What is the recommended best practice to query Presto using Syntasa Notebook?

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Mike Z

February 10, 2022 21:27

I'm hoping I can get some sample code that helps demonstrate the best method of connecting to Presto in a Syntasa Notebook.

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  Mike Z August 22, 2023 12:01 (Edited August 22, 2023 12:02)

  It looks like the suggested code may not work for all situations. A jar file needs to be downloaded to all executors in a notebook which isn't the easiest thing to do. The below is what was recommended instead:

  https://github.com/prestodb/presto-python-client

  Though, this is an edge case we ran into over 1.5 years ago when our notebook capability was still a bit new. Syntasa Notebooks have matured immensely these days. For probably 99% of our cases (so far) we're able to use spark.sql queries in notebooks to analyze the tables. Attaching an external cluster is not necessary for most high level exploratory understanding of datasets that don't require much in terms of resources.

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  Mahesh Shenoy August 01, 2022 06:39

  Mike Z  Was this been addressed? If yes please add the details so this can be referred later.

  Sarath Botlagunta / Shahdy Ali Hassan

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  Mahesh Shenoy November 17, 2022 13:15

  Sarath Botlagunta / Pradeepraj Chandrasekaran : Can you please provide more details on this

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  Pradeepraj Chandrasekaran August 21, 2023 16:51

  You can install this library and try it from notebook without using spark

  https://github.com/trinodb/trino-python-client

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  Labinot Dvorani August 21, 2023 18:51

  Mike Z, 
  
  Eric has suggested the below code:
  
  

  from pyspark.sql import SparkSession
  
  # Create a Spark session
  spark = SparkSession.builder \
      .appName("PrestoQuery") \
      .config("spark.jars.packages", "com.facebook.presto:presto-jdbc:0.240") \  # Replace with the appropriate version
      .getOrCreate()
  
  # Set Presto connection properties
  url = "jdbc:presto://presto-server.example.com:8080/"
  properties = {
      "user": "your_username"
  }
  
  # Query
  query = "SELECT * FROM your_table"
  
  # Read data using the jdbc data source
  df = spark.read \
      .format("jdbc") \
      .option("url", url) \
      .option("query", query) \
      .option("user", properties["user"]) \
      .load()

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