Storage Memory Usage Benchmark

Two components of etcd storage consume physical memory. The etcd process allocates an in-memory index to speed key lookup. The process's page cache, managed by the operating system, stores recently-accessed data from disk for quick re-use.

The in-memory index holds all the keys in a B-tree data structure, along with pointers to the on-disk data (the values). Each key in the B-tree may contain multiple pointers, pointing to different versions of its values. The theoretical memory consumption of the in-memory index can hence be approximated with the formula:

N * (c1 + avg_key_size) + N * (avg_versions_of_key) * (c2 + size_of_pointer)

where c1 is the key metadata overhead and c2 is the version metadata overhead.

The graph shows the detailed structure of the in-memory index B-tree.

                                In mem index

                               | key || ... |
  +--------------+             |     ||     |
  |              |             +------------+
  |              |             | v1  || ... |
  |   disk    <----------------|     ||     | Tree Node
  |              |             +------------+
  |              |             | v2  || ... |
  |           <----------------+     ||     |
  |              |             +------------+
  +--------------+       +-----+    |   |   |
                         |     |    |   |   |
                         |     +------------+
                      | ... |
                      |     |
                      | ... | Tree Node
                      |     |
                      | ... |
                      |     |

Page cache memory is managed by the operating system and is not covered in detail in this document.

Testing Environment

etcd version

  • git head

GCE n1-standard-2 machine type

  • 7.5 GB memory
  • 2x CPUs

In-memory index memory usage

In this test, we only benchmark the memory usage of the in-memory index. The goal is to find c1 and c2 mentioned above and to understand the hard limit of memory consumption of the storage.

We calculate the memory usage consumption via the Go runtime.ReadMemStats. We calculate the total allocated bytes difference before creating the index and after creating the index. It cannot perfectly reflect the memory usage of the in-memory index itself but can show the rough consumption pattern.

N versions key size memory usage
100K 1 64bytes 22MB
100K 5 64bytes 39MB
1M 1 64bytes 218MB
1M 5 64bytes 432MB
100K 1 256bytes 41MB
100K 5 256bytes 65MB
1M 1 256bytes 409MB
1M 5 256bytes 506MB

Based on the result, we can calculate c1=120bytes, c2=30bytes. We only need two sets of data to calculate c1 and c2, since they are the only unknown variable in the formula. The c1=120bytes and c2=30bytes are the average value of the 4 sets of c1 and c2 we calculated. The key metadata overhead is still relatively nontrivial (50%) for small key-value pairs. However, this is a significant improvement over the old store, which had at least 1000% overhead.

Overall memory usage

The overall memory usage captures how much RSS etcd consumes with the storage. The value size should have very little impact on the overall memory usage of etcd, since we keep values on disk and only retain hot values in memory, managed by the OS page cache.

N versions key size value size memory usage
100K 1 64bytes 256bytes 40MB
100K 5 64bytes 256bytes 89MB
1M 1 64bytes 256bytes 470MB
1M 5 64bytes 256bytes 880MB
100K 1 64bytes 1KB 102MB
100K 5 64bytes 1KB 164MB
1M 1 64bytes 1KB 587MB
1M 5 64bytes 1KB 836MB

Based on the result, we know the value size does not significantly impact the memory consumption. There is some minor increase due to more data held in the OS page cache.