Category: SQL

df = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:mysql://{server_name}:{port}/{database_name}") \
    .option("driver", "com.mysql.cj.jdbc.Driver") \
    .option("dbtable", "employee") \
    .option("user", "root") \
    .option("password", "root") \
    .load()

# Query table using jdbc()
# Parameters
server_name = "myserver.database.windows.net"
port = "1433"
database_name = "mydatabase"
table_name = "mytable"
username = "myusername"
password = "mypassword"

# Construct the JDBC URL
jdbc_url = f"jdbc:sqlserver://{server_name}:{port};databaseName={database_name}"
connection_properties = {
    "user": username,
    "password": password,
    "driver": "com.microsoft.sqlserver.jdbc.SQLServerDriver"
}

# Read data from the SQL database
df = spark.read.jdbc(url=jdbc_url, table=table_name, properties=connection_properties)
# show DataFrame
df_dep.show()
+-----+--------------+
|depid|           dep|
+-----+--------------+
|    1|            IT|
|    2|          Sale|
|    3|       Finance|
|    4|human resource|
+-----+--------------+

Query JDBC Table Parallel, specific Columns

df = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:mysql://localhost:3306/emp") \
    .option("driver", "com.mysql.cj.jdbc.Driver") \
    .option("query", "select id,age from emp where sex='M'") \
    .option("numPartitions",5) \
    .option("fetchsize", 20) \
    .option("user", "root") \
    .option("password", "root") \
    .load()

df.show()

Read CSV File

df = spark.read.format("csv")\
.options(delimiter=',') \
.options(inferSchema='True')\
.options(header='True')\
.load("/Folder/, /filePath2/")

Write CSV File

df = spark.write.format("csv")\
.options(delimiter=',') \
.options(inferSchema='True')\
.options(header='True')\
.mode("overwrite")
.save("/filePath/filename")

By default, If you try to write directly to a file (e.g., name1.csv), it conflicts because Spark doesn’t generate a single file but a collection of part files in a directory.

path
dbfs:/FileStore/name1.csv/_SUCCESS
dbfs:/FileStore/name1.csv/_committed_7114979947112568022
dbfs:/FileStore/name1.csv/_started_7114979947112568022
dbfs:/FileStore/name1.csv/part-00000-tid-7114979947112568022-b2482528-b2f8-4c82-82fb-7c763d91300e-12-1-c000.csv

PySpark writes data in parallel, which results in multiple part files rather than a single CSV file by default. However, you can consolidate the data into a single CSV file by performing a coalesce(1) or repartition(1) operation before writing, which reduces the number of partitions to one.

df.coalesce(1).write.format("csv").mode("overwrite").options(header=True).save("dbfs:/FileStore/name1.csv")

at this time, name1.csv in “dbfs:/FileStore/name1.csv” will treat as a directory, rather than a Filename. PySpark writes the single file with a random name like part-00000-<id>.csv

dbfs:/FileStore/name1.csv/part-00000-tid-4656450869948503591-85145263-6f01-4b56-ad37-3455ca9a8882-9-1-c000.csv

we need take additional step – “Rename the File to name1.csv“

# List all files in the directory
files = dbutils.fs.ls("dbfs:/FileStore/name1.csv")

# Filter for the part file
for file in files:
    if file.name.startswith("part-"):
        source_file = file.path  # Full path to the part file
        destination_file = "dbfs:/FileStore/name1.csv"
        
        # Move and rename the file
        dbutils.fs.mv(source_file, destination_file)
        break


display(dbutils.fs.ls ( "dbfs:/FileStore/"))

Direct Write with Custom File Name

PySpark doesn’t natively allow specifying a custom file name directly while writing a file because it writes data in parallel using multiple partitions. However, you can achieve a custom file name with a workaround. Here’s how:

Steps:

1. Use coalesce(1) to combine all data into a single partition.
2. Save the file to a temporary location.
3. Rename the part file to the desired name.

# Combine all data into one partition
df.coalesce(1).write.format("csv") \
    .option("header", "true") \
    .mode("overwrite") \
    .save("dbfs:/FileStore/temp_folder")

# Get the name of the part file
files = dbutils.fs.ls("dbfs:/FileStore/temp_folder")
for file in files:
    if file.name.startswith("part-"):
        part_file = file.path
        break

# Move and rename the part file
dbutils.fs.mv(part_file, "dbfs:/FileStore/name1.csv")

# Remove the temporary folder
dbutils.fs.rm("dbfs:/FileStore/temp_folder", True)

sample data save at /tmp/output/people.parquet

dbfs:/tmp/output/people.parquet/part-00007-tid-4023475225384587157-553904a7-7460-4fb2-a4b8-140ccc85024c-15-1-c000.snappy.parquet

read from parquet

sample data save at /tmp/output/people.parquet
df1=spark.read.parquet("/tmp/output/people.parquet")
+---------+----------+--------+-----+------+------+
|firstname|middlename|lastname|  dob|gender|salary|
+---------+----------+--------+-----+------+------+
|   James |          |   Smith|36636|     M|  3000|
| Michael |      Rose|        |40288|     M|  4000|
|  Robert |          |Williams|42114|     M|  4000|
|   Maria |      Anne|   Jones|39192|     F|  4000|
|      Jen|      Mary|   Brown|     |     F|    -1|
+---------+----------+--------+-----+------+------+

read a parquet is the same as reading from csv, nothing special.

write to a parquet

• append: appends the data from the DataFrame to the existing file, if the Destination files already exist. In Case the Destination files do not exists, it will create a new parquet file in the specified location.
  
  df.write.mode(“append”).parquet(“path/to/parquet/file”)
  
• overwrite: This mode overwrites the destination Parquet file with the data from the DataFrame. If the file does not exist, it creates a new Parquet file.
  
  df.write.mode(“overwrite”).parquet(“path/to/parquet/file”)
  
• ignore: If the destination Parquet file already exists, this mode does nothing and does not write the DataFrame to the file. If the file does not exist, it creates a new Parquet file.
  
  df.write.mode(“ignore”).parquet(“path/to/parquet/file”)
  
• Create Parquet partition file
  
  df.write.partitionBy(“gender”,”salary”).mode(“overwrite”).parquet(“/tmp/output/people2.parquet”)
  
  

Read a simple JSON file

# Read a simple JSON file into dataframe
{
  "RecordNumber": 10,
  "Zipcode": 709,
  "ZipCodeType": "STANDARD",
  "City": "BDA SAN LUIS",
  "State": "PR"
}

df_simple = spark.read.format("json") \
.option("multiline","True")\
.load("dbfs:/FileStore/simple_json.json")

df_simple.printSchema()
root
 |-- City: string (nullable = true)
 |-- RecordNumber: long (nullable = true)
 |-- State: string (nullable = true)
 |-- ZipCodeType: string (nullable = true)
 |-- Zipcode: long (nullable = true)

df_simple.show()
+------------+------------+-----+-----------+-------+
|        City|RecordNumber|State|ZipCodeType|Zipcode|
+------------+------------+-----+-----------+-------+
|BDA SAN LUIS|          10|   PR|   STANDARD|    709|
+------------+------------+-----+-----------+-------+

pay attention on “.option(“multiline”,”True”)“. since my json file is multiple lines (look at above sample data), if reading without this option, it can still run load. But the dataframe will not work. Once you show, you will get this error

df_simple = spark.read.format(“json”).load(“dbfs:/FileStore/simple_json.json”)
df_simple,show()

AnalysisException: Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named _corrupt_record by default).

Reading from Multiline JSON (JSON Array) File

[{
  "RecordNumber": 2,
  "Zipcode": 704,
  "ZipCodeType": "STANDARD",
  "City": "PASEO COSTA DEL SUR",
  "State": "PR"
},
{
  "RecordNumber": 10,
  "Zipcode": 709,
  "ZipCodeType": "STANDARD",
  "City": "BDA SAN LUIS",
  "State": "PR"
}]
df_jsonarray_simple = spark.read\
    .format("json")\
    .option("multiline", "true")\
    .load("dbfs:/FileStore/jsonArrary.json")

df_jsonarray_simple.show()
+-------------------+------------+-----+-----------+-------+
|               City|RecordNumber|State|ZipCodeType|Zipcode|
+-------------------+------------+-----+-----------+-------+
|PASEO COSTA DEL SUR|           2|   PR|   STANDARD|    704|
|       BDA SAN LUIS|          10|   PR|   STANDARD|    709|
+-------------------+------------+-----+-----------+-------+

read complex json

df_complexjson=spark.read\
    .option("multiline","true")\
    .json("dbfs:/FileStore/jsonArrary2.json")

df_complexjson.select("id","type","name","ppu","batters","topping").show(truncate=False, vertical=True)
-RECORD 0--------------------------------------------------------------------------------------------------------------------------------------------
 id      | 0001                                                                                                                                      
 type    | donut                                                                                                                                     
 name    | Cake                                                                                                                                      
 ppu     | 0.55                                                                                                                                      
 batters | {[{1001, Regular}, {1002, Chocolate}, {1003, Blueberry}, {1004, Devil's Food}]}                                                           
 topping | [{5001, None}, {5002, Glazed}, {5005, Sugar}, {5007, Powdered Sugar}, {5006, Chocolate with Sprinkles}, {5003, Chocolate}, {5004, Maple}] 
-RECORD 1--------------------------------------------------------------------------------------------------------------------------------------------
 id      | 0002                                                                                                                                      
 type    | donut                                                                                                                                     
 name    | Raised                                                                                                                                    
 ppu     | 0.55                                                                                                                                      
 batters | {[{1001, Regular}]}                                                                                                                       
 topping | [{5001, None}, {5002, Glazed}, {5005, Sugar}, {5003, Chocolate}, {5004, Maple}]                                                           
-RECORD 2--------------------------------------------------------------------------------------------------------------------------------------------
 id      | 0003                                                                                                                                      
 type    | donut                                                                                                                                     
 name    | Old Fashioned                                                                                                                             
 ppu     | 0.55                                                                                                                                      
 batters | {[{1001, Regular}, {1002, Chocolate}]}                                                                                                    
 topping | [{5001, None}, {5002, Glazed}, {5003, Chocolate}, {5004, Maple}]                                                                          

Reading from Multiple files at a time

# Read multiple files
df2 = spark.read.json(
    ['resources/zipcode1.json','resources/zipcode2.json'])

Reading from Multiple files at a time

# Read all JSON files from a folder
df3 = spark.read.json("resources/*.json")

Reading files with a user-specified custom schema

# Define custom schema
schema = StructType([
      StructField("RecordNumber",IntegerType(),True),
      StructField("Zipcode",IntegerType(),True),
      StructField("ZipCodeType",StringType(),True),
      StructField("City",StringType(),True),
      StructField("State",StringType(),True),
      StructField("LocationType",StringType(),True),
      StructField("Lat",DoubleType(),True),
      StructField("Long",DoubleType(),True),
      StructField("Xaxis",IntegerType(),True),
      StructField("Yaxis",DoubleType(),True),
      StructField("Zaxis",DoubleType(),True),
      StructField("WorldRegion",StringType(),True),
      StructField("Country",StringType(),True),
      StructField("LocationText",StringType(),True),
      StructField("Location",StringType(),True),
      StructField("Decommisioned",BooleanType(),True),
      StructField("TaxReturnsFiled",StringType(),True),
      StructField("EstimatedPopulation",IntegerType(),True),
      StructField("TotalWages",IntegerType(),True),
      StructField("Notes",StringType(),True)
  ])

df_with_schema = spark.read.schema(schema) \
        .json("resources/zipcodes.json")
df_with_schema.printSchema()

Reading File using PySpark SQL

spark.sql("CREATE OR REPLACE TEMPORARY VIEW zipcode USING json OPTIONS" + 
      " (path 'resources/zipcodes.json')")
spark.sql("select * from zipcode").show()

Write to JSON

Options

• path: Specifies the path where the JSON files will be saved.
• mode: Specifies the behavior when writing to an existing directory.
• compression: Specifies the compression codec to use when writing the JSON files (e.g., “gzip”, “snappy”).
  df2.write . option(“compression”, “gzip”)
  
• dateFormat: Specifies the format for date and timestamp columns.
  df.write . option(“dateFormat”, “yyyy-MM-dd”)
  
• timestampFormat: Specifies the format for timestamp columns.
  df.write . option(“timestampFormat“, “yyyy-MM-dd’T’HH:mm:ss.SSSXXX”)
  
• lineSep: Specifies the character sequence to use as a line separator between JSON objects. \n (Unix/Linux newline); \r\n (Windows newline)
  df . write . option(“lineSep”, “\r\n”)
  
• encoding: Specifies the character encoding to use when writing the JSON files.
  UTF-8, UTF-16, ISO-8859-1 (Latin-1), Other Java-supported encodings.
  df . write . option(“encoding”, “UTF-8”)

df2.write . format("json")
 . option("compression", "gzip") \
 . option("dateFormat", "yyyy-MM-dd") \ 
 . option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss.SSSXXX") \
 . option("encoding", "UTF-8") \
 . option("lineSep", "\r\n")
 . save("dbfs:/FileStore/output_json")

modes

1. Append: Appends the data to the existing data in the target location. If the target location does not exist, it creates a new one.
2. Overwrite: Overwrites the data in the target location if it already exists. If the target location does not exist, it creates a new one.
3. Ignore: Ignores the operation and does nothing if the target location already exists. If the target location does not exist, it creates a new one.
4. Error or ErrorIfExists: Throws an error and fails the operation if the target location already exists. This is the default behavior if no saving mode is specified.

# Write with savemode example
df2.write.mode('Overwrite').json("/tmp/spark_output/zipcodes.json")

Read from SQL Server

server_name = "mainri-sqldb.database.windows.net"
port=1433
username="my login name"
password="my login password"
database_name="mainri-sqldb"
table_name="dep"

jdbc_url = f"jdbc:sqlserver://{server_name}:{port};databaseName={database_name}"
connection_properties = {
    "user": username,
    "password": password,
    "driver": "com.microsoft.sqlserver.jdbc.SQLServerDriver"
}
df = spark.read.jdbc(url=jdbc_url, table=table_name, properties=connection_properties)
df.show()
+-----+--------------+
|depid|           dep|
+-----+--------------+
|    1|            IT|
|    2|          Sale|
|    3|       Finance|
|    4|human resource|
+-----+--------------+

alternative way

df1=spark.read \
  .format("jdbc") \
  .option("url", jdbc_url) \
  .option("dbtable", table_name) \
  .option("user", username) \
  .option("password", password) \
  .load()

Select Specific Columns to Read

In the above example, it reads the entire table into PySpark DataFrame. Sometimes you may not be required to select the entire table, so to select the specific columns, specify the query you wanted to select with dbtable option.

df1=spark.read \
  .format("jdbc") \
  .option("url", jdbc_url) \
  .option("databaseName",database_name) \
  .option("query", "select * from [dbo].[dep] where depid>3") \
  .option("user", username) \
  .option("password", password) \
  .load()

df.show()
+-----+--------------+
|depid|           dep|
+-----+--------------+
|    4|human resource|
|    5|         admin|
+-----+--------------+

write to table

write mode:

Append:
mode("append") to append the rows to the existing SQL Server table.

# we have define variables, here is show again
server_name = “mainri-sqldb.database.windows.net”
port=1433
username=”my login name”
password=”my login password”
database_name=”mainri-sqldb”
table_name=”dep”
jdbc_url = f”jdbc:sqlserver://{server_name}:{port};databaseName={database_name}”

# append the rows to the existing SQL Server table.

df_newrow.show()
+-----+-----+
|depid|  dep|
+-----+-----+
|    5|admin|
+-----+-----+

df_newrow.write \
  .format("jdbc") \
  .mode("append") \
  .option("url", jdbc_url) \
  .option("dbtable", table_name) \
  .option("user", username) \
  .option("password", password) \
  .save()
+-----+--------------+
|depid|           dep|
+-----+--------------+
|    1|            IT|
|    2|          Sale|
|    3|       Finance|
|    4|human resource|
|    5|         admin| <- new added row
+-----+--------------+

overwrite

The mode("overwrite") drops the table if already exists by default and re-creates a new one without indexes. Use option(“truncate”,”true”) to retain the index.

df_newrow.write \
  .format("jdbc") \
  .mode("overwrite") \
  .option("url", jdbc_url) \
  .option("dbtable", table_name) \
  .option("user", username) \
  .option("password", password) \
  .save()

Read from MySQL

# Read from MySQL Table
val df = spark.read \
    .format("jdbc") \
    .option("driver","com.mysql.cj.jdbc.Driver") \
    .option("url", "jdbc:mysql://localhost:3306/emp") \
    .option("dbtable", "employee") \
    .option("user", "root") \
    .option("password", "root") \
    .load()

Select Specific Columns to Read

# Read from MySQL Table
val df = spark.read \
    .format("jdbc") \
    .option("driver","com.mysql.cj.jdbc.Driver") \
    .option("url", "jdbc:mysql://localhost:3306/emp") \
    .option("query", "select id,age from employee where gender='M'") \
    .option("numPartitions",4) \
    .option("fetchsize", 20) \
    .option("user", "root") \
    .option("password", "root") \
    .load()

Write to MySQL

Some points to note while writing

• To re-write the existing table, use the mode("overwrite"). This drops the table if already exists by default and re-creates a new one without indexes.
• To retain the indexes, use option("truncate","true").
• By default, the connector uses READ_COMMITTED isolation level. To change this use option("mssqlIsolationLevel", "READ_UNCOMMITTED").
• The dbtable option is used in PySpark to specify the name of the table in a database that you want to read data from or write data to.

# Write to MySQL Table
sampleDF.write \
  .format("jdbc") \
  .option("driver","com.mysql.cj.jdbc.Driver") \
  .option("url", "jdbc:mysql://localhost:3306/emp") \
  .option("dbtable", "employee") \
  .option("user", "root") \
  .option("password", "root") \
  .save()

PySpark jdbc() method with the option numPartitions you can read the database table in parallel. This option is used with both reading and writing. 

The maximum number of partitions that can be used for parallelism in table reading and writing. This also determines the maximum number of concurrent JDBC connections. If the number of partitions to write exceeds this limit, we decrease it to this limit by calling coalesce(numPartitions) before writing.

# Read Table in Parallel
df = spark.read \
    .format("jdbc") \
    .option("driver","com.mysql.cj.jdbc.Driver") \
    .option("url", "jdbc:mysql://localhost:3306/emp") \
    .option("dbtable","employee") \
    .option("numPartitions",5) \
    .option("user", "root") \
    .option("password", "root") \
    .load()

Select columns with where clause

# Select columns with where clause
df = spark.read \
    .format("jdbc") \
    .option("driver","com.mysql.cj.jdbc.Driver") \
    .option("url", "jdbc:mysql://localhost:3306/emp") \
    .option("query","select id,age from employee where gender='M'") \
    .option("numPartitions",5) \
    .option("user", "root") \
    .option("password", "root") \
    .load()

Using fetchsize with numPartitions to Read

The fetchsize is another option which is used to specify how many rows to fetch at a time, by default it is set to 10. The JDBC fetch size determines how many rows to retrieve per round trip which helps the performance of JDBC drivers.  Do not set this to very large number as you might see issues.

# Using fetchsize
df = spark.read \
    .format("jdbc") \
    .option("driver","com.mysql.cj.jdbc.Driver") \
    .option("url", "jdbc:mysql://localhost:3306/emp") \
    .option("query","select id,age from employee where gender='M'") \
    .option("numPartitions",5) \
    .option("fetchsize", 20) \
    .option("user", "root") \
    .option("password", "root") \
    .load()

Read delta table, Catalog, Hive Table

# Read Hive table
df = spark.sql("select * from emp.employee")
df.show()

# Read Hive table
df = spark.read.table("employee")
df.show()

# Read delta table
df = spark.sql("select * from delta.` table path `  ")
df.show()

caution: ` Backticks 

Write / Save

To save a PySpark DataFrame to Hive table use saveAsTable() function or use SQL CREATE statement on top of the temporary view.

# Create Hive Internal table
sampleDF.write.mode('overwrite') \
    .saveAsTable("emp.employee")

use SQL, temporary view

# Create temporary view
sampleDF.createOrReplaceTempView("sampleView")

# Create a Database CT
spark.sql("CREATE DATABASE IF NOT EXISTS ct")

# Create a Table naming as sampleTable under CT database.
spark.sql("CREATE TABLE ct.sampleTable (id Int, name String, age Int, gender String)")

# Insert into sampleTable using the sampleView. 
spark.sql("INSERT INTO TABLE ct.sampleTable  SELECT * FROM sampleView")

# Lets view the data in the table
spark.sql("SELECT * FROM ct.sampleTable").show()