I am using Spark Structured streaming for processing the messages and I am using Java8. I am reading the message from the kafka and writing the message to the file and save the file in HDFS.
I got a requirement like I need to write a sequence number along with the message to file.
For example, if I get the first message from kafka, the output file content will be "message, 1" , for second message its "message,2" etc.. kind of count.
if the message count reaches some threshold let say "message, 999999", then I need to reset the sequence from 1 again from the next message I received.
if the spark streaming job is restarted, it should continue with the sequence where it left. So I need to save this number in HDFS kind of checkPointLocation.
What is the best approach I can use to implement this sequence. Can I use Accumulator to do that? or is there any other better to approach to implement during the distributed processing ? or is it not possible in distributed processing?
It wont be that hard.You can read each message using a map function and keep on adding the count to the messages.The count can be maintained with in your code logic.
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'm working on creating file uploader. I would like to load files first temp folder and then convert them to needed format. For this I'll create a queue with tasks that will be executed with the executor. But in case of server crash, this queue will be lost. So could anybody suggest me a library without using another server that can make my queue persistent?
Instead of using in-memory queue implementation, you can use persistent options like DB or a JMS Queue. This will avoid loosing the data even if server crashes.
You need to use DB,a and store the bytes in it. Invoke 2 threads one will only feed the data to DB and another will poll onto convert the file. You can maintain the status if the file is changed to the format you wanted, and also the format it needs to be changed in
Is there functionality built into Kafka Streams that allows for dynamically connecting a single input stream into multiple output streams? KStream.branch allows branching based on true/false predicates, but this isn't quite what I want. I'd like each incoming log to determine the topic it will be streamed to at runtime, e.g., a log {"date": "2017-01-01"} will be streamed to the topic topic-2017-01-01 and a log {"date": "2017-01-02"} will be streamed to the topic topic-2017-01-02.
I could call forEach on the stream, then write to a Kafka producer, but that doesn't seem very elegant. Is there a better way to do this within the Streams framework?
If you want to create topics dynamically based on your data, you do not get any support within Kafka's Streaming API at the moment (v0.10.2 and earlier). You will need to create a KafkaProducer and implement your dynamic "routing" by yourself (for example using KStream#foreach() or KStream#process()). Note, that you need to do synchronous writes to avoid data loss (which are not very performant unfortunately). There are plans to extend Streaming API with dynamic topic routing, but there is no concrete timeline for this feature right now.
There is one more consideration you should take into account. If you do not know your destination topic(s) ahead of time and just rely on the so-called "topic auto creation" feature, you should make sure that those topics are being created with the desired configuration settings (e.g., number of partitions or replication factor).
As an alternative to "topic auto creation" you can also use Admin Client (available since v0.10.1) to create topics with correct configuration. See https://cwiki.apache.org/confluence/display/KAFKA/KIP-4+-+Command+line+and+centralized+administrative+operations
I have to setup camel to process data files where the first line of the file is the metadata and then it follows with millions of lines of actual data. The metadata dictates how the data is to be processed. What I am looking for is something like this:
Read first line (metadata) and populate a bean (with metadata) --> 2. then send data 1000 lines at a time to the data processor which will refer to the bean in step # 1
Is it possible in Apache Camel?
Yes.
An example architecture might look something like this:
You could setup a simple queue that could be populated with file names (or whatever identifier you are using to locate each individual file).
From the queue, you could route through a message translator bean, whose sole is to translate a request for a filename into a POJO that contains the metadata from the first line of the file.
(You have a few options here)
Your approach to processing the 1000 line sets will depend on whether or not the output or resulting data created from the 1000 lines sets needs to be recomposed into a single message and processed again later. If so, you could implement a composed message processor made up of a message producer/consumer, a message aggregator and a router. The message producer/consumer would receive the POJO with the metadata created in step2 and enqueue as many new requests are necessary to process all of the lines in the file. The router would route from this queue through your processing pipeline and into the message aggregator. Once aggregated, a single unified message with all of your important data will be available for you to do what you will.
If instead each 1000 line set can be processed independently and rejoining is not required, than it is not necessary to agggregate the messages. Instead, you can use a router to route from step 2 to a producer/consumer that will, like above, enquene the necessary number of new requests for each file. Finally, the router will route from this final queue to a consumer that will do the processing.
Since you have a large quantity of data to deal with, it will likely be difficult to pass around 1000 line groups of data through messages, especially if they are being placed in a queue (you don't want to run out of memory). I recommend passing around some type of indicator that can be used to identify which line of the file a specific request was for, and then parse the 1000 lines when you need them. You could do this in a number of ways, like by calculating the number of bytes deep into a file a specific line is, and then using a file reader's skip() method to jump to that line when the request hits the bean that will be processing it.
Here are some resources provided on the Apache Camel website that describe the enterprise integration patterns that I mentioned above:
http://camel.apache.org/message-translator.html
http://camel.apache.org/composed-message-processor.html
http://camel.apache.org/pipes-and-filters.html
http://camel.apache.org/eip.html