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How to handle large http JSON response body [duplicate]

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Best way to process a huge HTTP JSON response
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Closed 1 year ago.
There are REST-API that return large JSON data
example result:
{
"arrayA":[
{
"data1":"data",
"data2":"data"
},..
],
"arrayB":[
{
"data1":"data"
},..
]
}
"arrayA" possible record just 0 to 100 records but "arrayB" can be possible 1 million to 10 million record
it make my java application out of memory.
My question is how to handle this case.

There are different concerns here and IMO the question is too broad because the best solution may depend on the actual use case.
You say, you have a REST API and you would like to “protect” the server from Out Of Memory Error, I get that.
However, assuming you’ll find the way to fix the OOM error on server, what kind of client will want to view tens of millions objects at once? If its a browser, is it really required? How long will take the JSON processing on the client side? Won’t the client side of application become too slow and the clients will start to complain? I’m sure you’ve got the point.
So the first way is to “re-think” why do you need such a big response. In this case, probably the best solution is refactoring and changing the logic of the client-server communication
Now, another possible case is that you have an “integration” - some kind of server to server communication.
In this case there is no point in adding the whole json response at once or even doing streaming. If you’re running in the cloud for example, you might want to add this huge json string to some file and upload to S3, for example and then provide a link to it (because S3 can deal with files like this). Of course there are other alternatives in non AWS environment.
As a “stripped-down” idea you might get the Request, create the temp file on the file system, write the data to it in chunks and then return the “FileResource” to the client. Working chunk-by-chunk will ensure that the memory consumption is low on your java application’s side. Basically it would be equal to downloading the file that gets generated dynamically. When you close the stream you might want to remove the file.
This would work best if you have some kind of “get heap dump” or any data dump in general functionality.

Ways to buffer REST response

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.

JavaMail API - Reading a large outlook mailbox (>3000) for message content

I have a requirement to read a mailbox having more than 3000 mails. I need to read these mails, fetch their mail contents and feed the body into another api. Its easy to do with a few mails (for me it was approx 250), but after that it slowed down significantly. Is the accepted answer in this link
the only choice, or is there any other alternative way.
NOTE: I have purposely not pasted any snippet,as I have used the straight forward approach, and yes I did use FetchProfile too.

JavaMail IMAP performance is usually controlled by the speed of the server, the number of network round trips required, and the ammount of data being read. Using a FetchProfile is essential to reducing the number of round trips. Don't forget to consider the IMAP-specific FetchProfile items.
JavaMail will fetch message contents a buffer at a time. Large messages will obviously require many buffer fetches, and thus many round trips. You can change the size of the buffer (default 16K) by setting the mail.imap.fetchsize property. Or you can disable these partial fetches and require it to fetch the entire contents in one operation by setting the mail.imap.partialfetch property to false. Obviously the latter will require significant memory on the client if large messages are being read.
The JavaMail IMAP provider does not (usually; see below) cache message contents on the client, but it does cache message headers. When processing a very large number of messages it is sometimes helpful to invalidate the cache of headers when done processing a message by calling the IMAPMessage.invalidateHeaders method. When using IMAPFolder.FetchProfileItem.MESSAGE, the message contents are cache, and will also be invalidated by the above call.
Beyond that, you should examine the JavaMail debug output to ensure only the expected IMAP commands are being issued and that you're not doing something in your program that would cause it to issue unnecessary IMAP commands. You can also look at time-stamps for the protocol commands to determine whether the time is being spent on the server or the client.
Only after all of that has failed to yield acceptable performance, and you're sure the performance problems are not on the server (which you can't fix), would you need to look into custom IMAP commands as suggested in the link you referred to.

Is there a way to check post request parameter size at runtime

I am struggling with the maxPostSize parameter in tomcat server.xml.
I increased it from the default 2 MByte to 6 mg to solve some problem that we had, and after one week the problem appears again and I increased the size to 10 MByte. I am not sure if it is a good idea to use the unlimited size.
I am trying to find a way to check this parameter size in run time, for specific post requests.( to find out the source of the problem). is there any way to check this param size at run time?

You can create a Filter, check if its a Post request, iterate the request parameters and summarize their sizes. But maybe you should try to change your application to not to send so much data in a single request.

You could use HttpServletRequest#getHeader to obtain content-length header which value should be size of the request. It's not exactly what you want but it's the easiest thing you could do for debugging purposes.

Rate Limit Exceeded - Custom Twitter app

I am working with a java Twitter app (using Twitter4J api). I have created the app and can view the current users timeline, user's profiles, etc..
However, when using the app it seems to quite quickly exceed the 150 requests an hour rate limit set on Twitter clients (i know developers can increase this to 350 on given accounts, but that would not resolve for other users).
Surely this is not affecting all clients, any ideas as to how to get around this?
Does anyone know what counts as a request? For example, when i view a user's profile, i load the User object (twitter4j) and then get the screenname, username, user description, user status, etc to put into a JSON object - would this be a single call to get the object or would it several to include all the user.get... calls?
Thanks in advance

You really do need to keep track what your current request count is when dealing with Twitter.
However, twitter does not seem to drop the count for 304 Not Modified (at least it didn't the last time I dealt with it), so make sure there isn't something breaking your normal use of HTTP caching, and your practical request per hour goes up.
Note that twitter suffers from a bug in mod_gzip on apache where the e-tag is mal-formed in changing it to reflect that the content-encoding is different to that of the non-gzipped entity (this is the Right Thing to do, there's just a bug in the implementation). Because of this, accepting gzipped content from twitter means it'll never send a 304, which increases your request count, and in many cases undermines the efficiency gains of using gzip.
Hence, if you are accepting gzip (your web-library may do so by default, see what you can see with a tool like Fiddler, I'm a .NET guy with only a little Java knowledge, answering at the level of how twitter deals with HTTP so I don't know the details of Java web libraries), try turning that off, and see if it improve things.

Almost every type of read from Twitter's servers (i.e. anything that calls HTTP GET) counts as a request. Getting user timelines, retweets, direct messages, getting user data all count as 1 request each. Pretty much the only Twitter API call that reads from the server without counting against your API limit is checking to see the rate limit status.