I have an app whose messaging granularity could be written two ways - sending many small messages vs. (possibly far) fewer larger ones. Conceptually what moves around is a set of 'alive' vertex IDs that might get filtered at each superstep based on a processed list (vertex value) that vertexes manage. The ones that survive to the end are the lucky winners. compute() calculates a set of 'new-to-me' incoming IDs that are perfect for the outgoing message, but I could easily send each ID one at a time. My guess is that sending fewer messages is more important, but then each set might contain thousands of IDs. Thank you.
P.S. A side question: The few custom message type examples I've found are relatively simple objects with a few primitive instance variables, rather than collections. Is it nutty to send around a collection of IDs as a message?
I have used lists and even maps to be sent or just stored as vertex data, so that isn’t a problem. I think it shouldn’t matter for giraph which you want to choose, and I’d rather go with many simple small messages, as you will use Giraph appropriately. Instead you will need to go in the compute function through the list of messages and for each message through the list of IDs.
Performance-wise it shouldn’t make any difference. What I’ve rather found to make a big difference is, try to compute as much as possible in on cycle, as the switching between cycles and synchronising the messages, ... takes a lot of time. As long as that doesn’t change it should be more or less the same and probably much easier to read and maintain when you keep the size of messages small.
In order to answer your question, you need understand the MessageStore interface and its implementations.
In a nutshell, under the hood, it took the following steps:
The worker receive the byte raw input of the messages and the destination IDs
The worker sort the messages and put them into A Map of A Map. The first map's key is the partition ID, the section map's key is the vertex ID. (It is kind of like the post office. The work is like the center hub, and it sort the letters into different zip code first, then in each zip code sorted by address)
When it is the vertex's turn of compute, a Iterable of that vertex's messages are passed to the vertex's compute method, and that's where you get the messages and use it.
So less and bigger messages are better because of less sorting if the total amount of bytes is the same for both cases.
Also, you could send many small messages, but let Giraph convert this into a long one (almost) automatically. You can use Combiners.
The documentation on this subject is terrible on Giraph site, but you maybe could extract an example from the book Practical Graph Analytics with Apache Giraph.
This depends on the type of messages that you are sending, mainly.
Related
is it better to define one topic with different types or to define typed-topic for each type?
For example,
[topic]
foo
[message]
{
type: 'x',
data: [1,2,3]
}
OR
[topic]
foo-x
[message]
{
data: [1,2,3]
}
According to pub/sub-pricing, it seems that latter case is a little bit cheaper because of the message size given that gross message sent and delivered is the same.
But this is only pricing, I think there may be some empirical design or schema to define message. What could be considered in this case of message?
Prefer the second solution. Indeed, if you want to plug processing (Function, Cloud Run, AppEngine or other compute platform) on a particular topic, you can do it because the topic is typed.
If the topic is not typed, you have to implement a filter in your process to do: "If my type is X then do else exit". And you are charged for this. For example, for function and Cloud Run, you are charged per request and by processing time, the minimal facturation in 100ms -> too much for a simple IF
However, if you want to plug the same function on several topics, you have to duplicate your function (except if you use HTTP trigger)
Hope this help!
There isn't one right or wrong answer here. In part, it depends on the nature of your subscribers. Are they going to be interested in messages of all types or in messages of only one type? If the former, then a single topic with the type in the message might be better. If the latter, then separate subscriptions might be better. Additionally, how many types do you have? For dozens or more types, managing individual topics may be a lot of overhead.
Price would likely not be a factor. If your messages are large, then the price will be dominated by the contents of the messages. If your messages are small, then they will be subject to the 1KB minimum billing volume per request. The only way the type would be a factor is if you have very small messages, but batch them such that they are at least 1KB.
If you do decide to encode the type in the message, consider putting it in the message attributes instead of the message data. That way, you can look at the type without having to decode the entire message, which can be helpful if you want to immediately ack messages of certain types instead of processing them.
I have a stream of objects that come in one at a time to a debugger node. I want to be able to grab the objects and store them into one array once the objects stop streaming from an rss feed. The issue is I won't know how many of these objects will be coming through.
I've tried pushing the objects into an array that i store into a flow context, but I have to believe there's a much better and less messy way of doing this in node-red.
Look at the join node, it has a number of modes for collecting messages and assembling an array of payloads.
Since you do not know how many, the time out node may be the best bet
I'm a student and starting to relearn again the basics of programming.
The problem I stated above starts when I have read some Facebook posts that most of the programmers use arrays in their application and arrays are useful. And I started to realize that I never use arrays in my program.
I read some books but they only show the syntax of array and didn't discuss on when to apply them in creating real world applications. I tried to research this on the Internet but I cannot find any. Do you guys have circumstance when you use arrays. Can you please share it to me so I can have an idea.
Also, to clear my doubts can you please explain to me why arrays are good to store information because database can also store information. When is the right time for me to use database and arrays?
I hope to get a clear answer because I have one remaining semester before the internship and I want to clear my head on this. I do not include any specific programming language because I know most of the programming language have arrays.
I hope to get an answer that can I can easily understand.
When is the right time for me to use database and arrays?
I can see how databases and arrays may seem like competing solutions to the same problem, but you're comparing apples and oranges. Arrays are a way to represent structured data in memory. Databases are a tool to store data on disk until you need to retrieve it.
The question you pose is kind of like asking: "When is the right time to use an integer to hold a value, vs a piece of paper?" One of them is a structural representation in memory; the other is a storage tool.
Do you guys have circumstance when you use arrays
In most applications, databases and arrays work together. Applications often retrieve data from a database, and hold it in an array for easy processing. Here is a simple example:
Google allows you to receive an alert when something of interest is mentioned on the news. Let's call it the event. Many people can be interested in the event, so Google needs to keep a list of people to alert. How? Probably in a database.
When the event occurs, what does Google do? Well it needs to:
Retrieve the list of interested users from the DB and place it in an array
Loop through the array and send a notification to each user.
In this example, arrays work really well because users form a collection of similarly shaped data structures that needs to be put through a similar process. That's exactly what arrays are for!
Some other common uses of arrays
A bank wants to send invoice and payment due reminders at the end of the day. So it retrieves the users with past due payments from the DB, and loops through the users' array sending notifications.
An IT admin panel wants to check whether all critical websites in a list are still online. So it loops through the array of domains, pings each one and records the results in a log
An educational program wants to perform statistical functions on student test results. So it puts the results in an array to easily perform operations such as average, sum, standardDev...
Arrays are also awesome at keeping things in a predictable order. You can be certain that as you loop forward through an array, you encounter values in the order you put them in. If you're trying to simulate a checkout line at the store, the customers in a queue are a perfect candidate to represent in an array because:
They are similarly shaped data: each customer has a name, cart contents, wait time, and position in line
They will be put through a similar process: each customer needs methods for enter queue, request checkout, approve payment, reject payment, exit queue
Their order should be consistent: When your program executes next(), you should expect that the next customer in line will be the one at the register, not some customer from the back of the line.
Trying to store the checkout queue in a database doesn't make sense because we want to actively work with the queue while we run our simulation, so we need data in memory. The database can hold a historical record of all customers and their checkout outcomes, perhaps for another program to retrieve and use in another way (maybe build customized statistical reports)
There are two different points. Let's me try to explain the simple way:
Array: container objects to keep a fixed number of values. The array is stored in your memory. So it depends on your requirements but when you need a fixed and fast one, just use array.
Database: when you have a relational data or you would like to store it in somewhere and not really worry about the size of the objects. You can store 10, 100, 1000 records to you DB. It's also flexible and you can select/query/update the data flexible. Simple way to use is: have a relational data, large amount and would like to flexible it, use database.
Hope this help.
There are a number of ways to store data when you have multiple instances of the same type of data. (For example, say you want to keep information on all the people in your city. There would be some sort of object to hold the information on each person, and you might want to have a data structure that holds the information on every person.)
Java has two main ways to store multiple instances of data in memory: arrays and Collections.
Databases are something different. The difference between a database and an array or collection, as I see it, are:
databases are persistent, i.e. the data will stay around after your program has finished running;
databases can be shared between programs, often programs running in all different parts of the world;
databases can be extremely large, much, much larger than could fit in your computer's memory.
Arrays and collections, however, are intended only for use by one program as it runs. Your program may want to keep track of some information in order to do its calculations. But the data will be in your computer's memory, and therefore other programs on other computers won't be able to access it. And when your program is done running, the data is gone. However, since the data is in memory, it's much faster to use it than data in a database, which is stored on some sort of external device. (This is really an overgeneralization, and doesn't consider things like virtual memory and caching. But it's good enough for someone learning the basics.)
The Java run time gives you three basic kinds of collections: sets, lists, and maps. A set is an unordered collection of unique elements; you use that when the data doesn't belong in any particular order, and the main operations you want are to see if something is in the set, or return all the data in the set without caring about the order. A list is ordered, though; the data has a particular order, and provides operations like "get the Nth element" for some number N, and adding to the ends of the list or inserting in a particular place in the list. A map is unordered like a set, but it also attaches keys to the data, so that you can look for data by giving the key. (Again, this is an overgeneralization. Some sets do have order, like SortedSet. And I haven't even gotten into queues, trees, multisets, etc., some of which are in third-party libraries.)
Java provides a List type for ordered lists, but there are several ways to implement it. One is ArrayList. Like all lists, it provides the capability to get the Nth item in the list. But an ArrayList provides this capability faster; under the hood, it's able to go directly to the Nth item. Some other list implementations don't do that--they have to go through the first, second, etc., items, until they get to the Nth.
An array is similar to an ArrayList, but it has a different syntax. For an array x, you can get the Nth element by referring to x[n], while for an ArrayList you'd say x.get(n). As far as functionality goes, the biggest difference is that for an array, you have to know how big it is before you create it, while an ArrayList can grow. So you'd want to use an ArrayList if you don't know beforehand how big your list will be. If you do know, then an array is a little more efficient, I think. Although you can probably get by mostly with just ArrayList, arrays are still fundamental structures in every computer language. The implementation of ArrayList depends on arrays under the hood, for instance.
Think of an array as a book, and database as library. You can't share the book with others at the same time, but you can share a library. You can't put the entire library in one book, but you can checkout 1 book at a time.
I am working on an Android application that uses the Overpass API at [1]. My goal is to get all circular ways that enclose a certain lat-long point.
In order to do so I build a request for a rectangle that contains my location, then parse the response XML and run a ray-casting algorithm to filter the ways that enclose the given lat-long position. This is too slow for the purpose of my application because sometimes the response has tens or hundreds of MB.
Is there any OSM API that I can call to get all ways that enclose a certain location? Otherwise, how could I optimize the process?
Thanks!
[1] http://overpass-api.de/
To my knowledge, there is no standard API in OSM to do this (it is indeed a very uncommon usecase).
I assume you define enclose as the point representing the current location is inside the inner area of the polygon. Furthermore I assume optimizing the process might including changing the entire concept of the algorithm.
First of all, you need to define the rectangle to fetch data. For that, you need to consider that querying a too large rectangle would yield too much data. As far as I know there is no specific API to query circular ways only, and even if there is, querying a too large rectangle would probably denied by the server, because the server load would be enormous.
Server-side precomputation / prefiltering
Therefore I suggest the first optimization: Instead of querying an API that is not specifically suited for your purpose, use an offline database saved on the Android device. OsmAnd and others save the whole database for a country offline, but in your specific usecase you only need to save a pre-filtered database of circular ways.
As far as I know, only a small fraction of the ways in OSM is circular. Therefore I suggest writing a script that regularly downloads OSM dumps e.g. from Geofabrik, remove non-circular ways (e.g. you could check if the last node ID in a way is equal to the first node ID, but you'd need to check if that captures any way you would define as circular). How often you would run it depends on your usecase.
This optimization solves:
The issue of downloading a large amount of data
The issue of overloading the API with large request
The issue of not being able to request large chunks of data
If that is not suitable for your usecase, I suggest to build a simple API for that on your server.
Re-chunking the data into appriopriate grids
However, you still would need to filter a large amount of data. In order to partially solve this, I suggest the second optimization: Re-chunk your data. For example, if your current location is in Virginia, you would not need to filter circular ways that have an area not beyond Texas. Because filtering by state etc. would by highly country-dependent and difficult (CPU-intensive), I suggest to choose a grid, say e.g. 0.05 lat/lon degree (I'd choose a equirectangular projection because it's easy to calculate if you already have lat/lon coordinates).
The script that preprocessed that data shall then create one chunk of data (that could be a file, but we don't know enough about your usecase to talk about specific data strucutres) for any rectangle in the area you want to use. A circular way is included in this chunk if and only if it has at least one node that is inside the chunk area.
You would then only request / filter the specific chunk your position is currently in. Choose the chunk size appropriately for your application (preferably rather small, but that depends on numerous factors!).
This optimization solves:
Assuming most of the circular ways are quite small in terms of their bounding rectangles, you only need to filter a tiny fraction of the overall ways
IO is minimized, especially if you
Hysteretic heuristics
If the aforementioned optimizations do not sufficiently reduce your computation time, I'd suggest the third optimization that depends on how many circular ways you want to find (if you really need to find all, it won't help at all): Use hysteresis. Save the circular ways you were inside of during the last computation (assuming the new current location is near to the last location) and check them first. If your location didn't change too much, you have a high chance of hitting a way you're inside of during the first few raycasts.
Leveraging relations between different circular ways
Also, a fourth optimization is possible: There will be some circular ways that are fully enclosed in another circular way. You could code your program so that it knows about that relation and checks the inner circular way first. If this check succeeds, you automatically now that the current position is also contained in the outer circular way. I think computing the information (server-side) could be incredibly CPU-intensive and implementing it might also be a hard task, so I'd suggest to use this optimization only if not avoidable.
Tuning the parameters of these optimizations should be sufficient to decrease the CPU time needed for your computation significantly. Please feel free to comment/ask if you have further questions regarding these suggestions.
hen I was thinking about implementing a system, enabling users to send messages to eachother, I thought about the ammount of data you could save if, instead of saving a copy of the message for each of the receivers, I would save one message with a list of receivers.
There would actually be 3 lists, one list of receivers, one list of booleans, where if b[i], then receiver[i] has read the message, and a third list containinging all the users that have not deleted the message. Every day, I would run a cron job, looking for messages with an empty list of such users, and remove them.
Could there be any problems with this model?
The first schema, it's like trying to replicate the email architecture, which is outdated and does not work quite well.
Definitely, the second approach it's better.
Problems? No one while your code does not have bugs. But consider replies, if you have to support them. Maybe a fourth list could be enough if the instance does not exceeds the 1M size limit.
But actually, a separated model for answers is more consistent and intuitive. This new model will also have lists like: readed_by, deleted_by, etc.
The cron job may be unnecessary. You could just delete the message after a user mark it as "deteleted" if message.deleted_by == message.receivers + message.from.