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https://issues.apache.org/jira/browse/BEAM-11266?focusedWorklogId=512674&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-512674
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ASF GitHub Bot logged work on BEAM-11266:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 17/Nov/20 00:10
Start Date: 17/Nov/20 00:10
Worklog Time Spent: 10m
Work Description: y1chi commented on a change in pull request #13350:
URL: https://github.com/apache/beam/pull/13350#discussion_r524486089
##########
File path: sdks/python/apache_beam/io/mongodbio.py
##########
@@ -241,6 +275,27 @@ def _get_split_keys(self, desired_chunk_size_in_mb,
start_pos, end_pos):
max={'_id': end_pos},
maxChunkSize=desired_chunk_size_in_mb)['splitKeys'])
+ def _get_buckets(self, desired_chunk_size, start_pos, end_pos):
+ if start_pos >= end_pos:
+ # single document not splittable
+ return []
+ size = self.estimate_size()
+ bucket_count = size // desired_chunk_size
Review comment:
The split function will likely be called recursively for dynamic
rebalancing, so for a range with start_pos and end_pos, it can be further split
upon backend request, so it might not be reasonable to always use the total
collection size divide by desired_chunk_size to calculate the bucket count. Is
it possible to only get the buckets within the give _id range? and we can
probably use an average document size times the number of documents to
calculate the size of the range being split.
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Issue Time Tracking
-------------------
Worklog Id: (was: 512674)
Time Spent: 1h (was: 50m)
> Cannot use Python MongoDB connector with Atlas MongoDB
> ------------------------------------------------------
>
> Key: BEAM-11266
> URL: https://issues.apache.org/jira/browse/BEAM-11266
> Project: Beam
> Issue Type: Bug
> Components: io-py-mongodb
> Affects Versions: 2.25.0
> Environment: Google Cloud Dataflow
> Reporter: Eugene Nikolaiev
> Assignee: Yichi Zhang
> Priority: P2
> Labels: mongodb, python
> Fix For: 2.27.0
>
> Time Spent: 1h
> Remaining Estimate: 0h
>
> Cannot use the Python MongoDB connector with a managed Atlas instance. The
> current implementations makes use of splitVector which is a high-privilege
> function that cannot be assigned to any user in Atlas. Getting error:
> {code:java}
> pymongo.errors.OperationFailure: not authorized on properties to execute
> command
> { splitVector: "properties.properties", keyPattern: { _id: 1 },
> ...{code}
> BEAM-4567 addressed the same issue in Java connector.
> Proposed solution for Python is to add {{bucket_auto}} option for the
> connector which would configure it to use {{@bucketAuto}} MongoDB aggregation
> instead of {{splitVector}} command:
> {code:java}
> pipeline | ReadFromMongoDB(uri='mongodb+srv://user:[email protected]',
> db='testdb',
> coll='input',
> bucket_auto=True)
> {code}
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