If your environment is so concurrent that such rare/hypothetical situation becomes an issue (or you positively can not afford to left even a single record behind) - then go for the most concurrent option in your DBMS and allow dirty reads (set it READ UNCOMMITTED in other words).
Cause, alas, no other offset tracking option (e.g. mode=timestamp+incrementing) would help you there, as timestamp is set at the moment insert/update physically happens, NOT at the moment of commit.
Other option here would be to establish a kind of reconciliation workflow there. But that's gonna complicate things significantly.
P.S. As for "redesign this setup" - think of the following: you, practically, are "publishing" record into a DB table, and then trigger "re-publishing" it into Kafka topic.
Why not skip the DB altogether & just publish direct to Kafka?
Or, if you need your DB table for other purposes - just initiate both events at once: insert into table AND produce into Kafka topic?
Old chat I know, but it helped me and allowed me to find another "quirk" I guess....
We have Report Builder 10, and it is fine with "LIKE". I use parameters with it to get Starts with or Contains.
In Select statements if you want "Starts with" I use LIKE @Parameter + '%'
But I did discover that to use it for "Contains" instead of "Starts With", I wasn't getting any results with
LIKE '%' + @Parameter + '%' as you would expect to do. I think it may be ok if you wanted text inside a word, but in my case the "Contains" may be second word in a name, after a space.... eg Mt Everest.
I found when I added a second wildcard eg. LIKE '%' +'%' + @Parameter + '%' it worked!
That's a relief to know. I traced the problem, it was not in the code. My text editor was not showing the final newline. When I used cat on the file, I could see that there was indeed a new line there.
A 30-second delay in 2025 is quite long, unless you're performing deep research that involves web crawling, compiling, and generating a report. For long running tasks, it's advisable to use an intermediate system like a Pub/Sub Queue. While this introduces the overhead of setting up new queues, managing message reception, and handling retries for failures, it's generally more efficient.
If you prefer to maintain a simpler system and a certain degree of latency is acceptable, consider the following:
Seems like You have a Scaffold inside another Scaffold.
Check it with the Widget Inspector, and on the top Scaffold set resizeToAvoidBottomInset: false
I hope this will help.
It's also worth noting the std::fmt::Pointer trait, because its name is very misleading, as it's actually not suitable for describing a generic pointer type.
In response to the question of ` is it possible to use retries and deadletter queues in celery? Answer:- Retries:- You can sell self.retry() in a task or set autoretry_for, max_retries, and retry_backoff to automatically retry failed tasks. Dead Letter Queues (DLQ):- Not built-in, but you can configure your broker (RabbitMQ, Redis) to route expired or rejected messages to a DLQ. Celebrity will work that configuration. Source of answer:- results from carried out experiments.
Mozilla signs extensions as a defense against malicious software. If an extension's files are altered, the signature becomes invalid (i.e., "corrupt"), so Firefox will refuse to load it.
You can disable signature verification by setting xpinstall.signatures.required in about:config to false in Firefox nightly, developer, and enterprise (esr). The setting exists for all versions of Firefox, but it has no effect in released and beta. (Nightly and developer are pre-release. Beta is pre-release, too, but it's supposed to behave as if it's released.) The setting might also work for some clones, but that depends on the clone.
I followed Mageworx answer above and added price_per_unit attribute to the attribute set where it was removed. However I threw it in the main group and it didn't work. There's one additional component to this. It has to go inside the mageworx-dynamic-options group.
From early mornings to late nights, 7 Brew keeps coffee lovers energized with a creative range of beverages. Popular picks like the Creme Brulee Breve, Caramel Macchiato, and Strawberry Smoothie are perfect for every mood. Along with shakes, teas, and the famous 7 Energy line, you’ll find endless flavor combinations to try. Discover all the latest drinks and specials by browsing the Latest Menu 2025.
As far as I'm concerned, a nibble is a 4-bit group. For example, the byte 0xA1 (0b10100001 ) is split into two nibbles, 0xA and 0x1 (0b1010 and 0b0001 respectively), and the most significant bit of that byte is 0x1, (I'm using the little-endian format). The sequence 0xA1B2C3 is split into 6 nibbles, 0xA, 0x1, 0xB, 0x2, 0xC and 0x3. Assuming a little-endian format, the most significante byte is 0xA1, and the most significante bit of all the 24-bit sequence is 0b1. The sane logic applies to any other byte sequence, such as the one you provided.
I have dataset in ms Excel in which customer id account no and account balance column are there. Also one customer id can have multiple account and corresponding amount. I want to add another column borrower total amount in which sum of amt of same customer having multiple account can be customised. What is the procedure to do it?
I would not recommend copying/pasting any tasks from package to package due to the GUID copying over and potentially causing a headache for other devs to figure out. Not my monkey, not my circus.
you can’t use a TypeScript method decorator on a standalone function (including React function components or hooks). TypeScript decorators (both legacy and the current TC39 proposal) only target classes and class elements. Hooks and function components are plain functions, so a MethodDecorator won’t apply.
Now in 2025, if you are using Visual Studio, there is an editor build in. Just right click on the "Folder" named Asset Catalogs in the Solution Explorer and click Add Asset Catalog . Maybe there is already a catalog you just have to fill with your images and you don't need to add a new one.
Jack Henry does not have a required maximum lifetime for a public key. We recommend using a jwks endpoint regardless of your chosen standard for key expiration/lifetime. The OIDC provider will automatically fetch new keys as they are rotated when using the JWKS endpoint for the configuration rather than hardcoding a public key in PEM format.
If your Python came from the Microsoft Store, VS Code/Jupyter may not find kernels. Install Python from python.org, recreate the venv, and register the kernel. That worked for me.
Weird as this may be - might still help someone. If switching from 1st gen to 2nd gen, give it a bit of time before trying to hit the API. OR if deploying for the first time too, giving it time can help.
A single attribute, "status" can be associated with the email messages or in gneral, the notifications.
It would constitute, therefore, the "state" of such messages or notifications.
Initially when they are "composed" but before being "submitted" for the "delivery", the attribute, "status" can assume the value, "readyforDelivery" with additional values. e.g., "readyToBeDelivered" subsequently.
POST /notifications would be used for "composing" the messages initially by setting the "status" to "readyforDelivery".
PATCH /notifications/{messageId} then, would provide the clients to initiate delivering the messages, which would change the "status" from "readyforDelivery" ---> "readyToBeDelivered"
This method does not require using verbs in the API Signatures, honors the REST principles for the HTTP Verbs, and free from any side- effects.
Really late answer but the problem continues to be relevant (I hit it trying to bring an existing git repo into p4 in order to practice migrating it back, haha). git p4 is intended ONLY for bringing git-side changes in a repo that was originally imported from p4. You can see this in git-p4.py, where the git branch to rebase or submit will be scanned by findUpstreamBranchPoint() to find the last commit that came from p4. This is done by looking for a log message containing text of the form "[git-p4: depot-paths = "//depot/path/": change = 3]". It keeps going backward until it finds the log or gets the error for going beyond the branch start. There is another tool gitp4transfer (untested) that can do this. Or you can work around by rebasing the foreign git repo branch onto one imported from p4 – see https://stackoverflow.com/a/29496432/10532990 – and then git p4 submit will work.
I am using VSCode version 1.103.0 in 2025 of system installer and marked all the options. But it did not work, even I have tried method that showed up earlier.
Then create a file name vscode_context_menu_fix.reg and paste below code.
Windows Registry Editor Version 5.00
[HKEY_CLASSES_ROOT\*\shell\Open with VSCode]
@="Open with VS Code"
"Icon"="C:\\Program Files\\Microsoft VS Code\\Code.exe"
[HKEY_CLASSES_ROOT\*\shell\Open with VSCode\command]
@="\"C:\\Program Files\\Microsoft VS Code\\Code.exe\" \"%1\""
[HKEY_CLASSES_ROOT\Directory\shell\Open with VSCode]
@="Open Folder with VS Code"
"Icon"="C:\\Program Files\\Microsoft VS Code\\Code.exe"
[HKEY_CLASSES_ROOT\Directory\shell\Open with VSCode\command]
@="\"C:\\Program Files\\Microsoft VS Code\\Code.exe\" \"%1\""
NoteVSCODE file path could be different. Please check your's first manually.
I ran into the same FDL shutdown. Ended up using SDDL (Simple Deferred Deep Linking) — covers web + mobile, supports Universal Links/App Links and deferred deep links. Has iOS/Android SDKs https://sddl.me
If your container is stateless then the ongoing process in the old pod just keeps running until it finishes or the pod is terminated, scaling out doesn’t interrupt it but the new pods wont pick up that same process they’ll just take new work. For statefull stuff its more tricky, if the process depends on local state and that pod goes away you can loose progress unless your app is coded to persist state to some external storage like a DB or volume. HPA itself doesnt migrate running tasks between pods so you gotta handle that logic in the app or job queue.
While the bail rule will only stop validating a specific field when it encounters a validation failure, the stopOnFirstFailure method will inform the validator that it should stop validating all attributes once a single validation failure has occurred
As you haven't changed your code it may be something with third party libraries that are not no longer compatible with the newer version of Visual Studio. A likely culprit is the C++/WinRT NuGet package. Update that one and try again.
i have on component named x , which is parent component, and another child component y. here i pass the props in the y from x and the props passed is fetched from api in X. but somehow the props is send empty in the state where api have not fetched , but whenever i use watch in the child the props change is seen. also, i have declared all the things in the data() properties, but idk why this is happening
Deploy an EPM Client Settings policy that enables EPM
If you do not have a default elevation behavior property set in the EPM Client Settings policy above, then ensure you have at least one Elevation Rules policy properly deployed
import time
from v1 import log, ServiceLogger
from logging.handlers import MemoryHandler
# Initialize handler once at module level
h = MemoryHandler(1000)
log.addHandler(h) # Add handler only once
class Service:
id = None
log = None
def __init__(self, id: int):
self.id = id
self.log = ServiceLogger(log) # No handler added here
def do(self):
print("Do some job per service!")
def service_exec():
service = Service(id=4)
service.do()
if __name__ == '__main__':
while True:
for i in range(10):
service_exec()
time.sleep(1)
Why do you want to have VPA in recommendation only mode instead of scaling the application both horizontally and vertically at the same time with a tool like Zesty Pod Rightsizing or something similar?
@Peter Cordes is right. I rewrite the atomic version (using 60 bytes padding on machine with 64 bytes cache line to separate r1,r2 into different cache lines):
Gradient clipping is used to limit the gradients of the model during training so they do not get too large and cause instability. It clip the gradient values before updating parameters. Suppose our clip range is (-5,5) if gradient value is 6.4 it will clip it down as 6.4/5 = 0.78. This is commonly used in where backpropagating through long sequences of hidden states is required such as in RNNs, LSTMs, and sometimes Transformers.
BatchNorm is a trainable layer. During training, it normalises the output of a layer so the mean is 0 and variance is 1 for each channel in the batch. This keeps all output elements on a similar scale — for example, preventing a case where one output value is 20000 and another is 20, which could make the model over-rely on the larger value. BatchNorm is mostly used in models such as CNNs, feed forward neural nets and other models that perform fixed computation.
Conclusion: Both solve different problems in different parts of the training process — gradient clipping handles exploding gradients in the backward pass, while BatchNorm2d stabilises activation scales in the forward pass.
I built a kubectl plugin that solves this exact problem!
kubectl-execrec is a transparent wrapper around kubectl exec that captures all commands and output. Just replace kubectl exec with kubectl execrec and everything gets logged to timestamped files.
To save memory and optimize memory it is default using parameter low_memory=True by reading the file in chunks to figure out the data type (dtype) for each column. The warning occurs when Pandas makes a decision based on an early chunk, but then finds conflicting data in a later chunk its either read it as string or whatsoever is it. Refer to this documentation for the parameter default.
From your experiment, i can see if pandas is processing your file as two chunk when processing 34926 lines while removing 1 is giving you warning. Which pandas for example, read your first chunk as integer, but in second chunk it seems wrongly take it as string or whatever it is which make giving you warning to declare what type of data should be that column is, that is why you should either supress it by declare dtype for each column or use low_memory=false.
Check and delete the hidden datasets which is created for every parameter. Right click on Datasets and enable show hidden dataset. Also check the parameter value in pop-up window
I found myself in the same boat migrating from Oracle to PostgreSQL in a project, and needing this table.
Your answer here https://stackoverflow.com/a/51636290/3756780 provided me with a good starting point, but was ultimately not fully working as i needed all columns in a constraint and their position (besides some small errors in the SQL which i also fixed in my view).
My goal was to provide an as close as possible to 1:1 version of the Oracle table user_cons_columns in Postgres. So i gave the columns of this view the same name as they had in Oracle. This means you don't have to update any existing queries that use this Oracle table (besides the considerations listed below).
I did need a full list of all columns in case of a constraint with more than one column, so thanks to the comment pointing to unnest, which i chose to implement with a Common Table Expression per this answer: https://stackoverflow.com/a/48010532/3756780
The performance considerations on this structure were no issue for me, this table/view is not queried often in my application. This might be different for your use case, then consider one of the alternatives in that same post.
A few considerations before we get to the final view which i created:
The position seems to match for basic constraints, but for others it dit not start at 1 for example. I think it is taking the column position of the original table in case of a Foreign Key constraint (and not the position in the FK constraint itself, like the Oracle version does).
If this matters for your code, you will need to adapt for this!
The constraint_type values are different between Oracle and Postgres, here are the mappings:
R -> f (referential becomes foreign key constraint)
Many values i found are in lower case in Postgres vs upper case in Oracle, you may need to adapt your queries accordingly (with for example lower() or upper() SQL functions)
This is the view i ended up with:
CREATE OR REPLACE VIEW user_cons_columns AS
WITH unnested_pg_constraint_attribute AS (select unnest(pgco.conkey) as con_key_id, *
from pg_constraint as pgco)
select isc.table_schema as owner,
pgco.conname as constraint_name,
isc.table_name as table_name,
isc.column_name as column_name,
pgco.con_key_id as position
from pg_attribute as pga
inner join pg_class as pgc on pga.attrelid = pgc.oid
inner join pg_namespace as pgn on pgn.oid = pgc.relnamespace
inner join information_schema.columns as isc on isc.column_name = pga.attname
and isc.table_name = pgc.relname
inner join unnested_pg_constraint_attribute as pgco on pgco.con_key_id = pga.attnum
and pgco.connamespace = pgc.relnamespace
and pgco.conrelid = pga.attrelid
order by owner, constraint_name, table_name, position;
Tested on PostgreSQL 12.22, compiled by Visual C++ build 1942, 64-bit
To answer the first half: If you don't need to ever use the double-less-than symbol, (and double-greater-than) you can simply redefine them using a show-rule:
The best course of action was to skip using a CollisionPolygon2D at all and instead just check Geometry2D.is_point_in_polygon(pos, polygon.polygon) on the visual Polygon2D. Where pos is the position of the mouse.
Assuming you are after the percentage of each count relative to their group instead of 100%, you need to include the groups in denominator of tbl_hierarchical().
tbl_list <- lapply(group_vars, function(var) {
gtsummary::tbl_hierarchical(
data = adsl,
denominator = adsl %>%
select(USUBJID, all_of(var)),
id = USUBJID,
by = .data[[var]],
variables = c(SITEGR1, SITEID)
)
})
It sounds like updateCurrentTranscriptItem() is being called on every playback time update without any limit, so multiple scroll events are triggered in quick succession and interrupt each other.
You can fix this by wrapping the call in a throttle() or debounce() function and adding behavior: "smooth" to keep the scroll consistent.
This kind of performance tuning is a common topic in front-end development — I’ve seen it come up often when mentoring new developers during our internship program at https://www.modsen-software.com/
Keycloak uses protocol mappers to map attributes on the user to claims in the resulting token.
If you want to map a custom, new attribute to the token, you need to configure a new protocol mapper. You do this either on a per-client basis by clicking on the client, then on its dedicated client scope and adding the mapper there or, if you want it for multiple clients, you can add a new client scope under the client scopes option, add the mapper and then add the scope to multiple clients.
Make sure to check the 'add to access token' toggle when defining the mapper.
You can find an example on how to map a custom attribute in this Baeldung tutorial.
In Chrome and Edge, backdrop-filter only blurs the content directly behind an element, not its children. This means that if your dropdown is placed inside a blurred navbar, it cannot “see” past the navbar’s background to blur the page content. While Safari and Firefox handle this as expected, Chrome and Edge fail because the dropdown is trapped within the navbar’s stacking context. The most reliable fix is to render the dropdown outside the navbar—either as a DOM sibling or via a React Portal to document.body—and position it absolutely beneath the trigger button so it can blur the page background directly. If you must keep it inside the navbar, give the dropdown its own semi-transparent background with backdrop-blur-lg (plus will-change: backdrop-filter and -webkit-backdrop-filter for WebKit) and ensure no ancestor has overflow: hidden or transform.
Like mentionned in the error message, you need to create an admin class for the ActionConfig entity with the function configureFormFields
Bellow an exemple of code.
<?php
namespace App\Admin;
use Sonata\AdminBundle\Admin\AbstractAdmin;
use Sonata\AdminBundle\Form\FormMapper;
final class ActionConfigAdmin extends AbstractAdmin
{
protected function configureFormFields(FormMapper $form): void
{
$form->add('your_field_name');
}
}
Don't forget to register it in the config/services.yaml with your name convention
I'm relatively new to this stuff, but I think it's impossible, for mathematical reasons. I'll try to explain below.
Any training metric must be differentiable with respect to the model parameters, so that the training process is able to optimise with respect to that metric, and find a local minimum / maxmimum.
The F1 score is defined as a function of precision and recall. These are both integer-valued 'counts' of the number of values in two blocks of the confusion matrix, and are therefore not differentiable with respect to model parameters.
I really enjoyed reading this post. Speaking of stylish outerwear, if you’re into premium designs, check out Oskar Jacket’s collection. Their Ross leather jacket is a perfect blend of timeless style and comfort definitely worth a look for fashion lovers
Prophet is a great time-series forecasting library, but it is known to struggle with count data, especially when being close to zero. I’ve encountered this issue frequently in my work, which ultimately led me to develop a new Prophet-inspired library: Gloria.
Gloria addresses this problem by introducing a number of new distributions beyond the normal distribution. For instance, count data can be handled using Poisson, Binomial, Negative Binomial, or Beta-Binomial distributions. The following code block showcases how I would try to treat your data, which is similar to what is shown in the Saturation tutorial:
import pandas as pd
from gloria import Gloria, cast_series_to_kind, CalendricData
# Load the data
data = pd.read_csv("headcounts.csv")
# Save the column names for later use
timestamp_name="Date"
metric_name="Headcount"
# Convert timestamp to datetime
data[timestamp_name] = pd.to_datetime(data[timestamp_name])
# Ensure metric is an unsigned integer
data[metric_name] = cast_series_to_kind(data[metric_name], "u")
# Set up the Gloria model
m = Gloria(
model="binomial",
timestamp_name=timestamp_name,
metric_name=metric_name,
sampling_period="15min",
n_changepoints = 0
)
# Create protocol for calendric data
calendric_protocol = CalendricData(country = "US")
# Add the protocol
m.add_protocol(calendric_protocol)
# Fit the model to the data
m.fit(data, capacity = 180)
# Predict
forecast = m.predict(periods=24 * 60 * 4)
# Plot the results
m.plot(forecast, show_capacity=True)
m.plot_components(forecast)
Some remarks:
Using Gloria, you need to pick the distribution model when instantiating the Gloria object. As your count data are capped at 180, it certainly makes sense to try model="binomial". The capacity is later passed as an argument to the fit() method. If your data show overdispersion (=excessive noise), you can try "beta-binomial" instead.
Gloria is rather strict enforcing data types. Count data cannot be floats and must be non-negative. Accordinly, they should be unsigned ints. We ensure that using the helper function cast_series_to_kind() (see in docs).
Gloria offers the possibility to handle non-daily data. Actually, you can do that too with Prophet, but its API is a bit inconvenient in this situation. For this reason, you tell the Gloria object on what time grid your input data live using sampling_period="15min" .
You are using a flat trend. Gloria does not (yet) include that, but you can achieve similar behaviour, by turning off changepoints setting n_changepoints=0 . Your trend function will have a nonzero intercept and growth rate, but no rate changes.
Gloria objects do not include any holidays or seasonalities by default. This design choice was made to prepare Gloria for use cases based on non-calendric data. Instead, we introduced protocols (see API documentation and Calendric Data tutorial). Protocols bundle default events and seasonalities for certain data sources. The CalendricData protocol emulates Prophet's default behaviour and adds weekly and yearly seasonalities as well as country holidays, if you specify the country of interest. Eventually you add the protocol to the model using m.add_protocol(calendric_data).
Your data show a drop lasting about a month around December 23 /Jan 24. My above code example won't be prepared for this drop and the fit in this range will be poor. You could model that allowing the trend to go to closer to zero. You would need to introduce custom changepoints as the Modeling Trends tutorial demonstrates, but there is another way: Gloria generalizes Prophet's holiday regressor concept to more general events. You could leverage this system by introducing a Gaussian-shaped pulse event of a month length (the length may need to be adapted), add it to your model using m.add_event() and setting its anchor time the center of the drop. The event will act as an envelope on your prediction and be optimized such as to suppress the weekly oscillations to match your drop in the data. You can model it in the following way
m = Gloria(...)
# Define the event profile
profile = Gaussian(width="30d") # one month Gaussian drop
# Add event to model
m.add_event(
name="drop",
regressor_type="SingleEvent", # choose the regressor class
profile=profile, # attach the profile
t_anchor="2024-01-01" # anchor time
)
m.fit(...)
Fitting your data in this way should give you a number of advantages
No more drops in the prediction below zero.
All predictions and confidence bands are integer values, respecting the lower and upper bounds of your data.
The prediction should better match your observed weekly peaks (Prophet pulls the prediction down towards zero due to the zero-inflated data. This way the prediction average fits the data at the expense of extreme values)
Fitting the drop with an event clearly separates this vacation effect from other patterns.
If you have target groups and it is saying unhealthy in health check then it will stall the first ever deployment process to on Install phase, so you have to de-register the instance or service connected to target group and then once the deployment is completed then you can register the service again in target groups and it will healthy if the targeted port is exposed or otherwise you will have to figure it out in logs. I used docker logs to check where things go wrong.. deployment was successful but my internal code was faulty.
This is just a general error for something going wrong with your chrome binary. To see if you're missing any dependency libraries on Linux, you can do ps aux | chrome and you may see an error pop up such as chrome: error while loading shared libraries: libxkbcommon.so.0: cannot open shared object file: No such file or directory Then you would know to download libxkbcommon0 (apt-get update && apt-get install -y libxkbcommon0)
