Random Library Design Notes

The Abseil Random library is a collection of classes and function templates which provide an API for generating random numbers. In these design notes we will focus on the parts of the library which provide meaningful additions to the standard library <random> header. For information on the design of <random> consider watching these talks:

Bit Generators

Most users are not experts in random number generation and do not know if they want std::minstd_rand or std::mt19937. While it is important and necessary to have these capabilities available for experts, in our experience, for most users it suffices to provide a single default bit generator type absl::BitGen.

Good Defaults

Seeding random bit generators is surprisingly difficult to get right, and often requires intimate knowledge of the bit generation algorithm used in the generators implementation. While we provide the standard facility for seeding (via std::seed_seq), we do not want the average user to concern themselves with the details of seeding. For this reason, absl::BitGen and absl::InsecureBitGen have default constructors which are well-seeded with random data. That is, simply default constructing an absl::BitGen does the right thing.


Non-determinism is strongly baked into the design of the bit generators provided by the Abseil Random library. A default-constructed absl::BitGen will be seeded with OS-level entropy when it is available. Consequently two absl::BitGens in the same binary will yield different sequences of generated bits. Rerunning the same binary will again produce different sequences.

Within the same binary, two bit generators constructed with the same seed sequence will generate the same sequence of bits. However, this sequence is not deterministic. Rerunning the same binary, the two generators will once again agree with each other, but they will not generate the the same sequence of bits as they did on the first binary execution.

The reasoning for this design choice is subtle, but important. While it often seems like determinism is a useful property for random number generation, our experience tells us that most users require determinism most often as a mechanism for testing.

If bit generators are deterministic, whether or not such determinism is promised as part of the API, users come to rely on it. With sufficiently many users, it becomes impossible to make any change to the bit generation API without breaking someone. (SeeHyrum’s Law). By introducing guaranteed non-determinism, we hope to fight against Hyrum’s Law, allowing us to safely make improvements to our bit generators in the future.

Distribution Function Templates

Abseil provides functions templates for getting results out of distributions. There are two reasons for this design. First, in some instances we want to provide a different API than the standard library to be explicit1 about the meanings of certain inputs. For example,

absl::BitGen gen;
absl::string_view kHello = "Hello, Abseil Random!";

// Using the standard library API.  Remember that both endpoints are always
// included.
std::uniform_int_distribution die_dist<int>(1, 6);
int die_roll1 = die_dist(gen);

std::uniform_int_distribution index_dist<size_t>(size_t{0}, kHello.size() - 1);
char c1 = kHello[index_dist(gen)];

// Using Abseil Random.  Specify the interval bounds explicitly.
// Roll a fair die.
int die_roll2 = absl::Uniform<int>(__absl::IntervalClosedClosed__, gen, 1, 6);

// Pick a random character from the string.
size_t index = absl::Uniform<size_t>(absl::IntervalClosedOpen,
                                     gen, 0, kHello.size());
char c2 = kHello[index];

We provide similar functions (named absl::Bernoulli, absl::Gaussian, etc.) for other distributions in absl/random/distributions.h.

The second benefit of these function templates is that they provide a useful extension point to enable testing application code that uses Abseil Random.


In addition to the bit generators discussed above, we also provide absl::MockingBitGen which works with all the distribution function templates mentioned above. Instead of calling out to the underlying distribution, they return values which can be mocked using GoogleTest infrastructure.

absl::MockingBitGen gen;

ON_CALL(absl::MockUniform<int>(), gen, testing::_, testing::Lt(100))

EXPECT_EQ(absl::Uniform(gen, absl::IntervalClosedClosed, 0, 25), 17);
EXPECT_EQ(absl::Uniform(gen, absl::IntervalClosedClosed, 0, 50), 17);
EXPECT_EQ(absl::Uniform(gen, absl::IntervalClosedClosed, 0, 75), 17);

This approach has a number of benefits. First, it gives test authors the ability to set a particular value as the result of a distribution sample, not just out of the bit generator. This allows users to easily test uncommon cases:

using ::testing::_;
using ::testing::Lt;
using ::testing::Return;

int ComputeValue(absl::BitGenRef gen) {
  if (absl::Bernoulli(gen, 0.00001)) {
    return 10;
  } else {
    return 0;

TEST(ComputeValueTest, UnlikelyCase) {
  absl::MockingBitGen gen;
  ON_CALL(absl::MockBernoulli(), Call(gen, Lt(0.01))

  // The value `10` can be returned by ComputeValue, but it is highly unlikely.
  // With absl::MockingBitGen, we have a hook to get deterministic coverage of
  // this case.
  EXPECT_EQ(ComputeValue(gen), 10);

Another key benefit to this approach is that testing code will use neither a production bit generator, nor an actual distribution object. At first glance this may seem to be of marginal utility, but this has the benefit of freeing the implementations of both bit generators and distribution implementations to change over time, as tests will not come to rely on specific values produced by these generators.

There is one notable drawback to this approach: It requires interfaces to either type-erase the bit generator (so that either absl::MockingBitGen or another bit generator can be passed in), or to itself take a bit generator type as a template parameter. The type absl::BitGenRef is used to do type erasure here and is (cheap but) not free, even when a real bit generator is being used. We believe the facilities provided are well worth the cost.

  1. Part of the reasoning behind wanting this explicitness is that this library grew out of a previous code that always used half-open intervals. Because distributions in <random> always use closed intervals, to avoid confusion, we wanted to be explicit about the meanings of the interval bounds.