AWS Glue takes a long time to finish
I just run a very simple job as follows
glueContext = GlueContext(SparkContext.getOrCreate())
l_table = glueContext.create_dynamic_frame.from_catalog(
database="gluecatalog",
table_name="fctable")
l_table = l_table.drop_fields(['seq','partition_0','partition_1','partition_2','partition_3']).rename_field('tbl_code','table_code')
print "Count: ", l_table.count()
l_table.printSchema()
l_table.select_fields(['trans_time']).toDF().distinct().show()
dfc = l_table.relationalize("table_root", "s3://my-bucket/temp/")
print "Before keys() call "
dfc.keys()
print "After keys() call "
l_table.select_fields('table').printSchema()
dfc.select('table_root_table').toDF().where("id = 1 or id = 2").orderBy(['id','index']).show()
dfc.select('table_root').toDF().where("table = 1 or table = 2").show()
The data structure is simple too
root
|-- table: array
| |-- element: struct
| | |-- trans_time: string
| | |-- seq: null
| | |-- operation: string
| | |-- order_date: string
| | |-- order_code: string
| | |-- tbl_code: string
| | |-- ship_plant_code: string
|-- partition_0
|-- partition_1
|-- partition_2
|-- partition_3
When I run job test, it took anywhere from 12 to 16 minutes to finish. But the cloud watch log showed that the job took 2 seconds to display all my data.
So my questions are: Where does AWS Glue job spend its time beyond the logging could show and is what it doing outside the logging period?
Answer
It's taking the time to setup the environment that allows your code to run. I had the same issue, contacted the AWS GLUE team and they were helpful. The reason it takes a long time is that GLUE builds an environment when you run the first job (which stays alive for 1 hours) if you run the same script twice or any other script within one hour, the next job will take significantly less time. They call this Cold Start when you run the first script, It took my first job 17 minutes, I ran the same job again right after the first one finished and it took 3 minutes only.