context.rs

// 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 std::collections::{HashMap, HashSet};
use std::path::PathBuf;
use std::ptr::NonNull;
use std::str::FromStr;
use std::sync::Arc;

use arrow::array::RecordBatchReader;
use arrow::ffi_stream::ArrowArrayStreamReader;
use arrow::pyarrow::FromPyArrow;
use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef};
use datafusion::arrow::pyarrow::PyArrowType;
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::catalog::{CatalogProvider, CatalogProviderList, TableProviderFactory};
use datafusion::common::{DFSchema, ScalarValue, TableReference, exec_err};
use datafusion::datasource::file_format::file_compression_type::FileCompressionType;
use datafusion::datasource::file_format::parquet::ParquetFormat;
use datafusion::datasource::listing::{
    ListingOptions, ListingTable, ListingTableConfig, ListingTableUrl,
};
use datafusion::datasource::{MemTable, TableProvider};
use datafusion::execution::TaskContextProvider;
use datafusion::execution::context::{
    DataFilePaths, SQLOptions, SessionConfig, SessionContext, TaskContext,
};
use datafusion::execution::disk_manager::DiskManagerMode;
use datafusion::execution::memory_pool::{FairSpillPool, GreedyMemoryPool, UnboundedMemoryPool};
use datafusion::execution::options::{ArrowReadOptions, ReadOptions};
use datafusion::execution::runtime_env::RuntimeEnvBuilder;
use datafusion::execution::session_state::SessionStateBuilder;
use datafusion::prelude::{
    AvroReadOptions, CsvReadOptions, DataFrame, JsonReadOptions, ParquetReadOptions,
};
use datafusion_ffi::catalog_provider::FFI_CatalogProvider;
use datafusion_ffi::catalog_provider_list::FFI_CatalogProviderList;
use datafusion_ffi::config::extension_options::FFI_ExtensionOptions;
use datafusion_ffi::execution::FFI_TaskContextProvider;
use datafusion_ffi::proto::logical_extension_codec::FFI_LogicalExtensionCodec;
use datafusion_ffi::proto::physical_extension_codec::FFI_PhysicalExtensionCodec;
use datafusion_ffi::table_provider_factory::FFI_TableProviderFactory;
use datafusion_proto::logical_plan::LogicalExtensionCodec;
use datafusion_proto::physical_plan::PhysicalExtensionCodec;
use datafusion_python_util::{
    create_logical_extension_capsule, create_physical_extension_capsule,
    ffi_logical_codec_from_pycapsule, get_global_ctx, get_tokio_runtime,
    physical_codec_from_pycapsule, physical_optimizer_rule_from_pycapsule, spawn_future,
    wait_for_future,
};
use object_store::ObjectStore;
use pyo3::IntoPyObjectExt;
use pyo3::exceptions::{PyKeyError, PyRuntimeError, PyValueError};
use pyo3::prelude::*;
use pyo3::types::{PyCapsule, PyDict, PyList, PyTuple};
use url::Url;
use uuid::Uuid;

use crate::catalog::{
    PyCatalog, PyCatalogList, RustWrappedPyCatalogProvider, RustWrappedPyCatalogProviderList,
};
use crate::codec::{PythonLogicalCodec, PythonPhysicalCodec};
use crate::common::data_type::PyScalarValue;
use crate::common::df_schema::PyDFSchema;
use crate::dataframe::PyDataFrame;
use crate::dataset::Dataset;
use crate::errors::{
    PyDataFusionError, PyDataFusionResult, from_datafusion_error, py_datafusion_err,
};
use crate::expr::PyExpr;
use crate::expr::sort_expr::PySortExpr;
use crate::options::PyCsvReadOptions;
use crate::physical_plan::PyExecutionPlan;
use crate::record_batch::PyRecordBatchStream;
use crate::sql::logical::PyLogicalPlan;
use crate::sql::util::replace_placeholders_with_strings;
use crate::store::StorageContexts;
use crate::table::{PyTable, RustWrappedPyTableProviderFactory};
use crate::udaf::PyAggregateUDF;
use crate::udf::PyScalarUDF;
use crate::udtf::PyTableFunction;
use crate::udwf::PyWindowUDF;

/// Configuration options for a SessionContext
#[pyclass(
    from_py_object,
    frozen,
    name = "SessionConfig",
    module = "datafusion",
    subclass
)]
#[derive(Clone, Default)]
pub struct PySessionConfig {
    pub config: SessionConfig,
}

impl From<SessionConfig> for PySessionConfig {
    fn from(config: SessionConfig) -> Self {
        Self { config }
    }
}

#[pymethods]
impl PySessionConfig {
    #[pyo3(signature = (config_options=None))]
    #[new]
    fn new(config_options: Option<HashMap<String, String>>) -> Self {
        let mut config = SessionConfig::new();
        if let Some(hash_map) = config_options {
            for (k, v) in &hash_map {
                config = config.set(k, &ScalarValue::Utf8(Some(v.clone())));
            }
        }

        Self { config }
    }

    fn with_create_default_catalog_and_schema(&self, enabled: bool) -> Self {
        Self::from(
            self.config
                .clone()
                .with_create_default_catalog_and_schema(enabled),
        )
    }

    fn with_default_catalog_and_schema(&self, catalog: &str, schema: &str) -> Self {
        Self::from(
            self.config
                .clone()
                .with_default_catalog_and_schema(catalog, schema),
        )
    }

    fn with_information_schema(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_information_schema(enabled))
    }

    fn with_batch_size(&self, batch_size: usize) -> Self {
        Self::from(self.config.clone().with_batch_size(batch_size))
    }

    fn with_target_partitions(&self, target_partitions: usize) -> Self {
        Self::from(
            self.config
                .clone()
                .with_target_partitions(target_partitions),
        )
    }

    fn with_repartition_aggregations(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_repartition_aggregations(enabled))
    }

    fn with_repartition_joins(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_repartition_joins(enabled))
    }

    fn with_repartition_windows(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_repartition_windows(enabled))
    }

    fn with_repartition_sorts(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_repartition_sorts(enabled))
    }

    fn with_repartition_file_scans(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_repartition_file_scans(enabled))
    }

    fn with_repartition_file_min_size(&self, size: usize) -> Self {
        Self::from(self.config.clone().with_repartition_file_min_size(size))
    }

    fn with_parquet_pruning(&self, enabled: bool) -> Self {
        Self::from(self.config.clone().with_parquet_pruning(enabled))
    }

    fn set(&self, key: &str, value: &str) -> Self {
        Self::from(self.config.clone().set_str(key, value))
    }

    pub fn with_extension(&self, extension: Bound<PyAny>) -> PyResult<Self> {
        if !extension.hasattr("__datafusion_extension_options__")? {
            return Err(pyo3::exceptions::PyAttributeError::new_err(
                "Expected extension object to define __datafusion_extension_options__()",
            ));
        }
        let capsule = extension.call_method0("__datafusion_extension_options__")?;
        let capsule = capsule.cast::<PyCapsule>()?;

        let extension: NonNull<FFI_ExtensionOptions> = capsule
            .pointer_checked(Some(c"datafusion_extension_options"))?
            .cast();
        let mut extension = unsafe { extension.as_ref() }.clone();

        let mut config = self.config.clone();
        let options = config.options_mut();
        if let Some(prior_extension) = options.extensions.get::<FFI_ExtensionOptions>() {
            extension
                .merge(prior_extension)
                .map_err(py_datafusion_err)?;
        }

        options.extensions.insert(extension);

        Ok(Self::from(config))
    }
}

/// Runtime options for a SessionContext
#[pyclass(
    from_py_object,
    frozen,
    name = "RuntimeEnvBuilder",
    module = "datafusion",
    subclass
)]
#[derive(Clone)]
pub struct PyRuntimeEnvBuilder {
    pub builder: RuntimeEnvBuilder,
}

#[pymethods]
impl PyRuntimeEnvBuilder {
    #[new]
    fn new() -> Self {
        Self {
            builder: RuntimeEnvBuilder::default(),
        }
    }

    fn with_disk_manager_disabled(&self) -> Self {
        let mut runtime_builder = self.builder.clone();

        let mut disk_mgr_builder = runtime_builder
            .disk_manager_builder
            .clone()
            .unwrap_or_default();
        disk_mgr_builder.set_mode(DiskManagerMode::Disabled);

        runtime_builder = runtime_builder.with_disk_manager_builder(disk_mgr_builder);
        Self {
            builder: runtime_builder,
        }
    }

    fn with_disk_manager_os(&self) -> Self {
        let mut runtime_builder = self.builder.clone();

        let mut disk_mgr_builder = runtime_builder
            .disk_manager_builder
            .clone()
            .unwrap_or_default();
        disk_mgr_builder.set_mode(DiskManagerMode::OsTmpDirectory);

        runtime_builder = runtime_builder.with_disk_manager_builder(disk_mgr_builder);
        Self {
            builder: runtime_builder,
        }
    }

    fn with_disk_manager_specified(&self, paths: Vec<String>) -> Self {
        let paths = paths.iter().map(|s| s.into()).collect();
        let mut runtime_builder = self.builder.clone();

        let mut disk_mgr_builder = runtime_builder
            .disk_manager_builder
            .clone()
            .unwrap_or_default();
        disk_mgr_builder.set_mode(DiskManagerMode::Directories(paths));

        runtime_builder = runtime_builder.with_disk_manager_builder(disk_mgr_builder);
        Self {
            builder: runtime_builder,
        }
    }

    fn with_unbounded_memory_pool(&self) -> Self {
        let builder = self.builder.clone();
        let builder = builder.with_memory_pool(Arc::new(UnboundedMemoryPool::default()));
        Self { builder }
    }

    fn with_fair_spill_pool(&self, size: usize) -> Self {
        let builder = self.builder.clone();
        let builder = builder.with_memory_pool(Arc::new(FairSpillPool::new(size)));
        Self { builder }
    }

    fn with_greedy_memory_pool(&self, size: usize) -> Self {
        let builder = self.builder.clone();
        let builder = builder.with_memory_pool(Arc::new(GreedyMemoryPool::new(size)));
        Self { builder }
    }

    fn with_temp_file_path(&self, path: &str) -> Self {
        let builder = self.builder.clone();
        let builder = builder.with_temp_file_path(path);
        Self { builder }
    }
}

/// `PySQLOptions` allows you to specify options to the sql execution.
#[pyclass(
    from_py_object,
    frozen,
    name = "SQLOptions",
    module = "datafusion",
    subclass
)]
#[derive(Clone)]
pub struct PySQLOptions {
    pub options: SQLOptions,
}

impl From<SQLOptions> for PySQLOptions {
    fn from(options: SQLOptions) -> Self {
        Self { options }
    }
}

#[pymethods]
impl PySQLOptions {
    #[new]
    fn new() -> Self {
        let options = SQLOptions::new();
        Self { options }
    }

    /// Should DDL data modification commands  (e.g. `CREATE TABLE`) be run? Defaults to `true`.
    fn with_allow_ddl(&self, allow: bool) -> Self {
        Self::from(self.options.with_allow_ddl(allow))
    }

    /// Should DML data modification commands (e.g. `INSERT and COPY`) be run? Defaults to `true`
    pub fn with_allow_dml(&self, allow: bool) -> Self {
        Self::from(self.options.with_allow_dml(allow))
    }

    /// Should Statements such as (e.g. `SET VARIABLE and `BEGIN TRANSACTION` ...`) be run?. Defaults to `true`
    pub fn with_allow_statements(&self, allow: bool) -> Self {
        Self::from(self.options.with_allow_statements(allow))
    }
}

/// `PySessionContext` is able to plan and execute DataFusion plans.
/// It has a powerful optimizer, a physical planner for local execution, and a
/// multi-threaded execution engine to perform the execution.
#[pyclass(
    from_py_object,
    frozen,
    name = "SessionContext",
    module = "datafusion",
    subclass
)]
#[derive(Clone)]
pub struct PySessionContext {
    pub ctx: Arc<SessionContext>,
    logical_codec: Arc<PythonLogicalCodec>,
    physical_codec: Arc<PythonPhysicalCodec>,
}

#[pymethods]
impl PySessionContext {
    #[pyo3(signature = (config=None, runtime=None, physical_optimizer_rules=None))]
    #[new]
    pub fn new(
        config: Option<PySessionConfig>,
        runtime: Option<PyRuntimeEnvBuilder>,
        physical_optimizer_rules: Option<Vec<Bound<'_, PyAny>>>,
    ) -> PyDataFusionResult<Self> {
        let config = if let Some(c) = config {
            c.config
        } else {
            SessionConfig::default().with_information_schema(true)
        };
        let runtime_env_builder = if let Some(c) = runtime {
            c.builder
        } else {
            RuntimeEnvBuilder::default()
        };
        let runtime = Arc::new(runtime_env_builder.build()?);
        let mut state_builder = SessionStateBuilder::new()
            .with_config(config)
            .with_runtime_env(runtime)
            .with_default_features();
        for rule in physical_optimizer_rules.unwrap_or_default() {
            let rule = physical_optimizer_rule_from_pycapsule(&rule)?;
            state_builder = state_builder.with_physical_optimizer_rule(rule);
        }
        let session_state = state_builder.build();
        let ctx = Arc::new(SessionContext::new_with_state(session_state));
        Ok(PySessionContext {
            ctx,
            logical_codec: Arc::new(PythonLogicalCodec::default()),
            physical_codec: Arc::new(PythonPhysicalCodec::default()),
        })
    }

    pub fn enable_url_table(&self) -> PyResult<Self> {
        Ok(PySessionContext {
            ctx: Arc::new(self.ctx.as_ref().clone().enable_url_table()),
            logical_codec: Arc::clone(&self.logical_codec),
            physical_codec: Arc::clone(&self.physical_codec),
        })
    }

    #[staticmethod]
    #[pyo3(signature = ())]
    pub fn global_ctx() -> PyResult<Self> {
        let ctx = get_global_ctx().clone();
        Ok(Self {
            ctx,
            logical_codec: Arc::new(PythonLogicalCodec::default()),
            physical_codec: Arc::new(PythonPhysicalCodec::default()),
        })
    }

    /// Register an object store with the given name
    #[pyo3(signature = (scheme, store, host=None))]
    pub fn register_object_store(
        &self,
        scheme: &str,
        store: StorageContexts,
        host: Option<&str>,
    ) -> PyResult<()> {
        // for most stores the "host" is the bucket name and can be inferred from the store
        let (store, upstream_host): (Arc<dyn ObjectStore>, String) = match store {
            StorageContexts::AmazonS3(s3) => (s3.inner, s3.bucket_name),
            StorageContexts::GoogleCloudStorage(gcs) => (gcs.inner, gcs.bucket_name),
            StorageContexts::MicrosoftAzure(azure) => (azure.inner, azure.container_name),
            StorageContexts::LocalFileSystem(local) => (local.inner, "".to_string()),
            StorageContexts::HTTP(http) => (http.store, http.url),
        };

        // let users override the host to match the api signature from upstream
        let derived_host = if let Some(host) = host {
            host
        } else {
            &upstream_host
        };
        let url_string = format!("{scheme}{derived_host}");
        let url = Url::parse(&url_string).map_err(|e| PyValueError::new_err(e.to_string()))?;
        self.ctx.runtime_env().register_object_store(&url, store);
        Ok(())
    }

    /// Deregister an object store with the given url
    #[pyo3(signature = (scheme, host=None))]
    pub fn deregister_object_store(
        &self,
        scheme: &str,
        host: Option<&str>,
    ) -> PyDataFusionResult<()> {
        let host = host.unwrap_or("");
        let url_string = format!("{scheme}{host}");
        let url = Url::parse(&url_string).map_err(|e| PyDataFusionError::Common(e.to_string()))?;
        self.ctx.runtime_env().deregister_object_store(&url)?;
        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (name, path, table_partition_cols=vec![],
    file_extension=".parquet",
    schema=None,
    file_sort_order=None))]
    pub fn register_listing_table(
        &self,
        name: &str,
        path: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        file_extension: &str,
        schema: Option<PyArrowType<Schema>>,
        file_sort_order: Option<Vec<Vec<PySortExpr>>>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let options = ListingOptions::new(Arc::new(ParquetFormat::new()))
            .with_file_extension(file_extension)
            .with_table_partition_cols(
                table_partition_cols
                    .into_iter()
                    .map(|(name, ty)| (name, ty.0))
                    .collect::<Vec<(String, DataType)>>(),
            )
            .with_file_sort_order(
                file_sort_order
                    .unwrap_or_default()
                    .into_iter()
                    .map(|e| e.into_iter().map(|f| f.into()).collect())
                    .collect(),
            );
        let table_path = ListingTableUrl::parse(path)?;
        let resolved_schema: SchemaRef = match schema {
            Some(s) => Arc::new(s.0),
            None => {
                let state = self.ctx.state();
                let schema = options.infer_schema(&state, &table_path);
                wait_for_future(py, schema)??
            }
        };
        let config = ListingTableConfig::new(table_path)
            .with_listing_options(options)
            .with_schema(resolved_schema);
        let table = ListingTable::try_new(config)?;
        self.ctx.register_table(name, Arc::new(table))?;
        Ok(())
    }

    pub fn register_udtf(&self, func: PyTableFunction) {
        let name = func.name.clone();
        let func = Arc::new(func);
        self.ctx.register_udtf(&name, func);
    }

    pub fn deregister_udtf(&self, name: &str) {
        self.ctx.deregister_udtf(name);
    }

    #[pyo3(signature = (query, options=None, param_values=HashMap::default(), param_strings=HashMap::default()))]
    pub fn sql_with_options(
        &self,
        py: Python,
        mut query: String,
        options: Option<PySQLOptions>,
        param_values: HashMap<String, PyScalarValue>,
        param_strings: HashMap<String, String>,
    ) -> PyDataFusionResult<PyDataFrame> {
        let options = if let Some(options) = options {
            options.options
        } else {
            SQLOptions::new()
        };

        let param_values = param_values
            .into_iter()
            .map(|(name, value)| (name, ScalarValue::from(value)))
            .collect::<HashMap<_, _>>();

        let state = self.ctx.state();
        let dialect = state.config().options().sql_parser.dialect.as_ref();

        if !param_strings.is_empty() {
            query = replace_placeholders_with_strings(&query, dialect, param_strings)?;
        }

        let mut df = wait_for_future(py, async {
            self.ctx.sql_with_options(&query, options).await
        })?
        .map_err(from_datafusion_error)?;

        if !param_values.is_empty() {
            df = df.with_param_values(param_values)?;
        }

        Ok(PyDataFrame::new(df))
    }

    #[pyo3(signature = (partitions, name=None, schema=None))]
    pub fn create_dataframe(
        &self,
        partitions: PyArrowType<Vec<Vec<RecordBatch>>>,
        name: Option<&str>,
        schema: Option<PyArrowType<Schema>>,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let schema = if let Some(schema) = schema {
            SchemaRef::from(schema.0)
        } else {
            partitions.0[0][0].schema()
        };

        let table = MemTable::try_new(schema, partitions.0)?;

        // generate a random (unique) name for this table if none is provided
        // table name cannot start with numeric digit
        let table_name = match name {
            Some(val) => val.to_owned(),
            None => {
                "c".to_owned()
                    + Uuid::new_v4()
                        .simple()
                        .encode_lower(&mut Uuid::encode_buffer())
            }
        };

        self.ctx.register_table(&*table_name, Arc::new(table))?;

        let table = wait_for_future(py, self._table(&table_name))??;

        let df = PyDataFrame::new(table);
        Ok(df)
    }

    /// Create a DataFrame from an existing logical plan
    pub fn create_dataframe_from_logical_plan(&self, plan: PyLogicalPlan) -> PyDataFrame {
        PyDataFrame::new(DataFrame::new(self.ctx.state(), plan.plan.as_ref().clone()))
    }

    /// Construct datafusion dataframe from Python list
    #[pyo3(signature = (data, name=None))]
    pub fn from_pylist(
        &self,
        data: Bound<'_, PyList>,
        name: Option<&str>,
    ) -> PyResult<PyDataFrame> {
        // Acquire GIL Token
        let py = data.py();

        // Instantiate pyarrow Table object & convert to Arrow Table
        let table_class = py.import("pyarrow")?.getattr("Table")?;
        let args = PyTuple::new(py, &[data])?;
        let table = table_class.call_method1("from_pylist", args)?;

        // Convert Arrow Table to datafusion DataFrame
        let df = self.from_arrow(table, name, py)?;
        Ok(df)
    }

    /// Construct datafusion dataframe from Python dictionary
    #[pyo3(signature = (data, name=None))]
    pub fn from_pydict(
        &self,
        data: Bound<'_, PyDict>,
        name: Option<&str>,
    ) -> PyResult<PyDataFrame> {
        // Acquire GIL Token
        let py = data.py();

        // Instantiate pyarrow Table object & convert to Arrow Table
        let table_class = py.import("pyarrow")?.getattr("Table")?;
        let args = PyTuple::new(py, &[data])?;
        let table = table_class.call_method1("from_pydict", args)?;

        // Convert Arrow Table to datafusion DataFrame
        let df = self.from_arrow(table, name, py)?;
        Ok(df)
    }

    /// Construct datafusion dataframe from Arrow Table
    #[pyo3(signature = (data, name=None))]
    pub fn from_arrow(
        &self,
        data: Bound<'_, PyAny>,
        name: Option<&str>,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let (schema, batches) =
            if let Ok(stream_reader) = ArrowArrayStreamReader::from_pyarrow_bound(&data) {
                // Works for any object that implements __arrow_c_stream__ in pycapsule.

                let schema = stream_reader.schema().as_ref().to_owned();
                let batches = stream_reader
                    .collect::<Result<Vec<RecordBatch>, arrow::error::ArrowError>>()?;

                (schema, batches)
            } else if let Ok(array) = RecordBatch::from_pyarrow_bound(&data) {
                // While this says RecordBatch, it will work for any object that implements
                // __arrow_c_array__ and returns a StructArray.

                (array.schema().as_ref().to_owned(), vec![array])
            } else {
                return Err(PyDataFusionError::Common(
                    "Expected either a Arrow Array or Arrow Stream in from_arrow().".to_string(),
                ));
            };

        // Because create_dataframe() expects a vector of vectors of record batches
        // here we need to wrap the vector of record batches in an additional vector
        let list_of_batches = PyArrowType::from(vec![batches]);
        self.create_dataframe(list_of_batches, name, Some(schema.into()), py)
    }

    /// Construct datafusion dataframe from pandas
    #[allow(clippy::wrong_self_convention)]
    #[pyo3(signature = (data, name=None))]
    pub fn from_pandas(&self, data: Bound<'_, PyAny>, name: Option<&str>) -> PyResult<PyDataFrame> {
        // Obtain GIL token
        let py = data.py();

        // Instantiate pyarrow Table object & convert to Arrow Table
        let table_class = py.import("pyarrow")?.getattr("Table")?;
        let args = PyTuple::new(py, &[data])?;
        let table = table_class.call_method1("from_pandas", args)?;

        // Convert Arrow Table to datafusion DataFrame
        let df = self.from_arrow(table, name, py)?;
        Ok(df)
    }

    /// Construct datafusion dataframe from polars
    #[pyo3(signature = (data, name=None))]
    pub fn from_polars(&self, data: Bound<'_, PyAny>, name: Option<&str>) -> PyResult<PyDataFrame> {
        // Convert Polars dataframe to Arrow Table
        let table = data.call_method0("to_arrow")?;

        // Convert Arrow Table to datafusion DataFrame
        let df = self.from_arrow(table, name, data.py())?;
        Ok(df)
    }

    pub fn register_table(&self, name: &str, table: Bound<'_, PyAny>) -> PyDataFusionResult<()> {
        let session = self.clone().into_bound_py_any(table.py())?;
        let table = PyTable::new(table, Some(session))?;

        self.ctx.register_table(name, table.table)?;
        Ok(())
    }

    pub fn deregister_table(&self, name: &str) -> PyDataFusionResult<()> {
        self.ctx.deregister_table(name)?;
        Ok(())
    }

    pub fn register_table_factory(
        &self,
        format: &str,
        mut factory: Bound<'_, PyAny>,
    ) -> PyDataFusionResult<()> {
        if factory.hasattr("__datafusion_table_provider_factory__")? {
            let py = factory.py();
            let ffi = self.ffi_logical_codec();
            let codec_capsule = create_logical_extension_capsule(py, ffi.as_ref())?;
            factory = factory
                .getattr("__datafusion_table_provider_factory__")?
                .call1((codec_capsule,))?;
        }

        let factory: Arc<dyn TableProviderFactory> =
            if let Ok(capsule) = factory.cast::<PyCapsule>().map_err(py_datafusion_err) {
                let data: NonNull<FFI_TableProviderFactory> = capsule
                    .pointer_checked(Some(c"datafusion_table_provider_factory"))?
                    .cast();
                let factory = unsafe { data.as_ref() };
                factory.into()
            } else {
                Arc::new(RustWrappedPyTableProviderFactory::new(
                    factory.into(),
                    self.ffi_logical_codec(),
                ))
            };

        let st = self.ctx.state_ref();
        let mut lock = st.write();
        lock.table_factories_mut()
            .insert(format.to_owned(), factory);

        Ok(())
    }

    pub fn register_catalog_provider_list(
        &self,
        mut provider: Bound<PyAny>,
    ) -> PyDataFusionResult<()> {
        if provider.hasattr("__datafusion_catalog_provider_list__")? {
            let py = provider.py();
            let ffi = self.ffi_logical_codec();
            let codec_capsule = create_logical_extension_capsule(py, ffi.as_ref())?;
            provider = provider
                .getattr("__datafusion_catalog_provider_list__")?
                .call1((codec_capsule,))?;
        }

        let provider = if let Ok(capsule) = provider.cast::<PyCapsule>() {
            let data: NonNull<FFI_CatalogProviderList> = capsule
                .pointer_checked(Some(c"datafusion_catalog_provider_list"))?
                .cast();
            let provider = unsafe { data.as_ref() };
            let provider: Arc<dyn CatalogProviderList> = provider.into();
            provider
        } else {
            match provider.extract::<PyCatalogList>() {
                Ok(py_catalog_list) => py_catalog_list.catalog_list,
                Err(_) => Arc::new(RustWrappedPyCatalogProviderList::new(
                    provider.into(),
                    self.ffi_logical_codec(),
                )) as Arc<dyn CatalogProviderList>,
            }
        };

        self.ctx.register_catalog_list(provider);

        Ok(())
    }

    pub fn register_catalog_provider(
        &self,
        name: &str,
        mut provider: Bound<'_, PyAny>,
    ) -> PyDataFusionResult<()> {
        if provider.hasattr("__datafusion_catalog_provider__")? {
            let py = provider.py();
            let ffi = self.ffi_logical_codec();
            let codec_capsule = create_logical_extension_capsule(py, ffi.as_ref())?;
            provider = provider
                .getattr("__datafusion_catalog_provider__")?
                .call1((codec_capsule,))?;
        }

        let provider = if let Ok(capsule) = provider.cast::<PyCapsule>() {
            let data: NonNull<FFI_CatalogProvider> = capsule
                .pointer_checked(Some(c"datafusion_catalog_provider"))?
                .cast();
            let provider = unsafe { data.as_ref() };
            let provider: Arc<dyn CatalogProvider> = provider.into();
            provider
        } else {
            match provider.extract::<PyCatalog>() {
                Ok(py_catalog) => py_catalog.catalog,
                Err(_) => Arc::new(RustWrappedPyCatalogProvider::new(
                    provider.into(),
                    self.ffi_logical_codec(),
                )) as Arc<dyn CatalogProvider>,
            }
        };

        let _ = self.ctx.register_catalog(name, provider);

        Ok(())
    }

    /// Construct datafusion dataframe from Arrow Table
    pub fn register_table_provider(
        &self,
        name: &str,
        provider: Bound<'_, PyAny>,
    ) -> PyDataFusionResult<()> {
        // Deprecated: use `register_table` instead
        self.register_table(name, provider)
    }

    pub fn register_record_batches(
        &self,
        name: &str,
        partitions: PyArrowType<Vec<Vec<RecordBatch>>>,
    ) -> PyDataFusionResult<()> {
        let schema = partitions.0[0][0].schema();
        let table = MemTable::try_new(schema, partitions.0)?;
        self.ctx.register_table(name, Arc::new(table))?;
        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (name, path, table_partition_cols=vec![],
                        parquet_pruning=true,
                        file_extension=".parquet",
                        skip_metadata=true,
                        schema=None,
                        file_sort_order=None))]
    pub fn register_parquet(
        &self,
        name: &str,
        path: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        parquet_pruning: bool,
        file_extension: &str,
        skip_metadata: bool,
        schema: Option<PyArrowType<Schema>>,
        file_sort_order: Option<Vec<Vec<PySortExpr>>>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let mut options = ParquetReadOptions::default()
            .table_partition_cols(
                table_partition_cols
                    .into_iter()
                    .map(|(name, ty)| (name, ty.0))
                    .collect::<Vec<(String, DataType)>>(),
            )
            .parquet_pruning(parquet_pruning)
            .skip_metadata(skip_metadata);
        options.file_extension = file_extension;
        options.schema = schema.as_ref().map(|x| &x.0);
        options.file_sort_order = file_sort_order
            .unwrap_or_default()
            .into_iter()
            .map(|e| e.into_iter().map(|f| f.into()).collect())
            .collect();

        let result = self.ctx.register_parquet(name, path, options);
        wait_for_future(py, result)??;
        Ok(())
    }

    #[pyo3(signature = (name,
                        path,
                        options=None))]
    pub fn register_csv(
        &self,
        name: &str,
        path: &Bound<'_, PyAny>,
        options: Option<&PyCsvReadOptions>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let options = options
            .map(|opts| opts.try_into())
            .transpose()?
            .unwrap_or_default();

        if path.is_instance_of::<PyList>() {
            let paths = path.extract::<Vec<String>>()?;
            let result = self.register_csv_from_multiple_paths(name, paths, options);
            wait_for_future(py, result)??;
        } else {
            let path = path.extract::<String>()?;
            let result = self.ctx.register_csv(name, &path, options);
            wait_for_future(py, result)??;
        }

        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (name,
                        path,
                        schema=None,
                        schema_infer_max_records=1000,
                        file_extension=".json",
                        table_partition_cols=vec![],
                        file_compression_type=None))]
    pub fn register_json(
        &self,
        name: &str,
        path: PathBuf,
        schema: Option<PyArrowType<Schema>>,
        schema_infer_max_records: usize,
        file_extension: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        file_compression_type: Option<String>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let path = path
            .to_str()
            .ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?;

        let mut options = JsonReadOptions::default()
            .file_compression_type(parse_file_compression_type(file_compression_type)?)
            .table_partition_cols(
                table_partition_cols
                    .into_iter()
                    .map(|(name, ty)| (name, ty.0))
                    .collect::<Vec<(String, DataType)>>(),
            );
        options.schema_infer_max_records = schema_infer_max_records;
        options.file_extension = file_extension;
        options.schema = schema.as_ref().map(|x| &x.0);

        let result = self.ctx.register_json(name, path, options);
        wait_for_future(py, result)??;

        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (name,
                        path,
                        schema=None,
                        file_extension=".avro",
                        table_partition_cols=vec![]))]
    pub fn register_avro(
        &self,
        name: &str,
        path: PathBuf,
        schema: Option<PyArrowType<Schema>>,
        file_extension: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let path = path
            .to_str()
            .ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?;

        let mut options = AvroReadOptions::default().table_partition_cols(
            table_partition_cols
                .into_iter()
                .map(|(name, ty)| (name, ty.0))
                .collect::<Vec<(String, DataType)>>(),
        );
        options.file_extension = file_extension;
        options.schema = schema.as_ref().map(|x| &x.0);

        let result = self.ctx.register_avro(name, path, options);
        wait_for_future(py, result)??;

        Ok(())
    }

    #[pyo3(signature = (name, path, schema=None, file_extension=".arrow", table_partition_cols=vec![]))]
    pub fn register_arrow(
        &self,
        name: &str,
        path: &str,
        schema: Option<PyArrowType<Schema>>,
        file_extension: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let mut options = ArrowReadOptions::default().table_partition_cols(
            table_partition_cols
                .into_iter()
                .map(|(name, ty)| (name, ty.0))
                .collect::<Vec<(String, DataType)>>(),
        );
        options.file_extension = file_extension;
        options.schema = schema.as_ref().map(|x| &x.0);

        let result = self.ctx.register_arrow(name, path, options);
        wait_for_future(py, result)??;
        Ok(())
    }

    pub fn register_batch(
        &self,
        name: &str,
        batch: PyArrowType<RecordBatch>,
    ) -> PyDataFusionResult<()> {
        self.ctx.register_batch(name, batch.0)?;
        Ok(())
    }

    // Registers a PyArrow.Dataset
    pub fn register_dataset(
        &self,
        name: &str,
        dataset: &Bound<'_, PyAny>,
        py: Python,
    ) -> PyDataFusionResult<()> {
        let table: Arc<dyn TableProvider> = Arc::new(Dataset::new(dataset, py)?);

        self.ctx.register_table(name, table)?;

        Ok(())
    }

    pub fn register_udf(&self, udf: PyScalarUDF) -> PyResult<()> {
        self.ctx.register_udf(udf.function);
        Ok(())
    }

    pub fn deregister_udf(&self, name: &str) {
        self.ctx.deregister_udf(name);
    }

    pub fn register_udaf(&self, udaf: PyAggregateUDF) -> PyResult<()> {
        self.ctx.register_udaf(udaf.function);
        Ok(())
    }

    pub fn deregister_udaf(&self, name: &str) {
        self.ctx.deregister_udaf(name);
    }

    pub fn register_udwf(&self, udwf: PyWindowUDF) -> PyResult<()> {
        self.ctx.register_udwf(udwf.function);
        Ok(())
    }

    pub fn deregister_udwf(&self, name: &str) {
        self.ctx.deregister_udwf(name);
    }

    #[pyo3(signature = (name="datafusion"))]
    pub fn catalog(&self, py: Python, name: &str) -> PyResult<Py<PyAny>> {
        let catalog = self.ctx.catalog(name).ok_or(PyKeyError::new_err(format!(
            "Catalog with name {name} doesn't exist."
        )))?;

        match catalog.downcast_ref::<RustWrappedPyCatalogProvider>() {
            Some(wrapped_schema) => Ok(wrapped_schema.catalog_provider.clone_ref(py)),
            None => {
                Ok(PyCatalog::new_from_parts(catalog, self.ffi_logical_codec()).into_py_any(py)?)
            }
        }
    }

    pub fn catalog_names(&self) -> HashSet<String> {
        self.ctx.catalog_names().into_iter().collect()
    }

    pub fn table(&self, name: &str, py: Python) -> PyResult<PyDataFrame> {
        let res = wait_for_future(py, self.ctx.table(name))
            .map_err(|e| PyKeyError::new_err(e.to_string()))?;
        match res {
            Ok(df) => Ok(PyDataFrame::new(df)),
            Err(e) => {
                if let datafusion::error::DataFusionError::Plan(msg) = &e
                    && msg.contains("No table named")
                {
                    return Err(PyKeyError::new_err(msg.to_string()));
                }
                Err(py_datafusion_err(e))
            }
        }
    }

    pub fn table_exist(&self, name: &str) -> PyDataFusionResult<bool> {
        Ok(self.ctx.table_exist(name)?)
    }

    pub fn empty_table(&self) -> PyDataFusionResult<PyDataFrame> {
        Ok(PyDataFrame::new(self.ctx.read_empty()?))
    }

    pub fn session_id(&self) -> String {
        self.ctx.session_id()
    }

    pub fn session_start_time(&self) -> String {
        self.ctx.session_start_time().to_rfc3339()
    }

    pub fn enable_ident_normalization(&self) -> bool {
        self.ctx.enable_ident_normalization()
    }

    pub fn parse_sql_expr(&self, sql: &str, schema: PyDFSchema) -> PyDataFusionResult<PyExpr> {
        let df_schema: DFSchema = schema.into();
        Ok(self.ctx.parse_sql_expr(sql, &df_schema)?.into())
    }

    pub fn execute_logical_plan(
        &self,
        plan: PyLogicalPlan,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let df = wait_for_future(
            py,
            self.ctx.execute_logical_plan(plan.plan.as_ref().clone()),
        )??;
        Ok(PyDataFrame::new(df))
    }

    pub fn refresh_catalogs(&self, py: Python) -> PyDataFusionResult<()> {
        wait_for_future(py, self.ctx.refresh_catalogs())??;
        Ok(())
    }

    pub fn remove_optimizer_rule(&self, name: &str) -> bool {
        self.ctx.remove_optimizer_rule(name)
    }

    pub fn table_provider(&self, name: &str, py: Python) -> PyResult<PyTable> {
        let provider = wait_for_future(py, self.ctx.table_provider(name))
            // Outer error: runtime/async failure
            .map_err(|e| PyRuntimeError::new_err(e.to_string()))?
            // Inner error: table not found
            .map_err(|e| PyKeyError::new_err(e.to_string()))?;
        Ok(PyTable { table: provider })
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (path, schema=None, schema_infer_max_records=1000, file_extension=".json", table_partition_cols=vec![], file_compression_type=None))]
    pub fn read_json(
        &self,
        path: PathBuf,
        schema: Option<PyArrowType<Schema>>,
        schema_infer_max_records: usize,
        file_extension: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        file_compression_type: Option<String>,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let path = path
            .to_str()
            .ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?;
        let mut options = JsonReadOptions::default()
            .table_partition_cols(
                table_partition_cols
                    .into_iter()
                    .map(|(name, ty)| (name, ty.0))
                    .collect::<Vec<(String, DataType)>>(),
            )
            .file_compression_type(parse_file_compression_type(file_compression_type)?);
        options.schema_infer_max_records = schema_infer_max_records;
        options.file_extension = file_extension;
        let df = if let Some(schema) = schema {
            options.schema = Some(&schema.0);
            let result = self.ctx.read_json(path, options);
            wait_for_future(py, result)??
        } else {
            let result = self.ctx.read_json(path, options);
            wait_for_future(py, result)??
        };
        Ok(PyDataFrame::new(df))
    }

    #[pyo3(signature = (
        path,
        options=None))]
    pub fn read_csv(
        &self,
        path: &Bound<'_, PyAny>,
        options: Option<&PyCsvReadOptions>,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let options = options
            .map(|opts| opts.try_into())
            .transpose()?
            .unwrap_or_default();

        if path.is_instance_of::<PyList>() {
            let paths = path.extract::<Vec<String>>()?;
            let paths = paths.iter().map(|p| p as &str).collect::<Vec<&str>>();
            let result = self.ctx.read_csv(paths, options);
            let df = PyDataFrame::new(wait_for_future(py, result)??);
            Ok(df)
        } else {
            let path = path.extract::<String>()?;
            let result = self.ctx.read_csv(path, options);
            let df = PyDataFrame::new(wait_for_future(py, result)??);
            Ok(df)
        }
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (
        path,
        table_partition_cols=vec![],
        parquet_pruning=true,
        file_extension=".parquet",
        skip_metadata=true,
        schema=None,
        file_sort_order=None))]
    pub fn read_parquet(
        &self,
        path: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        parquet_pruning: bool,
        file_extension: &str,
        skip_metadata: bool,
        schema: Option<PyArrowType<Schema>>,
        file_sort_order: Option<Vec<Vec<PySortExpr>>>,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let mut options = ParquetReadOptions::default()
            .table_partition_cols(
                table_partition_cols
                    .into_iter()
                    .map(|(name, ty)| (name, ty.0))
                    .collect::<Vec<(String, DataType)>>(),
            )
            .parquet_pruning(parquet_pruning)
            .skip_metadata(skip_metadata);
        options.file_extension = file_extension;
        options.schema = schema.as_ref().map(|x| &x.0);
        options.file_sort_order = file_sort_order
            .unwrap_or_default()
            .into_iter()
            .map(|e| e.into_iter().map(|f| f.into()).collect())
            .collect();

        let result = self.ctx.read_parquet(path, options);
        let df = PyDataFrame::new(wait_for_future(py, result)??);
        Ok(df)
    }

    #[allow(clippy::too_many_arguments)]
    #[pyo3(signature = (path, schema=None, table_partition_cols=vec![], file_extension=".avro"))]
    pub fn read_avro(
        &self,
        path: &str,
        schema: Option<PyArrowType<Schema>>,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        file_extension: &str,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let mut options = AvroReadOptions::default().table_partition_cols(
            table_partition_cols
                .into_iter()
                .map(|(name, ty)| (name, ty.0))
                .collect::<Vec<(String, DataType)>>(),
        );
        options.file_extension = file_extension;
        let df = if let Some(schema) = schema {
            options.schema = Some(&schema.0);
            let read_future = self.ctx.read_avro(path, options);
            wait_for_future(py, read_future)??
        } else {
            let read_future = self.ctx.read_avro(path, options);
            wait_for_future(py, read_future)??
        };
        Ok(PyDataFrame::new(df))
    }

    #[pyo3(signature = (path, schema=None, file_extension=".arrow", table_partition_cols=vec![]))]
    pub fn read_arrow(
        &self,
        path: &str,
        schema: Option<PyArrowType<Schema>>,
        file_extension: &str,
        table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
        py: Python,
    ) -> PyDataFusionResult<PyDataFrame> {
        let mut options = ArrowReadOptions::default().table_partition_cols(
            table_partition_cols
                .into_iter()
                .map(|(name, ty)| (name, ty.0))
                .collect::<Vec<(String, DataType)>>(),
        );
        options.file_extension = file_extension;
        options.schema = schema.as_ref().map(|x| &x.0);

        let result = self.ctx.read_arrow(path, options);
        let df = wait_for_future(py, result)??;
        Ok(PyDataFrame::new(df))
    }

    pub fn read_table(&self, table: Bound<'_, PyAny>) -> PyDataFusionResult<PyDataFrame> {
        let session = self.clone().into_bound_py_any(table.py())?;
        let table = PyTable::new(table, Some(session))?;
        let df = self.ctx.read_table(table.table())?;
        Ok(PyDataFrame::new(df))
    }

    fn __repr__(&self) -> PyResult<String> {
        let config = self.ctx.copied_config();
        let mut config_entries = config
            .options()
            .entries()
            .iter()
            .filter(|e| e.value.is_some())
            .map(|e| format!("{} = {}", e.key, e.value.as_ref().unwrap()))
            .collect::<Vec<_>>();
        config_entries.sort();
        Ok(format!(
            "SessionContext: id={}; configs=[\n\t{}]",
            self.session_id(),
            config_entries.join("\n\t")
        ))
    }

    /// Execute a partition of an execution plan and return a stream of record batches
    pub fn execute(
        &self,
        plan: PyExecutionPlan,
        part: usize,
        py: Python,
    ) -> PyDataFusionResult<PyRecordBatchStream> {
        let ctx: TaskContext = TaskContext::from(&self.ctx.state());
        let plan = plan.plan.clone();
        let stream = spawn_future(py, async move { plan.execute(part, Arc::new(ctx)) })?;
        Ok(PyRecordBatchStream::new(stream))
    }

    pub fn __datafusion_task_context_provider__<'py>(
        &self,
        py: Python<'py>,
    ) -> PyResult<Bound<'py, PyCapsule>> {
        let name = cr"datafusion_task_context_provider".into();

        let ctx_provider = Arc::clone(&self.ctx) as Arc<dyn TaskContextProvider>;
        let ffi_ctx_provider = FFI_TaskContextProvider::from(&ctx_provider);

        PyCapsule::new(py, ffi_ctx_provider, Some(name))
    }

    pub fn __datafusion_logical_extension_codec__<'py>(
        &self,
        py: Python<'py>,
    ) -> PyResult<Bound<'py, PyCapsule>> {
        let ffi = self.ffi_logical_codec();
        create_logical_extension_capsule(py, ffi.as_ref())
    }

    pub fn with_logical_extension_codec<'py>(
        &self,
        codec: Bound<'py, PyAny>,
    ) -> PyDataFusionResult<Self> {
        let inner_ffi = ffi_logical_codec_from_pycapsule(codec)?;
        let inner: Arc<dyn LogicalExtensionCodec> = (&inner_ffi).into();
        let logical_codec = Arc::new(PythonLogicalCodec::new(inner));

        Ok(Self {
            ctx: Arc::clone(&self.ctx),
            logical_codec,
            physical_codec: Arc::clone(&self.physical_codec),
        })
    }

    pub fn __datafusion_physical_extension_codec__<'py>(
        &self,
        py: Python<'py>,
    ) -> PyResult<Bound<'py, PyCapsule>> {
        let ffi = self.ffi_physical_codec();
        create_physical_extension_capsule(py, ffi.as_ref())
    }

    pub fn with_physical_extension_codec<'py>(
        &self,
        codec: Bound<'py, PyAny>,
    ) -> PyDataFusionResult<Self> {
        let inner = physical_codec_from_pycapsule(&codec)?;
        let physical_codec = Arc::new(PythonPhysicalCodec::new(inner));

        Ok(Self {
            ctx: Arc::clone(&self.ctx),
            logical_codec: Arc::clone(&self.logical_codec),
            physical_codec,
        })
    }
}

impl PySessionContext {
    async fn _table(&self, name: &str) -> datafusion::common::Result<DataFrame> {
        self.ctx.table(name).await
    }

    async fn register_csv_from_multiple_paths(
        &self,
        name: &str,
        table_paths: Vec<String>,
        options: CsvReadOptions<'_>,
    ) -> datafusion::common::Result<()> {
        let table_paths = table_paths.to_urls()?;
        let session_config = self.ctx.copied_config();
        let listing_options =
            options.to_listing_options(&session_config, self.ctx.copied_table_options());

        let option_extension = listing_options.file_extension.clone();

        if table_paths.is_empty() {
            return exec_err!("No table paths were provided");
        }

        // check if the file extension matches the expected extension
        for path in &table_paths {
            let file_path = path.as_str();
            if !file_path.ends_with(option_extension.clone().as_str()) && !path.is_collection() {
                return exec_err!(
                    "File path '{file_path}' does not match the expected extension '{option_extension}'"
                );
            }
        }

        let resolved_schema = options
            .get_resolved_schema(&session_config, self.ctx.state(), table_paths[0].clone())
            .await?;

        let config = ListingTableConfig::new_with_multi_paths(table_paths)
            .with_listing_options(listing_options)
            .with_schema(resolved_schema);
        let table = ListingTable::try_new(config)?;
        self.ctx
            .register_table(TableReference::Bare { table: name.into() }, Arc::new(table))?;
        Ok(())
    }

    /// Session-scoped logical codec. Sibling modules read this when they
    /// need to serialize/deserialize logical-layer types (LogicalPlan,
    /// Expr) against the user-installed (or default) codec stack.
    pub(crate) fn logical_codec(&self) -> &Arc<PythonLogicalCodec> {
        &self.logical_codec
    }

    /// Session-scoped physical codec. Sibling modules read this for
    /// ExecutionPlan / PhysicalExpr serialization.
    pub(crate) fn physical_codec(&self) -> &Arc<PythonPhysicalCodec> {
        &self.physical_codec
    }

    /// Build an FFI-wrapped clone of the session's logical codec on demand.
    /// Used at every site that exports the codec across an FFI boundary
    /// (capsule getters, Rust wrappers for Python-defined providers, etc.).
    pub(crate) fn ffi_logical_codec(&self) -> Arc<FFI_LogicalExtensionCodec> {
        let inner: Arc<dyn LogicalExtensionCodec> =
            Arc::clone(&self.logical_codec) as Arc<dyn LogicalExtensionCodec>;
        let runtime = get_tokio_runtime().handle().clone();
        let ctx_provider = Arc::clone(&self.ctx) as Arc<dyn TaskContextProvider>;
        Arc::new(FFI_LogicalExtensionCodec::new(
            inner,
            Some(runtime),
            &ctx_provider,
        ))
    }

    /// Build an FFI-wrapped clone of the session's physical codec on demand.
    pub(crate) fn ffi_physical_codec(&self) -> Arc<FFI_PhysicalExtensionCodec> {
        let inner: Arc<dyn PhysicalExtensionCodec + Send> =
            Arc::clone(&self.physical_codec) as Arc<dyn PhysicalExtensionCodec + Send>;
        let runtime = get_tokio_runtime().handle().clone();
        let ctx_provider = Arc::clone(&self.ctx) as Arc<dyn TaskContextProvider>;
        Arc::new(FFI_PhysicalExtensionCodec::new(
            inner,
            Some(runtime),
            &ctx_provider,
        ))
    }
}

pub fn parse_file_compression_type(
    file_compression_type: Option<String>,
) -> Result<FileCompressionType, PyErr> {
    FileCompressionType::from_str(&*file_compression_type.unwrap_or("".to_string()).as_str())
        .map_err(|_| {
            PyValueError::new_err("file_compression_type must one of: gzip, bz2, xz, zstd")
        })
}

impl From<PySessionContext> for SessionContext {
    fn from(ctx: PySessionContext) -> SessionContext {
        ctx.ctx.as_ref().clone()
    }
}

impl From<SessionContext> for PySessionContext {
    fn from(ctx: SessionContext) -> PySessionContext {
        PySessionContext {
            ctx: Arc::new(ctx),
            logical_codec: Arc::new(PythonLogicalCodec::default()),
            physical_codec: Arc::new(PythonPhysicalCodec::default()),
        }
    }
}