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| // Licensed to the Apache Software Foundation (ASF) under one | ||
| // 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 arrow::array::{BooleanArray, LargeStringArray, StringArray, StringBuilder}; | ||
| use log::info; | ||
|
|
||
| use datafusion::arrow::datatypes::{DataType, Field, Schema}; | ||
| use datafusion::arrow::record_batch::RecordBatch; | ||
| use datafusion::error::Result; | ||
| use datafusion::prelude::*; | ||
| use datafusion_common::assert_contains; | ||
|
|
||
| /// This example demonstrates how to use the regexp_* | ||
| /// functions in the DataFrame API as well as via sql. | ||
| #[tokio::main] | ||
| async fn main() -> Result<()> { | ||
| // define a schema. Regex are restricted to Utf8 and largeutf8 data | ||
| let schema = Arc::new(Schema::new(vec![ | ||
| Field::new("values", DataType::Utf8, false), | ||
| Field::new("patterns", DataType::LargeUtf8, false), | ||
| Field::new("flags", DataType::Utf8, true), | ||
| ])); | ||
|
|
||
| let mut sb = StringBuilder::new(); | ||
| sb.append_value("i"); | ||
| sb.append_value("i"); | ||
| sb.append_value("i"); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
| sb.append_null(); | ||
|
|
||
| // define data for our examples | ||
| let batch = RecordBatch::try_new( | ||
| schema, | ||
| vec![ | ||
| Arc::new(StringArray::from(vec![ | ||
| "abc", | ||
| "ABC", | ||
| "aBc", | ||
| "AbC", | ||
| "aBC", | ||
| "4000", | ||
| "4010", | ||
| "Düsseldorf", | ||
| "Москва", | ||
| "Köln", | ||
| "إسرائيل", | ||
| ])), | ||
| // the full list of supported features and | ||
| // syntax can be found at | ||
| // https://docs.rs/regex/latest/regex/#syntax | ||
|
|
||
| // NOTE: double slashes are required to escape the slash character | ||
| // NOTE: when not using the r"" syntax | ||
| Arc::new(LargeStringArray::from(vec![ | ||
| // simple regex examples | ||
| "^(a)", | ||
| "^(A).*", | ||
| "(b|d)", | ||
| "(B|D)", | ||
| "^(b|c)", | ||
| // word boundaries, grouping, etc | ||
| r"\b4([1-9]\d\d|\d[1-9]\d|\d\d[1-9])\b", | ||
| r"\b4([1-9]\d\d|\d[1-9]\d|\d\d[1-9])\b", | ||
| // unicode is supported | ||
| r"[\p{Letter}-]+", | ||
| r"[\p{L}-]+", | ||
| "[a-zA-Z]ö[a-zA-Z]{2}", | ||
| // unicode character classes work | ||
| r"^\p{Arabic}+$", | ||
| ])), | ||
| // supported flags can be found at | ||
| // https://docs.rs/regex/latest/regex/#grouping-and-flags | ||
| Arc::new(sb.finish()), | ||
| ], | ||
| )?; | ||
|
|
||
| // 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("examples", batch)?; | ||
| let df = ctx.table("examples").await?; | ||
|
|
||
| // | ||
| // | ||
| //regexp_like examples | ||
| // | ||
| // | ||
| // regexp_like format is (regexp_replace(text, regex[, flags]) | ||
| // | ||
|
|
||
| // use dataframe and regexp_like function to test col 'values', against patterns in col 'patterns' without flags | ||
| let df = df.with_column("a", regexp_like(vec![col("values"), col("patterns")]))?; | ||
| // use dataframe and regexp_like function to test col 'values', against patterns in col 'patterns' with flags | ||
| let df = df.with_column( | ||
| "b", | ||
| regexp_like(vec![col("values"), col("patterns"), col("flags")]), | ||
| )?; | ||
|
|
||
| // you can use literals as well with dataframe calls | ||
| let df = df.with_column( | ||
| "c", | ||
| regexp_like(vec![lit("foobarbequebaz"), lit("(bar)(beque)")]), | ||
| )?; | ||
|
|
||
| let df = df.select_columns(&["a", "b", "c"])?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
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||
| // use sql and regexp_like function to test col 'values', against patterns in col 'patterns' without flags | ||
| let df = ctx | ||
| .sql("select regexp_like(values, patterns) from examples") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // use dataframe and regexp_like function to test col 'values', against patterns in col 'patterns' with flags | ||
| let df = ctx | ||
| .sql("select regexp_like(values, patterns, flags) from examples") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // literals work as well | ||
| // to match against the entire input use ^ and $ in the regex | ||
| let df = ctx.sql("select regexp_like('John Smith', '^.*Smith$'), regexp_like('Smith Jones', '^Smith.*$')").await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
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||
| // look-around and back references are not supported for performance | ||
| // reasons. | ||
| // Note that an error may not always be returned but the result | ||
| // if returned will always be false | ||
| let df = ctx.read_empty()?.with_column( | ||
| "a", | ||
| regexp_like(vec![ | ||
| lit(r"(?<=[A-Z]\w* )Smith"), | ||
| lit("John Smith"), | ||
| lit("i"), | ||
| ]), | ||
| )?; | ||
| let df = df.select_columns(&["a"])?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
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| let result = ctx | ||
| .sql(r"select regexp_like('(?<=[A-Z]\w )Smith', 'John Smith', 'i') as a") | ||
| .await? | ||
| .collect() | ||
| .await; | ||
|
|
||
| let expected = RecordBatch::try_new( | ||
| Arc::new(Schema::new(vec![Field::new("a", DataType::Boolean, false)])), | ||
| vec![Arc::new(BooleanArray::from(vec![false]))], | ||
| ) | ||
| .unwrap(); | ||
|
|
||
| assert!(result.is_ok()); | ||
| let result = result.unwrap(); | ||
|
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| assert_eq!(result.len(), 1); | ||
| info!("{:?}", result[0]); | ||
| info!("{expected:?}"); | ||
|
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| assert_eq!(format!("{:?}", result[0]), format!("{expected:?}")); | ||
|
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| // invalid flags will result in an error | ||
| let result = ctx | ||
| .sql(r"select regexp_like('\b4(?!000)\d\d\d\b', 4010, 'g')") | ||
| .await? | ||
| .collect() | ||
| .await; | ||
|
|
||
| let expected = "regexp_like() does not support the \"global\" option"; | ||
| assert_contains!(result.unwrap_err().to_string(), expected); | ||
|
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||
| // there is a size limit on the regex during regex compilation | ||
| let result = ctx | ||
| .sql("select regexp_like('aaaaa', 'a{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}{5}')") | ||
| .await? | ||
| .collect() | ||
| .await; | ||
|
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| let expected = "Regular expression did not compile: CompiledTooBig("; | ||
| assert_contains!(result.unwrap_err().to_string(), expected); | ||
|
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||
| // | ||
| // | ||
| //regexp_match examples | ||
| // | ||
| // | ||
| // regexp_match format is (regexp_replace(text, regex[, flags]) | ||
| // | ||
|
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||
| let df = ctx.table("examples").await?; | ||
|
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||
| // use dataframe and regexp_match function to test col 'values', against patterns in col 'patterns' without flags | ||
| let df = df.with_column("a", regexp_match(vec![col("values"), col("patterns")]))?; | ||
| // use dataframe and regexp_match function to test col 'values', against patterns in col 'patterns' with flags | ||
| let df = df.with_column( | ||
| "b", | ||
| regexp_match(vec![col("values"), col("patterns"), col("flags")]), | ||
| )?; | ||
|
|
||
| // you can use literals as well with dataframe calls | ||
| let df = df.with_column( | ||
| "c", | ||
| regexp_match(vec![lit("foobarbequebaz"), lit("(bar)(beque)")]), | ||
| )?; | ||
|
|
||
| let df = df.select_columns(&["a", "b", "c"])?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // use sql and regexp_match function to test col 'values', against patterns in col 'patterns' without flags | ||
| let df = ctx | ||
| .sql("select regexp_match(values, patterns) from examples") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // use dataframe and regexp_match function to test col 'values', against patterns in col 'patterns' with flags | ||
| let df = ctx | ||
| .sql("select regexp_match(values, patterns, flags) from examples") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // literals work as well | ||
| // to match against the entire input use ^ and $ in the regex | ||
| let df = ctx.sql("select regexp_match('John Smith', '^.*Smith$'), regexp_match('Smith Jones', '^Smith.*$')").await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // | ||
| // | ||
| //regexp_replace examples | ||
| // | ||
| // | ||
| // regexp_replace format is (regexp_replace(text, regex, replace, flags) | ||
| // | ||
|
|
||
| // global flag example | ||
| let df = ctx | ||
| .sql("SELECT regexp_replace('foobarbaz', 'b(..)', 'X\\1Y', 'g')") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // without global flag | ||
| let df = ctx | ||
| .sql("SELECT regexp_replace('foobarbaz', 'b(..)', 'X\\1Y', null)") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| // null regex means null result | ||
| let df = ctx | ||
| .sql("SELECT regexp_replace('foobarbaz', NULL, 'X\\1Y', 'g')") | ||
| .await?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| Ok(()) | ||
| } |
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