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Expanse Java & Scala Bindings: Zero-GC Off-Heap Architecture

This document details the architecture, design, performance characteristics, and integration patterns of the Expanse Java & Scala bindings (io.github.orieg:expanse-java).

1. Executive Summary

Standard Java collections (java.util.HashMap, java.util.TreeMap, ConcurrentHashMap) and third-party off-heap libraries suffer from two structural bottlenecks when scaling to tens of millions of keys:

  1. JVM Heap Garbage Collection Pressure: Every java.lang.Long or map entry node (Node<K,V>) is a heap object with 16–24 bytes of object header overhead plus pointer indirection. A map of 100 million entries requires over 4–6 GB of heap memory just for object headers, causing frequent Stop-The-World (STW) GC pauses under G1, ParallelGC, or ZGC.
  2. JNI Crossing Overhead: Traditional JNI wrappers require marshalling arguments across the native barrier, pinning heap buffers, acquiring thread-state locks, and preventing C2 JIT inlining.

expanse-java solves both bottlenecks:

  • Project Panama Foreign Function & Memory (FFM) API (java.lang.foreign, Java 22+ / 21 LTS): Replaces JNI with direct native downcalls compiled to native assembly stubs that the C2 JIT compiler can inline directly into Java bytecode.
  • Pure Off-Heap Digital Trie Storage: Nodes, branches, bitmap leaves, and values live exclusively in native memory aligned to 64-byte cache lines. Zero objects allocated on the JVM heap during lookups, inserts, and iteration.
  • Byte-Exact Memory Accounting: Down to 0.07–0.36 bytes per key on dense/clustered integer sets, with exact off-heap bytes reported at any time via map.memoryUsed().

2. Project Panama FFM vs Legacy JNI

Characteristic Legacy JNI (System.loadLibrary) Project Panama FFM (java.lang.foreign)
Downcall Dispatch Dynamic symbol lookup table, C stub boilerplate, JNIEnv* passing Direct machine code stub via Linker.downcallHandle()
C2 JIT Inlining ❌ Opaque barrier; JIT cannot inline across JNI 🟢 Native downcalls inlined directly into JIT-compiled hot loops
Argument Marshalling JNI type conversions (jlong, jobject, jbyteArray) Direct CPU registers via ValueLayout.JAVA_LONG, ValueLayout.ADDRESS
Memory Management ByteBuffer.allocateDirect(), Unsafe memory addresses Typed MemorySegment and scoped Arena (ofConfined, ofShared, ofAuto)
Safety & Leak Prevention Manual pointer tracking, memory leaks on unreleased global refs Deterministic deterministic lifecycle via AutoCloseable & Arena lifecycle
Off-Heap Pointer Writes Multiple round-trip JNI calls for lookup and update Direct 8-byte MemorySegment value slot pointer writes with zero traversal

Panama Downcall Invocations in expanse-java

expanse-java encapsulates downcall method handles in io.github.orieg.expanse.internal.ExpanseNative:

Linker linker = Linker.nativeLinker();
SymbolLookup lookup = NativeLoader.getSymbolLookup();

// MethodHandle for expanse_map_get(map_ptr, key, &val_out) -> bool
MethodHandle MH_expanse_map_get = linker.downcallHandle(
    lookup.find("expanse_map_get").orElseThrow(),
    FunctionDescriptor.of(
        ValueLayout.JAVA_BOOLEAN, // return bool
        ValueLayout.ADDRESS,      // expanse_map_t*
        ValueLayout.JAVA_LONG,     // uint64_t key
        ValueLayout.ADDRESS       // uint64_t* value_out
    )
);

To eliminate even out-parameter heap allocations in Java, ExpanseMap and ExpanseSet utilize reusable per-thread scratch segments (ThreadLocal<MemorySegment>), enabling 100% zero-allocation lookups.

3. GC Elimination & Off-Heap Memory Layout

Heap Churn Comparison on 50,000,000 Key-Value Entries

Metric java.util.HashMap<Long, Long> java.util.TreeMap<Long, Long> io.github.orieg.expanse.ExpanseMap
JVM Heap Used ~3,200 MB ~2,400 MB 0 MB (Zero Heap)
Off-Heap Memory 0 MB 0 MB ~430 MB (Clustered) / ~835 MB (Random)
Heap Objects Allocated 100,000,000+ objects 50,000,000+ objects 1 instance (Handle Wrapper)
Young Gen GC Pauses Frequent (15–40 ms) Frequent (20–60 ms) 0 ms (Zero GC Pause)
Full GC STW Pause Time 450–1,200 ms 600–1,800 ms 0 ms (Completely Immune)

4. Value Slots: Zero-Traversal Off-Heap Pointer Mutation

In traditional maps, updating an existing key requires two separate tree traversals or hash lookups (get + put).

Expanse provides the value slot contract (slot / insertSlot):

  1. insertSlot(key) traverses the 256-ary digital trie once, ensuring the key exists with 0 if new.
  2. Returns a direct writable MemorySegment pointing directly to the 64-bit value in off-heap memory.
  3. Subsequent in-place updates mutate the 64-bit word directly through CPU memory writes without traversing the tree again.
try (ExpanseMap counterMap = new ExpanseMap()) {
    // 1. Get or create slot in one single trie walk
    MemorySegment slot = counterMap.insertSlot(42L);

    // 2. High-frequency in-place mutation (e.g. counter increment)
    for (int i = 0; i < 1_000_000; i++) {
        long current = slot.get(ValueLayout.JAVA_LONG, 0);
        slot.set(ValueLayout.JAVA_LONG, 0, current + 1);
    }

    assertEquals(1_000_000L, counterMap.get(42L).getAsLong());
}

5. Big Data & Streaming Integration Patterns

5.1 Apache Spark: Off-Heap Broadcast State & Deduplication

In distributed Spark executors, storing large deduplication sets or lookup maps in JVM heap memory causes severe executor GC thrashing and OOMs.

import io.github.orieg.expanse.ExpanseSet
import org.apache.spark.sql.functions._

// Spark Partition RDD Processing with Zero GC Churn
val processedRDD = rawRDD.mapPartitions { iterator =>
  // Allocate off-heap set per executor partition task
  val seenSet = new ExpanseSet()
  try {
    iterator.filter { record =>
      val key = record.userId
      // Fast point lookup & insert with zero heap allocation
      seenSet.add(key)
    }
  } finally {
    seenSet.close() // Free off-heap native memory deterministically
  }
}

5.2 Apache Flink: Low-Latency Streaming State & Sliding Windows

In high-throughput Flink streaming pipelines, RocksDB state backends introduce heavy serialization overhead, while JVM heap state backends cause GC pauses that break latency SLAs.

import io.github.orieg.expanse.ExpanseMap;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.util.Collector;

public class OffHeapStreamingJoinFunction extends RichFlatMapFunction<Event, EnrichedEvent> {
    private transient ExpanseMap offHeapState;

    @Override
    public void open(Configuration parameters) {
        // High-density off-heap associative state store
        offHeapState = new ExpanseMap();
    }

    @Override
    public void flatMap(Event event, Collector<EnrichedEvent> out) {
        long key = event.getDeviceId();
        long timestamp = event.getTimestamp();

        // O(depth) rank check: check how many events occurred in range
        long eventsInRange = offHeapState.countRange(timestamp - 3600_000L, timestamp);

        offHeapState.put(timestamp, event.getValue());
        out.collect(new EnrichedEvent(event, eventsInRange));
    }

    @Override
    public void close() {
        if (offHeapState != null) {
            offHeapState.close();
        }
    }
}

5.3 Low-Latency Financial Order Books & Market Depth

In ultra-low latency trading systems, ExpanseMap provides ordered price-level traversal (ceilingEntry, floorEntry, higherKey, lowerKey) and direct memory slot updates without Java memory locks.

try (ExpanseMap bidBook = new ExpanseMap();
     ExpanseMap askBook = new ExpanseMap()) {

    // Insert bid price level (price in cents -> cumulative quantity)
    bidBook.put(150_25L, 500L);
    bidBook.put(150_20L, 1200L);
    bidBook.put(150_10L, 3400L);

    // Fast Best Bid lookup
    bidBook.lastEntry().ifPresent(bestBid -> {
        System.out.printf("Best Bid: $%.2f (Qty: %d)%n", bestBid.key() / 100.0, bestBid.value());
    });

    // Level-2 depth iteration
    bidBook.forEach((price, qty) -> {
        // In-order traversal from lowest to highest bid
    });
}

6. Multi-Core Optimistic Concurrency Control (OCC)

SyncExpanseMap and SyncExpanseSet provide thread-safe concurrent access designed for read-heavy workloads:

  • Lock-Free Readers: Readers take an optimistic snapshot and validate subtrees without taking any read lock, avoiding cache line bouncing.
  • Single-Writer Serialization: Writers serialize internally without locking out concurrent readers.
  • Reader Reuse: Create a SyncExpanseMap.Reader per worker thread and reuse it across requests.
SyncExpanseMap concurrentMap = new SyncExpanseMap();

// Worker thread:
try (SyncExpanseMap.Reader reader = concurrentMap.reader()) {
    while (running) {
        long key = pollNextKey();
        OptionalLong val = reader.get(key);
        // ...
    }
}

7. Scala Language Integration

expanse-java works seamlessly with Scala 2.13 and Scala 3:

import io.github.orieg.expanse.{ExpanseMap, ExpanseSet}
import scala.util.Using

// Automatic resource management via Scala Using
Using.resource(new ExpanseMap()) { map =>
  map.put(100L, 1L)
  map.put(200L, 2L)
  map.put(300L, 3L)

  // Rank query
  val count = map.countRange(100L, 250L) // 2

  // Key navigation
  val next = map.higherKey(100L) // OptionalLong.of(200)
}

8. Artifact Coordinates & Native Packaging

Not yet published to Maven Central. No io.github.orieg artifact exists on Maven Central (returns 404 / numFound:0), and no release-workflow job currently builds or deploys the Java bindings — release.yml has no Maven/Gradle/Sonatype step. The coordinates and packaging layout below are the planned shape; build from bindings/java locally until first publish.

Maven Central Dependency (planned)

<dependency>
    <groupId>io.github.orieg</groupId>
    <artifactId>expanse-java</artifactId>
    <version>0.5.0</version>
</dependency>

Precompiled Multi-Arch Native Artifacts (planned)

Once published, the JAR is intended to bundle precompiled native binaries for:

  • linux-x86_64 (libexpanse.so, optimized with hardware vectorization)
  • linux-aarch64 (libexpanse.so, ARM64 NEON)
  • darwin-aarch64 (libexpanse.dylib, Apple Silicon M1/M2/M3/M4)
  • darwin-x86_64 (libexpanse.dylib, Intel macOS)
  • windows-x86_64 (expanse.dll, Windows MSVC)