sentry/tests/snuba/sessions/test_sessions_v2.py

import math
from datetime import datetime, timezone

import pytest
from django.http import QueryDict

from sentry.release_health.base import AllowedResolution, SessionsQueryConfig
from sentry.snuba.sessions_v2 import (
    InvalidParams,
    QueryDefinition,
    get_constrained_date_range,
    get_timestamps,
    massage_sessions_result,
)
from sentry.testutils.helpers.datetime import freeze_time
from sentry.testutils.pytest.fixtures import django_db_all


def _make_query(qs, allow_minute_resolution=True, params=None):
    query_config = SessionsQueryConfig(
        (AllowedResolution.one_minute if allow_minute_resolution else AllowedResolution.one_hour),
        allow_session_status_query=False,
        restrict_date_range=True,
    )
    return QueryDefinition(QueryDict(qs), params or {}, query_config)


def result_sorted(result):
    """sort the groups of the results array by the `by` object, ensuring a stable order"""

    def stable_dict(d):
        return tuple(sorted(d.items(), key=lambda t: t[0]))

    result["groups"].sort(key=lambda group: stable_dict(group["by"]))
    return result


@freeze_time("2018-12-11 03:21:00")
def test_round_range():
    start, end, interval = get_constrained_date_range({"statsPeriod": "2d"})
    assert start == datetime(2018, 12, 9, 3, tzinfo=timezone.utc)
    assert end == datetime(2018, 12, 11, 4, 00, tzinfo=timezone.utc)

    start, end, interval = get_constrained_date_range({"statsPeriod": "2d", "interval": "1d"})
    assert start == datetime(2018, 12, 9, tzinfo=timezone.utc)
    assert end == datetime(2018, 12, 12, 0, 0, tzinfo=timezone.utc)


def test_invalid_interval():
    with pytest.raises(InvalidParams):
        start, end, interval = get_constrained_date_range({"interval": "0d"})


def test_round_exact():
    start, end, interval = get_constrained_date_range(
        {"start": "2021-01-12T04:06:16", "end": "2021-01-17T08:26:13", "interval": "1d"},
    )
    assert start == datetime(2021, 1, 12, tzinfo=timezone.utc)
    assert end == datetime(2021, 1, 18, tzinfo=timezone.utc)


def test_inclusive_end():
    start, end, interval = get_constrained_date_range(
        {"start": "2021-02-24T00:00:00", "end": "2021-02-25T00:00:00", "interval": "1h"},
    )
    assert start == datetime(2021, 2, 24, tzinfo=timezone.utc)
    assert end == datetime(2021, 2, 25, 0, tzinfo=timezone.utc)


@freeze_time("2021-03-05T11:00:00.000Z")
def test_future_request():
    start, end, interval = get_constrained_date_range(
        {"start": "2021-03-05T12:00:00", "end": "2021-03-05T13:00:00", "interval": "1h"},
    )
    assert start == datetime(2021, 3, 5, 11, tzinfo=timezone.utc)
    assert end == datetime(2021, 3, 5, 13, 0, tzinfo=timezone.utc)


@freeze_time("2021-03-05T11:14:17.105Z")
def test_interval_restrictions():
    # making sure intervals are cleanly divisible
    with pytest.raises(InvalidParams, match="The interval has to be less than one day."):
        _make_query("statsPeriod=4d&interval=2d&field=sum(session)")
    with pytest.raises(
        InvalidParams, match="The interval should divide one day without a remainder."
    ):
        _make_query("statsPeriod=6h&interval=59m&field=sum(session)")
    with pytest.raises(
        InvalidParams, match="The interval should divide one day without a remainder."
    ):
        _make_query("statsPeriod=4d&interval=5h&field=sum(session)")

    _make_query("statsPeriod=6h&interval=90m&field=sum(session)")
    with pytest.raises(
        InvalidParams,
        match="The interval has to be a multiple of the minimum interval of one hour.",
    ):
        _make_query("statsPeriod=6h&interval=90m&field=sum(session)", False)

    with pytest.raises(
        InvalidParams,
        match="The interval has to be a multiple of the minimum interval of one minute.",
    ):
        _make_query("statsPeriod=1h&interval=90s&field=sum(session)")

    with pytest.raises(
        InvalidParams, match="Your interval and date range would create too many results."
    ):
        _make_query("statsPeriod=90d&interval=1h&field=sum(session)")


@freeze_time("2020-12-18T11:14:17.105Z")
def test_timestamps():
    query = _make_query("statsPeriod=1d&interval=12h&field=sum(session)")

    expected_timestamps = ["2020-12-17T00:00:00Z", "2020-12-17T12:00:00Z", "2020-12-18T00:00:00Z"]
    actual_timestamps = get_timestamps(query)
    assert actual_timestamps == expected_timestamps


@freeze_time("2021-03-08T09:34:00.000Z")
def test_hourly_rounded_start():
    query = _make_query("statsPeriod=30m&interval=1m&field=sum(session)")

    actual_timestamps = get_timestamps(query)

    assert actual_timestamps[0] == "2021-03-08T09:04:00Z"
    assert actual_timestamps[-1] == "2021-03-08T09:33:00Z"
    assert len(actual_timestamps) == 30

    # in this case "45m" means from 08:49:00-09:34:00, but since we round start/end
    # to hours, we extend the start time to 08:00:00.
    query = _make_query("statsPeriod=45m&interval=1m&field=sum(session)")

    actual_timestamps = get_timestamps(query)

    assert actual_timestamps[0] == "2021-03-08T08:49:00Z"
    assert actual_timestamps[-1] == "2021-03-08T09:33:00Z"
    assert len(actual_timestamps) == 45


def test_rounded_end():
    query = _make_query(
        "field=sum(session)&interval=1h&start=2021-02-24T00:00:00Z&end=2021-02-25T00:00:00Z"
    )

    expected_timestamps = [
        "2021-02-24T00:00:00Z",
        "2021-02-24T01:00:00Z",
        "2021-02-24T02:00:00Z",
        "2021-02-24T03:00:00Z",
        "2021-02-24T04:00:00Z",
        "2021-02-24T05:00:00Z",
        "2021-02-24T06:00:00Z",
        "2021-02-24T07:00:00Z",
        "2021-02-24T08:00:00Z",
        "2021-02-24T09:00:00Z",
        "2021-02-24T10:00:00Z",
        "2021-02-24T11:00:00Z",
        "2021-02-24T12:00:00Z",
        "2021-02-24T13:00:00Z",
        "2021-02-24T14:00:00Z",
        "2021-02-24T15:00:00Z",
        "2021-02-24T16:00:00Z",
        "2021-02-24T17:00:00Z",
        "2021-02-24T18:00:00Z",
        "2021-02-24T19:00:00Z",
        "2021-02-24T20:00:00Z",
        "2021-02-24T21:00:00Z",
        "2021-02-24T22:00:00Z",
        "2021-02-24T23:00:00Z",
    ]
    actual_timestamps = get_timestamps(query)

    assert len(actual_timestamps) == 24
    assert actual_timestamps == expected_timestamps


def test_simple_query():
    query = _make_query("statsPeriod=1d&interval=12h&field=sum(session)")

    assert query.query_columns == ["sessions"]


def test_groupby_query():
    query = _make_query("statsPeriod=1d&interval=12h&field=sum(session)&groupBy=release")

    assert sorted(query.query_columns) == ["release", "sessions"]
    assert query.query_groupby == ["release"]


def test_virtual_groupby_query():
    query = _make_query("statsPeriod=1d&interval=12h&field=sum(session)&groupBy=session.status")

    assert sorted(query.query_columns) == [
        "sessions",
        "sessions_abnormal",
        "sessions_crashed",
        "sessions_errored",
    ]
    assert query.query_groupby == []

    query = _make_query(
        "statsPeriod=1d&interval=12h&field=count_unique(user)&groupBy=session.status"
    )

    assert sorted(query.query_columns) == [
        "users",
        "users_abnormal",
        "users_crashed",
        "users_errored",
    ]
    assert query.query_groupby == []


@freeze_time("2022-05-04T09:00:00.000Z")
def _get_query_maker_params(project):
    # These parameters are computed in the API endpoint, before the
    # QueryDefinition is built. Since we're only testing the query
    # definition here, we can safely mock these.
    return {
        "start": datetime.now(),
        "end": datetime.now(),
        "organization_id": project.organization_id,
    }


@django_db_all
def test_filter_proj_slug_in_query(default_project):
    params = _get_query_maker_params(default_project)
    params["project_id"] = [default_project.id]
    query_def = _make_query(
        f"field=sum(session)&interval=2h&statsPeriod=2h&query=project%3A{default_project.slug}",
        params=params,
    )
    assert query_def.query == f"project:{default_project.slug}"
    assert query_def.params["project_id"] == [default_project.id]


@django_db_all
def test_filter_proj_slug_in_top_filter(default_project):
    params = _get_query_maker_params(default_project)
    params["project_id"] = [default_project.id]
    query_def = _make_query(
        f"field=sum(session)&interval=2h&statsPeriod=2h&project={default_project.id}",
        params=params,
    )
    assert query_def.query == ""
    assert query_def.params["project_id"] == [default_project.id]


@django_db_all
def test_filter_proj_slug_in_top_filter_and_query(default_project):
    params = _get_query_maker_params(default_project)
    params["project_id"] = [default_project.id]
    query_def = _make_query(
        f"field=sum(session)&interval=2h&statsPeriod=2h&project={default_project.id}&query=project%3A"
        f"{default_project.slug}",
        params=params,
    )
    assert query_def.query == f"project:{default_project.slug}"
    assert query_def.params["project_id"] == [default_project.id]


@django_db_all
def test_proj_neither_in_top_filter_nor_query(default_project):
    params = _get_query_maker_params(default_project)
    query_def = _make_query(
        "field=sum(session)&interval=2h&statsPeriod=2h",
        params=params,
    )
    assert query_def.query == ""
    assert "project_id" not in query_def.params


@django_db_all
def test_filter_env_in_query(default_project):
    env = "prod"
    params = _get_query_maker_params(default_project)
    query_def = _make_query(
        f"field=sum(session)&interval=2h&statsPeriod=2h&query=environment%3A{env}",
        params=params,
    )
    assert query_def.query == f"environment:{env}"


@django_db_all
def test_filter_env_in_top_filter(default_project):
    env = "prod"
    params = _get_query_maker_params(default_project)
    params["environment"] = "prod"
    query_def = _make_query(
        f"field=sum(session)&interval=2h&statsPeriod=2h&environment={env}",
        params=params,
    )
    assert query_def.query == ""


@django_db_all
def test_filter_env_in_top_filter_and_query(default_project):
    env = "prod"
    params = _get_query_maker_params(default_project)
    params["environment"] = "prod"
    query_def = _make_query(
        f"field=sum(session)&interval=2h&statsPeriod=2h&environment={env}&query=environment%3A{env}",
        params=params,
    )
    assert query_def.query == f"environment:{env}"


@django_db_all
def test_env_neither_in_top_filter_nor_query(default_project):
    params = _get_query_maker_params(default_project)
    query_def = _make_query(
        "field=sum(session)&interval=2h&statsPeriod=2h",
        params=params,
    )
    assert query_def.query == ""


@freeze_time("2020-12-18T11:14:17.105Z")
def test_massage_empty():
    query = _make_query("statsPeriod=1d&interval=1d&field=sum(session)")

    expected_result = {
        "end": "2020-12-19T00:00:00Z",
        "groups": [],
        "intervals": ["2020-12-17T00:00:00Z", "2020-12-18T00:00:00Z"],
        "query": "",
        "start": "2020-12-17T00:00:00Z",
    }

    actual_result = result_sorted(massage_sessions_result(query, [], []))

    assert actual_result == expected_result


@freeze_time("2020-12-18T11:14:17.105Z")
def test_massage_unbalanced_results():
    query = _make_query("statsPeriod=1d&interval=1d&field=sum(session)&groupBy=release")

    result_totals = [
        {"release": "test-example-release", "sessions": 1},
    ]

    expected_result = {
        "start": "2020-12-17T00:00:00Z",
        "end": "2020-12-19T00:00:00Z",
        "query": "",
        "intervals": ["2020-12-17T00:00:00Z", "2020-12-18T00:00:00Z"],
        "groups": [
            {
                "by": {"release": "test-example-release"},
                "series": {"sum(session)": [0, 0]},
                "totals": {"sum(session)": 1},
            }
        ],
    }

    actual_result = result_sorted(massage_sessions_result(query, result_totals, []))

    assert actual_result == expected_result

    result_totals = []
    result_timeseries = [
        {
            "release": "test-example-release",
            "sessions": 1,
            "bucketed_started": "2020-12-18T00:00:00+00:00",
        },
    ]

    expected_result = {
        "start": "2020-12-17T00:00:00Z",
        "end": "2020-12-19T00:00:00Z",
        "query": "",
        "intervals": ["2020-12-17T00:00:00Z", "2020-12-18T00:00:00Z"],
        "groups": [
            {
                "by": {"release": "test-example-release"},
                "series": {"sum(session)": [0, 1]},
                "totals": {"sum(session)": 0},
            }
        ],
    }

    actual_result = result_sorted(massage_sessions_result(query, result_totals, result_timeseries))

    assert actual_result == expected_result

    @freeze_time("2020-12-18T11:14:17.105Z")
    def test_massage_simple_timeseries():
        """A timeseries is filled up when it only receives partial data"""

        query = _make_query("statsPeriod=1d&interval=6h&field=sum(session)")
        result_totals = [{"sessions": 4}]
        # snuba returns the datetimes as strings for now
        result_timeseries = [
            {"sessions": 2, "bucketed_started": "2020-12-18T06:00:00+00:00"},
            {"sessions": 2, "bucketed_started": "2020-12-17T12:00:00+00:00"},
        ]

        expected_result = {
            "start": "2020-12-17T12:00:00Z",
            "end": "2020-12-18T11:15:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T12:00:00Z",
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
            ],
            "groups": [
                {"by": {}, "series": {"sum(session)": [2, 0, 0, 2]}, "totals": {"sum(session)": 4}}
            ],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result

    @freeze_time("2020-12-18T11:14:17.105Z")
    def test_massage_unordered_timeseries():
        query = _make_query("statsPeriod=1d&interval=6h&field=sum(session)")
        result_totals = [{"sessions": 10}]
        # snuba returns the datetimes as strings for now
        result_timeseries = [
            {"sessions": 3, "bucketed_started": "2020-12-18T00:00:00+00:00"},
            {"sessions": 2, "bucketed_started": "2020-12-17T18:00:00+00:00"},
            {"sessions": 4, "bucketed_started": "2020-12-18T06:00:00+00:00"},
            {"sessions": 1, "bucketed_started": "2020-12-17T12:00:00+00:00"},
        ]

        expected_result = {
            "start": "2020-12-17T12:00:00Z",
            "end": "2020-12-18T11:15:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T12:00:00Z",
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
            ],
            "groups": [
                {"by": {}, "series": {"sum(session)": [1, 2, 3, 4]}, "totals": {"sum(session)": 10}}
            ],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result

    @freeze_time("2020-12-18T11:14:17.105Z")
    def test_massage_no_timeseries():
        query = _make_query("statsPeriod=1d&interval=6h&field=sum(session)&groupby=projects")
        result_totals = [{"sessions": 4}]
        # snuba returns the datetimes as strings for now
        result_timeseries = None

        expected_result = {
            "start": "2020-12-17T12:00:00Z",
            "end": "2020-12-18T11:15:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T12:00:00Z",
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
            ],
            "groups": [{"by": {}, "totals": {"sum(session)": 4}}],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result

    def test_massage_exact_timeseries():
        query = _make_query(
            "start=2020-12-17T15:12:34Z&end=2020-12-18T11:14:17Z&interval=6h&field=sum(session)"
        )
        result_totals = [{"sessions": 4}]
        result_timeseries = [
            {"sessions": 2, "bucketed_started": "2020-12-18T06:00:00+00:00"},
            {"sessions": 2, "bucketed_started": "2020-12-17T12:00:00+00:00"},
        ]

        expected_result = {
            "start": "2020-12-17T12:00:00Z",
            "end": "2020-12-18T12:00:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T12:00:00Z",
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
            ],
            "groups": [
                {"by": {}, "series": {"sum(session)": [2, 0, 0, 2]}, "totals": {"sum(session)": 4}}
            ],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result

    @freeze_time("2020-12-18T11:14:17.105Z")
    def test_massage_groupby_timeseries():
        query = _make_query("statsPeriod=1d&interval=6h&field=sum(session)&groupBy=release")

        result_totals = [
            {"release": "test-example-release", "sessions": 4},
            {"release": "test-example-release-2", "sessions": 1},
        ]
        # snuba returns the datetimes as strings for now
        result_timeseries = [
            {
                "release": "test-example-release",
                "sessions": 2,
                "bucketed_started": "2020-12-18T06:00:00+00:00",
            },
            {
                "release": "test-example-release-2",
                "sessions": 1,
                "bucketed_started": "2020-12-18T06:00:00+00:00",
            },
            {
                "release": "test-example-release",
                "sessions": 2,
                "bucketed_started": "2020-12-17T12:00:00+00:00",
            },
        ]

        expected_result = {
            "start": "2020-12-17T12:00:00Z",
            "end": "2020-12-18T11:15:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T12:00:00Z",
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
            ],
            "groups": [
                {
                    "by": {"release": "test-example-release"},
                    "series": {"sum(session)": [2, 0, 0, 2]},
                    "totals": {"sum(session)": 4},
                },
                {
                    "by": {"release": "test-example-release-2"},
                    "series": {"sum(session)": [0, 0, 0, 1]},
                    "totals": {"sum(session)": 1},
                },
            ],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result

    @freeze_time("2020-12-18T13:25:15.769Z")
    def test_massage_virtual_groupby_timeseries():
        query = _make_query(
            "statsPeriod=1d&interval=6h&field=sum(session)&field=count_unique(user)&groupBy=session.status"
        )
        result_totals = [
            {
                "users": 1,
                "users_crashed": 1,
                "sessions": 31,
                "sessions_errored": 15,
                "users_errored": 1,
                "sessions_abnormal": 6,
                "sessions_crashed": 8,
                "users_abnormal": 0,
            }
        ]
        # snuba returns the datetimes as strings for now
        result_timeseries = [
            {
                "sessions_errored": 1,
                "users": 1,
                "users_crashed": 1,
                "sessions_abnormal": 0,
                "sessions": 3,
                "users_errored": 1,
                "users_abnormal": 0,
                "sessions_crashed": 1,
                "bucketed_started": "2020-12-18T12:00:00+00:00",
            },
            {
                "sessions_errored": 0,
                "users": 1,
                "users_crashed": 0,
                "sessions_abnormal": 0,
                "sessions": 3,
                "users_errored": 0,
                "users_abnormal": 0,
                "sessions_crashed": 0,
                "bucketed_started": "2020-12-18T06:00:00+00:00",
            },
            {
                "sessions_errored": 10,
                "users": 1,
                "users_crashed": 0,
                "sessions_abnormal": 2,
                "sessions": 15,
                "users_errored": 0,
                "users_abnormal": 0,
                "sessions_crashed": 4,
                "bucketed_started": "2020-12-18T00:00:00+00:00",
            },
            {
                "sessions_errored": 4,
                "users": 1,
                "users_crashed": 0,
                "sessions_abnormal": 4,
                "sessions": 10,
                "users_errored": 0,
                "users_abnormal": 0,
                "sessions_crashed": 3,
                "bucketed_started": "2020-12-17T18:00:00+00:00",
            },
        ]

        expected_result = {
            "start": "2020-12-17T18:00:00Z",
            "end": "2020-12-18T13:26:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
                "2020-12-18T12:00:00Z",
            ],
            "groups": [
                {
                    "by": {"session.status": "abnormal"},
                    "series": {"count_unique(user)": [0, 0, 0, 0], "sum(session)": [4, 2, 0, 0]},
                    "totals": {"count_unique(user)": 0, "sum(session)": 6},
                },
                {
                    "by": {"session.status": "crashed"},
                    "series": {"count_unique(user)": [0, 0, 0, 1], "sum(session)": [3, 4, 0, 1]},
                    "totals": {"count_unique(user)": 1, "sum(session)": 8},
                },
                {
                    "by": {"session.status": "errored"},
                    "series": {"count_unique(user)": [0, 0, 0, 0], "sum(session)": [0, 4, 0, 0]},
                    "totals": {"count_unique(user)": 0, "sum(session)": 1},
                },
                {
                    "by": {"session.status": "healthy"},
                    "series": {"count_unique(user)": [1, 1, 1, 0], "sum(session)": [6, 5, 3, 2]},
                    # while in one of the time slots, we have a healthy user, it is
                    # the *same* user as the one experiencing a crash later on,
                    # so in the *whole* time window, that one user is not counted as healthy,
                    # so the `0` here is expected, as that's an example of the `count_unique` behavior.
                    "totals": {"count_unique(user)": 0, "sum(session)": 16},
                },
            ],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result

    @freeze_time("2020-12-18T13:25:15.769Z")
    def test_clamping_in_massage_sessions_results_with_groupby_timeseries():
        query = _make_query(
            "statsPeriod=12h&interval=6h&field=sum(session)&field=count_unique(user)&groupBy=session.status"
        )
        # snuba returns the datetimes as strings for now
        result_timeseries = [
            {
                "sessions": 7,
                "sessions_errored": 3,
                "sessions_crashed": 2,
                "sessions_abnormal": 2,
                "users": 7,
                "users_errored": 3,
                "users_crashed": 2,
                "users_abnormal": 2,
                "bucketed_started": "2020-12-18T12:00:00+00:00",
            },
            {
                "sessions": 5,
                "sessions_errored": 10,
                "sessions_crashed": 0,
                "sessions_abnormal": 0,
                "users": 5,
                "users_errored": 10,
                "users_crashed": 0,
                "users_abnormal": 0,
                "bucketed_started": "2020-12-18T06:00:00+00:00",
            },
        ]
        expected_result = {
            "start": "2020-12-18T06:00:00Z",
            "end": "2020-12-18T13:26:00Z",
            "query": "",
            "intervals": [
                "2020-12-18T06:00:00Z",
                "2020-12-18T12:00:00Z",
            ],
            "groups": [
                {
                    "by": {"session.status": "abnormal"},
                    "series": {"count_unique(user)": [0, 2], "sum(session)": [0, 2]},
                    "totals": {"count_unique(user)": 0, "sum(session)": 0},
                },
                {
                    "by": {"session.status": "crashed"},
                    "series": {"count_unique(user)": [0, 2], "sum(session)": [0, 2]},
                    "totals": {"count_unique(user)": 0, "sum(session)": 0},
                },
                {
                    "by": {"session.status": "errored"},
                    "series": {"count_unique(user)": [10, 0], "sum(session)": [10, 0]},
                    "totals": {"count_unique(user)": 0, "sum(session)": 0},
                },
                {
                    "by": {"session.status": "healthy"},
                    "series": {"count_unique(user)": [0, 4], "sum(session)": [0, 4]},
                    "totals": {"count_unique(user)": 0, "sum(session)": 0},
                },
            ],
        }

        actual_result = result_sorted(massage_sessions_result(query, [], result_timeseries))

        assert actual_result == expected_result

    @freeze_time("2020-12-18T11:14:17.105Z")
    def test_nan_duration():
        query = _make_query(
            "statsPeriod=1d&interval=6h&field=avg(session.duration)&field=p50(session.duration)"
        )

        result_totals = [
            {
                "duration_avg": math.nan,
                "duration_quantiles": [math.inf, math.inf, math.inf, math.inf, math.inf, math.inf],
            },
        ]
        result_timeseries = [
            {
                "duration_avg": math.inf,
                "duration_quantiles": [math.inf, math.inf, math.inf, math.inf, math.inf, math.inf],
                "bucketed_started": "2020-12-18T06:00:00+00:00",
            },
            {
                "duration_avg": math.nan,
                "duration_quantiles": [math.nan, math.nan, math.nan, math.nan, math.nan, math.nan],
                "bucketed_started": "2020-12-17T12:00:00+00:00",
            },
        ]

        expected_result = {
            "start": "2020-12-17T12:00:00Z",
            "end": "2020-12-18T11:15:00Z",
            "query": "",
            "intervals": [
                "2020-12-17T12:00:00Z",
                "2020-12-17T18:00:00Z",
                "2020-12-18T00:00:00Z",
                "2020-12-18T06:00:00Z",
            ],
            "groups": [
                {
                    "by": {},
                    "series": {
                        "avg(session.duration)": [None, None, None, None],
                        "p50(session.duration)": [None, None, None, None],
                    },
                    "totals": {"avg(session.duration)": None, "p50(session.duration)": None},
                },
            ],
        }

        actual_result = result_sorted(
            massage_sessions_result(query, result_totals, result_timeseries)
        )

        assert actual_result == expected_result