File Formats

Quick reference table for reading and writing into several file formats in hdfs. 


File Format Action Procedure and points to remember
TEXT FILE READ sparkContext.textFile(<path to file>);
WRITE sparkContext.saveAsTextFile(<path to file>,classOf[compressionCodecClass]);
//use any codec here org.apache.hadoop.io.compress.(BZip2Codec or GZipCodec or SnappyCodec)
SEQUENCE FILE READ sparkContext.sequenceFile(<path location>,classOf[<class name>],classOf[<compressionCodecClass >]);
//read the head of sequence file to understand what two class names need to be used here
WRITE rdd.saveAsSequenceFile(<path location>, Some(classOf[compressionCodecClass]))
//use any codec here (BZip2Codec,GZipCodec,SnappyCodec)
//here rdd is MapPartitionRDD and not the regular pair RDD.
PARQUET FILE READ //use data frame to load the file.
sqlContext.read.parquet(<path to location>); //this results in a data frame object.
WRITE sqlContext.setConf("spark.sql.parquet.compression.codec","gzip") //use gzip, snappy, lzo or uncompressed here
dataFrame.write.parquet(<path to location>);
ORC FILE READ sqlContext.read.orc(<path to location>); //this results in a dataframe
WRITE df.write.mode(SaveMode.Overwrite).format("orc") .save(<path to location>)
AVRO FILE READ import com.databricks.spark.avro._;
sqlContext.read.avro(<path to location>); // this results in a data frame object
WRITE sqlContext.setConf("spark.sql.avro.compression.codec","snappy") //use snappy, deflate, uncompressed;
dataFrame.write.avro(<path to location>);
JSON FILE READ sqlContext.read.json();
WRITE dataFrame.toJSON().saveAsTextFile(<path to location>,classOf[Compression Codec])

153 comments:

  1. Arun , Are you preparing contents for Configurations as per the last module of CCA175 syllabus ???

    ReplyDelete
    Replies
    1. Yes, this is as per new syllabus. Please go through the prep plan page and the corresponding video.

      http://arun-teaches-u-tech.blogspot.com/p/certification-preparation-plan.html

      https://www.youtube.com/playlist?list=PLRLUm7no962j8cf-mpXjrQqusWvw-gIJx

      Delete
    2. Arun , I was expecting the Spark-Submit scenarios.

      Is there any specific deadline by which you will finish the entire PlayList ?

      I have already paid for the certification but due to syllabus change I have not yet given the certification exam.

      Delete
    3. I am working on a scenario that combines spark streaming and submit. Will have them ready by end of this week.

      Delete
    4. Hi Arun...Wonderful work. Thanks for sharing with us.
      As mentioned above, could you please post streaming and submit questions?

      Delete
    5. Hi Arun, your videos about this blogs are awesome. I prepared for CCA 175 last year but was not able to give exam. After going through these scenarios it feels like I refreshed my 3 months of preparation in 1 day. Thank you very much.. I know its 3 years but just asking if you have posted Spark streaming and submit scenario. I could not find it. Thank you very much.

      Delete
  2. Arun, Sequence file write working only if rdd.saveAsSequenceFile(,Some(classOf[])).

    Also is there a way to compress ORC file ?

    ReplyDelete
    Replies
    1. Thank you for the correction. I updated the blog accordingly. Given that ORC files come with excellent compression ration and by default are written as snappy files i did not think about a need to use something else. I will update the blog if i find something soon.

      Delete
  3. This comment has been removed by the author.

    ReplyDelete
  4. Thank you so much for this blog. It helped me a lot to clear the exam.

    ReplyDelete
  5. Hi Arun, just wanted to say a big thank you. Really appreciate your great work. This blog helped me a lot.

    ReplyDelete
  6. sqlContext.setConf("spark.sql.parquet.compression.codec","lzo")

    ordersDF.write.format("parquet").mode("overwrite").save("/data/output/orders_parquet_lzo")
    => failed with error Caused by: parquet.hadoop.BadConfigurationException: Class com.hadoop.compression.lzo.LzoCodec was not found

    facing above issue . could you please help?

    ReplyDelete
    Replies
    1. there is nothing wrong in the code your wrote. the problem is lzo libraries are not available in the CDH you are using. They will likely not be available in the environment you use during the exam as well OR the exam will only ask you to perform lzo compression only if the lzo libraries are configured and available. Hence, you will not face this issue during the exam.

      If you are trying to solve this for your project or for your company and your focus is not about the exam then please follow the solution available here.

      https://stackoverflow.com/questions/23441142/class-com-hadoop-compression-lzo-lzocodec-not-found-for-spark-on-cdh-5

      Delete
    2. Thanks Arun ... your expertise is helping me a lot to boost up my confidence...

      Delete
  7. Hi Arun,

    I want to appreciate and want to say big thank you for all videos and tutorials. please continue doing the same in future also and keep us motivated.

    ReplyDelete
  8. Hi Arun,

    Great work!! Much needed for guys like me to prepare for the Exam.

    I have query?

    Can We refer local documents(existing documents) during the exam? to cross check the syntax of the PIG/Hive commands.

    Please let know

    ReplyDelete
  9. I’m not sure where you’re getting your information, but good topic. I need to spend some time learning more or understanding more. Thanks for fantastic info I was looking for this information for my mission.

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  10. Thanks Arun for consolidating all the file formats. Just figured that parquet writing method works for orc and json as well. Just thought of sharing with others.

    var dataFile = sqlContext.read.avro("");

    .write.format works for parquet,orc and json

    dataFile.write.format("parquet") .save("")
    dataFile.write.format("orc").save("")
    dataFile.write.format("json").save("")

    ReplyDelete
    Replies
    1. Arun,
      Thanks for putting together a great stuff.

      Please correct if something wrong doing as below. For me it's simple this way.

      dataFrame.write.json("//path")
      dataFrame.write.orc("//path")
      dataFrame.write.parquet("//path")

      Delete
  11. This comment has been removed by the author.

    ReplyDelete
  12. Hi Arun,

    Thanks for providing such a wonderful compilation of problems and solutions. It enabled be to clear the CCA 175 certification yesterday. Many thanks.

    Regards
    Shubham

    ReplyDelete
  13. I definitely appreciate your blog. Excellent work!
    Startups

    ReplyDelete
  14. Hi Arun, Your blog is definitely the stepping stone towards successful CCA175 certification. Why you are not actively updating the posts. I see that the last updated was may-2017. Can you add few more for us? It was really helpful.

    ReplyDelete
    Replies
    1. it was reported by many who follow my blog that the content was sufficent to clear the current version of CCA 175 exam. I will update the content if i receive any feedback on the coverage of the formulated problems in addressing CCA 175 exam needs.

      Delete
  15. Hi Arun,
    Thanks for the good work, All problems are very well designed to cover all the important scenarios.

    I need a clarification, Can the data in Data frame be saved as text file or sequence file. It works for json/parquet and Avro.

    ReplyDelete
  16. Hi Arun,

    Thanks for the content. It's really helpful.
    I am confused on dataframes. The videos from itversity state that we shouldn't use data frames. Is it because the videos are older and at the time of recording there was Spark V1.2?

    Now, I see Spark 1.6 is being provided on CCA175 page from Cloudera. So can we use DataFrames?

    Regards
    Rajesh K

    ReplyDelete
  17. what about csv files? how to read and write them with compression? there is no API for csv in cloudera quickstart VM.

    ReplyDelete
  18. How to save a DataFrame (or) an RDD as a text file with the delimiter as "|" (pipe) or "\t" (tab). Is there any API to do it? or it needs to be done manually in a map transformation?

    ReplyDelete
  19. Hi I need to read json data which is on s3 in tar.gz format , can you help how to read it in spark using scala.

    sqlContext.read.json is not working/reading .

    ReplyDelete
  20. This comment has been removed by the author.

    ReplyDelete
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    ReplyDelete
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  24. The writer understand better the mind of people what they want to learn through their writing therefore this article is outstanding. Thanks!!! view publisher site

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  25. Excellent post, There are so many ways to save the file for each of the format, I always having trouble to remember all this methods. Listing them all in a single post is great help. This can be used just before the exam.

    ReplyDelete
  26. Hi Arun,

    I can see several ways/method to save a Dataframe as a parquet file format. I don't see any difference in the output. Can we use any of the method?

    val friendsDF = friendsMBV.toDF
    sqlContext.setConf("spark.sql.parquet.compression.codec", "snappy")
    friendsDF.saveAsParquetFile("/home/cloudera/workspace/fk1/parq10")

    or
    val friendsDF = friendsMBV.toDF
    sqlContext.setConf("spark.sql.parquet.compression.codec", "snappy")
    friendsDF.write.parquet("/home/cloudera/workspace/fk1/parq11")

    [root@quickstart retail_db_json]# hadoop fs -ls /home/cloudera/workspace/fk1/parq10
    Found 5 items
    -rw-r--r-- 1 cloudera supergroup 0 2018-02-11 09:08 /home/cloudera/workspace/fk1/parq10/_SUCCESS
    -rw-r--r-- 1 cloudera supergroup 314 2018-02-11 09:08 /home/cloudera/workspace/fk1/parq10/_common_metadata
    -rw-r--r-- 1 cloudera supergroup 779 2018-02-11 09:08 /home/cloudera/workspace/fk1/parq10/_metadata
    -rw-r--r-- 1 cloudera supergroup 688 2018-02-11 09:08 /home/cloudera/workspace/fk1/parq10/part-r-00000-1a0a76d6-6bb3-4d06-b68a-b2dfe7874c2a.snappy.parquet
    -rw-r--r-- 1 cloudera supergroup 681 2018-02-11 09:08 /home/cloudera/workspace/fk1/parq10/part-r-00001-1a0a76d6-6bb3-4d06-b68a-b2dfe7874c2a.snappy.parquet

    [root@quickstart retail_db_json]# hadoop fs -ls /home/cloudera/workspace/fk1/parq11
    Found 5 items
    -rw-r--r-- 1 cloudera supergroup 0 2018-02-11 09:09 /home/cloudera/workspace/fk1/parq11/_SUCCESS
    -rw-r--r-- 1 cloudera supergroup 314 2018-02-11 09:09 /home/cloudera/workspace/fk1/parq11/_common_metadata
    -rw-r--r-- 1 cloudera supergroup 779 2018-02-11 09:09 /home/cloudera/workspace/fk1/parq11/_metadata
    -rw-r--r-- 1 cloudera supergroup 688 2018-02-11 09:09 /home/cloudera/workspace/fk1/parq11/part-r-00000-9689ab16-7c4c-46c5-b72c-e2af028152bb.snappy.parquet
    -rw-r--r-- 1 cloudera supergroup 681 2018-02-11 09:09 /home/cloudera/workspace/fk1/parq11/part-r-00001-9689ab16-7c4c-46c5-b72c-e2af028152bb.snappy.parquet

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  30. avrodata.map(x=> (x(0).toString,x(0)+"\t"+x(1)+"\t"+x(2)+"\t"+x(3))).saveAsSequenceFile("/user/cloudera/problem5/ssequencezip",classOf[org.apache.hadoop.io.compress.GZipCodec]);
    running above code in scala shows GZipCodec is not member of org.apache.hadoop.io.compress

    ReplyDelete
    Replies
    1. I had the same issue. The class name is GzipCodec. It is not GZipCodec.

      Delete
  31. avrodata.map(x=> (x(0).toString,x(0)+"\t"+x(1)+"\t"+x(2)+"\t"+x(3))).saveAsSequenceFile("/user/cloudera/problem5/ssequencezip",Some(classOf[org.apache.hadoop.io.compress.GZipCodec])); please help ,
    give solution

    ReplyDelete
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  33. Hi! Thank you for your excellent work! I would like to add that I cannot save to json the way you show it, but have found an alternative way that does work:

    df.write.option("compression", "snappy").json("/user/cloudera/foo")

    It would be nice if you could add it to the table, so people could see it and use it ;)

    ReplyDelete
    Replies
    1. Hey Jose. Thanks for this. Can you tell me if there is a way to verify this output? The files have neither .snappy nor .json extension. Any input on this is appreciated :)

      Delete
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  44. Hi Arun,
    When I am trying to save the file as ORC for the 8th question, I get an error saying input path does not exist
    in the console, it shows input path as :
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    Why is /user/cloudera coming twice? I verified the code twice. I am not doing anything different

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