Omega359 commented on code in PR #9019: URL: https://github.com/apache/arrow-datafusion/pull/9019#discussion_r1497529417
########## datafusion-examples/examples/to_date.rs: ########## @@ -0,0 +1,60 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::sync::Arc; + +use datafusion::arrow::array::StringArray; +use datafusion::arrow::datatypes::{DataType, Field, Schema}; +use datafusion::arrow::record_batch::RecordBatch; +use datafusion::error::Result; +use datafusion::prelude::*; + +/// This example demonstrates how to use the to_timestamp series Review Comment: to_timestamp -> to_date ########## datafusion/physical-expr/src/datetime_expressions.rs: ########## @@ -424,6 +425,84 @@ fn to_timestamp_impl<T: ArrowTimestampType + ScalarType<i64>>( } } +/// # Examples +/// +/// ``` +/// use std::sync::Arc; + +/// use datafusion::arrow::array::StringArray; +/// use datafusion::arrow::datatypes::{DataType, Field, Schema}; +/// use datafusion::arrow::record_batch::RecordBatch; +/// use datafusion::error::Result; +/// use datafusion::prelude::*; + +/// #[tokio::main] +/// async fn main() -> Result<()> { +/// // define a schema. +/// let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Utf8, false)])); + +/// // define data. +/// let batch = RecordBatch::try_new( +/// schema, +/// vec![Arc::new(StringArray::from(vec![ +/// "2020-09-08T13:42:29Z", +/// "2020-09-08T13:42:29.190855-05:00", +/// "2020-08-09 12:13:29", +/// "2020-01-02", +/// ]))], +/// )?; + +/// // declare a new context. In spark API, this corresponds to a new spark SQLsession +/// let ctx = SessionContext::new(); + +/// // declare a table in memory. In spark API, this corresponds to createDataFrame(...). +/// ctx.register_batch("t", batch)?; +/// let df = ctx.table("t").await?; + +/// // use to_timestamp function to convert col 'a' to timestamp type using the default parsing +/// let df = df.with_column("a", to_date(vec![col("a")]))?; + +/// let df = df.select_columns(&["a"])?; + +/// // print the results +/// df.show().await?; + +/// Ok(()) +/// } +/// ``` +pub fn to_date(args: &[ColumnarValue]) -> Result<ColumnarValue> { + match args.len() { + 1 => handle::<Date32Type, _, Date32Type>( + args, + |s| { + string_to_timestamp_nanos_shim(s) + .map(|n| n / (1_000_000 * 24 * 60 * 60 * 1_000)) + .and_then(|v| { + v.try_into().map_err(|_| { + internal_datafusion_err!("Unable to cast to Date32 for converting from i64 to i32 failed") + }) + }) + }, + "to_date", + ), + n if n >= 2 => handle_multiple::<Date32Type, _, Date32Type, _>( + args, + |s, format| { + string_to_timestamp_nanos_formatted(s, format) + .map(|n| n / (1_000_000 * 24 * 60 * 60 * 1_000)) + .and_then(|v| { + v.try_into().map_err(|_| { + internal_datafusion_err!("Unable to cast to Date32 for converting from i64 to i32 failed") + }) + }) + }, + |n| n, + "to_date", + ), + _ => internal_err!("Unsupported 0 argument count for function to_date"), Review Comment: This should be an exec_err (See https://github.com/apache/arrow-datafusion/pull/9241 for details) ########## datafusion-examples/examples/to_date.rs: ########## @@ -0,0 +1,60 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::sync::Arc; + +use datafusion::arrow::array::StringArray; +use datafusion::arrow::datatypes::{DataType, Field, Schema}; +use datafusion::arrow::record_batch::RecordBatch; +use datafusion::error::Result; +use datafusion::prelude::*; + +/// This example demonstrates how to use the to_timestamp series +/// of functions in the DataFrame API as well as via sql. +#[tokio::main] +async fn main() -> Result<()> { + // define a schema. + let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Utf8, false)])); + + // define data. + let batch = RecordBatch::try_new( + schema, + vec![Arc::new(StringArray::from(vec![ + "2020-09-08T13:42:29Z", + "2020-09-08T13:42:29.190855-05:00", + "2020-08-09 12:13:29", + "2020-01-02", + ]))], + )?; + + // declare a new context. In spark API, this corresponds to a new spark SQLsession + let ctx = SessionContext::new(); + + // declare a table in memory. In spark API, this corresponds to createDataFrame(...). + ctx.register_batch("t", batch)?; + let df = ctx.table("t").await?; + + // use to_timestamp function to convert col 'a' to timestamp type using the default parsing Review Comment: to_timestamp -> to_date, convert 'a to date type ########## datafusion/physical-expr/src/datetime_expressions.rs: ########## @@ -424,6 +425,84 @@ fn to_timestamp_impl<T: ArrowTimestampType + ScalarType<i64>>( } } +/// # Examples +/// +/// ``` +/// use std::sync::Arc; + +/// use datafusion::arrow::array::StringArray; +/// use datafusion::arrow::datatypes::{DataType, Field, Schema}; +/// use datafusion::arrow::record_batch::RecordBatch; +/// use datafusion::error::Result; +/// use datafusion::prelude::*; Review Comment: Unfortunately we'll have to add an ignore for this because of https://github.com/apache/arrow-datafusion/issues/9277 See https://github.com/apache/arrow-datafusion/pull/9279/files#diff-67ae8808785b2e651767d7ff67dd7c53be04ca098857b52c82ed19927e071cdaR514 for an example. I believe once the functions are move to the new dataframe-functions crate we should be able to re-enable. ########## datafusion/physical-expr/src/datetime_expressions.rs: ########## @@ -424,6 +425,84 @@ fn to_timestamp_impl<T: ArrowTimestampType + ScalarType<i64>>( } } +/// # Examples +/// +/// ``` +/// use std::sync::Arc; + +/// use datafusion::arrow::array::StringArray; +/// use datafusion::arrow::datatypes::{DataType, Field, Schema}; +/// use datafusion::arrow::record_batch::RecordBatch; +/// use datafusion::error::Result; +/// use datafusion::prelude::*; + +/// #[tokio::main] +/// async fn main() -> Result<()> { +/// // define a schema. +/// let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Utf8, false)])); + +/// // define data. +/// let batch = RecordBatch::try_new( +/// schema, +/// vec![Arc::new(StringArray::from(vec![ +/// "2020-09-08T13:42:29Z", +/// "2020-09-08T13:42:29.190855-05:00", +/// "2020-08-09 12:13:29", +/// "2020-01-02", +/// ]))], +/// )?; + +/// // declare a new context. In spark API, this corresponds to a new spark SQLsession +/// let ctx = SessionContext::new(); + +/// // declare a table in memory. In spark API, this corresponds to createDataFrame(...). +/// ctx.register_batch("t", batch)?; +/// let df = ctx.table("t").await?; + +/// // use to_timestamp function to convert col 'a' to timestamp type using the default parsing Review Comment: timestamp => date ########## datafusion/physical-expr/src/datetime_expressions.rs: ########## @@ -1567,6 +1646,36 @@ fn validate_to_timestamp_data_types( None } +/// to_date SQL function implementation +pub fn to_date_invoke(args: &[ColumnarValue]) -> Result<ColumnarValue> { + if args.is_empty() { + return exec_err!( + "to_date function requires 1 or more arguments, got {}", + args.len() + ); + } + + // validate that any args after the first one are Utf8 + if args.len() > 1 { + if let Some(value) = validate_to_timestamp_data_types(args, "to_date") { + return value; + } + } + + match args[0].data_type() { + DataType::Int32 + | DataType::Int64 + | DataType::Null + | DataType::Float64 + | DataType::Date32 + | DataType::Date64 => cast_column(&args[0], &DataType::Date32, None), + DataType::Utf8 => to_date(args), + other => { + internal_err!("Unsupported data type {:?} for function to_date", other) Review Comment: exec_err -- This is an automated message from the Apache Git Service. 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