Run Spark workloads from VS Code, Jupyter Notebooks, or a terminal¶

Adicione uma conexão usando Snowflake CLI¶

Você pode usar Snowflake CLI para adicionar propriedades de conexão que Snowpark Connect for Spark pode usar para se conectar ao Snowflake. Suas alterações são salvas em um arquivo config.toml.

1. Execute o seguinte comando para adicionar uma conexão usando o comando de conexão snow: comado add.

   snow connection add
   

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2. Siga as instruções para definir uma conexão.

   Certifique-se de especificar spark-connect como o nome da conexão.

   Este comando adiciona uma conexão ao seu arquivo config.toml, como no exemplo a seguir:

   [connections.spark-connect]
   host = "example.snowflakecomputing.com"
   port = 443
   account = "example"
   user = "test_example"
   password = "password"
   protocol = "https"
   warehouse = "example_wh"
   database = "example_db"
   schema = "public"
   

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3. Execute o seguinte comando para confirmar que a conexão funciona.

   Você poderá testar a conexão dessa maneira quando a adicionar usando Snowflake CLI.

   snow connection list
   snow connection test --connection spark-connect
   

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Adição de uma conexão gravando manualmente um arquivo de conexão¶

Você pode escrever ou atualizar manualmente um arquivo connections.toml para que seu código possa se conectar ao Snowpark Connect for Spark no Snowflake.

1. Execute o seguinte comando para garantir que seu arquivo connections.toml permita que somente o proprietário (usuário) tenha acesso de leitura e gravação.

   chmod 0600 "~/.snowflake/connections.toml"
   

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2. Edite o arquivo connections.toml para que ele contenha uma conexão [spark-connect] com as propriedades de conexão no exemplo a seguir.

   Certifique-se de substituir os valores por suas próprias especificações de conexão.

   [spark-connect]
   host="my_snowflake_account.snowflakecomputing.com"
   account="my_snowflake_account"
   user="my_user"
   password="&&&&&&&&"
   warehouse="my_wh"
   database="my_db"
   schema="public"
   

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Instalação do Snowpark Connect for Spark¶

Você pode instalar Snowpark Connect for Spark como um pacote Python.

1. Crie um ambiente virtual Python.

   Confirm that your Python version is 3.10 or later and earlier than 3.13 by running python3 --version.

   python3 -m venv .venv
   source .venv/bin/activate
   

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2. Instale o pacote Snowpark Connect for Spark.

   pip install --upgrade --force-reinstall 'snowpark-connect[jdk]'
   

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3. Adicione o código Python para iniciar um Snowpark Connect for Spark e crie uma sessão de Snowpark Connect for Spark.

   from snowflake import snowflake.snowpark_connect
   
   # Import snowpark_connect *before* importing pyspark libraries
   from pyspark.sql.types import Row
   
   spark = snowflake.snowpark_connect.server.init_spark_session()
   

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Step 1: Set up your Snowpark Connect for Spark environment¶

Set up your environment by using steps described in the following topics:

1. Create a Python virtual environment and install Snowpark Connect.

2. Set up a connection.

Step 2: Create a Snowpark Connect for Spark server script and launch the server¶

1. Create a Python script to launch the Snowpark Connect for Spark server.

   # launch-snowpark-connect.py
   
   from snowflake import snowpark_connect
   
   def main():
       snowpark_connect.start_session(is_daemon=False, remote_url="sc://localhost:15002")
       print("SAS started on port 15002")
   
   if __name__ == "__main__":
       main()
   

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2. Launch the Snowpark Connect for Spark server.

   # Make sure you're in the correct Python environment
   pyenv activate your-snowpark-connect-env
   
   # Run the server script
   python launch-snowpark-connect.py
   

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Step 3: Set up your Scala application¶

1. Add the Spark Connect client dependency to your build.sbt file.

   libraryDependencies += "org.apache.spark" %% "spark-connect-client-jvm" % "3.5.3"
   
   // Add JVM options for Java 9+ module system compatibility
   javaOptions ++= Seq(
     "--add-opens=java.base/java.nio=ALL-UNNAMED"
   )
   

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2. Execute Scala code to connect to the Snowpark Connect for Spark server.

   import org.apache.spark.sql.SparkSession
   import org.apache.spark.sql.connect.client.REPLClassDirMonitor
   
   object SnowparkConnectExample {
     def main(args: Array[String]): Unit = {
       // Create Spark session with Snowpark Connect
       val spark = SparkSession.builder().remote("sc://localhost:15002").getOrCreate()
   
       // Register ClassFinder for UDF support (if needed)
       // val classFinder = new REPLClassDirMonitor("target/scala-2.12/classes")
       // spark.registerClassFinder(classFinder)
   
       try {
         // Simple DataFrame operations
         import spark.implicits._
   
         val data = Seq(
           (1, "Alice", 25),
           (2, "Bob", 30),
           (3, "Charlie", 35)
         )
   
         val df = spark.createDataFrame(data).toDF("id", "name", "age")
   
         println("Original DataFrame:")
         df.show()
   
         println("Filtered DataFrame (age > 28):")
         df.filter($"age" > 28).show()
   
         println("Aggregated result:")
         df.groupBy().avg("age").show()
   
       } finally {
         spark.stop()
       }
     }
   }
   

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3. Compile e execute o aplicativo.

   # Compile your Scala application
   sbt compile
   
   # Run the application
   sbt "runMain SnowparkConnectExample"
   

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Scala UDF support on Snowpark Connect for Spark¶

When using user-defined functions or custom code, do one of the following:

• Register a class finder to monitor and upload class files.

  import org.apache.spark.sql.connect.client.REPLClassDirMonitor
  
  val classFinder = new REPLClassDirMonitor("/absolute/path/to/target/scala-2.12/classes")
  spark.registerClassFinder(classFinder)
  

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• Carregue as dependências de JAR, se necessário.

  spark.addArtifact("/absolute/path/to/dependency.jar")
  

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Solução de problemas de instalação de Snowpark Connect for Spark¶

With the following list of checks, you can troubleshoot Snowpark Connect for Spark installation and use.

• Certifique-se de que Java e Python sejam baseados na mesma arquitetura.

• Use the most recent Snowpark Connect for Spark package file, as described in Instalação do Snowpark Connect for Spark.

• Confirme que o comando :com:`Python` Comando com o código PySpark está funcionando corretamente para execução local, ou seja, sem conectividade do Snowflake.

  Por exemplo, execute um comando como o seguinte:

  python your_pyspark_file.py
  

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Step 1: Install Required Packages¶

• Install pyspark. You don’t need to install any Snowflake packages.

  pip install "pyspark[connect]>=3.5.0,<4"
  

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Step 2: Setup and Authentication¶

1. Generate a programmatic access token (PAT).

   For more information, see the following topics:

   • Uso de tokens de acesso programático para autenticação

   • ALTER USER … ADD PROGRAMMATIC ACCESS TOKEN (PAT)

   The following example adds a PAT named TEST_PAT for the user sysadmin and sets the expiration to 30 days.

   ALTER USER add PAT TEST_PAT ROLE_RESTRICTION = sysadmin DAYS_TO_EXPIRY = 30;
   

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2. Find your Snowflake Spark Connect host URL.

   Run the following SQL in Snowflake to find the hostname for your account:

   SELECT t.VALUE:type::VARCHAR as type,
          t.VALUE:host::VARCHAR as host,
          t.VALUE:port as port
     FROM TABLE(FLATTEN(input => PARSE_JSON(SYSTEM$ALLOWLIST()))) AS t where type = 'SNOWPARK_CONNECT';
   

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Step 3: Connect to Spark Connect server¶

• To connect to the Spark Connect server, use code such as the following:

  from pyspark.sql import SparkSession
  import urllib.parse
  
  # Replace with your actual PAT.
  pat = urllib.parse.quote("<pat>", safe="")
  
  # Replace with your Snowpark Connect host from the above SQL query.
  snowpark_connect_host = ""
  
  # Define database/schema/warehouse for executing your Spark session in Snowflake (recommended); otherwise, it will be resolved from your default_namespace and default_warehouse
  
  db_name = urllib.parse.quote("TESTDB", safe="")
  schema_name = urllib.parse.quote("TESTSCHEMA", safe="")
  warehouse_name = urllib.parse.quote("TESTWH", safe="")
  
  spark = SparkSession.builder.remote(f"sc://{snowpark_connect_host}/;token={pat};token_type=PAT;database={db_name};schema={schema_name};warehouse={warehouse_name}").getOrCreate()
  
  # Spark session is ready to use. You can write regular Spark DataFrame code, as in the following example:
  
  from pyspark.sql import Row
  
  df = spark.createDataFrame([
      Row(a=1, b=2.),
      Row(a=2, b=3.),
      Row(a=4, b=5.),])
  print(df.count())
  

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