Merge pull request #36 from RachelTucker/updating_samples

Updating bulk get sample for multi-blob download and improved clarity

Author: RachelTucker

samples/gettingData.py

@@ -15,59 +15,97 @@
 
 from ds3 import ds3
 
-client = ds3.createClientFromEnv()
+# This example retrieves all objects in the specified bucket and lands them in the specified destination.
+# By default it looks for objects in bucket 'books' and lands them in the temporary directory.
+# At the end of running, those files are removed from the local system for testing reasons.
+#
+# This example assumes that a bucket named "books" containing some objects exist on the server.
 
-bucketName = "books"
-# this example assumes that a bucket named "books" containing some objects exist on the server 
+bucketName = "books"  # modify this value to match the BP bucket you wish to retrieve objects from
 
+destination = tempfile.gettempdir()  # modify this value to match where the object should be landed on your system
+
+client = ds3.createClientFromEnv()
+
+# retrieves a list of all objects in the bucket
 bucketContents = client.get_bucket(ds3.GetBucketRequest(bucketName))
 
+# Converting that list of objects into a list of objects to retrieve.
+# If you want to retrieve a subset of objects, or already know their names, then just make a list of ds3.DesGetObject
+# where each item describes one object you wish to retrieve from the BP.
 objectList = list([ds3.Ds3GetObject(obj['Key']) for obj in bucketContents.result['ContentsList']])
+
+# Create a dictionary to map the BP object name to the destination where your landing the object.
+# In this example, we are landing all objects to the path described in the destination variable.
+# Also, if the object name contains paths, this will normalize it for your OS and land that object
+# in a sub-folder of the destination.
+objectNameToDestinationPathMap = {}
+for obj in objectList:
+    objectNameToDestinationPathMap[obj.name] = os.path.join(destination, os.path.normpath(obj.name))
+
+# Create a bulk get job on the BP. This tells the BP what objects your going to retrieve.
+# This triggers the BP to start staging the objects in cache.
+# Large objects may have been broken up into several pieces, i.e. blobs.
+# The BP breaks up your retrieval job into "chunks".
+# These chunks represent bundles of data that are ready to be retrieved.
+# Each chunk which will contain one or more pieces of your files (blobs).
+# How the job will be broken up (chunked) is determined when you create the bulk get job.
 bulkGetResult = client.get_bulk_job_spectra_s3(ds3.GetBulkJobSpectraS3Request(bucketName, objectList))
 
-# create a set of the chunk ids which will be used to track
-# what chunks have not been retrieved
+# Create a set of the chunk ids that describe all units of work that make up the get job.
+# This will be used to track which chunks we still need to process.
 chunkIds = set([x['ChunkId'] for x in bulkGetResult.result['ObjectsList']])
 
-# create a dictionary to map our retrieved objects to temporary files
-# if you want to keep the retreived files on disk, this is not necessary
-tempFiles = {}
-
-# while we still have chunks to retrieve
+# Attempt to retrieve data from the BP while there are still chunks that need to be processed.
 while len(chunkIds) > 0:
-    # get a list of the available chunks that we can get
+    # Get a list of chunks for this job that are ready to be retrieved.
     availableChunks = client.get_job_chunks_ready_for_client_processing_spectra_s3(
         ds3.GetJobChunksReadyForClientProcessingSpectraS3Request(bulkGetResult.result['JobId']))
 
     chunks = availableChunks.result['ObjectsList']
 
-    # check to make sure we got some chunks, if we did not
-    # sleep and retry.  This could mean that the cache is full
+    # Check to make sure we got some chunks, if we did not sleep and retry.
+    # Having no chunks ready may indicate that the BP cache is currently full.
     if len(chunks) == 0:
         time.sleep(availableChunks.retryAfter)
         continue
 
-    # for each chunk that is available, check to make sure
-    # we have not gotten it, and if not, get that object
+    # For each chunk that is available, check to make sure we haven't processed it already.
+    # If we have not processed this chunk yet, then retrieve all its objects.
     for chunk in chunks:
         if not chunk['ChunkId'] in chunkIds:
+            # This chunk has already been processed
             continue
-        chunkIds.remove(chunk['ChunkId'])
+
+        # For each blob within this chunk, retrieve the data and land it on the destination.
         for obj in chunk['ObjectList']:
-            # if we haven't create a temporary file for this object yet, create one
-            if obj['Name'] not in list(tempFiles.keys()):
-                tempFiles[obj['Name']] = tempfile.mkstemp()
+            # Open the destination file and seek to the offset corresponding with this blob.
+            objectStream = open(objectNameToDestinationPathMap[obj['Name']], "wb")
+            objectStream.seek(int(obj['Offset']))
 
-            # get the object
-            objectStream = open(tempFiles[obj['Name']][1], "wb")
+            # Get the blob for the current object and write it to the destination.
             client.get_object(ds3.GetObjectRequest(bucketName,
                                                    obj['Name'],
                                                    objectStream,
                                                    offset=int(obj['Offset']),
                                                    job=bulkGetResult.result['JobId']))
 
-# iterate over the temporary files, printing out their names, then closing and and removing them
-for objName in list(tempFiles.keys()):
-    print(objName)
-    os.close(tempFiles[objName][0])
-    os.remove(tempFiles[objName][1])
+            # Close the file handle.
+            objectStream.close()
+
+        # We've finished processing this chunk. Remove it from our list of chunks that still need processing.
+        chunkIds.remove(chunk['ChunkId'])
+
+# Go through all items that were landed and check that they were created.
+# This is not needed in production code.
+for objName in objectNameToDestinationPathMap.keys():
+    destinationPath = objectNameToDestinationPathMap[objName]
+    if os.path.isfile(destinationPath):
+        fileSize = os.path.getsize(destinationPath)
+        print(f'Retrieved object={objName}, landed at destination={destinationPath}, has size={fileSize}')
+
+        # This removes the retrieved file from the destination.
+        # This is done to clean up the script for when people are using it to test connection only.
+        os.remove(destinationPath)  # Remove in production code.
+    else:
+        print(f'Failed to retrieve object={objName}')