| dotsMethods {methods} | R Documentation |
...
in Method SignaturesThe “...” argument in R functions is treated specially, in that it matches zero, one or more actual arguments (and so, objects). A mechanism has been added to R to allow “...” as the signature of a generic function. Methods defined for such functions will be selected and called when all the arguments matching “...” are from the specified class or from some subclass of that class.
Beginning with version 2.8.0 of R, S4 methods can be dispatched (selected and called) corresponding to the special argument “...”. Currently, “...” cannot be mixed with other formal arguments: either the signature of the generic function is “...” only, or it does not contain “...”. (This restriction may be lifted in a future version.)
Given a suitable generic function, methods are specified in the
usual way by a call to setMethod
. The method
definition must be written expecting all the arguments corresponding
to “...” to be from the class specified in the method's signature,
or from a class that extends that class (i.e., a subclass of that
class).
Typically the methods will pass “...” down to another function or will create a list of the arguments and iterate over that. See the examples below.
When you have a computation that is suitable for more than one existing
class, a convenient approach may be to define a union of these
classes by a call to setClassUnion
. See the example
below.
See Methods_Details for a general discussion. The following assumes you have read the “Method Selection and Dispatch” section of that documentation.
A method selecting on “...” is specified by a single class in the
call to setMethod
. If all the actual arguments
corresponding to “...” have this class, the corresponding method is
selected directly.
Otherwise, the class of each argument and that class' superclasses are computed, beginning with the first “...” argument. For the first argument, eligible methods are those for any of the classes. For each succeeding argument that introduces a class not considered previously, the eligible methods are further restricted to those matching the argument's class or superclasses. If no further eligible classes exist, the iteration breaks out and the default method, if any, is selected.
At the end of the iteration, one or more methods may be eligible.
If more than one, the selection looks for the method with the least
distance to the actual arguments. For each argument, any inherited
method corresponds to a distance, available from the contains
slot of the class definition. Since the same class can arise for
more than one argument, there may be several distances associated
with it. Combining them is inevitably arbitrary: the current
computation uses the minimum distance. Thus, for example, if a
method matched one argument directly, one as first generation
superclass and another as a second generation superclass, the
distances are 0, 1 and 2. The current selection computation would
use distance 0 for this
method. In particular, this selection criterion tends to use a method that
matches exactly one or more of the arguments' class.
As with ordinary method selection, there may be multiple methods with the same distance. A warning message is issued and one of the methods is chosen (the first encountered, which in this case is rather arbitrary).
Notice that, while the computation examines all arguments, the essential cost of dispatch goes up with the number of distinct classes among the arguments, likely to be much smaller than the number of arguments when the latter is large.
Methods dispatching on “...” were introduced in version 2.8.0 of
R. The initial implementation of the corresponding selection and
dispatch is in an R function, for flexibility while the new
mechanism is being studied. In this implementation, a local version
of setGeneric
is inserted in the generic function's
environment. The local version selects a method according to the
criteria above and calls that method, from the environment of the
generic function. This is slightly different from the action taken
by the C implementation when “...” is not involved. Aside from the
extra computing time required, the method is evaluated in a true
function call, as opposed to the special context constructed by the
C version (which cannot be exactly replicated in R code.) However,
situations in which different computational results would
be obtained have not been encountered so far, and seem very
unlikely.
Methods dispatching on arguments other than “...” are cached by storing
the inherited method in the table of all methods, where it will be
found on the next selection with the same combination of classes
in the actual arguments (but not used for inheritance searches).
Methods based on “...” are also cached, but not found quite
as immediately. As noted, the selected method depends only on the
set of classes that occur in the “...” arguments. Each of
these classes can appear one or more times, so many combinations of
actual argument classes will give rise to the same effective
signature. The selection computation first computes and sorts the
distinct classes encountered. This gives a label that will be
cached in the table of all methods, avoiding any further search for
inherited classes after the first occurrence. A call to
showMethods
will expose such inherited methods.
The intention is that the “...” features will be added to the standard C code when enough experience with them has been obtained. It is possible that at the same time, combinations of “...” with other arguments in signatures may be supported.
Chambers, John M. (2008) Software for Data Analysis: Programming with R Springer. (For the R version.)
Chambers, John M. (1998) Programming with Data Springer (For the original S4 version.)
For the general discussion of methods, see Methods_Details and links from there.
cc <- function(...)c(...) setGeneric("cc") setMethod("cc", "character", function(...)paste(...)) setClassUnion("Number", c("numeric", "complex")) setMethod("cc", "Number", function(...) sum(...)) setClass("cdate", contains = "character", slots = c(date = "Date")) setClass("vdate", contains = "vector", slots = c(date = "Date")) cd1 <- new("cdate", "abcdef", date = Sys.Date()) cd2 <- new("vdate", "abcdef", date = Sys.Date()) stopifnot(identical(cc(letters, character(), cd1), paste(letters, character(), cd1))) # the "character" method stopifnot(identical(cc(letters, character(), cd2), c(letters, character(), cd2))) # the default, because "vdate" doesn't extend "character" stopifnot(identical(cc(1:10, 1+1i), sum(1:10, 1+1i))) # the "Number" method stopifnot(identical(cc(1:10, 1+1i, TRUE), c(1:10, 1+1i, TRUE))) # the default stopifnot(identical(cc(), c())) # no arguments implies the default method setGeneric("numMax", function(...)standardGeneric("numMax")) setMethod("numMax", "numeric", function(...)max(...)) # won't work for complex data setMethod("numMax", "Number", function(...) paste(...)) # should not be selected w/o complex args stopifnot(identical(numMax(1:10, pi, 1+1i), paste(1:10, pi, 1+1i))) stopifnot(identical(numMax(1:10, pi, 1), max(1:10, pi, 1))) try(numMax(1:10, pi, TRUE)) # should be an error: no default method ## A generic version of paste(), dispatching on the "..." argument: setGeneric("paste", signature = "...") setMethod("paste", "Number", function(..., sep, collapse) c(...)) stopifnot(identical(paste(1:10, pi, 1), c(1:10, pi, 1)))


BIO_f_prefix, BIO_set_prefix, BIO_set_indent, BIO_get_indent - prefix BIO filter
#include <openssl/bio.h>
const BIO_METHOD *BIO_f_prefix(void);
long BIO_set_prefix(BIO *b, const char *prefix);
long BIO_set_indent(BIO *b, long indent);
long BIO_get_indent(BIO *b);
BIO_f_cipher() returns the prefix BIO method. This is a filter for text output, where each line gets automatically prefixed and indented according to user input.
The prefix and the indentation are combined. For each line of output going through this filter, the prefix is output first, then the amount of additional spaces indicated by the indentation, and then the line itself.
By default, there is no prefix, and indentation is set to 0.
BIO_set_prefix() sets the prefix to be used for future lines of text, using prefix. prefix may be NULL, signifying that there should be no prefix. If prefix isn't NULL, this function makes a copy of it.
BIO_set_indent() sets the indentation to be used for future lines of text, using indent. Negative values are not allowed.
BIO_get_indent() gets the current indentation.
BIO_set_prefix(), BIO_set_indent() and BIO_get_indent() are implemented as macros.
BIO_f_prefix() returns the prefix BIO method.
BIO_set_prefix() returns 1 if the prefix was correctly set, or <=0 on failure.
BIO_set_indent() returns 1 if the prefix was correctly set, or <=0 on failure.
BIO_get_indent() returns the current indentation, or a negative value for failure.
Copyright 2019 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Click on the buttons inside the tabbed menu:
London is the capital city of England. This is image 1.
Paris is the capital of France. This is image 2.
London is the capital city of England. This is image 3.
Paris is the capital of France. This is image 4.
Tokyo is the capital of Japan. This is image 5.
Tokyo is the capital of Japan. This is image 6.
Tokyo is the capital of Japan. This is image 7.


Land allotted for Christian burial ground
Members of the community thank Chief Minister
by Special CorrespondentThe State government has decided to earmark 68.32 acres of land for burial grounds for Christian community in different parts of Medchal and Rangareddy districts.
The members of the Christian community led by bishops and pastors called on State Planning Board Vice-Chairman B. Vinod Kumar on Monday and thanked the government for allotting the land.
The delegation told Mr. Vinod Kumar that their demand for land remained unresolved for the past several decades and the government, headed by Chief Minster K. Chandrasekhar Rao, took the initiative of fulfilling it. They said the community has planned to organise a public meeting as a thanksgiving gesture to the Chief Minister soon.
A preparatory meeting for deciding the date and venue of the public meeting would be held on Wednesday, they informed Mr. Vinod Kumar. The delegation expressed willingness to meet the CM soon.


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Tezvaz - Notes
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Former Beith star looking forward to clash with old side as Darvel hosts Bellsdale side
The Darvel assistant manager will be in the dugout against the team he played with for almost six years as Vale host Beith in the West of Scotland Cup first round.
by Lochlin Highet, https://www.dailyrecord.co.uk/authors/lochlin-highet/Kevin MacDonald admits he can’t wait to battle with his former side Beith this weekend.
The Darvel assistant manager will be in the dugout against the team he played with for almost six years as Vale host Beith in the West of Scotland Cup first round.
MacDonald reckons drawing the cup holders was as hard a draw as they could have got.
The former centre-back, who won the Premier Division League title and the Junior Cup at Bellsdale as well as the Ayrshire Cup, said: “I am looking forward to it. As a player I always enjoyed going up against a former team.
“Most of the staff and players are the same as when I left less than a year ago. And I have had a bit of banter with some of them this week.
“I am looking forward to seeing everyone again.
“I loved my time at Beith, everyone there was great from the fans, committee, players and staff.
“It will be great to catch up with them properly after the game on Saturday – saying that, if we lose I might just have to go into hiding.”
Beith, nicknamed The Mighty, drew 3-3 with fierce rivals Kilbirnie on Saturday, another side MacDonald played with.
He said: “I actually moved directly from Kilbirnie to Beith, so the fans have always been quick to remind me that I was a turncoat.
“But moving to Beith was a great decision as I had a successful spell there”
He added: “Even though it was a draw on Saturday, going by the highlights Beith could have won by a few.
“I believe they are still one of the strongest squads in Junior football.
“The game on Saturday is as tough as it gets.
“But it’s good for us that Paul Frize is suspended.”
MacDonald, who played 13 SPL games with Motherwell before moving to Stirling Albion and Dumbarton, reckons playing a Premiership side will be a great opportunity for Darvel.
He said: “This will be a good test to find out how we do against the level of teams we want to play.
“We beat them in pre-season but that was when we had a settled squad, so it might be different now.
“We have the quality to beat these top teams. We have got great players across the park.
“And then we’ve got the incredible Darren Miller who will probably tell you he’s the top scorer in Britain.”


If you find a bot/spider/crawler user agent that CrawlerDetect fails to detect, please submit a pull request with with the regex pattern added to the $data array in Fixtures/Crawlers.php and add the failing user agent to tests/crawlers.txt.
$data
Fixtures/Crawlers.php
tests/crawlers.txt


Wikidata id: Q174989
Individual Properties:
- block_size {#unsigned long long} (size of a single block / sector in bytes) - volume_size {#unsigned long long} (total size of the file system in bytes) - volume_name {#char[256]} - endianness {little, big} - version {#int} - UUID {#char[256]} - floppy_size {DD, HD} (Does the volume_size match the size of a DD/HD floppy?) - last_mounted {#char[256]} (location of the last mounting)
Instance of File System.
no properties
Found in [unix.c]
Instance of File System.
Inherited Properties:
+ volume_name (File System) + UUID (File System) + volume_size (File System) + block_size (File System)
Individual Properties:
- layout {4.0, Unknown}
Found in [linux.c]
Instance of File System.
(UDF)
Inherited Properties:
+ block_size (File System) + volume_name (File System)
Individual Properties:
- version_hex {#hexadecimal.hexadecimal} (version is given by two hexadecimal numbers seperated by a dot)
Found in [udf.c]
Instance of File System.
(UFS)
Inherited Properties:
+ version (File System) {1, 2} + endianness (File System) + last_mounted (File System) + volume_name (File System) (volume name by FreeBSD convention)
Individual Properties:
- volume_name_darwin {#char[256]} (volume name by Darwin convention) - offset {#int} - long_file_names {true} - fs_featurebits {true} - support_4GB {true} (support for sizes larger than 4GB)
Found in [unix.c]
Instance of File System.
Inherited Properties:
+ version (File System) + endianness (File System) + volume_size (File System) + block_size (File System)
Found in [unix.c]
Instance of File System.
Inherited Properties:
+ endianness (File System) + block_size (File System)
Found in [unix.c]
Instance of File System.
Inherited Properties:
+ version (File System) + volume_name (File System) + UUID (File System) + volume_size (File System) + block_size (File System)
Found in [unix.c]


Luis Enrique returns as Spain coach after daughter’s death
by Staff ReporterMADRID (Reuters) – Luis Enrique has been reappointed Spain coach, having resigned from the role in June as his late daughter battled cancer, Spanish Football Federation (RFEF) President Luis Rubiales said on Tuesday.
Rubiales said Robert Moreno, the former assistant coach who steered Spain to Euro 2020 qualification after Luis Enrique left, wanted to stand down so that he could return.
Luis Enrique’s daughter Xana died in September.
“Today we can confirm that Luis Enrique returns to his role as coach,” Rubiales told a news conference.
“Everyone knew that if Luis Enrique wanted to return he would have the doors to the national team open to him.”
Moreno, who led the team to seven victories and two draws as Spain comfortably qualified for next summer’s tournament, had said when appointed that he would step aside if Luis Enrique wanted to return.
Moreno did not speak to media after his side’s 5-0 win over Romania on Monday, and there have been reports that he has fallen out with Luis Enrique and will not return as his assistant.
“Luis Enrique’s decisions regarding his staff, as well as Robert’s decision not to continue when he found out Luis Enrique wanted to return, are personal things. You have to ask them,” added Rubiales.
“On Monday, Robert sent us a message saying he wanted to leave so as not to hinder the return of Luis Enrique.”
Rubiales confirmed that the 49-year-old Luis Enrique, the former Barcelona coach, has a contract until after the Qatar 2022 World Cup, and will stay on regardless of the team’s performance at Euro 2020.


It is integrated with a number of popular databases, including MySQL, PostgreSQL, Oracle, Sybase, Informix, and Microsoft SQL Server.
PHP is pleasingly zippy in its execution, especially when compiled as an Apache module on the Unix side. The MySQL server, once started, executes even very complex queries with huge result sets in record-setting time.
PHP supports a large number of major protocols such as POP3, IMAP, and LDAP. PHP4 added support for Java and distributed object architectures (COM and CORBA), making n-tier development a possibility for the first time.
PHP is forgiving: PHP language tries to be as forgiving as possible.
PHP Syntax is C-Like.
Common Uses of PHP
PHP performs system functions, i.e. from files on a system it can create, open, read, write, and close them. The other uses of PHP are:
PHP can handle forms, i.e. gather data from files, save data to a file, thru email you can send data, return data to the user.
You add, delete, modify elements within your database thru PHP.
Access cookies variables and set cookies.
Using PHP, you can restrict users to access some pages of your website.
It can encrypt data.
Some Five important characteristics make PHP's practical nature possible:
1)Simplicity
By maintaining Simplicity in our software, we reduce the amount of non-core code and functionality we need to design, code, test, and maintain.
2)Efficiency
Efficient programming is programming in a manner that, when the program is executed, uses a low amount of overall resources pertaining to computer hardware
3)Security
For Instance language-based security (LBS) is a set of techniques that may be used to strengthen the security of applications on a high level by using the properties of programming languages
4)Flexibility
The flexibility of programming languages refers to the unexpectedly many ways in which utterings in the language can be used. The flexibility in the design of a program is offered through source code: modifying a program's source code adapts the program's design.
5)Familiarity
“Familiar” means that I have used it for at least one project and could pick it up again rather quickly, but would have to reference documentation and/or previous code because I don't have much of it memorized
"Hello World" Script in PHP
As mentioned earlier, PHP is embedded in HTML. That means that in amongst your normal HTML (or XHTML if you're cutting-edge) you'll have PHP statements like this:
It will produce the following result:
Hello, DTP!
<html>
<head>
<title>Hello DTP</title>
<body>
<?php echo "Hello, DTP!";?>
</body>
</html>


--- layout: post title: Google Apps Script Basic Tutorial permalink: google-apps-script-basic-tutorial/ date: 2012-12-16 08:36:55.000000000 +08:00 categories: - google - howto tags: - getting started with google app script - google apps script - google apps script basic tutorial - google apps script for spreadsheet basic tutorial - google apps script hello world tutorial - what is google apps script status: publish type: post published: true meta: review_rating: '' _edit_last: '2' _wpas_done_all: '1' _wpas_skip_1611607: '1' _wpas_skip_2203490: '1' _wpas_skip_2452381: '1' _wpas_skip_2452385: '1' _wpas_skip_2452386: '1' dsq_thread_id: '2965369914' cleaned: true ---
If you are using Google Drive (Google Docs before), you might be interested to the fact that you can do some form of automation (mail merge for example) on Google Docs like how MS Office's Visual Basic for Application do some sort of "scripting" capability in all of it's Office Application.
That functionality as a whole is offered to what Google called as Google Apps Script. It basically provides an end-user to do some scripting and execute it on Google's server making it robust and handy since everything is stored in the cloud, unlike MS Office that you have to send the Office Doc itself and the user must download it to its hard disk, must have a MS Office installed.
Google App Script otherwise is very portable since you just need the link to that Google Doc, set some permission and the user dont need to install anything on his computer in able to run your script (a browser is all he need).
I will show you some "Hello World" tutorial to get you started with Google Apps Script, but this doesn't mean this technology is just about Office docs automation - there is a wide range of services and functionality that you can do with Google Apps Script. We will just tackle the very basics of it so that you can have a better grip of how this technology can change the way how you use Google Docs.
First, go to https://drive.google.com. Of Course you need to have a Google Account for you to do all this things so make sure you are logged in to your Google Account.
Next, create a new Spreadsheet document
Name the spreadsheet with something that you are able to remember (it could be anything that you want). Then make sure to save it (you dont have to enter any data in the cells).
Now, open that Spreadsheet document that you had just created.
Go to Script Editor
That should open another tab, which looks like an IDE (Integrated Environment Development) where you can write codes, create projects etc. There will be a default project/ file (Code.gs) which is provided by Google to you, but basically you can create more projects code files. For the purpose of this tutorial, we will edit the default and drop some lines of codes.
Clear the contents of Code.gs and put the ff. code:
function onOpen()
{
Browser.msgBox("Hello World");
}
The codes instructs the Spreadsheet to execute the codes enclosed in the parentheses when the Spreadsheet is "opened" by the user. In our case, It will open a "Message Box" on the browser.
So that's it, you just had made your first Google Apps Script!
Note that there are much wider use-cases for this aside from scripting a document, among others were:
Watch the following video to know more about Google Apps Script:
Or you can go directly to http://www.google.com/script/start/


[gecode-users] newbie questions on a possible Gecode use case
Christian Schulte
cschulte at kth.se
Wed Jan 15 11:54:30 CET 2014
Dear Rich,
Yes that sounds like a good application domain for constraint programming in
general and Gecode in particular.
One idea might be to first try to get a feel for how to model your problem.
There you might want to not use Gecode directly but something that is more
accessible, for example MiniZinc (www.minizinc.org) as a modeling language.
After you have convinced yourself how to model your problem I would switch
to Gecode and think about how to integrate it into your product/environment.
Cheers
Christian
--
Christian Schulte, Professor of Computer Science, KTH,
www.ict.kth.se/~cschulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Rich Morin
Sent: Friday, January 10, 2014 6:32 PM
To: users at gecode.org
Subject: [gecode-users] newbie questions on a possible Gecode use case
I'm working with a modeling approach (Inductive Process Modeling) that
composes sets of "model structures" from specifications of "entities"
and "processes". Some process combinations don't make sense, so we use sets
of simple constraints in generating candidate model structures, eg:
ALWAYS-TOGETHER
These processes must always be used together.
ATMOST-ONE
At most one of these processes may be used.
EXACTLY-ONE
Exactly one of these processes must be used.
FORBIDDEN
This process must not be used.
NECESSARY
All of these processes must be used.
Because the number of combinations could be very large, we generate
pseudo-random subsets (of a specified size). These are evaluated (eg,
fitting parameters to match observed data) and the best results are
presented to the user for inspection.
Currently, these combinations are being produced by means of complex Lisp
code that performs boolean satisfiability logic. I'm wondering whether this
task (along with possible follow-on extensions involving feedback from the
evaluation task) might be a good use case for Gecode.
(If this let me replace the code with a small set of constraints, I'd be a
really happy camper. :-)
I'm a complete newbie at Gecode, so I'm hoping for caveats, comments,
suggestions, etc. In addition, if any Gecoders out there are available for
small consulting projects, I'd like to hear from them (off-list!).
-r
--
http://www.cfcl.com/rdm Rich Morin rdm at cfcl.com
http://www.cfcl.com/rdm/resume San Bruno, CA, USA +1<PHONE_NUMBER>
Software system design, development, and documentation
_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
More information about the users
mailing list


While at Bright Pixel I worked closely with Público, one of the major newspapers in Portugal to develop a new version of the newspaper’s mobile app for iOS and Android.
On this page, I will try to describe the process behind the challenge: re-think and redesign a news mobile application that could have a significant impact on the business and user experience.
Download the applications here
Working with a prominent newspaper like Público, it's working in a thin line of balance between the commercial and editorial sides. They will be de decision-makers in the process.
When I lead a project and work closely with the product owners, I like to start with brainstorming sessions, sometimes close to design sprints, to try to get everyone in the same level field.
In this case, it took me a couple of visual concepts to try to set the application's mood/style/voice.
We knew that this, with a clear goal form both sides of the decision, would kickstart all the work.
The wireframe in this project where particularly relevant to lay down the complexity of screens and variations of news cards there are in the application.
After working close to the internal team that created the API that serves the website, I had to build a system that would mirror the same approach to mobile applications.
At the end of this phase, we started to play with some animations that roughly brought some feeling what tour be liker to navigate in the application.
To pass the work to the development team was another challenge, wed a very tight timeline before the project ran out of money and might put at risk the entire project.
I used Zepplin to create a direct line with the development team to inspect and download assets. But this project needed more. I create a cookbook on the form of seasons of a series to get a more visual statement to all the people involved, including non-techies.
It works pretty well. We had to have an approved pdf at the start of every sprint and the developer.
The looks of the print version inspired the final design of the application. I inset the grey lines dividing the cards, remove any shadow in the cards, try to replicate the symmetry in the relation of the title and the text, using the same color scheme, and try to the same dynamics in all the Público sub-brands.
We created an experience that brings the latest news with a feed always changing mixed with a more long-form call to read. We also brought a lot more attention to the podcast section and the subscriber's area, which would push the subscriptions.


Extends the S3 generic function knit_print() in 'knitr' to automatically print some objects using an appropriate format such as Markdown or LaTeX. For example, data frames are automatically printed as tables, and the help() pages can also be rendered in 'knitr' documents.
| Version: | 0.1 |
| Imports: | knitr (≥ 1.16) |
| Suggests: | tools, rmarkdown |
| Published: | 2017-05-19 |
| Author: | Yihui Xie |
| Maintainer: | Yihui Xie <xie at yihui.name> |
| BugReports: | https://github.com/yihui/printr/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | https://yihui.name/printr/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | printr results |
| Reference manual: | printr.pdf |
| Vignettes: |
An Introduction to the printr Package |
| Package source: | printr_0.1.tar.gz |
| Windows binaries: | r-devel: printr_0.1.zip, r-release: printr_0.1.zip, r-oldrel: printr_0.1.zip |
| OS X binaries: | r-release: printr_0.1.tgz, r-oldrel: printr_0.1.tgz |
| Reverse suggests: | codemetar, kdtools, pseval, SimCorrMix, SimMultiCorrData, SimRepeat, stocks |
Please use the canonical form https://CRAN.R-project.org/package=printr to link to this page.


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NOTE: It is useful to read about node affinity first.
This document describes a proposal for specifying and implementing inter-pod topological affinity and anti-affinity. By that we mean: rules that specify that certain pods should be placed in the same topological domain (e.g. same node, same rack, same zone, same power domain, etc.) as some other pods, or, conversely, should not be placed in the same topological domain as some other pods.
Here are a few example rules; we explain how to express them using the API described in this doc later, in the section “Examples.” * Affinity * Co-locate the pods from a particular service or Job in the same availability zone, without specifying which zone that should be. * Co-locate the pods from service S1 with pods from service S2 because S1 uses S2 and thus it is useful to minimize the network latency between them. Co-location might mean same nodes and/or same availability zone. * Anti-affinity * Spread the pods of a service across nodes and/or availability zones, e.g. to reduce correlated failures. * Give a pod “exclusive” access to a node to guarantee resource isolation – it must never share the node with other pods. * Don’t schedule the pods of a particular service on the same nodes as pods of another service that are known to interfere with the performance of the pods of the first service.
For both affinity and anti-affinity, there are three variants. Two variants have the property of requiring the affinity/anti-affinity to be satisfied for the pod to be allowed to schedule onto a node; the difference between them is that if the condition ceases to be met later on at runtime, for one of them the system will try to eventually evict the pod, while for the other the system may not try to do so. The third variant simply provides scheduling-time hints that the scheduler will try to satisfy but may not be able to. These three variants are directly analogous to the three variants of node affinity.
Note that this proposal is only about inter-pod topological affinity and anti-affinity. There are other forms of topological affinity and anti-affinity. For example, you can use node affinity to require (prefer) that a set of pods all be scheduled in some specific zone Z. Node affinity is not capable of expressing inter-pod dependencies, and conversely the API we describe in this document is not capable of expressing node affinity rules. For simplicity, we will use the terms “affinity” and “anti-affinity” to mean “inter-pod topological affinity” and “inter-pod topological anti-affinity,” respectively, in the remainder of this document.
We will add one field to PodSpec
Affinity *Affinity `json:"affinity,omitempty"`
The Affinity
type is defined as follows
type Affinity struct {
PodAffinity *PodAffinity `json:"podAffinity,omitempty"`
PodAntiAffinity *PodAntiAffinity `json:"podAntiAffinity,omitempty"`
}
type PodAffinity struct {
// If the affinity requirements specified by this field are not met at
// scheduling time, the pod will not be scheduled onto the node.
// If the affinity requirements specified by this field cease to be met
// at some point during pod execution (e.g. due to a pod label update), the
// system will try to eventually evict the pod from its node.
// When there are multiple elements, the lists of nodes corresponding to each
// PodAffinityTerm are intersected, i.e. all terms must be satisfied.
RequiredDuringSchedulingRequiredDuringExecution []PodAffinityTerm `json:"requiredDuringSchedulingRequiredDuringExecution,omitempty"`
// If the affinity requirements specified by this field are not met at
// scheduling time, the pod will not be scheduled onto the node.
// If the affinity requirements specified by this field cease to be met
// at some point during pod execution (e.g. due to a pod label update), the
// system may or may not try to eventually evict the pod from its node.
// When there are multiple elements, the lists of nodes corresponding to each
// PodAffinityTerm are intersected, i.e. all terms must be satisfied.
RequiredDuringSchedulingIgnoredDuringExecution []PodAffinityTerm `json:"requiredDuringSchedulingIgnoredDuringExecution,omitempty"`
// The scheduler will prefer to schedule pods to nodes that satisfy
// the affinity expressions specified by this field, but it may choose
// a node that violates one or more of the expressions. The node that is
// most preferred is the one with the greatest sum of weights, i.e.
// for each node that meets all of the scheduling requirements (resource
// request, RequiredDuringScheduling affinity expressions, etc.),
// compute a sum by iterating through the elements of this field and adding
// "weight" to the sum if the node matches the corresponding MatchExpressions; the
// node(s) with the highest sum are the most preferred.
PreferredDuringSchedulingIgnoredDuringExecution []WeightedPodAffinityTerm `json:"preferredDuringSchedulingIgnoredDuringExecution,omitempty"`
}
type PodAntiAffinity struct {
// If the anti-affinity requirements specified by this field are not met at
// scheduling time, the pod will not be scheduled onto the node.
// If the anti-affinity requirements specified by this field cease to be met
// at some point during pod execution (e.g. due to a pod label update), the
// system will try to eventually evict the pod from its node.
// When there are multiple elements, the lists of nodes corresponding to each
// PodAffinityTerm are intersected, i.e. all terms must be satisfied.
RequiredDuringSchedulingRequiredDuringExecution []PodAffinityTerm `json:"requiredDuringSchedulingRequiredDuringExecution,omitempty"`
// If the anti-affinity requirements specified by this field are not met at
// scheduling time, the pod will not be scheduled onto the node.
// If the anti-affinity requirements specified by this field cease to be met
// at some point during pod execution (e.g. due to a pod label update), the
// system may or may not try to eventually evict the pod from its node.
// When there are multiple elements, the lists of nodes corresponding to each
// PodAffinityTerm are intersected, i.e. all terms must be satisfied.
RequiredDuringSchedulingIgnoredDuringExecution []PodAffinityTerm `json:"requiredDuringSchedulingIgnoredDuringExecution,omitempty"`
// The scheduler will prefer to schedule pods to nodes that satisfy
// the anti-affinity expressions specified by this field, but it may choose
// a node that violates one or more of the expressions. The node that is
// most preferred is the one with the greatest sum of weights, i.e.
// for each node that meets all of the scheduling requirements (resource
// request, RequiredDuringScheduling anti-affinity expressions, etc.),
// compute a sum by iterating through the elements of this field and adding
// "weight" to the sum if the node matches the corresponding MatchExpressions; the
// node(s) with the highest sum are the most preferred.
PreferredDuringSchedulingIgnoredDuringExecution []WeightedPodAffinityTerm `json:"preferredDuringSchedulingIgnoredDuringExecution,omitempty"`
}
type WeightedPodAffinityTerm struct {
// weight is in the range 1-100
Weight int `json:"weight"`
PodAffinityTerm PodAffinityTerm `json:"podAffinityTerm"`
}
type PodAffinityTerm struct {
LabelSelector *LabelSelector `json:"labelSelector,omitempty"`
// namespaces specifies which namespaces the LabelSelector applies to (matches against);
// nil list means "this pod's namespace," empty list means "all namespaces"
// The json tag here is not "omitempty" since we need to distinguish nil and empty.
// See https://golang.org/pkg/encoding/json/#Marshal for more details.
Namespaces []api.Namespace `json:"namespaces,omitempty"`
// empty topology key is interpreted by the scheduler as "all topologies"
TopologyKey string `json:"topologyKey,omitempty"`
}
Note that the Namespaces
field is necessary because normal LabelSelector
is
scoped to the pod’s namespace, but we need to be able to match against all pods
globally.
To explain how this API works, let’s say that the PodSpec
of a pod P
has an
Affinity
that is configured as follows (note that we’ve omitted and collapsed
some fields for simplicity, but this should sufficiently convey the intent of
the design):
PodAffinity {
RequiredDuringScheduling: {{LabelSelector: P1, TopologyKey: "node"}},
PreferredDuringScheduling: {{LabelSelector: P2, TopologyKey: "zone"}},
}
PodAntiAffinity {
RequiredDuringScheduling: {{LabelSelector: P3, TopologyKey: "rack"}},
PreferredDuringScheduling: {{LabelSelector: P4, TopologyKey: "power"}}
}
Then when scheduling pod P, the scheduler:
* Can only schedule P onto nodes that are running pods that satisfy P1
.
(Assumes all nodes have a label with key node
and value specifying their node
name.)
* Should try to schedule P onto zones that are running pods that satisfy P2
.
(Assumes all nodes have a label with key zone
and value specifying their
zone.)
* Cannot schedule P onto any racks that are running pods that satisfy P3
.
(Assumes all nodes have a label with key rack
and value specifying their rack
name.)
* Should try not to schedule P onto any power domains that are running pods that
satisfy P4
. (Assumes all nodes have a label with key power
and value
specifying their power domain.)
When RequiredDuringScheduling
has multiple elements, the requirements are
ANDed. For PreferredDuringScheduling
the weights are added for the terms that
are satisfied for each node, and the node(s) with the highest weight(s) are the
most preferred.
In reality there are two variants of RequiredDuringScheduling
: one suffixed
with RequiredDuringExecution
and one suffixed with IgnoredDuringExecution
.
For the first variant, if the affinity/anti-affinity ceases to be met at some
point during pod execution (e.g. due to a pod label update), the system will try
to eventually evict the pod from its node. In the second variant, the system may
or may not try to eventually evict the pod from its node.
One thing that makes affinity and anti-affinity tricky is symmetry.
Imagine a cluster that is running pods from two services, S1 and S2. Imagine that the pods of S1 have a RequiredDuringScheduling anti-affinity rule “do not run me on nodes that are running pods from S2.” It is not sufficient just to check that there are no S2 pods on a node when you are scheduling a S1 pod. You also need to ensure that there are no S1 pods on a node when you are scheduling a S2 pod, even though the S2 pod does not have any anti-affinity rules. Otherwise if an S1 pod schedules before an S2 pod, the S1 pod’s RequiredDuringScheduling anti-affinity rule can be violated by a later-arriving S2 pod. More specifically, if S1 has the aforementioned RequiredDuringScheduling anti-affinity rule, then: * if a node is empty, you can schedule S1 or S2 onto the node * if a node is running S1 (S2), you cannot schedule S2 (S1) onto the node
Note that while RequiredDuringScheduling anti-affinity is symmetric, RequiredDuringScheduling affinity is not symmetric. That is, if the pods of S1 have a RequiredDuringScheduling affinity rule “run me on nodes that are running pods from S2,” it is not required that there be S1 pods on a node in order to schedule a S2 pod onto that node. More specifically, if S1 has the aforementioned RequiredDuringScheduling affinity rule, then: * if a node is empty, you can schedule S2 onto the node * if a node is empty, you cannot schedule S1 onto the node * if a node is running S2, you can schedule S1 onto the node * if a node is running S1+S2 and S1 terminates, S2 continues running * if a node is running S1+S2 and S2 terminates, the system terminates S1 (eventually)
However, although RequiredDuringScheduling affinity is not symmetric, there is an implicit PreferredDuringScheduling affinity rule corresponding to every RequiredDuringScheduling affinity rule: if the pods of S1 have a RequiredDuringScheduling affinity rule “run me on nodes that are running pods from S2” then it is not required that there be S1 pods on a node in order to schedule a S2 pod onto that node, but it would be better if there are.
PreferredDuringScheduling is symmetric. If the pods of S1 had a PreferredDuringScheduling anti-affinity rule “try not to run me on nodes that are running pods from S2” then we would prefer to keep a S1 pod that we are scheduling off of nodes that are running S2 pods, and also to keep a S2 pod that we are scheduling off of nodes that are running S1 pods. Likewise if the pods of S1 had a PreferredDuringScheduling affinity rule “try to run me on nodes that are running pods from S2” then we would prefer to place a S1 pod that we are scheduling onto a node that is running a S2 pod, and also to place a S2 pod that we are scheduling onto a node that is running a S1 pod.
Here are some examples of how you would express various affinity and anti-affinity rules using the API we described.
In the examples below, the word “put” is intentionally ambiguous; the rules are
the same whether “put” means “must put” (RequiredDuringScheduling) or “try to
put” (PreferredDuringScheduling)–all that changes is which field the rule goes
into. Also, we only discuss scheduling-time, and ignore the execution-time.
Finally, some of the examples use “zone” and some use “node,” just to make the
examples more interesting; any of the examples with “zone” will also work for
“node” if you change the TopologyKey
, and vice-versa.
Put the pod in zone Z: Tricked you! It is not possible express this using the API described here. For this you should use node affinity.
Put the pod in a zone that is running at least one pod from service S:
{LabelSelector: <selector that matches S's pods>, TopologyKey: "zone"}
Put the pod on a node that is already running a pod that requires a license
for software package P: Assuming pods that require a license for software
package P have a label {key=license, value=P}
:
{LabelSelector: "license" In "P", TopologyKey: "node"}
Put this pod in the same zone as other pods from its same service:
Assuming pods from this pod’s service have some label {key=service, value=S}
:
{LabelSelector: "service" In "S", TopologyKey: "zone"}
This last example illustrates a small issue with this API when it is used with a
scheduler that processes the pending queue one pod at a time, like the current
Kubernetes scheduler. The RequiredDuringScheduling rule
{LabelSelector: "service" In "S", TopologyKey: "zone"}
only “works” once one pod from service S has been scheduled. But if all pods in
service S have this RequiredDuringScheduling rule in their PodSpec, then the
RequiredDuringScheduling rule will block the first pod of the service from ever
scheduling, since it is only allowed to run in a zone with another pod from the
same service. And of course that means none of the pods of the service will be
able to schedule. This problem only applies to RequiredDuringScheduling
affinity, not PreferredDuringScheduling affinity or any variant of
anti-affinity. There are at least three ways to solve this problem:
* short-term: have the scheduler use a rule that if the
RequiredDuringScheduling affinity requirement matches a pod’s own labels, and
there are no other such pods anywhere, then disregard the requirement. This
approach has a corner case when running parallel schedulers that are allowed to
schedule pods from the same replicated set (e.g. a single PodTemplate): both
schedulers may try to schedule pods from the set at the same time and think
there are no other pods from that set scheduled yet (e.g. they are trying to
schedule the first two pods from the set), but by the time the second binding is
committed, the first one has already been committed, leaving you with two pods
running that do not respect their RequiredDuringScheduling affinity. There is no
simple way to detect this “conflict” at scheduling time given the current system
implementation.
* longer-term: when a controller creates pods from a PodTemplate, for
exactly one of those pods, it should omit any RequiredDuringScheduling
affinity rules that select the pods of that PodTemplate.
* very long-term/speculative: controllers could present the scheduler with a
group of pods from the same PodTemplate as a single unit. This is similar to the
first approach described above but avoids the corner case. No special logic is
needed in the controllers. Moreover, this would allow the scheduler to do proper
gang scheduling since
it could receive an entire gang simultaneously as a single unit.
As with the affinity examples, the examples here can be RequiredDuringScheduling
or PreferredDuringScheduling anti-affinity, i.e. “don’t” can be interpreted as
“must not” or as “try not to” depending on whether the rule appears in
RequiredDuringScheduling
or PreferredDuringScheduling
.
Spread the pods of this service S across nodes and zones:
{{LabelSelector: <selector that matches S's pods>, TopologyKey: "node"},
{LabelSelector: <selector that matches S's pods>, TopologyKey: "zone"}}
(note that if this is specified as a RequiredDuringScheduling anti-affinity,
then the first clause is redundant, since the second clause will force the
scheduler to not put more than one pod from S in the same zone, and thus by
definition it will not put more than one pod from S on the same node, assuming
each node is in one zone. This rule is more useful as PreferredDuringScheduling
anti-affinity, e.g. one might expect it to be common in
Cluster Federation clusters.)
Don’t co-locate pods of this service with pods from service “evilService”:
{LabelSelector: selector that matches evilService's pods, TopologyKey: "node"}
Don’t co-locate pods of this service with any other pods including pods of this service:
{LabelSelector: empty, TopologyKey: "node"}
Don’t co-locate pods of this service with any other pods except other pods of this service:
Assuming pods from the service have some label {key=service, value=S}
:
{LabelSelector: "service" NotIn "S", TopologyKey: "node"}
Note that this works because "service" NotIn "S"
matches pods with no key
“service” as well as pods with key “service” and a corresponding value that is
not “S.”
An example algorithm a scheduler might use to implement affinity and anti-affinity rules is as follows. There are certainly more efficient ways to do it; this is just intended to demonstrate that the API’s semantics are implementable.
Terminology definition: We say a pod P is “feasible” on a node N if P meets all of the scheduler predicates for scheduling P onto N. Note that this algorithm is only concerned about scheduling time, thus it makes no distinction between RequiredDuringExecution and IgnoredDuringExecution.
To make the algorithm slightly more readable, we use the term “HardPodAffinity” as shorthand for “RequiredDuringSchedulingScheduling pod affinity” and “SoftPodAffinity” as shorthand for “PreferredDuringScheduling pod affinity.” Analogously for “HardPodAntiAffinity” and “SoftPodAntiAffinity.”
TODO: Update this algorithm to take weight for SoftPod{Affinity,AntiAffinity} into account; currently it assumes all terms have weight 1.
Z = the pod you are scheduling
{N} = the set of all nodes in the system // this algorithm will reduce it to the set of all nodes feasible for Z
// Step 1a: Reduce {N} to the set of nodes satisfying Z's HardPodAffinity in the "forward" direction
X = {Z's PodSpec's HardPodAffinity}
foreach element H of {X}
P = {all pods in the system that match H.LabelSelector}
M map[string]int // topology value -> number of pods running on nodes with that topology value
foreach pod Q of {P}
L = {labels of the node on which Q is running, represented as a map from label key to label value}
M[L[H.TopologyKey]]++
{N} = {N} intersect {all nodes of N with label [key=H.TopologyKey, value=any K such that M[K]>0]}
// Step 1b: Further reduce {N} to the set of nodes also satisfying Z's HardPodAntiAffinity
// This step is identical to Step 1a except the M[K] > 0 comparison becomes M[K] == 0
X = {Z's PodSpec's HardPodAntiAffinity}
foreach element H of {X}
P = {all pods in the system that match H.LabelSelector}
M map[string]int // topology value -> number of pods running on nodes with that topology value
foreach pod Q of {P}
L = {labels of the node on which Q is running, represented as a map from label key to label value}
M[L[H.TopologyKey]]++
{N} = {N} intersect {all nodes of N with label [key=H.TopologyKey, value=any K such that M[K]==0]}
// Step 2: Further reduce {N} by enforcing symmetry requirement for other pods' HardPodAntiAffinity
foreach node A of {N}
foreach pod B that is bound to A
if any of B's HardPodAntiAffinity are currently satisfied but would be violated if Z runs on A, then remove A from {N}
// At this point, all node in {N} are feasible for Z.
// Step 3a: Soft version of Step 1a
Y map[string]int // node -> number of Z's soft affinity/anti-affinity preferences satisfied by that node
Initialize the keys of Y to all of the nodes in {N}, and the values to 0
X = {Z's PodSpec's SoftPodAffinity}
Repeat Step 1a except replace the last line with "foreach node W of {N} having label [key=H.TopologyKey, value=any K such that M[K]>0], Y[W]++"
// Step 3b: Soft version of Step 1b
X = {Z's PodSpec's SoftPodAntiAffinity}
Repeat Step 1b except replace the last line with "foreach node W of {N} not having label [key=H.TopologyKey, value=any K such that M[K]>0], Y[W]++"
// Step 4: Symmetric soft, plus treat forward direction of hard affinity as a soft
foreach node A of {N}
foreach pod B that is bound to A
increment Y[A] by the number of B's SoftPodAffinity, SoftPodAntiAffinity, and HardPodAffinity that are satisfied if Z runs on A but are not satisfied if Z does not run on A
// We're done. {N} contains all of the nodes that satisfy the affinity/anti-affinity rules, and Y is
// a map whose keys are the elements of {N} and whose values are how "good" of a choice N is for Z with
// respect to the explicit and implicit affinity/anti-affinity rules (larger number is better).
In this section we discuss three issues with RequiredDuringScheduling anti-affinity: Denial of Service (DoS), co-existing with daemons, and determining which pod(s) to kill. See issue #18265 for additional discussion of these topics.
Without proper safeguards, a pod using RequiredDuringScheduling anti-affinity can intentionally or unintentionally cause various problems for other pods, due to the symmetry property of anti-affinity.
The most notable danger is the ability for a pod that arrives first to some
topology domain, to block all other pods from scheduling there by stating a
conflict with all other pods. The standard approach to preventing resource
hogging is quota, but simple resource quota cannot prevent this scenario because
the pod may request very little resources. Addressing this using quota requires
a quota scheme that charges based on “opportunity cost” rather than based simply
on requested resources. For example, when handling a pod that expresses
RequiredDuringScheduling anti-affinity for all pods using a “node” TopologyKey
(i.e. exclusive access to a node), it could charge for the resources of the
average or largest node in the cluster. Likewise if a pod expresses
RequiredDuringScheduling anti-affinity for all pods using a “cluster”
TopologyKey
, it could charge for the resources of the entire cluster. If node
affinity is used to constrain the pod to a particular topology domain, then the
admission-time quota charging should take that into account (e.g. not charge for
the average/largest machine if the PodSpec constrains the pod to a specific
machine with a known size; instead charge for the size of the actual machine
that the pod was constrained to). In all cases once the pod is scheduled, the
quota charge should be adjusted down to the actual amount of resources allocated
(e.g. the size of the actual machine that was assigned, not the
average/largest). If a cluster administrator wants to overcommit quota, for
example to allow more than N pods across all users to request exclusive node
access in a cluster with N nodes, then a priority/preemption scheme should be
added so that the most important pods run when resource demand exceeds supply.
An alternative approach, which is a bit of a blunt hammer, is to use a capability mechanism to restrict use of RequiredDuringScheduling anti-affinity to trusted users. A more complex capability mechanism might only restrict it when using a non-“node” TopologyKey.
Our initial implementation will use a variant of the capability approach, which requires no configuration: we will simply reject ALL requests, regardless of user, that specify “all namespaces” with non-“node” TopologyKey for RequiredDuringScheduling anti-affinity. This allows the “exclusive node” use case while prohibiting the more dangerous ones.
A weaker variant of the problem described in the previous paragraph is a pod’s ability to use anti-affinity to degrade the scheduling quality of another pod, but not completely block it from scheduling. For example, a set of pods S1 could use node affinity to request to schedule onto a set of nodes that some other set of pods S2 prefers to schedule onto. If the pods in S1 have RequiredDuringScheduling or even PreferredDuringScheduling pod anti-affinity for S2, then due to the symmetry property of anti-affinity, they can prevent the pods in S2 from scheduling onto their preferred nodes if they arrive first (for sure in the RequiredDuringScheduling case, and with some probability that depends on the weighting scheme for the PreferredDuringScheduling case). A very sophisticated priority and/or quota scheme could mitigate this, or alternatively we could eliminate the symmetry property of the implementation of PreferredDuringScheduling anti-affinity. Then only RequiredDuringScheduling anti-affinity could affect scheduling quality of another pod, and as we described in the previous paragraph, such pods could be charged quota for the full topology domain, thereby reducing the potential for abuse.
We won’t try to address this issue in our initial implementation; we can consider one of the approaches mentioned above if it turns out to be a problem in practice.
A cluster administrator may wish to allow pods that express anti-affinity against all pods, to nonetheless co-exist with system daemon pods, such as those run by DaemonSet. In principle, we would like the specification for RequiredDuringScheduling inter-pod anti-affinity to allow “toleration” of one or more other pods (see #18263 for a more detailed explanation of the toleration concept). There are at least two ways to accomplish this:
Scheduler special-cases the namespace(s) where daemons live, in the
sense that it ignores pods in those namespaces when it is
determining feasibility for pods with anti-affinity. The name(s) of
the special namespace(s) could be a scheduler configuration
parameter, and default to kube-system
. We could allow
multiple namespaces to be specified if we want cluster admins to be
able to give their own daemons this special power (they would add
their namespace to the list in the scheduler configuration). And of
course this would be symmetric, so daemons could schedule onto a node
that is already running a pod with anti-affinity.
We could add an explicit “toleration” concept/field to allow the user to specify namespaces that are excluded when they use RequiredDuringScheduling anti-affinity, and use an admission controller/defaulter to ensure these namespaces are always listed.
Our initial implementation will use the first approach.
Because anti-affinity is symmetric, in the case of RequiredDuringSchedulingRequiredDuringExecution anti-affinity, the system must determine which pod(s) to kill when a pod’s labels are updated in such as way as to cause them to conflict with one or more other pods’ RequiredDuringSchedulingRequiredDuringExecution anti-affinity rules. In the absence of a priority/preemption scheme, our rule will be that the pod with the anti-affinity rule that becomes violated should be the one killed. A pod should only specify constraints that apply to namespaces it trusts to not do malicious things. Once we have priority/preemption, we can change the rule to say that the lowest-priority pod(s) are killed until all RequiredDuringSchedulingRequiredDuringExecution anti-affinity is satisfied.
The DoS potential of RequiredDuringScheduling anti-affinity stemmed from its symmetry: if a pod P requests anti-affinity, P cannot schedule onto a node with conflicting pods, and pods that conflict with P cannot schedule onto the node one P has been scheduled there. The design we have described says that the symmetry property for RequiredDuringScheduling affinity is weaker: if a pod P says it can only schedule onto nodes running pod Q, this does not mean Q can only run on a node that is running P, but the scheduler will try to schedule Q onto a node that is running P (i.e. treats the reverse direction as preferred). This raises the same scheduling quality concern as we mentioned at the end of the Denial of Service section above, and can be addressed in similar ways.
The nature of affinity (as opposed to anti-affinity) means that there is no issue of determining which pod(s) to kill when a pod’s labels change: it is obviously the pod with the affinity rule that becomes violated that must be killed. (Killing a pod never “fixes” violation of an affinity rule; it can only “fix” violation an anti-affinity rule.) However, affinity does have a different question related to killing: how long should the system wait before declaring that RequiredDuringSchedulingRequiredDuringExecution affinity is no longer met at runtime? For example, if a pod P has such an affinity for a pod Q and pod Q is temporarily killed so that it can be updated to a new binary version, should that trigger killing of P? More generally, how long should the system wait before declaring that P’s affinity is violated? (Of course affinity is expressed in terms of label selectors, not for a specific pod, but the scenario is easier to describe using a concrete pod.) This is closely related to the concept of forgiveness (see issue #1574). In theory we could make this time duration be configurable by the user on a per-pod basis, but for the first version of this feature we will make it a configurable property of whichever component does the killing and that applies across all pods using the feature. Making it configurable by the user would require a nontrivial change to the API syntax (since the field would only apply to RequiredDuringSchedulingRequiredDuringExecution affinity).
Affinity
field to PodSpec and the PodAffinity
and
PodAntiAffinity
types to the API along with all of their descendant types.RequiredDuringSchedulingIgnoredDuringExecution
affinity and anti-affinity into
account. Include a workaround for the issue described at the end of the Affinity
section of the Examples section (can’t schedule first pod).PreferredDuringSchedulingIgnoredDuringExecution
affinity and anti-affinity
into account.RequiredDuringScheduling
anti-affinity. This admission controller should be enabled by default.RequiredDuringSchedulingRequiredDuringExecution
field to affinity
and anti-affinity, and make sure the pieces of the system already implemented
for RequiredDuringSchedulingIgnoredDuringExecution
also take
RequiredDuringSchedulingRequiredDuringExecution
into account (e.g. the
scheduler predicate, the quota mechanism, the “co-existing with daemons”
solution).RequiredDuringSchedulingRequiredDuringExecution
for “node”
TopologyKey
to Kubelet’s admission decision.RequiredDuringSchedulingRequiredDuringExecution
. If Kubelet
then only for “node” TopologyKey
; if controller then potentially for all
TopologyKeys
’s. (see this comment).
Do so in a way that addresses the “determining which pod(s) to kill” issue.We assume Kubelet publishes labels describing the node’s membership in all of the relevant scheduling domains (e.g. node name, rack name, availability zone name, etc.). See #9044.
Old versions of the scheduler will ignore Affinity
.
Users should not start using Affinity
until the full implementation has been
in Kubelet and the master for enough binary versions that we feel comfortable
that we will not need to roll back either Kubelet or master to a version that
does not support them. Longer-term we will use a programmatic approach to
enforcing this (#4855).
The design described here is the result of careful analysis of use cases, a
decade of experience with Borg at Google, and a review of similar features in
other open-source container orchestration systems. We believe that it properly
balances the goal of expressiveness against the goals of simplicity and
efficiency of implementation. However, we recognize that use cases may arise in
the future that cannot be expressed using the syntax described here. Although we
are not implementing an affinity-specific extensibility mechanism for a variety
of reasons (simplicity of the codebase, simplicity of cluster deployment, desire
for Kubernetes users to get a consistent experience, etc.), the regular
Kubernetes annotation mechanism can be used to add or replace affinity rules.
The way this work would is:
1. Define one or more annotations to describe the new affinity rule(s)
1. User (or an admission controller) attaches the annotation(s) to pods to
request the desired scheduling behavior. If the new rule(s) replace one or
more fields of Affinity
then the user would omit those fields from Affinity
;
if they are additional rules, then the user would fill in Affinity
as well
as the annotation(s).
1. Scheduler takes the annotation(s) into account when scheduling.
If some particular new syntax becomes popular, we would consider upstreaming it
by integrating it into the standard Affinity
.
One can imagine that in the anti-affinity RequiredDuringScheduling case one
might want to associate a number with the rule, for example “do not allow this
pod to share a rack with more than three other pods (in total, or from the same
service as the pod).” We could allow this to be specified by adding an integer
Limit
to PodAffinityTerm
just for the RequiredDuringScheduling
case.
However, this flexibility complicates the system and we do not intend to
implement it.
It is likely that the specification and implementation of pod anti-affinity can be unified with taints and tolerations, and likewise that the specification and implementation of pod affinity can be unified with node affinity. The basic idea is that pod labels would be “inherited” by the node, and pods would only be able to specify affinity and anti-affinity for a node’s labels. Our main motivation for not unifying taints and tolerations with pod anti-affinity is that we foresee taints and tolerations as being a concept that only cluster administrators need to understand (and indeed in some setups taints and tolerations wouldn’t even be directly manipulated by a cluster administrator, instead they would only be set by an admission controller that is implementing the administrator’s high-level policy about different classes of special machines and the users who belong to the groups allowed to access them). Moreover, the concept of nodes “inheriting” labels from pods seems complicated; it seems conceptually simpler to separate rules involving relatively static properties of nodes from rules involving which other pods are running on the same node or larger topology domain.
Data/storage affinity is related to pod affinity, and is likely to draw on some of the ideas we have used for pod affinity. Today, data/storage affinity is expressed using node affinity, on the assumption that the pod knows which node(s) store(s) the data it wants. But a more flexible approach would allow the pod to name the data rather than the node.
The review for this proposal is in #18265.
The topic of affinity/anti-affinity has generated a lot of discussion. The main issue is #367 but #14484/#14485, #9560, #11369, #14543, #11707, #3945, #341, #1965, and #2906 all have additional discussion and use cases.
As the examples in this document have demonstrated, topological affinity is very useful in clusters that are spread across availability zones, e.g. to co-locate pods of a service in the same zone to avoid a wide-area network hop, or to spread pods across zones for failure tolerance. #17059, #13056, #13063, and #4235 are relevant.
Issue #15675 describes connection affinity, which is vaguely related.
This proposal is to satisfy #14816.
TODO: cite references


{% extends "desktop/base.html" %} {% set title = _('An Error Occurred') %} {% block content %} {{ title }} {{ _('Oh, no! It looks like an unexpected error occurred.') }} {% endblock %}
{{ _('Oh, no! It looks like an unexpected error occurred.') }}


wcps
Home
Employee Profile
Profile Update
Request For Claim
View Cliam Status
Logout
Claim Application Here
Employee Name:
Gender:
Date of Birth:
Email Id:
City:
Designation:
Contect No:
Date of Joining:


REPORT CORRUPTION
Corruption in Uganda is characterized by grand-scale theft of public funds and petty corruption involving public officials at all levels of society as well as widespread political patronage systems.
Therefore this plaftorm will help you report a corruptionincidence you have noticed anywhere in your area or community. let your vote count. Report Corruption.


Before Change
)
except HTTPError as err:
if err.response.status_code == HTTP_BAD_REQUEST:
_LOGGER.error(
"setup(): Failed to connect with the vendor"s web servers. "
"Check your username (%s), and password are correct."
"Unable to continue. Resolve any errors and restart HA.",
evo_data["params"][CONF_USERNAME]
)
elif err.response.status_code == HTTP_SERVICE_UNAVAILABLE:
_LOGGER.error(
"setup(): Failed to connect with the vendor"s web servers. "
"The server is not contactable. Unable to continue. "
"Resolve any errors and restart HA."
)
elif err.response.status_code == HTTP_TOO_MANY_REQUESTS:
_LOGGER.error(
"setup(): Failed to connect with the vendor"s web servers. "
"You have exceeded the api rate limit. Unable to continue. "
"Wait a while (say 10 minutes) and restart HA."
)
else:
raise // We don"t expect/handle any other HTTPErrors
return False
finally: // Redact username, password as no longer needed
evo_data["params"][CONF_USERNAME] = "REDACTED"
After Change
load_platform(hass, "climate", DOMAIN, {}, hass_config)
if "dhw" in evo_data["config"][GWS][0][TCS][0]:
_LOGGER.warning(
"setup(): DHW found, but this component doesn"t support DHW."
)
@callback
def _first_update(event):
When HA has started, the hub knows to retrieve it"s first update.
pkt = {"sender": "setup()", "signal": "refresh", "to": EVO_PARENT}


Welcome to DECCON. This accomplished company is thrilled for you to peruse the works on display here. Have a look at all the great things we’ve achieved so far, and make sure you don’t miss out on any future creations. If you have any questions, please get in touch below.
Within a short span of time, the company has been able to claim a considerable portion of the market. The commitment of Deccon is to use Eco-friendly materials, as much as possible. Currently, we are providing support to an over increasing greater than ever demand of interior design plans and the touch of perfection for offices, residence, hotels & restaurants.
We are the new leading design + planning & furniture solution providers in Bangladesh achieving our business vision through growth in market reach, increasing operation size. Wide distribution goals, total service and constant branding activities by being the most customer focused & innovative, cost effective & efficient, environmentally responsible & quality concerned company in the business.
Feel free to email us , give us a reason to be there for you, or to just say hello!
email to Sazzad Khan
4th floor, House 91/A, Road 9/A (New),
Dhanmondi R/A, Dhaka, Bangladesh


About Jhemm Fighters
Jhemm Fighters was created by Team Jhemm as a capstone project for Alchemy Code Lab's Software 201 course.
The source code for this project can be viewed on GitHub.
Jhemm Fighters was created by Team Jhemm as a capstone project for Alchemy Code Lab's Software 201 course.
The source code for this project can be viewed on GitHub.
My main hobbies are Overthinking Everything, Karaoke, and Making Goofy Stuff.
Games were my gateway into computer science and programming, so it just seemed right to join this team for my first major programming project. The world is burning, and sometimes a guy just needs to have some fun, y’know?
I am an outdoorsman through and through. The only time I stay inside is if it is almost flooding.
My hobbies are mainly getting outside and doing things, staying inside and doing things if it’s raining, and googling new activities.
 It all started with watching my dad play the original DOOM on the PS1, and when I was able to use a controller myself I couldn’t put it down. After video gaming for a few years, I got introduced to the world of DND and tabletop gaming, and have been doing both ever since. When I saw the project pitched, I saw the chance to add elements from both my passions and use it as an enjoyable way to improve as a developer.
From adventuring alone in various video games, to playing board games with friends, to participating in my national LARPing organization (Tunnel Snakes rule), gaming has always had a special place in my life. Other than that, I am an avid reader of fantasy, sci-fi, and mystery novels.
I came up with the base idea for this project when I saw another cohort make a single player game. I wanted to make a game as well and came up with an idea to switch players with a privacy screen. I find games bring people together and allow one to lose themselves in the moment, whether it’s fighting a monster, saving a member of royalty, or flying through the galaxy. Video games allow you to live another life.
First and foremost, I enjoy video games. Adventure, RPG, Action, whatever, if it is a good game I’ll play it. I also enjoy boarding: surfing, wakeboarding, snowboarding. Chances are that if you can do it on a board, I’ll like it.
Ever since I first picked up a controller, I had always thought about how cool it would be to make a game of my own. Strategy that I have learned from playing games has changed how I think about programming and the world. I was also told games were going to be difficult to code, so this was also an enjoyable way to improve my skills.
Video games have always been special to me. Growing up, it was my favorite activity to do with friends or family. More recently, I’ve been getting into lifting and sports (playing them recreationally and watching them at a professional level).
I wanted to work on this project because I knew it was something that I would be passionate about. Video games are my number one hobby, and because I’ve been playing them for so long, I knew that I could translate feature ideas from other games into JavaScript code. On top of that, gaming projects were described as difficult, so I wanted to put myself in a situation where I could struggle and grow.


Register
First Name
Second Name
First Surname
Second Surname
Birthdate
E-mail
Password
Register
Sign In


Comment Section
Yesterday we created the necessary API calls--get, add, delete, and getAll. Give me ten minutes to test each call and we'll turn to the front-end.
Okay. The question now is--Python returns a list of tuples, where each tuple represents a row. Javascript likes working with JSON objects, but no matter what, Javascript is going to receive a string. Python's string looks like this right now:
[(u'17', 1, u'2016-07-07 04:10:22', u'ivoirians', u'test', 0, None), (u'17', 2, u'2016-07-07 04:11:10', u'ivoirians', u'test2', 0, None), (u'17', 3, u'2016-07-07 04:11:21', u'ivoirians', u'This is the third comment.', 0, None)]
Besides the letter u representing unicode strings (that should be easy to fix), we have an ugly mix of brackets, and worst of all, no names of keys. It wouldn't be too hard to parse this mess on the front-end (actually, maybe it would be hard), so we should try and pre-process this into JSON with Python. It turns out, as always, Python has a way to do that. You can use the json module, convert your rows into dictionaries from column names to values, and call json.dumps() to get nicely formatted JSON. Or, you can use the sqlite3 connection.row_factory function. The example provided works excellently. All I needed to change was to cast the values to strings to remove the unicode prefix.
[{'intCommentNumber': '1', 'chvAuthor': 'ivoirians', 'chvPage': '17', 'dtmDeleted': 'None', 'dtmCreated': '2016-07-07 04:10:22', 'chvBody': 'test', 'bIsDeleted': '0'}, {'intCommentNumber': '2', 'chvAuthor': 'ivoirians', 'chvPage': '17', 'dtmDeleted': 'None', 'dtmCreated': '2016-07-07 04:11:10', 'chvBody': 'test2', 'bIsDeleted': '0'}, {'intCommentNumber': '3', 'chvAuthor': 'ivoirians', 'chvPage': '17', 'dtmDeleted': 'None', 'dtmCreated': '2016-07-07 04:11:21', 'chvBody': 'This is the third comment.', 'bIsDeleted': '0'}]
Every time I do anything, I notice something I should keep in mind for next time. In this case--because my JSON objects repeat the column names for every row returned, I should probably keep my column names shorter next time. Ah well.
Onto the front end. The entire task here is to take this JSON object and display it. Let's write an Angular controller.
A few minutes in--this will be a bit messy, because we can have as many comments as we want. There's an Angular.js ngRepeat directive that might help, but I'm not yet sure what the best way to go from JSON to HTML is.
Before we get to that--let's try to build a mechanism for actually submitting comments. I set up the Angular controller as usual, but before long we hit another easily anticipated issue: the POST data to my Python app is in a string format. I need to set up parsing on that end. Also, depending on the type of request, the data I'm looking for is alternatively in request.query_string, request.form, and request.data. Confusing, but that's what documentation is for.
And with that last bit of implementation, we have a comment section! Everything except the display. Check it out--submit a comment and click here One more hour should be enough time to get a passable looking comment section up. So, there you have it! Three hours to a working comment section.
The biggest lesson I've had in a little more than two weeks of doing this web dev thing? Workflow is key. Specifically, being able to rapidly debug your code is the most important thing by far. Log errors, print out line numbers in error messages, and aim for the fastest turnaround you possibly can in terms of redeploying a project. With this client-server setup, I can change a line of python, refresh my gunicorn server, and refresh a cached request in my browser to see what happened in four seconds flat. If it took me more than fifteen seconds per cycle, I'd probably lose my mind. So, that's my first real lesson to offer.
Last note: I should probably implement a rate-limiter now that there's a real (and easy) method of overloading my server with data. For now, I'll just clear the comment field when you hit submit so you can't submit repeatedly, forever. And I'll refuse blank submissions.


Learning that Tezzeret is behind recent events on Kaladesh, the Gatewatch seeks to overthrow his control of the plane. In doing so, they uncover hints of a sinister, mysterious plot put in motion by the Elder Dragon Planeswalker, Nicol Bolas.


Beegrid have a set of predefined layouts. With a minimum set of classes and few components you can laying out an entire website.
Components Overview
Layout Container: in which Header, Nav, Content and Footer can be nested, and can be placed.
Header: The top layout with default style, in which any element can be nested.
Nav: A simple sidebar with default style, in which any element can be nested.
Main: The content layout with default style, in which any element can be nested, such as a grid container.
Footer: The bottom layout with default style, in which any element can be nested.
Examples
HeaderMain
HeaderMain
HeaderMain
HeaderMain
HeaderMain
HeaderMain
HeaderMain
HeaderMain
HeaderMain
HeaderMain
Mobile behavior
BeeGrid include a JavaScript plugin called Navbar.
This plugin is usable only with the layouts with a nav component. If you include a button, or any HTML element, having class .--toggler-menu the plugin automatically change the layout default behavior and hide nav on small devices. This of course, not before you have included the plugin in your web page before closing the <body> tag.
If you run this script you’ll not see the button on large screens, but only from smaller and below. Just like this web site.


Works by using the websites' APIs or scrapping their web pages.
All restaurants are listed with food type, price, distance/waiting time and eventual promotions.
Ability to filter and sort by various criterias, and also filter by text.
Coded in Python 3 using the Flask framework.


Before Change
shape=(2, 3),
name="Input",
)
att_layer = get_multi_head_attention(
input_layer,
head_num=3,
dropout=1e-5,
name="MH"
)
dense_layer = keras.layers.Dense(units=3, name="Dense-1")(att_layer)
norm_layer = LayerNormalization(
name="Layer-Normalization",
trainable=False,
After Change
shape=(2, 3),
name="Input",
)
att_layer = MultiHeadAttention(
head_num=3,
dropout_rate=1e-5,
name="MH"
)(input_layer)
dense_layer = keras.layers.Dense(units=3, name="Dense-1")(att_layer)
norm_layer = LayerNormalization(
name="Layer-Normalization",
trainable=False,


--- layout: post status: publish published: true title: Welcome Facetime with me! author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 1429 wordpress_url: https://leaskh.com/?p=1429 date: '2010-10-23 16:20:17 +0800' date_gmt: '2010-10-23 08:20:17 +0800' categories: tags: [] comments: [] ---


_Widget.placeAt tests
Node2
This is where the tab starts. It should be above us in the id="container" div by now
BorderContainer sample
Add Stuff


Links
OLOB donates its proceeds to the Leukemia & Lymphoma Society.
Mariner's point, our field site, is a nesting site for the California Least Tern. The Least Tern is an endangered species whose numbers have been recovering from only 582 breeding pairs in 1974. OLOB takes place in the middle of the breeding season, and so it is very important that we do not disturb their nesting sites, located at the southern end of Mariner's Point.
OLOB is run as a partnership with the local Ultimate organization, DUDE.
Things to do in San Diego
Disc golf at Morley Field.
Play some pickup.
Like animals? Visit Sea World, the Birch Aquarium, or the San Diego Zoo.
Looking for things to do near the tournament site? Wave House, Belmont Park Roller Coaster.
Bring your dog to the beach at nearby Dog Beach.


mb
- command and control clusters of machines with Chef
mb
configure
mb
console
mb
purge HOST
mb
enable HOST
mb
disable HOST
mb
version
mb
template NAME PATH_OR_URL
mb
environment
mb
plugin
mb
environment
configure ENVIRONMENT FILE
mb
environment
destroy ENVIRONMENT
mb
environment
list
mb
environment
lock ENVIRONMENT
mb
environment
unlock ENVIRONMENT
mb
environment
from FILE
mb
environment
create ENVIRONMENT
mb
environment
examine ENVIRONMENT
mb
environment
help [COMMAND]
mb
plugin
init [PATH]
mb
plugin
install NAME
mb
plugin
list
mb
plugin
show NAME
mb
plugin
uninstall NAME --version=VERSION
mb
plugin
help [COMMAND]
mb
<plugin>
nodes
mb
<plugin>
bootstrap MANIFEST
mb
<plugin>
provision MANIFEST
mb
<plugin>
service [COMPONENT].[SERVICE] [STATE]
mb
<plugin>
upgrade
mb
<plugin>
attributes
mb
<plugin>
<component>
<command>
motherbrain is an orchestration framework for Chef. In the same way that you would use Chef's Knife command to create a single node, you can use motherbrain to create and control an entire application environment.
With the exception of a few top-level commands, most motherbrain interactions use the commands defined in the selected plugin.
Provisioning and bootstrapping nodes require manifests. Insert manifest documentation.
The following global options can be used on any motherbrain command to change the behavior.
-c
, --config
Path to a motherbrain JSON configuration file.
-v
, --verbose
Increase verbosity of output.
-d
, --debug
Output all log messages.
-L
, --logfile
Set the log file location.
-p
, --plugin_version
Plugin version to use
-e
, --environment
Chef environment
--on_environment_missing
What to do when the environment doesn't exist (Default: prompt)Possible values: prompt, create, quit
mb
configure
create a new configuration file based on a set of interactive questions
mb
console
Start an interactive motherbrain console
mb
purge HOST
Remove Chef from node and purge it's data from the Chef server
mb
enable HOST
Remove stop service attributes and 'disabled' run list entry from HOST's node.
mb
disable HOST
Stop services on HOST and prevent chef-client from running until reenabled.
mb
version
Display version and license information
mb
template NAME PATH_OR_URL
Download and install a bootstrap template
mb
environment
Environment level commands
mb
plugin
Plugin level commands
mb
environment
configure ENVIRONMENT FILE
configure a Chef environment
mb
environment
destroy ENVIRONMENT
Destroy a provisioned environment
mb
environment
list
List all environments
mb
environment
lock ENVIRONMENT
Lock an environment
mb
environment
unlock ENVIRONMENT
Unlock an environment
mb
environment
from FILE
Create an environment from JSON in a file
mb
environment
create ENVIRONMENT
Create an empty environment
mb
environment
examine ENVIRONMENT
Examine nodes in a Chef environment
mb
environment
help [COMMAND]
Describe subcommands or one specific subcommand
mb
plugin
init [PATH]
Generate a new motherbrain plugin in the target cookbook
mb
plugin
install NAME
Install a plugin from the remote Chef server
mb
plugin
list
List all installed plugins
mb
plugin
show NAME
Show information about a plugin
mb
plugin
uninstall NAME --version=VERSION
Uninstall a plugin from your Berkshelf
mb
plugin
help [COMMAND]
Describe subcommands or one specific subcommand
mb
<plugin>
nodes
List all nodes grouped by Component and Group
mb
<plugin>
bootstrap MANIFEST
Bootstrap a manifest of node groups
mb
<plugin>
provision MANIFEST
Create a cluster of nodes and add them to a Chef environment
mb
<plugin>
service [COMPONENT].[SERVICE] [STATE]
Change the specified service to a new state
mb
<plugin>
upgrade
Upgrade an environment to the specified versions
mb
<plugin>
attributes
View available attributes for plugin.
mb
<plugin>
<component>
<command>
Run a command defined in a component in the specified plugin. Options and usage are plugin-defined.
motherbrain is Copyright (C) 2012-2013 Riot Games, Inc.


This service allows you to modify and control the controller's (UUT) default IO behavior. You reference an IO control by ID.
To add a new item, right-click the object in the configuration tree and select Add from the shortcut menu.
To delete multiple items, right-click the object in the configuration tree and select Delete Multiple Items from the shortcut menu.


Behavior: Looking at a few aspects of a polynomial will tell us what is happening at either end of the graph of the polynomial function. We will be looking at the DEGREE of the polynomial and the SIGN of the leading coefficient to determine what is happening to the graph. Definition: End behavior refers to the appearance of a graph as it is followed indefinitely in either horizontal direction.


onStateChanged using RecordRTC
State preview:
inactive
Start Recording
Stop Recording
Pause Recording


Quick List Opened
{% else %}{{user_nickname_short}}
Member Since
{{user.user_from_db.sign_up_date_time}}
{% endif %}{% extends "padsweb/padsweb.html" %}
{# User Accounts Settings Template #}
{% block title %}Personal Archive of ‘Days Since’ Timers{% endblock %}
{% block content %}
{% if signed_in %}
{# Quick List Options #}
{{user.user_from_db.sign_up_date_time}} Choose from a selection of over 500 time zones,
including historical and test zones! Time zones are shown in Olson format. Please use
the Continent/City or Ocean/City time zones when
you are not hacking.
Timer Groups are used to organise your Timers.
They are automatically created for you when you assign group
names to a Timer.
To delete a Timer Group, select a group from the list, and
then press the Delete button. Deleting a group only unassigns
Timers from the deleted Group.
You can transfer timers in a Quick List to your account. Timers can be imported into a Timer Group. Please be aware that the Quick List will be deleted, and its password rendered permanently
unusable. You can change your password here when you need to.Quick List Opened
{% else %}
{{user_nickname_short}}
Member Since
Time Zone
Timer Groups
Delete A Timer Group
Quick Lists
Import Quick List
Passwords
Change Password
Please sign in first to check your user info. You should only mess with your own account!


All GIP projects are designed to improve land ecology and to increase the economic production of Utah's cattle, sheep and wildlife operations. Utah's livestock industry represents a significant percentage of the state's $17.5 billion dollar agricultural production and processing industry. Utah agriculture also generates more than 78,000 jobs and is a source of safe, high quality and affordable local food.


Class contining data preprocessing tools.
More...
#include <data_preprocessor.h>
|
| void | chooseTrainData (const std::string &filename) |
| | Split data to train and test Split data to training and testing set. More...
|
| |
| | DataPreprocessor (std::string curl_out_folder="output") |
| | Constructor. More...
|
| |
| bool | filterValidDomains (const std::string &input_file, const std::string &output_file) |
| | Filter domains that return quick http response Filer domains that give http reponse in less than 5 seconds and save them in "output_filename" file. More...
|
| |
| bool | get_attribs_from_link (const std::string &url) |
| | Get attributes from wiki link Get attributes from en.wikipedia.org article and save it in example/attribs.txt file. More...
|
| |
| const std::set< std::string > & | getAll_atribs () const |
| | Getter. More...
|
| |
| void | getAttribs (const std::string &filename) |
| | Get attributes from all websites in file Get attribites from websites in "filename" file and save them in curl_output_folder. More...
|
| |
| bool | parseHtmls (const std::string &filename, const std::string &from_which_tags) |
| | Parse html code. More...
|
| |
Class contining data preprocessing tools.
Data Preprocessot contains methods to get training and testing data from en.wikipedia.org for classification including parsing and attributes extracting
Definition at line 16 of file data_preprocessor.h.
| DataPreprocessor::DataPreprocessor |
( |
std::string |
curl_out_folder = "output" | ) |
|
Constructor.
- Parameters
-
| curl_out_folder | folder in which output of data preprocessing will be stored |
Definition at line 9 of file data_preprocessor.cpp.
| void DataPreprocessor::chooseTrainData |
( |
const std::string & |
inFilename | ) |
|
Split data to train and test Split data to training and testing set.
1/4 -testing, 3/4 training
- Parameters
-
| inFilename | file with links, every in new line |
Definition at line 132 of file data_preprocessor.cpp.
| bool DataPreprocessor::filterValidDomains |
( |
const std::string & |
input_filename, |
|
|
const std::string & |
output_filename |
|
) |
| |
Filter domains that return quick http response Filer domains that give http reponse in less than 5 seconds and save them in "output_filename" file.
- Parameters
-
| input_filename | name of file with links, every link in other line |
| output_filename | links that can be opened will be stored in this file |
- Returns
- false is cannot open file, otherwise return true
Definition at line 19 of file data_preprocessor.cpp.
| bool DataPreprocessor::get_attribs_from_link |
( |
const std::string & |
url | ) |
|
Get attributes from wiki link Get attributes from en.wikipedia.org article and save it in example/attribs.txt file.
Attributes are links found in text of article. Every attribute is in new line.
- Parameters
-
| url | url address of en.wikipedia.org article |
- Returns
- true - all went good, false - cannot open article/ no internet connection
Definition at line 182 of file data_preprocessor.cpp.
| const std::set< std::string > & DataPreprocessor::getAll_atribs |
( |
| ) |
const |
| void DataPreprocessor::getAttribs |
( |
const std::string & |
filename | ) |
|
Get attributes from all websites in file Get attribites from websites in "filename" file and save them in curl_output_folder.
Output files will be named from 1 to number of links. Every output will have name of file in first line as a name of category and attributes as all links found in text of link listed every in next line below.
- Parameters
-
| filename | file with links from en.wikipedia.org |
Definition at line 123 of file data_preprocessor.cpp.
| bool DataPreprocessor::parseHtmls |
( |
const std::string & |
filename, |
|
|
const std::string & |
from_which_tags |
|
) |
| |
Parse html code.
Parse html code from all links in filename. Html code is parsed depending on fromWhickTags parameter which is a
- Parameters
-
| filename | - file where html code is |
| from_which_tags | - from which tags from html (or xml) code structure the content should be |
Definition at line 59 of file data_preprocessor.cpp.
| std::string DataPreprocessor::_curl_output_folder |
|
private |
| std::set<std::string> DataPreprocessor::all_atribs |
|
private |
| int DataPreprocessor::fileCounter |
|
private |
| std::unique_ptr<Csv> DataPreprocessor::ptr_csv |
|
private |
The documentation for this class was generated from the following files:


Icons
Icons example below
Success
Used to indicate successful operation
Information
Used to indicate information prompts; also used for lack of conditional operation interception, prompting users for required information
Warning
Used to indicate that the operation will cause certain consequences
Danger
Used to indicate that the operation will cause serious irreversible consequences
Wait
Used to indicate that the operation will take time


Using Results Databases
GNSDK applications supports application created read/write databases for storing local and online query and image fetch results. These databases are referred to as Results databases Results databases are similar to the off-the-shelf monthly production gdbs in that they have the same files and schema and are configured, enabled and opened in the same way. The main differences are that they are created by the application and are read-write. An application can have multiple Results databases open at one time, depending on the need of the application. These databases store results that can be returned in future queries.
Results Databases:
- Support incremental Gracenote database updates that can eliminate the need to do a base replace of the entire database with the latest information.
- Support deferred (or tethered) online searches when a user attempts a search without a connection.
- Control disk space used by Gracenote databases.
- Do not support record deletion or expiration.
- Do not require the existence of a Gracenote delivered Production database in an application.
Application Flow
The process of using a Results database is similar to using any other GNSDK application. Each database must have a user supplied ID. This ID supports the use of a specific database.
- Typically initialize the GNSDK, provide license information and create a user.
- Open databases as necessary. Each database requires a unique ID and config_handle.
- Query and update as required.
- Manage database sizes, as necessary. This might be needed only when data is added to a database.
- Close databases and application.
Managing Disk Space
The Results database might need to be controlled to help maintain appropriate disk usage and content. If you are opening an existing Results database and intend to add to it, checking disk usage is a good step to take immediately after adding information. In addition, you can't delete individual pieces of data from within a Results database, so if it is necessary to retire and purge old data, you must delete and recreate the entire database. The following snippet will check the size of a database, and if it is too large, it deletes it.
/* Retrieve database size based on database ID */
gnsdk_lookupdatabase_size_get( db_identfier_id, &size ); /* Check if DB size is larger than a pre-determined value. If so, Remove old DB and start new one. */ if (size > MAX_DB_SIZE) { /* Close the database and delete it. */ gnsdk_lookupdatabase_close( db_identifier_id ); gnsdk_lookupdatabase_delete( config_handle ); /* code to reopen database here */ ... }
Querying and Updating a Results Database
Whenever a query occurs, such as with gnsdk_musicid_query_find_matches()
, all open databases are searched. There are no specific functions for a specific Results database search.
Gracenote Data Objects (GDOs) can be added to a Results database using the function gnsdk_lookupdatabase_record_add()
, and images can be added with gnsdk_lookupdatabase_image_add()
.
Note: If the same result is found in more than one open database, the result with the higher revision will be returned. Also, while you can always add a query result to a Results database, if it is already present with a revision that is equal to or higher than that of the one you are adding, the add will not take place.
Results Database Functions
The GNSDK provides several functions that allow you to open, read, write, or query databases. When making a database query, all open databases will be searched. You do not specify which database you wish to use. However, the API provides the following functions that allow you to use a specific database, based on the ID that you provided for it:
.
Examples: Results Databases
Sample Application: musicid_automotive/main.c
This application shows the intialization and shutdown of a GNSDK application, the opening of a Production database, and one Results database. It also shows database information lookup of the Results database, queries, and updates.
Sample Application: musicid_persist_and_manage/main.c
This more complex application shows the intialization and shutdown of a GNSDK application, the opening of a Production database, and two Results databases. In addition to queries and updates, it also shows size management of Results databases.


| var size, attribute, fileTime : int) |
The size parameter is the size of the file in bytes. The attribute parameter has its individual bits set as follows (the individual bits can be extracted using the bits operator):
Bit 0 attrDir set to 1 if entry is a directory.
Bit 1 attrRead set to 1 if the program can read the file.
Bit 2 attrWrite set to 1 if the program can write the file.
Bit 3 attrExecute set to 1 if the program can execute the file.
Bit 4 attrHidden set to 1 if the entry if a hidden file (PC, Mac).
Bit 5 attrSystem set to 1 if the entry is a system file (PC only).
Bit 6 attrVolume set to 1 if the entry is a volume name (PC only).
Bit 7 attrArchive set to 1 if the entry has archive bit set (PC only).
The attr... constants are defined in the Dir unit. They correspond to the values of attribute if a specified bit is set. For example, attrSystem is the value of the attribute parameter if bit 5 is set to 1. You can and or or these constants to get combinations of specific file attributes.
The fileTime is the time of last modification of the file. It is returned as the number of seconds since 00:00:00 GMT 1/1/1970. To convert this to a string, use Time.SecDate
var streamNumber : int
var fileName : string
var size, attribute, fileTime : int
streamNumber := Dir.Open ("datafiles")
assert streamNumber > 0
loop
Dir.GetLong (streamNumber, fileName, size, attribute, fileTime)
exit when fileName = ""
put fileName, " ", Time.SecDate (fileTime)
end loop
Dir.Close (streamNumber)
var streamNumber : int
var fileName : string
var size, attribute, fileTime : int
streamNumber := Dir.Open (Dir.Current)
assert streamNumber > 0
loop
Dir.GetLong (streamNumber, fileName, size, attribute, fileTime)
exit when fileName = ""
put fileName, " "..
if (attribute and attrDir) not= 0 then
put "Directory "..
end if
if (attribute and attrRead) not= 0 then
put "Readable "..
end if
if (attribute and attrWrite) not= 0 then
put "Writeable "..
end if
if (attribute and attrExecute) not= 0 then
put "Executable "..
end if
put ""
end loop
Dir.Close (streamNumber)
This means that you can only call the function by calling Dir.GetLong, not by calling GetLong.


@import models.User @(maybeUser: Option[User])(implicit messages: Messages) @main(Messages("open.title"), maybeUser) {
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