alamb commented on a change in pull request #7972:
URL: https://github.com/apache/arrow/pull/7972#discussion_r471094243
##########
File path: rust/datafusion/src/dataframe.rs
##########
@@ -15,42 +15,46 @@
// specific language governing permissions and limitations
// under the License.
-//! Table API for building a logical query plan. This is similar to the Table
API in Ibis
-//! and the DataFrame API in Apache Spark
+//! DataFrame API for building and executing query plans.
use crate::arrow::record_batch::RecordBatch;
use crate::error::Result;
-use crate::execution::context::ExecutionContext;
use crate::logicalplan::{Expr, LogicalPlan};
use arrow::datatypes::Schema;
use std::sync::Arc;
-/// Table is an abstraction of a logical query plan
-pub trait Table {
+/// DataFrame is an abstraction of a logical query plan
+pub trait DataFrame {
/// Select columns by name
- fn select_columns(&self, columns: Vec<&str>) -> Result<Arc<dyn Table>>;
+ fn select_columns(&self, columns: Vec<&str>) -> Result<Arc<dyn DataFrame>>;
/// Create a projection based on arbitrary expressions
- fn select(&self, expr: Vec<Expr>) -> Result<Arc<dyn Table>>;
+ fn select(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>;
/// Create a selection based on a filter expression
- fn filter(&self, expr: Expr) -> Result<Arc<dyn Table>>;
+ fn filter(&self, expr: Expr) -> Result<Arc<dyn DataFrame>>;
/// Perform an aggregate query
fn aggregate(
&self,
group_expr: Vec<Expr>,
aggr_expr: Vec<Expr>,
- ) -> Result<Arc<dyn Table>>;
+ ) -> Result<Arc<dyn DataFrame>>;
/// limit the number of rows
- fn limit(&self, n: usize) -> Result<Arc<dyn Table>>;
+ fn limit(&self, n: usize) -> Result<Arc<dyn DataFrame>>;
/// Return the logical plan
fn to_logical_plan(&self) -> LogicalPlan;
- /// Return an expression representing a column within this table
- fn col(&self, name: &str) -> Result<Expr>;
+ /// Collects the result as a vector of RecordBatch.
+ fn collect(&self, batch_size: usize) -> Result<Vec<RecordBatch>>;
+
+ /// Returns the schema
Review comment:
```suggestion
/// Returns the schema (names and types of columns) in this DataFrame
```
##########
File path: rust/datafusion/src/execution/context.rs
##########
@@ -89,12 +91,18 @@ impl ExecutionConfig {
}
/// Execution context for registering data sources and executing queries
-pub struct ExecutionContext {
- datasources: HashMap<String, Box<dyn TableProvider + Send + Sync>>,
- scalar_functions: HashMap<String, Box<ScalarFunction>>,
+#[derive(Clone)]
+pub struct ExecutionContextState {
+ datasources: Rc<RefCell<HashMap<String, Box<dyn TableProvider + Send +
Sync>>>>,
Review comment:
Can you explain the rationale for using `RefCell` here? It seems to me
like `Arc<HashMap<...>>` is easier to reason about. I can't think of an example
when running multi-threaded when you would want one thread to borrow mutably --
it seems like an ExecutionContextState would be created/configured by one
thread, and then when shared would be read only.
##########
File path: rust/datafusion/src/dataframe.rs
##########
@@ -15,42 +15,46 @@
// specific language governing permissions and limitations
// under the License.
-//! Table API for building a logical query plan. This is similar to the Table
API in Ibis
-//! and the DataFrame API in Apache Spark
+//! DataFrame API for building and executing query plans.
use crate::arrow::record_batch::RecordBatch;
use crate::error::Result;
-use crate::execution::context::ExecutionContext;
use crate::logicalplan::{Expr, LogicalPlan};
use arrow::datatypes::Schema;
use std::sync::Arc;
-/// Table is an abstraction of a logical query plan
-pub trait Table {
+/// DataFrame is an abstraction of a logical query plan
Review comment:
```suggestion
/// DataFrame represents a logical set of rows with the same named columns.
/// Similar to a [Pandas
DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)
or [Spark
DataFrame](https://spark.apache.org/docs/latest/sql-programming-guide.html)
```
##########
File path: rust/datafusion/src/execution/context.rs
##########
@@ -17,9 +17,11 @@
//! ExecutionContext contains methods for registering data sources and
executing queries
+use std::cell::RefCell;
use std::collections::{HashMap, HashSet};
use std::fs;
use std::path::Path;
+use std::rc::Rc;
Review comment:
As mentioned by @jorgecarleitao -- I suggest using `Arc` in this file
unless there is a good reason to use `Rc`
##########
File path: rust/datafusion/examples/memory_table_api.rs
##########
@@ -51,15 +51,15 @@ fn main() -> Result<()> {
// declare a table in memory. In spark API, this corresponds to
createDataFrame(...).
let provider = MemTable::new(schema, vec![vec![batch]])?;
ctx.register_table("t", Box::new(provider));
- let t = ctx.table("t")?;
+ let df = ctx.table("t")?;
Review comment:
FWIW I think `table` is still reasonable from a DataFusion point of view
as the data is creating a logical table in the context of a query, while being
backed by a Dataframe. Changing to `register_dataframe` would be fine too in my
opinion. I could go either way
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