There's 3 related questions here:
I can look at an account's NEAR balance before and after a near-api-js account.functionCall to determine how much gas the tx took, but is
there a better way to extrapolate that info from the metadata of the call?
Also, is there a cap for how much gas I can feed into a function call?
From within a functional call (in rust) is it possible to use env:: to see
how much gas is remaining in the course of operations?
I'm trying to set up the control flow so
that the function will quit working in time and never throw a 'ran out of gas' error, but only do as much work as it has gas to do.
I can look at an account's NEAR balance before and after a near-api-js account.functionCall to determine how much gas the tx took, but is there a better way to extrapolate that info from the metadata of the call?
Well, to compute the transaction fee you can query the transaction status (tx/EXPERIMENTAL_tx_status JSON RPC endpoints) and sum up the tokens_burnt from all the receipt_outcomes and transaction_outcome. If you want to profile the gas usage, you should sum up gas_burnt from all the receipt_outcomes and transaction_outcome.
Also, is there a cap for how much gas I can feed into a function call?
Yes, on mainnet genesis it is set to 1 PetaGas (1_000 TeraGas).
From within a functional call (in rust) is it possible to use env:: to see how much gas is remaining in the course of operations?
near_sdk::env::prepaid_gas() minus
near_sdk::env::gas_used()
Related
In chainlink keepers document here. There is a conf called checkGasLimit with 6,500,000 as the default value.
Since the computation in checkUpKeep is expected to be outsourced off-chain, why there is a configuration called checkGasLimit where computation is off-chain?
Or checkGasLimit is for the situation where function checkUpKeep is supposed to modify some state.
You got it!
checkUpkeep can be used to change the state of the blockchain. The Chainlink nodes will call the checkUpkeep function when it returns true - and if it costs gas, it will use gas.
The use of the checkGasLimit then, is to make sure they don't use too much gas. Per the docs:
The maximum amount of gas that can be used by your checkUpkeep for off-chain computation.
Small question regarding Spring Boot, some of the useful default metrics, and how to properly use them in Grafana please.
Currently with a Spring Boot 2.5.1+ (question applicable to 2.x.x.) with Actuator + Micrometer + Prometheus dependencies, there are lots of very handy default metrics that come out of the box.
I am seeing many many of them with pattern _max _count _sum.
Example, just to take a few:
spring_data_repository_invocations_seconds_max
spring_data_repository_invocations_seconds_count
spring_data_repository_invocations_seconds_sum
reactor_netty_http_client_data_received_bytes_max
reactor_netty_http_client_data_received_bytes_count
reactor_netty_http_client_data_received_bytes_sum
http_server_requests_seconds_max
http_server_requests_seconds_count
http_server_requests_seconds_sum
Unfortunately, I am not sure what to do with them, how to correctly use them, and feel like my ignorance makes me miss on some great application insights.
Searching on the web, I am seeing some using like this, to compute what seems to be an average with Grafana:
irate(http_server_requests_seconds::sum{exception="None", uri!~".*actuator.*"}[5m]) / irate(http_server_requests_seconds::count{exception="None", uri!~".*actuator.*"}[5m])
But Not sure if it is the correct way to use those.
May I ask what sort of queries are possible, usually used when dealing with metrics of type _max _count _sum please?
Thank you
UPD 2022/11: Recently I've had a chance to work with these metrics myself and I made a dashboard with everything I say in this answer and more. It's available on Github or Grafana.com. I hope this will be a good example of how you can use these metrics.
Original answer:
count and sum are generally used to calculate an average. count accumulates the number of times sum was increased, while sum holds the total value of something. Let's take http_server_requests_seconds for example:
http_server_requests_seconds_sum 10
http_server_requests_seconds_count 5
With the example above one can say that there were 5 HTTP requests and their combined duration was 10 seconds. If you divide sum by count you'll get the average request duration of 2 seconds.
Having these you can create at least two useful panels: average request duration (=average latency) and request rate.
Request rate
Using rate() or irate() function you can get how many there were requests per second:
rate(http_server_requests_seconds_count[5m])
rate() works in the following way:
Prometheus takes samples from the given interval ([5m] in this example) and calculates difference between current timepoint (not necessarily now) and [5m] ago.
The obtained value is then divided by the amount of seconds in the interval.
Short interval will make the graph look like a saw (every fluctuation will be noticeable); long interval will make the line more smooth and slow in displaying changes.
Average Request Duration
You can proceed with
http_server_requests_seconds_sum / http_server_requests_seconds_count
but it is highly likely that you will only see a straight line on the graph. This is because values of those metrics grow too big with time and a really drastic change must occur for this query to show any difference. Because of this nature, it will be better to calculate average on interval samples of the data. Using increase() function you can get an approximate value of how the metric changed during the interval. Thus:
increase(http_server_requests_seconds_sum[5m]) / increase(http_server_requests_seconds_count[5m])
The value is approximate because under the hood increase() is rate() multiplied by [inverval]. The error is insignificant for fast-moving counters (such as the request rate), just be ready that there can be an increase of 2.5 requests.
Aggregation and filtering
If you already ran one of the queries above, you have noticed that there is not one line, but many. This is due to labels; each unique set of labels that the metric has is considered a separate time series. This can be fixed by using an aggregation function (like sum()). For example, you can aggregate request rate by instance:
sum by(instance) (rate(http_server_requests_seconds_count[5m]))
This will show you a line for each unique instance label. Now if you want to see only some and not all instances, you can do that with a filter. For example, to calculate a value just for nodeA instance:
sum by(instance) (rate(http_server_requests_seconds_count{instance="nodeA"}[5m]))
Read more about selectors here. With labels you can create any number of useful panels. Perhaps you'd like to calculate the percentage of exceptions, or their rate of occurrence, or perhaps a request rate by status code, you name it.
Note on max
From what I found on the web, max shows the maximum recorded value during some interval set in settings (default is 2 minutes if to trust the source). This is somewhat uncommon metric and whether it is useful is up to you. Since it is a Gauge (unlike sum and count it can go both up and down) you don't need extra functions (such as rate()) to see dynamics. Thus
http_server_requests_seconds_max
... will show you the maximum request duration. You can augment this with aggregation functions (avg(), sum(), etc) and label filters to make it more useful.
Can any one help me understand
What happens to the fees of each transactions ? Does fees become part of the reward ?
When does the reward gets distributed . Is there any specific timeperiod. Can you please point me to couple of examples in explorer where reward is distribuited.
i got this docs for Reward - https://nomicon.io/Economics/README.html#rewards-calculation
coinbaseReward[t] The maximum inflation per epoch[t], as a function of REWARD_PCT_PER_YEAR / EPOCHS_A_YEAR
where can i get REWARD_PCT_PER_YEAR and EPOCHS_A_YEAR. Does it remain fixed for ceratin range of blocks
epochReward[t] = coinbaseReward[t] + epochFee[t]
epochFee[t] - does this mean fees of all transactions happened in an epoch .
Can i have some staking transactions from explorer if any
Sure,
I had exactly the same questions and came out with a doc.
In a few words:
Fees are burnt, they do not become a part of validators reward, though part of the fees of function calls and cross-contract function calls become a royalty reward to contract accounts.
Reward tokens are minted every epoch (something about every 12 hours) and gone to staking pools.
Please check the doc for the examples, I covered all possible situations of balance changing with all numbers and explanation what is going on.
UPD by the way, it's true, there is no information about royalties in Nomicon, we created the issue for that
I know that the "how to generate random number" in solidity is a very common question. However, after reading the great majority of answers I did not find one to fit my case.
A short description of what I want to do is: I have a list of objects that each have a unique id, a number. I need to produce a list that contains 25% of those objects, randomly selected each time the function is called. The person calling the function cannot be depended on to provide input that will somehow influence predictably the resulting list.
The only answer I found that gives a secure random number was Here. However, it depends on input coming from the participants and it is meant to address a gambling scenario. I cannot use it in my implementation.
All other cases mention that the number generated is going to be predictable, and even some of those depend on a singular input to produce a single random number. Once again, does not help me.
Summarising, I need a function that will give me multiple, non-predictable, random numbers.
Thanks for any help.
Here is an option:
function rand()
public
view
returns(uint256)
{
uint256 seed = uint256(keccak256(abi.encodePacked(
block.timestamp + block.difficulty +
((uint256(keccak256(abi.encodePacked(block.coinbase)))) / (now)) +
block.gaslimit +
((uint256(keccak256(abi.encodePacked(msg.sender)))) / (now)) +
block.number
)));
return (seed - ((seed / 1000) * 1000));
}
It generates a random number between 0-999, and basically it's impossible to predict it (It has been used by some famous Dapps like Fomo3D).
Smart Contracts are deterministic, so, basically every functions are predictable - if we know input, we will be and we should be know output. And you cannot get random number without any input - almost every language generates "pseudo random number" using clock. This means, you will not get random number in blockchain using simple method.
There are many interesting methods to generate random number using Smart Contract - using DAO, Oracle, etc. - but they all have some trade-offs.
So in conclusion, There is no method you are looking for. You need to sacrifice something.
:(
100% randomness is definitely impossible on Ethereum. The reason for that is that when distributed nodes are building from the scratch the blockchain they will build the state by running every single transaction ever created on the blockchain, and all of them have to achieve the exact same final status. In order to do that randomness is totally forbidden from the Ethereum Virtual Machine, since otherwise each execution of the exact same code would potentially yield a different result, which would make impossible to reach a common final status among all participants of the network.
That being said, there are projects like RanDAO that pretend to create trustable pseudorandomness on the blockchain.
In any case, there are approaches to achieve pseudandomness, being two of the most important ones commit-reveal techniques and using an oracle (or a combination of both).
As an example that just occurred to me: you could use Oraclize to call from time to time to a trusted external JSON API that returns pseudorandom numbers and verify on the contract that the call has truly been performed.
Of course the downside of these methods is that you and/or your users will have to spend more gas executing the smart contracts, but it's in my opinion a fair price for the huge benefits in security.
Anyone who's read Parse documentation has stumbled upon this
Caveat: Count queries are rate limited to a maximum of 160 requests per minute. They can also return inaccurate results for classes with more than 1,000 objects. Thus, it is preferable to architect your application to avoid this sort of count operation (by using counters, for example.)
Why's there such limitation and inaccuracy?
To quote the Parse Engineering Blog Post: Building Scalable Apps on Parse
Suppose you are building a product catalog. You might want to display
the count of products in each category on the top-level navigation
screen. If you run a count query for each of these UI elements, they
will not run efficiently on large data sets because MongoDB does not
use counting B-trees. Instead, we recommend that you use a separate
Parse Object to keep track of counts for each category. Whenever a
product gets added or deleted, you can increment or decrement the
counts in an afterSave or afterDelete Cloud Code handler.
To add on to this, here is another quote by Hector Ramos from the Parse Developers Google Group
Count queries have always been expensive once you throw some
constraints in. If you only care about the total size of the
collection, you can run a count query without any constraints and that
one should be pretty fast, as getting the total number of records is a
different problem than counting how many of these match an arbitrary
list of constraints. This is just the reality of working with database
systems.
The inaccuracy is not due to the 1000 request object limit. The count query will try to get the total number of records regardless of size, but since the operation may take a large amount of time to complete, it is possible that the database has changed during that window and the count value that is returned may no longer be valid.
The recommended way to handle counts is to essentially maintain your own index using before/after save hooks. However, this is also a non-ideal solution because save hooks can arbitrarily fail part way through and (worse) postSave hooks have no error propagation.
The limitation is simply to stop people using counts too much, they're just as runtime costly as full queries in effect.
The inaccuracy is because queries are limited to 1000 result objects (100 by default) and counts have the same hard limit.
You can run a recursive query to build up a count, but it's a crappy option. Hence the only really good option at this point in time (and as far as we can see in the future) is to keep an index of the things you're interested in counting and update the counts when anything changes. You would usually do that with save hooks in cloud code.