Optimizing Memcached Slab Allocation to Prevent Memory Waste

The Problem: Slab Waste and Premature Evictions

Memcached manages memory using a slab allocator to avoid memory fragmentation—the phenomenon where free memory is broken into small, non-contiguous blocks that cannot be used for larger objects. While this prevents the system from slowing down over time, it introduces a specific risk: slab waste.

Slab waste occurs when memory is locked into a specific slab class (a bucket for items of a certain size) that is not being used, while another slab class is full and forcing the eviction of active data. This results in a low cache hit rate even though the system reports available memory.

Choosing Your Memory Management Strategy

When configuring Memcached, you must decide between the default automatic allocation and manual tuning of slab growth factors. The decision depends on whether your data consists of uniform object sizes or a highly unpredictable mix of small and large values.

Strategy	Best For	Trade-off	Risk
Default Allocation	General purpose workloads; varying object sizes.	Easier setup; relies on internal rebalancing.	Higher chance of internal fragmentation.
Tuned Growth Factor	Predictable data sizes (e.g., all objects are ~1KB).	Maximum memory density; fewer evictions.	Requires precise knowledge of object sizes.

Understanding the Growth Factor

The -g (growth factor) parameter determines how the size of slab classes increases. For example, if the first slab is 64 bytes and the growth factor is 1.25, the next slab is 80 bytes. A smaller growth factor creates more slab classes with smaller gaps between them, reducing the amount of wasted space inside each slab, but increasing the total number of classes the system must manage.

The Trade-off: Precision vs. Overhead

• Low Growth Factor (e.g., 1.1): Minimizes the "gap" between the actual item size and the slab size. This increases memory efficiency for a wide variety of object sizes.
• High Growth Factor (e.g., 1.5): Reduces the number of slab classes. This is more efficient for the allocator but increases the likelihood that a 101-byte item is forced into a 150-byte slab, wasting 49 bytes.

Validating Slab Distribution

To determine if your current configuration is causing waste, you must inspect the slab distribution. You can do this by connecting to the Memcached instance via netcat or telnet.

Command Execution: Run the following on the server hosting Memcached (assuming default port 11211) as a user with network access to the port:

echo "stats slabs" | nc localhost 11211

Analyzing the Output: Look for the following indicators in the results:

• CURITEMS: The number of items currently stored in that slab class.
• TOTAL_ITEMS: The total number of items that have ever been in that class.
• CURRENT_CHUNKS: The amount of memory currently allocated to this class.

Diagnostic Decision: If you see a slab class with a high CURRENT_CHUNKS value but very few CURITEMS, while another class has high evictions (found in stats), your memory is improperly distributed. You are experiencing slab waste.

Implementation: Adjusting for Specific Workloads

If your analysis shows that items are consistently falling into slabs that are slightly too large, adjust the growth factor during service startup. This example assumes Memcached version 1.6+ on a Linux environment.

Configuration Change: Modify the startup command to include a tighter growth factor (e.g., 1.1) to reduce internal fragmentation:

# Run as root or via systemd service file
memcached -m 1024 -g 1.1

Permissions & Risks: This command requires permissions to bind to the network port and allocate the specified memory (-m 1024 for 1GB). Risk: Changing the growth factor requires a service restart, which will wipe all cached data because Memcached is purely volatile.

Verification of Result

After restarting with the new growth factor, monitor the stats slabs output again. You should observe a higher number of slab classes, but a more even distribution of CURITEMS across those classes, and a decrease in the evictions counter in the general stats output.

Rollback Procedure

Since this change modifies the service startup parameters, rollback involves reverting the command-line arguments and restarting the service:

1. Stop the Memcached service.
2. Remove the -g 1.1 flag or return it to the default (usually 1.25).
3. Restart the service.