[GitHub] [arrow] zeroshade edited a comment on pull request #10716: ARROW-13330: [Go][Parquet] Add the rest of the Encoding package

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zeroshade edited a comment on pull request #10716:
URL: https://github.com/apache/arrow/pull/10716#issuecomment-896915175


   @emkornfield  Just to tack on here, another interesting view is looking at a flame graph of the CPU profile for the `BenchmarkMemoTableAllUnique` benchmark case, just benchmarking the binary string case where the largest difference between the two is that in the builtin Go Map based implementation I use a `map[string]int` to map strings to their memo index, whereas in the custom implementation I use an `Int32HashTable` to map the hash of the string to the memo index, with the hash of the string being calculated with the custom hash implementation.
   
   ![image](https://user-images.githubusercontent.com/555095/129051817-ec6360bf-59e3-4db5-a697-4a676d60ad86.png)
   
   Looking at the flame graph you can see that a larger proportion of the CPU time for the builtin map-based implementation is spent in the map itself whether performing the hashes or accessing/growing/allocating vs adding the strings to the `BinaryBuilder` while in the xxh3 based custom implementation there's a smaller proportion of the time spent actually performing the hashing and the lookups/allocations. In the benchmarks I'm specifically using 0 when creating the new memo table to avoid pre-allocating in order to make the comparison between the go map implementation a closer / better comparison since, to my knowledge, there's no way to pre-allocate a size for the builtin golang map. But if I change that and have it actually use reserve to pre-allocate space the difference can become more pronounced.


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