Zarr-Style Workloads

# Use this install to work with the source code
# Mix.install(
#   [{:ex_codecs, path: Path.join(__DIR__, "..")}],
#   config: [rustler_precompiled: [force_build: [ex_codecs: true]]]
# )

Mix.install([{:ex_codecs, "~> 0.2.3"}])

Series

Scientific Dataset Compression

This notebook demonstrates compression patterns that array stores such as Zarr use, optimized for numerical and scientific data.

Array Data Layout

Scientific datasets are typically stored as multi-dimensional arrays with strong regularity:

• Numeric types (integers, floats) with fixed element sizes
• Regular dimension strides
• Column-oriented or row-oriented access patterns

Blosc2 was specifically designed for this workload:

# Generate a synthetic dataset (64-bit float array)
n_elements = 100_000
data = :binary.copy(<<3.14159265::float-64>>, n_elements)
data_size = byte_size(data)

# Compare codecs on regular numeric data
codecs = [:zstd, :lz4, :snappy, :bzip2, :blosc2]

results =
  for codec <- codecs do
    {time, {:ok, compressed}} = :timer.tc(fn -> ExCodecs.encode(codec, data) end)
    ratio = Float.round(data_size / byte_size(compressed), 2)
    {codec, byte_size: byte_size(compressed), ratio: ratio, time_us: time}
  end

IO.puts("Codec    | Compressed | Ratio | Time (us)")
IO.puts("---------|-----------|-------|----------")
for {codec, opts} <- results do
  IO.puts("#{String.pad_trailing("#{codec}", 8)} | #{String.pad_trailing("#{opts[:byte_size]}", 9)} | #{opts[:ratio]}   | #{opts[:time_us]}")
end

Blosc2 Shuffle for Numerical Data

The shuffle filter is the key to Blosc2’s effectiveness on array data:

data = :binary.copy(<<1.0::float-64>>, 50_000) <> :binary.copy(<<2.0::float-64>>, 50_000)

# No shuffle
{:ok, c_none} = ExCodecs.encode(:blosc2, data, typesize: 8, shuffle: :none)
# Byte shuffle - reorders bytes for better compression
{:ok, c_byte} = ExCodecs.encode(:blosc2, data, typesize: 8, shuffle: :byte)
# Bit shuffle - reorders bits for even better compression on some data
{:ok, c_bit} = ExCodecs.encode(:blosc2, data, typesize: 8, shuffle: :bit)

IO.puts("No shuffle:   #{byte_size(c_none)} bytes")
IO.puts("Byte shuffle: #{byte_size(c_byte)} bytes")
IO.puts("Bit shuffle:  #{byte_size(c_bit)} bytes")

Chunk-Based Compression

Large datasets are typically split into chunks, each compressed independently:

chunk_size = 1024 * 8  # 8 KiB chunks
large_data = :binary.copy(<<3.14159265::float-64>>, 131_072)  # 1 MiB of compressible floats

chunks =
  for <<chunk::binary-size(chunk_size) <- large_data>>, do: chunk

compressed_chunks =
  chunks
  |> Enum.map(fn chunk ->
    {:ok, compressed} = ExCodecs.encode(:zstd, chunk, level: 3)
    compressed
  end)

total_original = byte_size(large_data)
total_compressed = compressed_chunks |> Enum.map(&byte_size/1) |> Enum.sum()

IO.puts("Original:   #{total_original} bytes")
IO.puts("Compressed: #{total_compressed} bytes")
IO.puts("Ratio:      #{Float.round(total_original / total_compressed, 2)}x")

Decompressing Individual Chunks

Only the needed chunks need to be decompressed:

# Decompress just chunk 42
target = Enum.at(compressed_chunks, 42)
{:ok, decompressed} = ExCodecs.decode(:zstd, target)
IO.puts("Decompressed chunk size: #{byte_size(decompressed)} bytes")
IO.puts("Matches original: #{decompressed == Enum.at(chunks, 42)}")

Choosing the Right Codec for Your Data

data_patterns = %{
  "Repetitive" => String.duplicate("AAAA", 100_000),
  "Numeric array" => :binary.copy(<<3.14159::float-64>>, 100_000),
  "Mixed" => (:crypto.strong_rand_bytes(500) <> String.duplicate("X", 500)) |> String.duplicate(100),
  "Random" => :crypto.strong_rand_bytes(100_000)
}

codecs = [:zstd, :lz4, :snappy, :bzip2, :blosc2]

IO.puts(String.pad_trailing("Pattern", 15) <> " | " <> Enum.join(codecs, " | "))

for {name, data} <- data_patterns do
  ratios =
    for codec <- codecs do
      {:ok, c} = ExCodecs.encode(codec, data)
      Float.round(byte_size(data) / byte_size(c), 2)
    end

  ratio_strs = Enum.map(ratios, &String.pad_trailing("#{&1}", 5))
  IO.puts(String.pad_trailing(name, 15) <> " | " <> Enum.join(ratio_strs, " | "))
end

Key Takeaways

1. Blosc2 with byte shuffle excels on regular numeric arrays
2. Zstd is the best general-purpose codec
3. LZ4 and Snappy are fastest for real-time compression
4. Bzip2 gives the best ratios for archival
5. Chunk-based compression enables random access to large datasets

Navigation

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