# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import concurrent.futures
import datetime
import decimal
from typing import Tuple

from google.api_core import exceptions
import pytest

from google.cloud import bigquery
from google.cloud.bigquery.query import ArrayQueryParameter
from google.cloud.bigquery.query import ScalarQueryParameter
from google.cloud.bigquery.query import ScalarQueryParameterType
from google.cloud.bigquery.query import StructQueryParameter
from google.cloud.bigquery.query import StructQueryParameterType
from google.cloud.bigquery.query import RangeQueryParameter


@pytest.fixture(params=["INSERT", "QUERY"])
def query_api_method(request):
    return request.param


@pytest.fixture(scope="session")
def table_with_9999_columns_10_rows(bigquery_client, project_id, dataset_id):
    """Generate a table of maximum width via CREATE TABLE AS SELECT.

    The first column is named 'rowval', and has a value from 1..rowcount
    Subsequent columns are named col_<N> and contain the value N*rowval, where
    N is between 1 and 9999 inclusive.
    """
    table_id = "many_columns"
    row_count = 10
    col_projections = ",".join(f"r * {n} as col_{n}" for n in range(1, 10000))
    sql = f"""
    CREATE TABLE `{project_id}.{dataset_id}.{table_id}`
    AS
    SELECT
        r as rowval,
        {col_projections}
    FROM
      UNNEST(GENERATE_ARRAY(1,{row_count},1)) as r
    """
    query_job = bigquery_client.query(sql)
    query_job.result()

    return f"{project_id}.{dataset_id}.{table_id}"


def test_query_many_columns(
    bigquery_client, table_with_9999_columns_10_rows, query_api_method
):
    # Test working with the widest schema BigQuery supports, 10k columns.
    query_job = bigquery_client.query(
        f"SELECT * FROM `{table_with_9999_columns_10_rows}`",
        api_method=query_api_method,
    )
    rows = list(query_job)
    assert len(rows) == 10

    # check field representations adhere to expected values.
    for row in rows:
        rowval = row["rowval"]
        for column in range(1, 10000):
            assert row[f"col_{column}"] == rowval * column


def test_query_w_timeout(bigquery_client, query_api_method):
    job_config = bigquery.QueryJobConfig()
    job_config.use_query_cache = False

    query_job = bigquery_client.query(
        "SELECT * FROM `bigquery-public-data.github_repos.commits`;",
        location="US",
        job_config=job_config,
        api_method=query_api_method,
    )

    with pytest.raises(concurrent.futures.TimeoutError):
        query_job.result(timeout=1)

    # Even though the query takes >1 second, the call to getQueryResults
    # should succeed.
    assert not query_job.done(timeout=1)
    assert bigquery_client.cancel_job(query_job) is not None


def test_query_statistics(bigquery_client, query_api_method):
    """
    A system test to exercise some of the extended query statistics.

    Note:  We construct a query that should need at least three stages by
    specifying a JOIN query.  Exact plan and stats are effectively
    non-deterministic, so we're largely interested in confirming values
    are present.
    """

    job_config = bigquery.QueryJobConfig()
    job_config.use_query_cache = False

    query_job = bigquery_client.query(
        """
        SELECT
          COUNT(1)
        FROM
        (
          SELECT
            year,
            wban_number
          FROM `bigquery-public-data.samples.gsod`
          LIMIT 1000
        ) lside
        INNER JOIN
        (
          SELECT
            year,
            state
          FROM `bigquery-public-data.samples.natality`
          LIMIT 1000
        ) rside
        ON
        lside.year = rside.year
        """,
        location="US",
        job_config=job_config,
        api_method=query_api_method,
    )

    # run the job to completion
    query_job.result()

    # Must reload job to get stats if jobs.query was used.
    if query_api_method == "QUERY":
        query_job.reload()

    # Assert top-level stats
    assert not query_job.cache_hit
    assert query_job.destination is not None
    assert query_job.done
    assert not query_job.dry_run
    assert query_job.num_dml_affected_rows is None
    assert query_job.priority == "INTERACTIVE"
    assert query_job.total_bytes_billed > 1
    assert query_job.total_bytes_processed > 1
    assert query_job.statement_type == "SELECT"
    assert query_job.slot_millis > 1

    # Make assertions on the shape of the query plan.
    plan = query_job.query_plan
    assert len(plan) >= 3
    first_stage = plan[0]
    assert first_stage.start is not None
    assert first_stage.end is not None
    assert first_stage.entry_id is not None
    assert first_stage.name is not None
    assert first_stage.parallel_inputs > 0
    assert first_stage.completed_parallel_inputs > 0
    assert first_stage.shuffle_output_bytes > 0
    assert first_stage.status == "COMPLETE"

    # Query plan is a digraph.  Ensure it has inter-stage links,
    # but not every stage has inputs.
    stages_with_inputs = 0
    for entry in plan:
        if len(entry.input_stages) > 0:
            stages_with_inputs = stages_with_inputs + 1
    assert stages_with_inputs > 0
    assert len(plan) > stages_with_inputs


@pytest.mark.parametrize(
    ("sql", "expected", "query_parameters"),
    (
        (
            "SELECT @question",
            "What is the answer to life, the universe, and everything?",
            [
                ScalarQueryParameter(
                    name="question",
                    type_="STRING",
                    value="What is the answer to life, the universe, and everything?",
                )
            ],
        ),
        (
            "SELECT @answer",
            42,
            [ScalarQueryParameter(name="answer", type_="INT64", value=42)],
        ),
        (
            "SELECT @pi",
            3.1415926,
            [ScalarQueryParameter(name="pi", type_="FLOAT64", value=3.1415926)],
        ),
        (
            "SELECT @pi_numeric_param",
            decimal.Decimal("3.141592654"),
            [
                ScalarQueryParameter(
                    name="pi_numeric_param",
                    type_="NUMERIC",
                    value=decimal.Decimal("3.141592654"),
                )
            ],
        ),
        (
            "SELECT @bignum_param",
            decimal.Decimal("-{d38}.{d38}".format(d38="9" * 38)),
            [
                ScalarQueryParameter(
                    name="bignum_param",
                    type_="BIGNUMERIC",
                    value=decimal.Decimal("-{d38}.{d38}".format(d38="9" * 38)),
                )
            ],
        ),
        (
            "SELECT @truthy",
            True,
            [ScalarQueryParameter(name="truthy", type_="BOOL", value=True)],
        ),
        (
            "SELECT @beef",
            b"DEADBEEF",
            [ScalarQueryParameter(name="beef", type_="BYTES", value=b"DEADBEEF")],
        ),
        (
            "SELECT @naive",
            datetime.datetime(2016, 12, 5, 12, 41, 9),
            [
                ScalarQueryParameter(
                    name="naive",
                    type_="DATETIME",
                    value=datetime.datetime(2016, 12, 5, 12, 41, 9),
                )
            ],
        ),
        (
            "SELECT @naive_date",
            datetime.date(2016, 12, 5),
            [
                ScalarQueryParameter(
                    name="naive_date", type_="DATE", value=datetime.date(2016, 12, 5)
                )
            ],
        ),
        pytest.param(
            "SELECT @json",
            {"alpha": "abc", "num": [1, 2, 3]},
            [
                ScalarQueryParameter(
                    name="json",
                    type_="JSON",
                    value={"alpha": "abc", "num": [1, 2, 3]},
                )
            ],
            id="scalar-json",
        ),
        (
            "SELECT @naive_time",
            datetime.time(12, 41, 9, 62500),
            [
                ScalarQueryParameter(
                    name="naive_time",
                    type_="TIME",
                    value=datetime.time(12, 41, 9, 62500),
                )
            ],
        ),
        (
            "SELECT @zoned",
            datetime.datetime(2016, 12, 5, 12, 41, 9, tzinfo=datetime.timezone.utc),
            [
                ScalarQueryParameter(
                    name="zoned",
                    type_="TIMESTAMP",
                    value=datetime.datetime(
                        2016, 12, 5, 12, 41, 9, tzinfo=datetime.timezone.utc
                    ),
                )
            ],
        ),
        (
            "SELECT @array_param",
            [1, 2],
            [
                ArrayQueryParameter(
                    name="array_param", array_type="INT64", values=[1, 2]
                )
            ],
        ),
        (
            "SELECT (@hitchhiker.question, @hitchhiker.answer)",
            ({"_field_1": "What is the answer?", "_field_2": 42}),
            [
                StructQueryParameter(
                    "hitchhiker",
                    ScalarQueryParameter(
                        name="question",
                        type_="STRING",
                        value="What is the answer?",
                    ),
                    ScalarQueryParameter(
                        name="answer",
                        type_="INT64",
                        value=42,
                    ),
                ),
            ],
        ),
        (
            "SELECT "
            "((@rectangle.bottom_right.x - @rectangle.top_left.x) "
            "* (@rectangle.top_left.y - @rectangle.bottom_right.y))",
            100,
            [
                StructQueryParameter(
                    "rectangle",
                    StructQueryParameter(
                        "top_left",
                        ScalarQueryParameter("x", "INT64", 12),
                        ScalarQueryParameter("y", "INT64", 102),
                    ),
                    StructQueryParameter(
                        "bottom_right",
                        ScalarQueryParameter("x", "INT64", 22),
                        ScalarQueryParameter("y", "INT64", 92),
                    ),
                )
            ],
        ),
        (
            "SELECT ?",
            [
                {"name": "Phred Phlyntstone", "age": 32},
                {"name": "Bharney Rhubbyl", "age": 31},
            ],
            [
                ArrayQueryParameter(
                    name=None,
                    array_type="RECORD",
                    values=[
                        StructQueryParameter(
                            None,
                            ScalarQueryParameter(
                                name="name", type_="STRING", value="Phred Phlyntstone"
                            ),
                            ScalarQueryParameter(name="age", type_="INT64", value=32),
                        ),
                        StructQueryParameter(
                            None,
                            ScalarQueryParameter(
                                name="name", type_="STRING", value="Bharney Rhubbyl"
                            ),
                            ScalarQueryParameter(name="age", type_="INT64", value=31),
                        ),
                    ],
                )
            ],
        ),
        (
            "SELECT @empty_array_param",
            [],
            [
                ArrayQueryParameter(
                    name="empty_array_param",
                    values=[],
                    array_type=StructQueryParameterType(
                        ScalarQueryParameterType(name="foo", type_="INT64"),
                        ScalarQueryParameterType(name="bar", type_="STRING"),
                    ),
                )
            ],
        ),
        (
            "SELECT @roles",
            {
                "hero": {"name": "Phred Phlyntstone", "age": 32},
                "sidekick": {"name": "Bharney Rhubbyl", "age": 31},
            },
            [
                StructQueryParameter(
                    "roles",
                    StructQueryParameter(
                        "hero",
                        ScalarQueryParameter(
                            name="name", type_="STRING", value="Phred Phlyntstone"
                        ),
                        ScalarQueryParameter(name="age", type_="INT64", value=32),
                    ),
                    StructQueryParameter(
                        "sidekick",
                        ScalarQueryParameter(
                            name="name", type_="STRING", value="Bharney Rhubbyl"
                        ),
                        ScalarQueryParameter(name="age", type_="INT64", value=31),
                    ),
                ),
            ],
        ),
        (
            "SELECT ?",
            {"friends": ["Jack", "Jill"]},
            [
                StructQueryParameter(
                    None,
                    ArrayQueryParameter(
                        name="friends", array_type="STRING", values=["Jack", "Jill"]
                    ),
                )
            ],
        ),
        (
            "SELECT @range_date",
            {"end": None, "start": datetime.date(2016, 12, 5)},
            [
                RangeQueryParameter(
                    name="range_date",
                    range_element_type="DATE",
                    start=datetime.date(2016, 12, 5),
                )
            ],
        ),
        (
            "SELECT @range_datetime",
            {"end": None, "start": datetime.datetime(2016, 12, 5, 0, 0)},
            [
                RangeQueryParameter(
                    name="range_datetime",
                    range_element_type="DATETIME",
                    start=datetime.datetime(2016, 12, 5),
                )
            ],
        ),
        (
            "SELECT @range_unbounded",
            {"end": None, "start": None},
            [
                RangeQueryParameter(
                    name="range_unbounded",
                    range_element_type="DATETIME",
                )
            ],
        ),
    ),
)
def test_query_parameters(
    bigquery_client, query_api_method, sql, expected, query_parameters
):
    jconfig = bigquery.QueryJobConfig()
    jconfig.query_parameters = query_parameters
    query_job = bigquery_client.query(
        sql,
        job_config=jconfig,
        api_method=query_api_method,
    )
    rows = list(query_job.result())
    assert len(rows) == 1
    assert len(rows[0]) == 1
    assert rows[0][0] == expected


def test_dry_run(
    bigquery_client: bigquery.Client,
    query_api_method: str,
    scalars_table_multi_location: Tuple[str, str],
):
    location, full_table_id = scalars_table_multi_location
    query_config = bigquery.QueryJobConfig()
    query_config.dry_run = True

    query_string = f"SELECT * FROM {full_table_id}"
    query_job = bigquery_client.query(
        query_string,
        location=location,
        job_config=query_config,
        api_method=query_api_method,
    )

    # Note: `query_job.result()` is not necessary on a dry run query. All
    # necessary information is returned in the initial response.
    assert query_job.dry_run is True
    assert query_job.total_bytes_processed > 0
    assert len(query_job.schema) > 0


def test_query_error_w_api_method_query(bigquery_client: bigquery.Client):
    """No job is returned from jobs.query if the query fails."""

    with pytest.raises(exceptions.NotFound, match="not_a_real_dataset"):
        bigquery_client.query(
            "SELECT * FROM not_a_real_dataset.doesnt_exist", api_method="QUERY"
        )


def test_query_error_w_api_method_default(bigquery_client: bigquery.Client):
    """Test that an exception is not thrown until fetching the results.

    For backwards compatibility, jobs.insert is the default API method. With
    jobs.insert, a failed query job is "successfully" created. An exception is
    thrown when fetching the results.
    """

    query_job = bigquery_client.query("SELECT * FROM not_a_real_dataset.doesnt_exist")

    with pytest.raises(exceptions.NotFound, match="not_a_real_dataset"):
        query_job.result()


def test_session(bigquery_client: bigquery.Client, query_api_method: str):
    initial_config = bigquery.QueryJobConfig()
    initial_config.create_session = True
    initial_query = """
    CREATE TEMPORARY TABLE numbers(id INT64)
    AS
    SELECT * FROM UNNEST([1, 2, 3, 4, 5]) AS id;
    """
    initial_job = bigquery_client.query(
        initial_query, job_config=initial_config, api_method=query_api_method
    )
    initial_job.result()
    session_id = initial_job.session_info.session_id
    assert session_id is not None

    second_config = bigquery.QueryJobConfig()
    second_config.connection_properties = [
        bigquery.ConnectionProperty("session_id", session_id),
    ]
    second_job = bigquery_client.query(
        "SELECT COUNT(*) FROM numbers;", job_config=second_config
    )
    rows = list(second_job.result())

    assert len(rows) == 1
    assert rows[0][0] == 5