
Example restart policy:
restart: always
This ensures unhealthy containers do not impact the entire system.
Summary
Docker container memory usage often keeps increasing due to a mix of application memory leaks, garbage collection behavior, missing memory limits, caching issues, native memory usage, and long-running processes. In many cases, the memory is not truly leaked but retained by the operating system or runtime for reuse. By setting proper container limits, monitoring memory correctly, fixing application-level issues, and designing for cleanup and safe restarts, developers can keep Docker-based systems stable and reliable in production environments.
Sarthak VarshneyPosted Jan 16, 2026, 1:38 PM
Nice article