ThomasMoll

7 ms·

1. 1.▲
   by ThomasMoll 3y ago

   We (when I worked at LinkedIn) did it with ETL clusters, we already had built them out for moving data between datacenters nightly. They would mirror an HDFS cluster, then ran batch jobs to transfer either directly to the outbound cluster o
2. 2.▲
   by ThomasMoll 3y ago

   Not only that but the Hadoop team literally had the guy who wrote the original HDFS whitepaper. Moving a service with that much in house expertise first never made sense. I worked on one of the original Azure PoCs for Hadoop, even before Bl
3. 3.▲
   by ThomasMoll 4y ago

   Absolutely! It's some a great place to put the work I'm most proud of! Don't get me wrong, I do a lot of cool stuff at $JOB but there's a limit on how much info I can discuss about various internal systems. So just doing
4. 4.▲
   by ThomasMoll 5y ago

   I think the advantage here is that instead of centralizing the relationships between all types in one place, these relationships can be defined anywhere, including external packages. This makes composition of additional types extremely easy
5. 5.▲
   by ThomasMoll 5y ago

   Groovy supports multi-dispatch, if you needed to encapsulate a generic type, the compile would dynamically resolve that type correctly. See my example in Java (which has method overloading, but not MD)
6. 6.▲
   by ThomasMoll 5y ago

   Julia is fairly fast, since its type system _only_ does dynamic/runtime typing, the JIT is optimized towards that. You'll experience some minor startup lag, typically due to initial JIT'ing of any new used functions. However,
7. 7.▲
   by ThomasMoll 5y ago

   Hey, author here. 1) Including the "Onyx holding a hard stone executing Earthquake in a Sandstorm against a Flygon with the ability Hover?" state is just doing an N-constraint solver. Since multiple dispatch is a generalized syste
8. 8.▲
   by ThomasMoll 5y ago

   Yes! In fact I think Julia borrows a lot of concepts from CL and the like for their type system implementation.
9. 9.▲
   by ThomasMoll 5y ago

   Depending on the language target and method of execution dynamic resolution of runtime types can be quite hard. IIRC Java's JVM is super optimized for static codepaths, whereas Julia uses a JIT system that is "slow" on first
10. 10.▲
    by ThomasMoll 5y ago

    I'll admit, I'm a bit disappointed that my original title "Julia used Multiple Dispatch! It's Super Effective!" didn't make the HN frontpage.