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

Is your feature request related to a problem? Please describe.

A user in the community Slack wanted to filter out/select items from a JSON array of objects. Given records in the following format:

'[{"type": "AAA", "timestamp": "2021-09-27"}, {"type": "BBB", "timestamp": "2021-09-27"}, {"type": "AAA", "t

Add --add-exports jdk.management/com.ibm.lang.management.internal only when OpenJ9 detected.

Otherwise we got WARNING: package com.ibm.lang.management.internal not in jdk.management in logs

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.

This comment says that the message ID is optional,
but for SQL transport it is a mandatory attribute,
in turn it causes misunderstanding?

Is it possible to fix it or did I get something wrong?

https://github.com/ThreeDotsLabs/watermill/blob/b9928e750ba673cf93d442db88efc04706f67388/message/message.go#L20
https://github.com/ThreeDotsLabs/watermill/blob/b9928e750ba673cf93d442db88efc04706f6

I would like to convert a DataFrame to a JSON object the same way that Pandas does with to_dict().

toJSON() treats rows as elements in an array, and ignores the index labels. But to_dict() uses the index as keys.

Here is an example of what I have in mind:

function to_dict(df) {
    const rows = df.toJSON();
    const entries = df.index.map((e, i) => ({ [e]: rows[i] }));
  

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

Describe the bug

offset in select statements does not work.

To Reproduce

eric=> create table t1 (v1 int not null, v2 int not null, v3 int not null);
CREATE_TABLE
eric=> insert into t1 values (1,1,4), (5,1,4), (1,9,1), (9,8,1), (0,2,3);
INSERT 0 5
eric=> select * from t1 order by v1 limit 3;
 v1 | v2 | v3
----+----+----
  0 |  2 |  3
  1 |  1 |  4
  1 |  9 |  1
(3 

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.