comphead commented on code in PR #8886: URL: https://github.com/apache/arrow-datafusion/pull/8886#discussion_r1454007081
########## datafusion-examples/examples/dataframe_to_timestamp.rs: ########## @@ -0,0 +1,90 @@ +// 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 function 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), + Field::new("b", 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"])), + Arc::new(StringArray::from(vec!["2020-09-08T13:42:29Z", "2020-09-08T13:42:29.190855-05:00", "08-09-2020 13/42/29", "09-27-2020 13:42:29-05:30"])), + ], + )?; + + // 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_timestamp(vec![col("a")]))?; + // use to_timestamp_seconds function to convert col 'b' to timestamp(Seconds) type using a list of chrono formats to try + let df = df.with_column("b", to_timestamp_seconds(vec![col("b"), lit("%+"), lit("%d-%m-%Y %H/%M/%S"), lit("%m-%d-%Y %H:%M:%S%#z")]))?; + + let df = df.select_columns(&["a", "b"])?; + + // print the results + df.show().await?; + + // use sql to convert col 'a' to timestamp using the default parsing + let df = ctx.sql("select to_timestamp(a) from t").await?; + + // print the results + df.show().await?; + + // use sql to convert col 'b' to timestamp using a list of chrono formats to try + let df = ctx.sql("select to_timestamp(b, '%+', '%d-%m-%Y %H/%M/%S', '%m-%d-%Y %H:%M:%S%#z') from t").await?; + + // print the results + df.show().await?; + + // use sql to convert a static string to a timestamp using a list of chrono formats to try + let df = ctx.sql("select to_timestamp('01-14-2023 01:01:30+05:30', '%+', '%d-%m-%Y %H/%M/%S', '%m-%d-%Y %H:%M:%S%#z')").await?; + + // print the results + df.show().await?; + + // use sql to convert a static string to a timestamp using a non-matching chrono format to try + let result = ctx.sql("select to_timestamp('01-14-2023 01/01/30', '%d-%m-%Y %H:%M:%S')").await?.collect().await; + + if result.is_err() { Review Comment: would be great to have `.unwrap_err` and assert instead -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
