jorgecarleitao commented on a change in pull request #7972: URL: https://github.com/apache/arrow/pull/7972#discussion_r471127403
########## File path: rust/datafusion/src/dataframe.rs ########## @@ -0,0 +1,168 @@ +// 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. + +//! DataFrame API for building and executing query plans. + +use crate::arrow::record_batch::RecordBatch; +use crate::error::Result; +use crate::logicalplan::{Expr, LogicalPlan}; +use arrow::datatypes::Schema; +use std::sync::Arc; + +/// 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) +/// +/// DataFrames are typically created by the `read_csv` and `read_parquet` methods on the +/// [ExecutionContext](../execution/context/struct.ExecutionContext.html) and can then be modified +/// by calling the transformation methods, such as `filter`, `select`, `aggregate`, and `limit` +/// to build up a query definition. +/// +/// The query can be executed by calling the `collect` method. +/// +/// ``` +/// use datafusion::ExecutionContext; +/// use datafusion::execution::physical_plan::csv::CsvReadOptions; +/// use datafusion::logicalplan::col; +/// +/// let mut ctx = ExecutionContext::new(); +/// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); +/// let df = df.filter(col("a").lt_eq(col("b"))).unwrap() +/// .aggregate(vec![col("a")], vec![df.min(col("b")).unwrap()]).unwrap() +/// .limit(100).unwrap(); +/// let results = df.collect(4096); +/// ``` +pub trait DataFrame { + /// Filter the DataFrame by column. Returns a new DataFrame only containing the + /// specified columns. + /// + /// ``` + /// use datafusion::ExecutionContext; + /// use datafusion::execution::physical_plan::csv::CsvReadOptions; + /// let mut ctx = ExecutionContext::new(); + /// + /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); + /// let df = df.select_columns(vec!["a", "b"]).unwrap(); + /// ``` + fn select_columns(&self, columns: Vec<&str>) -> Result<Arc<dyn DataFrame>>; + + /// Create a projection based on arbitrary expressions. + /// + /// ``` + /// use datafusion::ExecutionContext; + /// use datafusion::execution::physical_plan::csv::CsvReadOptions; + /// use datafusion::logicalplan::col; + /// + /// let mut ctx = ExecutionContext::new(); + /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); + /// let df = df.select(vec![col("a").multiply(col("b")), col("c")]).unwrap(); + /// ``` + fn select(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>; + + /// Filter a DataFrame to only include rows that match the specified filter expression. + /// + /// ``` + /// use datafusion::ExecutionContext; + /// use datafusion::execution::physical_plan::csv::CsvReadOptions; + /// use datafusion::logicalplan::col; + /// + /// let mut ctx = ExecutionContext::new(); + /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); + /// let df = df.filter(col("a").lt_eq(col("b"))).unwrap(); + /// ``` + fn filter(&self, expr: Expr) -> Result<Arc<dyn DataFrame>>; + + /// Perform an aggregate query with optional grouping expressions. + /// + /// ``` + /// use datafusion::ExecutionContext; + /// use datafusion::execution::physical_plan::csv::CsvReadOptions; + /// use datafusion::logicalplan::col; + /// + /// let mut ctx = ExecutionContext::new(); + /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); + /// + /// // The following use is the equivalent of "SELECT MIN(b) GROUP BY a" + /// let _ = df.aggregate(vec![col("a")], vec![df.min(col("b")).unwrap()]).unwrap(); + /// + /// // The following use is the equivalent of "SELECT MIN(b)" + /// let _ = df.aggregate(vec![], vec![df.min(col("b")).unwrap()]).unwrap(); + /// ``` + fn aggregate( + &self, + group_expr: Vec<Expr>, + aggr_expr: Vec<Expr>, + ) -> Result<Arc<dyn DataFrame>>; + + /// limit the number of rows returned from this DataFrame. + /// + /// ``` + /// use datafusion::ExecutionContext; + /// use datafusion::execution::physical_plan::csv::CsvReadOptions; + /// use datafusion::logicalplan::col; + /// + /// let mut ctx = ExecutionContext::new(); + /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); + /// let df = df.limit(100).unwrap(); + /// ``` + fn limit(&self, n: usize) -> Result<Arc<dyn DataFrame>>; + + /// Executes this DataFrame and collects all results into a vector of RecordBatch. + /// + /// ``` + /// use datafusion::ExecutionContext; + /// use datafusion::execution::physical_plan::csv::CsvReadOptions; + /// use datafusion::logicalplan::col; + /// + /// let mut ctx = ExecutionContext::new(); + /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).unwrap(); + /// let batches = df.collect(4096).unwrap(); + /// ``` + fn collect(&self, batch_size: usize) -> Result<Vec<RecordBatch>>; + + /// Returns the schema describing the output of this DataFrame in terms of columns returned, + /// where each column has a name, data type, and nullability attribute. + Review comment: I am not sure if this won't break descriptions (add ` ///`?). 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