kandi X-RAY | gather Summary
kandi X-RAY | gather Summary
Gather is an ultra-lightweight, 37KB, no dependencies library to fire SQL like queries on Java collections and arrays. The data is not inserted into any in-memory database, nor any index is created. The query is matched against all the objects (could be different types as well) against the provided query. Useful to run ad-hoc queries and aggregation functions, say on XML/JSON/CSV and more.
Top functions reviewed by kandi - BETA
- Find unique values from an array of objects
- Find unique values for a given key
- Converts an Object to a Number
gather Key Features
gather Examples and Code Snippets
Trending Discussions on gather
Since I got Monterey 12.3 update (not sure it's related though), I have been getting this error when I try to run my python code in the terminal:
I am using python 3.10.3, Atom IDE, and run the code in terminal via atom-python-run package (which used to work perfectly fine). The settings for the package go like this:
which command in terminal returns the following (which is odd because earlier it would return something to just
I gather the error occurs because the terminal calls for
python instead of
python3, but I am super new to any coding and have no idea why it started now and how to fix it. Nothing of these has worked for me:
- I deleted and then reinstalled python from python.org.
- I tried
alias python='python3'(which I saw in one of the threads here).
- I tried
export PATH="/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin"(which I found here).
- To reset zsh and paths, I deleted all associated hidden files in
/local/users/and ran the terminal once again.
- I deleted evetyrhing and reinstalled macos and python only to get the same error.
ANSWERAnswered 2022-Mar-25 at 17:46
OK, after a couple of days trying, this is what has worked for me:
- I reinstalled Monterey (not sure it was essential, but I just figured I had messed with terminal and
- I installed
brewrather than from the official website. It would still return
command not founderror.
- I ran
echo "alias python=/usr/bin/python3" >> ~/.zshrcin terminal to alias
As far as I get it, there is no more pre-installed python 2.x in macOS as of 12.3 hence the error. I still find it odd though that
atom-python-run would call for
python instead of
python3 despite the settings.
I am writing a model Series class (kinda like the one in pandas) - and it should be both Positional and Associative....
ANSWERAnswered 2022-Mar-31 at 13:17
First, an MRE with an emphasis on the M1:
I'd like to confirm the battery usage of an app I am developing on iOS, specifically on Xcode 13 and iOS 15. (Note: This app previously showed no issues with battery usage on previous versions of iOS.)
Previously, it seems that there were two ways to gather energy usage information:
#1. On the device under Settings > Developer > Logging
- As per Apple's documentation described in the section titled "Log Energy Usage Directly on an iOS Device".
- However, on iOS15, I can't find any options for logging under Developer or anywhere under settings even when searching.
#2. Profiling via Instruments using the "Energy Log" template
- As per the same documentation from Apple described in the section "Use the Energy Diagnostics Profiling Template".
- While it is still available in Xcode 12, this template is missing in Xcode 13. Naturally, it's also not possible to profile an iOS15 device with Xcode 12.
Digging through the Xcode 13 release notes, I found the following:
Instruments no longer includes the Energy template; use metrics reporting in the Xcode Organizer instead. (74161279)
When I access the Organizer in Xcode (12 or 13), select an app and click "Energy" for all versions of the app, it shows the following:
"In some cases the pane shows “Insufficient usage data available,” because there may not be enough anonymized data reported by participating user devices. When this happens, try checking back in a few days."
Well over a year into production and having sufficient install numbers, I have a feeling that waiting a few days might not do much.
I would like to determine if this is a bug in my app or a bug in iOS15. How can energy usage data be gathered using Xcode 13 on iOS 15?...
ANSWERAnswered 2022-Feb-23 at 00:43
After contacting Apple Developer Technical Support (DTS) regarding this issue, they provided me with the following guidance.
Regarding "insufficient usage data available" for energy logs accessible via the Xcode organizer:
DTS indicated that they do not publish the thresholds for active users and usage logs would be expected to be present if you have more that a few thousand active users consistently on each version of your app. If your app meets this criteria and still does not show energy logs, DTS recommended opening a bug report with them.
Regarding how to collect energy log data for your app:
DTS recommended using MetricKit to get daily metric payloads. Payloads are delivered to your app every 24 hours and it is then possible to consume them and send them off device.
The instantiation of this is vey basic and can be as simple as:
I have the following 2 query plans for a particular query (second one was obtained by turning seqscan off):
The cost estimate for the second plan is lower than that for the first, however, pg only chooses the second plan if forced to do so (by turning seqscan off).
What could be causing this behaviour?
EDIT: Updating the question with information requested in a comment:
EXPLAIN (ANALYZE, BUFFERS, VERBOSE) for query 1 (seqscan on; does not use index). Also viewable at https://explain.depesz.com/s/cGLY:
ANSWERAnswered 2022-Feb-17 at 11:43
You should have those two indexes to speed up your query :
asyncpredicate, the "easy" way
One way would be to
Futures that compute the filters on every item. And then filters synchronously based on those:
ANSWERAnswered 2022-Feb-03 at 22:46
While you can't return an
impl Future from an
impl FnMut, you can return a boxed future, i.e. a
dyn Future which must be boxed because it's in return position. After a bit of borrow checker tetris, we arrive to this:
I have read several answers on SO already, and gathered these use-cases:
- When a function
- When a function has an infinite loop in it
But it is still unclear to me why we need to define the function like this:...
ANSWERAnswered 2022-Jan-22 at 13:23
The main difference between these signatures boils down to the fact that
! can coerce into any other type, and thus is compatible with any other type (since this code path is never taken, we can assume it to be of any type we need). It's important when we have multiple possible code paths, such as
For example, consider the following (probably contrived, but hopefully clear enough) code:
I am trying to define a function that takes a data frame or table as input with a specific number of ID columns (e.g., 2 or 3 ID columns), and the remaining columns are NAME1, NAME2, ..., NAMEK (numeric columns). The output should be a data table that consists of the same ID columns as before plus one additional ID column that groups each unique pairwise combination of the column names (NAME1, NAME2, ...). In addition, we must gather the actual values of the numeric columns into two new columns based on the ID column; an example with two ID columns and three numeric columns:...
ANSWERAnswered 2021-Dec-29 at 11:06
Here is an inspiring idea which is not fully satisfy OP's requirement (e.g., ID.new and number order) but I think it worth to be recoreded here.
You can turn
DT into long format by
shift value with the step
-nrow(DT) in order to do
the minus operation, i.e.
NAME1 - NAME2, NAME2 - NAME3, NAME3 - NAME1.
I want to get the last element of a lazy but finite Seq in Raku, e.g.:...
ANSWERAnswered 2021-Dec-04 at 19:49
What you're asking about ("get[ing] the last element of a lazy but finite Seq … while keeping the original Seq lazy") isn't possible. I don't mean that it's not possible with Raku – I mean that, in principle, it's not possible for any language that defines "laziness" the way Raku does with, for example, the
If particular, when a Seq is lazy in Raku, that "means that [the Seq's] values are computed on demand and stored for later use." Additionally, one of the defining features of a lazy iterable is that it cannot know its own length while remaining lazy – that's why calling
.elems on a lazy iterable throws an error:
I have a LocalDateTime object that I format as follows:...
ANSWERAnswered 2021-Oct-20 at 20:39
the ZonedDateTime can retrieve the current ZoneId. With the Zone, you can just use the
getDisplayName(TextStyle style, Locale locale) getter. Just experiment which TextStyle is the best for you.
As explained in the thread What is being pickled when I call multiprocessing.Process? there are circumstances where multiprocessing requires little to no data to be transferred via pickling. For example, on Unix systems, the interpreter uses
fork() to create the processes, and objects which already exist when multiprocessing starts can be accessed by each process without pickling.
However, I'm trying to consider scenarios beyond "here's how it's supposed to work". For example, the code may have a bug and an object which is supposed to read-only is inadvertently modified, leading to its pickling to be transferred to other processes.
Is there some way to determine what, or at least how much, is pickled during multiprocessing? The information doesn't necessarily have to be in real-time, but it would be helpful if there was a way to get some statistics (e.g., number of objects pickled) which might give a hint as to why something is taking longer to run than intended because of unexpected pickling overhead.
I'm looking for a solution internal to the Python environment. Process tracing (e.g. Linux
strace), network snooping, generalized IPC statistics, and similar solutions that might be used to count the number of bytes moving between processes aren't going to be specific enough to identify object pickling versus other types of communication.
Updated: Disappointingly, there appears to be no way to gather pickling statistics short of hacking up the module and/or interpreter sources. However, @aaron does explain this and clarified a few minor points, so I have accepted the answer....
ANSWERAnswered 2021-Aug-28 at 21:10
Multiprocessing isn't exactly a simple library, but once you're familiar with how it works, it's pretty easy to poke around and figure it out.
You usually want to start with context.py. This is where all the useful classes get bound depending on OS, and... well... the "context" you have active. There are 4 basic contexts: Fork, ForkServer, and Spawn for posix; and a separate Spawn for windows. These in turn each have their own "Popen" (called at
start()) to launch a new process to handle the separate implementations.
creating a process literally calls
os.fork(), and then in the child organizes to run
BaseProcess._bootstrap() which sets up some cleanup stuff then calls
self.run() to execute the code you give it. No pickling occurs to start a process this way because the entire memory space gets copied (with some exceptions. see: fork(2)).
I am most familiar with windows, but I assume both the win32 and posix versions operate in a very similar manner. A new python process is created with a simple crafted command line string including a pair of pipe handles to read/write from/to. The new process will import the __main__ module (generally equal to
sys.argv) in order to have access to all the needed references. Then it will execute a simple bootstrap function (from the command string) that attempts to read and un-pickle a
Process object from its pipe it was created with. Once it has the
Process instance (a new object which is a copy; not just a reference to the original), it will again arrange to call
The first time a new process is created with the "forkserver" context, a new process will be "spawn"ed running a simple server (listening on a pipe) which handles new process requests. Subsequent process requests all go to the same server (based on import mechanics and a module-level global for the server instance). New processes are then "fork"ed from that server in order to save the time of spinning up a new python instance. These new processes however can't have any of the same (as in same object and not a copy)
Process objects because the python process they were forked from was itself "spawn"ed. Therefore the
Process instance is pickled and sent much like with "spawn". The benefits of this method include: The process doing the forking is single threaded to avoid deadlocks. The cost of spinning up a new python interpreter is only paid once. The memory consumption of the interpreter, and any modules imported by __main__ can largely be shared due to "fork" generally using copy-on-write memory pages.
In all cases, once the split has occurred, you should consider the memory spaces totally separate, and the only communication between them is via pipes or shared memory. Locks and Semaphores are handled by an extension library (written in c), but are basically named semaphores managed by the OS.
multiprocessing.Manager's use pickling to synchronize changes to the proxy objects they return. The new-ish
multiprocessing.shared_memory uses a memory-mapped file or buffer to share data (managed by the OS like semaphores).
the code may have a bug and an object which is supposed to read-only is inadvertently modified, leading to its pickling to be transferred to other processes.
This only really applies to
multiprocessing.Manager proxy objects. As everything else requires you to be very intentional about sending and receiveing data, or instead uses some other transfer mechanism than pickling.
No vulnerabilities reported
You can use gather like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the gather component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
Reuse Trending Solutions
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page