A Smooth Sea Never Made a Skillful Sailor

I’m going to show you a neat way to work with CSV files and Apache Hive. Usually, you’d have to do some preparatory work on CSV data before you can consume it with Hive but I’d like to show you a built-in SerDe (Serializer/Deseriazlier) for Hive that will make it a lot more convenient to work with CSV. This work was merged in Hive 0.14 and there’s no additional steps necessary to work with CSV from Hive. Suppose you have a CSV file with the following entries  id first_name last_name email gender ip_address  1 James Coleman jcoleman0@cam.ac.uk Male 136.90.241.52  2 Lillian Lawrence llawrence1@statcounter.com Female 101.177.15.130  3 Theresa Hall thall2@sohu.com Female 114.123.153.64  4 Samuel Tucker stucker3@sun.com Male 89.60.227.31  5 Emily Dixon edixon4@surveymonkey.com Female 119.92.21.19 to consume it from within Hive, you’ll need to upload it to hdfs hdfs dfs -put sample .csv /tmp/serdes/ now all it takes is to create a table schema on top of the file drop table if

I must’ve been living under a rock because I’d just learned about Pig’s dynamic invokers. What if I told you that besides UDFs, you have another option to run your Java code without compiling your UDFs. I will let you read the docs on your own but even though I find it quite handy to use it, it is pretty limited in features. You’re limited to passing primitives only and only static methods work. There’s an example of using a non-static method “StringConcat” but I haven’t been able to make it work. So for the demo: suppose you have a file with numbers 4, 9, 16, etc, one on each line  upload the file to hdfs hdfs dfs - put numbers /user/guest/ then suppose you’d like to use Java Math’s Sqrt function to get square root of each number, you can of course write use built-in SQRT function but for the example purposes bare with me. The code to make it work with Pig and Java would look like so: DEFINE Sqrt InvokeForDouble( 'java.lang.Math.sqrt' , 'double' ); num

O'Reilly recently released a free ebook called Hadoop with Python by the author of MapReduce Design Patterns, Donald Miner. Needless to say that caught my eye. The book is a short read, I was able to run through it within two lunch hours. It has five chapters tackling different angles of Hadoop. It is an easy read with an excellent overview of each product discussed. 1st chapter discusses HDFS and Spotify's library written in Python called Snakebite that allows for Python shops interact with HDFS in a native way. This is pretty use-case specific because I don't see a reason to use the library unless you're a Python-heavy shop. The other drawback is that it's not Python3 compliant. That may be an issue going forward. The cool think about Snakebite is that the library does not require loading any Java libraries and promises to be really fast to load. It leverages RPC to speak to Namenode and uses protobuf, so interaction is native. 2nd chapter is on writing MapR

Apache Pig 0.11 added ability to write Pig UDFs in Groovy. The other possible languages to write Pig UDFs are Python, Ruby, Jython, Java, JavaScript. There are a lot of examples for UDFs in Python but the documentation does not give enough for beginners to get started with Groovy. I found the process of writing a Groovy UDF a lot more complicated than Python for example. First misconception is that you don't need to include Groovy groovy-all.jar in pig libraries, Pig is shipped with Groovy by default. Furthermore, you don't need to install Groovy on the client or any other machine, for the same reason as before. The other issue I was having and it was that I was getting type mismatch errors. The tuples arrive as byte arrays, at least with PigStorage loader function and before applying your custom logic, you need to cast the input to the appropriate class. import org.apache.pig.scripting.groovy.OutputSchemaFunction; import org.apache.pig.PigWarning; class GroovyUDFs {

I published a set of Pig, Hive and Spark scripts to interact with MongoDB using mongo-hadoop connector. Some of the published tutorials on Mongo and Hadoop on Databricks and MongoDB sites are no longer working, I decided to update them for HDP 2.3. Some things are still wonky, like Hive queries failing if you try to run anything other than select. Either way, give it a try and provide feedback. One more thing, I'm using Sandbox with HDP 2.3.2 and mongo is installed as an Ambari service using tutorial from github user nikunjness , made my work so much easier. The code is published on my github page as well as on Hortonworks Community Site. Thanks and enjoy. Sample tutorial on HDP integration with MongoDB using Ambari, Spark, Hive and Pig Prerequisites HDP 2.3.2 Sandbox Mongo 2.6.11 install MongoDB service as per  https://github.com/nikunjness/mongo-ambari IMPORTANT make sure you change directory to home after completing the mongo-ambari service insta