X-Git-Url: https://git.arvados.org/arvados.git/blobdiff_plain/b54a5ea817d3d2087eaa07dcf98ec8a82af56d06..9cc572d6a44262e21251372e28b549cfc09e681a:/doc/_includes/_tutorial_hash_script_py.liquid

diff --git a/doc/_includes/_tutorial_hash_script_py.liquid b/doc/_includes/_tutorial_hash_script_py.liquid
index f9b2ec0948..9eacb763dc 100644
--- a/doc/_includes/_tutorial_hash_script_py.liquid
+++ b/doc/_includes/_tutorial_hash_script_py.liquid
@@ -1,63 +1,50 @@
 #!/usr/bin/env python
+{% comment %}
+Copyright (C) The Arvados Authors. All rights reserved.
 
-# Import the hashlib module (part of the Python standard library) to compute md5.
-import hashlib
+SPDX-License-Identifier: CC-BY-SA-3.0
+{% endcomment %}
 
-# Import the Arvados sdk module
-import arvados
+import hashlib      # Import the hashlib module to compute MD5.
+import os           # Import the os module for basic path manipulation
+import arvados      # Import the Arvados sdk module
 
-# Get information about the task from the environment
-this_task = arvados.current_task()
-
-# Get the "input" field from "script_parameters" on the job creation object
-this_job_input = arvados.getjobparam('input')
-
-# Create the object access to the collection referred to in the input
-collection = arvados.CollectionReader(this_job_input)
-
-# Create an object to write a new collection as output
-out = arvados.CollectionWriter()
+# Automatically parallelize this job by running one task per file.
+# This means that if the input consists of many files, each file will
+# be processed in parallel on different nodes enabling the job to
+# be completed quicker.
+arvados.job_setup.one_task_per_input_file(if_sequence=0, and_end_task=True,
+                                          input_as_path=True)
 
-# Set the name of output file within the collection
-out.set_current_file_name("md5sum.txt")
-
-# Get an iterator over the files listed in the collection
-all_files = collection.all_files()
-
-# Iterate over each file
-for input_file in all_files:
-    # Create the object that will actually compute the md5 hash
-    digestor = hashlib.new('md5')
-
-    while True:
-        # read a 1 megabyte block from the file
-        buf = input_file.read(2**20)
+# Get object representing the current task
+this_task = arvados.current_task()
 
-        # break when there is no more data left
-        if len(buf) == 0:
-            break
+# Create the message digest object that will compute the MD5 hash
+digestor = hashlib.new('md5')
 
-        # update the md5 hash object
-        digestor.update(buf)
+# Get the input file for the task
+input_id, input_path = this_task['parameters']['input'].split('/', 1)
 
-    # Get the final hash code
-    hexdigest = digestor.hexdigest()
+# Open the input collection
+input_collection = arvados.CollectionReader(input_id)
 
-    # Get the file name from the StreamFileReader object
-    file_name = input_file.name()
+# Open the input file for reading
+with input_collection.open(input_path) as input_file:
+    for buf in input_file.readall():  # Iterate the file's data blocks
+        digestor.update(buf)          # Update the MD5 hash object
 
-    # The "stream name" is the subdirectory inside the collection in which
-    # the file is located; '.' is the root of the collection.
-    if input_file.stream_name() != '.':
-        file_name = os.join(input_file.stream_name(), file_name)
+# Write a new collection as output
+out = arvados.CollectionWriter()
 
-    # Write an output line with the md5 value and file name.
-    out.write("%s %s\n" % (hexdigest, file_name))
+# Write an output file with one line: the MD5 value and input path
+with out.open('md5sum.txt') as out_file:
+    out_file.write("{} {}/{}\n".format(digestor.hexdigest(), input_id,
+                                       os.path.normpath(input_path)))
 
-# Commit the output to keep.  This returns a Keep id.
-output_id = out.finish()
+# Commit the output to Keep.
+output_locator = out.finish()
 
-# Set the output for this task to the Keep id
-this_task.set_output(output_id)
+# Use the resulting locator as the output for this task.
+this_task.set_output(output_locator)
 
 # Done!