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expand data structure cache; move general cache sections together

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Damian Gryski 2018-11-26 07:19:21 -08:00
parent ed183e3093
commit c32429ddab
1 changed files with 19 additions and 13 deletions
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performance.md
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@ -311,18 +311,14 @@ Ideas for augmenting your data structure:
* If queries are expensive, add a cache. * If queries are expensive, add a cache.
We're all familiar with memcache, but there are in-process caches. The classic example of this is storing the length of a linked list in a field in
the root node. It takes a bit more work to keep it updated, but then querying
* Over the wire, the network + cost of serialization will hurt. the length becomes a simple field lookup instead of an O(n) traversal. Your
* In-process caches, but now you need to worry about expiration and added GC pressure data structure might present a similar win: a bit of bookkeeping during some
operations in exchange for some faster performance on a common use case. For
A cache saves information you've just spent time computing in the hopes that example, some skips lists keep a "search finger", where you store an pointer to
you'll be able to reuse it again soon and save the computation time. A cache where your just were in your data structure on the assumption it's a good
doesn't need to be complex. Even storing a single item -- the most recently starting point for your next operation.
seen query/response -- can be a big win. This "single item" idea can be extended
to a "search finger", where you store an pointer to where your just were in your
data structure on the assumption it's a good starting point for your next
operation.
These are all clear examples of "do less work" at the data structure level. These are all clear examples of "do less work" at the data structure level.
They all cost space. Most of the time if you're optimizing for CPU, your They all cost space. Most of the time if you're optimizing for CPU, your
@ -666,9 +662,19 @@ improve allowing you to stop when you hit an acceptable limit.
Cache common cases: Cache common cases:
We're all familiar with memcache, but there are in-process caches.
* Over the wire, the network + cost of serialization will hurt.
* In-process caches, but now you need to worry about expiration and added GC pressure
A cache saves information you've just spent time computing in the hopes that
you'll be able to reuse it again soon and save the computation time. A cache
doesn't need to be complex. Even storing a single item -- the most recently
seen query/response -- can be a big win.
* Your cache doesn't even need to be huge. * Your cache doesn't even need to be huge.
* see `time.Parse()` example below; just a single value made an impact * see `time.Parse()` example below; just a single value made an impact
* But beware cache invalidation, thread issues, etc. * But beware cache invalidation, concurrent access / updates, etc.
* Random cache eviction is fast and sufficiently effective. * Random cache eviction is fast and sufficiently effective.
* Random cache insertion can limit cache to popular items with minimal logic. * Random cache insertion can limit cache to popular items with minimal logic.
* Compare cost (time, complexity) of cache logic to cost of refetching the data. * Compare cost (time, complexity) of cache logic to cost of refetching the data.