## Books I read in year 2016

Here comes the last day of 2016 year. And it is also the time for me to review my harvest about knowledge, or books.

Frankly speaking, the book “All hard thing about hard things” literally frighten me, and cause me to give up any idea about joining a startup company in China. Maybe this is the best consequence, for many startup companies failed in this end of year and I fortunately avoid this tempest.

Diving more deeper into the ocean of “Hadoop Ecosystem”, or “Big Data”, I find out Spark is really a convenient and powerful framework (compare to MapReduce) which could implement complicated algorithm or data-flow with a few lines of code. Surely, Scala is also a key element for Spark’s efficiency and concision.

Today, even normal person could imagine a sci-fi story about how modern people will fight with Alien invaders. But, what will happen if Aliens attacked the earth in the ancient time? What about Medieval age? Then comes the funny and bold sci-fi novel “The High Crusade”. A group of Medieval army defeat the invader of AlienοΌ and did even more: occupied a frontline planet of a gigantic Alien Empire. It is really out of my imagination π

## The type of variables in Python

Haven’t written python code for more than one year, I met this simple problem:

Even the code have print out the value of “a” and “b” as 2 and 1, the condition check “if a >= b:” is false!

Spending more than 10 minutes, I eventually get the reason: the type of “a” is “int” but “b” is “string” (and the interpreter of Python will not report any warning about this “inconsistency”). I should have been taking enough care of the type of these variables.
Seems “print” can’t reveal adequate details of a variable, therefore it is highly suggested we using “pprint” instead of “print”.

The result will be

## My understanding of CNN (Convolutional Neural Network)

The classic Neural Network of Machine Learning usually use fully-connection, which will cost too much computing resource to get final result if the inputs are high-resolution images. So comes the Convolutional Neural Network. CNN (Convolutional Neural Network) splits the whole big image into small pieces (called Receptive Fields), and do some “Convolutional Operations” (actually are some image transformations, also called Kernels) on each Receptive Field, then the pooling operation (usually max-polling, which is simply collect a biggest feature weight in a 2X2 matrix).

Receptive Fields is easy to understand, but why do it use different kind of “Convolutional Operations” on them? In my opinion, “Convolutional Operations” means using different kind of Kernel Functions to transfer the same image (for example: sharpen the image, or detect the edge of object in image), so they could reveal different views of the same image.
These different Kernel Functions review different “Features” of a image, thus we call them “Feature Maps”:

From http://mxnet.io/tutorials/python/mnist.html
(The matrix of light-yellow is just transferred from light-gray matrix on its left)

By using Receptive Fields and max-pooling, the number of neurons will become very small gradually, which will make computing (or regression) much more easy and fast:

From http://www.cnblogs.com/bzjia-blog/p/3442788.html

Therefore, I reckon the main purpose of using CNN is to reduce the difficulty of computing result of a fully-connected Neural Network.

## Build dataflow to get monthly top price of Land Trading in UK

The dataset is downloaded from UK government data web(The total data size is more than 3GB). And, I am using Apache Oozie to run Hive and Sqoop job periodically.

The Hive script “land_price.hql”:

We want Hive job to run on queue “root.default” in YARN (and other jobs in “root.mr”), so we set the “mapred.job.queue.name” to “root.default”.

Remember to use SUBSTR() in Hive to erase quote charactor “\”” when importing data from raw CSV file.

The “coordinator.xml” for Apache Oozie:

The “workflow.xml” for Apache Oozie:

We run two jobs parallelly here: Hive and TeraSort (TeraSort is not useful in real productive environment, but it could be a good substitute for real private job in my company).

“job.properties” for Oozie:

Remember to set “oozie.use.system.libpath=true” therefore Oozie could run Hive and Sqoop job correctly.

The script to create MYSQL table:

After launch the Oozie coordinator, it will finally put consequent data into MYSQL table:

Looks the land price of “WOKINGHAM” in October 2015 is extremely expensive.

## Some tips about using Apache Flume

Question1: Flume process report “Expected timestamp in the Flume event headers, but it was null”
Solution1: The flume process expect to receive events with timestamp, but the event doesn’t have. For sending normal text event to flume, we need to tell it to generate timestamp with every events by itself. Put below line into configuration:

Question2: HDFS Sink generates tremendous small files with high frequency even we have set “a1.sinks.k2.hdfs.rollInterval=600”
Solution2: We still need to set “rollCount” and “rollSize”, as Flume will roll file if any condition of “rollInterval”, “rollCOunt”, or “rollSize” been fulfilled.

Question3: Flume process exit and report “Exception in thread “SinkRunner-PollingRunner-DefaultSinkProcessor” java.lang.OutOfMemoryError: GC overhead limit exceeded”
Solution3: Simply add “JAVA_OPTS=”-Xms12g -Xmx12g” (My server has more than 16G physical memory) into “/usr/lib/flume-ng/bin/flume-ng”

—— My configuration file for Flume ——

The startup command for Cloudera Environment:

## Use Oozie to run terasort

The better choice of “Action” for running terasort test case in Oozie is “Java Action” instead of “Mapreduce Action” because terasort need to run

first and then load ‘partitonFile’ by “TotalOrderPartitioner”. It’s not a simple Mapreduce job which need merely a few propertyies.

The directory of this”TerasortApp” which using “Java Action” of Oozie looks just like:

The core of this App is “workflow.xml”:

Note 1. In Cloudera environment, The Web UI will fail in the last step of creating sharelib for Oozie Service. To fix this problem:

Note 2. We can’t use property of ‘mapred.map.tasks’ to change the number of mappers in Terasort because it is actually decided by class ‘TotalOrderPartitioner’. Therefore I use ‘mapreduce.input.fileinputformat.split.minsize’ property to limit the number of mappers.