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## Assignment 9: Spark
### Due Dec 8, 2023, 11:59PM
Assignment 8 focuses on using Apache Spark for doing large-scale data analysis
tasks. For this assignment, we will use relatively small datasets and we won't
run anything in distributed mode; however Spark can be easily used to run the
same programs on much larger datasets.
## Getting Started with Spark
This guide is basically a summary of the excellent tutorials that can be found
at the [Spark website](http://spark.apache.org).
[Apache Spark](https://spark.apache.org) is a relatively new cluster computing
framework, developed originally at UC Berkeley. It significantly generalizes the
2-stage Map-Reduce paradigm (originally proposed by Google and popularized by
open-source Hadoop system); Spark is instead based on the abstraction of
**resilient distributed datasets (RDDs)**. An RDD is basically a distributed
collection of items, that can be created in a variety of ways. Spark provides a
set of operations to transform one or more RDDs into an output RDD, and analysis
tasks are written as chains of these operations.
Spark can be used with the Hadoop ecosystem, including the HDFS file system and
the YARN resource manager.
Note that bash is the default shell everywhere, but the `.cshrc` is set up
correctly if you feel like dropping into `tcsh`.
### Installing Spark
Mac: `brew install apache-spark`.
or
As before, we have provided a VagrantFile in the `assignment8` directory. Since
the Spark distribution is large, we ask you to download that directly from the
Spark website.
1. Download the Spark package
[here](https://archive.apache.org/dist/spark/spark-3.0.1/spark-3.0.1-bin-hadoop2.7.tgz).
2. Move the downloaded file to the `assignment8/` directory (so it is available
in '/vagrant' on the virtual machine), and uncompress it using: `tar zxvf
spark-3.0.1-bin-hadoop2.7.tgz` in your VM.
3. This will create a new directory: `spark-3.0.1-bin-hadoop2.7`.
4. (optional) This step is included in the VagrantFile, but if you get any error
related to `$SPARKHOME`, you can set the variable with: <br> `export
SPARKHOME=/vagrant/spark-3.0.1-bin-hadoop2.7` and then <br> `echo "export
SPARKHOME=/vagrant/spark-3.0.1-bin-hadoop2.7" >> .bashrc`
We are ready to use Spark.
### Spark and Python
Spark primarily supports three languages: Scala (Spark is written in Scala),
Java, and Python. We will use Python here -- you can follow the instructions at
the tutorial and quick start
(http://spark.apache.org/docs/latest/quick-start.html) for other languages. The
Java equivalent code can be very verbose and hard to follow. The below shows a
way to use the Python interface through the standard Python shell.
### PySpark Shell
You can also use the PySpark Shell directly.
1. `$SPARKHOME/bin/pyspark`: This will start a Python shell (it will also output
a bunch of stuff about what Spark is doing). The relevant variables are
initialized in this python shell, but otherwise it is just a standard Python
shell.
2. `>>> textFile = sc.textFile("README.md")`: This creates a new RDD, called
`textFile`, by reading data from a local file. The `sc.textFile` commands create
an RDD containing one entry per line in the file.
3. You can see some information about the RDD by doing `textFile.count()` or
`textFile.first()`, or `textFile.take(5)` (which prints an array containing 5
items from the RDD).
4. We recommend you follow the rest of the commands in the quick start guide
(http://spark.apache.org/docs/latest/quick-start.html). Here we will simply do
the Word Count application.
#### Word Count Application
The following command (in the pyspark shell) does a word count, i.e., it counts
the number of times each word appears in the file `README.md`. Use
`counts.take(5)` to see the output.
`>>> counts = textFile.flatMap(lambda line: line.split(" ")).map(lambda word:
(word, 1)).reduceByKey(lambda a, b: a + b)`
Here is the same code without the use of `lambda` functions.
```
def split(line):
return line.split(" ")
def generateone(word):
return (word, 1)
def sum(a, b):
return a + b
textfile.flatMap(split).map(generateone).reduceByKey(sum)
```
The `flatmap` splits each line into words, and the following `map` and `reduce`
do the counting (we will discuss this in the class, but here is an excellent and
detailed description: [Hadoop Map-Reduce
Tutorial](http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html#Source+Code)
(look for Walk-Through).
The `lambda` representation is more compact and preferable, especially for small
functions, but for large functions, it is better to separate out the
definitions.
### Running it as an Application
Instead of using a shell, you can also write your code as a python file, and
*submit* that to the spark cluster. The `assignment8` directory contains a
python file `wordcount.py`, which runs the program in a local mode. To run the
program, do: `$SPARKHOME/bin/spark-submit wordcount.py`
### More...
We encourage you to look at the [Spark Programming
Guide](https://spark.apache.org/docs/latest/programming-guide.html) and play
with the other RDD manipulation commands. You should also try out the Scala and
Java interfaces.
## Assignment Details
We have provided a Python file: `assignment.py`, that initializes the folllowing
RDDs:
* An RDD consisting of lines from a Shakespeare play (`play.txt`)
* An RDD consisting of lines from a log file (`NASA_logs_sample.txt`)
* An RDD consisting of 2-tuples indicating user-product ratings from Amazon
Dataset (`amazon-ratings.txt`)
* An RDD consisting of JSON documents pertaining to all the Noble Laureates over
last few years (`prize.json`)
The file also contains some examples of operations on these RDDs.
Your tasks are to fill out the 5 functions that are defined in the
`functions.py` file (starting with `task`). The amount of code that you write
would typically be small (several would be one-liners), with the exception of
the last one.
First 4 tasks are worth 1 point
each, and task 5 is worth 2 points.
- **Task 1**: This function takes as input the amazonInputRDD and calculate the
proportion of 1.0 rating review out of all reviews made by each customer. The
output will be an RDD where the key is the customer's user id, and the value
is the proportion in decimal. This can be completed by using `aggregateByKey`
or `reduceByKey` along with `map`.
- **Task 2**: Write just the flatmap function (`task2_flatmap`) that takes in a parsed JSON document (from `prize.json`) and returns the surnames of the Nobel Laureates. In other words, the following command should create an RDD with all the surnames. We will use `json.loads` to parse the JSONs (this is already done). Make sure to look at what it returns so you know how to access the information inside the parsed JSONs (these are basically nested dictionaries). (https://docs.python.org/2/library/json.html)
```
task2_result = nobelRDD.map(json.loads).flatMap(task2_flatmap)
```
- **Task 3**: This function operates on the `logsRDD`. It takes as input a list
of *dates* and returns an RDD with "hosts" that were present in the log on all
of those dates. The dates would be provided as strings, in the same format that
they appear in the logs (e.g., '01/Jul/1995' and '02/Jul/1995'). The format of
the log entries should be self-explanatory, but here are more details if you
need: [NASA Logs](http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html) Try to
minimize the number of RDDs you end up creating.
- **Task 4**: On the `logsRDD`, for two given days (provided as input analogous
to Task 9 above), use a 'cogroup' to create the following RDD: the key of the
RDD will be a host, and the value will be a 2-tuple, where the first element is
a list of all URLs fetched from that host on the first day, and the second
element is the list of all URLs fetched from that host on the second day. Use
`filter` to first create two RDDs from the input `logsRDD`.
- **Task 5**: NLP often needs to preprocess the input data and can benefit a lot
from cluster
computing. [Tokenization](https://en.wikipedia.org/wiki/Lexical_analysis#Tokenization)
is the process of chopping up the raw
text. [Bigrams](http://en.wikipedia.org/wiki/Bigram) are sequences of two
consecutive words. For example, the previous sentence contains the following
bigrams: "Bigrams are", "are simply", "simply sequences", "sequences of",
etc. Your task here is to tokenize each line in playRDD by first separating
out any punctuation and then converting each segment separated by a space as a
token (e.g. `Task 5: I'm easy.` will be tokenized into `["Task", "5", ":",
"I", "'", "m", "easy", "."]`); and count the appearance of each bigram in the
tokens. The return value should be a RDD where the key is a bigram, and the
value is its count.
### Sample results.txt File
You can use spark-submit to run the `assignment.py` file, but it would be easier
to develop with `pyspark` (by copying the commands over).
**results.txt** shows the results of running assignment.py on our code using:
`$SPARKHOME/bin/spark-submit assignment.py`
### Submission
Submit the `functions.py` file [on
gradescope](https://www.gradescope.com/courses/535193/assignments/2852211).
## Mac notes
Spark and jupyter can both be installed natively on the mac, though it can be a
bit tricky to get them to talk to each other. A link explaining how to install
Spark is
[here](https://www.tutorialkart.com/apache-spark/how-to-install-spark-on-mac-os),
and `jupyter` may be installed w/ `pip3 install jupyter`.
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