org.apache.spark.examples.sql.SQLDataSourceExample.scala Maven / Gradle / Ivy
The newest version!
/*
* 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.examples.sql
import java.util.Properties
import org.apache.spark.sql.SparkSession
object SQLDataSourceExample {
case class Person(name: String, age: Long)
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("Spark SQL data sources example")
.config("spark.some.config.option", "some-value")
.getOrCreate()
runBasicDataSourceExample(spark)
runGenericFileSourceOptionsExample(spark)
runBasicParquetExample(spark)
runParquetSchemaMergingExample(spark)
runJsonDatasetExample(spark)
runJdbcDatasetExample(spark)
spark.stop()
}
private def runGenericFileSourceOptionsExample(spark: SparkSession): Unit = {
// $example on:ignore_corrupt_files$
// enable ignore corrupt files
spark.sql("set spark.sql.files.ignoreCorruptFiles=true")
// dir1/file3.json is corrupt from parquet's view
val testCorruptDF = spark.read.parquet(
"examples/src/main/resources/dir1/",
"examples/src/main/resources/dir1/dir2/")
testCorruptDF.show()
// +-------------+
// | file|
// +-------------+
// |file1.parquet|
// |file2.parquet|
// +-------------+
// $example off:ignore_corrupt_files$
// $example on:recursive_file_lookup$
val recursiveLoadedDF = spark.read.format("parquet")
.option("recursiveFileLookup", "true")
.load("examples/src/main/resources/dir1")
recursiveLoadedDF.show()
// +-------------+
// | file|
// +-------------+
// |file1.parquet|
// |file2.parquet|
// +-------------+
// $example off:recursive_file_lookup$
spark.sql("set spark.sql.files.ignoreCorruptFiles=false")
// $example on:load_with_path_glob_filter$
val testGlobFilterDF = spark.read.format("parquet")
.option("pathGlobFilter", "*.parquet") // json file should be filtered out
.load("examples/src/main/resources/dir1")
testGlobFilterDF.show()
// +-------------+
// | file|
// +-------------+
// |file1.parquet|
// +-------------+
// $example off:load_with_path_glob_filter$
}
private def runBasicDataSourceExample(spark: SparkSession): Unit = {
// $example on:generic_load_save_functions$
val usersDF = spark.read.load("examples/src/main/resources/users.parquet")
usersDF.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
// $example off:generic_load_save_functions$
// $example on:manual_load_options$
val peopleDF = spark.read.format("json").load("examples/src/main/resources/people.json")
peopleDF.select("name", "age").write.format("parquet").save("namesAndAges.parquet")
// $example off:manual_load_options$
// $example on:manual_load_options_csv$
val peopleDFCsv = spark.read.format("csv")
.option("sep", ";")
.option("inferSchema", "true")
.option("header", "true")
.load("examples/src/main/resources/people.csv")
// $example off:manual_load_options_csv$
// $example on:manual_save_options_orc$
usersDF.write.format("orc")
.option("orc.bloom.filter.columns", "favorite_color")
.option("orc.dictionary.key.threshold", "1.0")
.option("orc.column.encoding.direct", "name")
.save("users_with_options.orc")
// $example off:manual_save_options_orc$
// $example on:direct_sql$
val sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
// $example off:direct_sql$
// $example on:write_sorting_and_bucketing$
peopleDF.write.bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed")
// $example off:write_sorting_and_bucketing$
// $example on:write_partitioning$
usersDF.write.partitionBy("favorite_color").format("parquet").save("namesPartByColor.parquet")
// $example off:write_partitioning$
// $example on:write_partition_and_bucket$
usersDF
.write
.partitionBy("favorite_color")
.bucketBy(42, "name")
.saveAsTable("users_partitioned_bucketed")
// $example off:write_partition_and_bucket$
spark.sql("DROP TABLE IF EXISTS people_bucketed")
spark.sql("DROP TABLE IF EXISTS users_partitioned_bucketed")
}
private def runBasicParquetExample(spark: SparkSession): Unit = {
// $example on:basic_parquet_example$
// Encoders for most common types are automatically provided by importing spark.implicits._
import spark.implicits._
val peopleDF = spark.read.json("examples/src/main/resources/people.json")
// DataFrames can be saved as Parquet files, maintaining the schema information
peopleDF.write.parquet("people.parquet")
// Read in the parquet file created above
// Parquet files are self-describing so the schema is preserved
// The result of loading a Parquet file is also a DataFrame
val parquetFileDF = spark.read.parquet("people.parquet")
// Parquet files can also be used to create a temporary view and then used in SQL statements
parquetFileDF.createOrReplaceTempView("parquetFile")
val namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19")
namesDF.map(attributes => "Name: " + attributes(0)).show()
// +------------+
// | value|
// +------------+
// |Name: Justin|
// +------------+
// $example off:basic_parquet_example$
}
private def runParquetSchemaMergingExample(spark: SparkSession): Unit = {
// $example on:schema_merging$
// This is used to implicitly convert an RDD to a DataFrame.
import spark.implicits._
// Create a simple DataFrame, store into a partition directory
val squaresDF = spark.sparkContext.makeRDD(1 to 5).map(i => (i, i * i)).toDF("value", "square")
squaresDF.write.parquet("data/test_table/key=1")
// Create another DataFrame in a new partition directory,
// adding a new column and dropping an existing column
val cubesDF = spark.sparkContext.makeRDD(6 to 10).map(i => (i, i * i * i)).toDF("value", "cube")
cubesDF.write.parquet("data/test_table/key=2")
// Read the partitioned table
val mergedDF = spark.read.option("mergeSchema", "true").parquet("data/test_table")
mergedDF.printSchema()
// The final schema consists of all 3 columns in the Parquet files together
// with the partitioning column appeared in the partition directory paths
// root
// |-- value: int (nullable = true)
// |-- square: int (nullable = true)
// |-- cube: int (nullable = true)
// |-- key: int (nullable = true)
// $example off:schema_merging$
}
private def runJsonDatasetExample(spark: SparkSession): Unit = {
// $example on:json_dataset$
// Primitive types (Int, String, etc) and Product types (case classes) encoders are
// supported by importing this when creating a Dataset.
import spark.implicits._
// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files
val path = "examples/src/main/resources/people.json"
val peopleDF = spark.read.json(path)
// The inferred schema can be visualized using the printSchema() method
peopleDF.printSchema()
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)
// Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")
// SQL statements can be run by using the sql methods provided by spark
val teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()
// +------+
// | name|
// +------+
// |Justin|
// +------+
// Alternatively, a DataFrame can be created for a JSON dataset represented by
// a Dataset[String] storing one JSON object per string
val otherPeopleDataset = spark.createDataset(
"""{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val otherPeople = spark.read.json(otherPeopleDataset)
otherPeople.show()
// +---------------+----+
// | address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+
// $example off:json_dataset$
}
private def runJdbcDatasetExample(spark: SparkSession): Unit = {
// $example on:jdbc_dataset$
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
// Loading data from a JDBC source
val jdbcDF = spark.read
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.load()
val connectionProperties = new Properties()
connectionProperties.put("user", "username")
connectionProperties.put("password", "password")
val jdbcDF2 = spark.read
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// Specifying the custom data types of the read schema
connectionProperties.put("customSchema", "id DECIMAL(38, 0), name STRING")
val jdbcDF3 = spark.read
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// Saving data to a JDBC source
jdbcDF.write
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.save()
jdbcDF2.write
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// Specifying create table column data types on write
jdbcDF.write
.option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// $example off:jdbc_dataset$
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy