0094b5fe72
## What changes were proposed in this pull request?
The `fit` method in `StringIndexer` sorts given labels in a sequential approach, if there are multiple input columns. When the number of input column increases, the time of label sorting dramatically increases too so it is hard to use in practice if dealing with hundreds of input columns.
This patch tries to make the label sorting parallel.
This runs benchmark like:
```scala
import org.apache.spark.ml.feature.StringIndexer
val numCol = 300
val data = (0 to 100).map { i =>
(i, 100 * i)
}
var df = data.toDF("id", "label0")
(1 to numCol).foreach { idx =>
df = df.withColumn(s"label$idx", col("label0") + 1)
}
val inputCols = (0 to numCol).map(i => s"label$i").toArray
val outputCols = (0 to numCol).map(i => s"labelIndex$i").toArray
val t0 = System.nanoTime()
val indexer = new StringIndexer().setInputCols(inputCols).setOutputCols(outputCols).setStringOrderType("alphabetDesc").fit(df)
val t1 = System.nanoTime()
println("Elapsed time: " + (t1 - t0) / 1000000000.0 + "s")
```
| numCol | 20 | 50 | 100 | 200 | 300 |
|--:|---|---|---|---|---|
| Before | 9.85 | 28.62 | 64.35 | 167.17 | 431.60 |
| After | 2.44 | 2.71 | 3.34 | 4.83 | 6.90 |
Unit: second
## How was this patch tested?
Passed existing tests. Manually test for performance.
Closes #25442 from viirya/improve_stringindexer2.
Authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Sean Owen <sean.owen@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
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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
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You can set the MASTER environment variable when running examples to submit
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"yarn" to run on YARN, and "local" to run
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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
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./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.
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