15 minute read

Spark Certification Study Guide - Part 2 (Application)

We now start diving deeper into code, and looking at specific applications of Spark to illustrate some fundamental use cases.



Once the driver is started, it configures an instance of SparkContext. Your Spark context is already preconfigured and available as the variable sc. When running a standalone Spark application by submitting a jar file, or by using Spark API from another program, your Spark application starts and configures the Spark context (i.e. Databricks).

There is usually one Spark context per JVM. Although the configuration option spark.driver.allowMultipleContexts exists, it’s misleading because usage of multiple Spark contexts is discouraged. This option is used only for Spark internal tests and we recommend you don’t use that option in your user programs. If you do, you may get unexpected results while running more than one Spark context in a single JVM.

SparkContext to control basic configuration settings such as spark.sql.shuffle.partitions

  • spark.sql.shuffle.partitions: Configures the number of partitions to use when shuffling data for joins or aggregations.
  • spark.executor.memory: Amount of memory to use per executor process, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. 512m, 2g).
  • spark.default.parallelism: Default number of partitions in RDDs returned by transformations like join, reduceByKey, and parallelize when not set by user. Note that this is ignored for DataFrames, and we can use df.repartition(numOfPartitions) instead.

Let's explore how to set the value of spark.sql.shuffle.partitions and spark.executor.memory using PySpark and SQL syntax.

# Print the default values of shuffle partition and the executor memory
print(spark.conf.get("spark.sql.shuffle.partitions"), ",", spark.conf.get("spark.executor.memory"))
# Set the number of shuffle partitions to 6
spark.conf.set("spark.sql.shuffle.partitions", 6)
# Set the memory of executors to 2 GB
spark.conf.set("spark.executor.memory", "2g")
# Print the values of the shuffle partition and the executor memory
print(spark.conf.get("spark.sql.shuffle.partitions"), ",", spark.conf.get("spark.executor.memory"))
SET spark.sql.shuffle.partitions = 200;
SET spark.executor.memory = 7284m;


Create a DataFrame/Dataset from a collection (e.g. list or set)

# import relevant modules
from pyspark.sql import *
from pyspark.sql.types import *
from pyspark.sql.functions import *
from pyspark import *
from pyspark import StorageLevel
import sys

Example: Create DataFrame from list with DataType specified

list_df = spark.createDataFrame([1, 2, 3, 4], IntegerType())

Example: Create DataFrame from Row

class pyspark.sql.Row

A row in DataFrame. The fields in it can be accessed:

  • like attributes (row.key)
  • like dictionary values (row[key])

In this scenario we have two tables to be joined employee and department. Both tables contains only a few records, but we need to join them to get to know the department of each employee. So, we join them using Spark DataFrames like this:

# Create Example Data - Departments and Employees

# Create the Employees
Employee = Row("name") # Define the Row `Employee' with one column/key
employee1 = Employee('Bob') # Define against the Row 'Employee'
employee2 = Employee('Sam') # Define against the Row 'Employee'

# Create the Departments
Department = Row("name", "department") # Define the Row `Department' with two columns/keys
department1 = Department('Bob', 'Accounts') # Define against the Row 'Department'
department2 = Department('Alice', 'Sales') # Define against the Row 'Department'
department3 = Department('Sam', 'HR') # Define against the Row 'Department'

# Create DataFrames from rows
employeeDF = spark.createDataFrame([employee1, employee2])
departmentDF = spark.createDataFrame([department1, department2, department3])

# Join employeeDF to departmentDF on "name"
display(employeeDF.join(departmentDF, "name"))

Example: Create DataFrame from Row, with Schema specified

createDataFrame(data, schema=None, samplingRatio=None, verifySchema=True)

Creates a DataFrame from an RDD, a list or a pandas.DataFrame. When schema is a list of column names, the type of each column will be inferred from data. When schema is None, it will try to infer the schema (column names and types) from data, which should be an RDD of Row, or namedtuple, or dict.

schema = StructType([
  StructField("letter", StringType(), True),
  StructField("position", IntegerType(), True)])

df = spark.createDataFrame([('A', 0),('B', 1),('C', 2)], schema)

Example: Create DataFrame from a list of Rows

# Create Example Data - Departments and Employees

# Create the Departments
Department = Row("id", "name")
department1 = Department('123456', 'Computer Science')
department2 = Department('789012', 'Mechanical Engineering')
department3 = Department('345678', 'Theater and Drama')
department4 = Department('901234', 'Indoor Recreation')
department5 = Department('000000', 'All Students')

# Create the Employees
Employee = Row("firstName", "lastName", "email", "salary")
employee1 = Employee('michael', 'armbrust', 'no-reply@berkeley.edu', 100000)
employee2 = Employee('xiangrui', 'meng', 'no-reply@stanford.edu', 120000)
employee3 = Employee('matei', None, 'no-reply@waterloo.edu', 140000)
employee4 = Employee(None, 'wendell', 'no-reply@berkeley.edu', 160000)
employee5 = Employee('michael', 'jackson', 'no-reply@neverla.nd', 80000)

# Create the DepartmentWithEmployees instances from Departments and Employees
DepartmentWithEmployees = Row("department", "employees")
departmentWithEmployees1 = DepartmentWithEmployees(department1, [employee1, employee2])
departmentWithEmployees2 = DepartmentWithEmployees(department2, [employee3, employee4])
departmentWithEmployees3 = DepartmentWithEmployees(department3, [employee5, employee4])
departmentWithEmployees4 = DepartmentWithEmployees(department4, [employee2, employee3])
departmentWithEmployees5 = DepartmentWithEmployees(department5, [employee1, employee2, employee3, employee4, employee5])

departmentsWithEmployeesSeq1 = [departmentWithEmployees1, departmentWithEmployees2, departmentWithEmployees3, departmentWithEmployees4, departmentWithEmployees5]
df1 = spark.createDataFrame(departmentsWithEmployeesSeq1)


Create a DataFrame for a range of numbers

range(start, end=None, step=1, numPartitions=None)

Create a DataFrame with single pyspark.sql.types.LongType column named id, containing elements in a range from start to end (exclusive) with step value step.


  • start – the start value
  • end – the end value (exclusive)
  • step – the incremental step (default: 1)
  • numPartitions – the number of partitions of the DataFrame


  • DataFrame

For example:

df = spark.range(1,8,2).toDF("number")
df = spark.range(5).toDF("number")

Access the DataFrameReaders

DataFrameReader — Loading Data From External Data Sources

class pyspark.sql.DataFrameReader(spark)

Interface used to load a DataFrame from external storage systems (e.g. file systems, key-value stores, etc). Use spark.read() to access this.

Before we start with using DataFrameReaders, let's mount a storage container that will contain different files for us to use.

# Localize with Storage Account name and key
storageAccountName = "storage--acount--name"
storageAccountAccessKey = "storage--acount--key"

# Function to mount a storage container
def mountStorageContainer(storageAccount, storageAccountKey, storageContainer, blobMountPoint):
  print("Mounting {0} to {1}:".format(storageContainer, blobMountPoint))
  # Attempt mount only if the storage container is not already mounted at the mount point
  if not any(mount.mountPoint == blobMountPoint for mount in dbutils.fs.mounts()):
    print("....Container is not mounted; Attempting mounting now..")
    mountStatus = dbutils.fs.mount(
                  source = "wasbs://{0}@{1}.blob.core.windows.net/".format(storageContainer, storageAccount),
                  mount_point = blobMountPoint,
                  extra_configs = {"fs.azure.account.key.{0}.blob.core.windows.net".format(storageAccount): storageAccountKey})
    print("....Status of mount is: " + str(mountStatus))
    print("....Container is already mounted.")
    print() # Provide a blank line between mounts

# Mount "bronze" storage container

# Display directory

We read in the auto-mpg.csv with a simple spark.read.csv command.

Note: This particular csv doesn't have a header specified.

df = spark.read.csv("/mnt/GoFast/bronze/auto-mpg.csv", header=False, inferSchema=True)

Register User Defined Functions (UDFs)

If we have a function that can use values from a row in the dataframe as input, then we can map it to the entire dataframe. The only difference is that with PySpark UDFs we have to specify the output data type.

We first define the udf below:

# Define UDF
def square(s):
  return s * s

# Register UDF to spark as 'squaredWithPython'
spark.udf.register("squaredWithPython", square, LongType())

Note that we can call squaredWithPython immediately from SparkSQL. We first register a temp table called 'table':

# Register temptable with range of numbers
spark.range(0, 19, 3).toDF("num").createOrReplaceTempView("table")
SELECT num, squaredWithPython(num) as num_sq FROM table

But note that with DataFrames, this will not work until we define the UDF explicitly (on top of registering it above) with the respective return DataType. In other words, when we call spark.udf.register above, that registers it with spark SQL only, and for DataFrame it must be explicitly defined as an UDF.

# Convert temp table to DataFrame
tabledf = spark.table("table")

# Define UDF
squaredWithPython = udf(square, LongType())

display(tabledf.select("num", squaredWithPython("num").alias("num_sq")))


Read data for the “core” data formats (CSV, JSON, JDBC, ORC, Parquet, text and tables)


Let's read airbnb-sf-listings.csv from our mount, with the header specified, and infer schema on read.

csvdf = spark.read.csv("/mnt/GoFast/bronze/airbnb-sf-listings.csv", header=True, inferSchema=True)


Let's first view zip.json from our mount, and then load it into a DataFrame.

%fs head "dbfs:/mnt/GoFast/bronze/zips.json"
jsondf = spark.read.json("/mnt/GoFast/bronze/zips.json")


We are going to be reading a table from this Azure SQL Database for this activity.

Azure SQL DB

Azure SQL DB
Azure SQL DB

Reading table in SQL Server Management Studio

SQL Query on SSMS
SQL Query on SSMS

Set up JDBC connection:

jdbcUsername = "your--SQL--username"
jdbcPassword = "your--SQL--password"
driverClass = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
jdbcHostname = "your-sql-svr.database.windows.net"
jdbcPort = 1433
jdbcDatabase = "your-sql-db"

# Create the JDBC URL without passing in the user and password parameters.
jdbcUrl = "jdbc:sqlserver://{0}:{1};database={2}".format(jdbcHostname, jdbcPort, jdbcDatabase)

# Create a Properties() object to hold the parameters.
connectionProperties = {
  "user" : jdbcUsername,
  "password" : jdbcPassword,
  "driver" : driverClass

Run Query against JDBC connection:

pushdown_query = "(SELECT * FROM SalesLT.Customer) Customers"
jdbcdf = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)


The Optimized Row Columnar (ORC) file format provides a highly efficient way to store Hive data. It was designed to overcome limitations of the other Hive file formats. Using ORC files improves performance when Hive is reading, writing, and processing data.

Let's read TestVectorOrcFile.testLzo.orc from our mount.

orcdf = spark.read.orc('/mnt/GoFast/bronze/TestVectorOrcFile.testLzo.orc')


Apache Parquet is a free and open-source column-oriented data storage format of the Apache Hadoop ecosystem. It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.

Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons.

Let's read wine.parquet from our mount.

parquetDF = spark.read.parquet("/mnt/GoFast/bronze/wine.parquet")


Let's read tweets.txt from our mount, then use a similar command as a csv earlier to load into DataFrame.

%fs head "dbfs:/mnt/GoFast/bronze/tweets.txt"
# Delimit on '='
txtdf = (spark.read
         .option("header", "false")
         .option("delimiter", "=")

# Display "Tweet" Column only


Let's read from one of our default Databricks Tables from the Hive Metastore:

tabledf = spark.sql("""SELECT * FROM databricks.citydata""")

How to configure options for specific formats

Let's read searose-env.csv from our mount, specify the schema, header and delimiter.

# Schema
Schema = StructType([
    StructField("UTC_date_time", StringType(), True),
    StructField("Unix_UTC_timestamp", FloatType(), True),
    StructField("latitude", FloatType(), True),
    StructField("longitude", FloatType(), True),
    StructField("callsign", StringType(), True),
    StructField("wind_from", StringType(), True),
    StructField("knots", StringType(), True),
    StructField("gust", StringType(), True),
    StructField("barometer", StringType(), True),
    StructField("air_temp", StringType(), True),
    StructField("dew_point", StringType(), True),
    StructField("water_temp", StringType(), True)

WeatherDF = (spark.read                                     # The DataFrameReader
             .option("header", "true")                      # Use first line of file as header
             .schema(Schema)                                # Enforce Schema
             .option("delimiter", ",")                      # Set delimiter to ,
             .csv("/mnt/GoFast/bronze/searose-env.csv")     # Creates a DataFrame from CSV after reading in the file


How to read data from non-core formats using format() and load()

We can read the same file above searose-env.csv with .format() and .load() syntax - as well as other "non-core" files.

Weather2DF = (spark.read
              .option("delimiter", ",")

How to specify a DDL-formatted schema

Data Definition Language (DDL) is a standard for commands that define the different structures in a database. DDL statements create, modify, and remove database objects such as tables, indexes, and users. Common DDL statements are CREATE, ALTER, and DROP.

We want to be able to specify our Schema in a DDL format prior to reading a file. Let's try this for our searose-env.csv above

SchemaDDL = """UTC_date_time VARCHAR(255),
               Unix_UTC_timestamp float,
               latitude float,
               longitude float,
               callsign VARCHAR(255),
               wind_from VARCHAR(255),
               knots VARCHAR(255),
               gust VARCHAR(255),
               barometer VARCHAR(255),
               air_temp VARCHAR(255),
               dew_point VARCHAR(255),
               water_temp VARCHAR(255)"""

Weather3DF = spark.read.csv('/mnt/GoFast/bronze/searose-env.csv', header=True, schema=SchemaDDL)

How to construct and specify a schema using the StructType classes

We've done this above.


Write data to the “core” data formats (csv, json, jdbc, orc, parquet, text and tables)

Let's create a DataFrame CustomerAddressDF from the table SalesLT.CustomerAddress in our Azure SQL Database. We will then write this to various different formats to our Storage Account's silver Container.

# Mount "silver" storage container
pushdown_query = "(SELECT * FROM SalesLT.CustomerAddress) CustomerAddresses"
CustomerAddressDF = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)


# Clean up directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/CSV"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path with header

We see this file in our Storage Account:

Committed CSV file
Committed CSV file

And the contents are equal to the length of the data received from Azure SQL:

CSV file contents
CSV file contents


# Clean up directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/JSON"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path


This time, we do an overwrite to our Azure SQL Database Table, by restoring CustomerAddresses to a DataFrame from our bucket.

# Restore a backup of our DataFrame from Silver Zone
CustomerAddressBackupDF = spark.read.csv("/mnt/GoFast/silver/CustomerAddresses/CSV", header=True, inferSchema=True)

# Create temporary view

# Perform overwrite on SQL table
spark.sql("""SELECT * FROM CustomerAddressView""").write \
    .format("jdbc") \
    .mode("overwrite")  \
    .option("url", jdbcUrl) \
    .option("dbtable", "SalesLT.CustomerAddress") \
    .option("user", jdbcUsername) \
    .option("password", jdbcPassword) \


# Clean up directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/ORC"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path


# Clean up directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/Parquet"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path


# Clean up directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/Text"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Note that text file does not support `int` data type, and also expects one column, so we must convert to one '|'' seperated string
CustomerAddressConcatDF = CustomerAddressDF \
                                     .withColumn("CustomerID", col("CustomerID").cast("string")) \
                                     .withColumn("AddressID", col("AddressID").cast("string")) \
                                     .withColumn("ModifiedDate", col("ModifiedDate").cast("string")) \
                                     .withColumn("Concatenated", concat(col("CustomerID"), lit('|'), \
                                                                        col("AddressID"), lit('|'), \
                                                                        col("AddressType"), lit('|'), \
                                                                        col("rowguid"), lit('|'), \
                                                                        col("ModifiedDate"))) \
                                     .drop(col("CustomerID")) \
                                     .drop(col("AddressID")) \
                                     .drop(col("AddressType")) \
                                     .drop(col("rowguid")) \

# Write DataFrame to path


Let's create a Hive table usign Parquet (location above).

USE databricks;

USING parquet
OPTIONS  (path "/mnt/GoFast/silver/CustomerAddresses/Parquet");

SELECT COUNT(*) FROM databricks.CustomerAddress

Overwriting existing files

We've already achieved this above when persisting a DataFrame to mount, by using .mode("overwrite") - we also do this below.

How to configure options for specific formats

Previously, we wrote a CSV file with the CustomerAddress data that was , seperated. Let's overwrite that file with a | seperated version now.


Pipe seperated CSV
Pipe seperated CSV

# Specify CSV directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/CSV"

# Write DataFrame to path with header and seperator
CustomerAddressDF.write.format("csv").option("header","true").option("delimiter", "|").mode("overwrite").save(dbfsDirPath)

How to write a data source to 1 single file or N separate files

Difference between coalesce and repartition

repartition() literally reshuffles the data to form as many partitions as we specify, i.e. the number of partitions can be increased/decreased. Whereas with coalesce() we avoid data movement and use the existing partitions, meaning the number of partitions can only be decreased.

Note that coalesce() results in partitions with different amounts of data per partition, whereas repartition() is distributed evenly. As a result, the coalesce() operation may run faster than repartition(), but the partitions themselves may work slower further on because Spark is built to work with equal sized partitions across the task slots on the executors.

50 partitions:

50 partitions
50 partitions
10 partitions:
10 partitions
10 partitions
1 partition:
1 partitions
1 partitions

# Clean up directory for 50 part CSV
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/CSV-50"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Redefine DataFrame with 50 partitions
CustomerAddressDF = CustomerAddressDF.repartition(50)

# Write DataFrame to path with header and repartition to 50 partitions

# Clean up directory for 10 part CSV
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/CSV-10"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path with header and coalesce to 10 partitions

# Clean up directory for 1 part CSV
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/CSV-1"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path with header and coalesce to 1 partition

How to write partitioned data

Partitioning by Columns

Partitioning uses partitioning columns to divide a dataset into smaller chunks (based on the values of certain columns) that will be written into separate directories.

With a partitioned dataset, Spark SQL can load only the parts (partitions) that are really needed (and avoid doing filtering out unnecessary data on JVM). That leads to faster load time and more efficient memory consumption which gives a better performance overall.

With a partitioned dataset, Spark SQL can also be executed over different subsets (directories) in parallel at the same time.

Let's partition by AddressType and write to storage on our earlier CustomerAddressDF dataframe:

# Clean up directory
dbfsDirPath = "/mnt/GoFast/silver/CustomerAddresses/CSV-partitioned-by-AddressType"
dbutils.fs.rm(dbfsDirPath, recurse=True)

# Write DataFrame to path with header and partition by AddressType

And we see:

Partitioned by Address
Partitioned by Address

How to bucket data by a given set of columns

What is Bucketing?

Bucketing is an optimization technique that uses buckets (and bucketing columns) to determine data partitioning and avoid data shuffle.

The motivation is to optimize performance of a join query by avoiding shuffles (aka exchanges) of tables participating in the join. Bucketing results in fewer exchanges (and so stages).

# Note that this is only supported to a table (and not to a location)
CustomerAddressDF.write \
  .mode("overwrite") \
  .bucketBy(10, "ModifiedDate") \

display(sql('''SELECT * FROM CustomerAddress_bucketed'''))


Have a working understanding of every action such as take()collect(), and foreach()

Let's demo the following table of Actions on our DataFrame:

collect()CollectionReturns an array that contains all of Rows in this Dataset.
count()LongReturns the number of rows in the Dataset.
first()RowReturns the first row.
foreach(f)-Applies a function f to all rows.
foreachPartition(f)-Applies a function f to each partition of this Dataset.
head()RowReturns the first row.
reduce(f)RowReduces the elements of this Dataset using the specified binary function.
show(..)-Displays the top 20 rows of Dataset in a tabular form.
take(n)CollectionReturns the first n rows in the Dataset.
toLocalIterator()IteratorReturn an iterator that contains all of Rows in this Dataset.

Note once again that while Transformations always return a DataFrame, Actions either return a result or write to disk.


Return a list that contains all of the elements in this RDD.

Note: This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver’s memory.

Array = CustomerAddressDF.collect()


Return the number of elements in this RDD.



Return the first element in this RDD/DataFrame.



Applies a function f to all elements of this RDD/DataFrame.

Note: RDD.foreach method runs on the cluster for each worker, and so we don't see the print output.

def f(x): print(x)


Applies a function to each partition of this RDD.

Note: RDD.foreachPartition method runs on the cluster for each worker, and so we don't see the print output.

def f(x): print(x)


Returns the first row.

At first glance, this looks identical to first() - let's look at the difference:

Sorted Data

If your data is sorted using either sort() or ORDER BY, these operations will be deterministic and return either the 1st element using first()/head() or the top-n using head(n)/take(n).

show()/show(n) return Unit (void) and will print up to the first 20 rows in a tabular form.

These operations may require a shuffle if there are any aggregations, joins, or sorts in the underlying query.

Unsorted Data

If the data is not sorted, these operations are not guaranteed to return the 1st or top-n elements - and a shuffle may not be required.

show()/show(n) return Unit (void) and will print up to 20 rows in a tabular form and in no particular order.

If no shuffle is required (no aggregations, joins, or sorts), these operations will be optimized to inspect enough partitions to satisfy the operation - likely a much smaller subset of the overall partitions of the dataset.



Reduces the elements of this RDD (note that it doesn't work on DataFrames) using the specified commutative and associative binary operator. Currently reduces partitions locally.

from operator import add
sc.parallelize([2, 4, 6]).reduce(add)


Displays the top 20 (default, can be overwritten) rows of Dataset in a tabular form



Take the first n elements of the RDD.

It works by first scanning one partition, and using the results from that partition to estimate the number of additional partitions needed to satisfy the limit.

Note: this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver’s memory.



Return an iterator that contains all of the elements in this RDD/DataFrame. The iterator will consume as much memory as the largest partition in this RDD.

[x for x in CustomerAddressDF.toLocalIterator()]

Have a working understanding of the various transformations and how they work

Such as producing a distinct set, filtering data, repartitioning and coalesceing, performing joins and unions as well as producing aggregates.


Projects a set of expressions and returns a new DataFrame.


  • cols – list of column names (string) or expressions (Column). If one of the column names is *, that column is expanded to include all columns in the current DataFrame.

Using our CustomerAddressDF from above, let's select out a couple of the rows only.

TruncatedDF = CustomerAddressDF.select("CustomerID", "rowguid")


Returns a new DataFrame containing the distinct rows in this DataFrame.

Let's create a DF with duplicates, and then run distinct on it.

DuplicateDF = TruncatedDF.withColumn("CustomerID", lit(29772)).withColumn("rowguid", lit("BF40660E-40B6-495B-99D0-753CE987B1D1"))


Groups the DataFrame using the specified columns, so we can run aggregation on them. See GroupedData for all the available aggregate functions.

groupby() is an alias for groupBy().


  • cols – list of columns to group by. Each element should be a column name (string) or an expression (Column).

Note: groupBy by itself doesn't return anything quantitative, we also have to agg by an aggregation function to get a tangible result back.

CustomerAddressAggDF = CustomerAddressDF.groupBy("AddressType").agg({'AddressType':'count'})


Compute the sum for each numeric columns for each group.


  • cols – list of column names (string). Non-numeric columns are ignored.
# Let's rename the column "count(AddressType)"
CustomerAddressAggDF2 = CustomerAddressAggDF.withColumn("AddressTypeCount", col("count(AddressType)")).drop(col("count(AddressType)"))

# We do a groupBy followed by a sum - to ultimately get back our number of rows in the original DataFrame

orderBy(*cols, *kwargs)

Returns a new DataFrame sorted by the specified column(s).


  • cols – list of Column or column names to sort by.
  • ascending – boolean or list of boolean (default True). Sort ascending vs. descending. Specify list for multiple sort orders. If a list is specified, length of the list must equal length of the cols.
display(CustomerAddressDF.orderBy("ModifiedDate", ascending = 0))


Filters rows using the given condition.

where() is an alias for filter().


  • condition – a Column of types.BooleanType or a string of SQL expression.
display(CustomerAddressDF.filter("AddressID = 484"))


Limits the result count to the number specified.


partition() and coalesce

We already discussed this in detail earlier.

join(other, on=None, how=None)

Joins with another DataFrame, using the given join expression.


  • other – Right side of the join
  • on – a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. If on is a string or a list of strings indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join.
  • how – str, default inner. Must be one of: inner, cross, outer, full, full_outer, left, left_outer, right, right_outer, left_semi, and left_anti.
# Let's query Azure SQL directly DataFrame we want to recreate
pushdown_query = """(SELECT DISTINCT C.CompanyName, A.City
                    FROM [SalesLT].[Customer] C
                    INNER JOIN [SalesLT].[CustomerAddress] CA ON C.CustomerID = CA.CustomerID
                    INNER JOIN [SalesLT].[Address] A ON CA.AddressID = A.AddressID
                    WHERE CA.AddressID IS NOT NULL AND A.AddressLine1 IS NOT NULL) CompanyAndAddress"""

CompanyAndAddressDF = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)
display(CompanyAndAddressDF.orderBy("CompanyName", ascending = 1))
# Get the underlying Tables as DataFrames from Azure SQL
pushdown_query = "(SELECT CustomerID, CompanyName FROM SalesLT.Customer) Customer"
CustomerDF = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)

pushdown_query = "(SELECT CustomerID, AddressID FROM SalesLT.CustomerAddress) CustomerAddress"
CustomerAddressDF = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)

pushdown_query = "(SELECT AddressID,City FROM SalesLT.Address) Address"
AddressDF = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)
# Perform joins identical to goal DataFrame above
display(CustomerDF.join(CustomerAddressDF, CustomerDF.CustomerID == CustomerAddressDF.CustomerID, 'inner') \
                  .join(AddressDF, CustomerAddressDF.AddressID == AddressDF.AddressID, 'inner') \
                  .select(CustomerDF.CompanyName, AddressDF.City) \
                  .orderBy("CompanyName", ascending = 1) \

And we get back the same DataFrame as CompanyAndAddressDF from our above SQL query.


Return a new DataFrame containing union of rows in this and another frame.

This is equivalent to UNION ALL in SQL. To do a SQL-style set union (that does deduplication of elements), use this function followed by distinct().

Also as standard in SQL, this function resolves columns by position (not by name).

# Create two DataFrames with fruit lists, with some multiple occurences

df1 = spark.createDataFrame(["apple", "orange", "apple", "mango"], StringType())
df2 = spark.createDataFrame(["cherries", "orange", "blueberry", "apple"], StringType())

# Perform union and display data

df3 = df1.union(df2)


Compute aggregates and returns the result as a DataFrame.

The available aggregate functions can be:

  1. built-in aggregation functions, such as avg, max, min, sum, count
  2. group aggregate pandas UDFs, created with pyspark.sql.functions.pandas_udf()

Note: There is no partial aggregation with group aggregate UDFs, i.e., a full shuffle is required. Also, all the data of a group will be loaded into memory, so the user should be aware of the potential OOM risk if data is skewed and certain groups are too large to fit in memory.

# Perform aggregation on `count` of the fruit names - i.e. the number of occurences in the union list per fruit.
display(df3.groupby("value").agg({'value':'count'}).orderBy("value", ascending = 1))

Know how to cache data, specifically to disk, memory or both

Caching in Spark

As we discussed earlier, Spark supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small “hot” dataset (required in many operations) or when running an iterative algorithm.

Here are some relevant functions:


Persists the DataFrame with the default storage level (MEMORY_AND_DISK). persist() without an argument is equivalent with cache().


Marks the DataFrame as non-persistent, and remove all blocks for it from memory and disk.


Sets the storage level to persist the contents of the DataFrame across operations after the first time it is computed. This can only be used to assign a new storage level if the DataFrame does not have a storage level set yet. If no storage level is specified defaults to (MEMORY_AND_DISK).

As we see below, there are 4 caching levels that can be fine-tuned with persist(). Let's look into the options available in more detail.

Storage LevelEquivalentDescription
MEMORY_ONLYStorageLevel(False, True, False, False, 1)Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will not be cached and will be recomputed on the fly each time they're needed. This is the default level.
MEMORY_AND_DISKStorageLevel(True, True, False, False, 1)Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the partitions that don't fit on disk, and read them from there when they're needed.
DISK_ONLYStorageLevel(True, False, False, False, 1)Store the RDD partitions only on disk.
MEMORY_ONLY_2StorageLevel(False, True, False, False, 2)Same as the levels above, but replicate each partition on two cluster nodes.
MEMORY_AND_DISK_2StorageLevel(True, True, False, False, 2)Same as the levels above, but replicate each partition on two cluster nodes.
OFF_HEAPStorageLevel(True, True, True, False, 1)Similar to MEMORY_ONLY_SER, but store the data in off-heap memory. This requires off-heap memory to be enabled.

As a simple example, let’s mark our CompanyAndAddressDF dataset to be cached. While it may seem silly to use Spark to explore and cache a small DataFrame, the interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes.

# Check the cache level before we do anything

# Let's cache the DataFrame with default - MEMORY_AND_DISK, and check the cache level

# Unpersist and proceed to Memory Only Cache
CompanyAndAddressDF.persist(storageLevel = StorageLevel.MEMORY_ONLY).storageLevel

# Unpersist and proceed to Memory Only Cache
CompanyAndAddressDF.persist(storageLevel = StorageLevel.MEMORY_ONLY).storageLevel

And so on.

Know how to uncache previously cached data

# Let's uncache the DataFrame

# Check the Cache level

Converting a DataFrame to a global or temp view.

Global Temporary View vs. Temporary View Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it terminates. If you want to have a temporary view that is shared among all sessions and keep alive until the Spark application terminates, you can create a global temporary view. Global temporary view is tied to a system preserved database global_temp, and we must use the qualified name to refer it, e.g. SELECT * FROM global_temp.view1. We use these two commands:

  • Global Temp View: df.createOrReplaceGlobalTempView("tempViewName") creates a global temporary view with this dataframe df. Lifetime of this view is dependent to spark application itself, if you want to drop this view: spark.catalog.dropGlobalTempView("tempViewName")
  • Temp View: df.createOrReplaceTempView("tempViewName") creates or replaces a local temporary view with this dataframe df. Lifetime of this view is dependent to SparkSession class, if you want to drop this view: spark.catalog.dropTempView("tempViewName")
# Create Global Temporary View
display(spark.sql("SELECT * FROM global_temp.CompanyAndAddressGlobal"))

# Create SQL Temporary View
display(spark.sql("SELECT * FROM CompanyAndAddressTemp"))

# Drop Temporary Views

Applying hints

Structured queries can be optimized using Hint Framework that allows for specifying query hints.

Query hints allow for annotating a query and give a hint to the query optimizer how to optimize logical plans. This can be very useful when the query optimizer cannot make optimal decision, e.g. with respect to join methods due to conservativeness or the lack of proper statistics.

Spark SQL supports COALESCE and REPARTITION and BROADCAST hints. All remaining unresolved hints are silently removed from a query plan at analysis.

display(CustomerDF.join(CustomerAddressDF.hint("broadcast"), "CustomerID"))

Row & Column

We can easily get back a single Column or Row and perform manipulations in Spark. For example, with our DataFrame CustomerDF:

# Extract First Row
display(CustomerDF.filter("CompanyName == 'A Bike Store' and CustomerID == 1"))

# Extract Distinct Second Column 'CompanyName', Not like "A **** ****", Order By descending, Pick Top 5
display(CustomerDF.select("CompanyName").filter("CompanyName not like 'A%'").distinct().orderBy("CompanyName", ascending = 1).take(5))

Spark SQL Functions

Let's use this Databricks table databricks.citydata for this section wherever applicable:

SELECT * FROM databricks.citydata

Aggregate functions: getting the first or last item from an array or computing the min and max values of a column.

first(expr[, isIgnoreNull])

Returns the first value of expr for a group of rows. If isIgnoreNull is true, returns only non-null values.

SELECT first(state), last(state) FROM
  (SELECT state FROM  databricks.citydata
  ORDER BY state ASC)


Aggregate function: returns the minimum value of the expression in a group.

SELECT min(estPopulation2016), max(estPopulation2016) FROM
  (SELECT estPopulation2016 FROM  databricks.citydata)

Collection functions: testing if an array contains a value, explodeing or flattening data

SELECT array_contains(array('a','b','c','d'), 'a') AS contains_a;
SELECT explode(array('a','b','c','d'));
SELECT flatten(array(array(1, 2), array(3, 4)));

Date time functions: parsing strings into timestamps or formatting timestamps into strings

to_date(date_str[, fmt])

Parses the date_str expression with the fmt expression to a date. Returns null with invalid input. By default, it follows casting rules to a date if the fmt is omitted.

SELECT to_date('2009-07-30 04:17:52') AS date, to_date('2016-12-31', 'yyyy-MM-dd') AS date_formatted

to_timestamp(timestamp[, fmt])

Parses the timestamp expression with the fmt expression to a timestamp. Returns null with invalid input. By default, it follows casting rules to a timestamp if the fmt is omitted.

SELECT to_timestamp('2016-12-31 00:12:00') AS timestamp, to_timestamp('2016-12-31', 'yyyy-MM-dd') AS timestamp_formatted;

date_format(timestamp, fmt)

Converts timestamp to a value of string in the format specified by the date format fmt.

SELECT date_format('2016-12-31T00:12:00.000+0000', 'y') AS year_only

Math functions: computing the cosign, floor or log of a number


Returns the cosine of expr.

SELECT cos(0) AS cos


Returns the largest integer not greater than expr.

SELECT floor(5.9) AS floored

log(base, expr)

Returns the logarithm of expr with base.

SELECT log(2, 8) AS log_2

Misc functions: converting a value to crc32, md5, sha1 or sha2


Returns a cyclic redundancy check value of the expr as a bigint.

SELECT crc32('hello')


Returns an MD5 128-bit checksum as a hex string of expr.

SELECT md5('hello')


Returns a sha1 hash value as a hex string of the expr.

SELECT sha1('hello')

sha2(expr, bitLength)

Returns a checksum of SHA-2 family as a hex string of expr. SHA-224, SHA-256, SHA-384, and SHA-512 are supported. Bit length of 0 is equivalent to 256.

SELECT sha2('hello', 256)

Non-aggregate functions: creating an array, testing if a column is null, not-null, nan, etc

array(expr, ...)

Returns an array with the given elements.

SELECT array(1, 2, 3) AS array


Returns true if expr is null, or false otherwise.

SELECT isnull(population2010) FROM databricks.citydata
limit 5


Returns true if expr is not null, or false otherwise.

SELECT isnotnull(population2010) FROM databricks.citydata
limit 5

Difference between null and NaN

null values represents "no value" or "nothing", it's not even an empty string or zero. It can be used to represent that nothing useful exists.

NaN stands for "Not a Number", it's usually the result of a mathematical operation that doesn't make sense, e.g. 0.0/0.0.


Returns true if expr is NaN, or false otherwise.

SELECT cast('NaN' as double), isnan(cast('NaN' as double)), cast('hello' as double), isnan(cast('hello' as double))

Sorting functions: sorting data in descending order, ascending order, and sorting with proper null handling

Let's use our WeatherDF for this section:

# We replace the `NULL` in column `gust` and `air_temp` above with `null`, and typecast to float
WeatherDFtemp = WeatherDF.withColumn("gust",when((ltrim(col("gust")) == "NULL"),lit(None)).otherwise(col("gust").cast("float"))) \
                     .withColumn("air_temp",when((ltrim(col("air_temp")) == "NULL"),lit(None)).otherwise(col("air_temp").cast("float")))

# Create temp SQL view

We demonstrate:

  • Filter for values where gust is not null and air_temp is not null
  • Order by gust ascending
  • Order by air_temp descending
SELECT gust, air_temp
FROM Weather
WHERE isnotnull(gust) AND isnotnull(air_temp)
ORDER BY gust ASC, air_temp DESC

String functions: applying a provided regular expression, trimming string and extracting substrings

regexp_extract(str, regexp[, idx])

Extracts a group that matches regexp.

SELECT regexp_extract('Verified by Stacy', '( by)')

regexp_replace(str, regexp, rep)

Replaces all substrings of str that match regexp with rep.

SELECT regexp_replace('Verified by Stacy', '( by)', ':')


Removes the leading and trailing space characters from str.

trim(BOTH trimStr FROM str)

Remove the leading and trailing trimStr characters from str

trim(LEADING trimStr FROM str)

Remove the leading trimStr characters from str

trim(TRAILING trimStr FROM str)

Remove the trailing trimStr characters from str


  • str - a string expression
  • trimStr - the trim string characters to trim, the default value is a single space
  • BOTH, FROM - these are keywords to specify trimming string characters from both ends of the string
  • LEADING, FROM - these are keywords to specify trimming string characters from the left end of the string
  • TRAILING, FROM - these are keywords to specify trimming string characters from the right end of the string
SELECT trim('    SparkSQL   '),
       trim('SL', 'SSparkSQLS'),
       trim(BOTH 'SL' FROM 'SSparkSQLS'),
       trim(LEADING 'SL' FROM 'SSparkSQLS'),
       trim(TRAILING 'SL' FROM 'SSparkSQLS')

UDF functions: employing a UDF function

We call the squaredWithPython UDF we defined earlier.

SELECT gust, squaredWithPython(floor(gust))
FROM Weather
WHERE isnotnull(gust)

Window functions: computing the rank or dense rank.

Let's create a DataFrame to demo these functions, and create a Window.

What are Window Functions?

A window function calculates a return value for every input row of a table based on a group of rows, called the Frame. Every input row can have a unique frame associated with it. This characteristic of window functions makes them more powerful than other functions and allows users to express various data processing tasks that are hard (if not impossible) to be expressed without window functions in a concise way.

schema = StructType([
  StructField("letter", StringType(), True),
  StructField("position", IntegerType(), True)])

df = spark.createDataFrame([("a", 10), ("a", 10), ("a", 20)], schema)
windowSpec = Window.partitionBy("letter").orderBy("position")


Computes the rank of a value in a group of values. The result is one plus the number of rows preceding or equal to the current row in the ordering of the partition. The values will produce gaps in the sequence.


Computes the rank of a value in a group of values. The result is one plus the previously assigned rank value. Unlike the function rank, dense_rank will not produce gaps in the ranking sequence.


Returns a sequential number starting at 1 within a window partition.

display(df.withColumn("rank", rank().over(windowSpec))
  .withColumn("dense_rank", dense_rank().over(windowSpec))
  .withColumn("row_number", row_number().over(windowSpec)))

Note that the value "10" exists twice in position within the same window (letter = "a"). That's when you see a difference between the three functions rank, dense_rank, row.

Another example

We create a productRevenue table as seen below.

We want to answer two questions:

  1. What are the best-selling and the second best-selling products in every category?
  2. What is the difference between the revenue of each product and the revenue of the best-selling product in the same category of that product?
# Create the Products
Product = Row("product", "category", "revenue")

# Create DataFrames from rows
ProductDF = spark.createDataFrame([
                                   Product('Thin', 'Cell phone', 6000),
                                   Product('Normal', 'Tablet', 1500),
                                   Product('Mini', 'Tablet', 5500),
                                   Product('Ultra thin', 'Cell phone', 5500),
                                   Product('Very thin', 'Cell phone', 6000),
                                   Product('Big', 'Tablet', 2500),
                                   Product('Bendable', 'Cell phone', 3000),
                                   Product('Foldable', 'Cell phone', 3000),
                                   Product('Pro', 'Tablet', 4500),
                                   Product('Pro2', 'Tablet', 6500)

To answer the first question “What are the best-selling and the second best-selling products in every category?”, we need to rank products in a category based on their revenue, and to pick the best selling and the second best-selling products based the ranking.

Below is the SQL query used to answer this question by using window function dense_rank.

    dense_rank() OVER (PARTITION BY category ORDER BY revenue DESC) as rank
  FROM productRevenue) tmp
  rank <= 2
ORDER BY category DESC

For the second question “What is the difference between the revenue of each product and the revenue of the best selling product in the same category as that product?”, to calculate the revenue difference for a product, we need to find the highest revenue value from products in the same category for each product.

Below is a Python DataFrame program used to answer this question.

windowSpec = Window.partitionBy(col("category")).orderBy(col("revenue").desc()).rangeBetween(-sys.maxsize, sys.maxsize)
revenue_difference = (max(col("revenue")).over(windowSpec) - col("revenue"))
dataFrame = sqlContext.table("productRevenue")
display(dataFrame.select("product", "category", "revenue", revenue_difference.alias("revenue_difference")).orderBy("category", ascending = 0))

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If you have any questions or suggestions, feel free to open an issue on GitHub!

© 2021 Raki Rahman.