[antonhag]

That's nice - I'd encourage you to play around with attaching e.g. JMC [1] to the process to better understand why things are as they are.

I tried recreating your DataInputStream + BufferedInputStream (wrote the 1brc data to separate output files, read using your code - I had to guess at ResultObserver implementation though). On my machine it roughly in the same time frame as yours - ~1min.

According to Flight Recorder:

  - ~49% of the time is spent in reading the strings (city names). Almost all of it in the DataInputStream.readUTF/readFully methods.
  - ~5% of the time is spent reading temperature (readShort)
  - ~41% of the time is spent doing hashmap look-ups for computeIfAbsent()
  - About 50GB of memory is allocated - %99.9 of it for Strings (and the wrapped byte[] array in them). This likely causes quite a bit of GC pressure.

Hash-map lookups are not de-serialization, yet the lookup likely affected the benchmarks quite a bit. The rest of the time is mostly spent in reading and allocating strings. I would guess that that is true for some of the other implementations in the original post as well.

[1] https://github.com/openjdk/jmc

edit: better link to JMC

[marginalia_nu]

JMC is indeed a valuable tool, though what you see in any java profiler is to be taken with a grain of salt. The string parsing and hash lookups are present in most of the implementations, yet some of them are up to 10 times faster than the DataInputStream + BufferedInputStream code.

It doesn't seem like it can be true that 90% of the time is spent in string parsing and hash lookups if the same operation takes 10% of the time when reading from a filechannel and bytebuffer.

[antonhag]

Aren't the versions that take 10% of the time only reading each city name once, and then doing an array lookup rather than a hashmap lookup?

[haglin]

JFR only samples running Java methods.

I would guess at least some of the bottlenecks are in hardware, the operating system or in native code (including the JVM) in this case.