import glob
import math
import os
import sys
import warnings
from decimal import Decimal

import numpy as np
import pandas as pd
import pytest
from packaging.version import parse as parse_version

import dask
import dask.dataframe as dd
import dask.multiprocessing
from dask.blockwise import Blockwise, optimize_blockwise
from dask.dataframe._compat import PANDAS_GT_110, PANDAS_GT_121, PANDAS_GT_130
from dask.dataframe.io.parquet.utils import _parse_pandas_metadata
from dask.dataframe.optimize import optimize_dataframe_getitem
from dask.dataframe.utils import assert_eq
from dask.layers import DataFrameIOLayer
from dask.utils import natural_sort_key
from dask.utils_test import hlg_layer

try:
    import fastparquet
except ImportError:
    fastparquet = False
    fastparquet_version = parse_version("0")
else:
    fastparquet_version = parse_version(fastparquet.__version__)


try:
    import pyarrow as pa
except ImportError:
    pa = False
    pa_version = parse_version("0")
else:
    pa_version = parse_version(pa.__version__)

try:
    import pyarrow.parquet as pq
except ImportError:
    pq = False


SKIP_FASTPARQUET = not fastparquet
FASTPARQUET_MARK = pytest.mark.skipif(SKIP_FASTPARQUET, reason="fastparquet not found")

if sys.platform == "win32" and pa and pa_version == parse_version("2.0.0"):
    SKIP_PYARROW = True
    SKIP_PYARROW_REASON = (
        "skipping pyarrow 2.0.0 on windows: "
        "https://github.com/dask/dask/issues/6093"
        "|https://github.com/dask/dask/issues/6754"
    )
else:
    SKIP_PYARROW = not pq
    SKIP_PYARROW_REASON = "pyarrow not found"
PYARROW_MARK = pytest.mark.skipif(SKIP_PYARROW, reason=SKIP_PYARROW_REASON)

# "Legacy" and "Dataset"-specific MARK definitions
SKIP_PYARROW_LE = SKIP_PYARROW
SKIP_PYARROW_LE_REASON = "pyarrow not found"
SKIP_PYARROW_DS = SKIP_PYARROW
SKIP_PYARROW_DS_REASON = "pyarrow not found"
if not SKIP_PYARROW_LE:
    # NOTE: We should use PYARROW_LE_MARK to skip
    # pyarrow-legacy tests once pyarrow officially
    # removes ParquetDataset support in the future.
    PYARROW_LE_MARK = pytest.mark.filterwarnings(
        "ignore::DeprecationWarning",
        "ignore::FutureWarning",
    )
else:
    PYARROW_LE_MARK = pytest.mark.skipif(SKIP_PYARROW_LE, reason=SKIP_PYARROW_LE_REASON)
PYARROW_DS_MARK = pytest.mark.skipif(SKIP_PYARROW_DS, reason=SKIP_PYARROW_DS_REASON)

ANY_ENGINE_MARK = pytest.mark.skipif(
    SKIP_FASTPARQUET and SKIP_PYARROW,
    reason="No parquet engine (fastparquet or pyarrow) found",
)


nrows = 40
npartitions = 15
df = pd.DataFrame(
    {
        "x": [i * 7 % 5 for i in range(nrows)],  # Not sorted
        "y": [i * 2.5 for i in range(nrows)],  # Sorted
    },
    index=pd.Index([10 * i for i in range(nrows)], name="myindex"),
)

ddf = dd.from_pandas(df, npartitions=npartitions)


@pytest.fixture(
    params=[
        pytest.param("fastparquet", marks=FASTPARQUET_MARK),
        pytest.param("pyarrow-legacy", marks=PYARROW_LE_MARK),
        pytest.param("pyarrow-dataset", marks=PYARROW_DS_MARK),
    ]
)
def engine(request):
    return request.param


def write_read_engines(**kwargs):
    """Product of both engines for write/read:

    To add custom marks, pass keyword of the form: `mark_writer_reader=reason`,
    or `mark_engine=reason` to apply to all parameters with that engine."""
    backends = {"pyarrow-dataset", "pyarrow-legacy", "fastparquet"}

    # Skip if uninstalled
    skip_marks = {
        "fastparquet": FASTPARQUET_MARK,
        "pyarrow-legacy": PYARROW_LE_MARK,
        "pyarrow-dataset": PYARROW_DS_MARK,
    }
    marks = {(w, r): [skip_marks[w], skip_marks[r]] for w in backends for r in backends}

    # Custom marks
    for kw, val in kwargs.items():
        kind, rest = kw.split("_", 1)
        key = tuple(rest.split("_"))
        if kind not in ("xfail", "skip") or len(key) > 2 or set(key) - backends:
            raise ValueError("unknown keyword %r" % kw)
        val = getattr(pytest.mark, kind)(reason=val)
        if len(key) == 2:
            marks[key].append(val)
        else:
            for k in marks:
                if key in k:
                    marks[k].append(val)

    return pytest.mark.parametrize(
        ("write_engine", "read_engine"),
        [pytest.param(*k, marks=tuple(v)) for (k, v) in sorted(marks.items())],
    )


pyarrow_fastparquet_msg = "pyarrow schema and pandas metadata may disagree"
write_read_engines_xfail = write_read_engines(
    **{
        "xfail_pyarrow-dataset_fastparquet": pyarrow_fastparquet_msg,
        "xfail_pyarrow-legacy_fastparquet": pyarrow_fastparquet_msg,
    }
)

if (
    fastparquet
    and fastparquet_version < parse_version("0.5")
    and PANDAS_GT_110
    and not PANDAS_GT_121
):
    # a regression in pandas 1.1.x / 1.2.0 caused a failure in writing partitioned
    # categorical columns when using fastparquet 0.4.x, but this was (accidentally)
    # fixed in fastparquet 0.5.0
    fp_pandas_msg = "pandas with fastparquet engine does not preserve index"
    fp_pandas_xfail = write_read_engines(
        **{
            "xfail_pyarrow-dataset_fastparquet": pyarrow_fastparquet_msg,
            "xfail_pyarrow-legacy_fastparquet": pyarrow_fastparquet_msg,
            "xfail_fastparquet_fastparquet": fp_pandas_msg,
            "xfail_fastparquet_pyarrow-dataset": fp_pandas_msg,
            "xfail_fastparquet_pyarrow-legacy": fp_pandas_msg,
        }
    )
else:
    fp_pandas_msg = "pandas with fastparquet engine does not preserve index"
    fp_pandas_xfail = write_read_engines()


@PYARROW_MARK
def test_pyarrow_getengine():
    from dask.dataframe.io.parquet.arrow import ArrowDatasetEngine
    from dask.dataframe.io.parquet.core import get_engine

    # Check that the default engine for "pyarrow"/"arrow"
    # is the `pyarrow.dataset`-based engine
    assert get_engine("pyarrow") == ArrowDatasetEngine
    assert get_engine("arrow") == ArrowDatasetEngine

    if SKIP_PYARROW_LE:
        with pytest.warns(FutureWarning):
            get_engine("pyarrow-legacy")


@write_read_engines()
def test_local(tmpdir, write_engine, read_engine):
    tmp = str(tmpdir)
    data = pd.DataFrame(
        {
            "i32": np.arange(1000, dtype=np.int32),
            "i64": np.arange(1000, dtype=np.int64),
            "f": np.arange(1000, dtype=np.float64),
            "bhello": np.random.choice(["hello", "yo", "people"], size=1000).astype(
                "O"
            ),
        }
    )
    df = dd.from_pandas(data, chunksize=500)

    df.to_parquet(tmp, write_index=False, engine=write_engine)

    files = os.listdir(tmp)
    assert "_common_metadata" in files
    assert "_metadata" in files
    assert "part.0.parquet" in files

    df2 = dd.read_parquet(tmp, index=False, engine=read_engine)

    assert len(df2.divisions) > 1

    out = df2.compute(scheduler="sync").reset_index()

    for column in df.columns:
        assert (data[column] == out[column]).all()


@pytest.mark.parametrize("index", [False, True])
@write_read_engines_xfail
def test_empty(tmpdir, write_engine, read_engine, index):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": ["a", "b", "b"], "b": [4, 5, 6]})[:0]
    if index:
        df.set_index("a", inplace=True, drop=True)
    ddf = dd.from_pandas(df, npartitions=2)

    ddf.to_parquet(fn, write_index=index, engine=write_engine)
    read_df = dd.read_parquet(fn, engine=read_engine)
    assert_eq(ddf, read_df)


@write_read_engines()
def test_simple(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    if write_engine != "fastparquet":
        df = pd.DataFrame({"a": [b"a", b"b", b"b"], "b": [4, 5, 6]})
    else:
        df = pd.DataFrame({"a": ["a", "b", "b"], "b": [4, 5, 6]})
    df.set_index("a", inplace=True, drop=True)
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(fn, engine=write_engine)
    read_df = dd.read_parquet(fn, index=["a"], engine=read_engine)
    assert_eq(ddf, read_df)


@write_read_engines()
def test_delayed_no_metadata(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": ["a", "b", "b"], "b": [4, 5, 6]})
    df.set_index("a", inplace=True, drop=True)
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(
        fn, engine=write_engine, compute=False, write_metadata_file=False
    ).compute()
    files = os.listdir(fn)
    assert "_metadata" not in files
    # Fastparquet doesn't currently handle a directory without "_metadata"
    read_df = dd.read_parquet(
        os.path.join(fn, "*.parquet"),
        index=["a"],
        engine=read_engine,
        gather_statistics=True,
    )
    assert_eq(ddf, read_df)


@write_read_engines()
def test_read_glob(tmpdir, write_engine, read_engine):
    tmp_path = str(tmpdir)
    ddf.to_parquet(tmp_path, engine=write_engine)
    if os.path.exists(os.path.join(tmp_path, "_metadata")):
        os.unlink(os.path.join(tmp_path, "_metadata"))
    files = os.listdir(tmp_path)
    assert "_metadata" not in files

    ddf2 = dd.read_parquet(
        os.path.join(tmp_path, "*.parquet"),
        engine=read_engine,
        index="myindex",  # Must specify index without _metadata
        gather_statistics=True,
    )
    assert_eq(ddf, ddf2)


@write_read_engines()
def test_gather_statistics_false(tmpdir, write_engine, read_engine):
    tmp_path = str(tmpdir)
    ddf.to_parquet(tmp_path, write_index=False, engine=write_engine)

    ddf2 = dd.read_parquet(
        tmp_path,
        engine=read_engine,
        index=False,
        gather_statistics=False,
    )
    assert_eq(ddf, ddf2, check_index=False, check_divisions=False)


@write_read_engines()
def test_read_list(tmpdir, write_engine, read_engine):
    if write_engine == read_engine == "fastparquet" and os.name == "nt":
        # fastparquet or dask is not normalizing filepaths correctly on
        # windows.
        pytest.skip("filepath bug.")

    tmpdir = str(tmpdir)
    ddf.to_parquet(tmpdir, engine=write_engine)
    files = sorted(
        (
            os.path.join(tmpdir, f)
            for f in os.listdir(tmpdir)
            if not f.endswith("_metadata")
        ),
        key=natural_sort_key,
    )

    ddf2 = dd.read_parquet(
        files, engine=read_engine, index="myindex", gather_statistics=True
    )
    assert_eq(ddf, ddf2)


@write_read_engines()
def test_columns_auto_index(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=write_engine)

    # XFAIL, auto index selection no longer supported (for simplicity)
    # ### Empty columns ###
    # With divisions if supported
    assert_eq(dd.read_parquet(fn, columns=[], engine=read_engine), ddf[[]])

    # No divisions
    assert_eq(
        dd.read_parquet(fn, columns=[], engine=read_engine, gather_statistics=False),
        ddf[[]].clear_divisions(),
        check_divisions=True,
    )

    # ### Single column, auto select index ###
    # With divisions if supported
    assert_eq(dd.read_parquet(fn, columns=["x"], engine=read_engine), ddf[["x"]])

    # No divisions
    assert_eq(
        dd.read_parquet(fn, columns=["x"], engine=read_engine, gather_statistics=False),
        ddf[["x"]].clear_divisions(),
        check_divisions=True,
    )


@write_read_engines()
def test_columns_index(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=write_engine)

    # With Index
    # ----------
    # ### Empty columns, specify index ###
    # With divisions if supported
    assert_eq(
        dd.read_parquet(fn, columns=[], engine=read_engine, index="myindex"), ddf[[]]
    )

    # No divisions
    assert_eq(
        dd.read_parquet(
            fn, columns=[], engine=read_engine, index="myindex", gather_statistics=False
        ),
        ddf[[]].clear_divisions(),
        check_divisions=True,
    )

    # ### Single column, specify index ###
    # With divisions if supported
    assert_eq(
        dd.read_parquet(fn, index="myindex", columns=["x"], engine=read_engine),
        ddf[["x"]],
    )

    # No divisions
    assert_eq(
        dd.read_parquet(
            fn,
            index="myindex",
            columns=["x"],
            engine=read_engine,
            gather_statistics=False,
        ),
        ddf[["x"]].clear_divisions(),
        check_divisions=True,
    )

    # ### Two columns, specify index ###
    # With divisions if supported
    assert_eq(
        dd.read_parquet(fn, index="myindex", columns=["x", "y"], engine=read_engine),
        ddf,
    )

    # No divisions
    assert_eq(
        dd.read_parquet(
            fn,
            index="myindex",
            columns=["x", "y"],
            engine=read_engine,
            gather_statistics=False,
        ),
        ddf.clear_divisions(),
        check_divisions=True,
    )


def test_nonsense_column(tmpdir, engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=engine)
    with pytest.raises((ValueError, KeyError)):
        dd.read_parquet(fn, columns=["nonesense"], engine=engine)
    with pytest.raises((Exception, KeyError)):
        dd.read_parquet(fn, columns=["nonesense"] + list(ddf.columns), engine=engine)


@write_read_engines()
def test_columns_no_index(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=write_engine)
    ddf2 = ddf.reset_index()

    # No Index
    # --------
    # All columns, none as index
    assert_eq(
        dd.read_parquet(fn, index=False, engine=read_engine, gather_statistics=True),
        ddf2,
        check_index=False,
        check_divisions=True,
    )

    # Two columns, none as index
    assert_eq(
        dd.read_parquet(
            fn,
            index=False,
            columns=["x", "y"],
            engine=read_engine,
            gather_statistics=True,
        ),
        ddf2[["x", "y"]],
        check_index=False,
        check_divisions=True,
    )

    # One column and one index, all as columns
    assert_eq(
        dd.read_parquet(
            fn,
            index=False,
            columns=["myindex", "x"],
            engine=read_engine,
            gather_statistics=True,
        ),
        ddf2[["myindex", "x"]],
        check_index=False,
        check_divisions=True,
    )


@write_read_engines()
def test_gather_statistics_no_index(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=write_engine, write_index=False)

    df = dd.read_parquet(fn, engine=read_engine, index=False)
    assert df.index.name is None
    assert not df.known_divisions


def test_columns_index_with_multi_index(tmpdir, engine):
    fn = os.path.join(str(tmpdir), "test.parquet")
    index = pd.MultiIndex.from_arrays(
        [np.arange(10), np.arange(10) + 1], names=["x0", "x1"]
    )
    df = pd.DataFrame(np.random.randn(10, 2), columns=["a", "b"], index=index)
    df2 = df.reset_index(drop=False)

    if engine == "fastparquet":
        fastparquet.write(fn, df.reset_index(), write_index=False)

    else:
        pq.write_table(pa.Table.from_pandas(df.reset_index(), preserve_index=False), fn)

    ddf = dd.read_parquet(fn, engine=engine, index=index.names)
    assert_eq(ddf, df)

    d = dd.read_parquet(fn, columns="a", engine=engine, index=index.names)
    assert_eq(d, df["a"])

    d = dd.read_parquet(fn, index=["a", "b"], columns=["x0", "x1"], engine=engine)
    assert_eq(d, df2.set_index(["a", "b"])[["x0", "x1"]])

    # Just index
    d = dd.read_parquet(fn, index=False, engine=engine)
    assert_eq(d, df2)

    d = dd.read_parquet(fn, columns=["b"], index=["a"], engine=engine)
    assert_eq(d, df2.set_index("a")[["b"]])

    d = dd.read_parquet(fn, columns=["a", "b"], index=["x0"], engine=engine)
    assert_eq(d, df2.set_index("x0")[["a", "b"]])

    # Just columns
    d = dd.read_parquet(fn, columns=["x0", "a"], index=["x1"], engine=engine)
    assert_eq(d, df2.set_index("x1")[["x0", "a"]])

    # Both index and columns
    d = dd.read_parquet(fn, index=False, columns=["x0", "b"], engine=engine)
    assert_eq(d, df2[["x0", "b"]])

    for index in ["x1", "b"]:
        d = dd.read_parquet(fn, index=index, columns=["x0", "a"], engine=engine)
        assert_eq(d, df2.set_index(index)[["x0", "a"]])

    # Columns and index intersect
    for index in ["a", "x0"]:
        with pytest.raises(ValueError):
            d = dd.read_parquet(fn, index=index, columns=["x0", "a"], engine=engine)

    # Series output
    for ind, col, sol_df in [
        ("x1", "x0", df2.set_index("x1")),
        (False, "b", df2),
        (False, "x0", df2[["x0"]]),
        ("a", "x0", df2.set_index("a")[["x0"]]),
        ("a", "b", df2.set_index("a")),
    ]:
        d = dd.read_parquet(fn, index=ind, columns=col, engine=engine)
        assert_eq(d, sol_df[col])


@write_read_engines()
def test_no_index(tmpdir, write_engine, read_engine):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(fn, engine=write_engine)
    ddf2 = dd.read_parquet(fn, engine=read_engine)
    assert_eq(df, ddf2, check_index=False)


def test_read_series(tmpdir, engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=engine)
    ddf2 = dd.read_parquet(fn, columns=["x"], index="myindex", engine=engine)
    assert_eq(ddf[["x"]], ddf2)

    ddf2 = dd.read_parquet(fn, columns="x", index="myindex", engine=engine)
    assert_eq(ddf.x, ddf2)


def test_names(tmpdir, engine):
    fn = str(tmpdir)
    ddf.to_parquet(fn, engine=engine)

    def read(fn, **kwargs):
        return dd.read_parquet(fn, engine=engine, **kwargs)

    assert set(read(fn).dask) == set(read(fn).dask)

    assert set(read(fn).dask) != set(read(fn, columns=["x"]).dask)

    assert set(read(fn, columns=("x",)).dask) == set(read(fn, columns=["x"]).dask)


@write_read_engines()
def test_roundtrip_from_pandas(tmpdir, write_engine, read_engine):
    fn = str(tmpdir.join("test.parquet"))
    dfp = df.copy()
    dfp.index.name = "index"
    dfp.to_parquet(
        fn, engine="pyarrow" if write_engine.startswith("pyarrow") else "fastparquet"
    )
    ddf = dd.read_parquet(fn, index="index", engine=read_engine)
    assert_eq(dfp, ddf)


@write_read_engines()
def test_categorical(tmpdir, write_engine, read_engine):
    tmp = str(tmpdir)
    df = pd.DataFrame({"x": ["a", "b", "c"] * 100}, dtype="category")
    ddf = dd.from_pandas(df, npartitions=3)
    dd.to_parquet(ddf, tmp, engine=write_engine)

    ddf2 = dd.read_parquet(tmp, categories="x", engine=read_engine)
    assert ddf2.compute().x.cat.categories.tolist() == ["a", "b", "c"]

    ddf2 = dd.read_parquet(tmp, categories=["x"], engine=read_engine)
    assert ddf2.compute().x.cat.categories.tolist() == ["a", "b", "c"]

    # autocat
    if read_engine == "fastparquet":
        ddf2 = dd.read_parquet(tmp, engine=read_engine)
        assert ddf2.compute().x.cat.categories.tolist() == ["a", "b", "c"]

        ddf2.loc[:1000].compute()
        assert assert_eq(df, ddf2)

    # dereference cats
    ddf2 = dd.read_parquet(tmp, categories=[], engine=read_engine)

    ddf2.loc[:1000].compute()
    assert (df.x == ddf2.x.compute()).all()


def test_append(tmpdir, engine):
    """Test that appended parquet equal to the original one."""
    tmp = str(tmpdir)
    df = pd.DataFrame(
        {
            "i32": np.arange(1000, dtype=np.int32),
            "i64": np.arange(1000, dtype=np.int64),
            "f": np.arange(1000, dtype=np.float64),
            "bhello": np.random.choice(["hello", "yo", "people"], size=1000).astype(
                "O"
            ),
        }
    )
    df.index.name = "index"

    half = len(df) // 2
    ddf1 = dd.from_pandas(df.iloc[:half], chunksize=100)
    ddf2 = dd.from_pandas(df.iloc[half:], chunksize=100)
    ddf1.to_parquet(tmp, engine=engine)
    ddf2.to_parquet(tmp, append=True, engine=engine)

    ddf3 = dd.read_parquet(tmp, engine=engine)
    assert_eq(df, ddf3)


def test_append_create(tmpdir, engine):
    """Test that appended parquet equal to the original one."""
    tmp_path = str(tmpdir)
    df = pd.DataFrame(
        {
            "i32": np.arange(1000, dtype=np.int32),
            "i64": np.arange(1000, dtype=np.int64),
            "f": np.arange(1000, dtype=np.float64),
            "bhello": np.random.choice(["hello", "yo", "people"], size=1000).astype(
                "O"
            ),
        }
    )
    df.index.name = "index"

    half = len(df) // 2
    ddf1 = dd.from_pandas(df.iloc[:half], chunksize=100)
    ddf2 = dd.from_pandas(df.iloc[half:], chunksize=100)
    ddf1.to_parquet(tmp_path, append=True, engine=engine)
    ddf2.to_parquet(tmp_path, append=True, engine=engine)

    ddf3 = dd.read_parquet(tmp_path, engine=engine)
    assert_eq(df, ddf3)


def test_append_with_partition(tmpdir, engine):
    tmp = str(tmpdir)
    df0 = pd.DataFrame(
        {
            "lat": np.arange(0, 10, dtype="int64"),
            "lon": np.arange(10, 20, dtype="int64"),
            "value": np.arange(100, 110, dtype="int64"),
        }
    )
    df0.index.name = "index"
    df1 = pd.DataFrame(
        {
            "lat": np.arange(10, 20, dtype="int64"),
            "lon": np.arange(10, 20, dtype="int64"),
            "value": np.arange(120, 130, dtype="int64"),
        }
    )
    df1.index.name = "index"

    # Check that nullable dtypes work
    # (see: https://github.com/dask/dask/issues/8373)
    df0["lat"] = df0["lat"].astype("Int64")
    df1["lat"].iloc[0] = np.nan
    df1["lat"] = df1["lat"].astype("Int64")

    dd_df0 = dd.from_pandas(df0, npartitions=1)
    dd_df1 = dd.from_pandas(df1, npartitions=1)
    dd.to_parquet(dd_df0, tmp, partition_on=["lon"], engine=engine)
    dd.to_parquet(
        dd_df1,
        tmp,
        partition_on=["lon"],
        append=True,
        ignore_divisions=True,
        engine=engine,
    )

    out = dd.read_parquet(
        tmp, engine=engine, index="index", gather_statistics=True
    ).compute()
    # convert categorical to plain int just to pass assert
    out["lon"] = out.lon.astype("int64")
    # sort required since partitioning breaks index order
    assert_eq(
        out.sort_values("value"), pd.concat([df0, df1])[out.columns], check_index=False
    )


def test_partition_on_cats(tmpdir, engine):
    tmp = str(tmpdir)
    d = pd.DataFrame(
        {
            "a": np.random.rand(50),
            "b": np.random.choice(["x", "y", "z"], size=50),
            "c": np.random.choice(["x", "y", "z"], size=50),
        }
    )
    d = dd.from_pandas(d, 2)
    d.to_parquet(tmp, partition_on=["b"], engine=engine)
    df = dd.read_parquet(tmp, engine=engine)
    assert set(df.b.cat.categories) == {"x", "y", "z"}


@PYARROW_MARK
@pytest.mark.parametrize("meta", [False, True])
@pytest.mark.parametrize("stats", [False, True])
def test_partition_on_cats_pyarrow(tmpdir, stats, meta):
    tmp = str(tmpdir)
    d = pd.DataFrame(
        {
            "a": np.random.rand(50),
            "b": np.random.choice(["x", "y", "z"], size=50),
            "c": np.random.choice(["x", "y", "z"], size=50),
        }
    )
    d = dd.from_pandas(d, 2)
    d.to_parquet(tmp, partition_on=["b"], engine="pyarrow", write_metadata_file=meta)
    df = dd.read_parquet(tmp, engine="pyarrow", gather_statistics=stats)
    assert set(df.b.cat.categories) == {"x", "y", "z"}


def test_partition_on_cats_2(tmpdir, engine):
    tmp = str(tmpdir)
    d = pd.DataFrame(
        {
            "a": np.random.rand(50),
            "b": np.random.choice(["x", "y", "z"], size=50),
            "c": np.random.choice(["x", "y", "z"], size=50),
        }
    )
    d = dd.from_pandas(d, 2)
    d.to_parquet(tmp, partition_on=["b", "c"], engine=engine)
    df = dd.read_parquet(tmp, engine=engine)
    assert set(df.b.cat.categories) == {"x", "y", "z"}
    assert set(df.c.cat.categories) == {"x", "y", "z"}

    df = dd.read_parquet(tmp, columns=["a", "c"], engine=engine)
    assert set(df.c.cat.categories) == {"x", "y", "z"}
    assert "b" not in df.columns
    assert_eq(df, df.compute())
    df = dd.read_parquet(tmp, index="c", engine=engine)
    assert set(df.index.categories) == {"x", "y", "z"}
    assert "c" not in df.columns
    # series
    df = dd.read_parquet(tmp, columns="b", engine=engine)
    assert set(df.cat.categories) == {"x", "y", "z"}


def test_append_wo_index(tmpdir, engine):
    """Test append with write_index=False."""
    tmp = str(tmpdir.join("tmp1.parquet"))
    df = pd.DataFrame(
        {
            "i32": np.arange(1000, dtype=np.int32),
            "i64": np.arange(1000, dtype=np.int64),
            "f": np.arange(1000, dtype=np.float64),
            "bhello": np.random.choice(["hello", "yo", "people"], size=1000).astype(
                "O"
            ),
        }
    )
    half = len(df) // 2
    ddf1 = dd.from_pandas(df.iloc[:half], chunksize=100)
    ddf2 = dd.from_pandas(df.iloc[half:], chunksize=100)
    ddf1.to_parquet(tmp, engine=engine)

    with pytest.raises(ValueError) as excinfo:
        ddf2.to_parquet(tmp, write_index=False, append=True, engine=engine)
    assert "Appended columns" in str(excinfo.value)

    tmp = str(tmpdir.join("tmp2.parquet"))
    ddf1.to_parquet(tmp, write_index=False, engine=engine)
    ddf2.to_parquet(tmp, write_index=False, append=True, engine=engine)

    ddf3 = dd.read_parquet(tmp, index="f", engine=engine)
    assert_eq(df.set_index("f"), ddf3)


def test_append_overlapping_divisions(tmpdir, engine):
    """Test raising of error when divisions overlapping."""
    tmp = str(tmpdir)
    df = pd.DataFrame(
        {
            "i32": np.arange(1000, dtype=np.int32),
            "i64": np.arange(1000, dtype=np.int64),
            "f": np.arange(1000, dtype=np.float64),
            "bhello": np.random.choice(["hello", "yo", "people"], size=1000).astype(
                "O"
            ),
        }
    )
    half = len(df) // 2
    ddf1 = dd.from_pandas(df.iloc[:half], chunksize=100)
    ddf2 = dd.from_pandas(df.iloc[half - 10 :], chunksize=100)
    ddf1.to_parquet(tmp, engine=engine)

    with pytest.raises(ValueError) as excinfo:
        ddf2.to_parquet(tmp, engine=engine, append=True)
    assert "Appended divisions" in str(excinfo.value)

    ddf2.to_parquet(tmp, engine=engine, append=True, ignore_divisions=True)


def test_append_different_columns(tmpdir, engine):
    """Test raising of error when non equal columns."""
    tmp = str(tmpdir)
    df1 = pd.DataFrame({"i32": np.arange(100, dtype=np.int32)})
    df2 = pd.DataFrame({"i64": np.arange(100, dtype=np.int64)})
    df3 = pd.DataFrame({"i32": np.arange(100, dtype=np.int64)})

    ddf1 = dd.from_pandas(df1, chunksize=2)
    ddf2 = dd.from_pandas(df2, chunksize=2)
    ddf3 = dd.from_pandas(df3, chunksize=2)

    ddf1.to_parquet(tmp, engine=engine)

    with pytest.raises(ValueError) as excinfo:
        ddf2.to_parquet(tmp, engine=engine, append=True)
    assert "Appended columns" in str(excinfo.value)

    with pytest.raises(ValueError) as excinfo:
        ddf3.to_parquet(tmp, engine=engine, append=True)
    assert "Appended dtypes" in str(excinfo.value)


def test_append_dict_column(tmpdir, engine):
    # See: https://github.com/dask/dask/issues/7492

    if engine == "fastparquet":
        pytest.xfail("Fastparquet engine is missing dict-column support")
    elif pa_version < parse_version("1.0.1"):
        pytest.skip("PyArrow 1.0.1+ required for dict-column support.")

    tmp = str(tmpdir)
    dts = pd.date_range("2020-01-01", "2021-01-01")
    df = pd.DataFrame(
        {"value": [{"x": x} for x in range(len(dts))]},
        index=dts,
    )
    ddf1 = dd.from_pandas(df, npartitions=1)

    # Write ddf1 to tmp, and then append it again
    ddf1.to_parquet(tmp, append=True, engine=engine)
    ddf1.to_parquet(tmp, append=True, engine=engine, ignore_divisions=True)

    # Read back all data (ddf1 + ddf1)
    ddf2 = dd.read_parquet(tmp, engine=engine)

    # Check computed result
    expect = pd.concat([df, df])
    result = ddf2.compute()
    assert_eq(expect, result)


@write_read_engines_xfail
def test_ordering(tmpdir, write_engine, read_engine):
    tmp = str(tmpdir)
    df = pd.DataFrame(
        {"a": [1, 2, 3], "b": [10, 20, 30], "c": [100, 200, 300]},
        index=pd.Index([-1, -2, -3], name="myindex"),
        columns=["c", "a", "b"],
    )
    ddf = dd.from_pandas(df, npartitions=2)
    dd.to_parquet(ddf, tmp, engine=write_engine)

    if read_engine == "fastparquet":
        pf = fastparquet.ParquetFile(tmp)
        assert pf.columns == ["myindex", "c", "a", "b"]

    ddf2 = dd.read_parquet(tmp, index="myindex", engine=read_engine)
    assert_eq(ddf, ddf2, check_divisions=False)


def test_read_parquet_custom_columns(tmpdir, engine):
    tmp = str(tmpdir)
    data = pd.DataFrame(
        {"i32": np.arange(1000, dtype=np.int32), "f": np.arange(1000, dtype=np.float64)}
    )
    df = dd.from_pandas(data, chunksize=50)
    df.to_parquet(tmp, engine=engine)

    df2 = dd.read_parquet(tmp, columns=["i32", "f"], engine=engine)
    assert_eq(df[["i32", "f"]], df2, check_index=False)

    fns = glob.glob(os.path.join(tmp, "*.parquet"))
    df2 = dd.read_parquet(fns, columns=["i32"], engine=engine).compute()
    df2.sort_values("i32", inplace=True)
    assert_eq(df[["i32"]], df2, check_index=False, check_divisions=False)

    df3 = dd.read_parquet(tmp, columns=["f", "i32"], engine=engine)
    assert_eq(df[["f", "i32"]], df3, check_index=False)


@pytest.mark.parametrize(
    "df,write_kwargs,read_kwargs",
    [
        (pd.DataFrame({"x": [3, 2, 1]}), {}, {}),
        (pd.DataFrame({"x": ["c", "a", "b"]}), {}, {}),
        (pd.DataFrame({"x": ["cc", "a", "bbb"]}), {}, {}),
        (pd.DataFrame({"x": [b"a", b"b", b"c"]}), {"object_encoding": "bytes"}, {}),
        (
            pd.DataFrame({"x": pd.Categorical(["a", "b", "a"])}),
            {},
            {"categories": ["x"]},
        ),
        (pd.DataFrame({"x": pd.Categorical([1, 2, 1])}), {}, {"categories": ["x"]}),
        (pd.DataFrame({"x": list(map(pd.Timestamp, [3000, 2000, 1000]))}), {}, {}),
        (pd.DataFrame({"x": [3000, 2000, 1000]}).astype("M8[ns]"), {}, {}),
        pytest.param(
            pd.DataFrame({"x": [3, 2, 1]}).astype("M8[ns]"),
            {},
            {},
        ),
        (pd.DataFrame({"x": [3, 2, 1]}).astype("M8[us]"), {}, {}),
        (pd.DataFrame({"x": [3, 2, 1]}).astype("M8[ms]"), {}, {}),
        (pd.DataFrame({"x": [3000, 2000, 1000]}).astype("datetime64[ns]"), {}, {}),
        (pd.DataFrame({"x": [3000, 2000, 1000]}).astype("datetime64[ns, UTC]"), {}, {}),
        (pd.DataFrame({"x": [3000, 2000, 1000]}).astype("datetime64[ns, CET]"), {}, {}),
        (pd.DataFrame({"x": [3, 2, 1]}).astype("uint16"), {}, {}),
        (pd.DataFrame({"x": [3, 2, 1]}).astype("float32"), {}, {}),
        (pd.DataFrame({"x": [3, 1, 2]}, index=[3, 2, 1]), {}, {}),
        (pd.DataFrame({"x": [3, 1, 5]}, index=pd.Index([1, 2, 3], name="foo")), {}, {}),
        (pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}), {}, {}),
        (pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}, columns=["y", "x"]), {}, {}),
        (pd.DataFrame({"0": [3, 2, 1]}), {}, {}),
        (pd.DataFrame({"x": [3, 2, None]}), {}, {}),
        (pd.DataFrame({"-": [3.0, 2.0, None]}), {}, {}),
        (pd.DataFrame({".": [3.0, 2.0, None]}), {}, {}),
        (pd.DataFrame({" ": [3.0, 2.0, None]}), {}, {}),
    ],
)
def test_roundtrip(tmpdir, df, write_kwargs, read_kwargs, engine):
    if "x" in df and df.x.dtype == "M8[ns]" and "arrow" in engine:
        pytest.xfail(reason="Parquet pyarrow v1 doesn't support nanosecond precision")
    if (
        "x" in df
        and df.x.dtype == "M8[ns]"
        and engine == "fastparquet"
        and fastparquet_version <= parse_version("0.6.3")
    ):
        pytest.xfail(reason="fastparquet doesn't support nanosecond precision yet")
    if (
        PANDAS_GT_130
        and read_kwargs.get("categories", None)
        and engine == "fastparquet"
        and fastparquet_version <= parse_version("0.6.3")
    ):
        pytest.xfail("https://github.com/dask/fastparquet/issues/577")

    tmp = str(tmpdir)
    if df.index.name is None:
        df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=2)

    oe = write_kwargs.pop("object_encoding", None)
    if oe and engine == "fastparquet":
        dd.to_parquet(ddf, tmp, engine=engine, object_encoding=oe, **write_kwargs)
    else:
        dd.to_parquet(ddf, tmp, engine=engine, **write_kwargs)
    ddf2 = dd.read_parquet(tmp, index=df.index.name, engine=engine, **read_kwargs)
    if str(ddf2.dtypes.get("x")) == "UInt16" and engine == "fastparquet":
        # fastparquet choooses to use masked type to be able to get true repr of
        # 16-bit int
        assert_eq(ddf.astype("UInt16"), ddf2)
    else:
        assert_eq(ddf, ddf2)


def test_categories(tmpdir, engine):
    fn = str(tmpdir)
    df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": list("caaab")})
    ddf = dd.from_pandas(df, npartitions=2)
    ddf["y"] = ddf.y.astype("category")
    ddf.to_parquet(fn, engine=engine)
    ddf2 = dd.read_parquet(fn, categories=["y"], engine=engine)

    # Shouldn't need to specify categories explicitly
    ddf3 = dd.read_parquet(fn, engine=engine)
    assert_eq(ddf3, ddf2)

    with pytest.raises(NotImplementedError):
        ddf2.y.cat.categories
    assert set(ddf2.y.compute().cat.categories) == {"a", "b", "c"}
    cats_set = ddf2.map_partitions(lambda x: x.y.cat.categories.sort_values()).compute()
    assert cats_set.tolist() == ["a", "c", "a", "b"]

    if engine == "fastparquet":
        assert_eq(ddf.y, ddf2.y, check_names=False)
        with pytest.raises(TypeError):
            # attempt to load as category that which is not so encoded
            ddf2 = dd.read_parquet(fn, categories=["x"], engine=engine).compute()

    with pytest.raises((ValueError, FutureWarning)):
        # attempt to load as category unknown column
        ddf2 = dd.read_parquet(fn, categories=["foo"], engine=engine)


def test_categories_unnamed_index(tmpdir, engine):
    # Check that we can handle an unnamed categorical index
    # https://github.com/dask/dask/issues/6885

    tmpdir = str(tmpdir)

    df = pd.DataFrame(
        data={"A": [1, 2, 3], "B": ["a", "a", "b"]}, index=["x", "y", "y"]
    )
    ddf = dd.from_pandas(df, npartitions=1)
    ddf = ddf.categorize(columns=["B"])

    ddf.to_parquet(tmpdir, engine=engine)
    ddf2 = dd.read_parquet(tmpdir, engine=engine)

    assert_eq(ddf.index, ddf2.index, check_divisions=False)


def test_empty_partition(tmpdir, engine):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": range(10), "b": range(10)})
    ddf = dd.from_pandas(df, npartitions=5)

    ddf2 = ddf[ddf.a <= 5]
    ddf2.to_parquet(fn, engine=engine)

    ddf3 = dd.read_parquet(fn, engine=engine)
    assert ddf3.npartitions < 5
    sol = ddf2.compute()
    assert_eq(sol, ddf3, check_names=False, check_index=False)


def test_timestamp_index(tmpdir, engine):
    fn = str(tmpdir)
    df = dd._compat.makeTimeDataFrame()
    df.index.name = "foo"
    ddf = dd.from_pandas(df, npartitions=5)
    ddf.to_parquet(fn, engine=engine)
    ddf2 = dd.read_parquet(fn, engine=engine)
    assert_eq(ddf, ddf2)


@FASTPARQUET_MARK
@PYARROW_MARK
def test_to_parquet_default_writes_nulls(tmpdir):
    fn = str(tmpdir.join("test.parquet"))

    df = pd.DataFrame({"c1": [1.0, np.nan, 2, np.nan, 3]})
    ddf = dd.from_pandas(df, npartitions=1)

    ddf.to_parquet(fn)
    table = pq.read_table(fn)
    assert table[1].null_count == 2


@PYARROW_LE_MARK
def test_to_parquet_pyarrow_w_inconsistent_schema_by_partition_fails_by_default(tmpdir):
    df = pd.DataFrame(
        {"partition_column": [0, 0, 1, 1], "strings": ["a", "b", None, None]}
    )

    ddf = dd.from_pandas(df, npartitions=2)
    # In order to allow pyarrow to write an inconsistent schema,
    # we need to avoid writing the _metadata file (will fail >0.17.1)
    # and need to avoid schema inference (i.e. use `schema=None`)
    ddf.to_parquet(
        str(tmpdir),
        engine="pyarrow",
        partition_on=["partition_column"],
        write_metadata_file=False,
        schema=None,
    )

    # Test that schema is not validated by default
    # (shouldn't raise error with legacy dataset)
    dd.read_parquet(
        str(tmpdir),
        engine="pyarrow-legacy",
        gather_statistics=False,
    ).compute()

    # Test that read fails when validate_schema=True
    # Note: This fails differently for pyarrow.dataset api
    with pytest.raises(ValueError) as e_info:
        dd.read_parquet(
            str(tmpdir),
            engine="pyarrow-legacy",
            gather_statistics=False,
            dataset={"validate_schema": True},
        ).compute()
        assert e_info.message.contains("ValueError: Schema in partition")
        assert e_info.message.contains("was different")


@PYARROW_MARK
def test_to_parquet_pyarrow_w_inconsistent_schema_by_partition_succeeds_w_manual_schema(
    tmpdir,
):
    # Data types to test: strings, arrays, ints, timezone aware timestamps
    in_arrays = [[0, 1, 2], [3, 4], np.nan, np.nan]
    out_arrays = [[0, 1, 2], [3, 4], None, None]
    in_strings = ["a", "b", np.nan, np.nan]
    out_strings = ["a", "b", None, None]
    tstamp = pd.Timestamp(1513393355, unit="s")
    in_tstamps = [tstamp, tstamp, pd.NaT, pd.NaT]
    out_tstamps = [
        # Timestamps come out in numpy.datetime64 format
        tstamp.to_datetime64(),
        tstamp.to_datetime64(),
        np.datetime64("NaT"),
        np.datetime64("NaT"),
    ]
    timezone = "US/Eastern"
    tz_tstamp = pd.Timestamp(1513393355, unit="s", tz=timezone)
    in_tz_tstamps = [tz_tstamp, tz_tstamp, pd.NaT, pd.NaT]
    out_tz_tstamps = [
        # Timezones do not make it through a write-read cycle.
        tz_tstamp.tz_convert(None).to_datetime64(),
        tz_tstamp.tz_convert(None).to_datetime64(),
        np.datetime64("NaT"),
        np.datetime64("NaT"),
    ]

    df = pd.DataFrame(
        {
            "partition_column": [0, 0, 1, 1],
            "arrays": in_arrays,
            "strings": in_strings,
            "tstamps": in_tstamps,
            "tz_tstamps": in_tz_tstamps,
        }
    )

    ddf = dd.from_pandas(df, npartitions=2)
    schema = pa.schema(
        [
            ("arrays", pa.list_(pa.int64())),
            ("strings", pa.string()),
            ("tstamps", pa.timestamp("ns")),
            ("tz_tstamps", pa.timestamp("ns", timezone)),
            ("partition_column", pa.int64()),
        ]
    )
    ddf.to_parquet(
        str(tmpdir), engine="pyarrow", partition_on="partition_column", schema=schema
    )
    ddf_after_write = (
        dd.read_parquet(str(tmpdir), engine="pyarrow", gather_statistics=False)
        .compute()
        .reset_index(drop=True)
    )

    # Check array support
    arrays_after_write = ddf_after_write.arrays.values
    for i in range(len(df)):
        assert np.array_equal(arrays_after_write[i], out_arrays[i]), type(out_arrays[i])

    # Check datetime support
    tstamps_after_write = ddf_after_write.tstamps.values
    for i in range(len(df)):
        # Need to test NaT separately
        if np.isnat(tstamps_after_write[i]):
            assert np.isnat(out_tstamps[i])
        else:
            assert tstamps_after_write[i] == out_tstamps[i]

    # Check timezone aware datetime support
    tz_tstamps_after_write = ddf_after_write.tz_tstamps.values
    for i in range(len(df)):
        # Need to test NaT separately
        if np.isnat(tz_tstamps_after_write[i]):
            assert np.isnat(out_tz_tstamps[i])
        else:
            assert tz_tstamps_after_write[i] == out_tz_tstamps[i]

    # Check string support
    assert np.array_equal(ddf_after_write.strings.values, out_strings)

    # Check partition column
    assert np.array_equal(ddf_after_write.partition_column, df.partition_column)


@PYARROW_MARK
@pytest.mark.parametrize("index", [False, True])
@pytest.mark.parametrize("schema", ["infer", "complex"])
def test_pyarrow_schema_inference(tmpdir, index, engine, schema):
    if schema == "complex":
        schema = {"index": pa.string(), "amount": pa.int64()}

    tmpdir = str(tmpdir)
    df = pd.DataFrame(
        {
            "index": ["1", "2", "3", "2", "3", "1", "4"],
            "date": pd.to_datetime(
                [
                    "2017-01-01",
                    "2017-01-01",
                    "2017-01-01",
                    "2017-01-02",
                    "2017-01-02",
                    "2017-01-06",
                    "2017-01-09",
                ]
            ),
            "amount": [100, 200, 300, 400, 500, 600, 700],
        },
        index=range(7, 14),
    )
    if index:
        df = dd.from_pandas(df, npartitions=2).set_index("index")
    else:
        df = dd.from_pandas(df, npartitions=2)

    df.to_parquet(tmpdir, engine="pyarrow", schema=schema)
    df_out = dd.read_parquet(tmpdir, engine=engine)
    df_out.compute()

    if index and engine == "fastparquet":
        # Fastparquet fails to detect int64 from _metadata
        df_out["amount"] = df_out["amount"].astype("int64")

        # Fastparquet not handling divisions for
        # pyarrow-written dataset with string index
        assert_eq(df, df_out, check_divisions=False)
    else:
        assert_eq(df, df_out)


def test_partition_on(tmpdir, engine):
    tmpdir = str(tmpdir)
    df = pd.DataFrame(
        {
            "a1": np.random.choice(["A", "B", "C"], size=100),
            "a2": np.random.choice(["X", "Y", "Z"], size=100),
            "b": np.random.random(size=100),
            "c": np.random.randint(1, 5, size=100),
            "d": np.arange(0, 100),
        }
    )
    d = dd.from_pandas(df, npartitions=2)
    d.to_parquet(tmpdir, partition_on=["a1", "a2"], engine=engine)
    # Note #1: Cross-engine functionality is missing
    # Note #2: The index is not preserved in pyarrow when partition_on is used
    out = dd.read_parquet(
        tmpdir, engine=engine, index=False, gather_statistics=False
    ).compute()
    for val in df.a1.unique():
        assert set(df.d[df.a1 == val]) == set(out.d[out.a1 == val])

    # Now specify the columns and allow auto-index detection
    out = dd.read_parquet(tmpdir, engine=engine, columns=["d", "a2"]).compute()
    for val in df.a2.unique():
        assert set(df.d[df.a2 == val]) == set(out.d[out.a2 == val])


def test_partition_on_duplicates(tmpdir, engine):
    # https://github.com/dask/dask/issues/6445
    tmpdir = str(tmpdir)
    df = pd.DataFrame(
        {
            "a1": np.random.choice(["A", "B", "C"], size=100),
            "a2": np.random.choice(["X", "Y", "Z"], size=100),
            "data": np.random.random(size=100),
        }
    )
    d = dd.from_pandas(df, npartitions=2)

    for _ in range(2):
        d.to_parquet(tmpdir, partition_on=["a1", "a2"], engine=engine)

    out = dd.read_parquet(tmpdir, engine=engine).compute()

    assert len(df) == len(out)
    for root, dirs, files in os.walk(tmpdir):
        for file in files:
            assert file in (
                "part.0.parquet",
                "part.1.parquet",
                "_common_metadata",
                "_metadata",
            )


@PYARROW_MARK
@pytest.mark.parametrize("partition_on", ["aa", ["aa"]])
def test_partition_on_string(tmpdir, partition_on):
    tmpdir = str(tmpdir)
    with dask.config.set(scheduler="single-threaded"):
        tmpdir = str(tmpdir)
        df = pd.DataFrame(
            {
                "aa": np.random.choice(["A", "B", "C"], size=100),
                "bb": np.random.random(size=100),
                "cc": np.random.randint(1, 5, size=100),
            }
        )
        d = dd.from_pandas(df, npartitions=2)
        d.to_parquet(
            tmpdir, partition_on=partition_on, write_index=False, engine="pyarrow"
        )
        out = dd.read_parquet(
            tmpdir, index=False, gather_statistics=False, engine="pyarrow"
        )
    out = out.compute()
    for val in df.aa.unique():
        assert set(df.bb[df.aa == val]) == set(out.bb[out.aa == val])


@write_read_engines()
def test_filters_categorical(tmpdir, write_engine, read_engine):
    tmpdir = str(tmpdir)
    cats = ["2018-01-01", "2018-01-02", "2018-01-03", "2018-01-04"]
    dftest = pd.DataFrame(
        {
            "dummy": [1, 1, 1, 1],
            "DatePart": pd.Categorical(cats, categories=cats, ordered=True),
        }
    )
    ddftest = dd.from_pandas(dftest, npartitions=4).set_index("dummy")
    ddftest.to_parquet(tmpdir, partition_on="DatePart", engine=write_engine)
    ddftest_read = dd.read_parquet(
        tmpdir,
        index="dummy",
        engine=read_engine,
        filters=[(("DatePart", "<=", "2018-01-02"))],
    )
    assert len(ddftest_read) == 2


@write_read_engines()
def test_filters(tmpdir, write_engine, read_engine):
    tmp_path = str(tmpdir)
    df = pd.DataFrame({"x": range(10), "y": list("aabbccddee")})
    ddf = dd.from_pandas(df, npartitions=5)
    assert ddf.npartitions == 5

    ddf.to_parquet(tmp_path, engine=write_engine)

    a = dd.read_parquet(tmp_path, engine=read_engine, filters=[("x", ">", 4)])
    assert a.npartitions == 3
    assert (a.x > 3).all().compute()

    b = dd.read_parquet(tmp_path, engine=read_engine, filters=[("y", "==", "c")])
    assert b.npartitions == 1
    assert (b.y == "c").all().compute()

    c = dd.read_parquet(
        tmp_path, engine=read_engine, filters=[("y", "==", "c"), ("x", ">", 6)]
    )
    assert c.npartitions <= 1
    assert not len(c)
    assert_eq(c, c)

    d = dd.read_parquet(
        tmp_path,
        engine=read_engine,
        filters=[
            # Select two overlapping ranges
            [("x", ">", 1), ("x", "<", 6)],
            [("x", ">", 3), ("x", "<", 8)],
        ],
    )
    assert d.npartitions == 3
    assert ((d.x > 1) & (d.x < 8)).all().compute()

    e = dd.read_parquet(tmp_path, engine=read_engine, filters=[("x", "in", (0, 9))])
    assert e.npartitions == 2
    assert ((e.x < 2) | (e.x > 7)).all().compute()

    f = dd.read_parquet(tmp_path, engine=read_engine, filters=[("y", "=", "c")])
    assert f.npartitions == 1
    assert len(f)
    assert (f.y == "c").all().compute()


@write_read_engines()
def test_filters_v0(tmpdir, write_engine, read_engine):
    if write_engine == "fastparquet" or read_engine == "fastparquet":
        pytest.importorskip("fastparquet", minversion="0.3.1")

    # Recent versions of pyarrow support full row-wise filtering
    # (fastparquet and older pyarrow versions do not)
    pyarrow_row_filtering = read_engine == "pyarrow-dataset"

    fn = str(tmpdir)
    df = pd.DataFrame({"at": ["ab", "aa", "ba", "da", "bb"]})
    ddf = dd.from_pandas(df, npartitions=1)

    # Ok with 1 partition and filters
    ddf.repartition(npartitions=1, force=True).to_parquet(
        fn, write_index=False, engine=write_engine
    )
    ddf2 = dd.read_parquet(
        fn, index=False, engine=read_engine, filters=[("at", "==", "aa")]
    ).compute()
    ddf3 = dd.read_parquet(
        fn, index=False, engine=read_engine, filters=[("at", "=", "aa")]
    ).compute()
    if pyarrow_row_filtering:
        assert_eq(ddf2, ddf[ddf["at"] == "aa"], check_index=False)
        assert_eq(ddf3, ddf[ddf["at"] == "aa"], check_index=False)
    else:
        assert_eq(ddf2, ddf)
        assert_eq(ddf3, ddf)

    # with >1 partition and no filters
    ddf.repartition(npartitions=2, force=True).to_parquet(fn, engine=write_engine)
    ddf2 = dd.read_parquet(fn, engine=read_engine).compute()
    assert_eq(ddf2, ddf)

    # with >1 partition and filters using base fastparquet
    if read_engine == "fastparquet":
        ddf.repartition(npartitions=2, force=True).to_parquet(fn, engine=write_engine)
        df2 = fastparquet.ParquetFile(fn).to_pandas(filters=[("at", "==", "aa")])
        df3 = fastparquet.ParquetFile(fn).to_pandas(filters=[("at", "=", "aa")])
        assert len(df2) > 0
        assert len(df3) > 0

    # with >1 partition and filters
    ddf.repartition(npartitions=2, force=True).to_parquet(fn, engine=write_engine)
    ddf2 = dd.read_parquet(
        fn, engine=read_engine, filters=[("at", "==", "aa")]
    ).compute()
    ddf3 = dd.read_parquet(
        fn, engine=read_engine, filters=[("at", "=", "aa")]
    ).compute()
    assert len(ddf2) > 0
    assert len(ddf3) > 0
    assert_eq(ddf2, ddf3)


def test_filtering_pyarrow_dataset(tmpdir, engine):
    pytest.importorskip("pyarrow", minversion="1.0.0")

    fn = str(tmpdir)
    df = pd.DataFrame({"aa": range(100), "bb": ["cat", "dog"] * 50})
    ddf = dd.from_pandas(df, npartitions=10)
    ddf.to_parquet(fn, write_index=False, engine=engine)

    # Filtered read
    aa_lim = 40
    bb_val = "dog"
    filters = [[("aa", "<", aa_lim), ("bb", "==", bb_val)]]
    ddf2 = dd.read_parquet(fn, index=False, engine="pyarrow-dataset", filters=filters)

    # Check that partitions are filetered for "aa" filter
    nonempty = 0
    for part in ddf[ddf["aa"] < aa_lim].partitions:
        nonempty += int(len(part.compute()) > 0)
    assert ddf2.npartitions == nonempty

    # Check that rows are filtered for "aa" and "bb" filters
    df = df[df["aa"] < aa_lim]
    df = df[df["bb"] == bb_val]
    assert_eq(df, ddf2.compute(), check_index=False)


def test_fiters_file_list(tmpdir, engine):
    df = pd.DataFrame({"x": range(10), "y": list("aabbccddee")})
    ddf = dd.from_pandas(df, npartitions=5)

    ddf.to_parquet(str(tmpdir), engine=engine)
    fils = str(tmpdir.join("*.parquet"))
    ddf_out = dd.read_parquet(
        fils, gather_statistics=True, engine=engine, filters=[("x", ">", 3)]
    )

    assert ddf_out.npartitions == 3
    assert_eq(df[df["x"] > 3], ddf_out.compute(), check_index=False)

    # Check that first parition gets filtered for single-path input
    ddf2 = dd.read_parquet(
        str(tmpdir.join("part.0.parquet")),
        gather_statistics=True,
        engine=engine,
        filters=[("x", ">", 3)],
    )
    assert len(ddf2) == 0


def test_pyarrow_filter_divisions(tmpdir):
    pytest.importorskip("pyarrow")

    # Write simple dataset with an index that will only
    # have a sorted index if certain row-groups are filtered out.
    # In this case, we filter "a" <= 3 to get a sorted
    # index. Otherwise, "a" is NOT monotonically increasing.
    df = pd.DataFrame({"a": [0, 1, 10, 12, 2, 3, 8, 9], "b": range(8)}).set_index("a")
    df.iloc[:4].to_parquet(
        str(tmpdir.join("file.0.parquet")), engine="pyarrow", row_group_size=2
    )
    df.iloc[4:].to_parquet(
        str(tmpdir.join("file.1.parquet")), engine="pyarrow", row_group_size=2
    )

    # Only works for ArrowDatasetEngine.
    # Legacy code will not apply filters on individual row-groups
    # when `split_row_groups=False`.
    ddf = dd.read_parquet(
        str(tmpdir),
        engine="pyarrow-dataset",
        split_row_groups=False,
        gather_statistics=True,
        filters=[("a", "<=", 3)],
    )
    assert ddf.divisions == (0, 2, 3)

    ddf = dd.read_parquet(
        str(tmpdir),
        engine="pyarrow-dataset",
        split_row_groups=True,
        gather_statistics=True,
        filters=[("a", "<=", 3)],
    )
    assert ddf.divisions == (0, 2, 3)


def test_divisions_read_with_filters(tmpdir):
    pytest.importorskip("fastparquet", minversion="0.3.1")
    tmpdir = str(tmpdir)
    # generate dataframe
    size = 100
    categoricals = []
    for value in ["a", "b", "c", "d"]:
        categoricals += [value] * int(size / 4)
    df = pd.DataFrame(
        {
            "a": categoricals,
            "b": np.random.random(size=size),
            "c": np.random.randint(1, 5, size=size),
        }
    )
    d = dd.from_pandas(df, npartitions=4)
    # save it
    d.to_parquet(tmpdir, write_index=True, partition_on=["a"], engine="fastparquet")
    # read it
    out = dd.read_parquet(tmpdir, engine="fastparquet", filters=[("a", "==", "b")])
    # test it
    expected_divisions = (25, 49)
    assert out.divisions == expected_divisions


def test_divisions_are_known_read_with_filters(tmpdir):
    pytest.importorskip("fastparquet", minversion="0.3.1")
    tmpdir = str(tmpdir)
    # generate dataframe
    df = pd.DataFrame(
        {
            "unique": [0, 0, 1, 1, 2, 2, 3, 3],
            "id": ["id1", "id2", "id1", "id2", "id1", "id2", "id1", "id2"],
        },
        index=[0, 0, 1, 1, 2, 2, 3, 3],
    )
    d = dd.from_pandas(df, npartitions=2)
    # save it
    d.to_parquet(tmpdir, partition_on=["id"], engine="fastparquet")
    # read it
    out = dd.read_parquet(tmpdir, engine="fastparquet", filters=[("id", "==", "id1")])
    # test it
    assert out.known_divisions
    expected_divisions = (0, 2, 3)
    assert out.divisions == expected_divisions


@FASTPARQUET_MARK
@pytest.mark.xfail(reason="No longer accept ParquetFile objects")
def test_read_from_fastparquet_parquetfile(tmpdir):
    fn = str(tmpdir)

    df = pd.DataFrame(
        {
            "a": np.random.choice(["A", "B", "C"], size=100),
            "b": np.random.random(size=100),
            "c": np.random.randint(1, 5, size=100),
        }
    )
    d = dd.from_pandas(df, npartitions=2)
    d.to_parquet(fn, partition_on=["a"], engine="fastparquet")

    pq_f = fastparquet.ParquetFile(fn)

    # OK with no filters
    out = dd.read_parquet(pq_f).compute()
    for val in df.a.unique():
        assert set(df.b[df.a == val]) == set(out.b[out.a == val])

    # OK with  filters
    out = dd.read_parquet(pq_f, filters=[("a", "==", "B")]).compute()
    assert set(df.b[df.a == "B"]) == set(out.b)

    # Engine should not be set to 'pyarrow'
    with pytest.raises(AssertionError):
        out = dd.read_parquet(pq_f, engine="pyarrow")


@pytest.mark.parametrize("scheduler", ["threads", "processes"])
def test_to_parquet_lazy(tmpdir, scheduler, engine):
    tmpdir = str(tmpdir)
    df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [1.0, 2.0, 3.0, 4.0]})
    df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=2)
    value = ddf.to_parquet(tmpdir, compute=False, engine=engine)

    assert hasattr(value, "dask")
    value.compute(scheduler=scheduler)
    assert os.path.exists(tmpdir)

    ddf2 = dd.read_parquet(tmpdir, engine=engine)

    assert_eq(ddf, ddf2)


@FASTPARQUET_MARK
def test_timestamp96(tmpdir):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": [pd.to_datetime("now", utc=True)]})
    ddf = dd.from_pandas(df, 1)
    ddf.to_parquet(fn, write_index=False, times="int96")
    pf = fastparquet.ParquetFile(fn)
    assert pf._schema[1].type == fastparquet.parquet_thrift.Type.INT96
    out = dd.read_parquet(fn, index=False).compute()
    assert_eq(out, df)


@FASTPARQUET_MARK
def test_drill_scheme(tmpdir):
    fn = str(tmpdir)
    N = 5
    df1 = pd.DataFrame({c: np.random.random(N) for i, c in enumerate(["a", "b", "c"])})
    df2 = pd.DataFrame({c: np.random.random(N) for i, c in enumerate(["a", "b", "c"])})
    files = []
    for d in ["test_data1", "test_data2"]:
        dn = os.path.join(fn, d)
        if not os.path.exists(dn):
            os.mkdir(dn)
        files.append(os.path.join(dn, "data1.parq"))

    fastparquet.write(files[0], df1)
    fastparquet.write(files[1], df2)

    df = dd.read_parquet(files)
    assert "dir0" in df.columns
    out = df.compute()
    assert "dir0" in out
    assert (np.unique(out.dir0) == ["test_data1", "test_data2"]).all()


def test_parquet_select_cats(tmpdir, engine):
    fn = str(tmpdir)
    df = pd.DataFrame(
        {
            "categories": pd.Series(
                np.random.choice(["a", "b", "c", "d", "e", "f"], size=100),
                dtype="category",
            ),
            "ints": pd.Series(list(range(0, 100)), dtype="int"),
            "floats": pd.Series(list(range(0, 100)), dtype="float"),
        }
    )

    ddf = dd.from_pandas(df, 1)
    ddf.to_parquet(fn, engine=engine)
    rddf = dd.read_parquet(fn, columns=["ints"], engine=engine)
    assert list(rddf.columns) == ["ints"]
    rddf = dd.read_parquet(fn, engine=engine)
    assert list(rddf.columns) == list(df)


def test_columns_name(tmpdir, engine):
    if engine == "fastparquet" and fastparquet_version <= parse_version("0.3.1"):
        pytest.skip("Fastparquet does not write column_indexes up to 0.3.1")
    tmp_path = str(tmpdir)
    df = pd.DataFrame({"A": [1, 2]}, index=pd.Index(["a", "b"], name="idx"))
    df.columns.name = "cols"
    ddf = dd.from_pandas(df, 2)

    ddf.to_parquet(tmp_path, engine=engine)
    result = dd.read_parquet(tmp_path, engine=engine, index=["idx"])
    assert_eq(result, df)


def check_compression(engine, filename, compression):
    if engine == "fastparquet":
        pf = fastparquet.ParquetFile(filename)
        md = pf.fmd.row_groups[0].columns[0].meta_data
        if compression is None:
            assert md.total_compressed_size == md.total_uncompressed_size
        else:
            assert md.total_compressed_size != md.total_uncompressed_size
    else:
        metadata = pa.parquet.ParquetDataset(filename).metadata
        names = metadata.schema.names
        for i in range(metadata.num_row_groups):
            row_group = metadata.row_group(i)
            for j in range(len(names)):
                column = row_group.column(j)
                if compression is None:
                    assert (
                        column.total_compressed_size == column.total_uncompressed_size
                    )
                else:
                    compress_expect = compression
                    if compression == "default":
                        compress_expect = "snappy"
                    assert compress_expect.lower() == column.compression.lower()
                    assert (
                        column.total_compressed_size != column.total_uncompressed_size
                    )


@pytest.mark.parametrize("compression,", ["default", None, "gzip", "snappy"])
def test_writing_parquet_with_compression(tmpdir, compression, engine):
    fn = str(tmpdir)
    if compression in ["snappy", "default"]:
        pytest.importorskip("snappy")

    df = pd.DataFrame({"x": ["a", "b", "c"] * 10, "y": [1, 2, 3] * 10})
    df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=3)

    ddf.to_parquet(fn, compression=compression, engine=engine)
    out = dd.read_parquet(fn, engine=engine)
    assert_eq(out, ddf)
    check_compression(engine, fn, compression)


@pytest.mark.parametrize("compression,", ["default", None, "gzip", "snappy"])
def test_writing_parquet_with_partition_on_and_compression(tmpdir, compression, engine):
    fn = str(tmpdir)
    if compression in ["snappy", "default"]:
        pytest.importorskip("snappy")

    df = pd.DataFrame({"x": ["a", "b", "c"] * 10, "y": [1, 2, 3] * 10})
    df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=3)

    ddf.to_parquet(fn, compression=compression, engine=engine, partition_on=["x"])
    check_compression(engine, fn, compression)


@pytest.fixture(
    params=[
        # fastparquet 0.1.3
        {
            "columns": [
                {
                    "metadata": None,
                    "name": "idx",
                    "numpy_type": "int64",
                    "pandas_type": "int64",
                },
                {
                    "metadata": None,
                    "name": "A",
                    "numpy_type": "int64",
                    "pandas_type": "int64",
                },
            ],
            "index_columns": ["idx"],
            "pandas_version": "0.21.0",
        },
        # pyarrow 0.7.1
        {
            "columns": [
                {
                    "metadata": None,
                    "name": "A",
                    "numpy_type": "int64",
                    "pandas_type": "int64",
                },
                {
                    "metadata": None,
                    "name": "idx",
                    "numpy_type": "int64",
                    "pandas_type": "int64",
                },
            ],
            "index_columns": ["idx"],
            "pandas_version": "0.21.0",
        },
        # pyarrow 0.8.0
        {
            "column_indexes": [
                {
                    "field_name": None,
                    "metadata": {"encoding": "UTF-8"},
                    "name": None,
                    "numpy_type": "object",
                    "pandas_type": "unicode",
                }
            ],
            "columns": [
                {
                    "field_name": "A",
                    "metadata": None,
                    "name": "A",
                    "numpy_type": "int64",
                    "pandas_type": "int64",
                },
                {
                    "field_name": "__index_level_0__",
                    "metadata": None,
                    "name": "idx",
                    "numpy_type": "int64",
                    "pandas_type": "int64",
                },
            ],
            "index_columns": ["__index_level_0__"],
            "pandas_version": "0.21.0",
        },
        # TODO: fastparquet update
    ]
)
def pandas_metadata(request):
    return request.param


def test_parse_pandas_metadata(pandas_metadata):
    index_names, column_names, mapping, column_index_names = _parse_pandas_metadata(
        pandas_metadata
    )
    assert index_names == ["idx"]
    assert column_names == ["A"]
    assert column_index_names == [None]

    # for new pyarrow
    if pandas_metadata["index_columns"] == ["__index_level_0__"]:
        assert mapping == {"__index_level_0__": "idx", "A": "A"}
    else:
        assert mapping == {"idx": "idx", "A": "A"}

    assert isinstance(mapping, dict)


def test_parse_pandas_metadata_null_index():
    # pyarrow 0.7.1 None for index
    e_index_names = [None]
    e_column_names = ["x"]
    e_mapping = {"__index_level_0__": None, "x": "x"}
    e_column_index_names = [None]

    md = {
        "columns": [
            {
                "metadata": None,
                "name": "x",
                "numpy_type": "int64",
                "pandas_type": "int64",
            },
            {
                "metadata": None,
                "name": "__index_level_0__",
                "numpy_type": "int64",
                "pandas_type": "int64",
            },
        ],
        "index_columns": ["__index_level_0__"],
        "pandas_version": "0.21.0",
    }
    index_names, column_names, mapping, column_index_names = _parse_pandas_metadata(md)
    assert index_names == e_index_names
    assert column_names == e_column_names
    assert mapping == e_mapping
    assert column_index_names == e_column_index_names

    # pyarrow 0.8.0 None for index
    md = {
        "column_indexes": [
            {
                "field_name": None,
                "metadata": {"encoding": "UTF-8"},
                "name": None,
                "numpy_type": "object",
                "pandas_type": "unicode",
            }
        ],
        "columns": [
            {
                "field_name": "x",
                "metadata": None,
                "name": "x",
                "numpy_type": "int64",
                "pandas_type": "int64",
            },
            {
                "field_name": "__index_level_0__",
                "metadata": None,
                "name": None,
                "numpy_type": "int64",
                "pandas_type": "int64",
            },
        ],
        "index_columns": ["__index_level_0__"],
        "pandas_version": "0.21.0",
    }
    index_names, column_names, mapping, column_index_names = _parse_pandas_metadata(md)
    assert index_names == e_index_names
    assert column_names == e_column_names
    assert mapping == e_mapping
    assert column_index_names == e_column_index_names


@PYARROW_MARK
def test_read_no_metadata(tmpdir, engine):
    # use pyarrow.parquet to create a parquet file without
    # pandas metadata
    tmp = str(tmpdir) + "table.parq"

    table = pa.Table.from_arrays(
        [pa.array([1, 2, 3]), pa.array([3, 4, 5])], names=["A", "B"]
    )
    pq.write_table(table, tmp)
    result = dd.read_parquet(tmp, engine=engine)
    expected = pd.DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]})
    assert_eq(result, expected)


def test_parse_pandas_metadata_duplicate_index_columns():
    md = {
        "column_indexes": [
            {
                "field_name": None,
                "metadata": {"encoding": "UTF-8"},
                "name": None,
                "numpy_type": "object",
                "pandas_type": "unicode",
            }
        ],
        "columns": [
            {
                "field_name": "A",
                "metadata": None,
                "name": "A",
                "numpy_type": "int64",
                "pandas_type": "int64",
            },
            {
                "field_name": "__index_level_0__",
                "metadata": None,
                "name": "A",
                "numpy_type": "object",
                "pandas_type": "unicode",
            },
        ],
        "index_columns": ["__index_level_0__"],
        "pandas_version": "0.21.0",
    }
    (
        index_names,
        column_names,
        storage_name_mapping,
        column_index_names,
    ) = _parse_pandas_metadata(md)
    assert index_names == ["A"]
    assert column_names == ["A"]
    assert storage_name_mapping == {"__index_level_0__": "A", "A": "A"}
    assert column_index_names == [None]


def test_parse_pandas_metadata_column_with_index_name():
    md = {
        "column_indexes": [
            {
                "field_name": None,
                "metadata": {"encoding": "UTF-8"},
                "name": None,
                "numpy_type": "object",
                "pandas_type": "unicode",
            }
        ],
        "columns": [
            {
                "field_name": "A",
                "metadata": None,
                "name": "A",
                "numpy_type": "int64",
                "pandas_type": "int64",
            },
            {
                "field_name": "__index_level_0__",
                "metadata": None,
                "name": "A",
                "numpy_type": "object",
                "pandas_type": "unicode",
            },
        ],
        "index_columns": ["__index_level_0__"],
        "pandas_version": "0.21.0",
    }
    (
        index_names,
        column_names,
        storage_name_mapping,
        column_index_names,
    ) = _parse_pandas_metadata(md)
    assert index_names == ["A"]
    assert column_names == ["A"]
    assert storage_name_mapping == {"__index_level_0__": "A", "A": "A"}
    assert column_index_names == [None]


def test_writing_parquet_with_kwargs(tmpdir, engine):
    fn = str(tmpdir)
    path1 = os.path.join(fn, "normal")
    path2 = os.path.join(fn, "partitioned")
    pytest.importorskip("snappy")

    df = pd.DataFrame(
        {
            "a": np.random.choice(["A", "B", "C"], size=100),
            "b": np.random.random(size=100),
            "c": np.random.randint(1, 5, size=100),
        }
    )
    df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=3)

    engine_kwargs = {
        "pyarrow-dataset": {
            "compression": "snappy",
            "coerce_timestamps": None,
            "use_dictionary": True,
        },
        "fastparquet": {"compression": "snappy", "times": "int64", "fixed_text": None},
    }
    engine_kwargs["pyarrow-legacy"] = engine_kwargs["pyarrow-dataset"]

    ddf.to_parquet(path1, engine=engine, **engine_kwargs[engine])
    out = dd.read_parquet(path1, engine=engine)
    assert_eq(out, ddf, check_index=(engine != "fastparquet"))

    # Avoid race condition in pyarrow 0.8.0 on writing partitioned datasets
    with dask.config.set(scheduler="sync"):
        ddf.to_parquet(
            path2, engine=engine, partition_on=["a"], **engine_kwargs[engine]
        )
    out = dd.read_parquet(path2, engine=engine).compute()
    for val in df.a.unique():
        assert set(df.b[df.a == val]) == set(out.b[out.a == val])


def test_writing_parquet_with_unknown_kwargs(tmpdir, engine):
    fn = str(tmpdir)

    with pytest.raises(TypeError):
        ddf.to_parquet(fn, engine=engine, unknown_key="unknown_value")


@ANY_ENGINE_MARK
def test_to_parquet_with_get(tmpdir):
    from dask.multiprocessing import get as mp_get

    tmpdir = str(tmpdir)

    flag = [False]

    def my_get(*args, **kwargs):
        flag[0] = True
        return mp_get(*args, **kwargs)

    df = pd.DataFrame({"x": ["a", "b", "c", "d"], "y": [1, 2, 3, 4]})
    ddf = dd.from_pandas(df, npartitions=2)

    ddf.to_parquet(tmpdir, compute_kwargs={"scheduler": my_get})
    assert flag[0]

    result = dd.read_parquet(os.path.join(tmpdir, "*"))
    assert_eq(result, df, check_index=False)


def test_select_partitioned_column(tmpdir, engine):
    pytest.importorskip("snappy")

    fn = str(tmpdir)
    size = 20
    d = {
        "signal1": np.random.normal(0, 0.3, size=size).cumsum() + 50,
        "fake_categorical1": np.random.choice(["A", "B", "C"], size=size),
        "fake_categorical2": np.random.choice(["D", "E", "F"], size=size),
    }
    df = dd.from_pandas(pd.DataFrame(d), 2)
    df.to_parquet(
        fn,
        compression="snappy",
        write_index=False,
        engine=engine,
        partition_on=["fake_categorical1", "fake_categorical2"],
    )

    df_partitioned = dd.read_parquet(fn, engine=engine)
    df_partitioned[df_partitioned.fake_categorical1 == "A"].compute()


def test_with_tz(tmpdir, engine):
    if engine == "fastparquet" and fastparquet_version < parse_version("0.3.0"):
        pytest.skip("fastparquet<0.3.0 did not support this")

    with warnings.catch_warnings():
        if engine == "fastparquet":
            # fastparquet-442
            warnings.simplefilter("ignore", FutureWarning)  # pandas 0.25
            fn = str(tmpdir)
            df = pd.DataFrame([[0]], columns=["a"], dtype="datetime64[ns, UTC]")
            df = dd.from_pandas(df, 1)
            df.to_parquet(fn, engine=engine)
            df2 = dd.read_parquet(fn, engine=engine)
            assert_eq(df, df2, check_divisions=False, check_index=False)


@PYARROW_MARK
def test_arrow_partitioning(tmpdir):
    # Issue #3518
    path = str(tmpdir)
    data = {
        "p": np.repeat(np.arange(3), 2).astype(np.int8),
        "b": np.repeat(-1, 6).astype(np.int16),
        "c": np.repeat(-2, 6).astype(np.float32),
        "d": np.repeat(-3, 6).astype(np.float64),
    }
    pdf = pd.DataFrame(data)
    ddf = dd.from_pandas(pdf, npartitions=2)
    ddf.to_parquet(path, engine="pyarrow", write_index=False, partition_on="p")

    ddf = dd.read_parquet(path, index=False, engine="pyarrow")

    ddf.astype({"b": np.float32}).compute()


def test_informative_error_messages():
    with pytest.raises(ValueError) as info:
        dd.read_parquet("foo", engine="foo")

    assert "foo" in str(info.value)
    assert "arrow" in str(info.value)
    assert "fastparquet" in str(info.value)


def test_append_cat_fp(tmpdir, engine):
    path = str(tmpdir)
    # https://github.com/dask/dask/issues/4120
    df = pd.DataFrame({"x": ["a", "a", "b", "a", "b"]})
    df["x"] = df["x"].astype("category")
    ddf = dd.from_pandas(df, npartitions=1)

    dd.to_parquet(ddf, path, engine=engine)
    dd.to_parquet(ddf, path, append=True, ignore_divisions=True, engine=engine)

    d = dd.read_parquet(path, engine=engine).compute()
    assert d["x"].tolist() == ["a", "a", "b", "a", "b"] * 2


@PYARROW_MARK
@pytest.mark.parametrize(
    "df",
    [
        pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]}),
        pd.DataFrame({"x": ["c", "a", "b"]}),
        pd.DataFrame({"x": ["cc", "a", "bbb"]}),
        pd.DataFrame({"x": [b"a", b"b", b"c"]}),
        pytest.param(pd.DataFrame({"x": pd.Categorical(["a", "b", "a"])})),
        pytest.param(pd.DataFrame({"x": pd.Categorical([1, 2, 1])})),
        pd.DataFrame({"x": list(map(pd.Timestamp, [3000000, 2000000, 1000000]))}),  # ms
        pd.DataFrame({"x": list(map(pd.Timestamp, [3000, 2000, 1000]))}),  # us
        pd.DataFrame({"x": [3000, 2000, 1000]}).astype("M8[ns]"),
        # pd.DataFrame({'x': [3, 2, 1]}).astype('M8[ns]'), # Casting errors
        pd.DataFrame({"x": [3, 2, 1]}).astype("M8[us]"),
        pd.DataFrame({"x": [3, 2, 1]}).astype("M8[ms]"),
        pd.DataFrame({"x": [3, 2, 1]}).astype("uint16"),
        pd.DataFrame({"x": [3, 2, 1]}).astype("float32"),
        pd.DataFrame({"x": [3, 1, 2]}, index=[3, 2, 1]),
        pd.DataFrame(
            {"x": [4, 5, 6, 1, 2, 3]}, index=pd.Index([1, 2, 3, 4, 5, 6], name="foo")
        ),
        pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}),
        pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}, columns=["y", "x"]),
        pd.DataFrame({"0": [3, 2, 1]}),
        pd.DataFrame({"x": [3, 2, None]}),
        pd.DataFrame({"-": [3.0, 2.0, None]}),
        pd.DataFrame({".": [3.0, 2.0, None]}),
        pd.DataFrame({" ": [3.0, 2.0, None]}),
    ],
)
def test_roundtrip_arrow(tmpdir, df):
    # Index will be given a name when preserved as index
    tmp_path = str(tmpdir)
    if not df.index.name:
        df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=2)
    dd.to_parquet(ddf, tmp_path, engine="pyarrow", write_index=True)
    ddf2 = dd.read_parquet(tmp_path, engine="pyarrow", gather_statistics=True)
    assert_eq(ddf, ddf2)


def test_datasets_timeseries(tmpdir, engine):
    tmp_path = str(tmpdir)
    df = dask.datasets.timeseries(
        start="2000-01-01", end="2000-01-10", freq="1d"
    ).persist()
    df.to_parquet(tmp_path, engine=engine)

    df2 = dd.read_parquet(tmp_path, engine=engine)
    assert_eq(df, df2)


def test_pathlib_path(tmpdir, engine):
    import pathlib

    df = pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]})
    df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=2)
    path = pathlib.Path(str(tmpdir))
    ddf.to_parquet(path, engine=engine)
    ddf2 = dd.read_parquet(path, engine=engine)
    assert_eq(ddf, ddf2)


@PYARROW_LE_MARK
def test_pyarrow_metadata_nthreads(tmpdir):
    tmp_path = str(tmpdir)
    df = pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]})
    df.index.name = "index"
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(tmp_path, engine="pyarrow")
    ops = {"dataset": {"metadata_nthreads": 2}}
    ddf2 = dd.read_parquet(tmp_path, engine="pyarrow-legacy", **ops)
    assert_eq(ddf, ddf2)


@FASTPARQUET_MARK
def test_categories_large(tmpdir, engine):
    # Issue #5112
    fn = str(tmpdir.join("parquet_int16.parq"))
    numbers = np.random.randint(0, 800000, size=1000000)
    df = pd.DataFrame(numbers.T, columns=["name"])
    df.name = df.name.astype("category")

    df.to_parquet(fn, engine="fastparquet", compression="uncompressed")
    ddf = dd.read_parquet(fn, engine=engine, categories={"name": 80000})

    assert_eq(sorted(df.name.cat.categories), sorted(ddf.compute().name.cat.categories))


@write_read_engines()
def test_read_glob_no_meta(tmpdir, write_engine, read_engine):
    tmp_path = str(tmpdir)
    ddf.to_parquet(tmp_path, engine=write_engine)

    ddf2 = dd.read_parquet(
        os.path.join(tmp_path, "*.parquet"), engine=read_engine, gather_statistics=False
    )
    assert_eq(ddf, ddf2, check_divisions=False)


@write_read_engines()
def test_read_glob_yes_meta(tmpdir, write_engine, read_engine):
    tmp_path = str(tmpdir)
    ddf.to_parquet(tmp_path, engine=write_engine)
    paths = glob.glob(os.path.join(tmp_path, "*.parquet"))
    paths.append(os.path.join(tmp_path, "_metadata"))
    ddf2 = dd.read_parquet(paths, engine=read_engine, gather_statistics=False)
    assert_eq(ddf, ddf2, check_divisions=False)


@pytest.mark.parametrize("statistics", [True, False, None])
@pytest.mark.parametrize("remove_common", [True, False])
@write_read_engines()
def test_read_dir_nometa(tmpdir, write_engine, read_engine, statistics, remove_common):
    tmp_path = str(tmpdir)
    ddf.to_parquet(tmp_path, engine=write_engine)
    if os.path.exists(os.path.join(tmp_path, "_metadata")):
        os.unlink(os.path.join(tmp_path, "_metadata"))
    files = os.listdir(tmp_path)
    assert "_metadata" not in files

    if remove_common and os.path.exists(os.path.join(tmp_path, "_common_metadata")):
        os.unlink(os.path.join(tmp_path, "_common_metadata"))

    ddf2 = dd.read_parquet(tmp_path, engine=read_engine, gather_statistics=statistics)
    assert_eq(ddf, ddf2, check_divisions=False)
    assert ddf.divisions == tuple(range(0, 420, 30))
    if statistics is False or statistics is None and read_engine.startswith("pyarrow"):
        assert ddf2.divisions == (None,) * 14
    else:
        assert ddf2.divisions == tuple(range(0, 420, 30))


@write_read_engines()
def test_statistics_nometa(tmpdir, write_engine, read_engine):
    tmp_path = str(tmpdir)
    ddf.to_parquet(tmp_path, engine=write_engine, write_metadata_file=False)

    ddf2 = dd.read_parquet(tmp_path, engine=read_engine, gather_statistics=True)
    assert_eq(ddf, ddf2)
    assert ddf.divisions == tuple(range(0, 420, 30))
    assert ddf2.divisions == tuple(range(0, 420, 30))


@pytest.mark.parametrize("schema", ["infer", None])
def test_timeseries_nulls_in_schema(tmpdir, engine, schema):
    # GH#5608: relative path failing _metadata/_common_metadata detection.
    tmp_path = str(tmpdir.mkdir("files"))
    tmp_path = os.path.join(tmp_path, "../", "files")

    ddf2 = (
        dask.datasets.timeseries(start="2000-01-01", end="2000-01-03", freq="1h")
        .reset_index()
        .map_partitions(lambda x: x.loc[:5])
    )
    ddf2 = ddf2.set_index("x").reset_index().persist()
    ddf2.name = ddf2.name.where(ddf2.timestamp == "2000-01-01", None)

    # Note: `append_row_groups` will fail with pyarrow>0.17.1 for _metadata write
    dataset = {"validate_schema": False} if engine == "pyarrow-legacy" else {}
    ddf2.to_parquet(tmp_path, engine=engine, write_metadata_file=False, schema=schema)
    ddf_read = dd.read_parquet(tmp_path, engine=engine, dataset=dataset)

    assert_eq(ddf_read, ddf2, check_divisions=False, check_index=False)


@PYARROW_LE_MARK
@pytest.mark.parametrize("numerical", [True, False])
@pytest.mark.parametrize(
    "timestamp", ["2000-01-01", "2000-01-02", "2000-01-03", "2000-01-04"]
)
def test_timeseries_nulls_in_schema_pyarrow(tmpdir, timestamp, numerical):
    tmp_path = str(tmpdir)
    ddf2 = dd.from_pandas(
        pd.DataFrame(
            {
                "timestamp": [
                    pd.Timestamp("2000-01-01"),
                    pd.Timestamp("2000-01-02"),
                    pd.Timestamp("2000-01-03"),
                    pd.Timestamp("2000-01-04"),
                ],
                "id": np.arange(4, dtype="float64"),
                "name": ["cat", "dog", "bird", "cow"],
            }
        ),
        npartitions=2,
    ).persist()
    if numerical:
        ddf2.id = ddf2.id.where(ddf2.timestamp == timestamp, None)
        ddf2.id = ddf2.id.astype("float64")
    else:
        ddf2.name = ddf2.name.where(ddf2.timestamp == timestamp, None)

    # There should be no schema error if you specify a schema on write
    schema = pa.schema(
        [("timestamp", pa.timestamp("ns")), ("id", pa.float64()), ("name", pa.string())]
    )
    ddf2.to_parquet(tmp_path, schema=schema, write_index=False, engine="pyarrow")
    assert_eq(
        dd.read_parquet(
            tmp_path,
            dataset={"validate_schema": True},
            index=False,
            engine="pyarrow-legacy",
        ),
        ddf2,
        check_divisions=False,
        check_index=False,
    )


@PYARROW_LE_MARK
def test_read_inconsistent_schema_pyarrow(tmpdir):
    # Note: This is a proxy test for a cudf-related issue fix
    # (see cudf#5062 github issue).  The cause of that issue is
    # schema inconsistencies that do not actually correspond to
    # different types, but whether or not the file/column contains
    # null values.

    df1 = pd.DataFrame({"id": [0, 1], "val": [10, 20]})
    df2 = pd.DataFrame({"id": [2, 3], "val": [30, 40]})

    desired_type = "int64"
    other_type = "int32"
    df1.val = df1.val.astype(desired_type)
    df2.val = df2.val.astype(other_type)

    df_expect = pd.concat([df1, df2], ignore_index=True)
    df_expect["val"] = df_expect.val.astype(desired_type)

    df1.to_parquet(os.path.join(tmpdir, "0.parquet"), engine="pyarrow")
    df2.to_parquet(os.path.join(tmpdir, "1.parquet"), engine="pyarrow")

    # Read Directory
    check = dd.read_parquet(
        str(tmpdir), dataset={"validate_schema": False}, engine="pyarrow-legacy"
    )
    assert_eq(check.compute(), df_expect, check_index=False)

    # Read List
    check = dd.read_parquet(
        os.path.join(tmpdir, "*.parquet"),
        dataset={"validate_schema": False},
        engine="pyarrow-legacy",
    )
    assert_eq(check.compute(), df_expect, check_index=False)


def test_graph_size_pyarrow(tmpdir, engine):
    import pickle

    fn = str(tmpdir)

    ddf1 = dask.datasets.timeseries(
        start="2000-01-01", end="2000-01-02", freq="60S", partition_freq="1H"
    )

    ddf1.to_parquet(fn, engine=engine)
    ddf2 = dd.read_parquet(fn, engine=engine)

    assert len(pickle.dumps(ddf2.__dask_graph__())) < 25000


@pytest.mark.parametrize("preserve_index", [True, False])
@pytest.mark.parametrize("index", [None, np.random.permutation(2000)])
def test_getitem_optimization(tmpdir, engine, preserve_index, index):
    tmp_path_rd = str(tmpdir.mkdir("read"))
    tmp_path_wt = str(tmpdir.mkdir("write"))
    df = pd.DataFrame(
        {"A": [1, 2] * 1000, "B": [3, 4] * 1000, "C": [5, 6] * 1000}, index=index
    )
    df.index.name = "my_index"
    ddf = dd.from_pandas(df, 2, sort=False)

    ddf.to_parquet(tmp_path_rd, engine=engine, write_index=preserve_index)
    ddf = dd.read_parquet(tmp_path_rd, engine=engine)["B"]

    # Write ddf back to disk to check that the round trip
    # preserves the getitem optimization
    out = ddf.to_frame().to_parquet(tmp_path_wt, engine=engine, compute=False)
    dsk = optimize_dataframe_getitem(out.dask, keys=[out.key])

    subgraph_rd = hlg_layer(dsk, "read-parquet")
    assert isinstance(subgraph_rd, DataFrameIOLayer)
    assert subgraph_rd.columns == ["B"]
    assert next(iter(subgraph_rd.dsk.values()))[0].columns == ["B"]

    subgraph_wt = hlg_layer(dsk, "to-parquet")
    assert isinstance(subgraph_wt, Blockwise)

    assert_eq(ddf.compute(optimize_graph=False), ddf.compute())


def test_getitem_optimization_empty(tmpdir, engine):
    df = pd.DataFrame({"A": [1] * 100, "B": [2] * 100, "C": [3] * 100, "D": [4] * 100})
    ddf = dd.from_pandas(df, 2)
    fn = os.path.join(str(tmpdir))
    ddf.to_parquet(fn, engine=engine)

    df2 = dd.read_parquet(fn, columns=[], engine=engine)
    dsk = optimize_dataframe_getitem(df2.dask, keys=[df2._name])

    subgraph = next(iter(dsk.layers.values()))
    assert isinstance(subgraph, DataFrameIOLayer)
    assert subgraph.columns == []


def test_getitem_optimization_multi(tmpdir, engine):
    df = pd.DataFrame({"A": [1] * 100, "B": [2] * 100, "C": [3] * 100, "D": [4] * 100})
    ddf = dd.from_pandas(df, 2)
    fn = os.path.join(str(tmpdir))
    ddf.to_parquet(fn, engine=engine)

    a = dd.read_parquet(fn, engine=engine)["B"]
    b = dd.read_parquet(fn, engine=engine)[["C"]]
    c = dd.read_parquet(fn, engine=engine)[["C", "A"]]

    a1, a2, a3 = dask.compute(a, b, c)
    b1, b2, b3 = dask.compute(a, b, c, optimize_graph=False)

    assert_eq(a1, b1)
    assert_eq(a2, b2)
    assert_eq(a3, b3)


def test_layer_creation_info(tmpdir, engine):
    df = pd.DataFrame({"a": range(10), "b": ["cat", "dog"] * 5})
    dd.from_pandas(df, npartitions=1).to_parquet(
        tmpdir, engine=engine, partition_on=["b"]
    )

    # Apply filters directly in dd.read_parquet
    filters = [("b", "==", "cat")]
    ddf1 = dd.read_parquet(tmpdir, engine=engine, filters=filters)
    assert "dog" not in ddf1["b"].compute()

    # Results will not match if we use dd.read_parquet
    # without filters
    ddf2 = dd.read_parquet(tmpdir, engine=engine)
    with pytest.raises(AssertionError):
        assert_eq(ddf1, ddf2)

    # However, we can use `creation_info` to regenerate
    # the same collection with `filters` defined
    info = ddf2.dask.layers[ddf2._name].creation_info
    kwargs = info.get("kwargs", {})
    kwargs["filters"] = filters
    ddf3 = info["func"](*info.get("args", []), **kwargs)
    assert_eq(ddf1, ddf3)


@ANY_ENGINE_MARK
def test_blockwise_parquet_annotations(tmpdir):
    df = pd.DataFrame({"a": np.arange(40, dtype=np.int32)})
    expect = dd.from_pandas(df, npartitions=2)
    expect.to_parquet(str(tmpdir))

    with dask.annotate(foo="bar"):
        ddf = dd.read_parquet(str(tmpdir))

    # `ddf` should now have ONE Blockwise layer
    layers = ddf.__dask_graph__().layers
    assert len(layers) == 1
    layer = next(iter(layers.values()))
    assert isinstance(layer, DataFrameIOLayer)
    assert layer.annotations == {"foo": "bar"}


@ANY_ENGINE_MARK
def test_optimize_blockwise_parquet(tmpdir):
    size = 40
    npartitions = 2
    tmp = str(tmpdir)
    df = pd.DataFrame({"a": np.arange(size, dtype=np.int32)})
    expect = dd.from_pandas(df, npartitions=npartitions)
    expect.to_parquet(tmp)
    ddf = dd.read_parquet(tmp)

    # `ddf` should now have ONE Blockwise layer
    layers = ddf.__dask_graph__().layers
    assert len(layers) == 1
    assert isinstance(list(layers.values())[0], Blockwise)

    # Check single-layer result
    assert_eq(ddf, expect)

    # Increment by 1
    ddf += 1
    expect += 1

    # Increment by 10
    ddf += 10
    expect += 10

    # `ddf` should now have THREE Blockwise layers
    layers = ddf.__dask_graph__().layers
    assert len(layers) == 3
    assert all(isinstance(layer, Blockwise) for layer in layers.values())

    # Check that `optimize_blockwise` fuses all three
    # `Blockwise` layers together into a singe `Blockwise` layer
    keys = [(ddf._name, i) for i in range(npartitions)]
    graph = optimize_blockwise(ddf.__dask_graph__(), keys)
    layers = graph.layers
    name = list(layers.keys())[0]
    assert len(layers) == 1
    assert isinstance(layers[name], Blockwise)

    # Check final result
    assert_eq(ddf, expect)


@PYARROW_MARK
def test_split_row_groups(tmpdir, engine):
    """Test split_row_groups read_parquet kwarg"""
    tmp = str(tmpdir)
    df = pd.DataFrame(
        {"i32": np.arange(800, dtype=np.int32), "f": np.arange(800, dtype=np.float64)}
    )
    df.index.name = "index"

    half = len(df) // 2
    dd.from_pandas(df.iloc[:half], npartitions=2).to_parquet(
        tmp, engine="pyarrow", row_group_size=100
    )

    ddf3 = dd.read_parquet(tmp, engine=engine, split_row_groups=True, chunksize=1)
    assert ddf3.npartitions == 4

    ddf3 = dd.read_parquet(
        tmp, engine=engine, gather_statistics=True, split_row_groups=False
    )
    assert ddf3.npartitions == 2

    dd.from_pandas(df.iloc[half:], npartitions=2).to_parquet(
        tmp, append=True, engine="pyarrow", row_group_size=50
    )

    ddf3 = dd.read_parquet(
        tmp,
        engine=engine,
        gather_statistics=True,
        split_row_groups=True,
        chunksize=1,
    )
    assert ddf3.npartitions == 12

    ddf3 = dd.read_parquet(
        tmp, engine=engine, gather_statistics=True, split_row_groups=False
    )
    assert ddf3.npartitions == 4


@PYARROW_MARK
@pytest.mark.parametrize("split_row_groups", [1, 12])
@pytest.mark.parametrize("gather_statistics", [True, False])
def test_split_row_groups_int(tmpdir, split_row_groups, gather_statistics, engine):
    tmp = str(tmpdir)
    row_group_size = 10
    npartitions = 4
    half_size = 400
    df = pd.DataFrame(
        {
            "i32": np.arange(2 * half_size, dtype=np.int32),
            "f": np.arange(2 * half_size, dtype=np.float64),
        }
    )
    half = len(df) // 2

    dd.from_pandas(df.iloc[:half], npartitions=npartitions).to_parquet(
        tmp, engine="pyarrow", row_group_size=row_group_size
    )
    dd.from_pandas(df.iloc[half:], npartitions=npartitions).to_parquet(
        tmp, append=True, engine="pyarrow", row_group_size=row_group_size
    )

    ddf2 = dd.read_parquet(
        tmp,
        engine=engine,
        split_row_groups=split_row_groups,
        gather_statistics=gather_statistics,
    )
    expected_rg_cout = int(half_size / row_group_size)
    assert ddf2.npartitions == 2 * math.ceil(expected_rg_cout / split_row_groups)


@PYARROW_MARK
@pytest.mark.parametrize("split_row_groups", [8, 25])
def test_split_row_groups_int_aggregate_files(tmpdir, engine, split_row_groups):
    # Use pyarrow to write a multi-file dataset with
    # multiple row-groups per file
    row_group_size = 10
    size = 800
    df = pd.DataFrame(
        {
            "i32": np.arange(size, dtype=np.int32),
            "f": np.arange(size, dtype=np.float64),
        }
    )
    dd.from_pandas(df, npartitions=4).to_parquet(
        str(tmpdir), engine="pyarrow", row_group_size=row_group_size, write_index=False
    )

    # Read back with both `split_row_groups>1` and
    # `aggregate_files=True`
    ddf2 = dd.read_parquet(
        str(tmpdir),
        engine=engine,
        split_row_groups=split_row_groups,
        aggregate_files=True,
    )

    # Check that we are aggregating files as expected
    npartitions_expected = math.ceil((size / row_group_size) / split_row_groups)
    assert ddf2.npartitions == npartitions_expected
    assert len(ddf2) == size
    assert_eq(df, ddf2, check_index=False)


@PYARROW_MARK
def test_split_row_groups_filter(tmpdir, engine):
    tmp = str(tmpdir)
    df = pd.DataFrame(
        {"i32": np.arange(800, dtype=np.int32), "f": np.arange(800, dtype=np.float64)}
    )
    df.index.name = "index"
    search_val = 600
    filters = [("f", "==", search_val)]

    dd.from_pandas(df, npartitions=4).to_parquet(
        tmp, append=True, engine="pyarrow", row_group_size=50
    )

    ddf2 = dd.read_parquet(tmp, engine=engine)
    ddf3 = dd.read_parquet(
        tmp,
        engine=engine,
        gather_statistics=True,
        split_row_groups=True,
        filters=filters,
    )

    assert (ddf3["i32"] == search_val).any().compute()
    assert_eq(
        ddf2[ddf2["i32"] == search_val].compute(),
        ddf3[ddf3["i32"] == search_val].compute(),
    )


@ANY_ENGINE_MARK
def test_optimize_getitem_and_nonblockwise(tmpdir):
    path = os.path.join(tmpdir, "path.parquet")
    df = pd.DataFrame(
        {"a": [3, 4, 2], "b": [1, 2, 4], "c": [5, 4, 2], "d": [1, 2, 3]},
        index=["a", "b", "c"],
    )
    df.to_parquet(path)

    df2 = dd.read_parquet(path)
    df2[["a", "b"]].rolling(3).max().compute()


@ANY_ENGINE_MARK
def test_optimize_and_not(tmpdir):
    path = os.path.join(tmpdir, "path.parquet")
    df = pd.DataFrame(
        {"a": [3, 4, 2], "b": [1, 2, 4], "c": [5, 4, 2], "d": [1, 2, 3]},
        index=["a", "b", "c"],
    )
    df.to_parquet(path)

    df2 = dd.read_parquet(path)
    df2a = df2["a"].groupby(df2["c"]).first().to_delayed()
    df2b = df2["b"].groupby(df2["c"]).first().to_delayed()
    df2c = df2[["a", "b"]].rolling(2).max().to_delayed()
    df2d = df2.rolling(2).max().to_delayed()
    (result,) = dask.compute(df2a + df2b + df2c + df2d)

    expected = [
        dask.compute(df2a)[0][0],
        dask.compute(df2b)[0][0],
        dask.compute(df2c)[0][0],
        dask.compute(df2d)[0][0],
    ]
    for a, b in zip(result, expected):
        assert_eq(a, b)


@write_read_engines()
def test_chunksize_empty(tmpdir, write_engine, read_engine):
    df = pd.DataFrame({"a": pd.Series(dtype="int"), "b": pd.Series(dtype="float")})
    ddf1 = dd.from_pandas(df, npartitions=1)
    ddf1.to_parquet(tmpdir, engine=write_engine)
    ddf2 = dd.read_parquet(tmpdir, engine=read_engine, chunksize="1MiB")
    assert_eq(ddf1, ddf2, check_index=False)


@PYARROW_MARK
@pytest.mark.parametrize("metadata", [True, False])
@pytest.mark.parametrize("partition_on", [None, "a"])
@pytest.mark.parametrize("chunksize", [4096, "1MiB"])
@write_read_engines()
def test_chunksize_files(
    tmpdir, chunksize, partition_on, write_engine, read_engine, metadata
):

    if partition_on and read_engine == "fastparquet" and not metadata:
        pytest.skip("Fastparquet requires _metadata for partitioned data.")

    df_size = 100
    df1 = pd.DataFrame(
        {
            "a": np.random.choice(["apple", "banana", "carrot"], size=df_size),
            "b": np.random.random(size=df_size),
            "c": np.random.randint(1, 5, size=df_size),
        }
    )
    ddf1 = dd.from_pandas(df1, npartitions=9)

    ddf1.to_parquet(
        str(tmpdir),
        engine=write_engine,
        partition_on=partition_on,
        write_metadata_file=metadata,
        write_index=False,
    )

    ddf2 = dd.read_parquet(
        str(tmpdir),
        engine=read_engine,
        chunksize=chunksize,
        aggregate_files=partition_on if partition_on else True,
    )

    # Check that files where aggregated as expected
    if chunksize == 4096:
        assert ddf2.npartitions < ddf1.npartitions
    elif chunksize == "1MiB":
        if partition_on:
            assert ddf2.npartitions == 3
        else:
            assert ddf2.npartitions == 1

    # Check that the final data is correct
    if partition_on:
        df2 = ddf2.compute().sort_values(["b", "c"])
        df1 = df1.sort_values(["b", "c"])
        assert_eq(df1[["b", "c"]], df2[["b", "c"]], check_index=False)
    else:
        assert_eq(ddf1, ddf2, check_divisions=False, check_index=False)


@write_read_engines()
@pytest.mark.parametrize("aggregate_files", ["a", "b"])
def test_chunksize_aggregate_files(tmpdir, write_engine, read_engine, aggregate_files):

    chunksize = "1MiB"
    partition_on = ["a", "b"]
    df_size = 100
    df1 = pd.DataFrame(
        {
            "a": np.random.choice(["apple", "banana", "carrot"], size=df_size),
            "b": np.random.choice(["small", "large"], size=df_size),
            "c": np.random.random(size=df_size),
            "d": np.random.randint(1, 100, size=df_size),
        }
    )
    ddf1 = dd.from_pandas(df1, npartitions=9)

    ddf1.to_parquet(
        str(tmpdir),
        engine=write_engine,
        partition_on=partition_on,
        write_index=False,
    )
    ddf2 = dd.read_parquet(
        str(tmpdir),
        engine=read_engine,
        chunksize=chunksize,
        aggregate_files=aggregate_files,
    )

    # Check that files where aggregated as expected
    if aggregate_files == "a":
        assert ddf2.npartitions == 3
    elif aggregate_files == "b":
        assert ddf2.npartitions == 6

    # Check that the final data is correct
    df2 = ddf2.compute().sort_values(["c", "d"])
    df1 = df1.sort_values(["c", "d"])
    assert_eq(df1[["c", "d"]], df2[["c", "d"]], check_index=False)


@pytest.mark.parametrize("metadata", [True, False])
@pytest.mark.parametrize("chunksize", [None, 1024, 4096, "1MiB"])
def test_chunksize(tmpdir, chunksize, engine, metadata):
    nparts = 2
    df_size = 100
    row_group_size = 5

    df = pd.DataFrame(
        {
            "a": np.random.choice(["apple", "banana", "carrot"], size=df_size),
            "b": np.random.random(size=df_size),
            "c": np.random.randint(1, 5, size=df_size),
            "index": np.arange(0, df_size),
        }
    ).set_index("index")

    ddf1 = dd.from_pandas(df, npartitions=nparts)
    ddf1.to_parquet(
        str(tmpdir),
        engine="pyarrow",
        row_group_size=row_group_size,
        write_metadata_file=metadata,
    )

    if metadata:
        path = str(tmpdir)
    else:
        dirname = str(tmpdir)
        files = os.listdir(dirname)
        assert "_metadata" not in files
        path = os.path.join(dirname, "*.parquet")

    ddf2 = dd.read_parquet(
        path,
        engine=engine,
        chunksize=chunksize,
        split_row_groups=True,
        gather_statistics=True,
        index="index",
        aggregate_files=True,
    )

    assert_eq(ddf1, ddf2, check_divisions=False)

    num_row_groups = df_size // row_group_size
    if not chunksize:
        assert ddf2.npartitions == num_row_groups
    else:
        # Check that we are really aggregating
        assert ddf2.npartitions < num_row_groups
        if chunksize == "1MiB":
            # Largest chunksize will result in
            # a single output partition
            assert ddf2.npartitions == 1


@write_read_engines()
def test_roundtrip_pandas_chunksize(tmpdir, write_engine, read_engine):
    path = str(tmpdir.join("test.parquet"))
    pdf = df.copy()
    pdf.index.name = "index"
    pdf.to_parquet(
        path, engine="pyarrow" if write_engine.startswith("pyarrow") else "fastparquet"
    )

    ddf_read = dd.read_parquet(
        path,
        engine=read_engine,
        chunksize="10 kiB",
        gather_statistics=True,
        split_row_groups=True,
        index="index",
    )

    assert_eq(pdf, ddf_read)


@FASTPARQUET_MARK
def test_read_pandas_fastparquet_partitioned(tmpdir, engine):
    pdf = pd.DataFrame(
        [{"str": str(i), "int": i, "group": "ABC"[i % 3]} for i in range(6)]
    )
    path = str(tmpdir)
    pdf.to_parquet(path, partition_cols=["group"], engine="fastparquet")
    ddf_read = dd.read_parquet(path, engine=engine)

    assert len(ddf_read["group"].compute()) == 6
    assert len(ddf_read.compute().group) == 6


def test_read_parquet_getitem_skip_when_getting_read_parquet(tmpdir, engine):
    # https://github.com/dask/dask/issues/5893
    pdf = pd.DataFrame({"A": [1, 2, 3, 4, 5, 6], "B": ["a", "b", "c", "d", "e", "f"]})
    path = os.path.join(str(tmpdir), "data.parquet")
    pd_engine = "pyarrow" if engine.startswith("pyarrow") else "fastparquet"
    pdf.to_parquet(path, engine=pd_engine)

    ddf = dd.read_parquet(path, engine=engine)
    a, b = dask.optimize(ddf["A"], ddf)

    # Make sure we are still allowing the getitem optimization
    ddf = ddf["A"]
    dsk = optimize_dataframe_getitem(ddf.dask, keys=[(ddf._name, 0)])
    read = [key for key in dsk.layers if key.startswith("read-parquet")][0]
    subgraph = dsk.layers[read]
    assert isinstance(subgraph, DataFrameIOLayer)
    assert subgraph.columns == ["A"]


@pytest.mark.parametrize("gather_statistics", [None, True])
@write_read_engines()
def test_filter_nonpartition_columns(
    tmpdir, write_engine, read_engine, gather_statistics
):
    tmpdir = str(tmpdir)
    df_write = pd.DataFrame(
        {
            "id": [1, 2, 3, 4] * 4,
            "time": np.arange(16),
            "random": np.random.choice(["cat", "dog"], size=16),
        }
    )
    ddf_write = dd.from_pandas(df_write, npartitions=4)
    ddf_write.to_parquet(
        tmpdir, write_index=False, partition_on=["id"], engine=write_engine
    )
    ddf_read = dd.read_parquet(
        tmpdir,
        index=False,
        engine=read_engine,
        gather_statistics=gather_statistics,
        filters=[(("time", "<", 5))],
    )
    df_read = ddf_read.compute()
    assert len(df_read) == len(df_read[df_read["time"] < 5])
    assert df_read["time"].max() < 5


@PYARROW_MARK
def test_pandas_metadata_nullable_pyarrow(tmpdir):
    tmpdir = str(tmpdir)

    ddf1 = dd.from_pandas(
        pd.DataFrame(
            {
                "A": pd.array([1, None, 2], dtype="Int64"),
                "B": pd.array(["dog", "cat", None], dtype="str"),
            }
        ),
        npartitions=1,
    )
    ddf1.to_parquet(tmpdir, engine="pyarrow")
    ddf2 = dd.read_parquet(tmpdir, engine="pyarrow")

    assert_eq(ddf1, ddf2, check_index=False)


@PYARROW_MARK
def test_pandas_timestamp_overflow_pyarrow(tmpdir):
    info = np.iinfo(np.dtype("int64"))
    arr_numeric = np.linspace(
        start=info.min + 2, stop=info.max, num=1024, dtype="int64"
    )
    arr_dates = arr_numeric.astype("datetime64[ms]")

    table = pa.Table.from_arrays([pa.array(arr_dates)], names=["ts"])
    pa.parquet.write_table(
        table, f"{tmpdir}/file.parquet", use_deprecated_int96_timestamps=False
    )

    # This will raise by default due to overflow
    with pytest.raises(pa.lib.ArrowInvalid) as e:
        dd.read_parquet(str(tmpdir), engine="pyarrow").compute()
    assert "out of bounds" in str(e.value)

    if pa_version >= parse_version("5.0.0"):
        from dask.dataframe.io.parquet.arrow import ArrowDatasetEngine as ArrowEngine
    else:
        from dask.dataframe.io.parquet.arrow import ArrowEngine

    class ArrowEngineWithTimestampClamp(ArrowEngine):
        @classmethod
        def clamp_arrow_datetimes(cls, arrow_table: pa.Table) -> pa.Table:
            """Constrain datetimes to be valid for pandas

            Since pandas works in ns precision and arrow / parquet defaults to ms
            precision we need to clamp our datetimes to something reasonable"""

            new_columns = []
            for i, col in enumerate(arrow_table.columns):
                if pa.types.is_timestamp(col.type) and (
                    col.type.unit in ("s", "ms", "us")
                ):
                    multiplier = {"s": 1_0000_000_000, "ms": 1_000_000, "us": 1_000}[
                        col.type.unit
                    ]

                    original_type = col.type

                    series: pd.Series = col.cast(pa.int64()).to_pandas()
                    info = np.iinfo(np.dtype("int64"))
                    # constrain data to be within valid ranges
                    series.clip(
                        lower=info.min // multiplier + 1,
                        upper=info.max // multiplier,
                        inplace=True,
                    )
                    new_array = pa.array(series, pa.int64())
                    new_array = new_array.cast(original_type)
                    new_columns.append(new_array)
                else:
                    new_columns.append(col)

            return pa.Table.from_arrays(new_columns, names=arrow_table.column_names)

        @classmethod
        def _arrow_table_to_pandas(
            cls, arrow_table: pa.Table, categories, **kwargs
        ) -> pd.DataFrame:
            fixed_arrow_table = cls.clamp_arrow_datetimes(arrow_table)
            return super()._arrow_table_to_pandas(
                fixed_arrow_table, categories, **kwargs
            )

    # this should not fail, but instead produce timestamps that are in the valid range
    dd.read_parquet(str(tmpdir), engine=ArrowEngineWithTimestampClamp).compute()


@pytest.mark.parametrize(
    "write_cols",
    [["part", "col"], ["part", "kind", "col"]],
)
def test_partitioned_column_overlap(tmpdir, engine, write_cols):

    tmpdir.mkdir("part=a")
    tmpdir.mkdir("part=b")
    path0 = str(tmpdir.mkdir("part=a/kind=x"))
    path1 = str(tmpdir.mkdir("part=b/kind=x"))
    path0 = os.path.join(path0, "data.parquet")
    path1 = os.path.join(path1, "data.parquet")

    _df1 = pd.DataFrame({"part": "a", "kind": "x", "col": range(5)})
    _df2 = pd.DataFrame({"part": "b", "kind": "x", "col": range(5)})
    df1 = _df1[write_cols]
    df2 = _df2[write_cols]
    df1.to_parquet(path0, index=False)
    df2.to_parquet(path1, index=False)

    if engine == "fastparquet":
        path = [path0, path1]
    else:
        path = str(tmpdir)

    if write_cols == ["part", "kind", "col"]:
        result = dd.read_parquet(path, engine=engine)
        expect = pd.concat([_df1, _df2], ignore_index=True)
        assert_eq(result, expect, check_index=False)
    else:
        # For now, partial overlap between partition columns and
        # real columns is not allowed
        with pytest.raises(ValueError):
            dd.read_parquet(path, engine=engine)


@PYARROW_MARK
@pytest.mark.parametrize(
    "write_cols",
    [["col"], ["part", "col"]],
)
def test_partitioned_no_pandas_metadata(tmpdir, engine, write_cols):
    # See: https://github.com/dask/dask/issues/8087

    # Manually construct directory-partitioned dataset
    path1 = tmpdir.mkdir("part=a")
    path2 = tmpdir.mkdir("part=b")
    path1 = os.path.join(path1, "data.parquet")
    path2 = os.path.join(path2, "data.parquet")

    # Write partitions without parquet metadata.
    # Note that we always use pyarrow to do this
    # (regardless of the `engine`)
    _df1 = pd.DataFrame({"part": "a", "col": range(5)})
    _df2 = pd.DataFrame({"part": "b", "col": range(5)})
    t1 = pa.Table.from_pandas(
        _df1[write_cols],
        preserve_index=False,
    ).replace_schema_metadata(metadata={})
    pq.write_table(t1, path1)
    t2 = pa.Table.from_pandas(
        _df2[write_cols],
        preserve_index=False,
    ).replace_schema_metadata(metadata={})
    pq.write_table(t2, path2)

    # Check results
    expect = pd.concat([_df1, _df2], ignore_index=True)
    result = dd.read_parquet(str(tmpdir), engine=engine)
    result["part"] = result["part"].astype("object")
    assert_eq(result[list(expect.columns)], expect, check_index=False)


@fp_pandas_xfail
def test_partitioned_preserve_index(tmpdir, write_engine, read_engine):
    tmp = str(tmpdir)
    size = 1_000
    npartitions = 4
    b = np.arange(npartitions).repeat(size // npartitions)
    data = pd.DataFrame(
        {
            "myindex": np.arange(size),
            "A": np.random.random(size=size),
            "B": pd.Categorical(b),
        }
    ).set_index("myindex")
    data.index.name = None
    df1 = dd.from_pandas(data, npartitions=npartitions)
    df1.to_parquet(tmp, partition_on="B", engine=write_engine)

    expect = data[data["B"] == 1]
    got = dd.read_parquet(tmp, engine=read_engine, filters=[("B", "==", 1)])
    assert_eq(expect, got)


def test_from_pandas_preserve_none_index(tmpdir, engine):
    if engine.startswith("pyarrow"):
        pytest.importorskip("pyarrow", minversion="0.15.0")

    fn = str(tmpdir.join("test.parquet"))
    df = pd.DataFrame({"a": [1, 2], "b": [4, 5], "c": [6, 7]}).set_index("c")
    df.index.name = None
    df.to_parquet(
        fn,
        engine="pyarrow" if engine.startswith("pyarrow") else "fastparquet",
        index=True,
    )

    expect = pd.read_parquet(fn)
    got = dd.read_parquet(fn, engine=engine)
    assert_eq(expect, got)


def test_multi_partition_none_index_false(tmpdir, engine):
    if engine.startswith("pyarrow"):
        pytest.importorskip("pyarrow", minversion="0.15.0")
        write_engine = "pyarrow"
    else:
        assert engine == "fastparquet"
        write_engine = "fastparquet"

    # Write dataset without dask.to_parquet
    ddf1 = ddf.reset_index(drop=True)
    for i, part in enumerate(ddf1.partitions):
        path = tmpdir.join(f"test.{i}.parquet")
        part.compute().to_parquet(str(path), engine=write_engine)

    # Read back with index=False
    ddf2 = dd.read_parquet(str(tmpdir), index=False, engine=engine)
    assert_eq(ddf1, ddf2)


@write_read_engines()
def test_from_pandas_preserve_none_rangeindex(tmpdir, write_engine, read_engine):
    # See GitHub Issue#6348
    fn = str(tmpdir.join("test.parquet"))
    df0 = pd.DataFrame({"t": [1, 2, 3]}, index=pd.RangeIndex(start=1, stop=4))
    df0.to_parquet(
        fn, engine="pyarrow" if write_engine.startswith("pyarrow") else "fastparquet"
    )

    df1 = dd.read_parquet(fn, engine=read_engine)
    assert_eq(df0, df1.compute())


def test_illegal_column_name(tmpdir, engine):
    # Make sure user is prevented from preserving a "None" index
    # name if there is already a column using the special `null_name`
    null_name = "__null_dask_index__"
    fn = str(tmpdir.join("test.parquet"))
    df = pd.DataFrame({"x": [1, 2], null_name: [4, 5]}).set_index("x")
    df.index.name = None
    ddf = dd.from_pandas(df, npartitions=2)

    # If we don't want to preserve the None index name, the
    # write should work, but the user should be warned
    with pytest.warns(UserWarning, match=null_name):
        ddf.to_parquet(fn, engine=engine, write_index=False)

    # If we do want to preserve the None index name, should
    # get a ValueError for having an illegal column name
    with pytest.raises(ValueError) as e:
        ddf.to_parquet(fn, engine=engine)
    assert null_name in str(e.value)


def test_divisions_with_null_partition(tmpdir, engine):
    df = pd.DataFrame({"a": [1, 2, None, None], "b": [1, 2, 3, 4]})
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(str(tmpdir), engine=engine, write_index=False)

    ddf_read = dd.read_parquet(str(tmpdir), engine=engine, index="a")
    assert ddf_read.divisions == (None, None, None)


@PYARROW_MARK
def test_pyarrow_dataset_simple(tmpdir, engine):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": [4, 5, 6], "b": ["a", "b", "b"]})
    df.set_index("a", inplace=True, drop=True)
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(fn, engine=engine)
    read_df = dd.read_parquet(fn, engine="pyarrow-dataset")
    read_df.compute()
    assert_eq(ddf, read_df)


@PYARROW_MARK
@pytest.mark.parametrize("test_filter", [True, False])
def test_pyarrow_dataset_partitioned(tmpdir, engine, test_filter):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": [4, 5, 6], "b": ["a", "b", "b"]})
    df["b"] = df["b"].astype("category")
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(fn, engine=engine, partition_on="b")
    read_df = dd.read_parquet(
        fn,
        engine="pyarrow",
        filters=[("b", "==", "a")] if test_filter else None,
    )

    if test_filter:
        assert_eq(ddf[ddf["b"] == "a"].compute(), read_df.compute())
    else:
        assert_eq(ddf, read_df)


@PYARROW_MARK
def test_pyarrow_dataset_read_from_paths(tmpdir):
    fn = str(tmpdir)
    df = pd.DataFrame({"a": [4, 5, 6], "b": ["a", "b", "b"]})
    df["b"] = df["b"].astype("category")
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(fn, engine="pyarrow", partition_on="b")

    with pytest.warns(FutureWarning):
        read_df_1 = dd.read_parquet(
            fn,
            engine="pyarrow",
            filters=[("b", "==", "a")],
            read_from_paths=False,
        )

    read_df_2 = dd.read_parquet(
        fn,
        engine="pyarrow",
        filters=[("b", "==", "a")],
    )

    assert_eq(read_df_1, read_df_2)
    assert_eq(ddf[ddf["b"] == "a"].compute(), read_df_2.compute())


@PYARROW_MARK
@pytest.mark.parametrize("split_row_groups", [True, False])
def test_pyarrow_dataset_filter_partitioned(tmpdir, split_row_groups):
    fn = str(tmpdir)
    df = pd.DataFrame(
        {
            "a": [4, 5, 6],
            "b": ["a", "b", "b"],
            "c": ["A", "B", "B"],
        }
    )
    df["b"] = df["b"].astype("category")
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(fn, engine="pyarrow", partition_on=["b", "c"])

    # Filter on a a non-partition column
    read_df = dd.read_parquet(
        fn,
        engine="pyarrow-dataset",
        split_row_groups=split_row_groups,
        filters=[("a", "==", 5)],
    )
    assert_eq(
        read_df.compute()[["a"]],
        df[df["a"] == 5][["a"]],
        check_index=False,
    )


@PYARROW_MARK
def test_parquet_pyarrow_write_empty_metadata(tmpdir):
    # https://github.com/dask/dask/issues/6600
    tmpdir = str(tmpdir)

    df_a = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [], "y": []}, dtype=("int", "int")
    )
    df_b = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [1, 1, 2, 2], "y": [1, 0, 1, 0]}, dtype=("int64", "int64")
    )
    df_c = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [1, 2, 1, 2], "y": [1, 0, 1, 0]}, dtype=("int64", "int64")
    )

    df = dd.from_delayed([df_a, df_b, df_c])

    try:
        df.to_parquet(
            tmpdir,
            engine="pyarrow",
            partition_on=["x"],
            append=False,
        )

    except AttributeError:
        pytest.fail("Unexpected AttributeError")

    # Check that metadata files where written
    files = os.listdir(tmpdir)
    assert "_metadata" in files
    assert "_common_metadata" in files

    # Check that the schema includes pandas_metadata
    schema_common = pq.ParquetFile(
        os.path.join(tmpdir, "_common_metadata")
    ).schema.to_arrow_schema()
    pandas_metadata = schema_common.pandas_metadata
    assert pandas_metadata
    assert pandas_metadata.get("index_columns", False)


@PYARROW_MARK
def test_parquet_pyarrow_write_empty_metadata_append(tmpdir):
    # https://github.com/dask/dask/issues/6600
    tmpdir = str(tmpdir)

    df_a = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [1, 1, 2, 2], "y": [1, 0, 1, 0]}, dtype=("int64", "int64")
    )
    df_b = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [1, 2, 1, 2], "y": [2, 0, 2, 0]}, dtype=("int64", "int64")
    )

    df1 = dd.from_delayed([df_a, df_b])
    df1.to_parquet(
        tmpdir,
        engine="pyarrow",
        partition_on=["x"],
        append=False,
    )

    df_c = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [], "y": []}, dtype=("int64", "int64")
    )
    df_d = dask.delayed(pd.DataFrame.from_dict)(
        {"x": [3, 3, 4, 4], "y": [1, 0, 1, 0]}, dtype=("int64", "int64")
    )

    df2 = dd.from_delayed([df_c, df_d])
    df2.to_parquet(
        tmpdir,
        engine="pyarrow",
        partition_on=["x"],
        append=True,
        ignore_divisions=True,
    )


@PYARROW_MARK
@pytest.mark.parametrize("partition_on", [None, "a"])
@write_read_engines()
def test_create_metadata_file(tmpdir, write_engine, read_engine, partition_on):
    tmpdir = str(tmpdir)

    # Write ddf without a _metadata file
    df1 = pd.DataFrame({"b": range(100), "a": ["A", "B", "C", "D"] * 25})
    df1.index.name = "myindex"
    ddf1 = dd.from_pandas(df1, npartitions=10)
    ddf1.to_parquet(
        tmpdir,
        write_metadata_file=False,
        partition_on=partition_on,
        engine=write_engine,
    )

    # Add global _metadata file
    if partition_on:
        fns = glob.glob(os.path.join(tmpdir, partition_on + "=*/*.parquet"))
    else:
        fns = glob.glob(os.path.join(tmpdir, "*.parquet"))
    dd.io.parquet.create_metadata_file(
        fns,
        engine="pyarrow",
        split_every=3,  # Force tree reduction
    )

    # Check that we can now read the ddf
    # with the _metadata file present
    ddf2 = dd.read_parquet(
        tmpdir,
        gather_statistics=True,
        split_row_groups=False,
        engine=read_engine,
        index="myindex",  # python-3.6 CI
    )
    if partition_on:
        ddf1 = df1.sort_values("b")
        ddf2 = ddf2.compute().sort_values("b")
        ddf2.a = ddf2.a.astype("object")
    assert_eq(ddf1, ddf2)

    # Check if we can avoid writing an actual file
    fmd = dd.io.parquet.create_metadata_file(
        fns,
        engine="pyarrow",
        split_every=3,  # Force tree reduction
        out_dir=False,  # Avoid writing file
    )

    # Check that the in-memory metadata is the same as
    # the metadata in the file.
    fmd_file = pq.ParquetFile(os.path.join(tmpdir, "_metadata")).metadata
    assert fmd.num_rows == fmd_file.num_rows
    assert fmd.num_columns == fmd_file.num_columns
    assert fmd.num_row_groups == fmd_file.num_row_groups


def test_read_write_overwrite_is_true(tmpdir, engine):
    # https://github.com/dask/dask/issues/6824

    # Create a Dask DataFrame if size (100, 10) with 5 partitions and write to local
    ddf = dd.from_pandas(
        pd.DataFrame(
            np.random.randint(low=0, high=100, size=(100, 10)),
            columns=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"],
        ),
        npartitions=5,
    )
    ddf = ddf.reset_index(drop=True)
    dd.to_parquet(ddf, tmpdir, engine=engine, overwrite=True)

    # Keep the contents of the DataFrame constatn but change the # of partitions
    ddf2 = ddf.repartition(npartitions=3)

    # Overwrite the existing Dataset with the new dataframe and evaluate
    # the number of files against the number of dask partitions
    dd.to_parquet(ddf2, tmpdir, engine=engine, overwrite=True)

    # Assert the # of files written are identical to the number of
    # Dask DataFrame partitions (we exclude _metadata and _common_metadata)
    files = os.listdir(tmpdir)
    files = [f for f in files if f not in ["_common_metadata", "_metadata"]]
    assert len(files) == ddf2.npartitions


def test_read_write_partition_on_overwrite_is_true(tmpdir, engine):
    # https://github.com/dask/dask/issues/6824
    from pathlib import Path

    # Create a Dask DataFrame with 5 partitions and write to local, partitioning on the column A and column B
    df = pd.DataFrame(
        np.vstack(
            (
                np.full((50, 3), 0),
                np.full((50, 3), 1),
                np.full((20, 3), 2),
            )
        )
    )
    df.columns = ["A", "B", "C"]
    ddf = dd.from_pandas(df, npartitions=5)
    dd.to_parquet(ddf, tmpdir, engine=engine, partition_on=["A", "B"], overwrite=True)

    # Get the total number of files and directories from the original write
    files_ = Path(tmpdir).rglob("*")
    files = [f.as_posix() for f in files_]
    # Keep the contents of the DataFrame constant but change the # of partitions
    ddf2 = ddf.repartition(npartitions=3)

    # Overwrite the existing Dataset with the new dataframe and evaluate
    # the number of files against the number of dask partitions
    # Get the total number of files and directories from the original write
    dd.to_parquet(ddf2, tmpdir, engine=engine, partition_on=["A", "B"], overwrite=True)
    files2_ = Path(tmpdir).rglob("*")
    files2 = [f.as_posix() for f in files2_]
    # After reducing the # of partitions and overwriting, we expect
    # there to be fewer total files than were originally written
    assert len(files2) < len(files)


def test_to_parquet_overwrite_raises(tmpdir, engine):
    # https://github.com/dask/dask/issues/6824
    # Check that overwrite=True will raise an error if the
    # specified path is the current working directory
    df = pd.DataFrame({"a": range(12)})
    ddf = dd.from_pandas(df, npartitions=3)
    with pytest.raises(ValueError):
        dd.to_parquet(ddf, "./", engine=engine, overwrite=True)
    with pytest.raises(ValueError):
        dd.to_parquet(ddf, tmpdir, engine=engine, append=True, overwrite=True)


def test_dir_filter(tmpdir, engine):
    # github #6898
    df = pd.DataFrame.from_dict(
        {
            "A": {
                0: 351.0,
                1: 355.0,
                2: 358.0,
                3: 266.0,
                4: 266.0,
                5: 268.0,
                6: np.nan,
            },
            "B": {
                0: 2063.0,
                1: 2051.0,
                2: 1749.0,
                3: 4281.0,
                4: 3526.0,
                5: 3462.0,
                6: np.nan,
            },
            "year": {0: 2019, 1: 2019, 2: 2020, 3: 2020, 4: 2020, 5: 2020, 6: 2020},
        }
    )
    ddf = dask.dataframe.from_pandas(df, npartitions=1)
    ddf.to_parquet(tmpdir, partition_on="year", engine=engine)
    dd.read_parquet(tmpdir, filters=[("year", "==", 2020)], engine=engine)
    assert all


@PYARROW_MARK
def test_roundtrip_decimal_dtype(tmpdir):
    # https://github.com/dask/dask/issues/6948
    tmpdir = str(tmpdir)

    data = [
        {
            "ts": pd.to_datetime("2021-01-01", utc="Europe/Berlin"),
            "col1": Decimal("123.00"),
        }
        for i in range(23)
    ]
    ddf1 = dd.from_pandas(pd.DataFrame(data), npartitions=1)

    ddf1.to_parquet(path=tmpdir, engine="pyarrow")
    ddf2 = dd.read_parquet(tmpdir, engine="pyarrow")

    assert ddf1["col1"].dtype == ddf2["col1"].dtype
    assert_eq(ddf1, ddf2, check_divisions=False)


def test_roundtrip_rename_columns(tmpdir, engine):
    # https://github.com/dask/dask/issues/7017

    path = os.path.join(str(tmpdir), "test.parquet")
    df1 = pd.DataFrame(columns=["a", "b", "c"], data=np.random.uniform(size=(10, 3)))
    df1.to_parquet(path)

    # read it with dask and rename columns
    ddf2 = dd.read_parquet(path, engine=engine)
    ddf2.columns = ["d", "e", "f"]
    df1.columns = ["d", "e", "f"]

    assert_eq(df1, ddf2.compute())


def test_custom_metadata(tmpdir, engine):
    # Write a parquet dataset with custom metadata

    # Define custom metadata
    custom_metadata = {b"my_key": b"my_data"}

    # Write parquet dataset
    path = str(tmpdir)
    df = pd.DataFrame({"a": range(10), "b": range(10)})
    dd.from_pandas(df, npartitions=2).to_parquet(
        path,
        engine=engine,
        custom_metadata=custom_metadata,
    )

    # Check that data is correct
    assert_eq(df, dd.read_parquet(path, engine=engine))

    # Require pyarrow.parquet to check key/value metadata
    if pq:
        # Read footer metadata and _metadata.
        # Check that it contains keys/values from `custom_metadata`
        files = glob.glob(os.path.join(path, "*.parquet"))
        files += [os.path.join(path, "_metadata")]
        for fn in files:
            _md = pq.ParquetFile(fn).metadata.metadata
            for k, v in custom_metadata.items():
                assert _md[k] == custom_metadata[k]

    # Make sure we raise an error if the custom metadata
    # includes a b"pandas" key
    custom_metadata = {b"pandas": b"my_new_pandas_md"}
    with pytest.raises(ValueError) as e:
        dd.from_pandas(df, npartitions=2).to_parquet(
            path,
            engine=engine,
            custom_metadata=custom_metadata,
        )
    assert "User-defined key/value" in str(e.value)


@pytest.mark.parametrize("gather_statistics", [True, False, None])
def test_ignore_metadata_file(tmpdir, engine, gather_statistics):
    tmpdir = str(tmpdir)
    dataset_with_bad_metadata = os.path.join(tmpdir, "data1")
    dataset_without_metadata = os.path.join(tmpdir, "data2")

    # Write two identical datasets without any _metadata file
    df1 = pd.DataFrame({"a": range(100), "b": ["dog", "cat"] * 50})
    ddf1 = dd.from_pandas(df1, npartitions=2)
    ddf1.to_parquet(
        path=dataset_with_bad_metadata, engine=engine, write_metadata_file=False
    )
    ddf1.to_parquet(
        path=dataset_without_metadata, engine=engine, write_metadata_file=False
    )

    # Copy "bad" metadata into `dataset_with_bad_metadata`
    assert "_metadata" not in os.listdir(dataset_with_bad_metadata)
    with open(os.path.join(dataset_with_bad_metadata, "_metadata"), "w") as f:
        f.write("INVALID METADATA")
    assert "_metadata" in os.listdir(dataset_with_bad_metadata)
    assert "_metadata" not in os.listdir(dataset_without_metadata)

    # Read back the datasets with `ignore_metadata_file=True`, and
    # test that the results are the same
    if engine != "pyarrow-legacy":
        ddf2a = dd.read_parquet(
            dataset_with_bad_metadata,
            engine=engine,
            ignore_metadata_file=True,
            gather_statistics=gather_statistics,
        )
        ddf2b = dd.read_parquet(
            dataset_without_metadata,
            engine=engine,
            ignore_metadata_file=True,
            gather_statistics=gather_statistics,
        )
        assert_eq(ddf2a, ddf2b)
    else:
        # Check that "pyarrow-legacy" raises a ValueError
        with pytest.raises(ValueError):
            dd.read_parquet(
                dataset_with_bad_metadata,
                engine=engine,
                ignore_metadata_file=True,
            )


@pytest.mark.parametrize("write_metadata_file", [True, False])
@pytest.mark.parametrize("metadata_task_size", [2, 0])
def test_metadata_task_size(tmpdir, engine, write_metadata_file, metadata_task_size):

    # Write simple dataset
    tmpdir = str(tmpdir)
    df1 = pd.DataFrame({"a": range(100), "b": ["dog", "cat"] * 50})
    ddf1 = dd.from_pandas(df1, npartitions=10)
    ddf1.to_parquet(
        path=str(tmpdir), engine=engine, write_metadata_file=write_metadata_file
    )

    # Read back
    if engine != "pyarrow-legacy" or not metadata_task_size:
        ddf2a = dd.read_parquet(
            str(tmpdir),
            engine=engine,
            gather_statistics=True,
        )
        ddf2b = dd.read_parquet(
            str(tmpdir),
            engine=engine,
            gather_statistics=True,
            metadata_task_size=metadata_task_size,
        )
        assert_eq(ddf2a, ddf2b)

        with dask.config.set(
            {"dataframe.parquet.metadata-task-size-local": metadata_task_size}
        ):
            ddf2c = dd.read_parquet(
                str(tmpdir),
                engine=engine,
                gather_statistics=True,
            )
        assert_eq(ddf2b, ddf2c)

    else:
        # Check that other engines raise a ValueError
        with pytest.raises(ValueError):
            dd.read_parquet(
                str(tmpdir),
                engine=engine,
                gather_statistics=True,
                metadata_task_size=metadata_task_size,
            )


def test_extra_file(tmpdir, engine):
    # Check that read_parquet can handle spark output
    # See: https://github.com/dask/dask/issues/8087
    tmpdir = str(tmpdir)
    df = pd.DataFrame({"a": range(100), "b": ["dog", "cat"] * 50})
    ddf = dd.from_pandas(df, npartitions=2)
    ddf.to_parquet(tmpdir, engine=engine)
    open(os.path.join(tmpdir, "_SUCCESS"), "w").close()
    open(os.path.join(tmpdir, "part.0.parquet.crc"), "w").close()
    os.remove(os.path.join(tmpdir, "_metadata"))
    out = dd.read_parquet(tmpdir, engine=engine)
    assert_eq(out, df)

    if engine != "pyarrow-legacy":
        # For "fastparquet" and "pyarrow-dataset", we can pass the
        # expected file extension, or avoid checking file extensions
        # by passing False. Check here that this works:

        # Should Work
        out = dd.read_parquet(
            tmpdir, engine=engine, dataset={"require_extension": ".parquet"}
        )
        assert_eq(out, df)

        # Should Fail (for not capturing the _SUCCESS and crc files)
        with pytest.raises((OSError, pa.lib.ArrowInvalid)):
            dd.read_parquet(
                tmpdir, engine=engine, dataset={"require_extension": False}
            ).compute()

        # Should Fail (for filtering out all files)
        # (Related to: https://github.com/dask/dask/issues/8349)
        with pytest.raises(ValueError):
            dd.read_parquet(
                tmpdir, engine=engine, dataset={"require_extension": ".foo"}
            ).compute()


def test_unsupported_extension_file(tmpdir, engine):
    # File extension shouldn't matter when we are only
    # reading a single file.
    # (See: https://github.com/dask/dask/issues/8349)
    fn = os.path.join(str(tmpdir), "multi.foo")
    df0 = pd.DataFrame({"a": range(10)})
    df0.to_parquet(fn, engine=engine.split("-")[0])
    assert_eq(df0, dd.read_parquet(fn, engine=engine, index=False))


def test_unsupported_extension_dir(tmpdir, engine):
    # File extensions shouldn't matter when we have
    # a _metadata file
    # (Related to: https://github.com/dask/dask/issues/8349)
    path = str(tmpdir)
    ddf0 = dd.from_pandas(pd.DataFrame({"a": range(10)}), 1)
    ddf0.to_parquet(path, engine=engine, name_function=lambda i: f"part.{i}.foo")
    assert_eq(ddf0, dd.read_parquet(path, engine=engine))


def test_custom_filename(tmpdir, engine):
    fn = str(tmpdir)
    pdf = pd.DataFrame(
        {"num1": [1, 2, 3, 4], "num2": [7, 8, 9, 10]},
    )
    df = dd.from_pandas(pdf, npartitions=2)
    df.to_parquet(fn, name_function=lambda x: f"hi-{x}.parquet", engine=engine)

    files = os.listdir(fn)
    assert "_common_metadata" in files
    assert "_metadata" in files
    assert "hi-0.parquet" in files
    assert "hi-1.parquet" in files
    assert_eq(df, dd.read_parquet(fn, engine=engine))


def test_custom_filename_works_with_pyarrow_when_append_is_true(tmpdir, engine):
    fn = str(tmpdir)
    pdf = pd.DataFrame(
        {"num1": [1, 2, 3, 4], "num2": [7, 8, 9, 10]},
    )
    df = dd.from_pandas(pdf, npartitions=2)
    df.to_parquet(fn, name_function=lambda x: f"hi-{x * 2}.parquet", engine=engine)

    pdf = pd.DataFrame(
        {"num1": [33], "num2": [44]},
    )
    df = dd.from_pandas(pdf, npartitions=1)
    if engine == "fastparquet":
        pytest.xfail(
            "fastparquet errors our with IndexError when ``name_function`` is customized "
            "and append is set to True.  We didn't do a detailed investigation for expediency. "
            "See this comment for the conversation: https://github.com/dask/dask/pull/7682#issuecomment-845243623"
        )
    df.to_parquet(
        fn,
        name_function=lambda x: f"hi-{x * 2}.parquet",
        engine=engine,
        append=True,
        ignore_divisions=True,
    )
    files = os.listdir(fn)
    assert "_common_metadata" in files
    assert "_metadata" in files
    assert "hi-0.parquet" in files
    assert "hi-2.parquet" in files
    assert "hi-4.parquet" in files
    expected_pdf = pd.DataFrame(
        {"num1": [1, 2, 3, 4, 33], "num2": [7, 8, 9, 10, 44]},
    )
    actual = dd.read_parquet(fn, engine=engine, index=False)
    assert_eq(actual, expected_pdf, check_index=False)


def test_throws_error_if_custom_filename_is_invalid(tmpdir, engine):
    fn = str(tmpdir)
    pdf = pd.DataFrame(
        {"num1": [1, 2, 3, 4], "num2": [7, 8, 9, 10]},
    )
    df = dd.from_pandas(pdf, npartitions=2)
    with pytest.raises(
        ValueError, match="``name_function`` must be a callable with one argument."
    ):
        df.to_parquet(fn, name_function="whatever.parquet", engine=engine)

    with pytest.raises(
        ValueError, match="``name_function`` must produce unique filenames."
    ):
        df.to_parquet(fn, name_function=lambda x: "whatever.parquet", engine=engine)


def test_custom_filename_with_partition(tmpdir, engine):
    fn = str(tmpdir)
    pdf = pd.DataFrame(
        {
            "first_name": ["frank", "li", "marcela", "luis"],
            "country": ["canada", "china", "venezuela", "venezuela"],
        },
    )
    df = dd.from_pandas(pdf, npartitions=4)
    df.to_parquet(
        fn,
        partition_on=["country"],
        name_function=lambda x: f"{x}-cool.parquet",
        write_index=False,
    )

    for _, dirs, files in os.walk(fn):
        for dir in dirs:
            assert dir in (
                "country=canada",
                "country=china",
                "country=venezuela",
            )
        for file in files:
            assert file in (
                "0-cool.parquet",
                "1-cool.parquet",
                "2-cool.parquet",
                "_common_metadata",
                "_metadata",
            )
    actual = dd.read_parquet(fn, engine=engine, index=False)
    assert_eq(
        pdf, actual, check_index=False, check_dtype=False, check_categorical=False
    )

numpy.array_equal numpy.random.choice numpy.random.rand numpy.random.random numpy.dtype numpy.full numpy.random.normal numpy.random.randint numpy.arange numpy.isnat numpy.random.randn numpy.datetime64 numpy.random.permutation numpy.random.uniform numpy.repeat numpy.linspace numpy.unique