Features

• Value-centric design: Manual codec construction using combinators (no code generation required)
• Pure OCaml: No external C dependencies for core functionality
• Schema evolution: Built-in support for reading data with different schemas
• Container files: Full support for Avro Object Container File format
• Compression: Multiple compression codecs (null, deflate, snappy, zstandard)
• Streaming: Memory-efficient block-level streaming for large files
• Type-safe: Codec-enforced types with composable combinators

Quick Start

Example Usage

open Avro_simple

(* Encode a string *)
let name = "Alice" in
let encoded = Codec.encode_to_bytes Codec.string name in
let decoded = Codec.decode_from_bytes Codec.string encoded in
assert (name = decoded)

(* Encode an array *)
let numbers = [| 1; 2; 3; 4; 5 |] in
let codec = Codec.array Codec.int in
let encoded = Codec.encode_to_bytes codec numbers in
let decoded = Codec.decode_from_bytes codec encoded in
assert (numbers = decoded)

(* Optional values *)
let codec = Codec.option Codec.string in
let some_value = Some "hello" in
let encoded = Codec.encode_to_bytes codec some_value in
let decoded = Codec.decode_from_bytes codec encoded in
assert (some_value = decoded)

Check out examples/basic_usage.ml for a working demonstration.

Compression Codecs

Using Built-in Codecs

Container files support compression with automatic codec detection:

open Avro_simple

(* Write compressed container file *)
let write_compressed () =
  let codec = Codec.array Codec.int in
  let writer = Container_writer.create ~compression:"zstandard" codec "data.avro" in
  Container_writer.write writer [|1; 2; 3; 4; 5|];
  Container_writer.close writer

(* Read compressed container file - codec auto-detected *)
let read_compressed () =
  let codec = Codec.array Codec.int in
  match Container_reader.read_all codec "data.avro" with
  | Ok data -> Array.iter (Printf.printf "%d ") data
  | Error msg -> Printf.eprintf "Error: %s\n" msg

Available Codecs

(* List all available compression codecs *)
let codecs = Codec_registry.list () in
List.iter print_endline codecs
(* Output (with all optional deps installed):
   null
   deflate
   zstandard
   snappy
*)

(* Check if a specific codec is available *)
match Codec_registry.get "zstandard" with
| Some _ -> print_endline "Zstandard available!"
| None -> print_endline "Zstandard not installed"

Registering Custom Codecs

You can register your own compression codec:

module My_LZ4_Codec = struct
  type t = unit
  let name = "lz4"
  let create () = ()
  let compress () data = Lz4.compress data
  let decompress () data = Lz4.decompress data
end

(* Register the codec *)
let () = Codec_registry.register "lz4" (module My_LZ4_Codec)

(* Use it in container files *)
let writer = Container_writer.create ~compression:"lz4" codec "data.avro" in
...

Compression Performance Tips:

• null: Use for already-compressed data or when speed is critical
• deflate: Good default, widely compatible, moderate compression
• zstandard: Best compression ratio, fast decompression (recommended)
• snappy: Fastest compression, lower ratio (good for real-time data)

Streaming Large Files

The library provides memory-efficient streaming for processing large Avro container files. Only one block is loaded into memory at a time, enabling processing of files larger than available RAM.

Lazy Sequences

Use to_seq for flexible, composable streaming with early termination:

open Avro_simple

(* Process only what you need *)
let reader = Container_reader.open_file ~path:"large.avro" ~codec () in

let first_100_purchases =
  Container_reader.to_seq reader
  |> Seq.filter (fun event -> event.event_type = "purchase")
  |> Seq.take 100
  |> List.of_seq
in

Container_reader.close reader
(* Only scans until 100 purchases found - doesn't load entire file! *)

Full Scan Iterator

Use iter for maximum throughput when processing all records:

let count = ref 0 in
Container_reader.iter (fun record ->
  incr count;
  process_record record
) reader
(* Memory usage: O(block_size), not O(file_size) *)

Aggregation with Fold

Use fold for accumulating results:

(* Count records by category *)
let counts = Container_reader.fold (fun acc record ->
  let count =
    try List.assoc record.category acc
    with Not_found -> 0
  in
  (record.category, count + 1) :: List.remove_assoc record.category acc
) [] reader

Streaming Pipelines

Combine streaming read and write for transformations:

let reader = Container_reader.open_file ~path:"input.avro" ~codec () in
let writer = Container_writer.create ~path:"output.avro" ~codec () in

(* Stream: read -> filter -> transform -> write *)
Container_reader.to_seq reader
|> Seq.filter (fun e -> e.value > 50)
|> Seq.map (fun e -> { e with value = e.value * 2 })
|> Seq.iter (Container_writer.write writer);

Container_reader.close reader;
Container_writer.close writer
(* No intermediate storage - constant memory usage! *)

Memory Characteristics:

• Streaming approach: O(block_size) memory - typically 1-2 MB
• Full load approach: O(file_size) memory - entire file in RAM
• Benefit: Process 10 GB files with only ~20 MB memory usage

See examples/large_file_streaming.ml for comprehensive examples and docs/STREAMING_IMPLEMENTATION.md for technical details.

Building

# Build the library
dune build src

# Build examples
dune build examples

# Build everything (requires test dependencies)
dune build

# Run tests (requires alcotest and qcheck)
dune runtest

# Run benchmarks (requires benchmark library)
dune build @bench/runbench

# Watch mode (rebuild on save)
dune build --watch

# Clean build artifacts
dune clean

Installation

Basic Installation

# Install with deflate compression support (recommended)
opam install avro-simple

With Optional Compression Codecs

The library supports optional compression codecs that are automatically enabled when their dependencies are installed:

# Install with Zstandard support (recommended for modern deployments)
opam install avro-simple zstd

# Install with Snappy support (common in Hadoop/Spark ecosystems)
opam install avro-simple snappy

# Install with all compression codecs
opam install avro-simple zstd snappy

Available Codecs:

See INSTALL.md for detailed installation instructions.

Acknowledgements and Alternatives

This library started as a port of avro-simple from Haskell, when I needed to process Avro files in OCaml. Hopefully it feels native-OCaml style. I also referenced the ocaml-avro library by c-cube, below I provide a detailed comparison to that library. If I've missed a point or something is inaccurate please make a PR correcting it.

Comparison: avro-simple vs ocaml-avro

This document compares the two OCaml Avro library implementations to help you choose the right one for your project.

Overview

Core Differences

Design Approach

Feature Comparison

Schema & Type System

Schema Evolution

Container Files

Compression Codecs

Use Cases

Choose avro-simple when you need:

• Dynamic schema handling - Working with schemas determined at runtime
• Schema evolution - Reading data written with different schema versions
• Large file processing - Stream files larger than available RAM
• Memory efficiency - Process multi-GB files with constant memory usage
• No build complexity - Simple library dependency, no code generation step
• Flexible data processing - Building ETL pipelines, data transformations
• Modern compression - Snappy or Zstandard for better performance
• Composable codecs - Building complex types from simpler ones
• Logical types - Full support for date, time, decimal, uuid

Example scenarios:

• Data migration tools that handle multiple schema versions
• Log processing systems with evolving schemas and large volumes
• Big data pipelines processing multi-GB Avro files
• Microservices with schema registry integration
• Interactive data exploration tools
• Streaming ETL jobs with constant memory requirements

Choose ocaml-avro when you need:

• Compile-time safety - Catch schema errors at compile time
• Static schemas - Schemas known and fixed at build time
• Simpler types - Generated types match your mental model exactly
• Minimal runtime - No codec setup overhead
• Legacy support - Support for older OCaml versions (4.08+)

Example scenarios:

• Fixed-schema data storage with known structure
• Performance-critical applications with static schemas
• Projects with existing schema files to generate from
• Simple serialization without schema evolution needs