Conversion from one file format to other in Apache Spark

Read -->
Write
|
V
Text file

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username retail_dba --password cloudera \
--table orders \
--target-dir /user/cloudera/ReadDiffFileFormat/text \
--as-textfile

Read:

scala> val textFile = sc.textFile("/user/cloudera/ReadDiffFileFormat/text")
textFile: org.apache.spark.rdd.RDD[String] = /user/cloudera/ReadDiffFileFormat/text MapPartitionsRDD[279] at textFile at <console>:30
 Text file
textFile.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/textout")

Using compression
textFile.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/text/textoutput/compressed", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Sequence file
For sequence file we need to have a key.
val textMap = textFile.map(e => (e.split(",")(0).toInt, e))
textMap.saveAsSequenceFile("/user/cloudera/ReadDiffFileFormat/sequenceout")

Using compression
textMap.saveAsSequenceFile("/user/cloudera/ReadDiffFileFormat/text/textoutput/sequenceout/compressed", Some(classOf[org.apache.hadoop.io.compress.BZip2Codec]))
JSON file
val textMapDF = textFile.map(e => (e.split(",")(0).toInt, e.split(",")(1), e.split(",")(2).toInt, e.split(",")(3))).toDF
textMapDF.toJSON.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/jsonout")

Using compression
textMapDF.toJSON.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/text/textoutput/jsonout/compressed", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Parquet
val textMapDF = textFile.map(e => (e.split(",")(0).toInt, e.split(",")(1), e.split(",")(2).toInt, e.split(",")(3))).toDF
textMapDF.write.parquet("/user/cloudera/ReadDiffFileFormat/parquetout")

Using compression
sqlContext.setConf("spark.sql.parquet.compression.codec", "gzip")
textMapDF.write.parquet("/user/cloudera/ReadDiffFileFormat/text/textoutput/parquetout/compressed")
ORC file
val textMapDF = textFile.map(e => (e.split(",")(0).toInt, e.split(",")(1), e.split(",")(2).toInt, e.split(",")(3))).toDF
textMapDF.write.orc("/user/cloudera/ReadDiffFileFormat/orcout")

Compression not required.
Avro file
textMapDF.write.avro("/user/cloudera/ReadDiffFileFormat/avroout")

Using compression

import com.databricks.spark.avro._
sqlContext.setConf("spark.sql.avro.compression.codec", "snappy")
textMapDF.write.avro("/user/cloudera/ReadDiffFileFormat/text/textoutput/avroout/compressed1")


Read -->
Write
|
V
JSON file

Read:

val jsonFile = sqlContext.read.json("file:///home/cloudera/workspace/scala/rajudata/data-master/retail_db_json/orders")
 Text file
val jsonFileMap = jsonFile.rdd.map(e => (e.getLong(0) + "," + e.getString(1) + "," + e.getLong(2) + "," + e.getString(3)))
jsonFileMap.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/jsontotext1")

Using compression
jsonFileMap.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/json/jsontotext/compressed", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Sequence file
val jsonFileMap = jsonFile.rdd.map(e => (e.getLong(0), (e.getLong(0) + "," + e.getString(1) + "," + e.getLong(2) + "," + e.getString(3))))
jsonFileMap.saveAsSequenceFile("/user/cloudera/problem5/ReadDiffFileFormat/jsontoSequence")

Using compression
jsonFileMap.saveAsSequenceFile("/user/cloudera/ReadDiffFileFormat/jsontoSequence/compressed", Some(classOf[org.apache.hadoop.io.compress.BZip2Codec]))
JSON file
jsonFile.toDF.write.json("/user/cloudera/problem5/ReadDiffFileFormat/jsontojson")

Using compression
jsonFile.toJSON.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/jsontojson/compressed", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Parquet
jsonFile.toDF.write.parquet("/user/cloudera/problem5/ReadDiffFileFormat/jsontoparquet")

Using compression
sqlContext.setConf("spark.sql.parquet.compression.codec", "gzip")
jsonFile.write.parquet("/user/cloudera/ReadDiffFileFormat/jsontoparquet/compressed")
ORC file
jsonFile.toDF.write.orc("/user/cloudera/problem5/ReadDiffFileFormat/jsontoorc")

scala> jsonFile.toDF.write.orc("/user/cloudera/problem5/ReadDiffFileFormat/jsontoorc")
java.lang.AssertionError: assertion failed: The ORC data source can only be used with HiveContext.

Lets use HiveContext
val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
val jsonFile = hiveContext.read.json("file:///home/cloudera/workspace/scala/rajudata/data-master/retail_db_json/orders")
jsonFile.toDF.write.orc("/user/cloudera/problem5/ReadDiffFileFormat/jsontoorc")
Avro file
jsonFile.toDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/jsontoavro")

Using compression
import com.databricks.spark.avro._
sqlContext.setConf("spark.sql.avro.compression.codec", "gzip")

  
Read -->
Write
|
V
Parquetfile

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username retail_dba --password cloudera \
--table orders \
--target-dir /user/cloudera/ReadDiffFileFormat/parquet \
--as-parquetfile

Read: Read parquet file
scala> val parquetFile = sqlContext.read.parquet("/user/cloudera/ReadDiffFileFormat/parquet")
parquetFile: org.apache.spark.sql.DataFrame = [order_id: int, order_date: bigint, order_customer_id: int, order_status: string]
 Text file
val parquetFileMap = parquetFile.map(e => (e.getInt(0) + "," + e.getLong(1) + "," + e.getInt(2) + "," + e.getString(3)))
parquetFileMap.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/parquettotext")

Using compression
parquetFileMap.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/parquettotext", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Sequence file
val parquetFileMap = parquetFile.map(e => (e.getInt(0), (e.getInt(0) + "," + e.getLong(1) + "," + e.getInt(2) + "," + e.getString(3))))
parquetFileMap.saveAsSequenceFile("/user/cloudera/ReadDiffFileFormat/parquettotext")

parquetFileMap.saveAsSequenceFile("/user/cloudera/ReadDiffFileFormat/parquettotext", Some(classOf[org.apache.hadoop.io.compress.BZip2Codec]))
JSON file
val parquetFileDF = parquetFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) ,  e.getString(3))).toDF
parquetFileDF.toJSON.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/parquettojson")

parquetFileDF.toJSON.saveAsTextFile("/user/cloudera/ReadDiffFileFormat/parquettojson", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Parquet
val parquetFileDF = parquetFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) ,  e.getString(3))).toDF
parquetFileDF.write.parquet("/user/cloudera/problem5/ReadDiffFileFormat/parquettoparquet")

Using compression
sqlContext.setConf("spark.sql.parquet.compression.codec", "gzip")
parquetFileDF.write.parquet("/user/cloudera/ReadDiffFileFormat/jsontoparquet/compressed")
ORC file
val parquetFileDF = parquetFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) ,  e.getString(3))).toDF
parquetFileDF.write.orc("/user/cloudera/problem5/ReadDiffFileFormat/parquettoorc")
Avro file
import com.databricks.spark.avro._
val parquetFileDF = parquetFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) ,  e.getString(3))).toDF
parquetFileDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/parquettoavro")

Using compression
import com.databricks.spark.avro._
sqlContext.setConf("spark.sql.avro.compression.codec", "gzip")
parquetFileDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/parquettoavro/compressed")


Read -->
Write
|
V
ORC file

Read:
val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
val orcfile = hiveContext.read.orc("/user/cloudera/problem5/ReadDiffFileFormat/parquettoorc")
 Text file
val orcFileMap = orcfile.map(e => (e.getInt(0) + "," + e.getLong(1) + "," + e.getInt(2) + "," + e.getString(3)))
orcFileMap.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/orctotext")

Using compression
orcFileMap.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/orctotext", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Sequence file
val orcFileMap = orcfile.map(e => (e.getInt(0), (e.getInt(0) + "," + e.getLong(1) + "," + e.getInt(2) + "," + e.getString(3))))
orcFileMap.saveAsSequenceFile("/user/cloudera/problem5/ReadDiffFileFormat/orctoSequence")

Using compression
orcFileMap.saveAsSequenceFile("/user/cloudera/problem5/ReadDiffFileFormat/orctoSequence", Some(classOf[org.apache.hadoop.io.compress.BZip2Codec]))
JSON file
orcfile.toDF.toJSON.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/orctojson")

Using compression
orcfile.toDF.toJSON.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/orctojson", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Parquet
orcfile.toDF.write.parquet("/user/cloudera/problem5/ReadDiffFileFormat/orctoparquet")

Using compression
sqlContext.setConf("spark.sql.parquet.compression.codec", "gzip")
orcfile.toDF.write.parquet("/user/cloudera/problem5/ReadDiffFileFormat/orctoparquet")
ORC file
orcfile.toDF.write.orc("/user/cloudera/problem5/ReadDiffFileFormat/orctoorc")
Avro file
orcfile.toDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/orctoavro")

Using compression
import com.databricks.spark.avro._
sqlContext.setConf("spark.sql.avro.compression.codec", "gzip")
orcfile.toDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/orctoavro")



Read -->
Write
|
V
Avro file

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username retail_dba --password cloudera \
--table orders \
--target-dir /user/cloudera/problem5/ReadDiffFileFormat/avro \
--as-avrodatafile
Read:
val avroFile = sqlContext.read.avro("/user/cloudera/problem5/ReadDiffFileFormat/avro")

scala> avroFile.take(2)
res90: Array[org.apache.spark.sql.Row] = Array([1,1512321804000,11599,CLOSED], [2,1512321804000,256,CLOSED])
 Text file

val avroFileToText = avroFile.map(e => (e.getInt(0) + "," + e.getLong(1) + "," + e.getInt(2) + "," + e.getString(3)))
 avroFileToText.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/avrototext")

Using compression
 avroFileToText.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/avrototext", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Sequence file
val avroFileToSequence = avroFile.map(e => (e.getInt(0), (e.getInt(0) + "," + e.getLong(1) + "," + e.getInt(2) + "," + e.getString(3))))

avroFileToSequence.saveAsSequenceFile("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToSequence")

Using compression
avroFileToSequence.saveAsSequenceFile("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToSequence", Some(classOf[org.apache.hadoop.io.compress.BZip2Codec]))
JSON file
val avroFileMap = avroFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) , e.getString(3)))
avroFileMap.toDF.toJSON.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/avroFileTojson1")

Using compression
avroFileMap.toDF.toJSON.saveAsTextFile("/user/cloudera/problem5/ReadDiffFileFormat/avroFileTojson1", classOf[org.apache.hadoop.io.compress.BZip2Codec])
Parquet
val avroFileMap = avroFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) , e.getString(3)))
avroFileMap.toDF.write.parquet("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToparquet1")

Using compression
sqlContext.setConf("spark.sql.parquet.compression.codec", "gzip")
avroFileMap.toDF.write.parquet("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToparquet1")
ORC file
val avroFileMap = avroFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) , e.getString(3)))
avroFileMap.toDF.write.orc("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToorc")
Avro file
val avroFileMap = avroFile.map(e => (e.getInt(0) , e.getLong(1) , e.getInt(2) , e.getString(3)))
avroFileMap.toDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToavro")

Using compression
import com.databricks.spark.avro._
sqlContext.setConf("spark.sql.avro.compression.codec", "gzip")
avroFileMap.toDF.write.avro("/user/cloudera/problem5/ReadDiffFileFormat/avroFileToavro")


Comments

Post a Comment

Popular posts from this blog

How to use (inner) JOIN and group by in apache spark SQL.

How to use (inner) JOIN and group by in spark SQL. Problem statement: Get daily revenue by product with CLOSED and COMPLETED orders. Save the output to a text, CSV, TSV and parguet file. 1. Load all the json file into spark DataSet using sqlContext. All the data files can be found at GitHub. https://github.com/dgadiraju/data/tree/master/retail_db_json scala> val products = sqlContext.read.json("file:///home/cloudera/workspace/scala/rajudata/data-master/retail_db_json/products/*") scala> val orders = sqlContext.read.json("file:///home/cloudera/workspace/scala/rajudata/data-master/retail_db_json/orders/*") scala> val order_items = sqlContext.read.json("file:///home/cloudera/workspace/scala/rajudata/data-master/retail_db_json/order_items/*") scala> products.limit(2).show +-------------------+-------------------+----------+--------------------+--------------------+-------------+ |product_category_id|product_desc...
Read more

Problem: Find top rated movie using HIVE and store the result to HDFS

1. Download data from below site. https://datasets.imdbws.com/ 2. Download the movies data title.ratings.tsv.gz and title.akas.tsv.gz 3. Find the top 50 rated movies with more than 100000 votes 4. Find the top 50 voted movies 5. Storage details Columns: titleId,title,region,language,averageRating,numVotes Store the result at below location: /home/cloudera/workspace/movies/hive/<formatname> Store the result in following format. a. Text file Columns to be seperated with tab "\t" b. Sequence file. c. RC file. d. Parquet. e. ORC file. Compression: SNAPPY f. Avro file. Use Hive to load data and output data to required location. Pre work: hadoop fs -mkdir /home/cloudera/workspace/movies [root@quickstart movies]# hadoop fs -mkdir /home/cloudera/workspace/movies [root@quickstart movies]# hadoop fs -put /home/cloudera/Downloads/movies/title.ratings.tsv.gz /home/cloudera/workspace/movies [root@quickstart movies]# hadoop fs -ls /home/cloudera/works...
Read more