Tutorial: Use a REPL shell with your development endpoint
In AWS Glue, you can create a development endpoint and then invoke a REPL (Read–Evaluate–Print Loop) shell to run PySpark code incrementally so that you can interactively debug your ETL scripts before deploying them.
In order to use a REPL on a development endpoint, you need to have authorization to SSH to the endpoint.
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On your local computer, open a terminal window that can run SSH commands, and paste in the edited SSH command. Run the command.
Assuming that you accepted AWS Glue version 1.0 with Python 3 for the development endpoint, the output will look like this:
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'. >>> Test that the REPL shell is working correctly by typing the statement,
print(spark.version). As long as that displays the Spark version, your REPL is now ready to use.-
Now you can try executing the following simple script, line by line, in the 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()
