releases | A powerful Sphinx changelog-generating extension | Generator Utils library

Store the valid ids in an array and loop through each one, then remove correspondingly.

As given in this answer, Storms Internal Metrics are just estimated by a percentage of the real data flow. Initially, it uses 5% of incoming tuples to make those estimations. This may lead to inaccuracies for extreme high or low throughputs.

EDIT: The documentation describes the following:

In general all of these tuple count metrics are randomly sub-sampled unless otherwise stated. This means that the counts you see both on the UI and from the built in metrics are not necessarily exact. In fact by default we sample only 5% of the events and estimate the total number of events from that. The sampling percentage is configurable per topology through the topology.stats.sample.rate config. Setting it to 1.0 will make the counts exact, but be aware that the more events we sample the slower your topology will run (as the metrics are counted in the same code path as tuples are processed). This is why we have a 5% sample rate as the default.

EDIT 2 In this post, there is more information about the estimation:

The way it works is that if you choose a sampling rate of 0.05, it will pick a random element of the next 20 events in which to increase the count by 20. So if you have 20 tasks for that bolt, your stats could be off by +-380.

By the way, execute_count is just an increasing number, while your tuple_inRate.count is a rate, isn`t it?

The Kubernetes project is making an effort to move away from wording that can be considered offensive, with one concrete recommendation being renaming master to control-plane. In other words control-plane and master mean essentially the same thing, and the goal is to switch the terminology to use control-plane exclusively going forward. (More info in this answer)

The kubectl command is a command line interface that executes on a client (i.e your computer) and interacts with the cluster through the control-plane. The IP address you are seing through cluster-info is the IP address through which you reach the control-plane

An FTS entity table always has a column named rowid that is the equivalent of an INTEGER PRIMARY KEY index.

In brief SQLite's INTEGER does not imply a Java/Kotlin Integer/Int/int it is a column affinity/type.

If you look at the Datatypes in SQLite3 then an INTEGER can be up to 8 bytes (64 bit signed). Which in Java/Kotlin is long/Long.

Further evidence can be seen with the SQLiteDatabase insert convenience method as it returns the id (rowid) of the inserted row not as an int but as a long.

Returns long - the row ID of the newly inserted row, or -1 if an error occurred.

SQLiteAutoincrement explains about rowid and that in theory it can be from 1-9223372036854775807 (you can even have negative values).

As such, it is wrong to use int/Int/Integer for the rowid as in theory (not that likely in practice), the rowid can be larger than an int/Int/Integer.

I should use INT not LONG.

I'd suggest that you should use Long. It makes no difference to the data as SQLite will store an integer in as little space as it can. Furthermore, it makes no difference to the table(s) that room creates as the COLUMN TYPE will be INTEGER.

You can try my model-relative-opacity component along with an animation component:

In Java 11, you can find out the flags that have been passed to the JVM and the "ergonomic" ones that have been set by the JVM by adding -XX:+PrintCommandLineFlags to the JVM options.

That should tell you if the container you are using is overriding the flags you have given.

Having said that, its is (IMO) unlikely that the container is what is overriding the parameters.

It is not unusual for a JVM to use more memory that the -Xmx option says. The explanation is that that option only controls the size of the Java heap. A JVM consumes a lot of memory that is not part of the Java heap; e.g. the executable and native libraries, the native heap, metaspace, off-heap memory allocations, stack frames, mapped files, and so on. Depending on your application, this could easily exceed 300MB.

Secondly, OOMEs are not necessarily caused by running out of heap space. Check what the "reason" string says.

Finally, this could be a difference in your app's memory utilization in a containerized environment versus when you run it locally.

The Printing API that allows programmatically printing the currently loaded web page using the PDF printer is not available in JxBrowser 7. It's already on our roadmap. We do our best to introduce this functionality in one of the next versions.

I recommend that you follow us on https://twitter.com/JxBrowserTeam to be notified when this functionality is released.

UPD: In JxBrowser 7.13 the Printing API has been extended with functionality that allows programmatically configure print settings and print a web page without displaying the Print Preview dialog. The API allows you to save the currently loaded web page as a PDF document. Read more at https://jxbrowser-support.teamdev.com/docs/guides/printing.html#configuring-settings

The following example demonstrates how to save a web page as PDF:

You can give fixed height and width to the images as required by the carousel:

According to the homepage for ppa:ondrej/nginx, here the PPA description: