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 clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Currently, ksqlDB causes a full topic scan whenever performing a pull query over a stream. This is inefficient when looking up specific sets of data, but necessary due to how pull queries are implemented over streams.

**Describe the

Describe the bug
The IMap.get() call will be blocked forever when a com.datastax.oss.driver.api.core.NoNodeAvailableException is thrown from the MapStore.

Setup:
Hazelcast v5.1.1
Cassandra SDK v4.14.1
Client-server setup
Read through cache, Cassandra DB gets called on a cache miss.

Test scenario: Read through cache, Cassandra server is down:

We tested the scenario whe

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] }));
  

dev=> create table t (v int);
CREATE_TABLE

dev=> drop materialized view t;
ERROR:  Invalid input syntax: Use `DROP TABLE` to drop a table.
dev=> drop source t;
ERROR:  Invalid input syntax: Use `DROP TABLE` to drop a table.

dev=> create materialized view mv as select * from t;
CREATE_MATERIALIZED_VIEW
dev=> drop table mv;
ERROR:  Invalid input syntax: Use `DROP MATERIALIZED VIEW`

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.