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// 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.

//! ExecutionContext contains methods for registering data sources and executing queries
use crate::{
    catalog::{
        catalog::{CatalogList, MemoryCatalogList},
        information_schema::CatalogWithInformationSchema,
    },
    optimizer::hash_build_probe_order::HashBuildProbeOrder,
    physical_optimizer::optimizer::PhysicalOptimizerRule,
};
use log::debug;
use std::fs;
use std::path::Path;
use std::string::String;
use std::sync::Arc;
use std::{
    collections::{HashMap, HashSet},
    sync::Mutex,
};

use futures::{StreamExt, TryStreamExt};
use tokio::task::{self, JoinHandle};

use arrow::csv;

use crate::catalog::{
    catalog::{CatalogProvider, MemoryCatalogProvider},
    schema::{MemorySchemaProvider, SchemaProvider},
    ResolvedTableReference, TableReference,
};
use crate::datasource::csv::CsvFile;
use crate::datasource::parquet::ParquetTable;
use crate::datasource::TableProvider;
use crate::error::{DataFusionError, Result};
use crate::execution::dataframe_impl::DataFrameImpl;
use crate::logical_plan::{
    FunctionRegistry, LogicalPlan, LogicalPlanBuilder, ToDFSchema,
};
use crate::optimizer::constant_folding::ConstantFolding;
use crate::optimizer::filter_push_down::FilterPushDown;
use crate::optimizer::limit_push_down::LimitPushDown;
use crate::optimizer::optimizer::OptimizerRule;
use crate::optimizer::projection_push_down::ProjectionPushDown;
use crate::physical_optimizer::coalesce_batches::CoalesceBatches;
use crate::physical_optimizer::merge_exec::AddMergeExec;
use crate::physical_optimizer::repartition::Repartition;

use crate::physical_plan::csv::CsvReadOptions;
use crate::physical_plan::planner::DefaultPhysicalPlanner;
use crate::physical_plan::udf::ScalarUDF;
use crate::physical_plan::ExecutionPlan;
use crate::physical_plan::PhysicalPlanner;
use crate::sql::{
    parser::{DFParser, FileType},
    planner::{ContextProvider, SqlToRel},
};
use crate::variable::{VarProvider, VarType};
use crate::{dataframe::DataFrame, physical_plan::udaf::AggregateUDF};
use parquet::arrow::ArrowWriter;
use parquet::file::properties::WriterProperties;

/// ExecutionContext is the main interface for executing queries with DataFusion. The context
/// provides the following functionality:
///
/// * Create DataFrame from a CSV or Parquet data source.
/// * Register a CSV or Parquet data source as a table that can be referenced from a SQL query.
/// * Register a custom data source that can be referenced from a SQL query.
/// * Execution a SQL query
///
/// The following example demonstrates how to use the context to execute a query against a CSV
/// data source using the DataFrame API:
///
/// ```
/// use datafusion::prelude::*;
/// # use datafusion::error::Result;
/// # fn main() -> Result<()> {
/// let mut ctx = ExecutionContext::new();
/// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
/// let df = df.filter(col("a").lt_eq(col("b")))?
///            .aggregate(vec![col("a")], vec![min(col("b"))])?
///            .limit(100)?;
/// let results = df.collect();
/// # Ok(())
/// # }
/// ```
///
/// The following example demonstrates how to execute the same query using SQL:
///
/// ```
/// use datafusion::prelude::*;
///
/// # use datafusion::error::Result;
/// # fn main() -> Result<()> {
/// let mut ctx = ExecutionContext::new();
/// ctx.register_csv("example", "tests/example.csv", CsvReadOptions::new())?;
/// let results = ctx.sql("SELECT a, MIN(b) FROM example GROUP BY a LIMIT 100")?;
/// # Ok(())
/// # }
/// ```
#[derive(Clone)]
pub struct ExecutionContext {
    /// Internal state for the context
    pub state: Arc<Mutex<ExecutionContextState>>,
}

impl ExecutionContext {
    /// Creates a new execution context using a default configuration.
    pub fn new() -> Self {
        Self::with_config(ExecutionConfig::new())
    }

    /// Creates a new execution context using the provided configuration.
    pub fn with_config(config: ExecutionConfig) -> Self {
        let catalog_list = Arc::new(MemoryCatalogList::new()) as Arc<dyn CatalogList>;

        if config.create_default_catalog_and_schema {
            let default_catalog = MemoryCatalogProvider::new();

            default_catalog.register_schema(
                config.default_schema.clone(),
                Arc::new(MemorySchemaProvider::new()),
            );

            let default_catalog: Arc<dyn CatalogProvider> = if config.information_schema {
                Arc::new(CatalogWithInformationSchema::new(
                    catalog_list.clone(),
                    Arc::new(default_catalog),
                ))
            } else {
                Arc::new(default_catalog)
            };

            catalog_list
                .register_catalog(config.default_catalog.clone(), default_catalog);
        }

        Self {
            state: Arc::new(Mutex::new(ExecutionContextState {
                catalog_list,
                scalar_functions: HashMap::new(),
                var_provider: HashMap::new(),
                aggregate_functions: HashMap::new(),
                config,
            })),
        }
    }

    /// Creates a dataframe that will execute a SQL query.
    pub fn sql(&mut self, sql: &str) -> Result<Arc<dyn DataFrame>> {
        let plan = self.create_logical_plan(sql)?;
        match plan {
            LogicalPlan::CreateExternalTable {
                ref schema,
                ref name,
                ref location,
                ref file_type,
                ref has_header,
            } => match file_type {
                FileType::CSV => {
                    self.register_csv(
                        name,
                        location,
                        CsvReadOptions::new()
                            .schema(&schema.as_ref().to_owned().into())
                            .has_header(*has_header),
                    )?;
                    let plan = LogicalPlanBuilder::empty(false).build()?;
                    Ok(Arc::new(DataFrameImpl::new(self.state.clone(), &plan)))
                }
                FileType::Parquet => {
                    self.register_parquet(name, location)?;
                    let plan = LogicalPlanBuilder::empty(false).build()?;
                    Ok(Arc::new(DataFrameImpl::new(self.state.clone(), &plan)))
                }
                _ => Err(DataFusionError::NotImplemented(format!(
                    "Unsupported file type {:?}.",
                    file_type
                ))),
            },

            plan => Ok(Arc::new(DataFrameImpl::new(
                self.state.clone(),
                &self.optimize(&plan)?,
            ))),
        }
    }

    /// Creates a logical plan.
    ///
    /// This function is intended for internal use and should not be called directly.
    pub fn create_logical_plan(&self, sql: &str) -> Result<LogicalPlan> {
        let statements = DFParser::parse_sql(sql)?;

        if statements.len() != 1 {
            return Err(DataFusionError::NotImplemented(
                "The context currently only supports a single SQL statement".to_string(),
            ));
        }

        // create a query planner
        let state = self.state.lock().unwrap().clone();
        let query_planner = SqlToRel::new(&state);
        query_planner.statement_to_plan(&statements[0])
    }

    /// Registers a variable provider within this context.
    pub fn register_variable(
        &mut self,
        variable_type: VarType,
        provider: Arc<dyn VarProvider + Send + Sync>,
    ) {
        self.state
            .lock()
            .unwrap()
            .var_provider
            .insert(variable_type, provider);
    }

    /// Registers a scalar UDF within this context.
    ///
    /// Note in SQL queries, function names are looked up using
    /// lowercase unless the query uses quotes. For example,
    ///
    /// `SELECT MY_FUNC(x)...` will look for a function named `"my_func"`
    /// `SELECT "my_FUNC"(x)` will look for a function named `"my_FUNC"`
    pub fn register_udf(&mut self, f: ScalarUDF) {
        self.state
            .lock()
            .unwrap()
            .scalar_functions
            .insert(f.name.clone(), Arc::new(f));
    }

    /// Registers an aggregate UDF within this context.
    ///
    /// Note in SQL queries, aggregate names are looked up using
    /// lowercase unless the query uses quotes. For example,
    ///
    /// `SELECT MY_UDAF(x)...` will look for an aggregate named `"my_udaf"`
    /// `SELECT "my_UDAF"(x)` will look for an aggregate named `"my_UDAF"`
    pub fn register_udaf(&mut self, f: AggregateUDF) {
        self.state
            .lock()
            .unwrap()
            .aggregate_functions
            .insert(f.name.clone(), Arc::new(f));
    }

    /// Creates a DataFrame for reading a CSV data source.
    pub fn read_csv(
        &mut self,
        filename: &str,
        options: CsvReadOptions,
    ) -> Result<Arc<dyn DataFrame>> {
        Ok(Arc::new(DataFrameImpl::new(
            self.state.clone(),
            &LogicalPlanBuilder::scan_csv(&filename, options, None)?.build()?,
        )))
    }

    /// Creates a DataFrame for reading a Parquet data source.
    pub fn read_parquet(&mut self, filename: &str) -> Result<Arc<dyn DataFrame>> {
        Ok(Arc::new(DataFrameImpl::new(
            self.state.clone(),
            &LogicalPlanBuilder::scan_parquet(
                &filename,
                None,
                self.state.lock().unwrap().config.concurrency,
            )?
            .build()?,
        )))
    }

    /// Creates a DataFrame for reading a custom TableProvider.
    pub fn read_table(
        &mut self,
        provider: Arc<dyn TableProvider>,
    ) -> Result<Arc<dyn DataFrame>> {
        let schema = provider.schema();
        let table_scan = LogicalPlan::TableScan {
            table_name: "".to_string(),
            source: provider,
            projected_schema: schema.to_dfschema_ref()?,
            projection: None,
            filters: vec![],
            limit: None,
        };
        Ok(Arc::new(DataFrameImpl::new(
            self.state.clone(),
            &LogicalPlanBuilder::from(&table_scan).build()?,
        )))
    }

    /// Registers a CSV data source so that it can be referenced from SQL statements
    /// executed against this context.
    pub fn register_csv(
        &mut self,
        name: &str,
        filename: &str,
        options: CsvReadOptions,
    ) -> Result<()> {
        self.register_table(name, Arc::new(CsvFile::try_new(filename, options)?))?;
        Ok(())
    }

    /// Registers a Parquet data source so that it can be referenced from SQL statements
    /// executed against this context.
    pub fn register_parquet(&mut self, name: &str, filename: &str) -> Result<()> {
        let table = ParquetTable::try_new(
            &filename,
            self.state.lock().unwrap().config.concurrency,
        )?;
        self.register_table(name, Arc::new(table))?;
        Ok(())
    }

    /// Registers a named catalog using a custom `CatalogProvider` so that
    /// it can be referenced from SQL statements executed against this
    /// context.
    ///
    /// Returns the `CatalogProvider` previously registered for this
    /// name, if any
    pub fn register_catalog(
        &self,
        name: impl Into<String>,
        catalog: Arc<dyn CatalogProvider>,
    ) -> Option<Arc<dyn CatalogProvider>> {
        let name = name.into();

        let state = self.state.lock().unwrap();
        let catalog = if state.config.information_schema {
            Arc::new(CatalogWithInformationSchema::new(
                state.catalog_list.clone(),
                catalog,
            ))
        } else {
            catalog
        };

        state.catalog_list.register_catalog(name, catalog)
    }

    /// Retrieves a `CatalogProvider` instance by name
    pub fn catalog(&self, name: &str) -> Option<Arc<dyn CatalogProvider>> {
        self.state.lock().unwrap().catalog_list.catalog(name)
    }

    /// Registers a table using a custom `TableProvider` so that
    /// it can be referenced from SQL statements executed against this
    /// context.
    ///
    /// Returns the `TableProvider` previously registered for this
    /// reference, if any
    pub fn register_table<'a>(
        &'a mut self,
        table_ref: impl Into<TableReference<'a>>,
        provider: Arc<dyn TableProvider>,
    ) -> Result<Option<Arc<dyn TableProvider>>> {
        let table_ref = table_ref.into();
        self.state
            .lock()
            .unwrap()
            .schema_for_ref(table_ref)?
            .register_table(table_ref.table().to_owned(), provider)
    }

    /// Deregisters the given table.
    ///
    /// Returns the registered provider, if any
    pub fn deregister_table<'a>(
        &'a mut self,
        table_ref: impl Into<TableReference<'a>>,
    ) -> Result<Option<Arc<dyn TableProvider>>> {
        let table_ref = table_ref.into();
        self.state
            .lock()
            .unwrap()
            .schema_for_ref(table_ref)?
            .deregister_table(table_ref.table())
    }

    /// Retrieves a DataFrame representing a table previously registered by calling the
    /// register_table function.
    ///
    /// Returns an error if no table has been registered with the provided reference.
    pub fn table<'a>(
        &self,
        table_ref: impl Into<TableReference<'a>>,
    ) -> Result<Arc<dyn DataFrame>> {
        let table_ref = table_ref.into();
        let schema = self.state.lock().unwrap().schema_for_ref(table_ref)?;

        match schema.table(table_ref.table()) {
            Some(ref provider) => {
                let schema = provider.schema();
                let table_scan = LogicalPlan::TableScan {
                    table_name: table_ref.table().to_owned(),
                    source: Arc::clone(provider),
                    projected_schema: schema.to_dfschema_ref()?,
                    projection: None,
                    filters: vec![],
                    limit: None,
                };
                Ok(Arc::new(DataFrameImpl::new(
                    self.state.clone(),
                    &LogicalPlanBuilder::from(&table_scan).build()?,
                )))
            }
            _ => Err(DataFusionError::Plan(format!(
                "No table named '{}'",
                table_ref.table()
            ))),
        }
    }

    /// Returns the set of available tables in the default catalog and schema.
    ///
    /// Use [`table`] to get a specific table.
    ///
    /// [`table`]: ExecutionContext::table
    #[deprecated(
        note = "Please use the catalog provider interface (`ExecutionContext::catalog`) to examine available catalogs, schemas, and tables"
    )]
    pub fn tables(&self) -> Result<HashSet<String>> {
        Ok(self
            .state
            .lock()
            .unwrap()
            // a bare reference will always resolve to the default catalog and schema
            .schema_for_ref(TableReference::Bare { table: "" })?
            .table_names()
            .iter()
            .cloned()
            .collect())
    }

    /// Optimizes the logical plan by applying optimizer rules.
    pub fn optimize(&self, plan: &LogicalPlan) -> Result<LogicalPlan> {
        let optimizers = &self.state.lock().unwrap().config.optimizers;

        let mut new_plan = plan.clone();
        debug!("Logical plan:\n {:?}", plan);
        for optimizer in optimizers {
            new_plan = optimizer.optimize(&new_plan)?;
        }
        debug!("Optimized logical plan:\n {:?}", new_plan);
        Ok(new_plan)
    }

    /// Creates a physical plan from a logical plan.
    pub fn create_physical_plan(
        &self,
        logical_plan: &LogicalPlan,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        let state = self.state.lock().unwrap();
        state
            .config
            .query_planner
            .create_physical_plan(logical_plan, &state)
    }

    /// Executes a query and writes the results to a partitioned CSV file.
    pub async fn write_csv(
        &self,
        plan: Arc<dyn ExecutionPlan>,
        path: String,
    ) -> Result<()> {
        // create directory to contain the CSV files (one per partition)
        let fs_path = Path::new(&path);
        match fs::create_dir(fs_path) {
            Ok(()) => {
                let mut tasks = vec![];
                for i in 0..plan.output_partitioning().partition_count() {
                    let plan = plan.clone();
                    let filename = format!("part-{}.csv", i);
                    let path = fs_path.join(&filename);
                    let file = fs::File::create(path)?;
                    let mut writer = csv::Writer::new(file);
                    let stream = plan.execute(i).await?;
                    let handle: JoinHandle<Result<()>> = task::spawn(async move {
                        stream
                            .map(|batch| writer.write(&batch?))
                            .try_collect()
                            .await
                            .map_err(DataFusionError::from)
                    });
                    tasks.push(handle);
                }
                futures::future::join_all(tasks).await;
                Ok(())
            }
            Err(e) => Err(DataFusionError::Execution(format!(
                "Could not create directory {}: {:?}",
                path, e
            ))),
        }
    }

    /// Executes a query and writes the results to a partitioned Parquet file.
    pub async fn write_parquet(
        &self,
        plan: Arc<dyn ExecutionPlan>,
        path: String,
        writer_properties: Option<WriterProperties>,
    ) -> Result<()> {
        // create directory to contain the Parquet files (one per partition)
        let fs_path = Path::new(&path);
        match fs::create_dir(fs_path) {
            Ok(()) => {
                let mut tasks = vec![];
                for i in 0..plan.output_partitioning().partition_count() {
                    let plan = plan.clone();
                    let filename = format!("part-{}.parquet", i);
                    let path = fs_path.join(&filename);
                    let file = fs::File::create(path)?;
                    let mut writer = ArrowWriter::try_new(
                        file.try_clone().unwrap(),
                        plan.schema(),
                        writer_properties.clone(),
                    )?;
                    let stream = plan.execute(i).await?;
                    let handle: JoinHandle<Result<()>> = task::spawn(async move {
                        stream
                            .map(|batch| writer.write(&batch?))
                            .try_collect()
                            .await
                            .map_err(DataFusionError::from)?;
                        writer.close().map_err(DataFusionError::from).map(|_| ())
                    });
                    tasks.push(handle);
                }
                futures::future::join_all(tasks).await;
                Ok(())
            }
            Err(e) => Err(DataFusionError::Execution(format!(
                "Could not create directory {}: {:?}",
                path, e
            ))),
        }
    }
}

impl From<Arc<Mutex<ExecutionContextState>>> for ExecutionContext {
    fn from(state: Arc<Mutex<ExecutionContextState>>) -> Self {
        ExecutionContext { state }
    }
}

impl FunctionRegistry for ExecutionContext {
    fn udfs(&self) -> HashSet<String> {
        self.state.lock().unwrap().udfs()
    }

    fn udf(&self, name: &str) -> Result<Arc<ScalarUDF>> {
        self.state.lock().unwrap().udf(name)
    }

    fn udaf(&self, name: &str) -> Result<Arc<AggregateUDF>> {
        self.state.lock().unwrap().udaf(name)
    }
}

/// A planner used to add extensions to DataFusion logical and physical plans.
pub trait QueryPlanner {
    /// Given a `LogicalPlan`, create an `ExecutionPlan` suitable for execution
    fn create_physical_plan(
        &self,
        logical_plan: &LogicalPlan,
        ctx_state: &ExecutionContextState,
    ) -> Result<Arc<dyn ExecutionPlan>>;
}

/// The query planner used if no user defined planner is provided
struct DefaultQueryPlanner {}

impl QueryPlanner for DefaultQueryPlanner {
    /// Given a `LogicalPlan`, create an `ExecutionPlan` suitable for execution
    fn create_physical_plan(
        &self,
        logical_plan: &LogicalPlan,
        ctx_state: &ExecutionContextState,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        let planner = DefaultPhysicalPlanner::default();
        planner.create_physical_plan(logical_plan, ctx_state)
    }
}

/// Configuration options for execution context
#[derive(Clone)]
pub struct ExecutionConfig {
    /// Number of concurrent threads for query execution.
    pub concurrency: usize,
    /// Default batch size when reading data sources
    pub batch_size: usize,
    /// Responsible for optimizing a logical plan
    optimizers: Vec<Arc<dyn OptimizerRule + Send + Sync>>,
    /// Responsible for optimizing a physical execution plan
    pub physical_optimizers: Vec<Arc<dyn PhysicalOptimizerRule + Send + Sync>>,
    /// Responsible for planning `LogicalPlan`s, and `ExecutionPlan`
    query_planner: Arc<dyn QueryPlanner + Send + Sync>,
    /// Default catalog name for table resolution
    default_catalog: String,
    /// Default schema name for table resolution
    default_schema: String,
    /// Whether the default catalog and schema should be created automatically
    create_default_catalog_and_schema: bool,
    /// Should DataFusion provide access to `information_schema`
    /// virtual tables for displaying schema information
    information_schema: bool,
    /// Should DataFusion repartition data using the join keys to execute joins in parallel
    /// using the provided `concurrency` level
    pub repartition_joins: bool,
}

impl ExecutionConfig {
    /// Create an execution config with default setting
    pub fn new() -> Self {
        Self {
            concurrency: num_cpus::get(),
            batch_size: 8192,
            optimizers: vec![
                Arc::new(ConstantFolding::new()),
                Arc::new(ProjectionPushDown::new()),
                Arc::new(FilterPushDown::new()),
                Arc::new(HashBuildProbeOrder::new()),
                Arc::new(LimitPushDown::new()),
            ],
            physical_optimizers: vec![
                Arc::new(Repartition::new()),
                Arc::new(CoalesceBatches::new()),
                Arc::new(AddMergeExec::new()),
            ],
            query_planner: Arc::new(DefaultQueryPlanner {}),
            default_catalog: "datafusion".to_owned(),
            default_schema: "public".to_owned(),
            create_default_catalog_and_schema: true,
            information_schema: false,
            repartition_joins: true,
        }
    }

    /// Customize max_concurrency
    pub fn with_concurrency(mut self, n: usize) -> Self {
        // concurrency must be greater than zero
        assert!(n > 0);
        self.concurrency = n;
        self
    }

    /// Customize batch size
    pub fn with_batch_size(mut self, n: usize) -> Self {
        // batch size must be greater than zero
        assert!(n > 0);
        self.batch_size = n;
        self
    }

    /// Replace the default query planner
    pub fn with_query_planner(
        mut self,
        query_planner: Arc<dyn QueryPlanner + Send + Sync>,
    ) -> Self {
        self.query_planner = query_planner;
        self
    }

    /// Adds a new [`OptimizerRule`]
    pub fn add_optimizer_rule(
        mut self,
        optimizer_rule: Arc<dyn OptimizerRule + Send + Sync>,
    ) -> Self {
        self.optimizers.push(optimizer_rule);
        self
    }

    /// Adds a new [`PhysicalOptimizerRule`]
    pub fn add_physical_optimizer_rule(
        mut self,
        optimizer_rule: Arc<dyn PhysicalOptimizerRule + Send + Sync>,
    ) -> Self {
        self.physical_optimizers.push(optimizer_rule);
        self
    }

    /// Selects a name for the default catalog and schema
    pub fn with_default_catalog_and_schema(
        mut self,
        catalog: impl Into<String>,
        schema: impl Into<String>,
    ) -> Self {
        self.default_catalog = catalog.into();
        self.default_schema = schema.into();
        self
    }

    /// Controls whether the default catalog and schema will be automatically created
    pub fn create_default_catalog_and_schema(mut self, create: bool) -> Self {
        self.create_default_catalog_and_schema = create;
        self
    }

    /// Enables or disables the inclusion of `information_schema` virtual tables
    pub fn with_information_schema(mut self, enabled: bool) -> Self {
        self.information_schema = enabled;
        self
    }

    /// Enables or disables the use of repartitioning for joins to improve parallelism
    pub fn with_repartition_joins(mut self, enabled: bool) -> Self {
        self.repartition_joins = enabled;
        self
    }
}

/// Execution context for registering data sources and executing queries
#[derive(Clone)]
pub struct ExecutionContextState {
    /// Collection of catalogs containing schemas and ultimately TableProviders
    pub catalog_list: Arc<dyn CatalogList>,
    /// Scalar functions that are registered with the context
    pub scalar_functions: HashMap<String, Arc<ScalarUDF>>,
    /// Variable provider that are registered with the context
    pub var_provider: HashMap<VarType, Arc<dyn VarProvider + Send + Sync>>,
    /// Aggregate functions registered in the context
    pub aggregate_functions: HashMap<String, Arc<AggregateUDF>>,
    /// Context configuration
    pub config: ExecutionConfig,
}

impl ExecutionContextState {
    fn resolve_table_ref<'a>(
        &'a self,
        table_ref: impl Into<TableReference<'a>>,
    ) -> ResolvedTableReference<'a> {
        table_ref
            .into()
            .resolve(&self.config.default_catalog, &self.config.default_schema)
    }

    fn schema_for_ref<'a>(
        &'a self,
        table_ref: impl Into<TableReference<'a>>,
    ) -> Result<Arc<dyn SchemaProvider>> {
        let resolved_ref = self.resolve_table_ref(table_ref.into());

        self.catalog_list
            .catalog(resolved_ref.catalog)
            .ok_or_else(|| {
                DataFusionError::Plan(format!(
                    "failed to resolve catalog: {}",
                    resolved_ref.catalog
                ))
            })?
            .schema(resolved_ref.schema)
            .ok_or_else(|| {
                DataFusionError::Plan(format!(
                    "failed to resolve schema: {}",
                    resolved_ref.schema
                ))
            })
    }
}

impl ContextProvider for ExecutionContextState {
    fn get_table_provider(&self, name: TableReference) -> Option<Arc<dyn TableProvider>> {
        let resolved_ref = self.resolve_table_ref(name);
        let schema = self.schema_for_ref(resolved_ref).ok()?;
        schema.table(resolved_ref.table)
    }

    fn get_function_meta(&self, name: &str) -> Option<Arc<ScalarUDF>> {
        self.scalar_functions.get(name).cloned()
    }

    fn get_aggregate_meta(&self, name: &str) -> Option<Arc<AggregateUDF>> {
        self.aggregate_functions.get(name).cloned()
    }
}

impl FunctionRegistry for ExecutionContextState {
    fn udfs(&self) -> HashSet<String> {
        self.scalar_functions.keys().cloned().collect()
    }

    fn udf(&self, name: &str) -> Result<Arc<ScalarUDF>> {
        let result = self.scalar_functions.get(name);

        result.cloned().ok_or_else(|| {
            DataFusionError::Plan(format!(
                "There is no UDF named \"{}\" in the registry",
                name
            ))
        })
    }

    fn udaf(&self, name: &str) -> Result<Arc<AggregateUDF>> {
        let result = self.aggregate_functions.get(name);

        result.cloned().ok_or_else(|| {
            DataFusionError::Plan(format!(
                "There is no UDAF named \"{}\" in the registry",
                name
            ))
        })
    }
}

#[cfg(test)]
mod tests {

    use super::*;
    use crate::physical_plan::functions::make_scalar_function;
    use crate::physical_plan::{collect, collect_partitioned};
    use crate::test;
    use crate::variable::VarType;
    use crate::{
        assert_batches_eq, assert_batches_sorted_eq,
        logical_plan::{col, create_udf, sum},
    };
    use crate::{
        datasource::MemTable, logical_plan::create_udaf,
        physical_plan::expressions::AvgAccumulator,
    };
    use arrow::array::{
        Array, ArrayRef, BinaryArray, DictionaryArray, Float64Array, Int32Array,
        Int64Array, LargeBinaryArray, LargeStringArray, StringArray,
        TimestampNanosecondArray,
    };
    use arrow::compute::add;
    use arrow::datatypes::*;
    use arrow::record_batch::RecordBatch;
    use std::fs::File;
    use std::thread::{self, JoinHandle};
    use std::{io::prelude::*, sync::Mutex};
    use tempfile::TempDir;
    use test::*;

    #[tokio::test]
    async fn parallel_projection() -> Result<()> {
        let partition_count = 4;
        let results = execute("SELECT c1, c2 FROM test", partition_count).await?;

        let expected = vec![
            "+----+----+",
            "| c1 | c2 |",
            "+----+----+",
            "| 3  | 1  |",
            "| 3  | 2  |",
            "| 3  | 3  |",
            "| 3  | 4  |",
            "| 3  | 5  |",
            "| 3  | 6  |",
            "| 3  | 7  |",
            "| 3  | 8  |",
            "| 3  | 9  |",
            "| 3  | 10 |",
            "| 2  | 1  |",
            "| 2  | 2  |",
            "| 2  | 3  |",
            "| 2  | 4  |",
            "| 2  | 5  |",
            "| 2  | 6  |",
            "| 2  | 7  |",
            "| 2  | 8  |",
            "| 2  | 9  |",
            "| 2  | 10 |",
            "| 1  | 1  |",
            "| 1  | 2  |",
            "| 1  | 3  |",
            "| 1  | 4  |",
            "| 1  | 5  |",
            "| 1  | 6  |",
            "| 1  | 7  |",
            "| 1  | 8  |",
            "| 1  | 9  |",
            "| 1  | 10 |",
            "| 0  | 1  |",
            "| 0  | 2  |",
            "| 0  | 3  |",
            "| 0  | 4  |",
            "| 0  | 5  |",
            "| 0  | 6  |",
            "| 0  | 7  |",
            "| 0  | 8  |",
            "| 0  | 9  |",
            "| 0  | 10 |",
            "+----+----+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn create_variable_expr() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let partition_count = 4;
        let mut ctx = create_ctx(&tmp_dir, partition_count)?;

        let variable_provider = test::variable::SystemVar::new();
        ctx.register_variable(VarType::System, Arc::new(variable_provider));
        let variable_provider = test::variable::UserDefinedVar::new();
        ctx.register_variable(VarType::UserDefined, Arc::new(variable_provider));

        let provider = test::create_table_dual();
        ctx.register_table("dual", provider)?;

        let results =
            plan_and_collect(&mut ctx, "SELECT @@version, @name FROM dual").await?;

        let expected = vec![
            "+----------------------+------------------------+",
            "| @@version            | @name                  |",
            "+----------------------+------------------------+",
            "| system-var-@@version | user-defined-var-@name |",
            "+----------------------+------------------------+",
        ];
        assert_batches_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn register_deregister() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let partition_count = 4;
        let mut ctx = create_ctx(&tmp_dir, partition_count)?;

        let provider = test::create_table_dual();
        ctx.register_table("dual", provider)?;

        assert!(ctx.deregister_table("dual")?.is_some());
        assert!(ctx.deregister_table("dual")?.is_none());

        Ok(())
    }

    #[tokio::test]
    async fn parallel_query_with_filter() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let partition_count = 4;
        let ctx = create_ctx(&tmp_dir, partition_count)?;

        let logical_plan =
            ctx.create_logical_plan("SELECT c1, c2 FROM test WHERE c1 > 0 AND c1 < 3")?;
        let logical_plan = ctx.optimize(&logical_plan)?;

        let physical_plan = ctx.create_physical_plan(&logical_plan)?;
        println!("{:?}", physical_plan);

        let results = collect_partitioned(physical_plan).await?;

        // note that the order of partitions is not deterministic
        let mut num_rows = 0;
        for partition in &results {
            for batch in partition {
                num_rows += batch.num_rows();
            }
        }
        assert_eq!(20, num_rows);

        let results: Vec<RecordBatch> = results.into_iter().flatten().collect();
        let expected = vec![
            "+----+----+",
            "| c1 | c2 |",
            "+----+----+",
            "| 1  | 1  |",
            "| 1  | 10 |",
            "| 1  | 2  |",
            "| 1  | 3  |",
            "| 1  | 4  |",
            "| 1  | 5  |",
            "| 1  | 6  |",
            "| 1  | 7  |",
            "| 1  | 8  |",
            "| 1  | 9  |",
            "| 2  | 1  |",
            "| 2  | 10 |",
            "| 2  | 2  |",
            "| 2  | 3  |",
            "| 2  | 4  |",
            "| 2  | 5  |",
            "| 2  | 6  |",
            "| 2  | 7  |",
            "| 2  | 8  |",
            "| 2  | 9  |",
            "+----+----+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn projection_on_table_scan() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let partition_count = 4;
        let ctx = create_ctx(&tmp_dir, partition_count)?;

        let table = ctx.table("test")?;
        let logical_plan = LogicalPlanBuilder::from(&table.to_logical_plan())
            .project(vec![col("c2")])?
            .build()?;

        let optimized_plan = ctx.optimize(&logical_plan)?;
        match &optimized_plan {
            LogicalPlan::Projection { input, .. } => match &**input {
                LogicalPlan::TableScan {
                    source,
                    projected_schema,
                    ..
                } => {
                    assert_eq!(source.schema().fields().len(), 3);
                    assert_eq!(projected_schema.fields().len(), 1);
                }
                _ => panic!("input to projection should be TableScan"),
            },
            _ => panic!("expect optimized_plan to be projection"),
        }

        let expected = "Projection: #c2\
        \n  TableScan: test projection=Some([1])";
        assert_eq!(format!("{:?}", optimized_plan), expected);

        let physical_plan = ctx.create_physical_plan(&optimized_plan)?;

        assert_eq!(1, physical_plan.schema().fields().len());
        assert_eq!("c2", physical_plan.schema().field(0).name().as_str());

        let batches = collect(physical_plan).await?;
        assert_eq!(40, batches.iter().map(|x| x.num_rows()).sum::<usize>());

        Ok(())
    }

    #[test]
    fn preserve_nullability_on_projection() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let ctx = create_ctx(&tmp_dir, 1)?;

        let schema: Schema = ctx.table("test").unwrap().schema().clone().into();
        assert_eq!(schema.field_with_name("c1")?.is_nullable(), false);

        let plan = LogicalPlanBuilder::scan_empty("", &schema, None)?
            .project(vec![col("c1")])?
            .build()?;

        let plan = ctx.optimize(&plan)?;
        let physical_plan = ctx.create_physical_plan(&Arc::new(plan))?;
        assert_eq!(
            physical_plan.schema().field_with_name("c1")?.is_nullable(),
            false
        );
        Ok(())
    }

    #[tokio::test]
    async fn projection_on_memory_scan() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
            Field::new("c", DataType::Int32, false),
        ]);
        let schema = SchemaRef::new(schema);

        let partitions = vec![vec![RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(Int32Array::from(vec![1, 10, 10, 100])),
                Arc::new(Int32Array::from(vec![2, 12, 12, 120])),
                Arc::new(Int32Array::from(vec![3, 12, 12, 120])),
            ],
        )?]];

        let plan = LogicalPlanBuilder::scan_memory(partitions, schema, None)?
            .project(vec![col("b")])?
            .build()?;
        assert_fields_eq(&plan, vec!["b"]);

        let ctx = ExecutionContext::new();
        let optimized_plan = ctx.optimize(&plan)?;
        match &optimized_plan {
            LogicalPlan::Projection { input, .. } => match &**input {
                LogicalPlan::TableScan {
                    source,
                    projected_schema,
                    ..
                } => {
                    assert_eq!(source.schema().fields().len(), 3);
                    assert_eq!(projected_schema.fields().len(), 1);
                }
                _ => panic!("input to projection should be InMemoryScan"),
            },
            _ => panic!("expect optimized_plan to be projection"),
        }

        let expected = "Projection: #b\
        \n  TableScan: projection=Some([1])";
        assert_eq!(format!("{:?}", optimized_plan), expected);

        let physical_plan = ctx.create_physical_plan(&optimized_plan)?;

        assert_eq!(1, physical_plan.schema().fields().len());
        assert_eq!("b", physical_plan.schema().field(0).name().as_str());

        let batches = collect(physical_plan).await?;
        assert_eq!(1, batches.len());
        assert_eq!(1, batches[0].num_columns());
        assert_eq!(4, batches[0].num_rows());

        Ok(())
    }

    #[tokio::test]
    async fn sort() -> Result<()> {
        let results =
            execute("SELECT c1, c2 FROM test ORDER BY c1 DESC, c2 ASC", 4).await?;
        assert_eq!(results.len(), 1);

        let expected: Vec<&str> = vec![
            "+----+----+",
            "| c1 | c2 |",
            "+----+----+",
            "| 3  | 1  |",
            "| 3  | 2  |",
            "| 3  | 3  |",
            "| 3  | 4  |",
            "| 3  | 5  |",
            "| 3  | 6  |",
            "| 3  | 7  |",
            "| 3  | 8  |",
            "| 3  | 9  |",
            "| 3  | 10 |",
            "| 2  | 1  |",
            "| 2  | 2  |",
            "| 2  | 3  |",
            "| 2  | 4  |",
            "| 2  | 5  |",
            "| 2  | 6  |",
            "| 2  | 7  |",
            "| 2  | 8  |",
            "| 2  | 9  |",
            "| 2  | 10 |",
            "| 1  | 1  |",
            "| 1  | 2  |",
            "| 1  | 3  |",
            "| 1  | 4  |",
            "| 1  | 5  |",
            "| 1  | 6  |",
            "| 1  | 7  |",
            "| 1  | 8  |",
            "| 1  | 9  |",
            "| 1  | 10 |",
            "| 0  | 1  |",
            "| 0  | 2  |",
            "| 0  | 3  |",
            "| 0  | 4  |",
            "| 0  | 5  |",
            "| 0  | 6  |",
            "| 0  | 7  |",
            "| 0  | 8  |",
            "| 0  | 9  |",
            "| 0  | 10 |",
            "+----+----+",
        ];

        // Note it is important to NOT use assert_batches_sorted_eq
        // here as we are testing the sortedness of the output
        assert_batches_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn sort_empty() -> Result<()> {
        // The predicate on this query purposely generates no results
        let results = execute(
            "SELECT c1, c2 FROM test WHERE c1 > 100000 ORDER BY c1 DESC, c2 ASC",
            4,
        )
        .await
        .unwrap();
        assert_eq!(results.len(), 0);
        Ok(())
    }

    #[tokio::test]
    async fn aggregate() -> Result<()> {
        let results = execute("SELECT SUM(c1), SUM(c2) FROM test", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+---------+---------+",
            "| SUM(c1) | SUM(c2) |",
            "+---------+---------+",
            "| 60      | 220     |",
            "+---------+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_empty() -> Result<()> {
        // The predicate on this query purposely generates no results
        let results = execute("SELECT SUM(c1), SUM(c2) FROM test where c1 > 100000", 4)
            .await
            .unwrap();

        assert_eq!(results.len(), 1);

        let expected = vec![
            "+---------+---------+",
            "| SUM(c1) | SUM(c2) |",
            "+---------+---------+",
            "|         |         |",
            "+---------+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_avg() -> Result<()> {
        let results = execute("SELECT AVG(c1), AVG(c2) FROM test", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+---------+---------+",
            "| AVG(c1) | AVG(c2) |",
            "+---------+---------+",
            "| 1.5     | 5.5     |",
            "+---------+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_max() -> Result<()> {
        let results = execute("SELECT MAX(c1), MAX(c2) FROM test", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+---------+---------+",
            "| MAX(c1) | MAX(c2) |",
            "+---------+---------+",
            "| 3       | 10      |",
            "+---------+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_min() -> Result<()> {
        let results = execute("SELECT MIN(c1), MIN(c2) FROM test", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+---------+---------+",
            "| MIN(c1) | MIN(c2) |",
            "+---------+---------+",
            "| 0       | 1       |",
            "+---------+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_grouped() -> Result<()> {
        let results = execute("SELECT c1, SUM(c2) FROM test GROUP BY c1", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+----+---------+",
            "| c1 | SUM(c2) |",
            "+----+---------+",
            "| 0  | 55      |",
            "| 1  | 55      |",
            "| 2  | 55      |",
            "| 3  | 55      |",
            "+----+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_grouped_avg() -> Result<()> {
        let results = execute("SELECT c1, AVG(c2) FROM test GROUP BY c1", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+----+---------+",
            "| c1 | AVG(c2) |",
            "+----+---------+",
            "| 0  | 5.5     |",
            "| 1  | 5.5     |",
            "| 2  | 5.5     |",
            "| 3  | 5.5     |",
            "+----+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn boolean_literal() -> Result<()> {
        let results =
            execute("SELECT c1, c3 FROM test WHERE c1 > 2 AND c3 = true", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+----+------+",
            "| c1 | c3   |",
            "+----+------+",
            "| 3  | true |",
            "| 3  | true |",
            "| 3  | true |",
            "| 3  | true |",
            "| 3  | true |",
            "+----+------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_grouped_empty() -> Result<()> {
        let results =
            execute("SELECT c1, AVG(c2) FROM test WHERE c1 = 123 GROUP BY c1", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec!["++", "||", "++", "++"];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_grouped_max() -> Result<()> {
        let results = execute("SELECT c1, MAX(c2) FROM test GROUP BY c1", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+----+---------+",
            "| c1 | MAX(c2) |",
            "+----+---------+",
            "| 0  | 10      |",
            "| 1  | 10      |",
            "| 2  | 10      |",
            "| 3  | 10      |",
            "+----+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_grouped_min() -> Result<()> {
        let results = execute("SELECT c1, MIN(c2) FROM test GROUP BY c1", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+----+---------+",
            "| c1 | MIN(c2) |",
            "+----+---------+",
            "| 0  | 1       |",
            "| 1  | 1       |",
            "| 2  | 1       |",
            "| 3  | 1       |",
            "+----+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_timestamps_sum() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;
        ctx.register_table("t", test::table_with_timestamps())
            .unwrap();

        let results = plan_and_collect(
            &mut ctx,
            "SELECT sum(nanos), sum(micros), sum(millis), sum(secs) FROM t",
        )
        .await
        .unwrap_err();

        assert_eq!(results.to_string(), "Error during planning: Coercion from [Timestamp(Nanosecond, None)] to the signature Uniform(1, [Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64, Float32, Float64]) failed.");

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_timestamps_count() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;
        ctx.register_table("t", test::table_with_timestamps())
            .unwrap();

        let results = plan_and_collect(
            &mut ctx,
            "SELECT count(nanos), count(micros), count(millis), count(secs) FROM t",
        )
        .await
        .unwrap();

        let expected = vec![
            "+--------------+---------------+---------------+-------------+",
            "| COUNT(nanos) | COUNT(micros) | COUNT(millis) | COUNT(secs) |",
            "+--------------+---------------+---------------+-------------+",
            "| 3            | 3             | 3             | 3           |",
            "+--------------+---------------+---------------+-------------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_timestamps_min() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;
        ctx.register_table("t", test::table_with_timestamps())
            .unwrap();

        let results = plan_and_collect(
            &mut ctx,
            "SELECT min(nanos), min(micros), min(millis), min(secs) FROM t",
        )
        .await
        .unwrap();

        let expected = vec![
            "+----------------------------+----------------------------+-------------------------+---------------------+",
            "| MIN(nanos)                 | MIN(micros)                | MIN(millis)             | MIN(secs)           |",
            "+----------------------------+----------------------------+-------------------------+---------------------+",
            "| 2011-12-13 11:13:10.123450 | 2011-12-13 11:13:10.123450 | 2011-12-13 11:13:10.123 | 2011-12-13 11:13:10 |",
            "+----------------------------+----------------------------+-------------------------+---------------------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_timestamps_max() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;
        ctx.register_table("t", test::table_with_timestamps())
            .unwrap();

        let results = plan_and_collect(
            &mut ctx,
            "SELECT max(nanos), max(micros), max(millis), max(secs) FROM t",
        )
        .await
        .unwrap();

        let expected = vec![
            "+-------------------------+-------------------------+-------------------------+---------------------+",
            "| MAX(nanos)              | MAX(micros)             | MAX(millis)             | MAX(secs)           |",
            "+-------------------------+-------------------------+-------------------------+---------------------+",
            "| 2021-01-01 05:11:10.432 | 2021-01-01 05:11:10.432 | 2021-01-01 05:11:10.432 | 2021-01-01 05:11:10 |",
            "+-------------------------+-------------------------+-------------------------+---------------------+",
];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn aggregate_timestamps_avg() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;
        ctx.register_table("t", test::table_with_timestamps())
            .unwrap();

        let results = plan_and_collect(
            &mut ctx,
            "SELECT avg(nanos), avg(micros), avg(millis), avg(secs) FROM t",
        )
        .await
        .unwrap_err();

        assert_eq!(results.to_string(), "Error during planning: Coercion from [Timestamp(Nanosecond, None)] to the signature Uniform(1, [Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64, Float32, Float64]) failed.");
        Ok(())
    }

    #[tokio::test]
    async fn join_partitioned() -> Result<()> {
        // self join on partition id (workaround for duplicate column name)
        let results = execute(
            "SELECT 1 FROM test JOIN (SELECT c1 AS id1 FROM test) ON c1=id1",
            4,
        )
        .await?;

        assert_eq!(
            results.iter().map(|b| b.num_rows()).sum::<usize>(),
            4 * 10 * 10
        );

        Ok(())
    }

    #[tokio::test]
    async fn count_basic() -> Result<()> {
        let results = execute("SELECT COUNT(c1), COUNT(c2) FROM test", 1).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+-----------+-----------+",
            "| COUNT(c1) | COUNT(c2) |",
            "+-----------+-----------+",
            "| 10        | 10        |",
            "+-----------+-----------+",
        ];
        assert_batches_sorted_eq!(expected, &results);
        Ok(())
    }

    #[tokio::test]
    async fn count_partitioned() -> Result<()> {
        let results = execute("SELECT COUNT(c1), COUNT(c2) FROM test", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+-----------+-----------+",
            "| COUNT(c1) | COUNT(c2) |",
            "+-----------+-----------+",
            "| 40        | 40        |",
            "+-----------+-----------+",
        ];
        assert_batches_sorted_eq!(expected, &results);
        Ok(())
    }

    #[tokio::test]
    async fn count_aggregated() -> Result<()> {
        let results = execute("SELECT c1, COUNT(c2) FROM test GROUP BY c1", 4).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+----+-----------+",
            "| c1 | COUNT(c2) |",
            "+----+-----------+",
            "| 0  | 10        |",
            "| 1  | 10        |",
            "| 2  | 10        |",
            "| 3  | 10        |",
            "+----+-----------+",
        ];
        assert_batches_sorted_eq!(expected, &results);
        Ok(())
    }

    #[tokio::test]
    async fn group_by_date_trunc() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = ExecutionContext::new();
        let schema = Arc::new(Schema::new(vec![
            Field::new("c2", DataType::UInt64, false),
            Field::new(
                "t1",
                DataType::Timestamp(TimeUnit::Microsecond, None),
                false,
            ),
        ]));

        // generate a partitioned file
        for partition in 0..4 {
            let filename = format!("partition-{}.{}", partition, "csv");
            let file_path = tmp_dir.path().join(&filename);
            let mut file = File::create(file_path)?;

            // generate some data
            for i in 0..10 {
                let data = format!("{},2020-12-{}T00:00:00.000\n", i, i + 10);
                file.write_all(data.as_bytes())?;
            }
        }

        ctx.register_csv(
            "test",
            tmp_dir.path().to_str().unwrap(),
            CsvReadOptions::new().schema(&schema).has_header(false),
        )?;

        let results = plan_and_collect(
            &mut ctx,
            "SELECT date_trunc('week', t1) as week, SUM(c2) FROM test GROUP BY date_trunc('week', t1)"
        ).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
            "+---------------------+---------+",
            "| week                | SUM(c2) |",
            "+---------------------+---------+",
            "| 2020-12-07 00:00:00 | 24      |",
            "| 2020-12-14 00:00:00 | 156     |",
            "+---------------------+---------+",
        ];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn group_by_dictionary() {
        async fn run_test_case<K: ArrowDictionaryKeyType>() {
            let mut ctx = ExecutionContext::new();

            // input data looks like:
            // A, 1
            // B, 2
            // A, 2
            // A, 4
            // C, 1
            // A, 1

            let dict_array: DictionaryArray<K> =
                vec!["A", "B", "A", "A", "C", "A"].into_iter().collect();
            let dict_array = Arc::new(dict_array);

            let val_array: Int64Array = vec![1, 2, 2, 4, 1, 1].into();
            let val_array = Arc::new(val_array);

            let schema = Arc::new(Schema::new(vec![
                Field::new("dict", dict_array.data_type().clone(), false),
                Field::new("val", val_array.data_type().clone(), false),
            ]));

            let batch = RecordBatch::try_new(schema.clone(), vec![dict_array, val_array])
                .unwrap();

            let provider = MemTable::try_new(schema.clone(), vec![vec![batch]]).unwrap();
            ctx.register_table("t", Arc::new(provider)).unwrap();

            let results = plan_and_collect(
                &mut ctx,
                "SELECT dict, count(val) FROM t GROUP BY dict",
            )
            .await
            .expect("ran plan correctly");

            let expected = vec![
                "+------+------------+",
                "| dict | COUNT(val) |",
                "+------+------------+",
                "| A    | 4          |",
                "| B    | 1          |",
                "| C    | 1          |",
                "+------+------------+",
            ];
            assert_batches_sorted_eq!(expected, &results);

            // Now, use dict as an aggregate
            let results =
                plan_and_collect(&mut ctx, "SELECT val, count(dict) FROM t GROUP BY val")
                    .await
                    .expect("ran plan correctly");

            let expected = vec![
                "+-----+-------------+",
                "| val | COUNT(dict) |",
                "+-----+-------------+",
                "| 1   | 3           |",
                "| 2   | 2           |",
                "| 4   | 1           |",
                "+-----+-------------+",
            ];
            assert_batches_sorted_eq!(expected, &results);
        }

        run_test_case::<Int8Type>().await;
        run_test_case::<Int16Type>().await;
        run_test_case::<Int32Type>().await;
        run_test_case::<Int64Type>().await;
        run_test_case::<UInt8Type>().await;
        run_test_case::<UInt16Type>().await;
        run_test_case::<UInt32Type>().await;
        run_test_case::<UInt64Type>().await;
    }

    async fn run_count_distinct_integers_aggregated_scenario(
        partitions: Vec<Vec<(&str, u64)>>,
    ) -> Result<Vec<RecordBatch>> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = ExecutionContext::new();
        let schema = Arc::new(Schema::new(vec![
            Field::new("c_group", DataType::Utf8, false),
            Field::new("c_int8", DataType::Int8, false),
            Field::new("c_int16", DataType::Int16, false),
            Field::new("c_int32", DataType::Int32, false),
            Field::new("c_int64", DataType::Int64, false),
            Field::new("c_uint8", DataType::UInt8, false),
            Field::new("c_uint16", DataType::UInt16, false),
            Field::new("c_uint32", DataType::UInt32, false),
            Field::new("c_uint64", DataType::UInt64, false),
        ]));

        for (i, partition) in partitions.iter().enumerate() {
            let filename = format!("partition-{}.csv", i);
            let file_path = tmp_dir.path().join(&filename);
            let mut file = File::create(file_path)?;
            for row in partition {
                let row_str = format!(
                    "{},{}\n",
                    row.0,
                    // Populate values for each of the integer fields in the
                    // schema.
                    (0..8)
                        .map(|_| { row.1.to_string() })
                        .collect::<Vec<_>>()
                        .join(","),
                );
                file.write_all(row_str.as_bytes())?;
            }
        }
        ctx.register_csv(
            "test",
            tmp_dir.path().to_str().unwrap(),
            CsvReadOptions::new().schema(&schema).has_header(false),
        )?;

        let results = plan_and_collect(
            &mut ctx,
            "
              SELECT
                c_group,
                COUNT(c_uint64),
                COUNT(DISTINCT c_int8),
                COUNT(DISTINCT c_int16),
                COUNT(DISTINCT c_int32),
                COUNT(DISTINCT c_int64),
                COUNT(DISTINCT c_uint8),
                COUNT(DISTINCT c_uint16),
                COUNT(DISTINCT c_uint32),
                COUNT(DISTINCT c_uint64)
              FROM test
              GROUP BY c_group
            ",
        )
        .await?;

        Ok(results)
    }

    #[tokio::test]
    async fn count_distinct_integers_aggregated_single_partition() -> Result<()> {
        let partitions = vec![
            // The first member of each tuple will be the value for the
            // `c_group` column, and the second member will be the value for
            // each of the int/uint fields.
            vec![
                ("a", 1),
                ("a", 1),
                ("a", 2),
                ("b", 9),
                ("c", 9),
                ("c", 10),
                ("c", 9),
            ],
        ];

        let results = run_count_distinct_integers_aggregated_scenario(partitions).await?;
        assert_eq!(results.len(), 1);

        let expected = vec!
[
    "+---------+-----------------+------------------------+-------------------------+-------------------------+-------------------------+-------------------------+--------------------------+--------------------------+--------------------------+",
    "| c_group | COUNT(c_uint64) | COUNT(DISTINCT c_int8) | COUNT(DISTINCT c_int16) | COUNT(DISTINCT c_int32) | COUNT(DISTINCT c_int64) | COUNT(DISTINCT c_uint8) | COUNT(DISTINCT c_uint16) | COUNT(DISTINCT c_uint32) | COUNT(DISTINCT c_uint64) |",
    "+---------+-----------------+------------------------+-------------------------+-------------------------+-------------------------+-------------------------+--------------------------+--------------------------+--------------------------+",
    "| a       | 3               | 2                      | 2                       | 2                       | 2                       | 2                       | 2                        | 2                        | 2                        |",
    "| b       | 1               | 1                      | 1                       | 1                       | 1                       | 1                       | 1                        | 1                        | 1                        |",
    "| c       | 3               | 2                      | 2                       | 2                       | 2                       | 2                       | 2                        | 2                        | 2                        |",
    "+---------+-----------------+------------------------+-------------------------+-------------------------+-------------------------+-------------------------+--------------------------+--------------------------+--------------------------+",
];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[tokio::test]
    async fn count_distinct_integers_aggregated_multiple_partitions() -> Result<()> {
        let partitions = vec![
            // The first member of each tuple will be the value for the
            // `c_group` column, and the second member will be the value for
            // each of the int/uint fields.
            vec![("a", 1), ("a", 1), ("a", 2), ("b", 9), ("c", 9)],
            vec![("a", 1), ("a", 3), ("b", 8), ("b", 9), ("b", 10), ("b", 11)],
        ];

        let results = run_count_distinct_integers_aggregated_scenario(partitions).await?;
        assert_eq!(results.len(), 1);

        let expected = vec![
    "+---------+-----------------+------------------------+-------------------------+-------------------------+-------------------------+-------------------------+--------------------------+--------------------------+--------------------------+",
    "| c_group | COUNT(c_uint64) | COUNT(DISTINCT c_int8) | COUNT(DISTINCT c_int16) | COUNT(DISTINCT c_int32) | COUNT(DISTINCT c_int64) | COUNT(DISTINCT c_uint8) | COUNT(DISTINCT c_uint16) | COUNT(DISTINCT c_uint32) | COUNT(DISTINCT c_uint64) |",
    "+---------+-----------------+------------------------+-------------------------+-------------------------+-------------------------+-------------------------+--------------------------+--------------------------+--------------------------+",
    "| a       | 5               | 3                      | 3                       | 3                       | 3                       | 3                       | 3                        | 3                        | 3                        |",
    "| b       | 5               | 4                      | 4                       | 4                       | 4                       | 4                       | 4                        | 4                        | 4                        |",
    "| c       | 1               | 1                      | 1                       | 1                       | 1                       | 1                       | 1                        | 1                        | 1                        |",
    "+---------+-----------------+------------------------+-------------------------+-------------------------+-------------------------+-------------------------+--------------------------+--------------------------+--------------------------+",
];
        assert_batches_sorted_eq!(expected, &results);

        Ok(())
    }

    #[test]
    fn aggregate_with_alias() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let ctx = create_ctx(&tmp_dir, 1)?;

        let schema = Arc::new(Schema::new(vec![
            Field::new("c1", DataType::Utf8, false),
            Field::new("c2", DataType::UInt32, false),
        ]));

        let plan = LogicalPlanBuilder::scan_empty("", schema.as_ref(), None)?
            .aggregate(vec![col("c1")], vec![sum(col("c2"))])?
            .project(vec![col("c1"), col("SUM(c2)").alias("total_salary")])?
            .build()?;

        let plan = ctx.optimize(&plan)?;

        let physical_plan = ctx.create_physical_plan(&Arc::new(plan))?;
        assert_eq!("c1", physical_plan.schema().field(0).name().as_str());
        assert_eq!(
            "total_salary",
            physical_plan.schema().field(1).name().as_str()
        );
        Ok(())
    }

    #[tokio::test]
    async fn limit() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;
        ctx.register_table("t", test::table_with_sequence(1, 1000).unwrap())
            .unwrap();

        let results =
            plan_and_collect(&mut ctx, "SELECT i FROM t ORDER BY i DESC limit 3")
                .await
                .unwrap();

        let expected = vec![
            "+------+", "| i    |", "+------+", "| 1000 |", "| 999  |", "| 998  |",
            "+------+",
        ];

        assert_batches_eq!(expected, &results);

        let results = plan_and_collect(&mut ctx, "SELECT i FROM t ORDER BY i limit 3")
            .await
            .unwrap();

        let expected = vec![
            "+---+", "| i |", "+---+", "| 1 |", "| 2 |", "| 3 |", "+---+",
        ];

        assert_batches_eq!(expected, &results);

        let results = plan_and_collect(&mut ctx, "SELECT i FROM t limit 3")
            .await
            .unwrap();

        // the actual rows are not guaranteed, so only check the count (should be 3)
        let num_rows: usize = results.into_iter().map(|b| b.num_rows()).sum();
        assert_eq!(num_rows, 3);

        Ok(())
    }

    #[tokio::test]
    async fn limit_multi_partitions() -> Result<()> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 1)?;

        let partitions = vec![
            vec![test::make_partition(0)],
            vec![test::make_partition(1)],
            vec![test::make_partition(2)],
            vec![test::make_partition(3)],
            vec![test::make_partition(4)],
            vec![test::make_partition(5)],
        ];
        let schema = partitions[0][0].schema();
        let provider = Arc::new(MemTable::try_new(schema, partitions).unwrap());

        ctx.register_table("t", provider).unwrap();

        // select all rows
        let results = plan_and_collect(&mut ctx, "SELECT i FROM t").await.unwrap();

        let num_rows: usize = results.into_iter().map(|b| b.num_rows()).sum();
        assert_eq!(num_rows, 15);

        for limit in 1..10 {
            let query = format!("SELECT i FROM t limit {}", limit);
            let results = plan_and_collect(&mut ctx, &query).await.unwrap();

            let num_rows: usize = results.into_iter().map(|b| b.num_rows()).sum();
            assert_eq!(num_rows, limit, "mismatch with query {}", query);
        }

        Ok(())
    }

    #[tokio::test]
    async fn case_sensitive_identifiers_functions() {
        let mut ctx = ExecutionContext::new();
        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let expected = vec![
            "+---------+",
            "| sqrt(i) |",
            "+---------+",
            "| 1       |",
            "+---------+",
        ];

        let results = plan_and_collect(&mut ctx, "SELECT sqrt(i) FROM t")
            .await
            .unwrap();

        assert_batches_sorted_eq!(expected, &results);

        let results = plan_and_collect(&mut ctx, "SELECT SQRT(i) FROM t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &results);

        // Using double quotes allows specifying the function name with capitalization
        let err = plan_and_collect(&mut ctx, "SELECT \"SQRT\"(i) FROM t")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Invalid function 'SQRT'"
        );

        let results = plan_and_collect(&mut ctx, "SELECT \"sqrt\"(i) FROM t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &results);
    }

    #[tokio::test]
    async fn case_sensitive_identifiers_user_defined_functions() -> Result<()> {
        let mut ctx = ExecutionContext::new();
        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let myfunc = |args: &[ArrayRef]| Ok(Arc::clone(&args[0]));
        let myfunc = make_scalar_function(myfunc);

        ctx.register_udf(create_udf(
            "MY_FUNC",
            vec![DataType::Int32],
            Arc::new(DataType::Int32),
            myfunc,
        ));

        // doesn't work as it was registered with non lowercase
        let err = plan_and_collect(&mut ctx, "SELECT MY_FUNC(i) FROM t")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Invalid function \'my_func\'"
        );

        // Can call it if you put quotes
        let result = plan_and_collect(&mut ctx, "SELECT \"MY_FUNC\"(i) FROM t").await?;

        let expected = vec![
            "+------------+",
            "| MY_FUNC(i) |",
            "+------------+",
            "| 1          |",
            "+------------+",
        ];
        assert_batches_eq!(expected, &result);

        Ok(())
    }

    #[tokio::test]
    async fn case_sensitive_identifiers_aggregates() {
        let mut ctx = ExecutionContext::new();
        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let expected = vec![
            "+--------+",
            "| MAX(i) |",
            "+--------+",
            "| 1      |",
            "+--------+",
        ];

        let results = plan_and_collect(&mut ctx, "SELECT max(i) FROM t")
            .await
            .unwrap();

        assert_batches_sorted_eq!(expected, &results);

        let results = plan_and_collect(&mut ctx, "SELECT MAX(i) FROM t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &results);

        // Using double quotes allows specifying the function name with capitalization
        let err = plan_and_collect(&mut ctx, "SELECT \"MAX\"(i) FROM t")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Invalid function 'MAX'"
        );

        let results = plan_and_collect(&mut ctx, "SELECT \"max\"(i) FROM t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &results);
    }

    #[tokio::test]
    async fn case_sensitive_identifiers_user_defined_aggregates() -> Result<()> {
        let mut ctx = ExecutionContext::new();
        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        // Note capitalizaton
        let my_avg = create_udaf(
            "MY_AVG",
            DataType::Float64,
            Arc::new(DataType::Float64),
            Arc::new(|| Ok(Box::new(AvgAccumulator::try_new(&DataType::Float64)?))),
            Arc::new(vec![DataType::UInt64, DataType::Float64]),
        );

        ctx.register_udaf(my_avg);

        // doesn't work as it was registered as non lowercase
        let err = plan_and_collect(&mut ctx, "SELECT MY_AVG(i) FROM t")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Invalid function \'my_avg\'"
        );

        // Can call it if you put quotes
        let result = plan_and_collect(&mut ctx, "SELECT \"MY_AVG\"(i) FROM t").await?;

        let expected = vec![
            "+-----------+",
            "| MY_AVG(i) |",
            "+-----------+",
            "| 1         |",
            "+-----------+",
        ];
        assert_batches_eq!(expected, &result);

        Ok(())
    }

    #[tokio::test]
    async fn write_csv_results() -> Result<()> {
        // create partitioned input file and context
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 4)?;

        // execute a simple query and write the results to CSV
        let out_dir = tmp_dir.as_ref().to_str().unwrap().to_string() + "/out";
        write_csv(&mut ctx, "SELECT c1, c2 FROM test", &out_dir).await?;

        // create a new context and verify that the results were saved to a partitioned csv file
        let mut ctx = ExecutionContext::new();

        let schema = Arc::new(Schema::new(vec![
            Field::new("c1", DataType::UInt32, false),
            Field::new("c2", DataType::UInt64, false),
        ]));

        // register each partition as well as the top level dir
        let csv_read_option = CsvReadOptions::new().schema(&schema);
        ctx.register_csv("part0", &format!("{}/part-0.csv", out_dir), csv_read_option)?;
        ctx.register_csv("allparts", &out_dir, csv_read_option)?;

        let part0 = plan_and_collect(&mut ctx, "SELECT c1, c2 FROM part0").await?;
        let allparts = plan_and_collect(&mut ctx, "SELECT c1, c2 FROM allparts").await?;

        let allparts_count: usize = allparts.iter().map(|batch| batch.num_rows()).sum();

        assert_eq!(part0[0].schema(), allparts[0].schema());

        assert_eq!(allparts_count, 40);

        Ok(())
    }

    #[tokio::test]
    async fn write_parquet_results() -> Result<()> {
        // create partitioned input file and context
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, 4)?;

        // execute a simple query and write the results to CSV
        let out_dir = tmp_dir.as_ref().to_str().unwrap().to_string() + "/out";
        write_parquet(&mut ctx, "SELECT c1, c2 FROM test", &out_dir, None).await?;

        // create a new context and verify that the results were saved to a partitioned csv file
        let mut ctx = ExecutionContext::new();

        // register each partition as well as the top level dir
        ctx.register_parquet("part0", &format!("{}/part-0.parquet", out_dir))?;
        ctx.register_parquet("part1", &format!("{}/part-1.parquet", out_dir))?;
        ctx.register_parquet("part2", &format!("{}/part-2.parquet", out_dir))?;
        ctx.register_parquet("part3", &format!("{}/part-3.parquet", out_dir))?;
        ctx.register_parquet("allparts", &out_dir)?;

        let part0 = plan_and_collect(&mut ctx, "SELECT c1, c2 FROM part0").await?;
        let allparts = plan_and_collect(&mut ctx, "SELECT c1, c2 FROM allparts").await?;

        let allparts_count: usize = allparts.iter().map(|batch| batch.num_rows()).sum();

        assert_eq!(part0[0].schema(), allparts[0].schema());

        assert_eq!(allparts_count, 40);

        Ok(())
    }

    #[tokio::test]
    async fn query_csv_with_custom_partition_extension() -> Result<()> {
        let tmp_dir = TempDir::new()?;

        // The main stipulation of this test: use a file extension that isn't .csv.
        let file_extension = ".tst";

        let mut ctx = ExecutionContext::new();
        let schema = populate_csv_partitions(&tmp_dir, 2, file_extension)?;
        ctx.register_csv(
            "test",
            tmp_dir.path().to_str().unwrap(),
            CsvReadOptions::new()
                .schema(&schema)
                .file_extension(file_extension),
        )?;
        let results =
            plan_and_collect(&mut ctx, "SELECT SUM(c1), SUM(c2), COUNT(*) FROM test")
                .await?;

        assert_eq!(results.len(), 1);
        let expected = vec![
            "+---------+---------+-----------------+",
            "| SUM(c1) | SUM(c2) | COUNT(UInt8(1)) |",
            "+---------+---------+-----------------+",
            "| 10      | 110     | 20              |",
            "+---------+---------+-----------------+",
        ];
        assert_batches_eq!(expected, &results);

        Ok(())
    }

    #[test]
    fn send_context_to_threads() -> Result<()> {
        // ensure ExecutionContexts can be used in a multi-threaded
        // environment. Usecase is for concurrent planing.
        let tmp_dir = TempDir::new()?;
        let partition_count = 4;
        let ctx = Arc::new(Mutex::new(create_ctx(&tmp_dir, partition_count)?));

        let threads: Vec<JoinHandle<Result<_>>> = (0..2)
            .map(|_| ctx.clone())
            .map(|ctx_clone| {
                thread::spawn(move || {
                    let ctx = ctx_clone.lock().expect("Locked context");
                    // Ensure we can create logical plan code on a separate thread.
                    ctx.create_logical_plan(
                        "SELECT c1, c2 FROM test WHERE c1 > 0 AND c1 < 3",
                    )
                })
            })
            .collect();

        for thread in threads {
            thread.join().expect("Failed to join thread")?;
        }
        Ok(())
    }
    #[test]
    fn ctx_sql_should_optimize_plan() -> Result<()> {
        let mut ctx = ExecutionContext::new();
        let plan1 =
            ctx.create_logical_plan("SELECT * FROM (SELECT 1) WHERE TRUE AND TRUE")?;

        let opt_plan1 = ctx.optimize(&plan1)?;

        let plan2 = ctx.sql("SELECT * FROM (SELECT 1) WHERE TRUE AND TRUE")?;

        assert_eq!(
            format!("{:?}", opt_plan1),
            format!("{:?}", plan2.to_logical_plan())
        );

        Ok(())
    }

    #[tokio::test]
    async fn scalar_udf() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]);

        let batch = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![
                Arc::new(Int32Array::from(vec![1, 10, 10, 100])),
                Arc::new(Int32Array::from(vec![2, 12, 12, 120])),
            ],
        )?;

        let mut ctx = ExecutionContext::new();

        let provider = MemTable::try_new(Arc::new(schema), vec![vec![batch]])?;
        ctx.register_table("t", Arc::new(provider))?;

        let myfunc = |args: &[ArrayRef]| {
            let l = &args[0]
                .as_any()
                .downcast_ref::<Int32Array>()
                .expect("cast failed");
            let r = &args[1]
                .as_any()
                .downcast_ref::<Int32Array>()
                .expect("cast failed");
            Ok(Arc::new(add(l, r)?) as ArrayRef)
        };
        let myfunc = make_scalar_function(myfunc);

        ctx.register_udf(create_udf(
            "my_add",
            vec![DataType::Int32, DataType::Int32],
            Arc::new(DataType::Int32),
            myfunc,
        ));

        // from here on, we may be in a different scope. We would still like to be able
        // to call UDFs.

        let t = ctx.table("t")?;

        let plan = LogicalPlanBuilder::from(&t.to_logical_plan())
            .project(vec![
                col("a"),
                col("b"),
                ctx.udf("my_add")?.call(vec![col("a"), col("b")]),
            ])?
            .build()?;

        assert_eq!(
            format!("{:?}", plan),
            "Projection: #a, #b, my_add(#a, #b)\n  TableScan: t projection=None"
        );

        let plan = ctx.optimize(&plan)?;
        let plan = ctx.create_physical_plan(&plan)?;
        let result = collect(plan).await?;

        let expected = vec![
            "+-----+-----+-------------+",
            "| a   | b   | my_add(a,b) |",
            "+-----+-----+-------------+",
            "| 1   | 2   | 3           |",
            "| 10  | 12  | 22          |",
            "| 10  | 12  | 22          |",
            "| 100 | 120 | 220         |",
            "+-----+-----+-------------+",
        ];
        assert_batches_eq!(expected, &result);

        let batch = &result[0];
        let a = batch
            .column(0)
            .as_any()
            .downcast_ref::<Int32Array>()
            .expect("failed to cast a");
        let b = batch
            .column(1)
            .as_any()
            .downcast_ref::<Int32Array>()
            .expect("failed to cast b");
        let sum = batch
            .column(2)
            .as_any()
            .downcast_ref::<Int32Array>()
            .expect("failed to cast sum");

        assert_eq!(4, a.len());
        assert_eq!(4, b.len());
        assert_eq!(4, sum.len());
        for i in 0..sum.len() {
            assert_eq!(a.value(i) + b.value(i), sum.value(i));
        }

        ctx.deregister_table("t")?;

        Ok(())
    }

    #[tokio::test]
    async fn simple_avg() -> Result<()> {
        let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);

        let batch1 = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
        )?;
        let batch2 = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![Arc::new(Int32Array::from(vec![4, 5]))],
        )?;

        let mut ctx = ExecutionContext::new();

        let provider =
            MemTable::try_new(Arc::new(schema), vec![vec![batch1], vec![batch2]])?;
        ctx.register_table("t", Arc::new(provider))?;

        let result = plan_and_collect(&mut ctx, "SELECT AVG(a) FROM t").await?;

        let batch = &result[0];
        assert_eq!(1, batch.num_columns());
        assert_eq!(1, batch.num_rows());

        let values = batch
            .column(0)
            .as_any()
            .downcast_ref::<Float64Array>()
            .expect("failed to cast version");
        assert_eq!(values.len(), 1);
        // avg(1,2,3,4,5) = 3.0
        assert_eq!(values.value(0), 3.0_f64);
        Ok(())
    }

    /// tests the creation, registration and usage of a UDAF
    #[tokio::test]
    async fn simple_udaf() -> Result<()> {
        let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);

        let batch1 = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
        )?;
        let batch2 = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![Arc::new(Int32Array::from(vec![4, 5]))],
        )?;

        let mut ctx = ExecutionContext::new();

        let provider =
            MemTable::try_new(Arc::new(schema), vec![vec![batch1], vec![batch2]])?;
        ctx.register_table("t", Arc::new(provider))?;

        // define a udaf, using a DataFusion's accumulator
        let my_avg = create_udaf(
            "my_avg",
            DataType::Float64,
            Arc::new(DataType::Float64),
            Arc::new(|| Ok(Box::new(AvgAccumulator::try_new(&DataType::Float64)?))),
            Arc::new(vec![DataType::UInt64, DataType::Float64]),
        );

        ctx.register_udaf(my_avg);

        let result = plan_and_collect(&mut ctx, "SELECT MY_AVG(a) FROM t").await?;

        let expected = vec![
            "+-----------+",
            "| my_avg(a) |",
            "+-----------+",
            "| 3         |",
            "+-----------+",
        ];
        assert_batches_eq!(expected, &result);

        Ok(())
    }

    #[tokio::test]
    async fn custom_query_planner() -> Result<()> {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_query_planner(Arc::new(MyQueryPlanner {})),
        );

        let df = ctx.sql("SELECT 1")?;
        df.collect().await.expect_err("query not supported");
        Ok(())
    }

    #[tokio::test]
    async fn information_schema_tables_not_exist_by_default() {
        let mut ctx = ExecutionContext::new();

        let err = plan_and_collect(&mut ctx, "SELECT * from information_schema.tables")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Table or CTE with name 'information_schema.tables' not found"
        );
    }

    #[tokio::test]
    async fn information_schema_tables_no_tables() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );

        let result =
            plan_and_collect(&mut ctx, "SELECT * from information_schema.tables")
                .await
                .unwrap();

        let expected = vec![
            "+---------------+--------------------+------------+------------+",
            "| table_catalog | table_schema       | table_name | table_type |",
            "+---------------+--------------------+------------+------------+",
            "| datafusion    | information_schema | columns    | VIEW       |",
            "| datafusion    | information_schema | tables     | VIEW       |",
            "+---------------+--------------------+------------+------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);
    }

    #[tokio::test]
    async fn information_schema_tables_tables_default_catalog() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );

        // Now, register an empty table
        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let result =
            plan_and_collect(&mut ctx, "SELECT * from information_schema.tables")
                .await
                .unwrap();

        let expected = vec![
            "+---------------+--------------------+------------+------------+",
            "| table_catalog | table_schema       | table_name | table_type |",
            "+---------------+--------------------+------------+------------+",
            "| datafusion    | information_schema | tables     | VIEW       |",
            "| datafusion    | information_schema | columns    | VIEW       |",
            "| datafusion    | public             | t          | BASE TABLE |",
            "+---------------+--------------------+------------+------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);

        // Newly added tables should appear
        ctx.register_table("t2", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let result =
            plan_and_collect(&mut ctx, "SELECT * from information_schema.tables")
                .await
                .unwrap();

        let expected = vec![
            "+---------------+--------------------+------------+------------+",
            "| table_catalog | table_schema       | table_name | table_type |",
            "+---------------+--------------------+------------+------------+",
            "| datafusion    | information_schema | columns    | VIEW       |",
            "| datafusion    | information_schema | tables     | VIEW       |",
            "| datafusion    | public             | t          | BASE TABLE |",
            "| datafusion    | public             | t2         | BASE TABLE |",
            "+---------------+--------------------+------------+------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);
    }

    #[tokio::test]
    async fn information_schema_tables_tables_with_multiple_catalogs() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );
        let catalog = MemoryCatalogProvider::new();
        let schema = MemorySchemaProvider::new();
        schema
            .register_table("t1".to_owned(), test::table_with_sequence(1, 1).unwrap())
            .unwrap();
        schema
            .register_table("t2".to_owned(), test::table_with_sequence(1, 1).unwrap())
            .unwrap();
        catalog.register_schema("my_schema", Arc::new(schema));
        ctx.register_catalog("my_catalog", Arc::new(catalog));

        let catalog = MemoryCatalogProvider::new();
        let schema = MemorySchemaProvider::new();
        schema
            .register_table("t3".to_owned(), test::table_with_sequence(1, 1).unwrap())
            .unwrap();
        catalog.register_schema("my_other_schema", Arc::new(schema));
        ctx.register_catalog("my_other_catalog", Arc::new(catalog));

        let result =
            plan_and_collect(&mut ctx, "SELECT * from information_schema.tables")
                .await
                .unwrap();

        let expected = vec![
            "+------------------+--------------------+------------+------------+",
            "| table_catalog    | table_schema       | table_name | table_type |",
            "+------------------+--------------------+------------+------------+",
            "| datafusion       | information_schema | columns    | VIEW       |",
            "| datafusion       | information_schema | tables     | VIEW       |",
            "| my_catalog       | information_schema | columns    | VIEW       |",
            "| my_catalog       | information_schema | tables     | VIEW       |",
            "| my_catalog       | my_schema          | t1         | BASE TABLE |",
            "| my_catalog       | my_schema          | t2         | BASE TABLE |",
            "| my_other_catalog | information_schema | columns    | VIEW       |",
            "| my_other_catalog | information_schema | tables     | VIEW       |",
            "| my_other_catalog | my_other_schema    | t3         | BASE TABLE |",
            "+------------------+--------------------+------------+------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);
    }

    #[tokio::test]
    async fn information_schema_show_tables_no_information_schema() {
        let mut ctx = ExecutionContext::with_config(ExecutionConfig::new());

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        // use show tables alias
        let err = plan_and_collect(&mut ctx, "SHOW TABLES").await.unwrap_err();

        assert_eq!(err.to_string(), "Error during planning: SHOW TABLES is not supported unless information_schema is enabled");
    }

    #[tokio::test]
    async fn information_schema_show_tables() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        // use show tables alias
        let result = plan_and_collect(&mut ctx, "SHOW TABLES").await.unwrap();

        let expected = vec![
            "+---------------+--------------------+------------+------------+",
            "| table_catalog | table_schema       | table_name | table_type |",
            "+---------------+--------------------+------------+------------+",
            "| datafusion    | information_schema | columns    | VIEW       |",
            "| datafusion    | information_schema | tables     | VIEW       |",
            "| datafusion    | public             | t          | BASE TABLE |",
            "+---------------+--------------------+------------+------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);

        let result = plan_and_collect(&mut ctx, "SHOW tables").await.unwrap();

        assert_batches_sorted_eq!(expected, &result);
    }

    #[tokio::test]
    async fn information_schema_show_columns_no_information_schema() {
        let mut ctx = ExecutionContext::with_config(ExecutionConfig::new());

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let err = plan_and_collect(&mut ctx, "SHOW COLUMNS FROM t")
            .await
            .unwrap_err();

        assert_eq!(err.to_string(), "Error during planning: SHOW COLUMNS is not supported unless information_schema is enabled");
    }

    #[tokio::test]
    async fn information_schema_show_columns_like_where() {
        let mut ctx = ExecutionContext::with_config(ExecutionConfig::new());

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let expected =
            "Error during planning: SHOW COLUMNS with WHERE or LIKE is not supported";

        let err = plan_and_collect(&mut ctx, "SHOW COLUMNS FROM t LIKE 'f'")
            .await
            .unwrap_err();
        assert_eq!(err.to_string(), expected);

        let err =
            plan_and_collect(&mut ctx, "SHOW COLUMNS FROM t WHERE column_name = 'bar'")
                .await
                .unwrap_err();
        assert_eq!(err.to_string(), expected);
    }

    #[tokio::test]
    async fn information_schema_show_columns() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let result = plan_and_collect(&mut ctx, "SHOW COLUMNS FROM t")
            .await
            .unwrap();

        let expected = vec![
            "+---------------+--------------+------------+-------------+-----------+-------------+",
            "| table_catalog | table_schema | table_name | column_name | data_type | is_nullable |",
            "+---------------+--------------+------------+-------------+-----------+-------------+",
            "| datafusion    | public       | t          | i           | Int32     | YES         |",
            "+---------------+--------------+------------+-------------+-----------+-------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);

        let result = plan_and_collect(&mut ctx, "SHOW columns from t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &result);

        // This isn't ideal but it is consistent behavior for `SELECT * from T`
        let err = plan_and_collect(&mut ctx, "SHOW columns from T")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Unknown relation for SHOW COLUMNS: T"
        );
    }

    // test errors with WHERE and LIKE
    #[tokio::test]
    async fn information_schema_show_columns_full_extended() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let result = plan_and_collect(&mut ctx, "SHOW FULL COLUMNS FROM t")
            .await
            .unwrap();
        let expected = vec![

    "+---------------+--------------+------------+-------------+------------------+----------------+-------------+-----------+--------------------------+------------------------+-------------------+-------------------------+---------------+--------------------+---------------+",
    "| table_catalog | table_schema | table_name | column_name | ordinal_position | column_default | is_nullable | data_type | character_maximum_length | character_octet_length | numeric_precision | numeric_precision_radix | numeric_scale | datetime_precision | interval_type |",
    "+---------------+--------------+------------+-------------+------------------+----------------+-------------+-----------+--------------------------+------------------------+-------------------+-------------------------+---------------+--------------------+---------------+",
    "| datafusion    | public       | t          | i           | 0                |                | YES         | Int32     |                          |                        | 32                | 2                       |               |                    |               |",
    "+---------------+--------------+------------+-------------+------------------+----------------+-------------+-----------+--------------------------+------------------------+-------------------+-------------------------+---------------+--------------------+---------------+",

        ];
        assert_batches_sorted_eq!(expected, &result);

        let result = plan_and_collect(&mut ctx, "SHOW EXTENDED COLUMNS FROM t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &result);
    }

    #[tokio::test]
    async fn information_schema_show_table_table_names() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );

        ctx.register_table("t", test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        let result = plan_and_collect(&mut ctx, "SHOW COLUMNS FROM public.t")
            .await
            .unwrap();

        let expected = vec![
            "+---------------+--------------+------------+-------------+-----------+-------------+",
            "| table_catalog | table_schema | table_name | column_name | data_type | is_nullable |",
            "+---------------+--------------+------------+-------------+-----------+-------------+",
            "| datafusion    | public       | t          | i           | Int32     | YES         |",
            "+---------------+--------------+------------+-------------+-----------+-------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);

        let result = plan_and_collect(&mut ctx, "SHOW columns from datafusion.public.t")
            .await
            .unwrap();
        assert_batches_sorted_eq!(expected, &result);

        let err = plan_and_collect(&mut ctx, "SHOW columns from t2")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Unknown relation for SHOW COLUMNS: t2"
        );

        let err = plan_and_collect(&mut ctx, "SHOW columns from datafusion.public.t2")
            .await
            .unwrap_err();
        assert_eq!(err.to_string(), "Error during planning: Unknown relation for SHOW COLUMNS: datafusion.public.t2");
    }

    #[tokio::test]
    async fn show_unsupported() {
        let mut ctx = ExecutionContext::with_config(ExecutionConfig::new());

        let err = plan_and_collect(&mut ctx, "SHOW SOMETHING_UNKNOWN")
            .await
            .unwrap_err();

        assert_eq!(err.to_string(), "This feature is not implemented: SHOW SOMETHING_UNKNOWN not implemented. Supported syntax: SHOW <TABLES>");
    }

    #[tokio::test]
    async fn information_schema_columns_not_exist_by_default() {
        let mut ctx = ExecutionContext::new();

        let err = plan_and_collect(&mut ctx, "SELECT * from information_schema.columns")
            .await
            .unwrap_err();
        assert_eq!(
            err.to_string(),
            "Error during planning: Table or CTE with name 'information_schema.columns' not found"
        );
    }

    fn table_with_many_types() -> Arc<dyn TableProvider> {
        let schema = Schema::new(vec![
            Field::new("int32_col", DataType::Int32, false),
            Field::new("float64_col", DataType::Float64, true),
            Field::new("utf8_col", DataType::Utf8, true),
            Field::new("large_utf8_col", DataType::LargeUtf8, false),
            Field::new("binary_col", DataType::Binary, false),
            Field::new("large_binary_col", DataType::LargeBinary, false),
            Field::new(
                "timestamp_nanos",
                DataType::Timestamp(TimeUnit::Nanosecond, None),
                false,
            ),
        ]);

        let batch = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![
                Arc::new(Int32Array::from(vec![1])),
                Arc::new(Float64Array::from(vec![1.0])),
                Arc::new(StringArray::from(vec![Some("foo")])),
                Arc::new(LargeStringArray::from(vec![Some("bar")])),
                Arc::new(BinaryArray::from(vec![b"foo" as &[u8]])),
                Arc::new(LargeBinaryArray::from(vec![b"foo" as &[u8]])),
                Arc::new(TimestampNanosecondArray::from_opt_vec(
                    vec![Some(123)],
                    None,
                )),
            ],
        )
        .unwrap();
        let provider = MemTable::try_new(Arc::new(schema), vec![vec![batch]]).unwrap();
        Arc::new(provider)
    }

    #[tokio::test]
    async fn information_schema_columns() {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().with_information_schema(true),
        );
        let catalog = MemoryCatalogProvider::new();
        let schema = MemorySchemaProvider::new();

        schema
            .register_table("t1".to_owned(), test::table_with_sequence(1, 1).unwrap())
            .unwrap();

        schema
            .register_table("t2".to_owned(), table_with_many_types())
            .unwrap();
        catalog.register_schema("my_schema", Arc::new(schema));
        ctx.register_catalog("my_catalog", Arc::new(catalog));

        let result =
            plan_and_collect(&mut ctx, "SELECT * from information_schema.columns")
                .await
                .unwrap();

        let expected = vec![
    "+---------------+--------------+------------+------------------+------------------+----------------+-------------+-----------------------------+--------------------------+------------------------+-------------------+-------------------------+---------------+--------------------+---------------+",
    "| table_catalog | table_schema | table_name | column_name      | ordinal_position | column_default | is_nullable | data_type                   | character_maximum_length | character_octet_length | numeric_precision | numeric_precision_radix | numeric_scale | datetime_precision | interval_type |",
    "+---------------+--------------+------------+------------------+------------------+----------------+-------------+-----------------------------+--------------------------+------------------------+-------------------+-------------------------+---------------+--------------------+---------------+",
    "| my_catalog    | my_schema    | t1         | i                | 0                |                | YES         | Int32                       |                          |                        | 32                | 2                       |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | binary_col       | 4                |                | NO          | Binary                      |                          | 2147483647             |                   |                         |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | float64_col      | 1                |                | YES         | Float64                     |                          |                        | 24                | 2                       |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | int32_col        | 0                |                | NO          | Int32                       |                          |                        | 32                | 2                       |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | large_binary_col | 5                |                | NO          | LargeBinary                 |                          | 9223372036854775807    |                   |                         |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | large_utf8_col   | 3                |                | NO          | LargeUtf8                   |                          | 9223372036854775807    |                   |                         |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | timestamp_nanos  | 6                |                | NO          | Timestamp(Nanosecond, None) |                          |                        |                   |                         |               |                    |               |",
    "| my_catalog    | my_schema    | t2         | utf8_col         | 2                |                | YES         | Utf8                        |                          | 2147483647             |                   |                         |               |                    |               |",
    "+---------------+--------------+------------+------------------+------------------+----------------+-------------+-----------------------------+--------------------------+------------------------+-------------------+-------------------------+---------------+--------------------+---------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);
    }

    #[tokio::test]
    async fn disabled_default_catalog_and_schema() -> Result<()> {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new().create_default_catalog_and_schema(false),
        );

        assert!(matches!(
            ctx.register_table("test", test::table_with_sequence(1, 1)?),
            Err(DataFusionError::Plan(_))
        ));

        assert!(matches!(
            ctx.sql("select * from datafusion.public.test"),
            Err(DataFusionError::Plan(_))
        ));

        Ok(())
    }

    #[tokio::test]
    async fn custom_catalog_and_schema() -> Result<()> {
        let mut ctx = ExecutionContext::with_config(
            ExecutionConfig::new()
                .create_default_catalog_and_schema(false)
                .with_default_catalog_and_schema("my_catalog", "my_schema"),
        );

        let catalog = MemoryCatalogProvider::new();
        let schema = MemorySchemaProvider::new();
        schema.register_table("test".to_owned(), test::table_with_sequence(1, 1)?)?;
        catalog.register_schema("my_schema", Arc::new(schema));
        ctx.register_catalog("my_catalog", Arc::new(catalog));

        for table_ref in &["my_catalog.my_schema.test", "my_schema.test", "test"] {
            let result = plan_and_collect(
                &mut ctx,
                &format!("SELECT COUNT(*) AS count FROM {}", table_ref),
            )
            .await?;

            let expected = vec![
                "+-------+",
                "| count |",
                "+-------+",
                "| 1     |",
                "+-------+",
            ];
            assert_batches_eq!(expected, &result);
        }

        Ok(())
    }

    #[tokio::test]
    async fn cross_catalog_access() -> Result<()> {
        let mut ctx = ExecutionContext::new();

        let catalog_a = MemoryCatalogProvider::new();
        let schema_a = MemorySchemaProvider::new();
        schema_a
            .register_table("table_a".to_owned(), test::table_with_sequence(1, 1)?)?;
        catalog_a.register_schema("schema_a", Arc::new(schema_a));
        ctx.register_catalog("catalog_a", Arc::new(catalog_a));

        let catalog_b = MemoryCatalogProvider::new();
        let schema_b = MemorySchemaProvider::new();
        schema_b
            .register_table("table_b".to_owned(), test::table_with_sequence(1, 2)?)?;
        catalog_b.register_schema("schema_b", Arc::new(schema_b));
        ctx.register_catalog("catalog_b", Arc::new(catalog_b));

        let result = plan_and_collect(
            &mut ctx,
            "SELECT cat, SUM(i) AS total FROM (
                    SELECT i, 'a' AS cat FROM catalog_a.schema_a.table_a
                    UNION ALL
                    SELECT i, 'b' AS cat FROM catalog_b.schema_b.table_b
                )
                GROUP BY cat
                ORDER BY cat
                ",
        )
        .await?;

        let expected = vec![
            "+-----+-------+",
            "| cat | total |",
            "+-----+-------+",
            "| a   | 1     |",
            "| b   | 3     |",
            "+-----+-------+",
        ];
        assert_batches_eq!(expected, &result);

        Ok(())
    }

    #[tokio::test]
    async fn create_external_table_with_timestamps() {
        let mut ctx = ExecutionContext::new();

        let data = "Jorge,2018-12-13T12:12:10.011\n\
                    Andrew,2018-11-13T17:11:10.011";

        let tmp_dir = TempDir::new().unwrap();
        let file_path = tmp_dir.path().join("timestamps.csv");

        // scope to ensure the file is closed and written
        {
            File::create(&file_path)
                .expect("creating temp file")
                .write_all(data.as_bytes())
                .expect("writing data");
        }

        let sql = format!(
            "CREATE EXTERNAL TABLE csv_with_timestamps (
                  name VARCHAR,
                  ts TIMESTAMP
              )
              STORED AS CSV
              LOCATION '{}'
              ",
            file_path.to_str().expect("path is utf8")
        );

        plan_and_collect(&mut ctx, &sql)
            .await
            .expect("Executing CREATE EXTERNAL TABLE");

        let sql = "SELECT * from csv_with_timestamps";
        let result = plan_and_collect(&mut ctx, &sql).await.unwrap();
        let expected = vec![
            "+--------+-------------------------+",
            "| name   | ts                      |",
            "+--------+-------------------------+",
            "| Andrew | 2018-11-13 17:11:10.011 |",
            "| Jorge  | 2018-12-13 12:12:10.011 |",
            "+--------+-------------------------+",
        ];
        assert_batches_sorted_eq!(expected, &result);
    }

    struct MyPhysicalPlanner {}

    impl PhysicalPlanner for MyPhysicalPlanner {
        fn create_physical_plan(
            &self,
            _logical_plan: &LogicalPlan,
            _ctx_state: &ExecutionContextState,
        ) -> Result<Arc<dyn ExecutionPlan>> {
            Err(DataFusionError::NotImplemented(
                "query not supported".to_string(),
            ))
        }
    }

    struct MyQueryPlanner {}

    impl QueryPlanner for MyQueryPlanner {
        fn create_physical_plan(
            &self,
            logical_plan: &LogicalPlan,
            ctx_state: &ExecutionContextState,
        ) -> Result<Arc<dyn ExecutionPlan>> {
            let physical_planner = MyPhysicalPlanner {};
            physical_planner.create_physical_plan(logical_plan, ctx_state)
        }
    }

    /// Execute SQL and return results
    async fn plan_and_collect(
        ctx: &mut ExecutionContext,
        sql: &str,
    ) -> Result<Vec<RecordBatch>> {
        ctx.sql(sql)?.collect().await
    }

    /// Execute SQL and return results
    async fn execute(sql: &str, partition_count: usize) -> Result<Vec<RecordBatch>> {
        let tmp_dir = TempDir::new()?;
        let mut ctx = create_ctx(&tmp_dir, partition_count)?;
        plan_and_collect(&mut ctx, sql).await
    }

    /// Execute SQL and write results to partitioned csv files
    async fn write_csv(
        ctx: &mut ExecutionContext,
        sql: &str,
        out_dir: &str,
    ) -> Result<()> {
        let logical_plan = ctx.create_logical_plan(sql)?;
        let logical_plan = ctx.optimize(&logical_plan)?;
        let physical_plan = ctx.create_physical_plan(&logical_plan)?;
        ctx.write_csv(physical_plan, out_dir.to_string()).await
    }

    /// Execute SQL and write results to partitioned parquet files
    async fn write_parquet(
        ctx: &mut ExecutionContext,
        sql: &str,
        out_dir: &str,
        writer_properties: Option<WriterProperties>,
    ) -> Result<()> {
        let logical_plan = ctx.create_logical_plan(sql)?;
        let logical_plan = ctx.optimize(&logical_plan)?;
        let physical_plan = ctx.create_physical_plan(&logical_plan)?;
        ctx.write_parquet(physical_plan, out_dir.to_string(), writer_properties)
            .await
    }

    /// Generate CSV partitions within the supplied directory
    fn populate_csv_partitions(
        tmp_dir: &TempDir,
        partition_count: usize,
        file_extension: &str,
    ) -> Result<SchemaRef> {
        // define schema for data source (csv file)
        let schema = Arc::new(Schema::new(vec![
            Field::new("c1", DataType::UInt32, false),
            Field::new("c2", DataType::UInt64, false),
            Field::new("c3", DataType::Boolean, false),
        ]));

        // generate a partitioned file
        for partition in 0..partition_count {
            let filename = format!("partition-{}.{}", partition, file_extension);
            let file_path = tmp_dir.path().join(&filename);
            let mut file = File::create(file_path)?;

            // generate some data
            for i in 0..=10 {
                let data = format!("{},{},{}\n", partition, i, i % 2 == 0);
                file.write_all(data.as_bytes())?;
            }
        }

        Ok(schema)
    }

    /// Generate a partitioned CSV file and register it with an execution context
    fn create_ctx(tmp_dir: &TempDir, partition_count: usize) -> Result<ExecutionContext> {
        let mut ctx =
            ExecutionContext::with_config(ExecutionConfig::new().with_concurrency(8));

        let schema = populate_csv_partitions(tmp_dir, partition_count, ".csv")?;

        // register csv file with the execution context
        ctx.register_csv(
            "test",
            tmp_dir.path().to_str().unwrap(),
            CsvReadOptions::new().schema(&schema),
        )?;

        Ok(ctx)
    }
}