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ClickHouse Proto Gen

CLI tool for generating Protocol Buffer schemas from ClickHouse tables, enabling type-safe data access and cross-platform compatibility.

ClickHouse Proto Gen is a standalone CLI tool that connects to a ClickHouse cluster, introspects table schemas, and generates corresponding Protocol Buffer (.proto) schema files. It bridges the gap between ClickHouse's powerful analytics capabilities and Protocol Buffers' type-safe, cross-platform data serialization.

Overview​

ClickHouse Proto Gen automates the tedious process of manually creating Protocol Buffer definitions from your ClickHouse tables:

  • Auto-discovery: Automatically extracts table schemas from ClickHouse
  • Intelligent Type Mapping: Maps ClickHouse types to appropriate Proto3 types
  • Preserves Documentation: Optionally includes table and column comments
  • Selective Generation: Generate proto for specific tables or entire databases
  • Configurable: Supports both CLI flags and YAML configuration files

Key Features​

Automatic Schema Discovery​

Point the tool at your ClickHouse database and it discovers:

  • Table structures with all columns
  • Data types with proper proto mappings
  • Column comments and documentation
  • Nullable fields and arrays

Smart Type Mapping​

Handles complex ClickHouse types intelligently:

  • Native types: Int, UInt, Float, String, DateTime, UUID
  • Large integers: Int128, Int256 mapped to strings
  • Arrays: ClickHouse Array(T) becomes proto repeated T
  • Nullable: Special nullable filter handling
  • Complex types: Map, Tuple, Enum with appropriate representations
  • UInt64 conversion: Optional string conversion for JavaScript/TypeScript safety

Developer Workflow Integration​

Designed to fit into modern development workflows:

  • CI/CD friendly: Use in automated pipelines
  • Version control: Generate proto files to commit alongside code
  • Type safety: Enable type-safe data access across languages
  • Cross-platform: Generate code for Go, Python, JavaScript, Rust, and more

Type Mappings​

Numeric Types​

ClickHouse TypeProto TypeNotes
Int8, Int16, Int32int32
Int64int64
Int128, Int256stringNo native protobuf support
UInt8, UInt16, UInt32uint32
UInt64uint64Optionally convert to string for JS
Float32float
Float64double
Decimal*stringPreserves precision

Other Types​

ClickHouse TypeProto TypeNotes
String, FixedStringstring
Date, DateTimestringISO 8601 format
Boolbool
UUIDstring
Array(T)repeated T
Nullable(T)Uses nullable filtersSpecial handling
MapstringJSON representation
TuplestringJSON representation
Enum8, Enum16stringEnum value as string
IPv4, IPv6stringIP address as string

UInt64 to String Conversion​

JavaScript's Number type cannot safely represent integers beyond Number.MAX_SAFE_INTEGER (2^53-1). For JavaScript/TypeScript clients, you can configure specific UInt64 fields to be converted to strings:

conversion:
  uint64_to_string:
    fct_my_table:
      - my_column_a
      - my_column_b
    int_my_other_table:
      - my_column_x

Or via CLI:

# Specific field in specific table
--uint64-to-string "fct_my_table.my_column_a"

# All fields in table
--uint64-to-string "int_my_other_table.*"

# Specific field in all tables
--uint64-to-string "*.my_column_x"

# All UInt64 fields everywhere
--uint64-to-string "*.*"

Use Cases​

Type-Safe Data Access​

Generate strongly-typed client libraries:

  • Go structs with proper field types
  • Python dataclasses with type hints
  • TypeScript interfaces with strict typing
  • Rust structs with compile-time guarantees

API Development​

Build APIs with consistent schemas:

  • Use with CBT API to generate REST endpoints
  • Create gRPC services with type-safe contracts
  • Generate OpenAPI specs from proto annotations

Data Pipeline Engineering​

Ensure type consistency across pipelines:

  • Validate data transformations with type checking
  • Generate consistent schemas for different environments
  • Version control your data contracts

Cross-Platform Development​

Enable multiple language clients:

  • Backend services in Go
  • Analytics scripts in Python
  • Frontend applications in TypeScript
  • Data processing in Rust

Examples​

Basic Usage​

# Generate proto for specific tables
clickhouse-proto-gen \
  --dsn "clickhouse://localhost:9000/mydb" \
  --tables users,orders,products \
  --out ./proto \
  --package myapp.v1

Using Docker​

# Generate with docker
docker run --rm -v "$(pwd):/workspace" \
  ethpandaops/clickhouse-proto-gen \
  --dsn "clickhouse://user:[email protected]:9000/mydb" \
  --tables beacon_blocks,attestations \
  --out /workspace/proto \
  --package ethereum.v1 \
  --include-comments

Configuration File​

# config.yaml
dsn: clickhouse://user:password@localhost:9000/mydb

tables:
  - users
  - orders
  - products

output_dir: ./proto
package: myapp.clickhouse.v1
go_package: github.com/myorg/myapp/gen/clickhousev1
include_comments: true

Run with:

clickhouse-proto-gen --config config.yaml

Cross-Database Generation​

Generate proto files for tables across multiple databases:

clickhouse-proto-gen \
  --dsn "clickhouse://localhost:9000" \
  --tables "db1.users,db2.products,db3.orders" \
  --out ./proto

Example Output​

Given this ClickHouse table:

CREATE TABLE users (
    id UInt64,
    email String,
    name Nullable(String),
    created_at DateTime,
    tags Array(String)
) ENGINE = MergeTree()
ORDER BY id
COMMENT = 'User accounts table';

ClickHouse Proto Gen generates:

syntax = "proto3";

package myapp.v1;

option go_package = "github.com/myorg/myapp/gen/v1";

// User accounts table
message Users {
  uint64 id = 11;
  string email = 12;
  optional string name = 13;
  string created_at = 14;
  repeated string tags = 15;
}

Integration with ethPandaOps Stack​

ClickHouse Proto Gen is used internally by:

  • CBT API: Automatically generates proto files for discovered tables
  • CBT: Can be used to generate type-safe models for transformations
  • Xatu Data Pipeline: Generates proto definitions for Xatu data tables

Resources​

Community​

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