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).
Related
As you know, Kappa architecture is some kind of simplification of Lambda architecture. Kappa doesn't need batch layer, instead speed layer have to guarantee computation precision and enough throughput (more parallelism/resources) on historical data re-computation.
Still Kappa architecture requires two serving layers in case when you need to do analytic based on historical data. For example, data that have age < 2 weeks are stored at Redis (streaming serving layer), while all older data are stored somewhere at HBase (batch serving layer).
When (due to Kappa architecture) I have to insert data to batch serving layer?
If streaming layer inserts data immidiately to both batch & stream serving layers - than how about late data arrival? Or streaming layer should backup speed serving layer to batch serving layer on regular basis?
Example: let say source of data is Kafka, data are processed by Spark Structured Streaming or Flink, sinks are Redis and HBase. When write to Redis & HBase should happen?
If we perform stream processing, we want to make sure that output data is firstly made available as a data stream. In your example that means we write to Kafka as a primary sink.
Now you have two options:
have secondary jobs that reads from that Kafka topic and writes to Redis and HBase. That is the Kafka way, in that Kafka Streams does not support writing directly to any of these systems and you set up a Kafka connect job. These secondary jobs can then be tailored to the specific sinks, but they add additional operations overhead. (That's a bit of the backup option that you mentioned).
with Spark and Flink you also have the option to have secondary sinks directly in your job. You may add additional processing steps to transform the Kafka output into a more suitable form for the sink, but you are more limited when configuring the job. For example in Flink, you need to use the same checkpointing settings for the Kafka sink and the Redis/HBase sink. Nevertheless, if the settings work out, you just need to run one streaming job instead of 2 or 3.
Late events
Now the question is what to do with late data. The best solution is to let the framework handle that through watermarks. That is, data is only committed at all sinks, when the framework is sure that no late data arrives. If that doesn't work out because you really need to process late events even if they arrive much, much later and still want to have temporary results, you have to use update events.
Update events
(as requested by the OP, I will add more details to the update events)
In Kafka Streams, elements are emitted through a continuous refinement mechanism by default. That means, windowed aggregations emit results as soon as they have any valid data point and update that result while receiving new data. Thus, any late event is processed and yield an updated result. While this approach nicely lowers the burden to users, as they do not need to understand watermarks, it has some severe short-comings that led the Kafka Streams developers to add Suppression in 2.1 and onward.
The main issue is that it poses quite big challenges to downward users to process intermediate results as also explained in the article about Suppression. If it's not obvious if a result is temporary or "final" (in the sense that all expected events have been processed) then many applications are much harder to implement. In particular, windowing operations need to be replicated on consumer side to get the "final" value.
Another issue is that the data volume is blown up. If you'd have a strong aggregation factor, using watermark-based emission will reduce your data volume heavily after the first operation. However, continuous refinement will add a constant volume factor as each record triggers a new (intermediate) record for all intermediate steps.
Lastly, and particularly interesting for you is how to offload data to external systems if you have update events. Ideally, you would offload the data with some time lag continuously or periodically. That approach simulates the watermark-based emission again on consumer side.
Mixing the options
It's possible to use watermarks for the initial emission and then use update events for late events. The volume is then reduced for all "on-time" events. For example, Flink offers allowed lateness to make windows trigger again for late events.
This setup makes offloading data much easier as data only needs to be re-emitted to the external systems if a late event actually happened. The system should be tweaked that a late event is a rare case though.
How hazelcast-jet achieves anything vastly different from what was earlier achievable by submitting EntryProcessors on keys in an IMap?
Curious to know.
Quoting the InfoQ article on Jet:
Sending a runnable to a partition is analogous to the work of a single DAG vertex. The advantage of Jet comes from the ability to have the vertex transform the data it reads, producing items which no longer belong to the same partition, then reshuffle them while sending to the downstream vertex so they are again correctly partitioned. This is essential for any kind of map-reduce operation where the reducing unit must observe all the data items with the same key. To minimize network traffic, Jet can first reduce the data slice produced on the local member, then send only one item per key to the remote member that combines the partial results.
And note that this is just an advantage in the context of the same or similar use cases currently covered by entry processors. Jet can take data from any source and make use of the whole cluster's computational resources to process it.
I'm new to Spark streaming and have following situation:
Multiple (health) devices send their data to my service, every event has at least following data inside (userId, timestamp, pulse, bloodPressure).
In the DB I have per user a threshold for pulse and bloodPressure.
Use Case:
I would like to make a sliding window with Spark streaming which calculates the average per user for pulse and bloodpressure, let's say within 10 min.
After 10 min I would like to check in the DB if the values exceed the threshold per user and execute an action, e.g. call a rest service to send an alarm.
Could somebody tell me if this is generally possible with Spark, and if yes, point me in the right direction?
This is definitely possible. It's not necessarily the best tool to do so though. It depends on the volume of input you expect. If you have hundreds of thousands devices sending one event every second, maybe Spark could be justified. Anyway it's not up to me to validate your architectural choices but keep in mind that resorting to Spark for these use cases make sense only if the volume of data cannot be handled by a single machine.
Also, if the latency of the alert is important and a second or two make a difference, Spark is not the best tool. A processor on a single machine can achieve lower latencies. Otherwise use something more streaming-oriented, like Apache Flink.
As a general advice, if you want to do it in Spark, you just need to create a source (I don't know where your data come from), load the thresholds in a broadcast variable (assuming they are constant over time) and write the logic. To make the rest call, use forEachRdd as the output sink and implement the call logic there.
We have been using spark streaming with kafka for a while and until now we were using the createStream method from KafkaUtils.
We just started exploring the createDirectStream and like it for two reasons:
1) Better/easier "exactly once" semantics
2) Better correlation of kafka topic partition to rdd partitions
I did notice that the createDirectStream is marked as experimental. The question I have is (sorry if this in not very specific):
Should we explore the createDirectStream method if exactly once is very important to us? Will be awesome if you guys can share your experience with it. Are we running the risk of having to deal with other issues such as reliability etc?
There is a great, extensive blog post by the creator of the direct approach (Cody) here.
In general, reading the Kafka delivery semantics section, the last part says:
So effectively Kafka guarantees at-least-once delivery by default and
allows the user to implement at most once delivery by disabling
retries on the producer and committing its offset prior to processing
a batch of messages. Exactly-once delivery requires co-operation with
the destination storage system but Kafka provides the offset which
makes implementing this straight-forward.
This basically means "we give you at least once out of the box, if you want exactly once, that's on you". Further, the blog post talks about the guarantee of "exactly once" semantics you get from Spark with both approaches (direct and receiver based, emphasis mine):
Second, understand that Spark does not guarantee exactly-once
semantics for output actions. When the Spark streaming guide talks
about exactly-once, it’s only referring to a given item in an RDD
being included in a calculated value once, in a purely functional
sense. Any side-effecting output operations (i.e. anything you do in
foreachRDD to save the result) may be repeated, because any stage of
the process might fail and be retried.
Also, this is what the Spark documentation says about receiver based processing:
The first approach (Receiver based) uses Kafka’s high level API to store consumed
offsets in Zookeeper. This is traditionally the way to consume data
from Kafka. While this approach (in combination with write ahead logs)
can ensure zero data loss (i.e. at-least once semantics), there is a
small chance some records may get consumed twice under some failures.
This basically means that if you're using the Receiver based stream with Spark you may still have duplicated data in case the output transformation fails, it is at least once.
In my project I use the direct stream approach, where the delivery semantics depend on how you handle them. This means that if you want to ensure exactly once semantics, you can store the offsets along with the data in a transaction like fashion, if one fails the other fails as well.
I recommend reading the blog post (link above) and the Delivery Semantics in the Kafka documentation page. To conclude, I definitely recommend you look into the direct stream approach.
I want to run a Spark job, where each RDD is responsible for sending certain traffic over a network connection. The return value from each RDD is not very important, but I could perhaps ask them to return the number of messages sent. The important part is the network traffic, which is basically a side effect for running a function over each RDD.
Is it a good idea to perform the above task in Spark?
I'm trying to simulate network traffic from multiple sources to test the data collection infrastructure on the receiving end. I could instead manually setup multiple machines to run the sender, but I thought it'd be nice if I could take advantage of Spark's existing distributed framework.
However, it seems like Spark is designed for programs to "compute" and then "return" something, not for programs to run for their side effects. I'm not sure if this is a good idea, and would appreciate input from others.
To be clear, I'm thinking of something like the following
IDs = sc.parallelize(range(0, n))
def f(x):
for i in range(0,100):
message = make_message(x, i)
SEND_OVER_NETWORK(message)
return (x, 100)
IDsOne = IDs.map(f)
counts = IDsOne.reduceByKey(add)
for (ID, count) in counts.collect():
print ("%i ran %i times" % (ID, count))
Generally speaking it doesn't make sense:
Spark is a heavyweight framework. At its core there is this huge machinery which ensures that data is properly distributed, collected, recovery is possible and so on. It has a significant impact on overall performance and latency but doesn't provide any benefits in case of side-effects-only tasks
Spark concurrency has a relatively low granularity with partition being the main unit of concurrency. At this level processing becomes synchronous. You cannot move on to the next partition before you finish the current one.
Lets say in your case there is a single slow SEND_OVER_NETWORK. If you use map you pretty much block processing on a whole partition. You can go at the lower level with mapPartitions, make SEND_OVER_NETWORK asynchronous, and return only when a whole partition has been processed. It is better but still suboptimal.
You can increase number of partitions, but it means higher bookkeeping overhead so at the end of the day you can make situation worse not better.
Spark API is designed mostly for side effects free operations. It makes it hard to express operations which doesn't fit into this model.
What is arguably more important is that Spark guarantees only that each operation is executed at-least-once (lets ignore zero-times if rdd is never materialized). If application requires for example exactly-once semantics things become tricky especially when you consider point 2.
It is possible to keep track of local state for each partition outside the main Spark logic but if you get there it is a really good sign that Spark is not the right tool.