I've been trying to solve this problem for quite sometime but I am having trouble with it.
Let's say on a trigger, you receive values.
First trigger: You get 1
Second trigger: You get 1, 2
Third trigger: You get 1, 2, 3
So, I store 1.
For 2nd trigger, I store 2 since 1 already exist.
For 3rd trigger, I store 3 since 1,2 already exist
so in total I have stored 1,2,3
As you can see, we can easily check for new values, if old != new.
Here's come the problem:
Fourth trigger: You get 1, 2, 4
For 4th trigger, I store 1, 2 because it exists
but how do I check against 3 and remove 3 from store and check if 4 is new?
If you are having problems understanding this, feel free to clarify. Thanks!
Use a std::set<int> container. When a trigger arrives, clear it an insert all the values from trigger. This should be ok if you work with just a few numbers (about ten or so). With more, a little bit more sophisticated approach might be required.
Hard to tell what you're asking exactly, but see std::set data structure if your main problem is trying to maintain a set of unique numbers and efficiently check for existence in the set.
Your logic changed between 1,2,3 and 1,2,4
(only stored 3 on former, but stored 1,2,4 on latter)
In that case, ignore data recv'd that already exists, only storing new data, unless some old data was not sent in which case you'll create a new set of data to store.
But, I'm guessing that's not what you had in mind at all :)
edit
I see it's been edited now, so my answer is invalid
edit-2
the fastest way is to drop all stored data on each iteration as comparisons will take as long (if not longer) than a complete save of sent data.
Your approach sounds like it is better served by using some basic set theory. A couple of answers already point you to STL sets for that matter. With that, you'll need to iterate through the reported values to test for membership in the set.
However, is there an opportunity to affect what is reported with each "trigger"? For example, if this is something like a select poll, you could just put whatever it is that you're polling into a different state so that it is not reported as ready in subsequent triggers.
Related
I'm making a time-spending tracker based on the work I do every hour of the day.
Now, suppose I have 28 types of work listed in my tracker (which I also have to increase from time to time), and I have about 8 significance values that I have decided to relate to these 28 types of work, predefined.
I want that, as soon as I enter a type of work in cell 1 - I want the adjacent cell 2 to get automatically populated with a significance value (from a range of 8 values) that is pre-definitely set by me.
Every time I input a new or old occurrence of a type of work, the adjacent cell should automatically get matched with its relevant significance value & automatically get populated in real-time.
I know how to do it using IF, IFS, and IF_OR conditions, but I feel that based on the ever-expanding types of work & significance values, the above formulas will be very big, complicated, and repetitive in the future. I feel there's a more efficient way to achieve it. Also, I don't want it to be selected from a drop-down list.
Guys, please help me out with the most efficient way to handle this. TUIA :)
Also, I've added a snapshot and a sample sheet describing the problem.
Sample sheet
XLOOKUP() may work. Try-
=XLOOKUP(D2,A2:A,B2:B)
Or FILTER() function like-
=FILTER(B2:B,A2:A=D2)
You can use this formula for a whole column:
=INDEX(IFERROR(VLOOKUP(C14:C,A2:B9,2,0)))
Adapt the ranges to your actual tables in order to include in the second argument all the potential values and their significances
This is the formula, that worked for me (for anybody's reference):
I created another reference sheet, stating the types of work & their significance. From that sheet, I'm using either vlookup, filter, xlookup.Using gforms for inputting my data.
=ARRAYFORMULA(IFS(ROW(D:D)=1,"Significance",A:A="","",TRUE,VLOOKUP(D:D,Reference!$A:$B,2,0)))
The concept of map-reduce is very familiar. It seems like a great fit for a problem I'm trying to solve, but it's either missing something (or I lack enough understanding of the concept).
I have a stream of items, structured as follows:
{
"jobId": 777,
"numberOfParts": 5,
"data": "some data..."
}
I want to do a map-reduce on many such items.
My mapping operation is straightforward - take the jobId.
My reduce operation is irrelevant for this phase, but all we know is that it takes multiple strings (the "some data..." part) and somehow reduces them to a single object.
The only problem is - I need all five parts of this job to complete before I can reduce all the strings into a single object. Every item has a "numberOfParts" property which indicates the number of items I must have before I apply the reduce operation. The items are not ordered, therefore I don't have a "partId" field.
Long story short - I need to apply some kind of a waiting mechanism that waits for all parts of the job to complete before initiating the reduce operation, and I need this waiting mechanism to rely on a value that exists within the payload (therefore solutions like kafka wouldn't work).
Is there a way to do that, hopefully using a single tool/framework?
I only want to write the map/reduce part and the "waiting" logic, the rest I believe should come out of the box.
**** EDIT ****
I'm currently in the design phase of the project and therefore not using any framework (such as spark, hadoop, etc...)
I asked this because I wanted to find out the best way to tackle this problem.
"Waiting" is not the correct approach.
Assuming your jobId is the key, and data contains some number of parts (zero or more), then you must have multiple reducers. One that gathers all parts of the same job, then another that processes all jobs with a collection of parts greater than or equal to numberOfParts while ignoring others
We are implementing support for tracking of Mailgun events in our application. We reviewed the proposed event polling algorithm but find ourselves not quite comfortable with it. First, we would prefer not to discard the data that we have already fetched and then retry from scratch after a pause. It is not very efficient and leaves a door open for a long loop of retries, as it is not clear when the loop is supposed to end. Second, the "threshold age" seems to be the key to determine "trustworthiness", but its value is not defined, only a very large "half an hour" is suggested.
It is our understanding that the events become "trustworthy" after some threshold delay, let us call it D_max, when the events are guaranteed to reside in the event storage. If so, we can implement this algorithm in a different way, so that we do not fetch the data that we know are not "trustworthy" and make use of all data which have been fetched.
We would be fetching data periodically, and on each iteration we would:
Make a request to the events API specifying an ascending time range from T_1 to T_2 = now() - D_max. For the first iteration, T_1 can be set to some time in the past, "e.g., half an hour ago". For the subsequent iterations, T_1 is set to the value of T_2 from the previous iteration.
Fetch all pages one by one while the next page URL is returned.
Use all fetched events, as they are all "trustworthy".
My questions are:
Q1: Are there any problems with this approach?
Q2: What is the minimum realistic value of D_max? Obviously, we can use "half an hour" for it, but we would like to be more agile in tracking events, so it would be great to know what is the minimum value we can set it to and still reliably fetch all events.
Thanks!
1: I see no problems with this solution (in fact I'm doing something very similar). I'm also storing ID's of the events to validate I'm not inserting duplicate entries.
2: I've been working through this similar process. Right now I am testing with D_max at 10 minutes.
Additionally, While going through a testing process I'm running an additional task nightly that goes back over the entire day to validate a few things:
Am I missing existing metrics?
Diagnose if there is a problem with the assumptions I've made about D_max.
So my professor assigned this project for us. It's pretty simple because it's our first hashing program. The program is to take 15 names as input and hash them and store them in something. I did it in a vector. Now once they are hashed the user enters another name and it will should hash that name and try to match it to one in the vector. Maybe it is me or maybe it is how he wrote the question but I'm a little confused. Our program is suppose to have collision protection. Which means we run an algorithm on the name inputted and it will spit out a number and that number is where we store the name in the vector. If there is another name already in that spot we are suppose to store it in the next available spot.
So lets say I enter the name jordan and jon. The algorithm will tell me to store these in the same spot (lets say spot 8) but collision protection will recognize that jordan is already taking up the spot (8) and it will move jon to the next available spot(lets say spot 9). Now when the user is entering names to see if it is in the vector already and he enters jon, the algorithm will see it should be in spot 8. Do you think I should just check to see if that spot is empty, if not say a match has been found in spot 8, even though the name in spot 8 is jordan and the name entered is jon. Or should I start at spot 8 and see if the strings match, and if they don't check the next spot and so on until i return to the original spot or find the match?
I wrote the program and it works fine, I just ran into this one dilemma and can't finish the program. Thanks
Your approach is Open Addressing collision handling.
According to this approach, when you are searching for an element, you should 'keep going' until you find the first empty spot - only then you are guaranteed the requested element is not stored.
Three things can happen when you look up a value.
You find the value you're looking for.
You find an empty spot in your hash table.
You find some other value in the assigned spot you're looking at.
In cases 1 and 2, it's clear what you should do. You know for sure that the value you're looking for is either in your table or it's not. In the third case, you should follow the same procedure that you do when adding items to the table. That is, keep looking in the next spot until you reach either condition 1 or 2 above.
Long story short, I'm rewriting a piece of a system and am looking for a way to store some hit counters in AWS SimpleDB.
For those of you not familiar with SimpleDB, the (main) problem with storing counters is that the cloud propagation delay is often over a second. Our application currently gets ~1,500 hits per second. Not all those hits will map to the same key, but a ballpark figure might be around 5-10 updates to a key every second. This means that if we were to use a traditional update mechanism (read, increment, store), we would end up inadvertently dropping a significant number of hits.
One potential solution is to keep the counters in memcache, and using a cron task to push the data. The big problem with this is that it isn't the "right" way to do it. Memcache shouldn't really be used for persistent storage... after all, it's a caching layer. In addition, then we'll end up with issues when we do the push, making sure we delete the correct elements, and hoping that there is no contention for them as we're deleting them (which is very likely).
Another potential solution is to keep a local SQL database and write the counters there, updating our SimpleDB out-of-band every so many requests or running a cron task to push the data. This solves the syncing problem, as we can include timestamps to easily set boundaries for the SimpleDB pushes. Of course, there are still other issues, and though this might work with a decent amount of hacking, it doesn't seem like the most elegant solution.
Has anyone encountered a similar issue in their experience, or have any novel approaches? Any advice or ideas would be appreciated, even if they're not completely flushed out. I've been thinking about this one for a while, and could use some new perspectives.
The existing SimpleDB API does not lend itself naturally to being a distributed counter. But it certainly can be done.
Working strictly within SimpleDB there are 2 ways to make it work. An easy method that requires something like a cron job to clean up. Or a much more complex technique that cleans as it goes.
The Easy Way
The easy way is to make a different item for each "hit". With a single attribute which is the key. Pump the domain(s) with counts quickly and easily. When you need to fetch the count (presumable much less often) you have to issue a query
SELECT count(*) FROM domain WHERE key='myKey'
Of course this will cause your domain(s) to grow unbounded and the queries will take longer and longer to execute over time. The solution is a summary record where you roll up all the counts collected so far for each key. It's just an item with attributes for the key {summary='myKey'} and a "Last-Updated" timestamp with granularity down to the millisecond. This also requires that you add the "timestamp" attribute to your "hit" items. The summary records don't need to be in the same domain. In fact, depending on your setup, they might best be kept in a separate domain. Either way you can use the key as the itemName and use GetAttributes instead of doing a SELECT.
Now getting the count is a two step process. You have to pull the summary record and also query for 'Timestamp' strictly greater than whatever the 'Last-Updated' time is in your summary record and add the two counts together.
SELECT count(*) FROM domain WHERE key='myKey' AND timestamp > '...'
You will also need a way to update your summary record periodically. You can do this on a schedule (every hour) or dynamically based on some other criteria (for example do it during regular processing whenever the query returns more than one page). Just make sure that when you update your summary record you base it on a time that is far enough in the past that you are past the eventual consistency window. 1 minute is more than safe.
This solution works in the face of concurrent updates because even if many summary records are written at the same time, they are all correct and whichever one wins will still be correct because the count and the 'Last-Updated' attribute will be consistent with each other.
This also works well across multiple domains even if you keep your summary records with the hit records, you can pull the summary records from all your domains simultaneously and then issue your queries to all domains in parallel. The reason to do this is if you need higher throughput for a key than what you can get from one domain.
This works well with caching. If your cache fails you have an authoritative backup.
The time will come where someone wants to go back and edit / remove / add a record that has an old 'Timestamp' value. You will have to update your summary record (for that domain) at that time or your counts will be off until you recompute that summary.
This will give you a count that is in sync with the data currently viewable within the consistency window. This won't give you a count that is accurate up to the millisecond.
The Hard Way
The other way way is to do the normal read - increment - store mechanism but also write a composite value that includes a version number along with your value. Where the version number you use is 1 greater than the version number of the value you are updating.
get(key) returns the attribute value="Ver015 Count089"
Here you retrieve a count of 89 that was stored as version 15. When you do an update you write a value like this:
put(key, value="Ver016 Count090")
The previous value is not removed and you end up with an audit trail of updates that are reminiscent of lamport clocks.
This requires you to do a few extra things.
the ability to identify and resolve conflicts whenever you do a GET
a simple version number isn't going to work you'll want to include a timestamp with resolution down to at least the millisecond and maybe a process ID as well.
in practice you'll want your value to include the current version number and the version number of the value your update is based on to more easily resolve conflicts.
you can't keep an infinite audit trail in one item so you'll need to issue delete's for older values as you go.
What you get with this technique is like a tree of divergent updates. you'll have one value and then all of a sudden multiple updates will occur and you will have a bunch of updates based off the same old value none of which know about each other.
When I say resolve conflicts at GET time I mean that if you read an item and the value looks like this:
11 --- 12
/
10 --- 11
\
11
You have to to be able to figure that the real value is 14. Which you can do if you include for each new value the version of the value(s) you are updating.
It shouldn't be rocket science
If all you want is a simple counter: this is way over-kill. It shouldn't be rocket science to make a simple counter. Which is why SimpleDB may not be the best choice for making simple counters.
That isn't the only way but most of those things will need to be done if you implement an SimpleDB solution in lieu of actually having a lock.
Don't get me wrong, I actually like this method precisely because there is no lock and the bound on the number of processes that can use this counter simultaneously is around 100. (because of the limit on the number of attributes in an item) And you can get beyond 100 with some changes.
Note
But if all these implementation details were hidden from you and you just had to call increment(key), it wouldn't be complex at all. With SimpleDB the client library is the key to making the complex things simple. But currently there are no publicly available libraries that implement this functionality (to my knowledge).
To anyone revisiting this issue, Amazon just added support for Conditional Puts, which makes implementing a counter much easier.
Now, to implement a counter - simply call GetAttributes, increment the count, and then call PutAttributes, with the Expected Value set correctly. If Amazon responds with an error ConditionalCheckFailed, then retry the whole operation.
Note that you can only have one expected value per PutAttributes call. So, if you want to have multiple counters in a single row, then use a version attribute.
pseudo-code:
begin
attributes = SimpleDB.GetAttributes
initial_version = attributes[:version]
attributes[:counter1] += 3
attributes[:counter2] += 7
attributes[:version] += 1
SimpleDB.PutAttributes(attributes, :expected => {:version => initial_version})
rescue ConditionalCheckFailed
retry
end
I see you've accepted an answer already, but this might count as a novel approach.
If you're building a web app then you can use Google's Analytics product to track page impressions (if the page to domain-item mapping fits) and then to use the Analytics API to periodically push that data up into the items themselves.
I haven't thought this through in detail so there may be holes. I'd actually be quite interested in your feedback on this approach given your experience in the area.
Thanks
Scott
For anyone interested in how I ended up dealing with this... (slightly Java-specific)
I ended up using an EhCache on each servlet instance. I used the UUID as a key, and a Java AtomicInteger as the value. Periodically a thread iterates through the cache and pushes rows to a simpledb temp stats domain, as well as writing a row with the key to an invalidation domain (which fails silently if the key already exists). The thread also decrements the counter with the previous value, ensuring that we don't miss any hits while it was updating. A separate thread pings the simpledb invalidation domain, and rolls up the stats in the temporary domains (there are multiple rows to each key, since we're using ec2 instances), pushing it to the actual stats domain.
I've done a little load testing, and it seems to scale well. Locally I was able to handle about 500 hits/second before the load tester broke (not the servlets - hah), so if anything I think running on ec2 should only improve performance.
Answer to feynmansbastard:
If you want to store huge amount of events i suggest you to use distributed commit log systems such as kafka or aws kinesis. They allow to consume stream of events cheap and simple (kinesis's pricing is 25$ per month for 1K events per seconds) – you just need to implement consumer (using any language), which bulk reads all events from previous checkpoint, aggregates counters in memory then flushes data into permanent storage (dynamodb or mysql) and commit checkpoint.
Events can be logged simply using nginx log and transfered to kafka/kinesis using fluentd. This is very cheap, performant and simple solution.
Also had similiar needs/challenges.
I looked at using google analytics and count.ly. the latter seemed too expensive to be worth it (plus they have a somewhat confusion definition of sessions). GA i would have loved to use, but I spent two days using their libraries and some 3rd party ones (gadotnet and one other from maybe codeproject). unfortunately I could only ever see counters post in GA realtime section, never in the normal dashboards even when the api reported success. we were probably doing something wrong but we exceeded our time budget for ga.
We already had an existing simpledb counter that updated using conditional updates as mentioned by previous commentor. This works well, but suffers when there is contention and conccurency where counts are missed (for example, our most updated counter lost several million counts over a period of 3 months, versus a backup system).
We implemented a newer solution which is somewhat similiar to the answer for this question, except much simpler.
We just sharded/partitioned the counters. When you create a counter you specify the # of shards which is a function of how many simulatenous updates you expect. this creates a number of sub counters, each which has the shard count started with it as an attribute :
COUNTER (w/5shards) creates :
shard0 { numshards = 5 } (informational only)
shard1 { count = 0, numshards = 5, timestamp = 0 }
shard2 { count = 0, numshards = 5, timestamp = 0 }
shard3 { count = 0, numshards = 5, timestamp = 0 }
shard4 { count = 0, numshards = 5, timestamp = 0 }
shard5 { count = 0, numshards = 5, timestamp = 0 }
Sharded Writes
Knowing the shard count, just randomly pick a shard and try to write to it conditionally. If it fails because of contention, choose another shard and retry.
If you don't know the shard count, get it from the root shard which is present regardless of how many shards exist. Because it supports multiple writes per counter, it lessens the contention issue to whatever your needs are.
Sharded Reads
if you know the shard count, read every shard and sum them.
If you don't know the shard count, get it from the root shard and then read all and sum.
Because of slow update propogation, you can still miss counts in reading but they should get picked up later. This is sufficient for our needs, although if you wanted more control over this you could ensure that- when reading- the last timestamp was as you expect and retry.