--- title: Where Your Bytes Live: Memory Layout in Rust and Java description: A deep dive compares how Rust and Java lay out data in memory, explaining that the real performance driver is boxing, indirection, and contiguity rather than... canonical: https://daily.dev/posts/where-your-bytes-live-memory-layout-in-rust-and-java-tjqya8qqa twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Where Your Bytes Live: Memory Layout in Rust and Java | daily.dev og:description: A deep dive compares how Rust and Java lay out data in memory, explaining that the real performance driver is boxing, indirection, and contiguity rather than... og:url: https://daily.dev/posts/where-your-bytes-live-memory-layout-in-rust-and-java-tjqya8qqa og:image: https://api.daily.dev/og/posts/tJqYA8qqa.png og:image:alt: Where Your Bytes Live: Memory Layout in Rust and Java og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Where Your Bytes Live: Memory Layout in Rust and Java **[Medium](https://daily.dev/sources/medium_js)** · 20 min read · 4 upvotes · 1 comments ## Summary A deep dive compares how Rust and Java lay out data in memory, explaining that the real performance driver is boxing, indirection, and contiguity rather than the common 'stack versus heap' myth. It walks through concrete examples: a million-integer ArrayList uses about 20MB versus 4MB for int[] or Vec, due to boxing overhead. It covers cache lines, padding and alignment, struct layout differences between Rust and Java, borrowed string slices versus Java's copying substring behavior, ownership-based Drop versus garbage collection, and Array-of-Structs versus Struct-of-Arrays layouts for cache-friendly loops. It closes with tooling advice (size_of, JOL, Criterion, JMH) for measuring actual memory layouts rather than guessing. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does a Java ArrayList use so much more memory than an int array for the same numbers? A million integers take about 4MB in a primitive int[] or Rust Vec, but about 20MB in a Java ArrayList. The difference comes from autoboxing: Java generics cannot hold primitives, so each int is wrapped in an Integer object with its own 12-byte header plus a 4-byte reference stored separately in the backing array. _Developers sizing data structures for memory-heavy workloads can dig into layout tradeoffs like these on daily.dev._ ### Does storing data on the stack instead of the heap make Rust programs faster than Java? No, the speed advantage comes from contiguous memory layout, not from stack versus heap placement. Both Rust's Vec and Java's int[] store their numbers on the heap, yet they are equally fast because the values sit side by side, letting the CPU read many values per 64-byte cache line fetch instead of chasing pointers. _Anyone debating stack-versus-heap performance myths can compare real layout tradeoffs on daily.dev._ ### What is the memory layout difference between Vec and Vec> in Rust? Vec stores every Point inline inside one contiguous buffer with no per-element heap allocation, while Vec> stores a buffer of pointers with each Point allocated separately elsewhere on the heap, behaving like Java's Point[] array of references. For a million points this works out to roughly 8MB inline versus about 28MB when each Point is boxed through references under typical JVM object header assumptions. _Rust developers choosing between inline and boxed collections can explore these layout patterns on daily.dev._ ## Community discussion Top comments from developers on daily.dev. **@theacademe** · 0 upvotes > These are the kind of fundamentals that I find some engineers do not understand today. > > > Assuming an engineer was involved in writing the code at all. > > > It's also the kind of smell that an engineer knows to look for in some AI-generated slop code. ## Similar posts on daily.dev - [Who Owns the Memory? 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