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]) |
Arun , Are you preparing contents for Configurations as per the last module of CCA175 syllabus ???
ReplyDeleteYes, this is as per new syllabus. Please go through the prep plan page and the corresponding video.
Deletehttp://arun-teaches-u-tech.blogspot.com/p/certification-preparation-plan.html
https://www.youtube.com/playlist?list=PLRLUm7no962j8cf-mpXjrQqusWvw-gIJx
Arun , I was expecting the Spark-Submit scenarios.
DeleteIs 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.
I am working on a scenario that combines spark streaming and submit. Will have them ready by end of this week.
DeleteHi Arun...Wonderful work. Thanks for sharing with us.
DeleteAs mentioned above, could you please post streaming and submit questions?
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.
DeleteArun, Sequence file write working only if rdd.saveAsSequenceFile(,Some(classOf[])).
ReplyDeleteAlso is there a way to compress ORC file ?
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.
DeleteThis comment has been removed by the author.
ReplyDeleteThank you so much for this blog. It helped me a lot to clear the exam.
ReplyDeleteyou are welcome. Congratulations!
DeleteHi Arun, just wanted to say a big thank you. Really appreciate your great work. This blog helped me a lot.
ReplyDeleteyou are welcome. Congratulations!
DeletesqlContext.setConf("spark.sql.parquet.compression.codec","lzo")
ReplyDeleteordersDF.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?
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.
DeleteIf 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
Thanks Arun ... your expertise is helping me a lot to boost up my confidence...
DeleteHi Arun,
ReplyDeleteI 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.
Hi Arun,
ReplyDeleteGreat 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
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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.
ReplyDeletevar 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("")
Arun,
DeleteThanks 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")
This comment has been removed by the author.
ReplyDeleteHi Arun,
ReplyDeleteThanks for providing such a wonderful compilation of problems and solutions. It enabled be to clear the CCA 175 certification yesterday. Many thanks.
Regards
Shubham
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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.
ReplyDeleteit 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.
DeleteHi Arun,
ReplyDeleteThanks 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.
Hi Arun,
ReplyDeleteThanks 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
what about csv files? how to read and write them with compression? there is no API for csv in cloudera quickstart VM.
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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?
ReplyDeletemap transformation
DeleteHi 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.
ReplyDeletesqlContext.read.json is not working/reading .
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ReplyDeleteExcellent 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.
ReplyDeleteHi Arun,
ReplyDeleteI 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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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]);
ReplyDeleterunning above code in scala shows GZipCodec is not member of org.apache.hadoop.io.compress
I had the same issue. The class name is GzipCodec. It is not GZipCodec.
Deleteavrodata.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 ,
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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:
ReplyDeletedf.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 ;)
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 :)
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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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