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

The apply button does not make an api call

Describe the bug
There are a few functions (AS_MAP, ARRAY_SORT and URL_ENCODE_PARAM for example) that don't do null checks on inputs. We should do null checks because these errors are left uncaught and end up crashing queries. An example of one that has been fixed recently is confluentinc/ksql#8400

Avoid controlling endless loop with exception in loadAnonymousClasses, e.g. by extracting loading class to the method:

  private boolean tryLoadClass(String innerClassName) {
        try {
            parent.loadClass(innerClassName);
        } catch (ClassNotFoundException ex) {
            return false;
        }
        return true;
    }

https://sonarcloud.io/project

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'm attempting to write a health check for a service that uses watermill and i'd like to be able to easily determine if the router is still up and running

Router exposes a channel at .Running(), and it's possible to infer if the router is closed with a little indirection:

running := false
go func() {
  r := router.Running()

  for {
    if _, open := <-r; open {
      running 

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.