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I have each record spread across multiple lines in the input file(Very huge file).
Ex:
Id: 2
ASIN: 0738700123
title: Test tile for this product
group: Book
salesrank: 168501
similar: 5 0738700811 1567184912 1567182813 0738700514 0738700915
categories: 2
|Books[283155]|Subjects[1000]|Religion & Spirituality[22]|Earth-Based Religions[12472]|Wicca[12484]
|Books[283155]|Subjects[1000]|Religion & Spirituality[22]|Earth-Based Religions[12472]|Witchcraft[12486]
reviews: total: 12 downloaded: 12 avg rating: 4.5
2001-12-16 cutomer: A11NCO6YTE4BTJ rating: 5 votes: 5 helpful: 4
2002-1-7 cutomer: A9CQ3PLRNIR83 rating: 4 votes: 5 helpful: 5
How to identify and process each multi line record in spark?
604
You can use triple-quotes at the start/end of the SQL code or a backslash at the end of each line. Triple quotes (both double and single) can be used in Python as well. Also backslashes are obsolete.
Spark JSON data source API provides the multiline option to read records from multiple lines. By default, spark considers every record in a JSON file as a fully qualified record in a single line hence, we need to use the multiline option to process JSON from multiple lines.
You can use either backslash or parenthesis to break the lines in pyspark as you do in python.
If the multi-line data has a defined record separator, you could use the hadoop support for multi-line records, providing the separator through a hadoop.Configuration object:
Something like this should do:
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.io.{LongWritable, Text}
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
val conf = new Configuration
conf.set("textinputformat.record.delimiter", "id:")
val dataset = sc.newAPIHadoopFile("/path/to/data", classOf[TextInputFormat], classOf[LongWritable], classOf[Text], conf)
val data = dataset.map(x=>x._2.toString)
This will provide you with an RDD[String] where each element corresponds to a record. Afterwards you need to parse each record following your application requirements.
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