Bulk Unload from Redshift to S3 Interrupted










1















I wrote a python script that will do a bulk unload of all tables within a schema to s3, which scales to petabytes of data. While my script was running perfectly okay, my python script got interrupted due to a network disconnection.



Now, I'm in the midst of an unload job, unsure of how I can resume from the last point of failure. While I am debating about rerunning everything from the start, I am obviously thinking of using a Jenkins slave to run my job, however, I don't want to lose the hours of unload that had already completed. Is there a way to resume from where it stopped? My thinking is that it is hard to figure out the point to resume when the files are coming in as a ZIP.



What are the best practices I could use to avoid this in the future?



Any strategies to be able to pick up from where it left off?



Part of my code snippet that does unload:



#function to unload the data from tables into s3
def unloadData(passed_tables,schema,cur):
#loop through each table from the specified schema
for table in passed_tables:
#extract the table name string and store it in a variable
i=(table[0])
try:
#unload query to migrate the table to s3
unload='''unload('select * from 0.1') to 's3://<bucket>/2/3/'
iam_role 'arn:aws:iam::*****:role/***';'''.format(schema,i,schema,i)
cur.execute(unload)
print("Unload in progress! Check S3 bucket in a while to confirm.")
except Exception, e:
print("Failed to unload data! Try again or check the query.")
print(sys.stderr, "Exception: %s" % str(e))
sys.exit(1)









share|improve this question




























    1















    I wrote a python script that will do a bulk unload of all tables within a schema to s3, which scales to petabytes of data. While my script was running perfectly okay, my python script got interrupted due to a network disconnection.



    Now, I'm in the midst of an unload job, unsure of how I can resume from the last point of failure. While I am debating about rerunning everything from the start, I am obviously thinking of using a Jenkins slave to run my job, however, I don't want to lose the hours of unload that had already completed. Is there a way to resume from where it stopped? My thinking is that it is hard to figure out the point to resume when the files are coming in as a ZIP.



    What are the best practices I could use to avoid this in the future?



    Any strategies to be able to pick up from where it left off?



    Part of my code snippet that does unload:



    #function to unload the data from tables into s3
    def unloadData(passed_tables,schema,cur):
    #loop through each table from the specified schema
    for table in passed_tables:
    #extract the table name string and store it in a variable
    i=(table[0])
    try:
    #unload query to migrate the table to s3
    unload='''unload('select * from 0.1') to 's3://<bucket>/2/3/'
    iam_role 'arn:aws:iam::*****:role/***';'''.format(schema,i,schema,i)
    cur.execute(unload)
    print("Unload in progress! Check S3 bucket in a while to confirm.")
    except Exception, e:
    print("Failed to unload data! Try again or check the query.")
    print(sys.stderr, "Exception: %s" % str(e))
    sys.exit(1)









    share|improve this question


























      1












      1








      1








      I wrote a python script that will do a bulk unload of all tables within a schema to s3, which scales to petabytes of data. While my script was running perfectly okay, my python script got interrupted due to a network disconnection.



      Now, I'm in the midst of an unload job, unsure of how I can resume from the last point of failure. While I am debating about rerunning everything from the start, I am obviously thinking of using a Jenkins slave to run my job, however, I don't want to lose the hours of unload that had already completed. Is there a way to resume from where it stopped? My thinking is that it is hard to figure out the point to resume when the files are coming in as a ZIP.



      What are the best practices I could use to avoid this in the future?



      Any strategies to be able to pick up from where it left off?



      Part of my code snippet that does unload:



      #function to unload the data from tables into s3
      def unloadData(passed_tables,schema,cur):
      #loop through each table from the specified schema
      for table in passed_tables:
      #extract the table name string and store it in a variable
      i=(table[0])
      try:
      #unload query to migrate the table to s3
      unload='''unload('select * from 0.1') to 's3://<bucket>/2/3/'
      iam_role 'arn:aws:iam::*****:role/***';'''.format(schema,i,schema,i)
      cur.execute(unload)
      print("Unload in progress! Check S3 bucket in a while to confirm.")
      except Exception, e:
      print("Failed to unload data! Try again or check the query.")
      print(sys.stderr, "Exception: %s" % str(e))
      sys.exit(1)









      share|improve this question
















      I wrote a python script that will do a bulk unload of all tables within a schema to s3, which scales to petabytes of data. While my script was running perfectly okay, my python script got interrupted due to a network disconnection.



      Now, I'm in the midst of an unload job, unsure of how I can resume from the last point of failure. While I am debating about rerunning everything from the start, I am obviously thinking of using a Jenkins slave to run my job, however, I don't want to lose the hours of unload that had already completed. Is there a way to resume from where it stopped? My thinking is that it is hard to figure out the point to resume when the files are coming in as a ZIP.



      What are the best practices I could use to avoid this in the future?



      Any strategies to be able to pick up from where it left off?



      Part of my code snippet that does unload:



      #function to unload the data from tables into s3
      def unloadData(passed_tables,schema,cur):
      #loop through each table from the specified schema
      for table in passed_tables:
      #extract the table name string and store it in a variable
      i=(table[0])
      try:
      #unload query to migrate the table to s3
      unload='''unload('select * from 0.1') to 's3://<bucket>/2/3/'
      iam_role 'arn:aws:iam::*****:role/***';'''.format(schema,i,schema,i)
      cur.execute(unload)
      print("Unload in progress! Check S3 bucket in a while to confirm.")
      except Exception, e:
      print("Failed to unload data! Try again or check the query.")
      print(sys.stderr, "Exception: %s" % str(e))
      sys.exit(1)






      python database amazon-redshift database-migration fault-tolerance






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      edited Nov 15 '18 at 0:40







      Praneeth Turlapati

















      asked Nov 15 '18 at 0:04









      Praneeth TurlapatiPraneeth Turlapati

      63




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