stream-processing

I figured out a way to get the (x,y,z) data points for each frame from one hand previously. but im not sure how to do that for the new holistic model that they released. I am trying to get the all landmark data points for both hands as well as parts of the chest and face. does anyone know how to extract the holistic landmark data/print it to a text file? or at least give me some directions as to h

Bug description

When saving retention metrics it looks like the process is allocating space for saving the data serially when OK is clicked on the dialog as it blocks for a noticeable period and you feel the application is unresponsive.

Expected behavior

Instead the space creation for data should happen asynchronously and the dialog should return immediately, or at least show a spi

Describe the bug
If you try to create a KAFKA formatted source with a BYTES column, the command will return

CREATE STREAM TEST (ID BYTES KEY, b BYTES) WITH (kafka_topic='test', format='DELIMITED');
The 'KAFKA' format does not support type 'BYTES'

This is because the BYTES type is missing [here](https://github.com/confluentinc/ksql/blob/a27e5e7501891e644196f8d164d078672e0feecd

Use try-with-resources or close this "HazelcastServerCachingProvider" in a "finally" clause.

Change the implementation or suppress sonar warning

https://sonarcloud.io/project/issues?fileUuids=AW9qkIoKkrN6v4yKaPtE&id=hz-os-master&resolved=false&severities=BLOCKER&types=BUG

Under the hood, Benthos csv input uses the standard encoding/csv packages's csv.Reader struct.

The current implementation of csv input doesn't allow setting the LazyQuotes field.

We have a use case where we need to set the LazyQuotes field in order to make things work correctly.

I have this implemented in a custom marshaler now but wondering if it makes sense to push back upstream - when we are integrating with legacy services we find it useful to use the correlation ID as the message ID when first brought into watermill - from there they get sent on headers to subsequent services and work normally.

Its a simple change if it would make sense for other users.

It would be really useful if there was a method that could insert a column into an existing Dataframe between two existing columns. I know about .addColumn, but that seems to place the new column at the end of the Dataframe.

For example:

df.print()

A | B 
======
7 | 5
3 | 6

df.insert({ "afterColumn": "A", "newColumnName": "C", "data": [4,1], inplace: true })
df.print()

It can be very difficult to piece together a reasonably estimate of a history of events from the current workers logs because none of them have timestamps.

So for that end, I think we should add timestamps to the logs.

This has some cons:

1. We can't just use @printf like we have been until now. We need to either include a timestamp in every @printf call (laborious and error prone) or c

For example, given a simple pipeline such as:

Pipeline p = Pipeline.create();
p.readFrom(TestSources.items("the", "quick", "brown", "fox"))
 .aggregate(aggregator)
 .writeTo(Sinks.logger());

I'd like aggregator to be something requiring a non-serialisable dependency to do its work.

I know I can do this:

Pipeline p = Pipeline.create();
p.readFrom(TestSource

The mapcat function seems to choke if you pass in a mapping function that returns a stream instead of a sequence:

user> (s/stream->seq (s/mapcat (fn [x] (s/->source [x])) (s/->source [1 2 3])))
()
Aug 18, 2019 2:23:39 PM clojure.tools.logging$eval5577$fn__5581 invoke
SEVERE: error in message propagation
java.lang.IllegalArgumentException: Don't know how to create ISeq from: manifold.