I am simply trying to deactivate a constraint in textViewDidBeginEditing and the deactivate in textViewDidEndEditing.
My NSLayoutConstraint is an outlet. The deactivation in textViewDidBeginEditing works, but then the reactivation on textViewDidEndEditing finds nil for the constraint.
I have tried this with both the .isActive instance value as well as the NSLayoutConstraint.activate/deactivate functions.
The Answer
I played around with Vasil Hristov's answer and that didn't work because he simply suggested 0 and 1000. From Documentation:
Priorities may not change from nonrequired to required, or from required to nonrequired. An exception will be thrown if a priority of NSLayoutPriorityRequired in OS X or UILayoutPriorityRequired in iOS is changed to a lower priority, or if a lower priority is changed to a required priority after the constraints is added to a view. Changing from one optional priority to another optional priority is allowed even after the constraint is installed on a view.
So, I had to change my priority in IB first to be something else (I chose 750 - High) and then just switched it to 250 when it wasn't needed, and created a second constraint conflicting with the first that had a 500 priority(do something else) at all times, which got me the behavior I wanted.
According to the above documentation, since High is not required, I should have been able to switch to 0, but that was still resulting in crashes so I must be reading that wrong. Hence, why I did the second constraint thing.
You can try to change the priority of constraint.
For example:
constraint.priority = 0; // it's turn off
constraint.priority = 1000; // it's turn on
Related
I'm having trouble getting my head around the purpose of supply {…} blocks/the on-demand supplies that they create.
Live supplies (that is, the types that come from a Supplier and get new values whenever that Supplier emits a value) make sense to me – they're a version of asynchronous streams that I can use to broadcast a message from one or more senders to one or more receivers. It's easy to see use cases for responding to a live stream of messages: I might want to take an action every time I get a UI event from a GUI interface, or every time a chat application broadcasts that it has received a new message.
But on-demand supplies don't make a similar amount of sense. The docs say that
An on-demand broadcast is like Netflix: everyone who starts streaming a movie (taps a supply), always starts it from the beginning (gets all the values), regardless of how many people are watching it right now.
Ok, fair enough. But why/when would I want those semantics?
The examples also leave me scratching my head a bit. The Concurancy page currently provides three examples of a supply block, but two of them just emit the values from a for loop. The third is a bit more detailed:
my $bread-supplier = Supplier.new;
my $vegetable-supplier = Supplier.new;
my $supply = supply {
whenever $bread-supplier.Supply {
emit("We've got bread: " ~ $_);
};
whenever $vegetable-supplier.Supply {
emit("We've got a vegetable: " ~ $_);
};
}
$supply.tap( -> $v { say "$v" });
$vegetable-supplier.emit("Radish"); # OUTPUT: «We've got a vegetable: Radish»
$bread-supplier.emit("Thick sliced"); # OUTPUT: «We've got bread: Thick sliced»
$vegetable-supplier.emit("Lettuce"); # OUTPUT: «We've got a vegetable: Lettuce»
There, the supply block is doing something. Specifically, it's reacting to the input of two different (live) Suppliers and then merging them into a single Supply. That does seem fairly useful.
… except that if I want to transform the output of two Suppliers and merge their output into a single combined stream, I can just use
my $supply = Supply.merge:
$bread-supplier.Supply.map( { "We've got bread: $_" }),
$vegetable-supplier.Supply.map({ "We've got a vegetable: $_" });
And, indeed, if I replace the supply block in that example with the map/merge above, I get exactly the same output. Further, neither the supply block version nor the map/merge version produce any output if the tap is moved below the calls to .emit, which shows that the "on-demand" aspect of supply blocks doesn't really come into play here.
At a more general level, I don't believe the Raku (or Cro) docs provide any examples of a supply block that isn't either in some way transforming the output of a live Supply or emitting values based on a for loop or Supply.interval. None of those seem like especially compelling use cases, other than as a different way to transform Supplys.
Given all of the above, I'm tempted to mostly write off the supply block as a construct that isn't all that useful, other than as a possible alternate syntax for certain Supply combinators. However, I have it on fairly good authority that
while Supplier is often reached for, many times one would be better off writing a supply block that emits the values.
Given that, I'm willing to hazard a pretty confident guess that I'm missing something about supply blocks. I'd appreciate any insight into what that might be.
Given you mentioned Supply.merge, let's start with that. Imagine it wasn't in the Raku standard library, and we had to implement it. What would we have to take care of in order to reach a correct implementation? At least:
Produce a Supply result that, when tapped, will...
Tap (that is, subscribe to) all of the input supplies.
When one of the input supplies emits a value, emit it to our tapper...
...but make sure we follow the serial supply rule, which is that we only emit one message at a time; it's possible that two of our input supplies will emit values at the same time from different threads, so this isn't an automatic property.
When all of our supplies have sent their done event, send the done event also.
If any of the input supplies we tapped sends a quit event, relay it, and also close the taps of all of the other input supplies.
Make very sure we don't have any odd races that will lead to breaking the supply grammar emit* [done|quit].
When a tap on the resulting Supply we produce is closed, be sure to close the tap on all (still active) input supplies we tapped.
Good luck!
So how does the standard library do it? Like this:
method merge(*#s) {
#s.unshift(self) if self.DEFINITE; # add if instance method
# [I elided optimizations for when there are 0 or 1 things to merge]
supply {
for #s {
whenever $_ -> \value { emit(value) }
}
}
}
The point of supply blocks is to greatly ease correctly implementing reusable operations over one or more Supplys. The key risks it aims to remove are:
Not correctly handling concurrently arriving messages in the case that we have tapped more than one Supply, potentially leading us to corrupt state (since many supply combinators we might wish to write will have state too; merge is so simple as not to). A supply block promises us that we'll only be processing one message at a time, removing that danger.
Losing track of subscriptions, and thus leaking resources, which will become a problem in any longer-running program.
The second is easy to overlook, especially when working in a garbage-collected language like Raku. Indeed, if I start iterating some Seq and then stop doing so before reaching the end of it, the iterator becomes unreachable and the GC eats it in a while. If I'm iterating over lines of a file and there's an implicit file handle there, I risk the file not being closed in a very timely way and might run out of handles if I'm unlucky, but at least there's some path to it getting closed and the resources released.
Not so with reactive programming: the references point from producer to consumer, so if a consumer "stops caring" but hasn't closed the tap, then the producer will retain its reference to the consumer (thus causing a memory leak) and keep sending it messages (thus doing throwaway work). This can eventually bring down an application. The Cro chat example that was linked is an example:
my $chat = Supplier.new;
get -> 'chat' {
web-socket -> $incoming {
supply {
whenever $incoming -> $message {
$chat.emit(await $message.body-text);
}
whenever $chat -> $text {
emit $text;
}
}
}
}
What happens when a WebSocket client disconnects? The tap on the Supply we returned using the supply block is closed, causing an implicit close of the taps of the incoming WebSocket messages and also of $chat. Without this, the subscriber list of the $chat Supplier would grow without bound, and in turn keep alive an object graph of some size for each previous connection too.
Thus, even in this case where a live Supply is very directly involved, we'll often have subscriptions to it that come and go over time. On-demand supplies are primarily about resource acquisition and release; sometimes, that resource will be a subscription to a live Supply.
A fair question is if we could have written this example without a supply block. And yes, we can; this probably works:
my $chat = Supplier.new;
get -> 'chat' {
web-socket -> $incoming {
my $emit-and-discard = $incoming.map(-> $message {
$chat.emit(await $message.body-text);
Supply.from-list()
}).flat;
Supply.merge($chat, $emit-and-discard)
}
}
Noting it's some effort in Supply-space to map into nothing. I personally find that less readable - and this didn't even avoid a supply block, it's just hidden inside the implementation of merge. Trickier still are cases where the number of supplies that are tapped changes over time, such as in recursive file watching where new directories to watch may appear. I don't really know how'd I'd express that in terms of combinators that appear in the standard library.
I spent some time teaching reactive programming (not with Raku, but with .Net). Things were easy with one asynchronous stream, but got more difficult when we started getting to cases with multiple of them. Some things fit naturally into combinators like "merge" or "zip" or "combine latest". Others can be bashed into those kinds of shapes with enough creativity - but it often felt contorted to me rather than expressive. And what happens when the problem can't be expressed in the combinators? In Raku terms, one creates output Suppliers, taps input supplies, writes logic that emits things from the inputs into the outputs, and so forth. Subscription management, error propagation, completion propagation, and concurrency control have to be taken care of each time - and it's oh so easy to mess it up.
Of course, the existence of supply blocks doesn't stop being taking the fragile path in Raku too. This is what I meant when I said:
while Supplier is often reached for, many times one would be better off writing a supply block that emits the values
I wasn't thinking here about the publish/subscribe case, where we really do want to broadcast values and are at the entrypoint to a reactive chain. I was thinking about the cases where we tap one or more Supply, take the values, do something, and then emit things into another Supplier. Here is an example where I migrated such code towards a supply block; here is another example that came a little later on in the same codebase. Hopefully these examples clear up what I had in mind.
Google Cloud Datastore documents that if an entity id needs to be pre-allocated, then one should use the allocateIds method:
https://cloud.google.com/datastore/docs/best-practices#keys
That method seems to make a REST or RPC call which has latency. I'd like to avoid that latency by using a PRNG in my Kubernetes Engine application. Here's the scala code:
import java.security.SecureRandom
class RandomFactory {
protected val r = new SecureRandom
def randomLong: Long = r.nextLong
def randomLong(min: Long, max: Long): Long =
// Unfortunately, Java didn't make Random.internalNextLong public,
// so we have to get to it in an indirect way.
r.longs(1, min, max).toArray.head
// id may be any value in the range (1, MAX_SAFE_INTEGER),
// so that it can be represented in Javascript.
// TODO: randomId is used in production, and might be susceptible to
// TODO: blocking if /dev/random does not contain entropy.
// TODO: Keep an eye on this concern.
def randomId: Long =
randomLong(1, RandomFactory.MAX_SAFE_INTEGER)
}
object RandomFactory extends RandomFactory {
// MAX_SAFE_INTEGER is es6 Number.MAX_SAFE_INTEGER
val MAX_SAFE_INTEGER = 9007199254740991L
}
I also plan to install haveged in the pod to help with entropy.
I understand allocateIds ensures that an ID is not already in use. But in my particular use case, there are two mitigating factors to overlooking that concern:
Based on entity count, the chance of a conflict is 1 in 100 million.
This particular entity type is non-essential, and can afford a "once in a blue moon" conflict.
I am more concerned about even distribution in keyspace, because that is normal use case concern.
Will this approach work, particularly with even distribution in keyspace? Is the allocatedIds method essential, or does it just help developers avoid simple mistakes?
To get rid of collisions use more bits -- for all practical purposes 128 [See statistics behind UUID V4] will never generate a collision.
Another technique is to insert new entities with a shorter random number and handle the error Cloud Datastore returns if they already exist by trying again with a new ID (until you happen upon one that isn't currently in use).
As far as the key distribution goes: the keys will be randomly distributed within the key space will keep Cloud Datastore happy.
Given that you don't want the entity identifier to be based on an external value, you should allow Cloud Datastore to allocate IDs for you. This way you won't have any conflicts. The IDs allocated by Cloud Datastore will be appropriately scattered through the key space.
So I'm getting this error every so often when running the exact same test.
StaleElementReferenceException: Message: stale element reference: element is not attached to the page document
(Session info: chrome=69.0.3497.100)
(Driver info: chromedriver=2.41.578706 (5f725d1b4f0a4acbf5259df887244095596231db),platform=Mac OS X 10.12.6 x86_64)
The only problem is that it seems to happen inconsistently to different areas of the code. It's when trying to access DOM elements, like a search field, of my ReactJS page. I'm running this through ROBOT Automation Framework, using a mix of the SeleniumLibrary and a custom library.
I've read that it's simply as it sounds, the xPath as become outdated on the DOM, but that doesn't help me figure out why it's an inconsistent error happening almost anywhere at any point.
EDIT: It seems to be happening in this:
def filter_modality(self, filter):
filter_value = '//span[#title="{}"]//parent::li'.format(filter)
self.selib.click_element(filter_locator)
self.selib.wait_until_page_contains_element('//*[#class="multi-selector-options open"]')
self.selib.wait_until_element_is_visible(filter_value)
self.selib.click_element(filter_value )
self.selib.wait_until_page_contains_element('//div...[#class="option selector-item active"]',
error=("Could not select filter: {}".format(filter_value )))
#I get the stale element message from or after executing this click_element
self.selib.click_element(filter_locator)
self.selib.wait_until_page_does_not_contain_element('//*[#class="multi-selector-options open"]',
error="Filter dropdown did not disappear after selection")
The exception comes when SE has found an element, but shortly afterwards something (a JS function) has changed it - removed from/rearranged it in the DOM, or changed its attributes significantly, so it's no longer tracked as the same element.
This comes for the fact that SE builds an internal cache of the DOM, which may become desynchronized from the actual one; thus the name "stale" - the el. is cached in some state, but its actual form is now different.
The issue is that common, that there is a specific SO tag for it - https://stackoverflow.com/questions/tagged/staleelementreferenceexception (I myself was surprised from this).
The common solutions are:
sleep for some seconds before an event you know will cause the issue
re-get an element before its usage (if you store a reference to it in a WebElement object, not really the case with robotframework)
have a rertry mechanism on working with an element that you know may cause the execution
swallow the exception and move along (I've done it in a few places, where the element was just a confirmation an operation was executed - it has been shown/found by SE once, afterwards I don't care is it changed in the DOM)
I'm learning every day more about dds, so my question my sound weird. I hope it makes sense.
One of the requirements of some dds wrapper I'm writing, is that it times out after some timeout period if it fails to write. My question: How can I do that?
On Prism Tech's website's tutorial, there's explanation on how to use a WaitSet to block a read operation, but what about write?
Here's some code including the question:
dds::domain::DomainParticipant dp(0);
dds::topic::Topic<MyType> topic(dp, "MyTopic");
dds::pub::Publisher pub(dp);
dds::pub::DataWriter<MyType> dw(pub, topic);
MyType t;
dw.write(t); //how can I make this block for 5 seconds (tops), and then throw an error on failure?
I noticed there exists a function in the API DataWriter::wait_for_acknowledgements(int timeout), but this seems to be bound to the DataWriter object, not to the specific call of writing. Can I bind it with the call above?
This is configured in QoS, cf RELIABILITY, field "max_blocking_time". How you set this value will depend on the vendor's implementation. Generally you get the current QoS, update the field, write the QoS back. Keep in mind that certain QoS policies must be set before something else happens. Reliability is "Before Enable" (at least in the implementation I'm most familiar with), which means you need to create the data-writer disabled, update the QoS, then enable the writer.
If QoS can be set outside the application (via XML for example), then you can set the policy easily. Otherwise, you need to do it in code.
From the spec:
The value of the max_blocking_time indicates the maximum time the operation DataWriter::write is allowed to block if the DataWriter does not have space to store the value written. The default max_blocking_time=100ms.
Background
I have a 2-tier web service - just my app server and an RDBMS. I want to move to a pool of identical app servers behind a load balancer. I currently cache a bunch of objects in-process. I hope to move them to a shared Redis.
I have a dozen or so caches of simple, small-sized business objects. For example, I have a set of Foos. Each Foo has a unique FooId and an OwnerId.
One "owner" may own multiple Foos.
In a traditional RDBMS this is just a table with an index on the PK FooId and one on OwnerId. I'm caching this in one process simply:
Dictionary<int,Foo> _cacheFooById;
Dictionary<int,HashSet<int>> _indexFooIdsByOwnerId;
Reads come straight from here, and writes go here and to the RDBMS.
I usually have this invariant:
"For a given group [say by OwnerId], the whole group is in cache or none of it is."
So when I cache miss on a Foo, I pull that Foo and all the owner's other Foos from the RDBMS. Updates make sure to keep the index up to date and respect the invariant. When an owner calls GetMyFoos I never have to worry that some are cached and some aren't.
What I did already
The first/simplest answer seems to be to use plain ol' SET and GET with a composite key and json value:
SET( "ServiceCache:Foo:" + theFoo.Id, JsonSerialize(theFoo));
I later decided I liked:
HSET( "ServiceCache:Foo", theFoo.FooId, JsonSerialize(theFoo));
That lets me get all the values in one cache as HVALS. It also felt right - I'm literally moving hashtables to Redis, so perhaps my top-level items should be hashes.
This works to first order. If my high-level code is like:
UpdateCache(myFoo);
AddToIndex(myFoo);
That translates into:
HSET ("ServiceCache:Foo", theFoo.FooId, JsonSerialize(theFoo));
var myFoos = JsonDeserialize( HGET ("ServiceCache:FooIndex", theFoo.OwnerId) );
myFoos.Add(theFoo.OwnerId);
HSET ("ServiceCache:FooIndex", theFoo.OwnerId, JsonSerialize(myFoos));
However, this is broken in two ways.
Two concurrent operations can read/modify/write at the same time. The latter "wins" the final HSET and the former's index update is lost.
Another operation could read the index in between the first and second lines. It would miss a Foo that it should find.
So how do I index properly?
I think I could use a Redis set instead of a json-encoded value for the index.
That would solve part of the problem since the "add-to-index-if-not-already-present" would be atomic.
I also read about using MULTI as a "transaction" but it doesn't seem like it does what I want. Am I right that I can't really MULTI; HGET; {update}; HSET; EXEC since it doesn't even do the HGET before I issue the EXEC?
I also read about using WATCH and MULTI for optimistic concurrency, then retrying on failure. But WATCH only works on top-level keys. So it's back to SET/GET instead of HSET/HGET. And now I need a new index-like-thing to support getting all the values in a given cache.
If I understand it right, I can combine all these things to do the job. Something like:
while(!succeeded)
{
WATCH( "ServiceCache:Foo:" + theFoo.FooId );
WATCH( "ServiceCache:FooIndexByOwner:" + theFoo.OwnerId );
WATCH( "ServiceCache:FooIndexAll" );
MULTI();
SET ("ServiceCache:Foo:" + theFoo.FooId, JsonSerialize(theFoo));
SADD ("ServiceCache:FooIndexByOwner:" + theFoo.OwnerId, theFoo.FooId);
SADD ("ServiceCache:FooIndexAll", theFoo.FooId);
EXEC();
//TODO somehow set succeeded properly
}
Finally I'd have to translate this pseudocode into real code depending how my client library uses WATCH/MULTI/EXEC; it looks like they need some sort of context to hook them together.
All in all this seems like a lot of complexity for what has to be a very common case;
I can't help but think there's a better, smarter, Redis-ish way to do things that I'm just not seeing.
How do I lock properly?
Even if I had no indexes, there's still a (probably rare) race condition.
A: HGET - cache miss
B: HGET - cache miss
A: SELECT
B: SELECT
A: HSET
C: HGET - cache hit
C: UPDATE
C: HSET
B: HSET ** this is stale data that's clobbering C's update.
Note that C could just be a really-fast A.
Again I think WATCH, MULTI, retry would work, but... ick.
I know in some places people use special Redis keys as locks for other objects. Is that a reasonable approach here?
Should those be top-level keys like ServiceCache:FooLocks:{Id} or ServiceCache:Locks:Foo:{Id}?
Or make a separate hash for them - ServiceCache:Locks with subkeys Foo:{Id}, or ServiceCache:Locks:Foo with subkeys {Id} ?
How would I work around abandoned locks, say if a transaction (or a whole server) crashes while "holding" the lock?
For your use case, you don't need to use watch. You simply use a multi + exec block and you'd have eliminated the race condition.
In pseudo code -
MULTI();
SET ("ServiceCache:Foo:" + theFoo.FooId, JsonSerialize(theFoo));
SADD ("ServiceCache:FooIndexByOwner:" + theFoo.OwnerId, theFoo.FooId);
SADD ("ServiceCache:FooIndexAll", theFoo.FooId);
EXEC();
This is sufficient because multi makes the following promise :
"It can never happen that a request issued by another client is served in the middle of the execution of a Redis transaction"
You don't need the watch and retry mechanism because you are not reading and writing in the same transaction.