A Dartmoor Christmas tree will take a starring role in this year's General Election
Downing Street's Christmas tree has been grown in Devon
by Colleen SmithA Christmas tree grown on Dartmoor will be at the centre of the drama that plays out on the steps of Number 10 Downing Street for this year's General Election.
The tree will be supplied once again by Dartmoor Christmas Trees after they won the title of British Christmas Tree Grower of the Year. They have previously also supplied trees for inside the Prime Minister's residence and the wreath for the famous front door at Number 10.
But this year will be different. Because of the rarity of December General Elections, the famous front steps at Downing Street are not usually in the spotlight as much as they will be in 2019 during the festive season.
This is only the fifth time a General Election has been held in December - the last was held in 1923 and it was the first time Labour won, under Ramsay MacDonald.
Stuart and Jennie Kirkup who run Dartmoor Christmas Trees say it's been a great growing year for trees in Devon.
Stuart said: “This year has been a great year for the trees with a mixture of intense sunshine and showers, which has been great for growth".
The family have won Champion Grower twice, in 2016 and 2019. In this year's competition they also won three other awards: Best Natural Wreath, Runner up Best Other Fir (Fraser) and Best Nordman Fir.
Stuart, Jennie and their family who have been growing Christmas Trees in the heart of Dartmoor for nearly 20 years have won 36 industry awards for their trees and wreaths.
Stuart attributes their success to their passion about what they do and the dedication to producing beautiful trees that are local and sustainable.
Dartmoor Christmas Trees work with Forestry England at Haldon Forest in Exeter and will be selling their trees in the retail centre there this year. Whether you prefer the low drop Nordman fir or Fraser fir or the traditional smells and shape of the Norway spruce they can help you decide on your perfect Christmas Tree.
Dartmoor Christmas Trees can also be bought at Morrison superstores in Paignton and Totnes.


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Hugo Arregui
- Birth date:
- December 2, 1986
- Nationality:
- Argentina
- Email:
- hugo.arregui.laboral at gmail.com
- Links:
- LinkedIn, GitHub, GitLab
Professional Experience
03/2022 - [PRESENT]: Staff Engineer at Decentraland (Working remotely)
02/2021 - 03/2022: Engineer at PolarSteps (Working remotely)
03/2020 - 02/2021: Engineer at Big Time Studios (Working remotely)
- Design and implement backend services
- PoC Research and development
- Stack: typescript, Unreal Engine
09/2018 - 03/2020: Tech Leader at Decentraland (Working remotely)
- Design and implement Decentraland's platform: identity, communications, user's shared state.
- Research and development
- Stack: golang, webrtc, auth0, C++, Unreal Engine
03/2016 - 09/2018: Engineer at Auth0 (Working remotely)
- Development and maintenance of Auth0's Management API
- Support users search engine using ElasticSearch
- Stack: nodejs, express, JWT, elasticsearch, mongodb, hapi.js, AWS, git, GitHub, Kibana, Datadog, valgrind
01/2015 - 06/2015: Senior Python Developer at Jampp (Working remotely)
- Worked on Jampp's Machine Learning backend.
- Stack: zeromq, sklearn, pandas, numpy, machine learning, PostgreSQL
02/2013 - 02/2015: Scheme Developer at bevuta IT GmbH (Working remotely)
- Worked on the android version of sayHey (a chat app) using Java + CHICKEN scheme + JNI.
- Stack: android, scheme, CHICKEN scheme, C, JNI, SQLite
- Part of this work is open-source, for example, the CHICKEN bindings for Java (in which I worked a lot)
08/2010 - 01/2014: C++ Developer (Ad honorem) at FuDePAN (Working remotely)
- Developed and implemented bioinformatic tools.
- Stack: C++, Template Metaprogramming, googletest, googlemock, g++, make, Scons, Jenkins, SVN, Mercurial, gdb
07/2011 - 01/2013: Regional Coordinator for Buenos Aires (Ad honorem) at FuDePAN (Working remotely)
- Volunteers coordination. Project management.
07/2010 - 03/2016: Freelance Software Engineer (Working remotely)
- Developed issue tracking system for an important petrol company. (python django psql jQuery javascript)
- Provided consulting services for a real estate online portal. (ruby sequel sinatra redis)
- Developed a multi-platform mobile app to organize and manage social events. (clojurescript clojure phonegap/cordova)
- Provided freelance development services for Ticketek. (python django)
- Developed a crawler infrastructure for an important deal network. (python scrapy flask)
04/2010 - 01/2011: Ssr. Python Developer at TeraCode
- Worked on Performable's website/platform.
- Stack: python, Tornado, django, SQLAlchemy, jQuery, MySQL, Scrum
04/2008 - 04/2010: Ssr. Java Developer at Livra
- Worked on Livra’s website. Creating and maintaining marketing tools for surveys, statistics, etc.
- Stack: java, Apache Wicket, wicket, Hibernate, JUnit, Velocity, Lucene, PostgreSQL, Web Services, Rest, Jersey, SOAP, Axis, prototype, scriptaculous, Eclipse, Tomcat, Jetty, ant, Maven, SVN, Prototype
08/2007 - 04/2008: Java Developer at Thales Information Systems
- Stack: IVR, asterix, structs, hibernate, spring
Publications
- 2012 - ACCU Overload Magazine #109, June 2012: Curiously Recursive Template Problems with Aspect Oriented Programming. Hugo Arregui, Carlos Castro, Daniel Gutson
- 2011 - 2do Congreso Argentino de Biologia Computacional: Biopp2, biological alphabet manipulation library. Hugo Arregui, Daniel Gutson
- 2011 - 2do Congreso Argentino de Biologia Computacional: Backbones Generator v2. Hugo Arregui, Rodrigo Castaño, Lucía Lauría, Rafael Garabato, Elmer Fernández, Marcos Villarreal
Education
- 2010 - 2014
- System Analyst at CAECE University
- 2017 - Elastic
- Advanced Elasticsearch: Data Modeling
- 2013 - Coursera
- Image and video processing: From Mars to Hollywood with a stop at the hospital
- 2012 - Coursera
- Algorithms: Design and Analysis Part I
- 2011 - ECI 2011 (UBA)
- Programación de Seguridad mediante Bibliotecas - (Security programming using libraries)
- 2011 - ECI 2011 (UBA)
- Programación distribuida y paralela usando MPI - (Distributed and parallel programming using MPI)
- 2010 - FuDePAN
- C++ Internals
- 2010 - FuDePAN
- C/C++ Good Practices
- 2005 - 2010
- System Engineering at Universidad Tecnologica Nacional (Unfinished)


doh_mvc_search-results-repeat-store.html automated tests (non-robot)
Employee Search
Master Detail Example - With repeat container.
Name:
Details
Details for selected index:
First Name:
Last Name:
Location:
Office:
Email:
Telephone:
Fax:
Commit
Reset


Threads, Networking and Sharing
Changing the Unchangeable
If you're feeling pig-headed (as I get) you wonder if it's ever possible to get around the restrictions of the borrow checker.
Consider the following little program, which compiles and runs without problems.
// cell.rs use std::cell::Cell; fn main() { let answer = Cell::new(42); assert_eq!(answer.get(), 42); answer.set(77); assert_eq!(answer.get(), 77); }
The answer was changed - and yet the variable answer
was not mutable!
This is obviously perfectly safe, since the value inside the cell is only accessed
through set
and get
. This goes by the grand name of interior mutability. The
usual is called inherited mutability: if I have a struct value v
, then I can only
write to a field v.a
if v
itself is writeable. Cell
values relax this rule, since
we can change the value contained within them with set
even if the cell itself is
not mutable.
However, Cell
only works with Copy
types
(e.g primitive types and user types deriving the Copy
trait).
For other values, we have to get a reference we can work on, either mutable or immutable.
This is what RefCell
provides - you ask it explicitly for a reference to the contained
value:
// refcell.rs use std::cell::RefCell; fn main() { let greeting = RefCell::new("hello".to_string()); assert_eq!(*greeting.borrow(), "hello"); assert_eq!(greeting.borrow().len(), 5); *greeting.borrow_mut() = "hola".to_string(); assert_eq!(*greeting.borrow(), "hola"); }
Again, greeting
was not declared as mutable!
The explicit dereference operator *
can be a bit confusing in Rust, because
often you don't need it - for instance greeting.borrow().len()
is fine since method
calls will dereference implicitly. But you do need *
to pull out the underlying
&String
from greeting.borrow()
or the &mut String
from greeting.borrow_mut()
.
Using a RefCell
isn't always safe, because any references returned from these
methods must follow the usual rules.
# #![allow(unused_variables)] # #fn main() { let mut gr = greeting.borrow_mut(); // gr is a mutable borrow *gr = "hola".to_string(); assert_eq!(*greeting.borrow(), "hola"); // <== we blow up here! .... thread 'main' panicked at 'already mutably borrowed: BorrowError' #}
You cannot borrow immutably if you have already borrowed mutably! Except - and this
is important - the violation of the rules happens at runtime. The solution (as always)
is to keep the scope of mutable borrows as limited as possible - in this case, you could
put a block around the first two lines here so that the mutable reference gr
gets
dropped before we borrow again.
So, this is not a feature you use without good reason, since you will not get a compile-time error. These types provide dynamic borrowing in cases where the usual rules make some things impossible.
Shared References
Up to now, the relationship between a value and its borrowed references has been clear
and known at compile time. The value is the owner, and the references cannot outlive it.
But many cases simply don't fit into this neat pattern. For example, say we have
a Player
struct and a Role
struct. A Player
keeps a vector of references to Role
objects. There isn't a neat one-to-one relationship between these values, and persuading
rustc
to cooperate becomes nasty.
Rc
works like Box
- heap memory is allocated and the value is moved to it. If you
clone a Box
, it allocates a full cloned copy of the value. But cloning an Rc
is
cheap, because each time you clone it just updates a reference count to the same data.
This is an old and very popular strategy for memory management,
for example it's used in the Objective C runtime on iOS/MacOS.
(In modern C++, it is implemented with std::shared_ptr
.)
When a Rc
is dropped, the reference count is decremented. When that count goes to zero
the owned value is dropped and the memory freed.
// rc1.rs use std::rc::Rc; fn main() { let s = "hello dolly".to_string(); let rs1 = Rc::new(s); // s moves to heap; ref count 1 let rs2 = rs1.clone(); // ref count 2 println!("len {}, {}", rs1.len(), rs2.len()); } // both rs1 and rs2 drop, string dies.
You may make as many references as you like to the original value - it's dynamic borrowing
again. You do not have to carefully track the relationship between the value T
and
its references &T
. There is some runtime cost involved, so it isn't the first
solution you choose, but it makes patterns of sharing possible which would fall foul
of the borrow checker. Note that Rc
gives you immutable shared references, since
otherwise that would break one of the very basic rules of borrowing.
A leopard can't change its spots without ceasing to be a leopard.
In the case of a Player
, it can now keep its roles as a Vec<Rc<Role>>
and things
work out fine - we can add or remove roles but not change them after their creation.
However, what if each Player
needs to keep references to a team as a vector of
Player
references? Then everything becomes immutable, because all the Player
values
need to be stored as Rc
! This is the place where RefCell
becomes necessary. The team
may be then defined as Vec<Rc<RefCell<Player>>>
. It is now possible to change
a Player
value using borrow_mut
, provided no-one has 'checked out' a reference
to a Player
at the same time. For example, say we have a rule that if something special
happens to a player, then all of their team gets stronger:
# #![allow(unused_variables)] # #fn main() { for p in &self.team { p.borrow_mut().make_stronger(); } #}
So the application code isn't too bad, but the type signatures get a bit scary. You can
always simplify them with a type
alias:
# #![allow(unused_variables)] # #fn main() { type PlayerRef = Rc<RefCell<Player>>; #}
Multithreading
Over the last twenty years, there has been a shift away from raw processing speed
to CPUs having multiple cores. So the only way to get the most out of a modern computer
is to keep all of those cores busy. It's certainly possible to spawn child processes
in the background as we saw with Command
but there's still a synchronization problem:
we don't know exactly when those children are finished without waiting on them.
There are other reasons for needing separate threads of execution, of course. You cannot afford to lock up your whole process just to wait on blocking i/o, for instance.
Spawning threads is straightforward in Rust - you feed spawn
a closure which is
executed in the background.
// thread1.rs use std::thread; use std::time; fn main() { thread::spawn(|| println!("hello")); thread::spawn(|| println!("dolly")); println!("so fine"); // wait a little bit thread::sleep(time::Duration::from_millis(100)); } // so fine // hello // dolly
Well obviously just 'wait a little bit' is not a very rigorous solution! It's better
to call join
on the returned object - then the main thread waits for the
spawned thread to finish.
// thread2.rs use std::thread; fn main() { let t = thread::spawn(|| { println!("hello"); }); println!("wait {:?}", t.join()); } // hello // wait Ok(())
Here's an interesting variation: force the new thread to panic.
# #![allow(unused_variables)] # #fn main() { let t = thread::spawn(|| { println!("hello"); panic!("I give up!"); }); println!("wait {:?}", t.join()); #}
We get a panic as expected, but only the panicking thread dies! We still manage
to print out the error message from the join
. So yes, panics are not always fatal,
but threads are relatively expensive, so this should not be seen as a routine way
of handling panics.
hello
thread '<unnamed>' panicked at 'I give up!', thread2.rs:7
note: Run with `RUST_BACKTRACE=1` for a backtrace.
wait Err(Any)
The returned objects can be used to keep track of multiple threads:
// thread4.rs use std::thread; fn main() { let mut threads = Vec::new(); for i in 0..5 { let t = thread::spawn(move || { println!("hello {}", i); }); threads.push(t); } for t in threads { t.join().expect("thread failed"); } } // hello 0 // hello 2 // hello 4 // hello 3 // hello 1
Rust insists that we handle the case where the join failed - i.e. that thread panicked. (You would typically not bail out of the main program when this happens, just note the error, retry etc)
There is no particular order to thread execution (this program gives different orders for different runs), and this is key - they really are independent threads of execution. Multithreading is easy; what's hard is concurrency - managing and synchronizing multiple threads of execution.
Threads Don't Borrow
It's possible for the thread closure to capture values, but by moving, not by borrowing!
// thread3.rs use std::thread; fn main() { let name = "dolly".to_string(); let t = thread::spawn(|| { println!("hello {}", name); }); println!("wait {:?}", t.join()); }
And here's the helpful error message:
error[E0373]: closure may outlive the current function, but it borrows `name`, which is owned by the current function
--> thread3.rs:6:27
|
6 | let t = thread::spawn(|| {
| ^^ may outlive borrowed value `name`
7 | println!("hello {}", name);
| ---- `name` is borrowed here
|
help: to force the closure to take ownership of `name` (and any other referenced variables), use the `move` keyword, as shown:
| let t = thread::spawn(move || {
That's fair enough! Imagine spawning this thread from a function - it will exist
after the function call has finished and name
gets dropped. So adding move
solves our
problem.
But this is a move, so name
may only appear in one thread! I'd like to emphasize
that it is possible to share references, but they need to have static
lifetime:
# #![allow(unused_variables)] # #fn main() { let name = "dolly"; let t1 = thread::spawn(move || { println!("hello {}", name); }); let t2 = thread::spawn(move || { println!("goodbye {}", name); }); #}
name
exists for the whole duration of the program (static
), so
rustc
is satisfied that the closure will never outlive name
. However, most interesting
references do not have static
lifetimes!
Threads can't share the same environment - by design in Rust. In particular, they cannot share regular references because the closures move their captured variables.
shared references are fine however, because their lifetime is 'as long as needed' -
but you cannot use Rc
for this. This is because
Rc
is not thread safe - it's optimized to be fast for the non-threaded case.
Fortunately it is a compile error to use Rc
here; the compiler is watching your
back as always.
For threads, you need std::sync::Arc
- 'Arc' stands for 'Atomic Reference Counting'.
That is, it guarantees that the reference count will be modified in one logical operation.
To make this guarantee, it must ensure that the operation is locked so that only the current
thread has access. clone
is still much cheaper than actually making a copy however.
// thread5.rs use std::thread; use std::sync::Arc; struct MyString(String); impl MyString { fn new(s: &str) -> MyString { MyString(s.to_string()) } } fn main() { let mut threads = Vec::new(); let name = Arc::new(MyString::new("dolly")); for i in 0..5 { let tname = name.clone(); let t = thread::spawn(move || { println!("hello {} count {}", tname.0, i); }); threads.push(t); } for t in threads { t.join().expect("thread failed"); } }
I"ve deliberately created a wrapper type for String
here (a 'newtype') since
our MyString
does not implement Clone
. But the shared reference can be cloned!
The shared reference name
is passed to each new thread by making a new reference
with clone
and moving it into the closure. It's a little verbose, but this is a safe
pattern. Safety is important in concurrency precisely because the problems are so
unpredictable. A program may run fine on your machine, but occasionally crash on the
server, usually on the weekend. Worse still, the symptoms of such problems are
not easy to diagnose.
Channels
There are ways to send data between threads. This
is done in Rust using channels. std::sync::mpsc::channel()
returns a tuple consisting
of the receiver channel and the sender channel. Each thread is passed a copy
of the sender with clone
, and calls send
. Meanwhile the main thread calls
recv
on the receiver.
'MPSC' stands for 'Multiple Producer Single Consumer'. We create multiple threads which attempt to send to the channel, and the main thread 'consumes' the channel.
// thread9.rs use std::thread; use std::sync::mpsc; fn main() { let nthreads = 5; let (tx, rx) = mpsc::channel(); for i in 0..nthreads { let tx = tx.clone(); thread::spawn(move || { let response = format!("hello {}", i); tx.send(response).unwrap(); }); } for _ in 0..nthreads { println!("got {:?}", rx.recv()); } } // got Ok("hello 0") // got Ok("hello 1") // got Ok("hello 3") // got Ok("hello 4") // got Ok("hello 2")
There's no need to join here since the threads send their response just before they
end execution, but obviously this can happen at any time. recv
will block, and will
return an error if the sender channel is disconnected. recv_timeout
will only block
for a given time period, and may return a timeout error as well.
send
never blocks, which is useful because threads can push out data without waiting
for the receiver to process. In addition, the channel is buffered so multiple
send operations can take place, which will be received in order.
However, not blocking means that Ok
does not automatically mean 'successfully delivered message'!
A sync_channel
does block on send. With an argument of zero, the send blocks until the
recv happens. The threads must meet up or rendezvous (on the sound principle that most things
sound better in French.)
# #![allow(unused_variables)] # #fn main() { let (tx, rx) = mpsc::sync_channel(0); let t1 = thread::spawn(move || { for i in 0..5 { tx.send(i).unwrap(); } }); for _ in 0..5 { let res = rx.recv().unwrap(); println!("{}",res); } t1.join().unwrap(); #}
We can easily cause an error here by calling recv
when there has been no corresponding send
, e.g
by looping for i in 0..4
. The thread ends, and tx
drops, and then recv
will fail. This will also
happen if the thread panics, which causes its stack to be unwound, dropping any values.
If the sync_channel
was created with a non-zero argument n
, then it acts like a queue with a
maximum size of n
- send
will only block when it tries to add more than n
values to the queue.
Channels are strongly typed - here the channel had type i32
- but type inference makes this implicit.
If you need to pass different kinds of data, then enums are a good way to express this.
Synchronization
Let's look at synchronization. join
is very basic, and merely waits until a
particular thread has finished. A sync_channel
synchronizes two threads - in the last example, the
spawned thread and the main thread are completely locked together.
Barrier synchronization is a checkpoint where the threads must wait until all of
them have reached that point. Then they can keep going as before. The barrier is
created with the number of threads that we want to wait for. As before we use use Arc
to share the barrier with all the threads.
// thread7.rs use std::thread; use std::sync::Arc; use std::sync::Barrier; fn main() { let nthreads = 5; let mut threads = Vec::new(); let barrier = Arc::new(Barrier::new(nthreads)); for i in 0..nthreads { let barrier = barrier.clone(); let t = thread::spawn(move || { println!("before wait {}", i); barrier.wait(); println!("after wait {}", i); }); threads.push(t); } for t in threads { t.join().unwrap(); } } // before wait 2 // before wait 0 // before wait 1 // before wait 3 // before wait 4 // after wait 4 // after wait 2 // after wait 3 // after wait 0 // after wait 1
The threads do their semi-random thing, all meet up, and then continue. It's like a kind
of resumable join
and useful when you need to farm off pieces of a job to
different threads and want to take some action when all the pieces are finished.
Shared State
How can threads modify shared state?
Recall the Rc<RefCell<T>>
strategy for dynamically doing a
mutable borrow on shared references. The threading equivalent to RefCell
is
Mutex
- you may get your mutable reference by calling lock
. While this reference
exists, no other thread can access it. mutex
stands for 'Mutual Exclusion' - we lock
a section of code so that only one thread can access it, and then unlock it. You get the
lock with the lock
method, and it is unlocked when the reference is dropped.
// thread9.rs use std::thread; use std::sync::Arc; use std::sync::Mutex; fn main() { let answer = Arc::new(Mutex::new(42)); let answer_ref = answer.clone(); let t = thread::spawn(move || { let mut answer = answer_ref.lock().unwrap(); *answer = 55; }); t.join().unwrap(); let ar = answer.lock().unwrap(); assert_eq!(*ar, 55); }
This isn't so straightforward as using RefCell
because asking for the lock on
the mutex might fail, if another thread has panicked while holding the lock.
(In this case, the documentation actually recommends just exiting the thread with unwrap
because things have gone seriously wrong!)
It's even more important to keep this mutable borrow as short as possible, because as long as the mutex is locked, other threads are blocked. This is not the place for expensive calculations! So typically such code would be used like this:
# #![allow(unused_variables)] # #fn main() { // ... do something in the thread // get a locked reference and use it briefly! { let mut data = data_ref.lock().unwrap(); // modify data } //... continue with the thread #}
Higher-Level Operations
It's better to find higher-level ways of doing threading, rather than managing the synchronization
yourself. An example is when you need to do things in parallel and collect the results. One very
cool crate is pipeliner which has a very straightforward
API. Here's the 'Hello, World!' - an iterator feeds us inputs and we execute up to n
of the operations
on the values in parallel.
extern crate pipeliner; use pipeliner::Pipeline; fn main() { for result in (0..10).with_threads(4).map(|x| x + 1) { println!("result: {}", result); } } // result: 1 // result: 2 // result: 5 // result: 3 // result: 6 // result: 7 // result: 8 // result: 9 // result: 10 // result: 4
It's a silly example of course, because the operation is so cheap to calculate, but shows how easy it is to run code in parallel.
Here's something more useful. Doing network operations in parallel is very useful, because they can take time, and you don't want to wait for them all to finish before starting to do work.
This example is pretty crude (believe me, there are better ways of doing it) but here we want to focus
on the principle. We reuse the shell
function defined in section 4 to call ping
on a range
of IP4 addresses.
extern crate pipeliner; use pipeliner::Pipeline; use std::process::Command; fn shell(cmd: &str) -> (String,bool) { let cmd = format!("{} 2>&1",cmd); let output = Command::new("/bin/sh") .arg("-c") .arg(&cmd) .output() .expect("no shell?"); ( String::from_utf8_lossy(&output.stdout).trim_right().to_string(), output.status.success() ) } fn main() { let addresses: Vec<_> = (1..40).map(|n| format!("ping -c1 192.168.0.{}",n)).collect(); let n = addresses.len(); for result in addresses.with_threads(n).map(|s| shell(&s)) { if result.1 { println!("got: {}", result.0); } } }
And the result on my home network looks like this:
got: PING <IP_ADDRESS> (<IP_ADDRESS>) 56(84) bytes of data.
64 bytes from <IP_ADDRESS>: icmp_seq=1 ttl=64 time=43.2 ms
--- <IP_ADDRESS> ping statistics ---
1 packets transmitted, 1 received, 0% packet loss, time 0ms
rtt min/avg/max/mdev = 43.284/43.284/43.284/0.000 ms
got: PING <IP_ADDRESS> (<IP_ADDRESS>) 56(84) bytes of data.
64 bytes from <IP_ADDRESS>: icmp_seq=1 ttl=64 time=0.029 ms
--- <IP_ADDRESS> ping statistics ---
1 packets transmitted, 1 received, 0% packet loss, time 0ms
rtt min/avg/max/mdev = 0.029/0.029/0.029/0.000 ms
got: PING <IP_ADDRESS> (<IP_ADDRESS>) 56(84) bytes of data.
64 bytes from <IP_ADDRESS>: icmp_seq=1 ttl=64 time=110 ms
--- <IP_ADDRESS> ping statistics ---
1 packets transmitted, 1 received, 0% packet loss, time 0ms
rtt min/avg/max/mdev = 110.008/110.008/110.008/0.000 ms
got: PING <IP_ADDRESS> (<IP_ADDRESS>) 56(84) bytes of data.
64 bytes from <IP_ADDRESS>: icmp_seq=1 ttl=64 time=207 ms
...
The active addresses come through pretty fast within the first half-second, and we then wait for the negative
results to come in. Otherwise, we would wait for the better part of a minute! You can now proceed
to scrape things like ping times from the output, although this would only work on Linux. ping
is universal, but the exact output format is different for each platform. To do better we need to use
the cross-platform Rust networking API, and so let's move onto Networking.
A Better Way to Resolve Addresses
If you just want availability and not detailed ping statistics, the std::net::ToSocketAddrs
trait
will do any DNS resolution for you:
use std::net::*; fn main() { for res in "google.com:80".to_socket_addrs().expect("bad") { println!("got {:?}", res); } } // got V4(<IP_ADDRESS>:80) // got V6([2c0f:fb50:4002:803::200e]:80)
It's an iterator because there is often more than one interface associated with a domain - there are both IPV4 and IPV6 interfaces to Google.
So, let's naively use this method to rewrite the pipeliner example. Most networking protocols use both an address and a port:
extern crate pipeliner; use pipeliner::Pipeline; use std::net::*; fn main() { let addresses: Vec<_> = (1..40).map(|n| format!("192.168.0.{}:0",n)).collect(); let n = addresses.len(); for result in addresses.with_threads(n).map(|s| s.to_socket_addrs()) { println!("got: {:?}", result); } } // got: Ok(IntoIter([V4(<IP_ADDRESS>:0)])) // got: Ok(IntoIter([V4(<IP_ADDRESS>:0)])) // got: Ok(IntoIter([V4(<IP_ADDRESS>:0)])) // got: Ok(IntoIter([V4(<IP_ADDRESS>:0)])) // got: Ok(IntoIter([V4(<IP_ADDRESS>:0)])) // ....
This is much faster than the ping example because it's just checking that the IP address is valid - if we fed it a list of actual domain names the DNS lookup could take some time, hence the importance of parallelism.
Suprisingly, it sort-of Just Works. The fact that everything in the standard library implements Debug
is great for exploration as well as debugging. The iterator is returning Result
(hence Ok
) and
in that Result
is an IntoIter
into a SocketAddr
which is an enum with either a ipv4 or a ipv6 address.
Why IntoIter
? Because a socket may have multiple addresses (e.g. both ipv4 and ipv6).
# #![allow(unused_variables)] # #fn main() { for result in addresses.with_threads(n) .map(|s| s.to_socket_addrs().unwrap().next().unwrap()) { println!("got: {:?}", result); } // got: V4(<IP_ADDRESS>:0) // got: V4(<IP_ADDRESS>:0) // got: V4(<IP_ADDRESS>:0) #}
This also works, surprisingly enough, at least for our simple example. The first unwrap
gets rid of
the Result
, and then we explicitly pull the first value out of the iterator. The Result
will get
bad typically when we give a nonsense address (like an address name without a port.)
TCP Client Server
Rust provides a straightforward interface to the most commonly used network protocol, TCP. It is very fault-resistant and is the base on which our networked world is built - packets of data are sent and received, with acknowledgement.  (Jokes about networking are only funny for a specialized meaning of the word 'funny')
However, error handling is very important with networking, because anything can happen, and will, eventually.
TCP works as a client/server model; the server listens on a address and a particular network port, and the client connects to that server. A connection is established and thereafter the client and server can communicate with a socket.
TcpStream::connect
takes anything that can convert into a SocketAddr
, in particular the plain strings
we have been using.
A simple TCP client in Rust is easy - a TcpStream
struct is both readable and writeable. As usual, we
have to bring the Read
, Write
and other std::io
traits into scope:
// client.rs use std::net::TcpStream; use std::io::prelude::*; fn main() { let mut stream = TcpStream::connect("<IP_ADDRESS>:8000").expect("connection failed"); write!(stream,"hello from the client!\n").expect("write failed"); }
The server is not much more complicated; we set up a listener and wait for connections. When a
client connects, we get a TcpStream
on the server side. In this
case, we read everything that the client has written into a string.
// server.rs use std::net::TcpListener; use std::io::prelude::*; fn main() { let listener = TcpListener::bind("<IP_ADDRESS>:8000").expect("could not start server"); // accept connections and get a TcpStream for connection in listener.incoming() { match connection { Ok(mut stream) => { let mut text = String::new(); stream.read_to_string(&mut text).expect("read failed"); println!("got '{}'", text); } Err(e) => { println!("connection failed {}", e); } } } }
Here I've chosen a port number moreorless at random, but most ports are assigned some meaning.
Note that both parties have to agree on a protocol - the client expects it can write text to the stream, and the server expects to read text from the stream. If they don't play the same game, then situations can occur where one party is blocked, waiting for bytes that never come.
Error checking is important - network I/O can fail for many reasons, and errors that might appear once in a blue moon on a local filesystem can happen on a regular basis. Someone can trip over the network cable, the other party could crash, and so forth. This little server isn't very robust, because it will fall over on the first read error.
Here is a more solid server that handles the error without failing. It also specifically reads a line
from the stream, which is done using io::BufReader
to create an io::BufRead
on which we can call
read_line
.
// server2.rs use std::net::{TcpListener, TcpStream}; use std::io::prelude::*; use std::io; fn handle_connection(stream: TcpStream) -> io::Result<()>{ let mut rdr = io::BufReader::new(stream); let mut text = String::new(); rdr.read_line(&mut text)?; println!("got '{}'", text.trim_right()); Ok(()) } fn main() { let listener = TcpListener::bind("<IP_ADDRESS>:8000").expect("could not start server"); // accept connections and get a TcpStream for connection in listener.incoming() { match connection { Ok(stream) => { if let Err(e) = handle_connection(stream) { println!("error {:?}", e); } } Err(e) => { print!("connection failed {}\n", e); } } } }
read_line
might fail in handle_connection
, but the resulting error is safely handled.
One-way communications like this are certainly useful - for instance. a set of services across a network which want to collect their status reports together in one central place. But it's reasonable to expect a polite reply, even if just 'ok'!
A simple example is a basic 'echo' server. The client writes some text ending in a newline to the server, and receives the same text back with a newline - the stream is readable and writeable.
// client_echo.rs use std::io::prelude::*; use std::net::TcpStream; fn main() { let mut stream = TcpStream::connect("<IP_ADDRESS>:8000").expect("connection failed"); let msg = "hello from the client!"; write!(stream,"{}\n", msg).expect("write failed"); let mut resp = String::new(); stream.read_to_string(&mut resp).expect("read failed"); let text = resp.trim_right(); assert_eq!(msg,text); }
The server has an interesting twist. Only handle_connection
changes:
# #![allow(unused_variables)] # #fn main() { fn handle_connection(stream: TcpStream) -> io::Result<()>{ let mut ostream = stream.try_clone()?; let mut rdr = io::BufReader::new(stream); let mut text = String::new(); rdr.read_line(&mut text)?; ostream.write_all(text.as_bytes())?; Ok(()) } #}
This is a common gotcha with simple two-way socket communication; we want to read a line, so
need to feed the readable stream to BufReader
- but it consumes the stream! So we have to
clone the stream, creating a new struct which refers to the same underlying socket. Then we
have happiness.


New in version 1.1: Horizontal reuse was added in Twig 1.1.
Note
Horizontal reuse is an advanced Twig feature that is hardly ever needed in regular templates. It is mainly used by projects that need to make template blocks reusable without using inheritance.
Template inheritance is one of the most powerful Twig’s feature but it is limited to single inheritance; a template can only extend one other template. This limitation makes template inheritance simple to understand and easy to debug:
{% extends "base.html" %}
{% block title %}{% endblock %}
{% block content %}{% endblock %}
Horizontal reuse is a way to achieve the same goal as multiple inheritance, but without the associated complexity:
{% extends "base.html" %}
{% use "blocks.html" %}
{% block title %}{% endblock %}
{% block content %}{% endblock %}
The use statement tells Twig to import the blocks defined in `blocks.html into the current template (it’s like macros, but for blocks):
# blocks.html
{% block sidebar %}{% endblock %}
In this example, the use statement imports the sidebar block into the main template. The code is mostly equivalent to the following one (the imported blocks are not outputted automatically):
{% extends "base.html" %}
{% block sidebar %}{% endblock %}
{% block title %}{% endblock %}
{% block content %}{% endblock %}
Note
The use tag only imports a template if it does not extend another template, if it does not define macros, and if the body is empty. But it can use other templates.
Note
Because use statements are resolved independently of the context passed to the template, the template reference cannot be an expression.
The main template can also override any imported block. If the template already defines the sidebar block, then the one defined in blocks.html is ignored. To avoid name conflicts, you can rename imported blocks:
{% extends "base.html" %}
{% use "blocks.html" with sidebar as base_sidebar %}
{% block sidebar %}{% endblock %}
{% block title %}{% endblock %}
{% block content %}{% endblock %}
New in version 1.3: The parent() support was added in Twig 1.3.
The parent() function automatically determines the correct inheritance tree, so it can be used when overriding a block defined in an imported template:
{% extends "base.html" %}
{% use "blocks.html" %}
{% block sidebar %}
{{ parent() }}
{% endblock %}
{% block title %}{% endblock %}
{% block content %}{% endblock %}
In this example, parent() will correctly call the sidebar block from the blocks.html template.
Tip
In Twig 1.2, renaming allows you to simulate inheritance by calling the “parent” block:
{% extends "base.html" %}
{% use "blocks.html" with sidebar as parent_sidebar %}
{% block sidebar %}
{{ block('parent_sidebar') }}
{% endblock %}
Note
You can use as many use statements as you want in any given template. If two imported templates define the same block, the latest one wins.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import model._ @import config.FrontendAppConfig @import views.html.templates.page_template @this(form: FormWithCSRF, textarea: govukTextarea, button: govukButton, textInput: GovukInput, page: page_template) @(id: String, journeyData: JourneyDataV2, lookupForm: Form[Lookup], isWelsh: Boolean, isUKMode: Boolean = true, message: Option[String] = None )(implicit request: Request[_], messages: Messages, appConfig: FrontendAppConfig) @errorPrefix = @{s"${messages("constants.error.prefix")} "} @titleWithError = @{ val baseTitle = if(isUKMode) messages("lookupPage.title.ukMode") else messages("lookupPage.title") if(lookupForm.errors) { errorPrefix + baseTitle } else { baseTitle } } @page(titleWithError, Some(lookupForm), journeyData) { @form(routes.AddressLookupController.select(id)) {


Before Change
result = [self.image_count, self.object_count, self.bin_counts_count]
for x in (self.images, self.objects, self.bin_counts):
for settings in x:
result += settings.settings()
return result
def visible_settings(self):
result = []
After Change
result = [self.image_count, self.object_count, self.bin_counts_count]
for x in (self.images, self.objects, self.bin_counts):
for settings in x:
temp = [s for s in settings.unpack_group()
if not (isinstance(s, cps.Divider) or isinstance(s, cps.RemoveSettingButton))]
result += temp
return result
def visible_settings(self):


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Comments
-
Greg Hurrell
Created an attachment (id=63) Synergy Advance preferences window
Attaching a screen shot of the Synergy Advance preferences window showing the user interface for automatic and manual version checking.
-
Greg Hurrell
Changing assignment to reflect my new email address.
http://wincent.com/a/news/archives/2006/05/change_of_email.php
-
Greg Hurrell
I'm marking all WinSwitch issues closed seeing as I personally no longer use it, and in fact haven't for around 4 years now.
WinSwitch addressed a real problem with the initial implementation of Fast User Switching in Panther (released October 2003), namely, the excessive screen real estate that it chewed up. Apple fixed that problem in Tiger, if I recall correctly, which came out in April 2005 (or if I'm wrong about it being Tiger, then it was definitely fixed by the time Leopard came out, in October 2007).
With this change, most of the justification for WinSwitch's existence went away, at least for me. So that's why I'm going to close all these tickets: I can't really support something that I don't use myself.
But it's open source (see http://git.wincent.com/WinSwitch.git), so if any one wants to tackle any of these issues and submit patches, I'll be happy to accept them.
-
Greg Hurrell
Status changed:
- From: new
- To: closed
Add a comment
Comments are now closed for this issue.


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This is an introduction to the concept of Spatials, the elements of the 3D scene graph. The scene graph is a data structure that manages all objects in your 3D world. For example, the scene graph keeps track of the 3D models that you load and position. When you extend a Java class from com.jme3.app.SimpleApplication, you automatically inherit the scene graph and its rootNode.
The rootNode is the central element of the scene graph. Even if the scenegraph is empty, it always has at least its rootNode. All other Spatials are attached to the rootNode in a parent-child relationship. If you think you need to understand the scene graph concept better, please read Scenegraph for dummies first.
In your Java code, a Spatial is either a com.jme3.scene.Node or a com.jme3.scene.Geometry. You use the two for different purposes:
| com.jme3.scene.Spatial | ||
|---|---|---|
| Purpose: | A Spatial is an abstract data structure that stores transformations (translation, rotation, scale) of elements of the scene graph. Spatials can be saved and loaded using the AssetManager. | |
| com.jme3.scene.Geometry | com.jme3.scene.Node | |
| Visibility: | A Geometry represents a visible 3-D object in the scene graph. | A Node is an invisible "handle" for a group of objects in the scene graph. |
| Purpose: | Use Geometries to represent an object's looks: Every Geometry contains a polygon mesh and a material, specifying its shape, color, texture, and opacity/transparency. You can attach a Geometry to a Node. | Use Nodes to structure and group Geometries and other Nodes. Every Node is attached to one parent node, and each node can have zero or more children attached to itself. When you transform a parent node, all its children are transformed as well. |
| Content: | Transformations; custom user data; mesh, material; | Transformations; custom user data; no mesh, no material. |
| Examples: | A box, a sphere, player, a building, a piece of terrain, a vehicle, missiles, NPCs, etc… | The rootNode, the guiNode, an audio node, a custom grouping node, etc… |
Spatial s = new Spatial();
! A Spatial is an abstract concept, like a mammal (there is no actual creature called "mammal" walking around here). You create a Node, or load a Geometry object. Some methods however require a Spatial argement: This is because they are able to accept both Nodes and Geometries as arguments. In this case, you must cast a Node or Geometry to Spatial.
The polygon Mesh inside a Geometry can be one of three things:
You can include custom Java objects in Nodes and Geometries. This is useful for maintaining information about a game element, such as health, budget, ammunition, inventory, equipment, etc for players, or landmark locations for terrains, and much more. Where ever the spatial is accessible, you can also access the object's game data.
// create and instance of your custom data class PlayerData playerData = new PlayerData("joe", 0, 100); // store custom data in Node or Geometry player.setUserData("player data", playerData); ... // Elsewhere: retrieved data from Node or Geometry... PlayerData playerData = player.getUserData("player data"); // ... set the data... playerData.setHealth("99"); // ... or get the data for tests or to display it in the HUD. health = playerData.getHealth();
You can add as many data objects to a Spatial as you need. Just make sure to label them with different Strings (player data
, player inventory
, player equipment
, etc).
You can also list all data keys that are defined for one Spatial:
for(String key : geom.getUserDataKeys()){ System.out.println(geom.getName()+"'s keys: "+key); }
Often after you load a scene or model, you need to access a part of it as an individual Geometry in the scene graph. Maybe you want to swap a character's weapon, or you want to play a door-opening animation. First you need to know the unique name of the sub-mesh.
In the following example, the Node house
is the loaded model. The sub-meshes in the Node are called its children. The String, here door 12
, is the name of the mesh that you are searching.
Geometry submesh = (Geometry) houseScene.getChild("door 12");


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Master of space and time by Rudy Rucker
About the book
Author: Rucker, Rudy v. B. (Rudy von Bitter), 1946-
Title: Master of space and time
Publication: New York : Baen, 1984. ISBN<PHONE_NUMBER>693.
DAISY audio format narrated by John Ihrig. CELA library call number DC05893. 229 pages; 5hrs., 15mins. as a recorded book.
About the story
This CELA book was transferred from the original audiocassette. This means that there is no DAISY navigation beyond the 90-minute bookmarks, which bear no relation to the contents. Among other things, this means you can’t skip the table of contents and publication information, and you will need to insert manual bookmarks if you ever want to find anything again.
So, I know the book was written in 1984, but it’s still a bit shocking to read the way that women are described, treated and portrayed. Like, not shocking in a nice or fun way. I guess it reminds us how far we have come (…until recently, it seems. So maybe it’s topical after all).
The story was fine, in a bit sexist and non-inclusive sort of way. A guy invents some kind of mind-probing device that gives him access to a selection of worlds, depending on how many gluons he has. A gluon is a particle, described in this book as coming in three colours (only they don’t, of course, but … willing suspension of disbelief and all that), and each colour is rarer than the previous one, so it only permits a certain number of “wishes”.
Some interesting devices: parallel universes, infinite regression, particles, etc., but on the whole I only finished it because I started it, and this is – after all – an RA exercise. (That’s “reader’s advisory” for you non-librarians in the crowd.)
I can’t say I enjoyed it, but at least it was short. As an author, Rucker is important as one of the founders of the cyberpunk movement.
Because it was transferred from tape, the quality was not as high as the more modern recordings, and the temporal dialect was a real blast from the past. The narrator, John Ihrig, did a good job, though.
Recommended?
People who did enjoy this book use words like “zany hijinks” and “madcap adventure”. Many people do enjoy it. I did not.


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9781500975982-peck-s-bad-boy-with-the-cowboys-paperback.pdf
[65.55 KB]