5cf475d288
### What changes were proposed in this pull request? https://bugs.openjdk.java.net/browse/JDK-8170259 https://bugs.openjdk.java.net/browse/JDK-8170563 When we cast string type to decimal type, we rely on java.math. BigDecimal. It can't accept leading and training spaces, as you can see in the above links. This behavior is not consistent with other numeric types now. we need to fix it and keep consistency. ### Why are the changes needed? make string to numeric types be consistent ### Does this PR introduce any user-facing change? yes, string removed trailing or leading white spaces will be able to convert to decimal if the trimmed is valid ### How was this patch tested? 1. modify ut #### Benchmark ```scala /* * 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. */ package org.apache.spark.sql.execution.benchmark import org.apache.spark.benchmark.Benchmark /** * Benchmark trim the string when casting string type to Boolean/Numeric types. * To run this benchmark: * {{{ * 1. without sbt: * bin/spark-submit --class <this class> --jars <spark core test jar> <spark sql test jar> * 2. build/sbt "sql/test:runMain <this class>" * 3. generate result: SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain <this class>" * Results will be written to "benchmarks/CastBenchmark-results.txt". * }}} */ object CastBenchmark extends SqlBasedBenchmark { override def runBenchmarkSuite(mainArgs: Array[String]): Unit = { val title = "Cast String to Integral" runBenchmark(title) { withTempPath { dir => val N = 500L << 14 val df = spark.range(N) val types = Seq("decimal") (1 to 5).by(2).foreach { i => df.selectExpr(s"concat(id, '${" " * i}') as str") .write.mode("overwrite").parquet(dir + i.toString) } val benchmark = new Benchmark(title, N, minNumIters = 5, output = output) Seq(true, false).foreach { trim => types.foreach { t => val str = if (trim) "trim(str)" else "str" val expr = s"cast($str as $t) as c_$t" (1 to 5).by(2).foreach { i => benchmark.addCase(expr + s" - with $i spaces") { _ => spark.read.parquet(dir + i.toString).selectExpr(expr).collect() } } } } benchmark.run() } } } } ``` #### string trim vs not trim ```java [info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_231-b11 on Mac OS X 10.15.1 [info] Intel(R) Core(TM) i9-9980HK CPU 2.40GHz [info] Cast String to Integral: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] cast(trim(str) as decimal) as c_decimal - with 1 spaces 3362 5486 NaN 2.4 410.4 1.0X [info] cast(trim(str) as decimal) as c_decimal - with 3 spaces 3251 5655 NaN 2.5 396.8 1.0X [info] cast(trim(str) as decimal) as c_decimal - with 5 spaces 3208 5725 NaN 2.6 391.7 1.0X [info] cast(str as decimal) as c_decimal - with 1 spaces 13962 16233 1354 0.6 1704.3 0.2X [info] cast(str as decimal) as c_decimal - with 3 spaces 14273 14444 179 0.6 1742.4 0.2X [info] cast(str as decimal) as c_decimal - with 5 spaces 14318 14535 125 0.6 1747.8 0.2X ``` #### string trim vs this fix ```java [info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_231-b11 on Mac OS X 10.15.1 [info] Intel(R) Core(TM) i9-9980HK CPU 2.40GHz [info] Cast String to Integral: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] cast(trim(str) as decimal) as c_decimal - with 1 spaces 3265 6299 NaN 2.5 398.6 1.0X [info] cast(trim(str) as decimal) as c_decimal - with 3 spaces 3183 6241 693 2.6 388.5 1.0X [info] cast(trim(str) as decimal) as c_decimal - with 5 spaces 3167 5923 1151 2.6 386.7 1.0X [info] cast(str as decimal) as c_decimal - with 1 spaces 3161 5838 1126 2.6 385.9 1.0X [info] cast(str as decimal) as c_decimal - with 3 spaces 3046 3457 837 2.7 371.8 1.1X [info] cast(str as decimal) as c_decimal - with 5 spaces 3053 4445 NaN 2.7 372.7 1.1X [info] ``` Closes #26640 from yaooqinn/SPARK-30000. Authored-by: Kent Yao <yaooqinn@hotmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.