How to choose the right value for the TTL? We need a push messsage delivered reliably, not being dropped, but at the same time we would like it delivered faster, because it is used to initiate live calls. I understand that 0 is not an option for us, since it has a good chance to be dropped? But then should it be 60*60 (an hour) or 60 (a minute) or what is the right way of thinking here?
You must remember that the value of TTL paramater must be a duration from 0 to 2,419,200 seconds, and it corresponds to the maximum period of time of push message to live on the push service before it's delivered.
If you set a TTL of zero, the push service will attempt to deliver the
message immediately, but if the device can't be reached, your message
will be immediately dropped from the push service queue.
You can also consider the following best practice of using TTL:
The higher the TTL, the less frequently caching name servers need to query authoritative name servers.
A higher TTL reduces the perceived latency of a site and decreases the dependency on the authoritative name servers.
The lower the TTL, the sooner the cached record expires. This allows queries for the records to occur more frequently.
Related
I have a problem with "quick" assets transferring through several accounts. For example, I have 3 accounts: A, B(no assets), C.
I transfer some amount of asset from A to B account, then look for "A->B" transaction (/transactions/info/{id}).
If the transaction was found I transfer the same amount from B to C account. In most cases everything is fine, but sometimes I get an error:
StateCheckFailedException: State check failed. Reason: Attempt to
transfer unavailable funds: Transaction application leads to negative
asset 'IssuedAsset(...)' balance to (at least) temporary negative
state, the current balance is 0 ...
If I will wait for 10 seconds, for example, "B->C" transfer will be succeeded. So, it seems that I should wait for some synchronization of the account's balances in the node.
Is there any guaranteed way in my case to make "B->C" transfer without waiting for an undetermined time? "A->B" transaction's presence in a blockchain doesn't work sometimes.
I use my own node for the broadcasting transactions. Node's configuration is the default. Version 1.1.7
This is due the fact from microblocks. Microblocks did transfer the funds, but didn't confirmed them yet permanently and therefore aren't yet fully in the blockchain.
If you want to be fully sure, I would say, wait 1 block (or 60 seconds). But even then it might not always be confirmed, since even waveschain has some moments it's being overloaded with thousands of tx's for a few minutes.
It's the same with other actions like creating assets etc.
While reading article from google about chubby, I didn't really understand the purpose of sequencers
Assume we have 4 entities :
Chubby cell
Client 1
Client 2
Service we want to use and where we will send the requests (for which we need the lock)
As far as I understood the steps are:
Client 1 send lock_request() to Chubby cell, Chubby responses with Sequencer (assume SequenceNumber = 1)
Client 1 send request modify_data() with Sequencer (SequenceNumber = 1) to Service
Service asks Chubby cell if SequenceNumber is valid (=1)
Chubby acknowledges it, set LeasePeriod (period of lock expiration to (assume) 60 seconds)
! during this time no one is able to acquire the lock
After acknowledge, Service cache the data about Client 1 (SequenceNumber = 1) for (assume) 40 seconds
Now:
if Client 2 tries to acquire lock during these 60 seconds we set, it will be rejected by Chubby cell
that means it is impossible that Client 2 will acquire the lock with the next SequenceNumber = 2 and send anything to the Service
As far as I understand all purpose of SequenceNumber is just for situation when 2 requests come to Service and Service can just compare 2 SequenceNumbers and reject the lower, without need to ask Chubby cell
but how this situation will ever happen if we have caches and impossibility to get the lock by Client 2 while Client 1 is holding this lock?
It will be a mistake to think about timing in distributed systems with actual times (like seconds), but I'll try to answer using the same semantics.
As you said, say client1 acquires write lock named foo1,
foo here being the lock name and 1 being the generation number.
Now say, lease period is 60 seconds. 58th second now Client1 sends a write, say R1.
And soon enough, Client1 is now dead.
Now, here's the catch. You assumed in your analysis, that R1 would reach
the server inside the 2 seconds, before another client, say Client2 becomes master.
THAT'S JUST NOT CERTAIN.
In a distributed system, with fractions of milliseconds network latencies on one hand and network partitions on the other hand,
you just cannot ascertain what reaches the master first, R1 or client2's request to become master.
This is where sequence numbers would help.
Master, now having known that there is foo2, can reject R1 that came with foo1 in metadata.
Read more about generational clocks/logical clocks here.
A logical clock is a mechanism for capturing chronological and causal relationships in a distributed system. Often, distributed systems may have no physically synchronous global clock. Fortunately, in many applications (such as distributed GNU make), if two processes never interact, the lack of synchronization is unobservable. Moreover, in these applications, it suffices for the processes to agree on the event ordering (i.e., logical clock) rather than the wall-clock time.[1]
In my application when I send messages I use the Metadata in the callback to save the offset of the record for future usage. However sometimes the metadata.offset() returns -1 which makes things hard later.
Why does this happen and is there a way to get the offset without consuming the topic to find it.
Edit: I am on ack 0 currently, when I pass to ack 1 I don't have these errors anymore however my performance drops drastically. From 100k message in 10 sec to 1 min.
acks=0 If set to zero then the producer will not wait for any
acknowledgment from the server at all. The record will be immediately
added to the socket buffer and considered sent. No guarantee can be
made that the server has received the record in this case, and the
retries configuration will not take effect (as the client won't
generally know of any failures). The offset given back for each
record will always be set to -1.
This is not exactly true as out of 100k messages I got 95k with offsets but I guess it's normal.
Still will need to find another solution to get the offset with ack=0
I have some questions regarding Azure Insights REST Api for Events.
When I make HTTP request to Inisghts API for events, I receive the header "
x-ms-ratelimit-remaining-subscription-reads", with value "14999".
But next query in 1s returns me the same value of remaining reads.
I see there is some throttling policy there, but I would like to understand how it works and what is the correct way to deal with that.
In particular,
1) how many reads I am able to do per second?
2) if I exceed the whole remaining reads parameter, how much time should I wait before it will again be maximum?
3) is it decreased on every query attempt, despite of the $top parameter setted and how many results has been returned?
Thank you!
This article seems to have the responses you need.
To answer the questions based on it:
There is no limit to the number of requests per second, but you have 15k
requests/hour/subscription/region/instance of ARM region. Worst case scenario you will get throttled after 15k requests but you'd have to be extremely unlucky for that.
If you exceed the limit, you are
told how much you have to wait and you can integrate that logic by
looking at the Retry-After header. Happily, it's a matter of
seconds.
I believe the $top parameter doesn't affect the query since
no matter how many results are brought back, a paging request is
still just one request.
As for the fact that you get 14999 requests
remaining multiple times, as they say in their documentation it is
expected since an ARM region has multiple instances and each instance has
15k requests limit/subscription/hour. If you hit simultaneously and
you get the same number remaining, it just means that you were lucky
enough to hit different instances within the same ARM region.
1) how many reads I am able to do per second?
Based on the rate limits published here - https://azure.microsoft.com/en-in/documentation/articles/azure-subscription-service-limits/#subscription-limits, you can perform 15000 reads / hour (not sure it would translate to 4 reads / second).
2) if I exceed the whole remaining reads parameter, how much time
should I wait before it will again be maximum?
Given the rates are defined per hour, my guess would be to wait till next hour if you exhaust 15000 read request limit.
3) is it decreased on every query attempt, despite of the $top
parameter setted and how many results has been returned?
This is based on the number of API calls and not the amount of data returned. So I would say defining $top parameter should not have any impact on this.
When I make HTTP request to Inisghts API for events, I receive the
header " x-ms-ratelimit-remaining-subscription-reads", with value
"14999". But next query in 1s returns me the same value of remaining
reads.
I would assume there's some caching in play here. Is it the same request you're repeating or a different request all together?
I am trying to implement a poker server. An http server forwards data packets to the backend servers which handle the state of all the poker hands. In any given hand the player to act gets 10 seconds to act (bet,fold,call,raise,etc.). If there is no response within 10 seconds the server automatically folds for them. To check that 10 seconds has passed an event list of when actions must be received is maintained. It is a priority queue ordered by time and each poker hand currently being played has an entry in the priority queue.
Consider the following scenario since the last action 9.99 seconds pass before the next action arrives at the http server. By the time the action is forwarded to the backend servers extra time passes so now a total of 10.1 seconds have passed. The backend servers will have declared the hand folded, but I would like the action to be processed since technically it arrived at the http server after 9.99 seconds. Now one solution would be to have the backends wait some extra time before declaring a hand folded to see if an action timestamped at 9.99 seconds comes. But that would result in delaying when the next person in the hand gets to act.
The goals I would like are
Handle actions reaching the http server at 9.99 seconds instead of folding their hand.
Aggressively minimize delay resulting from having to do idle waiting to "solve" problem mentioned in bullet point 1.
What are the various solutions? To experts in distributed systems is there known literature on what the trade offs are to various solutions. I would like to know the various solutions deemed acceptable by distributed systems literature. Not just various ad hocs solution.
Maybe on the server side when client request arrives you could take the timestamp?
So you would take "start" and "stop" timestamps, to measure exactly 9.9s?