There's a REST endpoint, which serves large (tens of gigabytes) chunks of data to my application.
Application processes the data in it's own pace, and as incoming data volumes grow, I'm starting to hit REST endpoint timeout.
Meaning, processing speed is less then network throughoutput.
Unfortunately, there's no way to raise processing speed enough, as there's no "enough" - incoming data volumes may grow indefinitely.
I'm thinking of a way to store incoming data locally before processing, in order to release REST endpoint connection before timeout occurs.
What I've came up so far, is downloading incoming data to a temporary file and reading (processing) said file simultaneously using OutputStream/InputStream.
Sort of buffering, using a file.
This brings it's own problems:
what if processing speed becomes faster then downloading speed for
some time and I get EOF?
file parser operates with
ObjectInputStream and it behaves weird in cases of empty file/EOF
and so on
Are there conventional ways to do such a thing?
Are there alternative solutions?
Please provide some guidance.
Upd:
I'd like to point out: http server is out of my control.
Consider it to be a vendor data provider. They have many consumers and refuse to alter anything for just one.
Looks like we're the only ones to use all of their data, as our client app processing speed is far greater than their sample client performance metrics. Still, we can not match our app performance with network throughoutput.
Server does not support http range requests or pagination.
There's no way to divide data in chunks to load, as there's no filtering attribute to guarantee that every chunk will be small enough.
Shortly: we can download all the data in a given time before timeout occurs, but can not process it.
Having an adapter between inputstream and outpustream, to pefrorm as a blocking queue, will help a ton.
You're using something like new ObjectInputStream(new FileInputStream(..._) and the solution for EOF could be wrapping the FileInputStream first in an WriterAwareStream which would block when hitting EOF as long a the writer is writing.
Anyway, in case latency don't matter much, I would not bother start processing before the download finished. Oftentimes, there isn't much you can do with an incomplete list of objects.
Maybe some memory-mapped-file-based queue like Chronicle-Queue may help you. It's faster than dealing with files directly and may be even simpler to use.
You could also implement a HugeBufferingInputStream internally using a queue, which reads from its input stream, and, in case it has a lot of data, it spits them out to disk. This may be a nice abstraction, completely hiding the buffering.
There's also FileBackedOutputStream in Guava, automatically switching from using memory to using a file when getting big, but I'm afraid, it's optimized for small sizes (with tens of gigabytes expected, there's no point of trying to use memory).
Are there alternative solutions?
If your consumer (the http client) is having trouble keeping up with the stream of data, you might want to look at a design where the client manages its own work in progress, pulling data from the server on demand.
RFC 7233 describes the Range Requests
devices with limited local storage might benefit from being able to request only a subset of a larger representation, such as a single page of a very large document, or the dimensions of an embedded image
HTTP Range requests on the MDN Web Docs site might be a more approachable introduction.
This is the sort of thing that queueing servers are made for. RabbitMQ, Kafka, Kinesis, any of those. Perhaps KStream would work. With everything you get from the HTTP server (given your constraint that it cannot be broken up into units of work), you could partition it into chunks of bytes of some reasonable size, maybe 1024kB. Your application would push/publish those records/messages to the topic/queue. They would all share some common series ID so you know which chunks match up, and each would need to carry an ordinal so they can be put back together in the right order; with a single Kafka partition you could probably rely upon offsets. You might publish a final record for that series with a "done" flag that would act as an EOF for whatever is consuming it. Of course, you'd send an HTTP response as soon as all the data is queued, though it may not necessarily be processed yet.
not sure if this would help in your case because you haven't mentioned what structure & format the data are coming to you in, however, i'll assume a beautifully normalised, deeply nested hierarchical xml (ie. pretty much the worst case for streaming, right? ... pega bix?)
i propose a partial solution that could allow you to sidestep the limitation of your not being able to control how your client interacts with the http data server -
deploy your own webserver, in whatever contemporary tech you please (which you do control) - your local server will sit in front of your locally cached copy of the data
periodically download the output of the webservice using a built-in http querying library, a commnd-line util such as aria2c curl wget et. al, an etl (or whatever you please) directly onto a local device-backed .xml file - this happens as often as it needs to
point your rest client to your own-hosted 127.0.0.1/modern_gigabyte_large/get... 'smart' server, instead of the old api.vendor.com/last_tested_on_megabytes/get... server
some thoughts:
you might need to refactor your data model to indicate that the xml webservice data that you and your clients are consuming was dated at the last successful run^ (ie. update this date when the next ingest process completes)
it would be theoretically possible for you to transform the underlying xml on the way through to better yield records in a streaming fashion to your webservice client (if you're not already doing this) but this would take effort - i could discuss this more if a sample of the data structure was provided
all of this work can run in parallel to your existing application, which continues on your last version of the successfully processed 'old data' until the next version 'new data' are available
^
in trade you will now need to manage a 'sliding window' of data files, where each 'result' is a specific instance of your app downloading the webservice data and storing it on disc, then successfully ingesting it into your model:
last (two?) good result(s) compressed (in my experience, gigabytes of xml packs down a helluva lot)
next pending/ provisional result while you're streaming to disc/ doing an integrity check/ ingesting data - (this becomes the current 'good' result, and the last 'good' result becomes the 'previous good' result)
if we assume that you're ingesting into a relational db, the current (and maybe previous) tables with the webservice data loaded into your app, and the next pending table
switching these around becomes a metadata operation, but now your database must store at least webservice data x2 (or x3 - whatever fits in your limitations)
... yes you don't need to do this, but you'll wish you did after something goes wrong :)
Looks like we're the only ones to use all of their data
this implies that there is some way for you to partition or limit the webservice feed - how are the other clients discriminating so as not to receive the full monty?
You can use in-memory caching techniques OR you can use Java 8 streams. Please see the following link for more info:
https://www.conductor.com/nightlight/using-java-8-streams-to-process-large-amounts-of-data/
Camel could maybe help you the regulate the network load between the REST producer and producer ?
You might for instance introduce a Camel endpoint acting as a proxy in front of the real REST endpoint, apply some throttling policy, before forwarding to the real endpoint:
from("http://localhost:8081/mywebserviceproxy")
.throttle(...)
.to("http://myserver.com:8080/myrealwebservice);
http://camel.apache.org/throttler.html
http://camel.apache.org/route-throttling-example.html
My 2 cents,
Bernard.
If you have enough memory, Maybe you can use in-memory data store like Redis.
When you get data from your Rest endpoint you can save your data into Redis list (or any other data structure which is appropriate for you).
Your consumer will consume data from the list.
I am currently using Spring as a RESTful Web Service for a mobile application I am writing. The server-side of things is pretty well completed (in terms of functionality) but now I am trying to improve its performance.
What I want to do is find an efficient way to return 304 (Not Modified) response codes from my server to the client with an implementation that is not shallow. Meaning, that I want to save both bandwidth and processing cycles.
What I figured I need to do is determine the last time an object has been modified and compare it to the if-modified-since HTTP header. The question here is, how should I get the last updated time of an object quickly (i.e. with zero-to-minimal access to the persistence layer)? Or is there a better approach to this altogether?
Note: I have been referencing this and this.
I'm not sure mongoDB support getting the last update time. I would do it manually. Check the $currentDate operator, here: http://docs.mongodb.org/manual/reference/operator/update/currentDate/
I am using 'n' number of web services in my systems. I am very well taking care. But, in recent days I am just seeing a strange behaviour while handling response of one my external systems.
Here is my problem,
When I request one of my downstream system for getting data, i am getting response with one very big xml. During parsing the response in system, the complete JAVA thread itself got struck more than configured time. So for temporary fix, we request downstream system to limit the response.
But, how this is happening? Irrespective of how big the data, the unmarshlling process should complete right.
So may i know what was the root cause of this issue ?
If you are unmarshalling then the whole XML will be converted to one object graph containing all the objects specified in the XML. So the bigger the XML the bigger the resulting object graph. Of course this takes more memory, perhaps more than your application has to its disposition, which could lead to an OutOfMemoryException.
If the XML received contains some kind of a list of items you can consider handling it item by item. You will read in one item at a time and then process it and dispose of it. You will then need only the amount of memory to fit one item's object graph in memory. But to do this you would have to rewrite your processing code to use a library like SAX.
We are using Rest using Jersey. There are few scenarios where server(WAS 8.5) sends large amount of data to client, which is RCP application. In some cases data is so huge(150MB) in xml format that client gets an OutOfMemoryError exception.
I have below questions
How much size is increased when java object is converted in xml?
How we can send large java object to client and still use rest calls?
1) Tough question to answer without seeing the XML schema, I've seen well designed schemas that result in tight, lean XML, and others that are a mess and very bloated. To test it write some test code that serializes your Java objects to a byte[] and compare it's size to the XML payload you currently produce.
2) Might be worth looking into a chunking process, 150MB is pretty large for a single payload. Also are you using GZIP compression for this already? Also may be worth looking at Fast Infoset. Basically it's a binary encoding for XML that generally helps reduce the size of an XML Document.
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Preferred Java way to ping an HTTP URL for availability
(6 answers)
Closed 9 years ago.
I'm trying to find the most efficient way to test 300,000+ URLs in a database to basically check if the URLs are still valid.
Having looked around the site I've found many excellent answers and am now using something along the lines of:
Read URL from file....
Test URL:
final URL url = new URL("http://" + address);
final HttpURLConnection urlConn = (HttpURLConnection) url.openConnection();
urlConn.setConnectTimeout(1000 * 10);
urlConn.connect();
urlConn.getResponseCode(); // Do something with the code
urlConn.disconnect();
Write details back to file....
So a couple of questions:
1) Is there a more efficient way to test URLs and get response codes?
2) Initially I am able to test about 50 URLs per minute, but after 5 or so minutes things really slow down - I imagine there is some resources I'm not releasing but am not sure what
3) Certain URLs (e.g. www.bhs.org.au) will cause the above to hang for minutes (not good when I have so many URLs to test) even with the connect timeout set, is there anyway I can tighten this up?
Thanks in advance for any help, it's been a quite a few years since I've written any code and I'm starting again from scratch :-)
By far the fastest way to do this would be to use java.nio to open a regular TCP connection to your target host on port 80. Then, simply send it a minimal HTTP request and process the result yourself.
The main advantage of this is that you can have a pool of 10 or 100 or even 1000 connections open and loading at the same time rather than having to do them one after the other. With this, for example, it won't matter much if one server (www.bhs.org.au) takes several minutes to respond. It'll simply hog one of your many connections in the pool, but others will keep running.
You could also achieve that same thing with a little more overhead but a lot less complex coding by using a Thread Pool to run many HttpURLConnections (the way you are doing it now) in parallel in multiple threads.
This may or may not help, but you might want to change your request method to HEAD instead of using the default, which is GET:
urlConn.setRequestMethod("HEAD");
This tells the server that you do not really need a response back, other than the response code.
The article What Is a HTTP HEAD Request Good for describes some uses for HEAD, including link verification:
[Head] asks for the response identical to the one that would correspond to a GET request, but without the response body. This is useful for retrieving meta-information written in response headers, without having to transport the entire content.... This can be used for example for creating a faster link verification service.