Let us create a comprehensive SAS project that involves merging, joining, transposing large tables, applying PROC SQL lead/rank functions, performing data validation with PROC FREQ, and incorporating error handling, macro variables, and macros for various functional tasks.

Step 1: Set Up Macro Variables for Date Values

%let start_date = '01JAN2023'd;
%let end_date = '31DEC2023'd;

Step 2: Define Macros for Various Functional Tasks

%macro import_data(libname=, dataset=, filepath=);
    proc import datafile="&filepath" out=&libname..&dataset dbms=csv replace;
    getnames=yes;
    run;
%mend import_data;

%macro join_tables(libname=, table1=, table2=, out_table=, join_condition=);
    proc sql;
        create table &libname..&out_table as
        select a.*, b.*
        from &libname..&table1 as a
        left join &libname..&table2 as b
        on &join_condition;
    quit;
%mend join_tables;

%macro transpose_table(libname=, in_table=, out_table=, id_var=, var=);
    proc transpose data=&libname..&in_table out=&libname..&out_table;
    by &id_var;
    var &var;
    run;
%mend transpose_table;

%macro validate_data(libname=, table=, var=);
    proc freq data=&libname..&table;
    tables &var / missing;
    run;
%mend validate_data;

%macro apply_rank(libname=, table=, var=, out_table=);
    proc sql;
        create table &libname..&out_table as
        select *, rank() over (order by &var desc) as rank
        from &libname..&table;
    quit;
%mend apply_rank;

Step 3: Import Large Tables

%import_data(libname=work, dataset=table1, filepath='/path/to/table1.csv');
%import_data(libname=work, dataset=table2, filepath='/path/to/table2.csv');

Step 4: Merge and Join Tables

%join_tables(libname=work, table1=table1, table2=table2, out_table=merged_table, join_condition=a.id=b.id);

Step 5: Transpose the Data

%transpose_table(libname=work, in_table=merged_table, out_table=transposed_table, id_var=id, var=some_var);

Step 6: Apply PROC SQL Lead/Rank Functions

%apply_rank(libname=work, table=transposed_table, var=some_var, out_table=ranked_table);

Step 7: Validate Data with PROC FREQ

%validate_data(libname=work, table=ranked_table, var=some_var);

Step 8: Error Handling

Ensure error handling by checking the existence of datasets and proper execution of steps.

%macro check_dataset(libname=, dataset=);
    %if %sysfunc(exist(&libname..&dataset)) %then %do;
        %put NOTE: Dataset &libname..&dataset exists.;
    %end;
    %else %do;
        %put ERROR: Dataset &libname..&dataset does not exist.;
        %abort cancel;
    %end;
%mend check_dataset;

%check_dataset(libname=work, dataset=ranked_table);

Full Example in One Go

Putting it all together:

%let start_date = '01JAN2023'd;
%let end_date = '31DEC2023'd;

%macro import_data(libname=, dataset=, filepath=);
    proc import datafile="&filepath" out=&libname..&dataset dbms=csv replace;
    getnames=yes;
    run;
%mend import_data;

%macro join_tables(libname=, table1=, table2=, out_table=, join_condition=);
    proc sql;
        create table &libname..&out_table as
        select a.*, b.*
        from &libname..&table1 as a
        left join &libname..&table2 as b
        on &join_condition;
    quit;
%mend join_tables;

%macro transpose_table(libname=, in_table=, out_table=, id_var=, var=);
    proc transpose data=&libname..&in_table out=&libname..&out_table;
    by &id_var;
    var &var;
    run;
%mend transpose_table;

%macro validate_data(libname=, table=, var=);
    proc freq data=&libname..&table;
    tables &var / missing;
    run;
%mend validate_data;

%macro apply_rank(libname=, table=, var=, out_table=);
    proc sql;
        create table &libname..&out_table as
        select *, rank() over (order by &var desc) as rank
        from &libname..&table;
    quit;
%mend apply_rank;

%macro check_dataset(libname=, dataset=);
    %if %sysfunc(exist(&libname..&dataset)) %then %do;
        %put NOTE: Dataset &libname..&dataset exists.;
    %end;
    %else %do;
        %put ERROR: Dataset &libname..&dataset does not exist.;
        %abort cancel;
    %end;
%mend check_dataset;

/* Import large tables */
%import_data(libname=work, dataset=table1, filepath='/path/to/table1.csv');
%import_data(libname=work, dataset=table2, filepath='/path/to/table2.csv');

/* Merge and join tables */
%join_tables(libname=work, table1=table1, table2=table2, out_table=merged_table, join_condition=a.id=b.id);

/* Transpose the data */
%transpose_table(libname=work, in_table=merged_table, out_table=transposed_table, id_var=id, var=some_var);

/* Apply PROC SQL lead/rank functions */
%apply_rank(libname=work, table=transposed_table, var=some_var, out_table=ranked_table);

/* Validate data with PROC FREQ */
%validate_data(libname=work, table=ranked_table, var=some_var);

/* Check the existence of the final dataset */
%check_dataset(libname=work, dataset=ranked_table);

Explanation:

1. Macro Variables: Defined for date values.
2. Macros for Functional Tasks: Defined for importing data, joining tables, transposing tables, validating data, and applying ranking functions.
3. Data Import: Two large tables are imported using PROC IMPORT.
4. Data Merge and Join: Tables are merged and joined using PROC SQL.
5. Data Transposition: Transposing the merged table.
6. Rank Application: Applying the rank function.
7. Data Validation: Validating data using PROC FREQ.
8. Error Handling: Ensuring datasets exist and handling errors accordingly.

This setup provides a robust framework for handling complex data processing tasks in SAS, incorporating various best practices and functionalities.

Pyspark Migration

Method1

Here is the equivalent PySpark code for the provided SAS script:

from pyspark.sql import SparkSession
from pyspark.sql.functions import rank, col

# Create a SparkSession object
spark = SparkSession.builder.appName("My App").getOrCreate()

# Define the start and end dates
start_date = "2023-01-01"
end_date = "2023-12-31"

# Import the data
table1 = spark.read.csv("/path/to/table1.csv", header=True, inferSchema=True)
table2 = spark.read.csv("/path/to/table2.csv", header=True, inferSchema=True)

# Register the DataFrames as temporary views
table1.createOrReplaceTempView("table1")
table2.createOrReplaceTempView("table2")

# Join the tables
merged_table = spark.sql("SELECT a.*, b.* FROM table1 a LEFT JOIN table2 b ON a.id = b.id")
merged_table.createOrReplaceTempView("merged_table")

# Transpose the data
transposed_table = merged_table.groupBy("id").pivot("some_var").sum()
transposed_table.createOrReplaceTempView("transposed_table")

# Apply the rank function
ranked_table = transposed_table.withColumn("rank", rank().over(Window.orderBy(col("some_var").desc())))
ranked_table.createOrReplaceTempView("ranked_table")

# Validate the data
ranked_table.select("some_var").summary().show()

# Check the existence of the final dataset
if ranked_table.count() > 0:
    print("Dataset exists.")
else:
    print("Dataset does not exist.")

Here’s a step-by-step explanation of the PySpark code:

1. Create a SparkSession object, which is the entry point to programming Spark with the Dataset and DataFrame API.
2. Define the start and end dates as strings.
3. Import the data from CSV files using the read.csv method, which returns a DataFrame.
4. Register the DataFrames as temporary views using the createOrReplaceTempView method, so they can be queried using SQL.
5. Join the tables using a SQL query, which is executed using the sql method.
6. Transpose the data using the groupBy and pivot methods, which are equivalent to the SAS proc transpose step.
7. Apply the rank function using the withColumn and rank methods, which are equivalent to the SAS proc sql step with the rank function.
8. Validate the data by computing summary statistics using the summary method, which is equivalent to the SAS proc freq step.
9. Check the existence of the final dataset by checking the count of rows using the count method. If the count is greater than 0, the dataset exists; otherwise, it does not exist.

Note that the PySpark code uses the DataFrame API, which is a more concise and expressive way of working with data compared to the SAS script. Additionally, the PySpark code uses SQL queries to perform the join and rank operations, which can be more efficient than using the SAS proc sql step.

Method 2

Here’s the modified PySpark code that translates SAS macros to functions and adds error handling:

Python

from pyspark.sql import SparkSession
from pyspark.sql.functions import rank, col

# Create a SparkSession object
spark = SparkSession.builder.appName("My App").getOrCreate()

# Define a function for importing data
def import_data(libname, dataset, filepath):
    try:
        df = spark.read.csv(filepath, header=True, inferSchema=True)
        df.createOrReplaceTempView(f"{libname}_{dataset}")
        return df
    except Exception as e:
        print(f"Error importing data: {e}")
        return None

# Define a function for joining tables
def join_tables(libname, table1, table2, out_table, join_condition):
    try:
        spark.sql(f"""
            CREATE TABLE {libname}_{out_table} AS
            SELECT a.*, b.*
            FROM {libname}_{table1} a
            LEFT JOIN {libname}_{table2} b
            ON {join_condition}
        """)
        return spark.table(f"{libname}_{out_table}")
    except Exception as e:
        print(f"Error joining tables: {e}")
        return None

# Define a function for transposing data
def transpose_table(libname, in_table, out_table, id_var, var):
    try:
        spark.sql(f"""
            CREATE TABLE {libname}_{out_table} AS
            SELECT {id_var}, {var}
            FROM {libname}_{in_table}
            GROUP BY {id_var}
            PIVOT ({var})
        """)
        return spark.table(f"{libname}_{out_table}")
    except Exception as e:
        print(f"Error transposing data: {e}")
        return None

# Define a function for applying the rank function
def apply_rank(libname, table, var, out_table):
    try:
        spark.sql(f"""
            CREATE TABLE {libname}_{out_table} AS
            SELECT *, RANK() OVER (ORDER BY {var} DESC) AS rank
            FROM {libname}_{table}
        """)
        return spark.table(f"{libname}_{out_table}")
    except Exception as e:
        print(f"Error applying rank function: {e}")
        return None

# Define a function for validating data
def validate_data(libname, table, var):
    try:
        spark.sql(f"""
            SELECT {var}, COUNT(*) AS count
            FROM {libname}_{table}
            GROUP BY {var}
        """).show()
    except Exception as e:
        print(f"Error validating data: {e}")

# Define a function for checking the existence of a dataset
def check_dataset(libname, dataset):
    try:
        if spark.catalog.tableExists(f"{libname}_{dataset}"):
            print("Dataset exists.")
        else:
            print("Dataset does not exist.")
    except Exception as e:
        print(f"Error checking dataset existence: {e}")

# Call the functions
table1 = import_data("work", "table1", "/path/to/table1.csv")
table2 = import_data("work", "table2", "/path/to/table2.csv")

merged_table = join_tables("work", "table1", "table2", "merged_table", "a.id = b.id")

transposed_table = transpose_table("work", "merged_table", "transposed_table", "id", "some_var")

ranked_table = apply_rank("work", "transposed_table", "some_var", "ranked_table")

validate_data("work", "ranked_table", "some_var")

check_dataset("work", "ranked_table")

In this modified code, I’ve defined separate functions for each of the SAS macros, and added error handling using try-except blocks. If an error occurs during the execution of a function, the error message is printed and the function returns None.

Method 3

Here’s the modified PySpark code that uses the sql method for the transpose and rank operations, and defines classes, objects, and functions to mimic the SAS macros:

Python

from pyspark.sql import SparkSession
from pyspark.sql.functions import rank, col

# Create a SparkSession object
spark = SparkSession.builder.appName("My App").getOrCreate()

# Define the start and end dates
start_date = "2023-01-01"
end_date = "2023-12-31"

# Define a class for importing data
class ImportData:
    def __init__(self, libname, dataset, filepath):
        self.libname = libname
        self.dataset = dataset
        self.filepath = filepath

    def execute(self):
        df = spark.read.csv(self.filepath, header=True, inferSchema=True)
        df.createOrReplaceTempView(f"{self.libname}_{self.dataset}")
        return df

# Define a class for joining tables
class JoinTables:
    def __init__(self, libname, table1, table2, out_table, join_condition):
        self.libname = libname
        self.table1 = table1
        self.table2 = table2
        self.out_table = out_table
        self.join_condition = join_condition

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT a.*, b.*
            FROM {self.libname}_{self.table1} a
            LEFT JOIN {self.libname}_{self.table2} b
            ON {self.join_condition}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

# Define a class for transposing data
class TransposeTable:
    def __init__(self, libname, in_table, out_table, id_var, var):
        self.libname = libname
        self.in_table = in_table
        self.out_table = out_table
        self.id_var = id_var
        self.var = var

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT {self.id_var}, {self.var}
            FROM {self.libname}_{self.in_table}
            GROUP BY {self.id_var}
            PIVOT ({self.var})
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

# Define a class for applying the rank function
class ApplyRank:
    def __init__(self, libname, table, var, out_table):
        self.libname = libname
        self.table = table
        self.var = var
        self.out_table = out_table

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT *, RANK() OVER (ORDER BY {self.var} DESC) AS rank
            FROM {self.libname}_{self.table}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

# Define a class for validating data
class ValidateData:
    def __init__(self, libname, table, var):
        self.libname = libname
        self.table = table
        self.var = var

    def execute(self):
        spark.sql(f"""
            SELECT {self.var}, COUNT(*) AS count
            FROM {self.libname}_{self.table}
            GROUP BY {self.var}
        """).show()

# Define a class for checking the existence of a dataset
class CheckDataset:
    def __init__(self, libname, dataset):
        self.libname = libname
        self.dataset = dataset

    def execute(self):
        if spark.catalog.tableExists(f"{self.libname}_{self.dataset}"):
            print("Dataset exists.")
        else:
            print("Dataset does not exist.")

# Create instances of the classes and execute the methods
import_data = ImportData("work", "table1", "/path/to/table1.csv")
table1 = import_data.execute()

import_data = ImportData("work", "table2", "/path/to/table2.csv")
table2 = import_data.execute()

join_tables = JoinTables("work", "table1", "table2", "merged_table", "a.id = b.id")
merged_table = join_tables.execute()

transpose_table = TransposeTable("work", "merged_table", "transposed_table", "id", "some_var")
transposed_table = transpose_table.execute()

apply_rank = ApplyRank("work", "transposed_table", "some_var", "ranked_table")
ranked_table = apply_rank.execute()

validate_data = ValidateData("work", "ranked_table", "some_var")
validate_data.execute()

check_dataset = CheckDataset("work", "ranked_table")
check_dataset.execute()

In this modified code, we define classes for each of the SAS macros, and create instances of these classes to execute

step by step explanation is still left

Here is a step-by-step explanation of the modified PySpark code:

Step 1: Create a SparkSession object

spark = SparkSession.builder.appName("My App").getOrCreate()

This line creates a SparkSession object, which is the entry point to programming Spark with the Dataset and DataFrame API.

Step 2: Define the start and end dates

start_date = "2023-01-01"
end_date = "2023-12-31"

These lines define the start and end dates as strings.

Step 3: Define a class for importing data

class ImportData:
    def __init__(self, libname, dataset, filepath):
        self.libname = libname
        self.dataset = dataset
        self.filepath = filepath

    def execute(self):
        df = spark.read.csv(self.filepath, header=True, inferSchema=True)
        df.createOrReplaceTempView(f"{self.libname}_{self.dataset}")
        return df

This class defines a method for importing data from a CSV file. The execute method reads the CSV file using the spark.read.csv method, creates a temporary view using the createOrReplaceTempView method, and returns the DataFrame.

Step 4: Define a class for joining tables

class JoinTables:
    def __init__(self, libname, table1, table2, out_table, join_condition):
        self.libname = libname
        self.table1 = table1
        self.table2 = table2
        self.out_table = out_table
        self.join_condition = join_condition

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT a.*, b.*
            FROM {self.libname}_{self.table1} a
            LEFT JOIN {self.libname}_{self.table2} b
            ON {self.join_condition}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

This class defines a method for joining two tables. The execute method uses the spark.sql method to execute a SQL query that joins the two tables, creates a new table with the joined data, and returns the resulting DataFrame.

Step 5: Define a class for transposing data

class TransposeTable:
    def __init__(self, libname, in_table, out_table, id_var, var):
        self.libname = libname
        self.in_table = in_table
        self.out_table = out_table
        self.id_var = id_var
        self.var = var

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT {self.id_var}, {self.var}
            FROM {self.libname}_{self.in_table}
            GROUP BY {self.id_var}
            PIVOT ({self.var})""")
        return spark.table(f"{self.libname}_{self.out_table}")

This class defines a method for transposing data. The execute method uses the spark.sql method to execute a SQL query that transposes the data, creates a new table with the transposed data, and returns the resulting DataFrame.

Step 6: Define a class for applying the rank function

class ApplyRank:
    def __init__(self, libname, table, var, out_table):
        self.libname = libname
        self.table = table
        self.var = var
        self.out_table = out_table

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT *, RANK() OVER (ORDER BY {self.var} DESC) AS rank
            FROM {self.libname}_{self.table}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

This class defines a method for applying the rank function. The execute method uses the spark.sql method to execute a SQL query that applies the rank function, creates a new table with the ranked data, and returns the resulting DataFrame.

Step 7: Define a class for validating data

class ValidateData:
    def __init__(self, libname, table, var):
        self.libname = libname
        self.table = table
        self.var = var

    def execute(self):
        spark.sql(f"""
            SELECT {self.var}, COUNT(*) AS count
            FROM {self.libname}_{self.table}
            GROUP BY {self.var}
        """).show()

This class defines a method for validating data. The execute method uses the spark.sql method to execute a SQL query that groups the data by the specified variable and counts the number of rows in each group, and displays the results.