The only thing which doesn't make much sense in Spark Streaming (and I am not saying it is done better in Storm) is the iterative and "redundant" shipping of the essentially the same tasks (closures/lambdas/functions) to the cluster nodes AND re-launching them there again and again
This is a legacy from Spark Batch where such approach DOES make sense So to recap - in Spark Streaming, the driver keeps serializing and transmitting the same Tasks (comprising a Job) for every new DStream RDD, which then get re-launched ie new JVM Threads launched within each Executor (JVM) and then the tasks report their final execution status to the driver (only the last has real functional purpose) An optimization (provided Spark Streaming was implemented from scratch) could be to launch the Stages/Tasks of a Streaming Job as constantly running Threads (Demons/Agents) within the Executors and leave the DStream RDD "stream" through them as only the final execution status for each DSTream RDD and some periodical heartbeats (of the Agents) are reported to the driver Essentially this gives you are Pipeline Architecture (of stringed Agents) which is a well known Parallel Programming Patterns especially suitable for streaming data From: Matei Zaharia [mailto:matei.zaha...@gmail.com] Sent: Wednesday, June 17, 2015 7:14 PM To: Enno Shioji Cc: Ashish Soni; ayan guha; Sabarish Sasidharan; Spark Enthusiast; Will Briggs; user; Sateesh Kavuri Subject: Re: Spark or Storm This documentation is only for writes to an external system, but all the counting you do within your streaming app (e.g. if you use reduceByKeyAndWindow to keep track of a running count) is exactly-once. When you write to a storage system, no matter which streaming framework you use, you'll have to make sure the writes are idempotent, because the storage system can't know whether you meant to write the same data again or not. But the place where Spark Streaming helps over Storm, etc is for tracking state within your computation. Without that facility, you'd not only have to make sure that writes are idempotent, but you'd have to make sure that updates to your own internal state (e.g. reduceByKeyAndWindow) are exactly-once too. Matei On Jun 17, 2015, at 8:26 AM, Enno Shioji <eshi...@gmail.com> wrote: The thing is, even with that improvement, you still have to make updates idempotent or transactional yourself. If you read http://spark.apache.org/docs/latest/streaming-programming-guide.html#fault-t olerance-semantics that refers to the latest version, it says: Semantics of output operations Output operations (like foreachRDD) have at-least once semantics, that is, the transformed data may get written to an external entity more than once in the event of a worker failure. While this is acceptable for saving to file systems using the saveAs***Files operations (as the file will simply get overwritten with the same data), additional effort may be necessary to achieve exactly-once semantics. There are two approaches. . Idempotent updates: Multiple attempts always write the same data. For example, saveAs***Files always writes the same data to the generated files. . Transactional updates: All updates are made transactionally so that updates are made exactly once atomically. One way to do this would be the following. o Use the batch time (available in foreachRDD) and the partition index of the transformed RDD to create an identifier. This identifier uniquely identifies a blob data in the streaming application. o Update external system with this blob transactionally (that is, exactly once, atomically) using the identifier. That is, if the identifier is not already committed, commit the partition data and the identifier atomically. Else if this was already committed, skip the update. So either you make the update idempotent, or you have to make it transactional yourself, and the suggested mechanism is very similar to what Storm does. On Wed, Jun 17, 2015 at 3:51 PM, Ashish Soni <asoni.le...@gmail.com> wrote: @Enno As per the latest version and documentation Spark Streaming does offer exactly once semantics using improved kafka integration , Not i have not tested yet. Any feedback will be helpful if anyone is tried the same. http://koeninger.github.io/kafka-exactly-once/#7 https://databricks.com/blog/2015/03/30/improvements-to-kafka-integration-of- spark-streaming.html On Wed, Jun 17, 2015 at 10:33 AM, Enno Shioji <eshi...@gmail.com> wrote: AFAIK KCL is *supposed* to provide fault tolerance and load balancing (plus additionally, elastic scaling unlike Storm), Kinesis providing the coordination. My understanding is that it's like a naked Storm worker process that can consequently only do map. I haven't really used it tho, so can't really comment how it compares to Spark/Storm. Maybe somebody else will be able to comment. On Wed, Jun 17, 2015 at 3:13 PM, ayan guha <guha.a...@gmail.com> wrote: Thanks for this. It's kcl based kinesis application. But because its just a Java application we are thinking to use spark on EMR or storm for fault tolerance and load balancing. Is it a correct approach? On 17 Jun 2015 23:07, "Enno Shioji" <eshi...@gmail.com> wrote: Hi Ayan, Admittedly I haven't done much with Kinesis, but if I'm not mistaken you should be able to use their "processor" interface for that. In this example, it's incrementing a counter: https://github.com/awslabs/amazon-kinesis-data-visualization-sample/blob/mas ter/src/main/java/com/amazonaws/services/kinesis/samples/datavis/kcl/Countin gRecordProcessor.java Instead of incrementing a counter, you could do your transformation and send it to HBase. On Wed, Jun 17, 2015 at 1:40 PM, ayan guha <guha.a...@gmail.com> wrote: Great discussion!! One qs about some comment: Also, you can do some processing with Kinesis. If all you need to do is straight forward transformation and you are reading from Kinesis to begin with, it might be an easier option to just do the transformation in Kinesis - Do you mean KCL application? Or some kind of processing withinKineis? Can you kindly share a link? I would definitely pursue this route as our transformations are really simple. Best On Wed, Jun 17, 2015 at 10:26 PM, Ashish Soni <asoni.le...@gmail.com> wrote: My Use case is below We are going to receive lot of event as stream ( basically Kafka Stream ) and then we need to process and compute Consider you have a phone contract with ATT and every call / sms / data useage you do is an event and then it needs to calculate your bill on real time basis so when you login to your account you can see all those variable as how much you used and how much is left and what is your bill till date ,Also there are different rules which need to be considered when you calculate the total bill one simple rule will be 0-500 min it is free but above it is $1 a min. How do i maintain a shared state ( total amount , total min , total data etc ) so that i know how much i accumulated at any given point as events for same phone can go to any node / executor. Can some one please tell me how can i achieve this is spark as in storm i can have a bolt which can do this ? Thanks, On Wed, Jun 17, 2015 at 4:52 AM, Enno Shioji <eshi...@gmail.com> wrote: I guess both. In terms of syntax, I was comparing it with Trident. If you are joining, Spark Streaming actually does offer windowed join out of the box. We couldn't use this though as our event stream can grow "out-of-sync", so we had to implement something on top of Storm. If your event streams don't become out of sync, you may find the built-in join in Spark Streaming useful. Storm also has a join keyword but its semantics are different. > Also, what do you mean by "No Back Pressure" ? So when a topology is overloaded, Storm is designed so that it will stop reading from the source. Spark on the other hand, will keep reading from the source and spilling it internally. This maybe fine, in fairness, but it does mean you have to worry about the persistent store usage in the processing cluster, whereas with Storm you don't have to worry because the messages just remain in the data store. Spark came up with the idea of rate limiting, but I don't feel this is as nice as back pressure because it's very difficult to tune it such that you don't cap the cluster's processing power but yet so that it will prevent the persistent storage to get used up. On Wed, Jun 17, 2015 at 9:33 AM, Spark Enthusiast <sparkenthusi...@yahoo.in> wrote: When you say Storm, did you mean Storm with Trident or Storm? My use case does not have simple transformation. There are complex events that need to be generated by joining the incoming event stream. Also, what do you mean by "No Back PRessure" ? On Wednesday, 17 June 2015 11:57 AM, Enno Shioji <eshi...@gmail.com> wrote: We've evaluated Spark Streaming vs. Storm and ended up sticking with Storm. Some of the important draw backs are: Spark has no back pressure (receiver rate limit can alleviate this to a certain point, but it's far from ideal) There is also no exactly-once semantics. (updateStateByKey can achieve this semantics, but is not practical if you have any significant amount of state because it does so by dumping the entire state on every checkpointing) There are also some minor drawbacks that I'm sure will be fixed quickly, like no task timeout, not being able to read from Kafka using multiple nodes, data loss hazard with Kafka. It's also not possible to attain very low latency in Spark, if that's what you need. The pos for Spark is the concise and IMO more intuitive syntax, especially if you compare it with Storm's Java API. I admit I might be a bit biased towards Storm tho as I'm more familiar with it. Also, you can do some processing with Kinesis. If all you need to do is straight forward transformation and you are reading from Kinesis to begin with, it might be an easier option to just do the transformation in Kinesis. On Wed, Jun 17, 2015 at 7:15 AM, Sabarish Sasidharan <sabarish.sasidha...@manthan.com> wrote: Whatever you write in bolts would be the logic you want to apply on your events. In Spark, that logic would be coded in map() or similar such transformations and/or actions. Spark doesn't enforce a structure for capturing your processing logic like Storm does. Regards Sab Probably overloading the question a bit. In Storm, Bolts have the functionality of getting triggered on events. Is that kind of functionality possible with Spark streaming? During each phase of the data processing, the transformed data is stored to the database and this transformed data should then be sent to a new pipeline for further processing How can this be achieved using Spark? On Wed, Jun 17, 2015 at 10:10 AM, Spark Enthusiast <sparkenthusi...@yahoo.in> wrote: I have a use-case where a stream of Incoming events have to be aggregated and joined to create Complex events. The aggregation will have to happen at an interval of 1 minute (or less). The pipeline is : send events enrich event Upstream services -------------------> KAFKA ---------> event Stream Processor ------------> Complex Event Processor ------------> Elastic Search. >From what I understand, Storm will make a very good ESP and Spark Streaming will make a good CEP. But, we are also evaluating Storm with Trident. How does Spark Streaming compare with Storm with Trident? Sridhar Chellappa On Wednesday, 17 June 2015 10:02 AM, ayan guha <guha.a...@gmail.com> wrote: I have a similar scenario where we need to bring data from kinesis to hbase. Data volecity is 20k per 10 mins. Little manipulation of data will be required but that's regardless of the tool so we will be writing that piece in Java pojo. All env is on aws. Hbase is on a long running EMR and kinesis on a separate cluster. TIA. Best Ayan On 17 Jun 2015 12:13, "Will Briggs" <wrbri...@gmail.com> wrote: The programming models for the two frameworks are conceptually rather different; I haven't worked with Storm for quite some time, but based on my old experience with it, I would equate Spark Streaming more with Storm's Trident API, rather than with the raw Bolt API. Even then, there are significant differences, but it's a bit closer. If you can share your use case, we might be able to provide better guidance. Regards, Will On June 16, 2015, at 9:46 PM, asoni.le...@gmail.com wrote: Hi All, I am evaluating spark VS storm ( spark streaming ) and i am not able to see what is equivalent of Bolt in storm inside spark. Any help will be appreciated on this ? Thanks , Ashish --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org -- Best Regards, Ayan Guha
