some code v3

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

use async_trait::async_trait;
use datafusion::arrow::array::{UInt64Builder, UInt8Builder};
use datafusion::arrow::datatypes::{DataType, Field, Schema, SchemaRef};
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::dataframe::DataFrame;
use datafusion::datasource::{TableProvider, TableType};
use datafusion::error::Result;
use datafusion::execution::context::{SessionState, TaskContext};
use datafusion::logical_plan::{provider_as_source, Expr, LogicalPlanBuilder};
use datafusion::physical_plan::expressions::PhysicalSortExpr;
use datafusion::physical_plan::memory::MemoryStream;
use datafusion::physical_plan::{
    project_schema, ExecutionPlan, SendableRecordBatchStream, Statistics,
};
use datafusion::prelude::*;
use std::any::Any;
use std::collections::{BTreeMap, HashMap};
use std::fmt::{Debug, Formatter};
use std::sync::{Arc, Mutex};
use std::time::Duration;
use tokio::time::timeout;

/// This example demonstrates executing a simple query against a custom datasource
#[tokio::main]
async fn main() -> Result<()> {
    // create our custom datasource and adding some users
    let db = CustomDataSource::default();
    db.populate_users();

    let ctx = SessionContext::new();
    ctx.register_table("abc", Arc::new(db))?;

    let df = ctx.sql("select * from abc").await?;
    df.show().await?;
    // println!("{:?}", df.s);
    // search_accounts(db.clone(), None, 3).await?;
    // search_accounts(db.clone(), Some(col("bank_account").gt(lit(8000u64))), 1).await?;
    // search_accounts(db.clone(), Some(col("bank_account").gt(lit(200u64))), 2).await?;

    Ok(())
}


/// A User, with an id and a bank account
#[derive(Clone, Debug)]
struct User {
    id: u8,
    bank_account: u64,
}

/// A custom datasource, used to represent a datastore with a single index
#[derive(Clone)]
pub struct CustomDataSource {
    inner: Arc<Mutex<CustomDataSourceInner>>,
}

struct CustomDataSourceInner {
    data: HashMap<u8, User>,
    bank_account_index: BTreeMap<u64, u8>,
}

impl Debug for CustomDataSource {
    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
        f.write_str("custom_db")
    }
}

impl CustomDataSource {
    pub(crate) async fn create_physical_plan(
        &self,
        projections: &Option<Vec<usize>>,
        schema: SchemaRef,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        Ok(Arc::new(CustomExec::new(projections, schema, self.clone())))
    }

    pub(crate) fn populate_users(&self) {
        self.add_user(User {
            id: 1,
            bank_account: 9_000,
        });
        self.add_user(User {
            id: 2,
            bank_account: 100,
        });
        self.add_user(User {
            id: 3,
            bank_account: 1_000,
        });
    }

    fn add_user(&self, user: User) {
        let mut inner = self.inner.lock().unwrap();
        inner.bank_account_index.insert(user.bank_account, user.id);
        inner.data.insert(user.id, user);
    }
}

impl Default for CustomDataSource {
    fn default() -> Self {
        CustomDataSource {
            inner: Arc::new(Mutex::new(CustomDataSourceInner {
                data: Default::default(),
                bank_account_index: Default::default(),
            })),
        }
    }
}

#[async_trait]
impl TableProvider for CustomDataSource {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn schema(&self) -> SchemaRef {
        SchemaRef::new(Schema::new(vec![
            Field::new("id", DataType::UInt8, false),
            Field::new("bank_account", DataType::UInt64, true),
        ]))
    }

    fn table_type(&self) -> TableType {
        TableType::Base
    }

    async fn scan(
        &self,
        _state: &SessionState,
        projection: &Option<Vec<usize>>,
        // filters and limit can be used here to inject some push-down operations if needed
        _filters: &[Expr],
        _limit: Option<usize>,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        return self.create_physical_plan(projection, self.schema()).await;
    }
}

#[derive(Debug, Clone)]
struct CustomExec {
    db: CustomDataSource,
    projected_schema: SchemaRef,
}

impl CustomExec {
    fn new(
        projections: &Option<Vec<usize>>,
        schema: SchemaRef,
        db: CustomDataSource,
    ) -> Self {
        let projected_schema = project_schema(&schema, projections.as_ref()).unwrap();
        Self {
            db,
            projected_schema,
        }
    }
}

impl ExecutionPlan for CustomExec {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn schema(&self) -> SchemaRef {
        self.projected_schema.clone()
    }

    fn output_partitioning(&self) -> datafusion::physical_plan::Partitioning {
        datafusion::physical_plan::Partitioning::UnknownPartitioning(1)
    }

    fn output_ordering(&self) -> Option<&[PhysicalSortExpr]> {
        None
    }

    fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> {
        vec![]
    }

    fn with_new_children(
        self: Arc<Self>,
        _: Vec<Arc<dyn ExecutionPlan>>,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        Ok(self)
    }

    fn execute(
        &self,
        _partition: usize,
        _context: Arc<TaskContext>,
    ) -> Result<SendableRecordBatchStream> {
        let users: Vec<User> = {
            let db = self.db.inner.lock().unwrap();
            db.data.values().cloned().collect()
        };

        let mut id_array = UInt8Builder::new(users.len());
        let mut account_array = UInt64Builder::new(users.len());

        for user in users {
            id_array.append_value(user.id);
            account_array.append_value(user.bank_account);
        }

        Ok(Box::pin(MemoryStream::try_new(
            vec![RecordBatch::try_new(
                self.projected_schema.clone(),
                vec![
                    Arc::new(id_array.finish()),
                    Arc::new(account_array.finish()),
                ],
            )?],
            self.schema(),
            None,
        )?))
    }

    fn statistics(&self) -> Statistics {
        todo!()
    }
}

// ================================================================================================================================

async fn search_accounts(
  db: CustomDataSource,
  filter: Option<Expr>,
  expected_result_length: usize,
) -> Result<()> {
  // create local execution context
  let ctx = SessionContext::new();

  // create logical plan composed of a single TableScan
  let logical_plan = LogicalPlanBuilder::scan_with_filters(
      "accounts",
      provider_as_source(Arc::new(db)),
      None,
      vec![],
  )
  .unwrap()
  .build()
  .unwrap();

  let mut dataframe = DataFrame::new(ctx.state, &logical_plan)
      .select_columns(&["id", "bank_account"])?;

  if let Some(f) = filter {
      dataframe = dataframe.filter(f)?;
  }

  timeout(Duration::from_secs(10), async move {
      let result = dataframe.collect().await.unwrap();
      let record_batch = result.get(0).unwrap();

      assert_eq!(expected_result_length, record_batch.column(1).len());
      dbg!(record_batch.columns());
  })
  .await
  .unwrap();

  Ok(())
}