def display_q1(spark: SparkSession):
st.title("Building your first DataFrames")
st.markdown("""
A `Dataset` is a distributed collection of data which combines the benefits of RDDs (strong typing, ability to use lambda functions)
and SparkSQL's optimized execution engine.
A `DataFrame` is a `Dataset` organized into named columns.
It is conceptually equivalent to a table in a relational database, or a data frame in Python/R.
Conceptually, a `DataFrame` is a `Dataset` of `Row`s.
As with RDDs, applications can create DataFrames from an existing RDD, a Hive table or from Spark data sources.
""")
st.subheader("Question 1 - Convert a RDD of Row to a DataFrame")
st.markdown("""
Recall from the previous assignment how we used two tables on students :
one for students to grades, another one for students to gender.
Let's create a function which takes a `RDD` of `Row`s and a schema as arguments
and generates the corresponding DataFrame.
Edit `create_dataframe` in `src/session3/sparksql.py` to solve the issue.
""")
test_create_dataframe(spark)
display_exercise_solved()
st.subheader("Question 2 - Load a CSV file to a DataFrame")
st.markdown("""
Let's reload the `FL_insurance_sample.csv` file from last session and freely interact with it.
Edit `read_csv` in `src/session3/sparksql.py` to solve the issue.
""")
test_read_csv(spark)
display_goto_next_section()
def display_q1(sc: SparkContext):
st.title("Building your first RDDs")
with st.beta_expander("Introduction"):
st.markdown("""
In this section, we are going to introduce Spark's core abstraction for working with data
in a distributed and resilient way: the **Resilient Distributed Dataset**, or RDD.
Under the hood, Spark automatically performs the distribution of RDDs and its processing around
the cluster, so we can focus on our code and not on distributed processing problems,
such as the handling of data locality or resiliency in case of node failure.
A RDD consists of a collection of elements partitioned accross the nodes of a cluster of machines
that can be operated on in parallel.
In Spark, work is expressed by the creation and transformation of RDDs using Spark operators.
""")
st.image("./img/spark-rdd.png", use_column_width=True)
st.markdown("""
_Note_: RDD is the core data structure to Spark, but the style of programming we are studying
in this lesson is considered the _lowest-level API_ for Spark.
The Spark community is pushing the use of Structured programming with Dataframes/Datasets instead,
an optimized interface for working with structured and semi-structured data,
which we will learn later.
Understanding RDDs is still important because it teaches you how Spark works under the hood
and will serve you to understand and optimize your application when deployed into production.
There are two ways to create RDDs: parallelizing an existing collection in your driver program,
or referencing a dataset in an external storage system,
such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.
""")
st.subheader("Question 1 - From Python collection to RDD")
st.markdown("""
Edit the `rdd_from_list` method in `src/session2/rdd.py`
to generate a Python list and transform it into a Spark RDD.
Ex:
```python
rdd_from_list([1, 2, 3]) should be a RDD with values [1, 2, 3]
```
""")
test_rdd_from_list(sc)
display_exercise_solved()
st.subheader("Question 2 - From text file to RDD")
st.markdown("""
Edit the `load_file_to_rdd` method in `src/session2/rdd.py`
to generate a Spark RDD from a text file.
Each line of the file will be an element of the RDD.
Ex:
```python
load_file_to_rdd("./data/FL_insurance_sample.csv") should be a RDD with each line an element of the RDD
```
""")
test_rdd_from_list(sc)
display_exercise_solved()
display_goto_next_section()
def display_q2():
st.subheader("Question 2 - Square numbers in the list")
st.markdown("""
Edit the `squared` method in `src/session1/hello.py` to square all elements in a list.
Ex:
```python
squared([1, 2, 3]) should be [1, 4, 9]
```
""")
test_squared()
display_exercise_solved()
display_goto_next_section()
def display_q1():
st.subheader("Question 1 - Sum of two numbers")
st.markdown("""
Edit the `add` method in `src/session1/hello.py` to return the sum of 2 numbers.
Ex:
```python
add(1, 2) should be 3
```
""")
test_add()
display_exercise_solved()
display_goto_next_section()
def display_q3():
st.subheader("Question 3 - Are all elements unique ?")
st.markdown("""
Edit the `is_unique` method in `src/session1/hello.py` to return `True` if all elements are unique
and `False` otherwise.
Ex:
```python
is_unique([2, 5, 9, 7]) should be True
is_unique([2, 5, 5, 7]) should be False
```
""")
test_is_unique()
display_exercise_solved()
display_goto_next_session()
def display_q4(sc: SparkContext):
st.title("Manipulating a CSV file")
st.markdown("""
We provide a `FL_insurance_sample.csv` file inside the `data` folder to use in our computations,
it will be loaded through `load_file_to_rdd()` you have previously implemented.
The first line of the CSV is the header, and it is annoying to have it mixed with the data.
In the lower-level RDD API we need to write code to specifically filter that first line.
Edit the `filter_header` method to remove the first element of a RDD.
""")
with st.beta_expander("Hint ?"):
st.markdown("""
**Hint** : `rdd.zipwithindex()` is a useful function when you need to filter by position
in a file _(though it is computationally expensive)_.
""")
test_filter_header(sc)
display_exercise_solved()
st.markdown("""
Let's try some statistics on the `county` variable, which is the second column of the dataset.
Edit `test_county_count` to return the number of times each county appears
""")
test_county_count(sc)
display_exercise_solved()
st.markdown("""
A little bonus. Streamlit can display plots directly:
* Matplotlib: `st.pyplot`
* Plotly: `st.plotly_chart`
* Bokeh: `st.bokeh_chart`
* Altair: `st.altair_chart`
So as a bonus question, display a bar chart of number of occurrences
for each county directly in the app by editing the `bar_chart_county` method.
""")
bar_chart_county(sc)
display_goto_next_section()
def display_q2(spark: SparkSession):
st.title("Running queries on DataFrames")
st.subheader("Question 1 - The comeback of 'Mean grades per student'")
st.markdown("""
Let's generate a Dataframe of the students tables for the incoming questions,
using our newly created `create_dataframe` function.
""")
with st.beta_expander(
"The following code is run automatically by the Streamlit app."):
st.markdown("""
```python
genders_rdd = spark.sparkContext.parallelize(
[("1", "M"), ("2", "M"), ("3", "F"), ("4", "F"), ("5", "F"), ("6", "M")]
)
grades_rdd = spark.sparkContext.parallelize(
[("1", 5), ("2", 12), ("3", 7), ("4", 18), ("5", 9), ("6", 5)]
)
genders_schema = StructType(
[
StructField("ID", StringType(), True),
StructField("gender", StringType(), True),
]
)
grades_schema = StructType(
[
StructField("ID", StringType(), True),
StructField("grade", StringType(), True),
]
)
genders_df = create_dataframe(spark, genders_rdd, genders_schema)
grades_df = create_dataframe(spark, grades_rdd, grades_schema)
```
""")
with st.beta_expander("There are 2 ways of interacting with DataFrames"):
st.markdown("""
* DataFrames provide a domain-specific language for structured manipulation :
```python
>> genders_df.filter(genders_df['ID'] > 2)
+---+------+
| ID|gender|
+---+------+
| 3| F|
| 4| F|
| 5| F|
| 6| M|
+---+------+
```
In the more simple cases, you can interact with DataFrames with a syntax close to the Pandas syntax.
```python
>> genders_df[genders_df['ID'] > 2]
+---+------+
| ID|gender|
+---+------+
| 3| F|
| 4| F|
| 5| F|
| 6| M|
+---+------+
```
* The `sql` function of a SparkSession enables to run SQL queries directly on the frame
and returns a DataFrame, on which you can queue other computations.
Before doing that, you must create a temporary views for those DataFrames
so we can interact with them within the SQL query..
```python
# Register the DataFrame as a SQL temporary view beforehand
>> genders_df.createOrReplaceTempView('genders')
# Now use the temporary view inside a SQL query, the compiler will map the name to the actual object
>> spark.sql('SELECT * FROM genders WHERE ID > 2').show()
+---+------+
| ID|gender|
+---+------+
| 3| F|
| 4| F|
| 5| F|
| 6| M|
+---+------+
```
Don't hesitate to check the [DataFrame Function Reference](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#module-pyspark.sql.functions)
for all of the operators you can use on a DataFrame. Use the following cell to experiment :)
""")
st.markdown("""
---
Remember the mean grade per gender question from last assignment ? Remember how unpleasant it was ?
Let's do that directly in SparkSQL in the `mean_grade_per_gender` method in `src/session3/sparksql.py`.
PS : if you are using programmatic SQL interaction, you can define a temporary view of temporary variables.
You may want to delete those views at the end of your function with `spark.catalog.dropTempView('your_view')`.
""")
test_mean_grade_per_gender(spark)
display_exercise_solved()
st.subheader("Question 2 - The comeback of 'counting counties'")
st.markdown("""
Let's plot the number of different counties in a histogram, like in the previous assignment.
To do that, in `count_county` return a Pandas a dataframe which contains, for each county,
the number of its occurences in the dataset.
> Hint: a Spark Dataframe is distributed on a number of workers, so it cannot be plotted as is.
> You will need to collect the data you want to plot back in the driver.
> The `toPandas` is usable to retrieve a Pandas local Dataframe,
> be careful to only use it on small Dataframes !
""")
test_count_county(spark)
display_exercise_solved()
st.markdown("""
A little bonus. Streamlit can display plots directly:
* Matplotlib: `st.pyplot`
* Plotly: `st.plotly_chart`
* Bokeh: `st.bokeh_chart`
* Altair: `st.altair_chart`
So as a bonus question, display a bar chart of number of occurrences
for each county directly in the app by editing the `bar_chart_county` method.
""")
bar_chart_county(spark)
display_goto_next_section()
def display_pagerank(sc: SparkContext):
st.title("Application - Computing Pagerank")
st.warning("""
This final bit is algorithmically heavy :skull:
Take your time, and good luck!
""")
st.markdown("""
PageRank is a function that assigns a real number to each page in the Web
(or at least to that portion of the Web that has been crawled and its links discovered).
The intent is that the higher the PageRank of a page, the more "important" it is.
Hadoop has its origins in Apache Nutch, an open source web search engine,
and one of the first use cases for Big Data technologies and MapReduce was the indexing
of millions of webpages.
> In the following application, we will delve into an implementation of an iterative algorithm,
the PageRank, which is well suited to Spark. You have to imagine your Pagerank implementation
will run on Petabytes of web crawler logs to compute the ranking of the web URLs!
We will deal with the following simplified web system :
""")
st.image("./img/pagerank.png", width=400)
st.markdown("""
We have four web pages (a, b, c, and d) in our system :
* Web page A has outbound links to pages B, C, D
* Web page B has outbound links to C, D
* Web page C has outbound link to B
* Web page D has outbound link to A, C
We will implement a [simpler version of PageRank](https://en.wikipedia.org/wiki/PageRank#Simplified_algorithm)
in PySpark, in the `src/session2/pagerank.py` file.
## Input Data
When crawling the Web for URLs and their outbound links,
web crawlers will append all the new data in the same file.
As such we expect the format to be the following when reading from the filesystem,
which is easier to append on. Let's call it the **long form**.
```
URL1 neighbor1
URL1 neighbor2
URL2 neighbor1
...
```
We could also work on a data structure where all neighbors of the same URL are grouped on one line.
Let's call this the **wide form**.
```
URL1 [neighbor1, neighbor2]
URL2 [neighbor1]
...
```
Let's build two functions to alternate between both representations.
""")
st.subheader("Question 1.1 - From wide to long")
st.markdown("""
Generate a web system as a RDD of tuples (page name, neighbor page name)
from a RDD of tuples (page name, list of all neighbors), by editing the `ungroup_input` method.
_Hint: we are working with PairedRDDs here, don't hesitate to check the_
_[API reference](https://spark.apache.org/docs/2.2.0/api/python/pyspark.html#pyspark.RDD)_
_of RDDs for functions like `mapValues` or `flatMapValues`_
""")
test_ungroup_input(sc)
display_exercise_solved()
st.subheader("Question 1.2 - From long to wide")
st.markdown("""
Generate a web system as a RDD of tuples (page name, list of all neighbors)
from a RDD of tuples (page name, neighbor page name), by editing the `group_input` method.
_Hint: Never hesitate to collect and print your RDD in the method using Streamlit._
_You may most notably find out that `pyspark.resultiterable.ResultIterable` is not the result we want to return._
""")
test_group_input(sc)
display_exercise_solved()
st.header("Question 2 - Page Contributions")
st.image("./img/pagerank.png", width=400)
st.markdown("""
PageRank is an iterative program, for each iteration a page gets empowered
by every other page that links to it.
At each iteration, we need to compute what a given URL contributes to the rank of other URLs.
The PageRank transferred from a given page to the targets of its outbound links
upon the next iteration is divided equally among all outbound links.
""")
st.latex(
r"Contribution\ to\ a\ page = \frac{pagerank\ of\ contributing\ page}{number\ outbound\ links\ from\ contributing\ page}"
)
st.markdown("""
> So for example, if page A has pagerank 3, it will contribute 1 to B, 1 to C and 1 to D.
Then, for a page, we will sum the contributions of every page linking to it.
> So B should receive contributions from A anc C's pageranks.
Finally, the update pagerank for a page, given a damping factor $s$ will be :
""")
st.latex(
r"pagerank(u) = 1 - s + s \times \sum_{v \in B_u} \frac{pagerank(v)}{L(v)}"
)
st.markdown("""
i.e. the PageRank value for a page $u$ is dependent on the PageRank values for each page $v$
contained in the set $B_u$ (the set containing all pages linking to page $u$),
divided by the number $L(v)$ of links from page $v$.
""")
st.subheader(
"Question 2.1 - Split a pagerank into contributions to outbound links")
st.markdown("""
First, we are going to build the page contribution of a page to a set of outbound urls
in `compute_contribs`.
The first arugment gives the outbound links of a page,
the second argument gives the rank of the page,
and we return a list of tuples (outbound url, contribution to the outbound url).
""")
st.warning(""":balloon: We will **NOT** be using Spark for this question.
We assume a Python worker is capable of working this out.
""")
st.markdown("""
Ex:
```python
assert compute_contributions(['b', 'c', 'd'], 1) == [('b', 1/3), ('c', 1/3), ('d', 1/3)]
```
""")
test_compute_contributions()
display_exercise_solved()
st.subheader("Question 2.2 - Generate a RDD of all contributions")
st.markdown("""
Let's assume we maintain the following two key-value RDD structures:
* links RDD
```
page1 [list of neighbors to page1]
page2 [list of neighbors to page2]
...
```
* ranks RDD
```
page1 rank1
page2 rank2
...
```
With the help from `compute_contributions`,
build the `generate_contributions` method,
which returns a key-value RDD of all generated contributions of the form
(URL, contributed rank by one of its parent page) using the links and ranks RDD.
```
A contribution_to_B
A contribution_to_C
A contribution_to_D
B contribution_to_C
B contribution_to_D
...
```
""")
test_generate_contributions(sc)
display_exercise_solved()
st.subheader("Question 2.3 - Apply damping and generate the new pagerank")
st.markdown("""
In `generate_ranks`, compute new ranks for each URL by summing all contributions from outbound URL,
and applying the damping factor.
""")
test_generate_ranks(sc)
display_exercise_solved()
st.subheader("Question 3 - Building the main algorithm")
st.markdown("""
Let's build the iteration. At each step :
1. Generate all contributions with `generate_contributions`
2. Update ranks RDD with `generate_ranks`
We provide a function to initialize the web system from the image and initial ranls:
""")
with st.echo("below"):
def initialize(sc):
"""
Initialize links and ranks RDDs
"""
# Loads all URLs from input file and initialize their neighbors.
links = sc.parallelize([
("a", "b"),
("a", "c"),
("a", "d"),
("c", "b"),
("b", "c"),
("b", "d"),
("d", "a"),
("d", "c"),
])
links = group_input(sc, links).cache(
) # put the links RDD in cache because it will be reused a lot
# Initialize all ranks to 0.25
ranks = links.keys().map(lambda url: (url, 0.25))
return (links, ranks)
st.markdown("""
Let's create a Pandas dataframe with columns \[a, b, c, d\].
At each iteration, add a row with the Pagerank value for each node in the corresponding column.
Then return the Pandas dataframe as a result so we can investigate it.
""")
test_main(sc)
display_exercise_solved()
st.markdown(
"""So do you want to visualize the convergence of pagerank ? Let's do this !"""
)
if st.button("Run Pagerank algorithm"):
links, ranks = initialize(sc)
result = main(sc, 25, 0.85, links, ranks)
st.write(result.plot())
display_goto_next_session()
def display_q3(sc: SparkContext):
st.title("Using Key-value RDDs")
st.markdown("""
If you recall the classic MapReduce paradigm, you were dealing with key/value pairs
to reduce your data in a distributed manner.
We define a pair as a tuple of two elements,
the first element being the key and the second the value.
Key/value pairs are good for solving many problems efficiently in a parallel fashion
so let us delve into them.
```python
pairs = [('b', 3), ('d', 4), ('a', 6), ('f', 1), ('e', 2)]
pairs_rdd = sc.parallelize(pairs)
```
### reduceByKey
The `.reduceByKey()` method works in a similar way to the `.reduce()`,
but it performs a reduction on a key-by-key basis.
The following counts the sum of all values for each key.
```python
pairs = [('b', 3), ('d', 4), ('a', 6), ('f', 1), ('e', 2)]
pairs_rdd = sc.parallelize(pairs).reduceByKey(lambda x,y: x+y)
```
""")
st.header("Time for the classic Hello world question !")
st.markdown(
"You know the drill. Edit `wordcount()` to count the number of occurences of each word."
)
test_wordcount(sc)
display_exercise_solved()
st.subheader("Question 2 - Joins")
with st.beta_expander("About joins"):
st.markdown("""
The `.join()` method joins two RDD of pairs together on their key element.
```python
genders_rdd = sc.parallelize([('1', 'M'), ('2', 'M'), ('3', 'F'), ('4', 'F'), ('5', 'F'), ('6', 'M')])
grades_rdd = sc.parallelize([('1', 5), ('2', 12), ('3', 7), ('4', 18), ('5', 9), ('6', 5)])
genders_rdd.join(grades_rdd)
```
""")
st.markdown("""
Let's give ourselves a `student-gender` RDD and a `student-grade` RDD.
Compute the mean grade for each gender.
""")
with st.beta_expander("Hint ?"):
st.markdown("""
_This is a long exercise._
Remember that the mean for a gender equals the sum of all grades
divided by the count of the number of grades.
You already know how to sum by key,
and you can use the `countByKey()` function for returning a hashmap of gender to count of grades,
then use that hashmap inside a map function to divide.
Good luck !
""")
test_mean_grade_per_gender()
display_goto_next_section()
def display_q2(sc: SparkContext):
st.title("Operations on RDDs")
with st.beta_expander("Introduction"):
st.markdown("""
RDDs have two sets of parallel operations:
* transformations : which return pointers to new RDDs without computing them, it rather waits for an action to compute itself.
* actions : which return values to the driver after running the computation. The `collect()` funcion is an operation which retrieves all elements of the distributed RDD to the driver.
RDD transformations are _lazy_ in a sense they do not compute their results immediately.
The following exercises study the usage of the most common Spark RDD operations.
""")
st.subheader("Question 1 - Map")
with st.beta_expander(".map() and flatMap() transformation"):
st.markdown("""
The `.map(function)` transformation applies the function given in argument to each of the elements
inside the RDD.
The following sums every number in the RDD by one, in a distributed manner.
```python
sc.parallelize([1,2,3]).map(lambda num: num+1)
```
The `.flatMap(function)` transformation applies the function given in argument to each of the elements
inside the RDD, then flattens the list so that there are no more nested elements inside it.
The following splits each line of the RDD by the comma and returns all numbers in a unique RDD.
```python
sc.parallelize(["1,2,3", "2,3,4", "4,5,3"]).flatMap(lambda csv_line: csv_line.split(","))
```
---
""")
st.markdown("""
What would be the result of:
```python
sc.parallelize(["1,2,3", "2,3,4", "4,5,3"]).map(lambda csv_line: csv_line.split(","))
```
?
---
""")
st.markdown("""
Suppose we have a RDD containing only lists of 2 elements :
```python
matrix = [[1,3], [2,5], [8,9]]
matrix_rdd = sc.parallelize(matrix)
```
This data structure is reminiscent of a matrix.
Edit the method `op1()` to multiply the first column (or first coordinate of each element)
of the matrix by 2, and removes 3 to the second column (second coordinate).
""")
test_op1(sc)
display_exercise_solved()
st.subheader("Question 2 - Extracting words from sentences")
st.markdown("""
Suppose we have a RDD containing sentences :
```python
sentences_rdd = sc.parallelize(
['Hi everybody', 'My name is Fanilo', 'and your name is Antoine everybody'
])
```
Edit `op2()` which returns all the words in the rdd, after splitting each sentence by the whitespace character.
""")
test_op2(sc)
display_exercise_solved()
st.subheader("Question 3 - Filtering")
st.markdown("""
The `.filter(function)` transformation let's us filter elements verify a certain function.
Suppose we have a RDD containing numbers.
Edit `op3()` to returns all the odd numbers.
""")
test_op3(sc)
display_exercise_solved()
st.subheader("Question 4 - Reduce")
with st.beta_expander("About reduce"):
st.markdown("""
The `.reduce(function)` transformation reduces all elements of the RDD into one
using a specific method.
This next example sums elements 2 by 2 in a distributed manner,
which will produce the sum of all elements in the RDD.
```python
sc.parallelize([1,2,3,4,5]).map(lambda x,y: x + y)
```
Do take note that, as in the Hadoop ecosystem, the function used to reduce the dataset
should be associative and commutative.
---
""")
st.markdown("""
Suppose we have a RDD containing numbers.
Create an operation `.op4()` which returns the sum of
all squared odd numbers in the RDD, using the `.reduce()` operation.
""")
test_op4(sc)
display_goto_next_section()
