In this blog, we are going to learn different spark join types. We will also write code and validate data output for each join type to better understand them.
In the last blog, we have loaded our data to Spark Dataframe. We have also used "inferschema" option to let spark figure out the schema of the Dataframe on its own. But in many cases, you would like to specify a schema for Dataframe. This will give you much better control over column names and especially data types. Let us see how we can add our custom schema while reading data in Spark.
In spark, schema is array StructField of type StructType. Each StructType has 4 parameters.
Let us write our custom schema for our flights data.
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from pyspark.sql.types import StructField, StructType, StringType,LongType custom_schema = StructType([ StructField("destination", StringType(), True), StructField("source", StringType(), True), StructField("total_flights", LongType(), True), ]) df = spark.read.format("csv") \ .schema(custom_schema) \ .option("header", True) \ .load("data/flights.csv") df.show(2) +-------------+-------+-------------+ | destination| source|total_flights| +-------------+-------+-------------+ |United States|Romania| 15| |United States|Croatia| 1| +-------------+-------+-------------+ only showing top 2 rows |
Now our data frame has column names and data types which we have specified. If you want to print schema for any dataframe you can use below function.
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df.printSchema() |
We can add extra information about columns using the metadata filed. This filed takes key-value pairs and we can choose any number of keys and values depending on our needs.
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custom_schema_with_metadata = StructType([ StructField("destination", StringType(), True, metadata={"desc": "destination country for flight"}), StructField("source", StringType(), True, metadata={"desc": "source country of flight"}), StructField("total_flights", LongType(), True, metadata={"desc": "Number of flights"}), ]) df = spark.read.format("csv") \ .schema(custom_schema_with_metadata) \ .option("header", True) \ .load("data/flights.csv") |
We can check our data frame and its schema now.
If you want to check schema with its metadata then we need to use following code. We can read all of schema with this function or also read schema for one column as well.
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df.schema.json() df.schema.fields[0].metadata["desc"] |
This is how we can add a custom schema to our dataframes. I hope this helps. See you in the next blog.
In this blog, we are going to learn different spark join types. We will also write code and validate data output for each join type to better understand them.
In this blog, we are going to integrate spark with jupyter notebook and visual studio code to create easy-to-use development environment.
In this blog, we are going to learn about reading data from SQL tables in Spark. We will create Spark data frames from tables and query results as well.
Hi mahesh
I have txt file say temp.txt with following data
a,(1,2)
b,(1,2)
readFile=spark.read.format("csv") \
.option("header",False) \
.option("inferSchema",True)\
.load("/home/data/temp.txt")
Now my question is how to define schema for such type of dataset.
Hello Vicky,
Thanks for reaching out.
To solve this issue you need to such data enclosed in quotes
"a","(1,2)"
"b","(1,2)"
Then you can use option("escapeQuotes", "true") while reading data.
If this is not possible, then it is slightly difficult to get it working.
You can read entire row as single column. And then format data according to your needs.
df = spark.read\
.option("header", "true")\
.option("inferSchema", "true")\
.option("sep", "\n")\
.csv("csv/sample_data1.csv")
df.show()
+---------+
|col1,col2|
+---------+
| a,(1,2)|
| b,(1,2)|
+---------+
You will need to run some transformations to get that data in required format.
Please let me know if you need my help in this.