sadamingh / apache-spark-practice

Apache-Spark-Practice

This is a practice problem set of Apache Spark.

1 Set Up the Environment

Before you start, you need to have,

• Python 3.8+
• PySpark 3.2.0+
• A Python IDE (e.g. Pycharm is recommended)

Then, clone this repository somewhere,

$ git clone https://github.com/Sadamingh/Apache-Spark-Practice.git

2 What to Do?

For each problem, what you have to do is to,

• Implement the start.py script by following the instructions in that script
• Make sure you followed the PEP8 pattern (or you won't pass the sanity test)

You can test the result by,

$ pytest -v start_test.py

A successful result should be something like,

For the solutions, you can refer to the solution.py file in each problem folder.

3. Spark SQL Questions

• How to transfer RDD to dataframes?

.toDF()

• How to transfer dataframes to RDD?

.rdd

• How to create a Spark session?

ss = SparkSession.builder.getOrCreate()

• How to create a Spark context from Spark session?

sc = ss.sparkContext

• What is the method we should call when transfer RDD to dataframes with schema?

createDataFrame

• How to show only the column with column_name?

select

• How to show the columns without column_name?

drop

4. Spark SQL R/W

• How to save the spark DataFrame as a table in the database?

use .write.saveAsTable()

• Suppose we have a spark dataframe df, then how to save it as a table named test in the database?

df.write.saveAsTable("test")

• What will be the content in the output? Suppose we have all the used variables defined.

ss = SparkSession.builder.getOrCreate()
df = ss.createDataFrame(business, business_schema)
df.write.saveAsTable('Business')
ss.stop()

ss = SparkSession.builder.getOrCreate()
ss.sql("select * from Business").show(5)
ss.stop()

Answer:

Error in line 7, because table path should be assigned.

• What will be the content in the output? Suppose we have all the used variables defined.

ss = SparkSession.builder.getOrCreate()
df = ss.createDataFrame(business, business_schema)
df.write.saveAsTable('Business')
ss.stop()

ss = SparkSession.builder.getOrCreate()
ss.sql("select * from parquet.`./spark-warehouse/Business`").show(5)
ss.stop()

Answer:

This code will print the first 5 lines of the table `Business`.

• What will be in the ./spark-warehouse directory after executing the following program?

ss = SparkSession.builder.getOrCreate()
df = ss.createDataFrame(business, business_schema)
df.write.saveAsTable('Business')
ss.sql("drop table IF EXISTS Business")
ss.stop()

Answer:

Empty

• How to read a json file with name ./Business.json and schema business_schema to spark dataframe df?

df = ss.read.json("./Business.json", business_schema)

• What's the data types in schema for the spark dataframe df after the following read?

df = ss.read.json("./Business.json")

Answer:

inferred data types

• How to read a csv file with name ./Business.csv and schema business_schema to spark dataframe df?

df = ss.read.csv("./Business.csv", business_schema)

• What's the data types in schema for the spark dataframe df after the following read?

df = ss.read.csv("./Business.json")

Answer:

strings

• What's the data types in schema for the spark dataframe df after the following read?

df = ss.read.csv("./Business.json", inferSchema=True)

Answer:

inferred data types

• How to read parquet files from directory ./spark-warehouse/Business and schema business_schema to spark dataframe df?

df = ss.read.parquet("./spark-warehouse/Business", business_schema)

• How to write a json file to path to/json/path from spark dataframe df?

df.write.json("to/json/path")

• How to write a json file to path to/csv/path from spark dataframe df?

df.write.csv("to/csv/path")

• How to write parquet files to path to/parquet/path from spark dataframe df?

df.write.parquet("to/parquet/path")

• How to write parquet files to path to/parquet/path from spark dataframe df named as Business?

df.write.option("path", "to/parquet/path").saveAsTable("Business")

• Suppose the default partition number is 3. Then how many files in the business_sf directory after executing the following code?

business_df.write.csv("business_sf")

Answer:

4, 3 with a _SUCCESS file

• Suppose the default partition number is 3. How many files in the business_sf directory after executing the following code?

business_df.coalesce(1).write.csv("business_sf")

Answer:

2

• Suppose the default partition number is 3. How many files in the business_sf directory after executing the following code?

business_df.coalesce(2).write.csv("business_sf")

Answer:

3

• Suppose the default partition number is 3. How many files in the business_sf directory after executing the following code?

business_df.coalesce(5).write.csv("business_sf")

Answer:

4

• Suppose the default partition number is 3. How many files in the business_sf directory after executing the following code?

business_df.repartition(2).write.csv("business_sf")

Answer:

3

• Suppose the default partition number is 3. How many files in the business_sf directory after executing the following code?

business_df.repartition(5).write.csv("business_sf")

Answer:

6

• To read from/write to s3 bucket or MongoDB using Spark, it requires extra Jar files.

Answer:

True

• How to read spark dataframe from s3a://example-test/mydata.json?

df = ss.read.json("s3a://example-test/mydata.json")

• How to write parquet files to spark dataframe df to s3a://example-test?

df.write.parquet("s3a://example-test/mydata")

• Which field should we modify if we want to read from mongodb+srv://...?

spark.mongodb.input.uri

• Which field should we modify if we want to write to mongodb+srv://...?

spark.mongodb.output.uri

• How to read spark dataframe from default MongoDB database?

df = spark.read.format('com.mongodb.spark.sql.DefaultSource').load()

• How to write spark dataframe df to the given uri with database db and collection business?

df.write.format("com.mongodb.spark.sql.DefaultSource")\
        .mode("overwrite")\
        .option("database","db")\
        .option("collection", "business")\
        .save()

5. Spark MLlib

• Spark ML are designed to run in parallel.

True

• What are the three components for spark ML?

transformers, estimators, evaluators

• What are the two Machine Learning API for Spark?

RDD-based, DataFrame-based

• What is the primary machine learning API for Spark now?

DataFrame-based

• Which of the following algorithm is not present in MLlib?
  • Streaming Linear Regression
  • Streaming KMeans
  • None of the above

None of the above

• What is the definition of a transformer in Spark MLlib?

A transformer is an algorithm that transforms one dataframe into another dataframe.

• What is the definition of an estimator in Spark MLlib?

An estimator is an algorithm that can be applied on a dataframe to produce a Transformer.

• What is the definition of pipeline in Spark MLlib?

A Pipeline chains multiple Transformers and Estimators together to specify an ML workflow.

• Give three examples of transformers.

VectorAssembler, or the estimator after fitting the data

• Give four examples of estimators.

StringIndexer, OneHotEncoder, KMeans, RandomForestClassifier, DecisionTreeClassifier, LogisticRegression

• Give two examples of evaluators.

BinaryClassificationEvaluator, MulticlassClassificationEvaluator, RegressionEvaluator, ClusteringEvaluator

• Why do we use .cache() for ML models?

We don't have to reload the data or processing the data for a second time.

• Suppose we have an evaluator eval and a model rf, and we are give the validation set valid_data. Then how can we know the default evaluation of eval with valid_data for rf?

eval.evaluate(rf.transform(valid_data))

• Suppose we have a transformer ohe and the training and validation data are given as train_data and valid_data. Then what will be the output of the following program?

ohe.fit(train_data)
valid_data = ohe.transform(valid_data)
valid_data.show(5)

Answer:

Error. We should store thee ohe as oheModel and use this model to transform.

oheModel = ohe.fit(train_data)
valid_data = oheModel.transform(valid_data)
valid_data.show(5)

• Suppose we have a spark dataframe df and we want to split it into training and testing set with a proportion of 8:2. Then which method should we called?

training, testing = df.randomSplit([0.8, 0.2])

• It makes sense to use StandardScaler for decision tree.

False

• It makes sense to use VectorAssembler for decision tree and logistic regression.

True

• Suppose we have the transforms work in the order of si , ohe , va. And we have a estimator of rf. Build a pipeline for showing for this workflow.

pipeline = Pipeline(stages=[si, ohe, va, rf])

• Suppose we have a pipeline estimator p. Then how to make predictions of valid_data based on train_data?

pModel = p.fit(train_data)
pred = pModel.transform(valid_data)

• Suppose we have a variable paramGrid = ParamGridBuilder() and a model dt. Then how to build a grid search of its attribute maxDepth with value of grid_list?

paramGridMap = paramGrid.addGrid(dt.maxDepth, grid_list).build()

• Kmeans prediction value is the same as the label.

False

• Spark MLlib algorithms are distributed.

True

6. Spark H2O

• H2O machine learning algorithms are distributed.

True

• H2O services on each node of a Spark cluster.

True

• What's the relationship between Sparkling water and Spark?

Sparkling water is the integration of H2O into spark.

• How to create a H2O context?

hc = H2OContext.getOrCreate()

• Which method should we call if we want to import a csv file with file_path as h2o dataframe?

h2o.import_file(file_path)

• What will be the column names for the output of the following code?

h2odf = h2o.import_file(file_path)
h2odf.show()

Answer:

C1, C2, ...

• What will be the column names for the output of the following code?

h2odf = h2o.import_file(file_path)
h2odf.set_names(col_names)

Answer:

col_names

• What will be the column names for the output of the following code?

h2odf = h2o.import_file(file_path)
h2odf = h2odf.set_names(col_names)

Answer:

col_names

• How can we convert a spark dataframe df to a h2o dataframe?

hc.asH2OFrame(df, frameName)

• How can we convert a h2o dataframe df to a spark dataframe?

hc.asSparkFrame(df)

• All the schma of the original Spark Dataframe is preserved when you convert to an h2o dataframe.

False

• What's Auto ML?

Auto ML means automatic training and tuning of many models within a time limit or model count.

• H2O autoML builds ensemble models as well.

True

• Suppose we have a H2O AutoML model model after training. Then which method should we call to see the rank of tried models?

model.leaderboard

• Suppose we have a H2O AutoML model model after training. Then which method should we call to see the details of best tried models?

model.leaderboard.get_best_model()

• How to see the performance of a H2O model model on the validation set val?

model.explain(val)

• How to get the predictions of a H2O model model on the validation set val?

model.predict(val)