Building Apache Spark Jars

Apache Spark seems to have taken over the Big Data world. Apache Spark is an in-memory solution that reads data from HDFS. Ever since its first release, Spark has received much attention from the Big Data community and now has a huge fan following from academicians, researchers and the industry. Unfortunately, the documentation for the work has not been keeping up with the code development (in my opinion). There is especially one issue that took me a long time to figure out and I am hoping this post will cut-short the time spent by others trying to solve the same problem.

The Goal: I already have a cluster of spark set up on a set of machines. Let me call this the "lab" cluster. This lab cluster came pre-installed with Hadoop. I want to run spark jobs (written in scala) on this cluster. There are two ways to do it

a) Running sbt run from the root of the sbt project that contains the scala code.

b) Run the fat jar created by sbt assembly as follows

    java -cp path-to-fat-jar MainClassName

The problem: According to the guidelines provided on Spark's documentation, the sbt plugin needs to specify library dependencies to spark and to the relevant hadoop version as follows:

libraryDependencies += "org.apache.spark" %% "spark-core" % "0.9.1"

libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "your-hadoop-version"

 The issue with this is that the fat jar thus compiled now contains libraries that are already present on my cluster. If there any version difference in your hadoop dependency and in hadoop installed on your cluster you will run into a common client mismatch error

The solution:    I found this great resource finally. Here they specially mention that you need to eliminate spark as well as hadoop dependencies and the fat jar should contain nothing but the code for your application. This can be done simply by adding "provided" keyword ahead of each of the library dependency in your build.sbt or build.scala. With this change watch how your sbt excludes all spark, akka and hadoop dependencies. The final jar can be launched using the above mentioned command without any issues

Next Up:    While the cloudera page (and the selflessness of a colleague at work) saved my day, I have now run into another problem. I would like to run this jar using oozie (hadoop's job scheduler) but then again, there is very limited documentation (except for may be one hint). Hopefully I will figure out the puzzle and write another blog to help folks out there who are struggling like me.

Hive: Lessons learned

The past few days I have been playing with Hive for some data analysis and I wanted to put down what I learned

a) Exporting data from hive to csv

If you are using hue, then it provides a convenient way to export to csv or excel format. But if not then you can use the following preamble before the select statement

INSERT OVERWRITE LOCAL DIRECTORY '/path/out.csv' ROW FORMATTED DELIMITED FIELDS TERMINATED BY ','

b) Hive does not allow "select" statements in the "where" clause. for example

SELECT file_id, file_type

FROM file_metadatas

WHERE file_type IN (select types from allowed_file_types)

WILL NOT WORK in Hive, but will work in MySQL

Instead one can use a join 

SELECT a.file_id, a.file_type

FROM file_metadatas a

JOIN allowed_file_types b

ON b.types = a.file_type

c) Date functions: Computing a date that is ~6 months behind the current date

DATE_SUB(TO_DATE(FROM_UNIXTIME(UNIX_TIMESTAMP( ))),180)

d) hive regex: Regular expressions can be used to extract parts of a text field. Here is an example

 

Extracting the file extension:  regexp_extract(file_name,'\\.[0-9a-zA-Z]+$',0))  extracts the 0th (1st) match of file_name with the input string

e) string manipulation support: Strings can be manipulated in hive. One such example is the "lower" function that converts all characters to lower

Matlab Tip: Changing line-width of all the lines via commandline

Get the handle to the line using:
hline = findobj(gcf, 'type', 'line');
Then  you can change some property for all the line objects:
set(hline,'LineWidth',3)
or just for some of them :
set(hline(1),'LineWidth',3)
set(hline(2:3),'LineStyle',':')
idx = [4 5];
set(hline(idx),'Marker','*')
This is not an original post. Here is the original post

Surviving the PhD program

As I am nearing the end of the longest phase of my academic life, I wanted to put together all resources that are useful to keep one going during the frustrating times of a PhD program. I truly believe that keeping a positive emotional state and using our energies in the right direction is the key to getting through the program. I am grateful to have a good support of friends and family but the internet is a great place to seek support in times of trouble. Here are some of the most useful links that I found.

• Dissertation Writers Toolkit : A great resource for those who are procrastinating on writing. This webpage also contains tons of other helpful material, like balanced-life chart, tools on staying organized, positive affirmations on writing and so on.
• Life is easier when you can laugh at yourself. Here are some daily affirmations for doctoral students. But I stayed away from PhD comics as much as I could.
• The Thesis Whisperer is another useful blog, that helped me realize I was not alone in some of my struggles. In fact my struggles are perfectly normal for a PhD student.
• Here are some productivity tricks for graduate students that I found useful. In fact, following one of the suggestions from this page, I purchased multiple chargers for my laptop so I could save time on getting started with my day.
• The 3-month thesis is also a good resource for thesis writing

Trailing Slash in rsync command

Just making a quick reference to the rysnc manual in order to synchronize directories

The command used to copy folders is as follows:

rsync -avz foo:src/bar /data/tmp

This command copies the directory bar in /data/tmp .. 

meaning at the end of this command, you will have /data/tmp/bar folder

If you just want to sync the folders then use the trailing slash like this

rsync -avz foo:src/bar/ /data/tmp 

Now only the contents of bar will be copied into /data/tmp folder;

 you will not find a folder called bar in /data/tmp

The caveats of using tar to create archives

So I recently created a bunch of tar files that had a long path name when unzipped. This long path name was the absolute path on the computer where the tar files were created. For example

tar c /home/shivani/testfile.txt

will create a tar that when unzipped will create /home/shivani/testfile.txt in the folder where you untar the file. 

In order to ensure this does not happen use the -C option, that cds into the directory /home/shivani and then creates the tar. 

This caveat was found on this webapge

Review of Available Implementations of the Latent Dirichlet Allocation

A part of my dissertation is based on the Latent Dirichlet Allocation Model and I have extensively searched for played with various LDA implementations.

Gibbs Sampling implementation of LDA in C++
The input format is basically a single text file that contains one line for each document. Each line contains the actual terms. I found this input format to be bulky. The code itself is very clean and easy to understand. Gibbs Sampling by itself is a little slow and nothing can be done about it.

Gibbs Sampling Implementation of LDA in Java
Written by the same folks who wrote the c++ implementation. Leaving the programming language aside everything else is kept same.

Variational Inference of LDA in Matlab and C
This is easy to use code that uses the input format as that of  SVMLight software, which is basically a text file with each line containing a sequence of tuples of the format : where the feature_id is the word_id based on a dictionary and count is the number of times it appears in the corpus. Implementation wise, I have one comment though. I was hoping that the algorithm would include estimation of the \eta parameter, the smoothing parameter on \beta.

MALLET toolbox
This is written in Java and uses plain text or SVM light format as input. It implements the Gibbs Sampling for LDA and allows for optimization of the hyper parameters \alpha and \eta after burn-in iterations. This allows us to exercise greater control over the impact of topics on the entire collection. Given that LDA itself is slow and that for estimation of hyper parameters we need to wait for burn-in number of iterations, I feel discouraged to use this tool.

Hybrid approach to LDA inference
This approach apparently uses both Variational and Gibbs Sampling based approach to learn the parameters. The author also presents a vanilla LDA implementation.

I will keep updating this post as and when I find more useful stuff.