6 ms·

I'm sorry, this hasn't been my experience, or the experience of anyone I know writing stream processing systems. Having written one each over a GC and not (Naia

• pron 11y ago
  Thanks, I'll read your references, but there are two general points to make: 1. Memory with very clear lifespan is obviously easier to manage without a GC. 2. The fact that GCs are a performance issue, does not mean that using a non-GC environment would yield better performance. It is often easier to avoid GC in GCed environments than to handle concurrent access to data in non-GC environments.
  • vitalyd 11y ago
    GC definitely makes some types of systems easier to develop and reason about, but it also tends to make people be a bit sloppy with respect to memory management. For systems where managing data is one of their reasons for existence, it creates a negative effect. GC'd runtimes also bloat memory consumption to support GC, as well as perform additional "hidden" operations in support of GC (e.g. card marking), even if you manage to not trigger GCs. In addition, GC works well when the generational hypothesis holds. For data intensive applications with mutation/churn, that doesn't tend to be the case. I disagree that avoiding GC is easier; you're swimming upstream the entire time. Managing memory manually in GC environments is annoying because the platform wasn't designed for that. As Frank said, you want to avoid sharing mutable data irrespective of GC or not - that won't scale well with larger core counts. Instead, you want to delineate ownership so you have single writer and many readers, and share the data that way.
    • pron 11y ago
      The key is that most of the effort is not where most of the data is. It's the small percentage of data (say, database index) that requires the biggest development effort, and you want the best mechanism to assist not where most bytes are stored, but where the most effort is invested. > For systems where managing data is one of their reasons for existence, it creates a negative effect. I think that people who author such systems/libraries know when to rely on the GC and when not to. > GC'd runtimes also bloat memory consumption to support GC When you write, say, an in-memory OLTP database (that's the use-case I know best), you keep the index -- that's where concurrency is managed -- on the heap and the user data off-heap. User data is the bulk of the RAM, but you only pay the GC footprint-overhead for the index. > you're swimming upstream the entire time. Going off-heap for large, known-lifespan data sets in Java has been widespread -- idiomatic, almost -- for at least five years now, if not ten. There are so many libraries that do that, that it's very, very simple. Again -- that's just where most data lies, not where the most effort is required. You end up with a very small percentage of your code that is not quite "beginner's Java" -- that's not swimming against the current; that's hitting a few strokes for one tiny section of the journey. It's managing the transactions and update to the indexes that requires 95% of the code, and where you end up thanking your lucky stars for a good GC. I haven't measured exactly, but the entire off-heap code in our database is under 3%, even though over 85-95% of the data is stored off-heap. > Instead, you want to delineate ownership so you have single writer and many readers, and share the data that way. Even single writer/many readers still require a concurrent access mechanism. A GC tremendously helps with this pattern. Besides, the best way to manage data -- and that's based on how caching mechanisms work at every level -- is single-writer/multiple-readers approximately, namely the single-writer may change identity at any time, but you ensure that it happens rarely. Once you try to make ownership permanent, system-wide locking is required. It's that flexibility that helps with amortizing the costs and making sure you're never hit with the worst case at every transaction[1]. There's some literature touching on that required flexibility, but here's my own take: http://highscalability.com/blog/2012/8/20/the-performance-of-distributed-data-structures-running-on-a.html http://highscalability.com/blog/2012/8/20/the-performance-of... (that flexibility is required to keep the B-tree evenly distributed across nodes -- they can be across the network or even NUMA sockets). [1]: Changing the owner is the amortization pressure-release valve (all amortized algorithms work on that idea of slowly increasing pressure, that is then released seldom but regularly). It replaces sharding's rebalancing, which usually requires a stop-the-world lock (or most of the world).