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#
# 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.
#
from __future__ import print_function
import os
import sys
from pyspark.sql import SparkSession
from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("PythonSQL")\
.getOrCreate()
# A list of Rows. Infer schema from the first row, create a DataFrame and print the schema
rows = [Row(name="John", age=19), Row(name="Smith", age=23), Row(name="Sarah", age=18)]
some_df = spark.createDataFrame(rows)
some_df.printSchema()
# A list of tuples
tuples = [("John", 19), ("Smith", 23), ("Sarah", 18)]
# Schema with two fields - person_name and person_age
schema = StructType([StructField("person_name", StringType(), False),
StructField("person_age", IntegerType(), False)])
# Create a DataFrame by applying the schema to the RDD and print the schema
another_df = spark.createDataFrame(tuples, schema)
another_df.printSchema()
# root
# |-- age: long (nullable = true)
# |-- name: string (nullable = true)
# A JSON dataset is pointed to by path.
# The path can be either a single text file or a directory storing text files.
if len(sys.argv) < 2:
path = "file://" + \
os.path.join(os.environ['SPARK_HOME'], "examples/src/main/resources/people.json")
else:
path = sys.argv[1]
# Create a DataFrame from the file(s) pointed to by path
people = spark.read.json(path)
# root
# |-- person_name: string (nullable = false)
# |-- person_age: integer (nullable = false)
# The inferred schema can be visualized using the printSchema() method.
people.printSchema()
# root
# |-- age: long (nullable = true)
# |-- name: string (nullable = true)
# Register this DataFrame as a temporary table.
people.createOrReplaceTempView("people")
# SQL statements can be run by using the sql methods provided by `spark`
teenagers = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
for each in teenagers.collect():
print(each[0])
spark.stop()
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