forked from GoogleCloudDataproc/spark-bigquery-connector
/
Shakespeare.scala
46 lines (40 loc) · 1.64 KB
/
Shakespeare.scala
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/*
* Copyright 2018 Google Inc. All Rights Reserved.
*
* Licensed 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 com.google.cloud.spark.bigquery.examples
import com.google.cloud.spark.bigquery._
import org.apache.spark.sql.SparkSession
object Shakespeare {
def main(args: Array[String]) {
val spark = SparkSession.builder()
.appName("spark-bigquery-demo")
.getOrCreate()
// Use the Cloud Storage bucket for temporary BigQuery export data used
// by the connector. This assumes the Cloud Storage connector for
// Hadoop is configured.
val bucket = spark.sparkContext.hadoopConfiguration.get("fs.gs.system.bucket")
spark.conf.set("temporaryGcsBucket", bucket)
// Load data in from BigQuery.
val wordsDF = spark.read.bigquery("bigquery-public-data.samples.shakespeare").cache()
wordsDF.show()
wordsDF.printSchema()
wordsDF.createOrReplaceTempView("words")
// Perform word count.
val wordCountDF = spark.sql(
"SELECT word, SUM(word_count) AS word_count FROM words GROUP BY word")
// Saving the data to BigQuery
wordCountDF.write.bigquery("wordcount_dataset.wordcount_output")
}
}