"""
Generates data for performance testing of warehouse sources.

In the future, we could try to create a more realistic dataset
by anonymizing and reduplicating a production datahub instance's data.
We could also get more human data by using Faker.

This is a work in progress, built piecemeal as needed.
"""
import random
import uuid
from dataclasses import dataclass
from datetime import datetime, timedelta, timezone
from typing import Iterable, List, TypeVar

from faker import Faker

from tests.performance.data_model import (
    Container,
    FieldAccess,
    Query,
    StatementType,
    Table,
    View,
)

T = TypeVar("T")

OPERATION_TYPES: List[StatementType] = [
    "INSERT",
    "UPDATE",
    "DELETE",
    "CREATE",
    "ALTER",
    "DROP",
    "CUSTOM",
    "UNKNOWN",
]


@dataclass(frozen=True)
class NormalDistribution:
    mu: float
    sigma: float

    def sample(self) -> int:
        return int(random.gauss(mu=self.mu, sigma=self.sigma))

    def sample_with_floor(self, floor: int = 1) -> int:
        return max(int(random.gauss(mu=self.mu, sigma=self.sigma)), floor)


@dataclass
class SeedMetadata:
    containers: List[Container]
    tables: List[Table]
    views: List[View]
    start_time: datetime
    end_time: datetime


def generate_data(
    num_containers: int,
    num_tables: int,
    num_views: int,
    columns_per_table: NormalDistribution = NormalDistribution(5, 2),
    parents_per_view: NormalDistribution = NormalDistribution(2, 1),
    view_definition_length: NormalDistribution = NormalDistribution(150, 50),
    time_range: timedelta = timedelta(days=14),
) -> SeedMetadata:
    containers = [Container(f"container-{i}") for i in range(num_containers)]
    tables = [
        Table(
            f"table-{i}",
            container=random.choice(containers),
            columns=[
                f"column-{j}-{uuid.uuid4()}"
                for j in range(columns_per_table.sample_with_floor())
            ],
        )
        for i in range(num_tables)
    ]
    views = [
        View(
            f"view-{i}",
            container=random.choice(containers),
            columns=[
                f"column-{j}-{uuid.uuid4()}"
                for j in range(columns_per_table.sample_with_floor())
            ],
            definition=f"{uuid.uuid4()}-{'*' * view_definition_length.sample_with_floor(10)}",
            parents=random.sample(tables, parents_per_view.sample_with_floor()),
        )
        for i in range(num_views)
    ]

    now = datetime.now(tz=timezone.utc)
    return SeedMetadata(
        containers=containers,
        tables=tables,
        views=views,
        start_time=now - time_range,
        end_time=now,
    )


def generate_queries(
    seed_metadata: SeedMetadata,
    num_selects: int,
    num_operations: int,
    num_unique_queries: int,
    num_users: int,
    tables_per_select: NormalDistribution = NormalDistribution(3, 5),
    columns_per_select: NormalDistribution = NormalDistribution(10, 5),
    upstream_tables_per_operation: NormalDistribution = NormalDistribution(2, 2),
    query_length: NormalDistribution = NormalDistribution(100, 50),
) -> Iterable[Query]:
    faker = Faker()
    query_texts = [
        faker.paragraph(query_length.sample_with_floor(30) // 30)
        for _ in range(num_unique_queries)
    ]

    all_tables = seed_metadata.tables + seed_metadata.views
    users = [f"user-{i}@xyz.com" for i in range(num_users)]
    for i in range(num_selects):  # Pure SELECT statements
        tables = _sample_list(all_tables, tables_per_select)
        all_columns = [
            FieldAccess(column, table) for table in tables for column in table.columns
        ]
        yield Query(
            text=random.choice(query_texts),
            type="SELECT",
            actor=random.choice(users),
            timestamp=_random_time_between(
                seed_metadata.start_time, seed_metadata.end_time
            ),
            fields_accessed=_sample_list(all_columns, columns_per_select),
        )

    for i in range(num_operations):
        modified_table = random.choice(seed_metadata.tables)
        n_col = len(modified_table.columns)
        num_columns_modified = NormalDistribution(n_col / 2, n_col / 2)
        upstream_tables = _sample_list(all_tables, upstream_tables_per_operation)

        all_columns = [
            FieldAccess(column, table)
            for table in upstream_tables
            for column in table.columns
        ]
        yield Query(
            text=random.choice(query_texts),
            type=random.choice(OPERATION_TYPES),
            actor=random.choice(users),
            timestamp=_random_time_between(
                seed_metadata.start_time, seed_metadata.end_time
            ),
            # Can have no field accesses, e.g. on a standard INSERT
            fields_accessed=_sample_list(all_columns, num_columns_modified, 0),
            object_modified=modified_table,
        )


def _sample_list(lst: List[T], dist: NormalDistribution, floor: int = 1) -> List[T]:
    return random.sample(lst, min(dist.sample_with_floor(floor), len(lst)))


def _random_time_between(start: datetime, end: datetime) -> datetime:
    return start + timedelta(seconds=(end - start).total_seconds() * random.random())