midway | js Serverless Framework for front-end/full-stack developers | Serverless library

• you can't use keys like under without defining the,

• south south is not a valid anchor point

• you can only use the coordinates of non-empty cells

A quick workaround will be: to check

time.timezone.substring(0, 4) ==="(GMT"

and if true add GMT to the returned value before "PM" / "AM"

something like this:

Getting the center point of a line

couldn't get your problem exactly, if you want to print the value call it by its property name ".name" as you did, but you have to be sure that video is a single object.

Disclaimer: This question is broad and I am somewhat involved with the Apache Arrow project so my answer may/or may not be biased.

This question is broad in the sense that a question like a "When should I use NoSQL?" type of question is broad. It depends. This answer is based on the assumption that you already have a Spark pipeline. This answer is not an attempt at Spark Vs. Arrow (which is even broader to the point I wouldn't touch it).

Many Apache Spark pipelines would never need to use Arrow. Spark, unlike Arrow-based pipelines, has its own in-memory dataframe format (https://spark.apache.org/docs/1.6.1/api/java/org/apache/spark/sql/DataFrame.html) which, to my knowledge, cannot be zero-copied to Arrow. So converting from one format to the other is likely to introduce a performance hit of some kind and any benefit you achieve is going to have to be weighed against that.

You brought up one great example, which is switching to other languages / libraries. For example, Spark currently uses Arrow to apply a Pandas UDF (https://spark.apache.org/docs/latest/api/python/user_guide/arrow_pandas.html). In this case, whenever you are going to a library that doesn't use Spark's in-memory format (which means any non-Java library and some Java libraries) you are going to have to do a translation between in-memory formats and so you are going to pay the performance hit anyways and you might as well switch to Arrow.

There are some things that are faster with Arrow's format than Spark's format. I'm not going to try and list those here because, for the most part, the benefit isn't going to outweigh the cost of going Spark -> Arrow in the first place and I don't know that I have enough information to do so in any sort of comprehensive way. Instead, I'll provide one concrete example:

A common case for Arrow is when you need to transfer a table between processes that are on the same machine (or have a very fast I/O channel in between). In that case the cost of serializing to parquet and then deserializing back (Spark must do this to go Spark Dataframe -> Parquet -> Wire -> Parquet -> Spark Dataframe) is more expensive than the I/O saved (Parquet is more compact than Spark Dataframe so you will save some in transmission). If you have a lot of this type of communication it may be beneficial to leave Spark, do these transmissions in Arrow, and then return to Spark.

You don't need to wait for # between each command. You can just send all the commands and ignore the shell prompts. The shell buffers all the inputs.

You only need to wait for the username and password prompts, and then the final # after the last command.

You also need to send an exit command at the end, otherwise you won't get EOF.

After many hours of debugging I found the problem:

In MainActivity.onDestroy() I was attempting to stop my Service LocationService. The problem is that I attach a location callback to the Service and therefore the Service is leaked when the phone is rotated because MainActivity.onDestroy() is called which attempts to stop the service but can't apparently because of the attached Location Listener and Android seems to assume that it successfully stopped the service and so creates a new instance of it the next time around.

*** Seems to be an Android OS Bug ***

Turns out the code in //do something with connection was more important than I thought. Even at the tls.Dial level you have to read off the "body". My, now obviously wrong assumption, was that tls.Dial just setup the connection and that since a GET / HTTP 1.1 request hadn't been sent yet no data needed to be read off the wire. This was causing all those buffers full of server response to sit around.

_, _= ioutil.ReadAll(tConn) fixed it all in one line. I feel much wiser and also dumb at the same time. As a side note, at this level, ReadAll() can hang for a long time if the server responds slowly. tConn.SetReadDeadline(time.Now().Add(time.Second * timeout)) solved that too.

You can do it with the $event parameter. Make sure to compare your values safely.

If your value is not in the right type or has spaces or unwanted chars, this c.state == val might not work.

You can use the trim function to compare your value safely: c.state.trim() == val.trim()

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