Analyze with Apache Spark
In this tutorial, you'll learn the basic steps to load and analyze data with Apache Spark for Azure Synapse.
Create a serverless Apache Spark pool
- In Synapse Studio, on the left-side pane, select Manage > Apache Spark pools.
- Select New
- For Apache Spark pool name enter Spark1.
- For Node size enter Small.
- For Number of nodes Set the minimum to 3 and the maximum to 3
- Select Review + create > Create. Your Apache Spark pool will be ready in a few seconds.
Understanding serverless Apache Spark pools
A serverless Spark pool is a way of indicating how a user wants to work with Spark. When you start using a pool, a Spark session is created if needed. The pool controls how many Spark resources will be used by that session and how long the session will last before it automatically pauses. You pay for spark resources used during that session and not for the pool itself. This way a Spark pool lets you use Apache Spark without managing clusters. This is similar to how a serverless SQL pool works.
Analyze NYC Taxi data with a Spark pool
Make sure you have placed the sample data in the primary storage account.
In Synapse Studio, go to the Develop hub.
Create a new notebook.
Create a new code cell and paste the following code in that cell:
%%pyspark df = spark.read.load('abfss://email@example.com/NYCTripSmall.parquet', format='parquet') display(df.limit(10))
Modify the load URI, so it references the sample file in your storage account according to the abfss URI scheme.
In the notebook, in the Attach to menu, choose the Spark1 serverless Spark pool that we created earlier.
Select Run on the cell. Synapse will start a new Spark session to run this cell if needed. If a new Spark session is needed, initially it will take about two seconds to be created.
If you just want to see the schema of the dataframe run a cell with the following code:
Load the NYC Taxi data into the Spark nyctaxi database
Data is available via the dataframe named df. Load it into a Spark database named nyctaxi.
Add a new code cell to the notebook, and then enter the following code:
%%pyspark spark.sql("CREATE DATABASE IF NOT EXISTS nyctaxi") df.write.mode("overwrite").saveAsTable("nyctaxi.trip")
Analyze the NYC Taxi data using Spark and notebooks
Create a new code cell and enter the following code.
%%pyspark df = spark.sql("SELECT * FROM nyctaxi.trip") display(df)
Run the cell to show the NYC Taxi data we loaded into the nyctaxi Spark database.
Create a new code cell and enter the following code. We'll analyze this data and save the results into a table called nyctaxi.passengercountstats.
%%pyspark df = spark.sql(""" SELECT passenger_count, SUM(trip_distance) as SumTripDistance, AVG(trip_distance) as AvgTripDistance FROM nyctaxi.trip WHERE trip_distance > 0 AND passenger_count > 0 GROUP BY passenger_count ORDER BY passenger_count """) display(df) df.write.saveAsTable("nyctaxi.passengercountstats")
In the cell results, select Chart to see the data visualized.
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