"""Kafka event schemas."""

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, ConfigDict, Field

from src.enums import DebeziumOperation


class DebeziumCDCEvent(BaseModel):
    """Debezium Change Data Capture event.

    Represents a database change event captured by Debezium.
    """

    model_config = ConfigDict(extra='ignore')

    op: DebeziumOperation = Field(
        ..., description='Operation type (c=create, u=update, d=delete, r=read)'
    )
    before: dict[str, Any] | None = Field(
        None, description='Row state before the change (null for inserts)'
    )
    after: dict[str, Any] | None = Field(
        None, description='Row state after the change (null for deletes)'
    )
    ts_ms: int | None = Field(None, description='Timestamp in milliseconds')


class KafkaRecord(BaseModel):
    """Individual Kafka record from MSK event source.

    Represents a single message from a Kafka topic partition.
    """

    topic: str = Field(..., description='Kafka topic name')
    partition: int = Field(..., description='Topic partition number')
    offset: int = Field(..., description='Message offset within partition')
    timestamp: int = Field(..., description='Message timestamp in milliseconds')
    key: str | None = Field(None, description='Base64-encoded message key')
    value: str = Field(..., description='Base64-encoded message value')


class KafkaEvent(BaseModel):
    """AWS Lambda event from MSK/Kafka event source.

    Lambda receives Kafka messages in batches grouped by topic-partition.
    The records field is a dictionary where keys are "{topic}-{partition}"
    and values are lists of KafkaRecord objects.
    """

    model_config = ConfigDict(extra='ignore')

    eventSource: str = Field(
        ..., description="Event source identifier (e.g., 'aws:kafka')"
    )
    records: dict[str, list[KafkaRecord]] = Field(
        ...,
        description="Records grouped by topic-partition (key format: 'topic-partition')",
    )