1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
|
/*
* 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]) {
val spark = SparkSession
.builder()
.appName("Spark SQL data sources example")
.config("spark.some.config.option", "some-value")
.getOrCreate()
runBasicDataSourceExample(spark)
runBasicParquetExample(spark)
runParquetSchemaMergingExample(spark)
runJsonDatasetExample(spark)
runJdbcDatasetExample(spark)
spark.stop()
}
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")
// $example off:manual_load_options$
// $example on:direct_sql$
val sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
// $example off:direct_sql$
}
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)
// 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)
// $example off:jdbc_dataset$
}
}
|
