# Tutorial: usar um shell REPL com seu endpoint de desenvolvimento
<a name="dev-endpoint-tutorial-repl"></a>

 No AWS Glue, você pode criar um endpoint de desenvolvimento e, em seguida, invocar um shell REPL (Read–Evaluate–Print Loop) para executar o código PySpark de forma incremental. Dessa forma, você poderá depurar interativamente seus scripts de ETL antes de implantá-los.

 Para usar um REPL em um endpoint de desenvolvimento, você precisa ter autorização para SSH no endpoint. 

1. No computador local, abra uma janela de terminal que possa executar comandos SSH e cole o comando SSH editado. Execute o comando.

   Pressupondo que você aceitou a versão 1.0 padrão do AWS Glue com Python 3 para o endpoint de desenvolvimento, a saída será assim:

   ```
   Python 3.6.8 (default, Aug  2 2019, 17:42:44)
   [GCC 4.8.5 20150623 (Red Hat 4.8.5-28)] on linux
   Type "help", "copyright", "credits" or "license" for more information.
   SLF4J: Class path contains multiple SLF4J bindings.
   SLF4J: Found binding in [jar:file:/usr/share/aws/glue/etl/jars/glue-assembly.jar!/org/slf4j/impl/StaticLoggerBinder.class]
   SLF4J: Found binding in [jar:file:/usr/lib/spark/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
   SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
   SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
   Setting default log level to "WARN".
   To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
   2019-09-23 22:12:23,071 WARN  [Thread-5] yarn.Client (Logging.scala:logWarning(66)) - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
   2019-09-23 22:12:26,562 WARN  [Thread-5] yarn.Client (Logging.scala:logWarning(66)) - Same name resource file:/usr/lib/spark/python/lib/pyspark.zip added multiple times to distributed cache
   2019-09-23 22:12:26,580 WARN  [Thread-5] yarn.Client (Logging.scala:logWarning(66)) - Same path resource file:///usr/share/aws/glue/etl/python/PyGlue.zip added multiple times to distributed cache.
   2019-09-23 22:12:26,581 WARN  [Thread-5] yarn.Client (Logging.scala:logWarning(66)) - Same path resource file:///usr/lib/spark/python/lib/py4j-src.zip added multiple times to distributed cache.
   2019-09-23 22:12:26,581 WARN  [Thread-5] yarn.Client (Logging.scala:logWarning(66)) - Same path resource file:///usr/share/aws/glue/libs/pyspark.zip added multiple times to distributed cache.
   Welcome to
         ____              __
        / __/__  ___ _____/ /__
       _\ \/ _ \/ _ `/ __/  '_/
      /__ / .__/\_,_/_/ /_/\_\   version 2.4.3
         /_/
   
   Using Python version 3.6.8 (default, Aug  2 2019 17:42:44)
   SparkSession available as 'spark'.
   >>>
   ```

1. Digite a instrução `print(spark.version)` para ver se o shell REPL está funcionando corretamente. Se a versão Spark for exibida, seu REPL está pronto para ser usado.

1. Agora você pode tentar executar o seguinte script simples, linha por linha, no shell:

   ```
   import sys
   from pyspark.context import SparkContext
   from awsglue.context import GlueContext
   from awsglue.transforms import *
   glueContext = GlueContext(SparkContext.getOrCreate())
   persons_DyF = glueContext.create_dynamic_frame.from_catalog(database="legislators", table_name="persons_json")
   print ("Count:  ", persons_DyF.count())
   persons_DyF.printSchema()
   ```