dataflow | Efficient Data Loading Pipeline in Pure Python | Machine Learning library

I've created TPL Dataflow pipeline as shown below

I installed ubuntu server VM on Azure there I installed couchbase community edition on now i need to access the couchbase using dotnet SDK but code gives me bucket not found or unreachable error. even i try configuring a public dns and gave it as ip during cluster creation but still its giving the same. even i added public dns to the host file like below 127.0.0.1 public dns The SDK log includes below 2 statements Attempted bootstrapping on endpoint "name.eastus.cloudapp.azure.com" has failed. (e80489ed) A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond.

SDK Doctor Log:

I'm using F# and have an AsyncSeq<'t>>. Each item will take a varying amount of time to process and does I/O that's rate-limited.

I want to run all the operations in parallel and then pass them down the chain as an AsyncSeq<'t> so I can perform further manipulations on them and ultimately AsyncSeq.fold them into a final outcome.

The following AsyncSeq operations almost meet my needs:

• mapAsyncParallel - does the parallelism, but it's unconstrained, (and I don't need the order preserved)
• iterAsyncParallelThrottled - parallel and has a max degree of parallelism but doesn't let me return results (and I don't need the order preserved)

What I really need is like a mapAsyncParallelThrottled. But, to be more precise, really the operation would be entitled mapAsyncParallelThrottledUnordered.

Things I'm considering:

1. use mapAsyncParallel but use a Semaphore within the function to constrain the parallelism myself, which is probably not going to be optimal in terms of concurrency, and due to buffering the results to reorder them.
2. use iterAsyncParallelThrottled and do some ugly folding of the results into an accumulator as they arrive guarded by a lock kinda like this - but I don't need the ordering so it won't be optimal.
3. build what I need by enumerating the source and emitting results via AsyncSeqSrc like this. I'd probably have a set of Async.StartAsTask tasks in flight and start more after each Task.WaitAny gives me something to AsyncSeqSrc.put until I reach the maxDegreeOfParallelism

Surely I'm missing a simple answer and there's a better way?

Failing that, would love someone to sanity check my option 3 in either direction!

I'm open to using AsyncSeq.toAsyncEnum and then use an IAsyncEnumerable way of achieving the same outcome if that exists, though ideally without getting into TPL DataFlow or RX land if it can be avoided (I've done extensive SO searching for that without results...).

I'm trying to load multiple files from a location into DB using Foreach Loop Container & DataFlow task in SSIS.

It's getting crashed while I try to execute the package. It's not giving any error message, whenever I execute the package it crashes and closes the visual studio app immediately. I have to kill the debug task in the task manager for the next execution of the package.

So I tried the below steps:

1. I used a FileSystem task instead of DataFlow task to just move all the files from the source to the archive directory, which ran fine without any issues.

1. Ran the DataFlow task individually to load a single file into DB, which was also executed successfully.

I couldn't figure out what was going wrong here. Any help would be appreciated! Thanks!

Screenshots

This is a React web app. When I run

We have a data pipeline built in Google Cloud Dataflow that consumes messages from a pubsub topic and streams them into BigQuery. In order to test that it works successfully we have some tests that run in a CI pipeline, these tests post messages onto the pubsub topic and verify that the messages are written to BigQuery successfully.

This is the code that posts to the pubsub topic:

I am following this tutorial on migrating data from an oracle database to a Cloud SQL PostreSQL instance.

I am using the Google Provided Streaming Template Datastream to PostgreSQL

At a high level this is what is expected:

1. Datastream exports in Avro format backfill and changed data into the specified Cloud Bucket location from the source Oracle database
2. This triggers the Dataflow job to pickup the Avro files from this cloud storage location and insert into PostgreSQL instance.

When the Avro files are uploaded into the Cloud Storage location, the job is indeed triggered but when I check the target PostgreSQL database the required data has not been populated.

When I check the job logs and worker logs, there are no error logs. When the job is triggered these are the logs that logged:

We have Beam data pipeline running on GCP dataflow written using both Python and Java. In the beginning, we had some simple and straightforward python beam jobs that works very well. So most recently we decided to transform more java beam to python beam job. When we having more complicated job, especially the job requiring windowing in the beam, we noticed that there is a significant slowness in python job than java job which end up using more cpu and memory and cost much more.

some sample python code looks like:

I'm currently building PoC Apache Beam pipeline in GCP Dataflow. In this case, I want to create streaming pipeline with main input from PubSub and side input from BigQuery and store processed data back to BigQuery.

Side pipeline code