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Large off-heap arrays and memory-mapped file (mmap) for Java and Scala
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A library for managing large off-heap arrays that can hold more than 2G (2^31) entries in Java and Scala.


  • LArray can create arrays with more than 2G(2^31) entries.
    • 2^31 -1 (2G) is the limitation of the default Java/Scala array size, because these arrays use 32-bit signed integer (int) as indexes. LArray uses long type indexes of 64-bit signed integers to resolve this limitation.
    • For example, the entire human genome data (3GB) can be stored in LArray.
  • LArray can be released immediately from the memory.
    • Call
    • The default arrays in Java/Scala stay in JVM heaps until they are collected by GC, so it is generally difficult to avoid OutOfMemoryException when working with large amount of data. For example, call new Array[Int](1000) x 10,000 times. You are lucky if you don't see OutOfMemoryException.
  • LArray can be collected by Garbage Collector (GC)
    • Even if you forget to call, the acquired memory will be released when GC sweeps LArray instances.
    • To prevent accidental memory release, keep a reference to LArray somewhere (e.g., in List) as in the standard Java/Scala program.
  • LArray resides in off-heap memory
    • LArray uses a memory space outside the JVM heap, so creating LArrays with more than -Xmx(maximum heap size) is possible. This is useful when you need large amount of memory, or it is unknown how much memory is required in your application.
  • Fast memory allocation
    • LArray internally uses a concurrent memory allocator suited to multi-threaded programs, which is faster than the default JVM memory allocator.
    • LArray by default skips the array initialization (zero-filling), which improves the memory allocation speed significantly.
  • LArray can be used as DirectBuffer
    • Enables zero-copy transfer to/from files, network, etc.
    • Zero-copy compression with snappy-java (supported since version 1.1.0-M4. Pass LArray.address to Snappy.rawCompress etc.)
  • Rich set of operations for LArray[A]
    • map, filter, reduce, zip, etc. Almost all collection operations in Scala are already implemented for LArray[A].
  • Supports Memory-mapped file larger than 2GB
    • Use LArray.mmap
    • It can create memory regions that can be shared between processes.


  • LArray[A] of generic objects (e.g., LArray[String], LArray[AnyRef]) cannot be released immedeately from the main memory, because objects other than primitive types need to be created on JVM heaps and they are under the control of GC.
    • To release objects from main memory, you need to create off-heap objects. For example, create a large LArray[Byte], then align your object data on the array. Object parameters can be retrieved with LArray[Byte].getInt(offset), getFloat(offset), etc.


Memory allocation

Here is a simple benchmark result that compares concurrent memory-allocation performances of LArray (with or without zero-filling), java arrays, ByteBuffer.allocate and ByteBuffer.allocateDirect, using Mac OS X with 2.9GHz Intelli Core i7. This test allocates 100 x 1MB of memory space concurrently using multiple threads, and repeats this process 20 times.

-concurrent allocation  total:2.426 sec. , count:   10, avg:0.243 sec. , core avg:0.236 sec. , min:0.159 sec. , max:0.379 sec.
  -without zero-filling total:0.126 sec. , count:   20, avg:6.279 msec., core avg:2.096 msec., min:1.405 msec., max:0.086 sec.
  -with zero-filling    total:0.476 sec. , count:   20, avg:0.024 sec. , core avg:0.023 sec. , min:0.017 sec. , max:0.037 sec.
  -java array           total:0.423 sec. , count:   20, avg:0.021 sec. , core avg:0.021 sec. , min:0.014 sec. , max:0.029 sec.
  -byte buffer          total:1.028 sec. , count:   20, avg:0.051 sec. , core avg:0.044 sec. , min:0.014 sec. , max:0.216 sec.
  -direct byte buffer   total:0.360 sec. , count:   20, avg:0.018 sec. , core avg:0.018 sec. , min:0.015 sec. , max:0.026 sec.

All allocators except LArray are orders of magnitude slower than LArray, and consumes CPUs because they need to fill the allocated memory with zeros due to their specification.

Snappy Compression

LArray (and LBuffer) has memory address that can be used for seamlessly interacting with fast native methods through JNI. Here is an example of using rawCompress(...) in snappy-java, which can take raw-memory address to compress/uncompress the data using C++ code, and is generally faster than Dain's pure-java version of Snappy.

-compress           total:0.017 sec. , count:   10, avg:1.669 msec., core avg:0.769 msec., min:0.479 msec., max:0.010 sec.
  -LBuffer -> LBuffer (raw) total:1.760 msec., count:   50, avg:0.035 msec., core avg:0.030 msec., min:0.024 msec., max:0.278 msec.
  -Array -> Array (raw)     total:1.450 msec., count:   50, avg:0.029 msec., core avg:0.027 msec., min:0.023 msec., max:0.110 msec.
  -Array -> Array (dain)    total:0.011 sec. , count:   50, avg:0.225 msec., core avg:0.141 msec., min:0.030 msec., max:4.441 msec.
-decompress         total:7.722 msec., count:   10, avg:0.772 msec., core avg:0.473 msec., min:0.418 msec., max:3.521 msec.
  -LBuffer -> LBuffer (raw) total:1.745 msec., count:   50, avg:0.035 msec., core avg:0.029 msec., min:0.020 msec., max:0.331 msec.
  -Array -> Array (raw)     total:1.189 msec., count:   50, avg:0.024 msec., core avg:0.021 msec., min:0.018 msec., max:0.149 msec.
  -Array -> Array (dain)    total:2.571 msec., count:   50, avg:0.051 msec., core avg:0.027 msec., min:0.025 msec., max:1.240 msec.


LArray consists of three-modules.

  • larray-buffer (Java) Off-heap memory buffer LBuffer and its allocator with GC support.
  • larray-mmap (Java + JNI (C code)) Memory-mapped file implementaiton MMapBuffer
  • larray (Scala and Java API) Provdes rich set of array operations through LArray class.

You can use each module independently. For example, if you only need an off-heap memory allocator that collects memory upon GC, use LBuffer in larray-buffer.

Simply you can include larray to the dependency in Maven or SBT so that all modules will be added to your classpaths.

Supported Platforms

A standard JVM, (e.g. Oracle JVM (standard JVM, HotSpotVM) or OpenJDK) must be used since larray-buffer depends on sun.misc.Unsafe class to allocate off-heap memory.

larray-mmap (MMapBuffer and LArray.mmap) uses JNI and is available for the following major CPU architecutres:

  • Windows (32/64-bit)
  • Linux (i368, amd64 (Intel 64-bit), arm, armhf)
  • Mac OSX (Intel 64bit)


  • November 11, 2013 version 0.2 - Extracted pure-java modules (larray-buffer.jar and larray-mmap.jar) from larray.jar (for Scala).
  • August 28, 2013 version 0.1.2 - improved memory layout
  • August 28, 2013 version 0.1.1 (for Scala 2.10.2)
  • Apr 23, 2013 Released version 0.1

Usage (Scala)

sbt settings

Add the following sbt dependency to your project settings:

libraryDependencies += "org.xerial" % "larray" % "0.2"
  • Using snapshot versions:
resolvers += "Sonatype shapshot repo" at ""

libraryDependencies += "org.xerial" % "larray" % "0.2-SNAPSHOT"


LArray can be used in the same manner with the standard Scala Arrays:

import xerial.larray._

val l = LArray(1, 2, 3)
val e = l(0) // 1
println(l.mkString(", ")) // 1, 2, 3
l(1) = 5
println(l.mkString(", ")) // 1, 5, 3

// Create an LArray of Int type
val l2 = LArray.of[Int](10000L)

// Release the memory resource 

l2(0) // The result of accessing released LArray is undefined

For more examples, see xerial/larray/example/LArrayExample.scala

Usage (Java)

Add the following dependency to your pom.xml (Maven):



In Java we cannot provide concise syntaxes as in Scala. Instead, use apply and update methods to read/write values in an array.

import xerial.larray.japi.LArrayJ;
import xerial.larray.*;

LIntArray l = LArrayJ.newLIntArray(10000L);
l.update(0L, 20); // Set l[0L] = 20
int e0 = l.apply(0L);  //  Get l[0L]

// release;

For more examples, see xerial/larray/example/LArrayJavaExample.scala


For developers

  • Building LArray: ./sbt compile
  • Run tests: ./sbt ~test
  • Creating IntelliJ IDEA project: ./sbt gen-idea
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