// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements.  See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership.  The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied.  See the License for the
// specific language governing permissions and limitations
// under the License.

use crate::execution::operators::ExecutionError;
use arrow::array::{FixedSizeBinaryArray, ListArray, MapArray, StringArray};
use arrow::buffer::NullBuffer;
use arrow::compute::can_cast_types;
use arrow::datatypes::{FieldRef, Fields};
use arrow::{
    array::{
        cast::AsArray, new_null_array, types::Int32Type, types::TimestampMicrosecondType, Array,
        ArrayRef, DictionaryArray, StructArray,
    },
    compute::{cast_with_options, take, CastOptions},
    datatypes::{DataType, TimeUnit},
    util::display::FormatOptions,
};
use datafusion::common::{Result as DataFusionResult, ScalarValue};
use datafusion::error::DataFusionError;
use datafusion::execution::object_store::ObjectStoreUrl;
use datafusion::execution::runtime_env::RuntimeEnv;
use datafusion::physical_plan::ColumnarValue;
use datafusion_comet_spark_expr::EvalMode;
use log::debug;
use object_store::path::Path;
use object_store::{parse_url, ObjectStore};
use parquet::arrow::PARQUET_FIELD_ID_META_KEY;
use std::collections::HashMap;
use std::sync::OnceLock;
use std::time::Duration;
use std::{collections::hash_map::DefaultHasher, hash::Hasher, sync::RwLock};
use std::{fmt::Debug, hash::Hash, sync::Arc};
use url::Url;

use super::objectstore;

// This file originates from cast.rs. While developing native scan support and implementing
// SparkSchemaAdapter we observed that Spark's type conversion logic on Parquet reads does not
// always align to the CAST expression's logic, so it was duplicated here to adapt its behavior.

static TIMESTAMP_FORMAT: Option<&str> = Some("%Y-%m-%d %H:%M:%S%.f");

static PARQUET_OPTIONS: CastOptions = CastOptions {
    safe: true,
    format_options: FormatOptions::new()
        .with_timestamp_tz_format(TIMESTAMP_FORMAT)
        .with_timestamp_format(TIMESTAMP_FORMAT),
};

/// Spark Parquet type conversion options
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub struct SparkParquetOptions {
    /// Spark evaluation mode
    pub eval_mode: EvalMode,
    /// When cast from/to timezone related types, we need timezone, which will be resolved with
    /// session local timezone by an analyzer in Spark.
    // TODO we should change timezone to Tz to avoid repeated parsing
    pub timezone: String,
    /// Allow casts that are supported but not guaranteed to be 100% compatible
    pub allow_incompat: bool,
    /// Support casting unsigned ints to signed ints (used by Parquet SchemaAdapter)
    pub allow_cast_unsigned_ints: bool,
    /// Whether to always represent decimals using 128 bits. If false, the native reader may represent decimals using 32 or 64 bits, depending on the precision.
    pub use_decimal_128: bool,
    /// Whether to read dates/timestamps that were written in the legacy hybrid Julian + Gregorian calendar as it is. If false, throw exceptions instead. If the spark type is TimestampNTZ, this should be true.
    pub use_legacy_date_timestamp_or_ntz: bool,
    // Whether schema field names are case sensitive
    pub case_sensitive: bool,
    /// SPARK-53535 (Spark 4.1+): when reading a struct whose requested fields are all
    /// missing in the Parquet file, true returns the entire struct as null (pre-4.1
    /// legacy behavior); false preserves the parent struct's nullness from the file
    /// so non-null parents return a struct of all-null fields.
    pub return_null_struct_if_all_fields_missing: bool,
    /// When true, resolve fields by parquet.field.id metadata instead of name
    /// (mirrors Spark's `spark.sql.parquet.fieldId.read.enabled`). Only takes effect
    /// when both physical and logical fields actually carry IDs.
    pub use_field_id: bool,
    /// When false (Spark's default), reading a file that has no field ids while the
    /// requested schema does carry ids raises a runtime error rather than silently
    /// producing nulls (mirrors `spark.sql.parquet.fieldId.read.ignoreMissing`).
    pub ignore_missing_field_id: bool,
}

impl SparkParquetOptions {
    pub fn new(eval_mode: EvalMode, timezone: &str, allow_incompat: bool) -> Self {
        Self {
            eval_mode,
            timezone: timezone.to_string(),
            allow_incompat,
            allow_cast_unsigned_ints: false,
            use_decimal_128: false,
            use_legacy_date_timestamp_or_ntz: false,
            case_sensitive: false,
            return_null_struct_if_all_fields_missing: true,
            use_field_id: false,
            ignore_missing_field_id: false,
        }
    }

    pub fn new_without_timezone(eval_mode: EvalMode, allow_incompat: bool) -> Self {
        Self {
            eval_mode,
            timezone: "".to_string(),
            allow_incompat,
            allow_cast_unsigned_ints: false,
            use_decimal_128: false,
            use_legacy_date_timestamp_or_ntz: false,
            case_sensitive: false,
            return_null_struct_if_all_fields_missing: true,
            use_field_id: false,
            ignore_missing_field_id: false,
        }
    }
}

/// Spark-compatible cast implementation. Defers to DataFusion's cast where that is known
/// to be compatible, and returns an error when a not supported and not DF-compatible cast
/// is requested.
pub fn spark_parquet_convert(
    arg: ColumnarValue,
    data_type: &DataType,
    parquet_options: &SparkParquetOptions,
) -> DataFusionResult<ColumnarValue> {
    match arg {
        ColumnarValue::Array(array) => Ok(ColumnarValue::Array(parquet_convert_array(
            array,
            data_type,
            parquet_options,
        )?)),
        ColumnarValue::Scalar(scalar) => {
            // Note that normally CAST(scalar) should be fold in Spark JVM side. However, for
            // some cases e.g., scalar subquery, Spark will not fold it, so we need to handle it
            // here.
            let array = scalar.to_array()?;
            let scalar = ScalarValue::try_from_array(
                &parquet_convert_array(array, data_type, parquet_options)?,
                0,
            )?;
            Ok(ColumnarValue::Scalar(scalar))
        }
    }
}

fn parquet_convert_array(
    array: ArrayRef,
    to_type: &DataType,
    parquet_options: &SparkParquetOptions,
) -> DataFusionResult<ArrayRef> {
    use DataType::*;
    let from_type = array.data_type().clone();

    let array = match &from_type {
        Dictionary(key_type, value_type)
            if key_type.as_ref() == &Int32
                && (value_type.as_ref() == &Utf8 || value_type.as_ref() == &LargeUtf8) =>
        {
            let dict_array = array
                .as_any()
                .downcast_ref::<DictionaryArray<Int32Type>>()
                .expect("Expected a dictionary array");

            let casted_dictionary = DictionaryArray::<Int32Type>::new(
                dict_array.keys().clone(),
                parquet_convert_array(Arc::clone(dict_array.values()), to_type, parquet_options)?,
            );

            let casted_result = match to_type {
                Dictionary(_, _) => Arc::new(casted_dictionary.clone()),
                _ => take(casted_dictionary.values().as_ref(), dict_array.keys(), None)?,
            };
            return Ok(casted_result);
        }
        _ => array,
    };
    let from_type = array.data_type();

    // Try Comet specific handlers first, then arrow-rs cast if supported,
    // return uncasted data otherwise
    match (from_type, to_type) {
        (Struct(_), Struct(_)) => Ok(parquet_convert_struct_to_struct(
            array.as_struct(),
            from_type,
            to_type,
            parquet_options,
        )?),
        (List(_), List(to_inner_type)) => {
            let list_arr: &ListArray = array.as_list();
            let cast_field = parquet_convert_array(
                Arc::clone(list_arr.values()),
                to_inner_type.data_type(),
                parquet_options,
            )?;

            Ok(Arc::new(ListArray::new(
                Arc::clone(to_inner_type),
                list_arr.offsets().clone(),
                cast_field,
                list_arr.nulls().cloned(),
            )))
        }
        (Timestamp(TimeUnit::Microsecond, None), Timestamp(TimeUnit::Microsecond, Some(tz))) => {
            Ok(Arc::new(
                array
                    .as_primitive::<TimestampMicrosecondType>()
                    .reinterpret_cast::<TimestampMicrosecondType>()
                    .with_timezone(Arc::clone(tz)),
            ))
        }
        (Map(_, ordered_from), Map(_, ordered_to)) if ordered_from == ordered_to =>
            parquet_convert_map_to_map(array.as_map(), to_type, parquet_options, *ordered_to)
            ,
        // Iceberg stores UUIDs as 16-byte fixed binary but Spark expects string representation.
        // Arrow doesn't support casting FixedSizeBinary to Utf8, so we handle it manually.
        (FixedSizeBinary(16), Utf8) => {
            let binary_array = array
                .as_any()
                .downcast_ref::<FixedSizeBinaryArray>()
                .expect("Expected a FixedSizeBinaryArray");

            let string_array: StringArray = binary_array
                .iter()
                .map(|opt_bytes| {
                    opt_bytes.map(|bytes| {
                        let uuid = uuid::Uuid::from_bytes(
                            bytes.try_into().expect("Expected 16 bytes")
                        );
                        uuid.to_string()
                    })
                })
                .collect();

            Ok(Arc::new(string_array))
        }
        // If Arrow cast supports the cast, delegate the cast to Arrow
        _ if can_cast_types(from_type, to_type) => {
            Ok(cast_with_options(&array, to_type, &PARQUET_OPTIONS)?)
        }
        _ => Ok(array),
    }
}

/// Read the Parquet field id stored under arrow-rs's `PARQUET_FIELD_ID_META_KEY`.
fn field_id(field: &arrow::datatypes::Field) -> Option<i32> {
    field
        .metadata()
        .get(PARQUET_FIELD_ID_META_KEY)
        .and_then(|v| v.parse::<i32>().ok())
}

/// Cast between struct types based on logic in
/// `org.apache.spark.sql.catalyst.expressions.Cast#castStruct`.
fn parquet_convert_struct_to_struct(
    array: &StructArray,
    from_type: &DataType,
    to_type: &DataType,
    parquet_options: &SparkParquetOptions,
) -> DataFusionResult<ArrayRef> {
    match (from_type, to_type) {
        (DataType::Struct(from_fields), DataType::Struct(to_fields)) => {
            // Match `from` (file) fields to `to` (logical) fields. Mirrors Spark's
            // `clipParquetGroupFields`: when the logical struct carries Parquet field IDs
            // anywhere, ID-bearing logical fields match ONLY by ID; non-ID-bearing fields
            // fall back to name match. When no logical field carries an ID, fall back to
            // name match across the board.
            let should_match_by_id =
                parquet_options.use_field_id && to_fields.iter().any(|f| field_id(f).is_some());

            let from_id_to_index: HashMap<i32, usize> = if should_match_by_id {
                let mut map = HashMap::new();
                for (i, field) in from_fields.iter().enumerate() {
                    if let Some(id) = field_id(field) {
                        map.entry(id).or_insert(i);
                    }
                }
                map
            } else {
                HashMap::new()
            };

            let normalize_name = |name: &str| -> String {
                if parquet_options.case_sensitive {
                    name.to_string()
                } else {
                    name.to_lowercase()
                }
            };
            let mut field_name_to_index_map = HashMap::new();
            for (i, field) in from_fields.iter().enumerate() {
                field_name_to_index_map.insert(normalize_name(field.name()), i);
            }
            assert_eq!(field_name_to_index_map.len(), from_fields.len());

            let mut field_overlap = false;
            let mut cast_fields: Vec<ArrayRef> = Vec::with_capacity(to_fields.len());
            for to_field in to_fields.iter() {
                let from_index = match (should_match_by_id, field_id(to_field)) {
                    // Spark treats a missing ID match as a missing column rather than
                    // falling back to name match.
                    (true, Some(id)) => from_id_to_index.get(&id).copied(),
                    _ => field_name_to_index_map
                        .get(&normalize_name(to_field.name()))
                        .copied(),
                };

                if let Some(from_index) = from_index {
                    cast_fields.push(parquet_convert_array(
                        Arc::clone(array.column(from_index)),
                        to_field.data_type(),
                        parquet_options,
                    )?);
                    field_overlap = true;
                } else {
                    cast_fields.push(new_null_array(to_field.data_type(), array.len()));
                }
            }

            // When the file's struct contains none of the requested fields, the
            // returned validity buffer depends on Spark's
            // `spark.sql.legacy.parquet.returnNullStructIfAllFieldsMissing` (SPARK-53535,
            // Spark 4.1+). Legacy mode marks the whole column null; the new default
            // preserves the file's parent-row nullness so non-null parents materialize
            // as a struct of all-null fields.
            let nulls =
                if !field_overlap && parquet_options.return_null_struct_if_all_fields_missing {
                    Some(NullBuffer::new_null(array.len()))
                } else {
                    array.nulls().cloned()
                };

            Ok(Arc::new(StructArray::new(
                to_fields.clone(),
                cast_fields,
                nulls,
            )))
        }
        _ => unreachable!(),
    }
}

/// Cast a map type to another map type. The same as arrow-cast except we recursively call our own
/// parquet_convert_array
fn parquet_convert_map_to_map(
    from: &MapArray,
    to_data_type: &DataType,
    parquet_options: &SparkParquetOptions,
    to_ordered: bool,
) -> Result<ArrayRef, DataFusionError> {
    match to_data_type {
        DataType::Map(entries_field, _) => {
            let key_field = key_field(entries_field).ok_or(DataFusionError::Internal(
                "map is missing key field".to_string(),
            ))?;
            let value_field = value_field(entries_field).ok_or(DataFusionError::Internal(
                "map is missing value field".to_string(),
            ))?;

            let key_array = parquet_convert_array(
                Arc::clone(from.keys()),
                key_field.data_type(),
                parquet_options,
            )?;
            let value_array = parquet_convert_array(
                Arc::clone(from.values()),
                value_field.data_type(),
                parquet_options,
            )?;

            Ok(Arc::new(MapArray::new(
                Arc::<arrow::datatypes::Field>::clone(entries_field),
                from.offsets().clone(),
                StructArray::new(
                    Fields::from(vec![key_field, value_field]),
                    vec![key_array, value_array],
                    from.entries().nulls().cloned(),
                ),
                from.nulls().cloned(),
                to_ordered,
            )))
        }
        dt => Err(DataFusionError::Internal(format!(
            "Expected MapType. Got: {dt}"
        ))),
    }
}

/// Gets the key field from the entries of a map.  For all other types returns None.
fn key_field(entries_field: &FieldRef) -> Option<FieldRef> {
    if let DataType::Struct(fields) = entries_field.data_type() {
        fields.first().cloned()
    } else {
        None
    }
}

/// Gets the value field from the entries of a map.  For all other types returns None.
fn value_field(entries_field: &FieldRef) -> Option<FieldRef> {
    if let DataType::Struct(fields) = entries_field.data_type() {
        fields.get(1).cloned()
    } else {
        None
    }
}

pub fn is_hdfs_scheme(url: &Url, object_store_configs: &HashMap<String, String>) -> bool {
    const COMET_LIBHDFS_SCHEMES_KEY: &str = "fs.comet.libhdfs.schemes";
    let scheme = url.scheme();
    if let Some(libhdfs_schemes) = object_store_configs.get(COMET_LIBHDFS_SCHEMES_KEY) {
        use itertools::Itertools;
        libhdfs_schemes.split(",").contains(scheme)
    } else {
        scheme == "hdfs"
    }
}

// Creates an HDFS object store from a URL using the native HDFS implementation
#[cfg(all(feature = "hdfs", not(feature = "hdfs-opendal")))]
fn create_hdfs_object_store(
    url: &Url,
) -> Result<(Box<dyn ObjectStore>, Path), object_store::Error> {
    match datafusion_comet_objectstore_hdfs::object_store::hdfs::HadoopFileSystem::new(url.as_ref())
    {
        Some(object_store) => {
            let path = object_store.get_path(url.as_str());
            Ok((Box::new(object_store), path))
        }
        _ => Err(object_store::Error::Generic {
            store: "HadoopFileSystem",
            source: "Could not create hdfs object store".into(),
        }),
    }
}

// Creates an OpenDAL HDFS Operator from a URL with optional configuration
#[cfg(feature = "hdfs-opendal")]
pub(crate) fn create_hdfs_operator(url: &Url) -> Result<opendal::Operator, object_store::Error> {
    let name_node = get_name_node_uri(url)?;
    let builder = opendal::services::Hdfs::default().name_node(&name_node);

    opendal::Operator::new(builder)
        .map_err(|error| object_store::Error::Generic {
            store: "hdfs-opendal",
            source: error.into(),
        })
        .map(|op| op.finish())
}

// Creates an HDFS object store from a URL using OpenDAL
#[cfg(feature = "hdfs-opendal")]
pub(crate) fn create_hdfs_object_store(
    url: &Url,
) -> Result<(Box<dyn ObjectStore>, Path), object_store::Error> {
    let op = create_hdfs_operator(url)?;
    let store = object_store_opendal::OpendalStore::new(op);
    let path = Path::parse(url.path())?;
    Ok((Box::new(store), path))
}

#[cfg(feature = "hdfs-opendal")]
fn get_name_node_uri(url: &Url) -> Result<String, object_store::Error> {
    use std::fmt::Write;
    if let Some(host) = url.host() {
        let schema = url.scheme();
        let mut uri_builder = String::new();
        write!(&mut uri_builder, "{schema}://{host}").unwrap();

        if let Some(port) = url.port() {
            write!(&mut uri_builder, ":{port}").unwrap();
        }
        Ok(uri_builder)
    } else {
        Err(object_store::Error::InvalidPath {
            source: object_store::path::Error::InvalidPath {
                path: std::path::PathBuf::from(url.as_str()),
            },
        })
    }
}

// Stub implementation when HDFS support is not enabled
#[cfg(all(not(feature = "hdfs"), not(feature = "hdfs-opendal")))]
fn create_hdfs_object_store(
    _url: &Url,
) -> Result<(Box<dyn ObjectStore>, Path), object_store::Error> {
    Err(object_store::Error::Generic {
        store: "HadoopFileSystem",
        source: "Hdfs support is not enabled in this build".into(),
    })
}

type ObjectStoreCache = RwLock<HashMap<(String, u64), Arc<dyn ObjectStore>>>;

/// Process-wide cache of object stores, keyed by `(scheme://host:port, config_hash)`.
///
/// ## Why static / process lifetime?
///
/// Comet's JNI architecture calls `initRecordBatchReader` once per Parquet file, and each
/// call constructs a fresh `RuntimeEnv`.  There is therefore no executor-scoped Rust object
/// with a lifetime longer than a single file read that could own this cache.  The executor
/// process itself is the natural scope for HTTP connection-pool reuse, so process lifetime
/// (i.e. `static`) is the appropriate choice here.  In the standard Spark-on-Kubernetes
/// deployment model each executor process is dedicated to a single Spark application, so
/// process lifetime and application lifetime are equivalent; the cache is reclaimed when
/// the executor pod terminates.
///
/// ## Unbounded size
///
/// Cache entries are indexed by `(scheme://host:port, hash-of-configs)`.  A typical Spark
/// job accesses a small, fixed set of buckets with a stable configuration, so the number of
/// distinct keys is O(buckets × credential-configs) and remains small throughout the job.
/// Entries are cheap relative to the cost of creating a new object store (new HTTP
/// connection pool + DNS resolution), and there is no meaningful benefit from eviction, so
/// no eviction policy is applied.
///
/// ## Credential invalidation
///
/// Object stores that use dynamic credentials (IMDS, WebIdentity, ECS role, STS assume-role)
/// delegate credential refresh to a `CometCredentialProvider` that fetches fresh credentials
/// on every request, so credential rotation is transparent and requires no cache
/// invalidation.  Object stores whose credentials are embedded in the Hadoop configuration
/// (e.g. `fs.s3a.access.key` / `fs.s3a.secret.key`) produce a different `config_hash` when
/// those values change, which causes a new store to be created and inserted under the new
/// key; the old entry is harmlessly superseded.
fn object_store_cache() -> &'static ObjectStoreCache {
    static CACHE: OnceLock<ObjectStoreCache> = OnceLock::new();
    CACHE.get_or_init(|| RwLock::new(HashMap::new()))
}

/// Compute a hash of the object store configuration for cache keying.
fn hash_object_store_configs(configs: &HashMap<String, String>) -> u64 {
    let mut hasher = DefaultHasher::new();
    let mut keys: Vec<&String> = configs.keys().collect();
    keys.sort();
    for key in keys {
        key.hash(&mut hasher);
        configs[key].hash(&mut hasher);
    }
    hasher.finish()
}

/// Parses the url, registers the object store with configurations, and returns a tuple of the object store url
/// and object store path
pub(crate) fn prepare_object_store_with_configs(
    runtime_env: Arc<RuntimeEnv>,
    url: String,
    object_store_configs: &HashMap<String, String>,
) -> Result<(ObjectStoreUrl, Path), ExecutionError> {
    let mut url = Url::parse(url.as_str())
        .map_err(|e| ExecutionError::GeneralError(format!("Error parsing URL {url}: {e}")))?;
    let is_hdfs_scheme = is_hdfs_scheme(&url, object_store_configs);
    let mut scheme = url.scheme();
    if !is_hdfs_scheme && scheme == "s3a" {
        scheme = "s3";
        url.set_scheme("s3").map_err(|_| {
            ExecutionError::GeneralError("Could not convert scheme from s3a to s3".to_string())
        })?;
    }
    let url_key = format!(
        "{}://{}",
        scheme,
        &url[url::Position::BeforeHost..url::Position::AfterPort],
    );

    let config_hash = hash_object_store_configs(object_store_configs);
    let cache_key = (url_key.clone(), config_hash);

    // Check the cache first to reuse existing object store instances.
    // This enables HTTP connection pooling and avoids redundant DNS lookups.
    let cached = {
        let cache = object_store_cache()
            .read()
            .map_err(|e| ExecutionError::GeneralError(format!("Object store cache error: {e}")))?;
        cache.get(&cache_key).cloned()
    };

    let (object_store, object_store_path): (Arc<dyn ObjectStore>, Path) =
        if let Some(store) = cached {
            debug!("Reusing cached object store for {url_key}");
            let path = Path::from_url_path(url.path())
                .map_err(|e| ExecutionError::GeneralError(e.to_string()))?;
            (store, path)
        } else {
            debug!("Creating new object store for {url_key}");
            let (store, path): (Box<dyn ObjectStore>, Path) = if is_hdfs_scheme {
                create_hdfs_object_store(&url)
            } else if scheme == "s3" {
                objectstore::s3::create_store(&url, object_store_configs, Duration::from_secs(300))
            } else {
                parse_url(&url)
            }
            .map_err(|e| ExecutionError::GeneralError(e.to_string()))?;

            let store: Arc<dyn ObjectStore> = Arc::from(store);
            // Insert into cache
            if let Ok(mut cache) = object_store_cache().write() {
                cache.insert(cache_key, Arc::clone(&store));
            }
            (store, path)
        };

    let object_store_url = ObjectStoreUrl::parse(url_key.clone())?;
    runtime_env.register_object_store(&url, object_store);
    Ok((object_store_url, object_store_path))
}

#[cfg(test)]
mod tests {
    #[cfg(any(
        all(not(feature = "hdfs"), not(feature = "hdfs-opendal")),
        feature = "hdfs"
    ))]
    use datafusion::execution::object_store::ObjectStoreUrl;
    #[cfg(any(
        all(not(feature = "hdfs"), not(feature = "hdfs-opendal")),
        feature = "hdfs"
    ))]
    use datafusion::execution::runtime_env::RuntimeEnv;
    #[cfg(any(
        all(not(feature = "hdfs"), not(feature = "hdfs-opendal")),
        feature = "hdfs"
    ))]
    use object_store::path::Path;
    #[cfg(any(
        all(not(feature = "hdfs"), not(feature = "hdfs-opendal")),
        feature = "hdfs"
    ))]
    use std::sync::Arc;
    #[cfg(any(
        all(not(feature = "hdfs"), not(feature = "hdfs-opendal")),
        feature = "hdfs"
    ))]
    use url::Url;

    #[cfg(all(not(feature = "hdfs"), not(feature = "hdfs-opendal")))]
    use crate::execution::operators::ExecutionError;
    #[cfg(all(not(feature = "hdfs"), not(feature = "hdfs-opendal")))]
    use std::collections::HashMap;

    /// Parses the url, registers the object store, and returns a tuple of the object store url and object store path
    #[cfg(all(not(feature = "hdfs"), not(feature = "hdfs-opendal")))]
    pub(crate) fn prepare_object_store(
        runtime_env: Arc<RuntimeEnv>,
        url: String,
    ) -> Result<(ObjectStoreUrl, Path), ExecutionError> {
        use crate::parquet::parquet_support::prepare_object_store_with_configs;
        prepare_object_store_with_configs(runtime_env, url, &HashMap::new())
    }

    /// Parses the url, registers the object store, and returns a tuple of the object store url and object store path
    #[cfg(feature = "hdfs")]
    pub(crate) fn prepare_object_store(
        runtime_env: Arc<RuntimeEnv>,
        url: String,
    ) -> Result<(ObjectStoreUrl, Path), crate::execution::operators::ExecutionError> {
        use crate::parquet::parquet_support::prepare_object_store_with_configs;
        use std::collections::HashMap;
        prepare_object_store_with_configs(runtime_env, url, &HashMap::new())
    }

    #[cfg(all(not(feature = "hdfs"), not(feature = "hdfs-opendal")))]
    #[test]
    fn test_prepare_object_store() {
        use crate::execution::operators::ExecutionError;

        let local_file_system_url = "file:///comet/spark-warehouse/part-00000.snappy.parquet";
        let hdfs_url = "hdfs://localhost:8020/comet/spark-warehouse/part-00000.snappy.parquet";

        let all_urls = [local_file_system_url, hdfs_url];
        let expected: Vec<Result<(ObjectStoreUrl, Path), ExecutionError>> = vec![
            Ok((
                ObjectStoreUrl::parse("file://").unwrap(),
                Path::from("/comet/spark-warehouse/part-00000.snappy.parquet"),
            )),
            Err(ExecutionError::GeneralError(
                "Generic HadoopFileSystem error: Hdfs support is not enabled in this build"
                    .parse()
                    .unwrap(),
            )),
        ];

        for (i, url_str) in all_urls.iter().enumerate() {
            let url = &Url::parse(url_str).unwrap();
            let res = prepare_object_store(Arc::new(RuntimeEnv::default()), url.to_string());

            let expected = expected.get(i).unwrap();
            match expected {
                Ok((o, p)) => {
                    let (r_o, r_p) = res.unwrap();
                    assert_eq!(r_o, *o);
                    assert_eq!(r_p, *p);
                }
                Err(e) => {
                    assert!(res.is_err());
                    let Err(res_e) = res else {
                        panic!("test failed")
                    };
                    assert_eq!(e.to_string(), res_e.to_string())
                }
            }
        }
    }

    #[test]
    #[cfg(feature = "hdfs")]
    fn test_prepare_object_store() {
        // we use a local file system url instead of an hdfs url because the latter requires
        // a running namenode
        let hdfs_url = "file:///comet/spark-warehouse/part-00000.snappy.parquet";
        let expected: (ObjectStoreUrl, Path) = (
            ObjectStoreUrl::parse("file://").unwrap(),
            Path::from("/comet/spark-warehouse/part-00000.snappy.parquet"),
        );

        let url = &Url::parse(hdfs_url).unwrap();
        let res = prepare_object_store(Arc::new(RuntimeEnv::default()), url.to_string());

        let res = res.unwrap();
        assert_eq!(res.0, expected.0);
        assert_eq!(res.1, expected.1);
    }
}
