parquet-ruby

Read and write Apache Parquet files from Ruby. This gem wraps the official Apache parquet rust crate, providing:

High performance columnar data storage and retrieval
Memory-efficient streaming APIs for large datasets
Full compatibility with the Apache Parquet specification
Simple, Ruby-native APIs that feel natural

Why Use This Library?

Apache Parquet is the de facto standard for analytical data storage, offering:

Efficient compression - typically 2-10x smaller than CSV
Fast columnar access - read only the columns you need
Rich type system - preserves data types, including nested structures
Wide ecosystem support - works with Spark, Pandas, DuckDB, and more

Installation

Add this line to your application's Gemfile:

gem 'parquet'

Then execute:

$ bundle install

Or install it directly:

$ gem install parquet

Quick Start

Reading Data

require "parquet"

# Read Parquet files row by row
Parquet.each_row("data.parquet") do |row|
  puts row  # => {"id" => 1, "name" => "Alice", "score" => 95.5}
end

# Or column by column for better performance
Parquet.each_column("data.parquet", batch_size: 1000) do |batch|
  puts batch  # => {"id" => [1, 2, ...], "name" => ["Alice", "Bob", ...]}
end

Writing Data

# Define your schema
schema = [
  { "id" => "int64" },
  { "name" => "string" },
  { "score" => "double" }
]

# Write row by row
rows = [
  [1, "Alice", 95.5],
  [2, "Bob", 82.3]
]

Parquet.write_rows(rows.each, schema: schema, write_to: "output.parquet")

Reading Parquet Files

The library provides two APIs for reading data, each optimized for different use cases:

Row-wise Reading (Sequential Access)

Best for: Processing records one at a time, data transformations, ETL pipelines

# Basic usage - returns hashes
Parquet.each_row("data.parquet") do |row|
  puts row  # => {"id" => 1, "name" => "Alice"}
end

# Memory-efficient array format
Parquet.each_row("data.parquet", result_type: :array) do |row|
  puts row  # => [1, "Alice"]
end

# Read specific columns only
Parquet.each_row("data.parquet", columns: ["id", "name"]) do |row|
  # Only requested columns are loaded from disk
end

# Works with IO objects
File.open("data.parquet", "rb") do |file|
  Parquet.each_row(file) do |row|
    # Process row
  end
end

Column-wise Reading (Analytical Access)

Best for: Analytics, aggregations, when you need few columns from wide tables

# Process data in column batches
Parquet.each_column("data.parquet", batch_size: 1000) do |batch|
  # batch is a hash of column_name => array_of_values
  ids = batch["id"]      # => [1, 2, 3, ..., 1000]
  names = batch["name"]  # => ["Alice", "Bob", ...]

  # Perform columnar operations
  avg_id = ids.sum.to_f / ids.length
end

# Array format for more control
Parquet.each_column("data.parquet",
                    result_type: :array,
                    columns: ["id", "name"]) do |batch|
  # batch is an array of arrays
  # [[1, 2, ...], ["Alice", "Bob", ...]]
end

File Metadata

Inspect file structure without reading data:

metadata = Parquet.metadata("data.parquet")

puts metadata["num_rows"]           # Total row count
puts metadata["created_by"]         # Writer identification
puts metadata["schema"]["fields"]   # Column definitions
puts metadata["row_groups"].size    # Number of row groups

Writing Parquet Files

Row-wise Writing

Best for: Streaming data, converting from other formats, memory-constrained environments

# Basic schema definition
schema = [
  { "id" => "int64" },
  { "name" => "string" },
  { "active" => "boolean" },
  { "balance" => "double" }
]

# Stream data from any enumerable
rows = CSV.foreach("input.csv").map do |row|
  [row[0].to_i, row[1], row[2] == "true", row[3].to_f]
end

Parquet.write_rows(rows,
  schema: schema,
  write_to: "output.parquet",
  batch_size: 5000  # Rows per batch (default: 1000)
)

Column-wise Writing

Best for: Pre-columnar data, better compression, higher performance

# Prepare columnar data
ids = [1, 2, 3, 4, 5]
names = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
scores = [95.5, 82.3, 88.7, 91.2, 79.8]

# Create batches
batches = [[
  ids,    # First column
  names,  # Second column
  scores  # Third column
]]

schema = [
  { "id" => "int64" },
  { "name" => "string" },
  { "score" => "double" }
]

Parquet.write_columns(batches.each,
  schema: schema,
  write_to: "output.parquet",
  compression: "snappy"  # Options: none, snappy, gzip, lz4, zstd
)

Data Types

Basic Types

schema = [
  # Integers
  { "tiny" => "int8" },         # -128 to 127
  { "small" => "int16" },       # -32,768 to 32,767
  { "medium" => "int32" },      # ±2 billion
  { "large" => "int64" },       # ±9 quintillion

  # Unsigned integers
  { "ubyte" => "uint8" },       # 0 to 255
  { "ushort" => "uint16" },     # 0 to 65,535
  { "uint" => "uint32" },       # 0 to 4 billion
  { "ulong" => "uint64" },      # 0 to 18 quintillion

  # Floating point
  { "price" => "float" },       # 32-bit precision
  { "amount" => "double" },     # 64-bit precision

  # Other basics
  { "name" => "string" },
  { "data" => "binary" },
  { "active" => "boolean" }
]

Date and Time Types

schema = [
  # Date (days since Unix epoch)
  { "date" => "date32" },

  # Timestamps (with different precisions)
  { "created_sec" => "timestamp_second" },
  { "created_ms" => "timestamp_millis" },    # Most common
  { "created_us" => "timestamp_micros" },
  { "created_ns" => "timestamp_nanos" },

  # Time of day (without date)
  { "time_ms" => "time_millis" },    # Milliseconds since midnight
  { "time_us" => "time_micros" }     # Microseconds since midnight
]

Decimal Type (Financial Data)

For exact decimal arithmetic (no floating-point errors):

require "bigdecimal"

schema = [
  # Financial amounts with 2 decimal places
  { "price" => "decimal", "precision" => 10, "scale" => 2 },  # Up to 99,999,999.99
  { "balance" => "decimal", "precision" => 15, "scale" => 2 }, # Larger amounts

  # High-precision calculations
  { "rate" => "decimal", "precision" => 10, "scale" => 8 }     # 8 decimal places
]

# Use BigDecimal for exact values
data = [[
  BigDecimal("19.99"),
  BigDecimal("1234567.89"),
  BigDecimal("0.00000123")
]]

Complex Data Structures

The library includes a powerful Schema DSL for defining nested data:

Using the Schema DSL

schema = Parquet::Schema.define do
  # Simple fields
  field :id, :int64, nullable: false      # Required field
  field :name, :string                    # Optional by default

  # Nested structure
  field :address, :struct do
    field :street, :string
    field :city, :string
    field :location, :struct do
      field :lat, :double
      field :lng, :double
    end
  end

  # Lists
  field :tags, :list, item: :string
  field :scores, :list, item: :int32

  # Maps (dictionaries)
  field :metadata, :map, key: :string, value: :string

  # Complex combinations
  field :contacts, :list, item: :struct do
    field :name, :string
    field :email, :string
    field :primary, :boolean
  end
end

Writing Complex Data

data = [[
  1,                              # id
  "Alice Johnson",                # name
  {                               # address
    "street" => "123 Main St",
    "city" => "Springfield",
    "location" => {
      "lat" => 40.7128,
      "lng" => -74.0060
    }
  },
  ["ruby", "parquet", "data"],    # tags
  [85, 92, 88],                   # scores
  { "dept" => "Engineering" },    # metadata
  [                               # contacts
    { "name" => "Bob", "email" => "bob@example.com", "primary" => true },
    { "name" => "Carol", "email" => "carol@example.com", "primary" => false }
  ]
]]

Parquet.write_rows(data.each, schema: schema, write_to: "complex.parquet")

⚠️ Important Limitations

Timezone Handling in Parquet

The Parquet specification has a fundamental limitation with timezone storage:

UTC-normalized: Any timestamp with timezone info (including "+09:00" or "America/New_York") is converted to UTC
Local/unzoned: Timestamps without timezone info are stored as-is

The original timezone information is permanently lost. This is not a limitation of this library but of the Parquet format itself.

schema = Parquet::Schema.define do
  # These BOTH store in UTC - timezone info is lost!
  field :timestamp_utc, :timestamp_millis, timezone: "UTC"
  field :timestamp_tokyo, :timestamp_millis, timezone: "+09:00"

  # This stores as local time (no timezone)
  field :timestamp_local, :timestamp_millis
end

# If you need timezone preservation, store it separately:
schema = Parquet::Schema.define do
  field :timestamp, :timestamp_millis, has_timezone: true  # UTC storage
  field :original_tz, :string                              # "America/New_York"
end

Performance Tips

Use column-wise reading when you need only a few columns from wide tables
Specify columns parameter to avoid reading unnecessary data
Choose appropriate batch sizes:
- Larger batches = better throughput but more memory
- Smaller batches = less memory but more overhead
Pre-sort data by commonly filtered columns for better compression

Memory Management

Control memory usage with flush thresholds:

Parquet.write_rows(huge_dataset.each,
  schema: schema,
  write_to: "output.parquet",
  batch_size: 1000,              # Rows before considering flush
  flush_threshold: 32 * 1024**2  # Flush if batch exceeds 32MB
)

Architecture

This gem uses a modular architecture:

parquet-core: Language-agnostic Rust core for Parquet operations
parquet-ruby-adapter: Ruby-specific FFI adapter layer
parquet gem: High-level Ruby API

Take a look at ARCH.md

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/njaremko/parquet-ruby.

License

The gem is available as open source under the terms of the MIT License.

parquet

Development

Runtime