Crate griddle[][src]

Expand description

A HashMap variant that spreads resize load across inserts.

Most hash table implementations (including hashbrown, the one in Rust’s standard library) must occasionally “resize” the backing memory for the map as the number of elements grows. This means allocating a new table (usually of twice the size), and moving all the elements from the old table to the new one. As your table gets larger, this process takes longer and longer.

For most applications, this behavior is fine — if some very small number of inserts take longer than others, the application won’t even notice. And if the map is relatively small anyway, even those “slow” inserts are quite fast. Similarly, if your map grow for a while, and then stops growing, the “steady state” of your application won’t see any resizing pauses at all.

Where resizing becomes a problem is in applications that use maps to keep ever-growing state where tail latency is important. At large scale, it is simply not okay for one map insert to take 30 milliseconds when most take single-digit microseconds. Worse yet, these resize pauses can compound to create significant spikes in tail latency.

This crate implements a technique referred to as “incremental resizing”, in contrast to the common “all-at-once” approached outlined above. At its core, the idea is pretty simple: instead of moving all the elements to the resized map immediately, move a couple each time an insert happens. This spreads the cost of moving the elements so that each insert becomes a little slower until the resize has finished, instead of one insert becoming a lot slower.

This approach isn’t free, however. While the resize is going on, the old table must be kept around (so memory isn’t reclaimed immediately), and all reads must check both the old and new map, which makes them slower. Only once the resize completes is the old table reclaimed and full read performance restored.

To help you decide whether this implementation is right for you, here’s a handy reference for how this implementation compares to the standard library map:

  • Inserts all take approximately the same time. After a resize, they will be slower for a while, but only by a relatively small factor.
  • Memory is not reclaimed immediately upon resize.
  • Reads and removals of old or missing keys are slower for a while after a resize.
  • The incremental map is slightly larger on the stack.
  • The “efficiency” of the resize is slightly lower as the all-at-once resize moves the items from the small table to the large one in batch, whereas the incremental does a series of inserts.

Under the hood, griddle uses hashbrown::raw to avoid re-implementing the core pieces of hashbrown. It also stays very close to hashbrown’s HashMap and HashSet wrappers, and even exposes a raw module so that you can build your own map on top of griddle’s modified table behavior.

Why “griddle”?

You can amortize the cost of making hashbrowns by using a griddle..?



A hash map implemented with quadratic probing and SIMD lookup.


A hash set implemented as a HashMap where the value is ().


Experimental and unsafe RawTable API. This module is only available if the raw feature is enabled.



A HashMap variant that spreads resize load across inserts.


A hash set implemented as a HashMap where the value is ().



The error type for try_reserve methods.