I recently found that a topic i've been using is multi-partition rather than single partition. I need to reconfigure my consumer class to handle the multiple partitions, but i'm a touch confused. I am currently using an offset group, let's call it test_offset_group for sake of the below example. Under normal circumstances, I will always be parsing linearly and continuing forward in time; as messages get added to the topic I will parse them and move on, but in the event of a crash or the need to go back and re-run the feed for the previous day, I need to be able to seek by timestamp. Kafka is mandatory in this project so I have no ability to change the type of streaming data service i'm using.
I configure my consumer like this:
test_consumer = KafkaConsumer("test_topic", bootstrap_servers="bootstrap_string", enable_auto_commit=False, group_id="test_offset_group"
In the event I need to seek to a timestamp, i'll provide a timestamp and then seek using the following method:
test_consumer.poll()
tp = TopicPartition("test_topic", 0)
needed_date = datetime.timestamp(timestamp)
rec_in = test_consumer.offsets_for_times({tp: needed_date * 1000})
test_consumer.seek(tp, rec_in[tp].offset)
The above functions perfectly for a single partition consumer, but this feels very clunky and difficult when you consider numerous partitions. I guess I could fetch the number of partitions using
test_consumer.partitions_for_topic('test_topic")
and then iterate over each of them, but again, that seems like i'm going against the grain of Kafka and I feel like there should be an easier way to do this.
In summary: I'd like to understand how to seek to a number of offsets with multiple partitions utilizing the offset_group functionality and i'd like to confirm that, by conducting the above operation, I am effectively ignoring all partitions aside from 0?
You are doing the right logic, you just need to perform it on all partitions asigned to this consumer instance.
You can retrieve the current assignment using assignment().
I'm still quite new to the world of stream and batch processing and trying to understnad concepts and speach. It is admitedly very possible that the answer to my question well known, easy to find or even answered a hundred times here at SO, but I was not able to find it.
The background:
I am working in a big scientific project (nuclear fusion research), and we are producing tons of measurement data during experiment runs. Those data are mostly streams of samples tagged with a nanosecond timestamp, where samples can be anything from a single by ADC value, via an array of such, via deeply structured data (with up to hundreds of entries from 1 bit booleans to 64bit double precision floats) to raw HD video frames or even string text messages. If I understand the common terminologies right, I would regard our data as "tabular data", for the most part.
We are working with mostly selfmade software solutions from data acquisition over simple online (streaming) analysis (like scaling, subsampling and such) to our own data sotrage, management and access facilities.
In view of the scale of the operation and the effort for maintaining all those implementations, we are investigating the possibilities to use standard frameworks and tools for more of our tasks.
My question:
In particular at this stage, we are facing the need for more and more sofisticated (automated and manual) data analytics on live/online/realtime data as well as "after the fact" offline/batch analytics of "historic" data. In this endavor, I am trying to understand if and how existing analytics frameworks like Spark, Flink, Storm etc. (possibly supported by message queues like Kafka, Pulsar,...) can support a scenario, where
data is flowing/streamed into the platform/framework, attached an identifier like a URL or an ID or such
the platform interacts with integrated or external storage to persist the streaming data (for years), associated with the identifier
analytics processes can now transparently query/analyse data addressed by an identifier and an arbitrary (open or closed) time window, and the framework suplies data batches/samples for the analysis either from backend storage or coming in live from data acquisition
Simply streaming the online data into storage and querying from there seems no option as we need both raw and analysed data for live monitoring and realtime feedback control of the experiment.
Also, letting the user query either a live input signal or a historic batch from storage differently would not be ideal, as our physicists mostly are no data scientists and we would like to keep such "technicalities" away from them and idealy the exact same algorithms should be used for analysing new real time data and old stored data from previous experiments.
Sitenotes:
we are talking about peek data loads in the range of 10th of gigabits per second coming in bursts of increasing length of seconds up to minutes - could this be handled by the candidates?
we are using timestamps in nanosecond resolution, even thinking about pico - this poses some limitations on the list of possible candidates if I unserstand correctly?
I would be very greatfull if anyone would be able to understand my question and to shed some light on the topic for me :-)
Many Thanks and kind regards,
Beppo
I don't think anyone can say "yes, framework X can definitely handle your workload", because it depends a lot on what you need out of your message processing, e.g. regarding messaging reliability, and how your data streams can be partitioned.
You may be interested in BenchmarkingDistributedStreamProcessingEngines. The paper is using versions of Storm/Flink/Spark that are a few years old (looks like they were released in 2016), but maybe the authors would be willing to let you use their benchmark to evaluate newer versions of the three frameworks?
A very common setup for streaming analytics is to go data source -> Kafka/Pulsar -> analytics framework -> long term data store. This decouples processing from data ingest, and lets you do stuff like reprocessing historical data as if it were new.
I think the first step for you should be to see if you can get the data volume you need through Kafka/Pulsar. Either generate a test set manually, or grab some data you think could be representative from your production environment, and see if you can put it through Kafka/Pulsar at the throughput/latency you need.
Remember to consider partitioning of your data. If some of your data streams could be processed independently (i.e. ordering doesn't matter), you should not be putting them in the same partitions. For example, there is probably no reason to mix sensor measurements and the video feed streams. If you can separate your data into independent streams, you are less likely to run into bottlenecks both in Kafka/Pulsar and the analytics framework. Separate data streams would also allow you to parallelize processing in the analytics framework much better, as you could run e.g. video feed and sensor processing on different machines.
Once you know whether you can get enough throughput through Kafka/Pulsar, you should write a small example for each of the 3 frameworks. To start, I would just receive and drop the data from Kafka/Pulsar, which should let you know early whether there's a bottleneck in the Kafka/Pulsar -> analytics path. After that, you can extend the example to do something interesting with the example data, e.g. do a bit of processing like what you might want to do in production.
You also need to consider which kinds of processing guarantees you need for your data streams. Generally you will pay a performance penalty for guaranteeing at-least-once or exactly-once processing. For some types of data (e.g. the video feed), it might be okay to occasionally lose messages. Once you decide on a needed guarantee, you can configure the analytics frameworks appropriately (e.g. disable acking in Storm), and try benchmarking on your test data.
Just to answer some of your questions more explicitly:
The live data analysis/monitoring use case sounds like it fits the Storm/Flink systems fairly well. Hooking it up to Kafka/Pulsar directly, and then doing whatever analytics you need sounds like it could work for you.
Reprocessing of historical data is going to depend on what kind of queries you need to do. If you simply need a time interval + id, you can likely do that with Kafka plus a filter or appropriate partitioning. Kafka lets you start processing at a specific timestamp, and if you data is partitioned by id or you filter it as the first step in your analytics, you could start at the provided timestamp and stop processing when you hit a message outside the time window. This only applies if the timestamp you're interested in is when the message was added to Kafka though. I also don't believe Kafka supports below-millisecond resolution on the timestamps it generates.
If you need to do more advanced queries (e.g. you need to look at timestamps generated by your sensors), you could look at using Cassandra or Elasticsearch or Solr as your permanent data store. You will also want to investigate how to get the data from those systems back into your analytics system. For example, I believe Spark ships with a connector for reading from Elasticsearch, while Elasticsearch provides a connector for Storm. You should check whether such a connector exists for your data store/analytics system combination, or be willing to write your own.
Edit: Elaborating to answer your comment.
I was not aware that Kafka or Pulsar supported timestamps specified by the user, but sure enough, they both do. I don't see that Pulsar supports sub-millisecond timestamps though?
The idea you describe can definitely be supported by Kafka.
What you need is the ability to start a Kafka/Pulsar client at a specific timestamp, and read forward. Pulsar doesn't seem to support this yet, but Kafka does.
You need to guarantee that when you write data into a partition, they arrive in order of timestamp. This means that you are not allowed to e.g. write first message 1 with timestamp 10, and then message 2 with timestamp 5.
If you can make sure you write messages in order to Kafka, the example you describe will work. Then you can say "Start at timestamp 'last night at midnight'", and Kafka will start there. As live data comes in, it will receive it and add it to the end of its log. When the consumer/analytics framework has read all the data from last midnight to current time, it will start waiting for new (live) data to arrive, and process it as it comes in. You can then write custom code in your analytics framework to make sure it stops processing when it reaches the first message with timestamp 'tomorrow night'.
With regard to support of sub-millisecond timestamps, I don't think Kafka or Pulsar will support it out of the box, but you can work around it reasonably easily. Just put the sub-millisecond timestamp in the message as a custom field. When you want to start at e.g. timestamp 9ms 10ns, you ask Kafka to start at 9ms, and use a filter in the analytics framework to drop all messages between 9ms and 9ms 10ns.
Allow me to add the following suggestions on how Apache Pulsar might help address some of your requirements. Food for thought as it were.
"data is flowing/streamed into the platform/framework, attached an identifier like a URL or an ID or such"
You might want to look at Pulsar Functions, which allows you to write simple functions (In Java or Python) that gets executed on each individual message that is published to a topic. They are ideal for this type of data augmentation use case.
the platform interacts with integrated or external storage to persist the streaming data (for years), associated with the identifier
Pulsar has recently added tiered-storage, that allows you to retain event streams in S3, Azure Blob Store, or Google Cloud storage. This would allow you to keep the data for years in a cheap and reliable data store
analytics processes can now transparently query/analyse data addressed by an identifier and an arbitrary (open or closed) time window, and the framework suplies data batches/samples for the analysis either from backend storage or coming in live from data acquisition
Apache Pulsar has also added integration with the Presto query engine, which would allow you to query the data over a given time period (including data from tiered-storage) and place it into a topic for processing.
Not sure the title is well suited to what I'm trying to achieve, so bear with me.
I'll start with defining my use case:
Many(say millions) IoT devices are sending data to my Spark stream. These devices are sending the current temperature level every 10 seconds.
The owner of all of these IoT devices has the ability to define a preset rules, for example: if temperature > 50 then do something.
I'm trying to figure out if I can output how many of these devices have met this if > 50 criteria in some time period. The catch is that the rules are defined in real time and should be applied to the Spark job at real time.
How would I do that. Is Spark the right tool for the job?
Many thanks
Is Spark the right tool for the job?
I think so.
the rules are defined in real time and should be applied to the Spark job at real time.
Let's assume the rules are in a database so every batch interval Spark would fetch them and apply one by one. They could also be in a file or any other storage. That's just orthogonal to the main requirement.
How would I do that?
The batch interval would be "some time period". I assume that the payload would have deviceId and temperature. With that you can just use regular filter over temperature and get deviceId back. You don't need stateful pipeline for this unless you want to accumulate data over time that is longer than your batch interval.
I am trying to get some change event from Kafka that I would like to propagate downstream in another system. However the Change order matters. Hence I wonder what is the appropriate way to do that with some Spark transformation in the middle.
The only thing I see is to loose the parallelism and make the DStream on one partition. Maybe there is a way to do operation in parallel and bring everything back in one partition and then send it to the external system or back in Kafka and then use a Kafka Sink for the matter.
What approach can I try?
In a distributed environment, with some form of cashing/buffering at most layer, message generated from same machine may reach back-end in different order. Also the definition of order is subjective. Implementing a global definition of order will be restrictive (may not be correct) for the data as a whole.
So, Kafka is meant for keeping the data in order in the order of put but partition comes as a catch!!! Partition defines the level of parallelism per topic.
Typically, the level of abstraction at which kafka is kept, it should not bother much about order. It should be optimised for maximum throughput, where partitioning will come handy!!! Consider ordering just a side effect of supporting streaming!!!
Now, what ever logic ensures, that data is put in to kafka in order, that makes more sense in your application (spark job).
I would like to understand if the following would be a correct use case for Spark.
Requests to an application are received either on a message queue, or in a file which contains a batch of requests. For the message queue, there are currently about 100 requests per second, although this could increase. Some files just contain a few requests, but more often there are hundreds or even many thousands.
Processing for each request includes filtering of requests, validation, looking up reference data, and calculations. Some calculations reference a Rules engine. Once these are completed, a new message is sent to a downstream system.
We would like to use Spark to distribute the processing across multiple nodes to gain scalability, resilience and performance.
I am envisaging that it would work like this:
Load a batch of requests into Spark as as RDD (requests received on the message queue might use Spark Streaming).
Separate Scala functions would be written for filtering, validation, reference data lookup and data calculation.
The first function would be passed to the RDD, and would return a new RDD.
The next function would then be run against the RDD output by the previous function.
Once all functions have completed, a for loop comprehension would be run against the final RDD to send each modified request to a downstream system.
Does the above sound correct, or would this not be the right way to use Spark?
Thanks
We have done something similar working on a small IOT project. we tested receiving and processing around 50K mqtt messages per second on 3 nodes and it was a breeze. Our processing included parsing of each JSON message, some manipulation of the object created and saving of all the records to a time series database.
We defined the batch time for 1 second, the processing time was around 300ms and RAM ~100sKB.
A few concerns with streaming. Make sure your downstream system is asynchronous so you wont get into memory issue. Its True that spark supports back pressure, but you will need to make it happen. another thing, try to keep the state to minimal. more specifically, your should not keep any state that grows linearly as your input grows. this is extremely important for your system scalability.
what impressed me the most is how easy you can scale with spark. with each node we added we grew linearly in the frequency of messages we could handle.
I hope this helps a little.
Good luck