One of the premises here is that if you can restrict your workload to fewer cores - which is easier with FiloDB and careful data modeling - you can make this work for much higher concurrency and lower latency than most typical Spark use cases.
The reason why it typically does not work in production is that most people are using HDFS and files. These data sources are designed for running queries and workloads on all your cores across many workers, and not for filtering your workload down to only one or two cores. There is actually nothing inherent in Spark that prevents people from using it as an app server. However, the insistence on using it with HDFS is what kills concurrency. This is why FiloDB is important. I agree there are more optimized stacks for running app servers, but the choices that you mentioned: ES is targeted at text search; Cass and HBase by themselves are not fast enough for analytical queries that the OP wants; and MySQL is great but not scalable. Probably something like VectorWise, HANA, Vertica would work well, but those are mostly not free solutions. Druid could work too if the use case is right. Anyways, great discussion! On Thu, Mar 10, 2016 at 2:46 PM, Chris Fregly <ch...@fregly.com> wrote: > you are correct, mark. i misspoke. apologies for the confusion. > > so the problem is even worse given that a typical job requires multiple > tasks/cores. > > i have yet to see this particular architecture work in production. i would > love for someone to prove otherwise. > > On Thu, Mar 10, 2016 at 5:44 PM, Mark Hamstra <m...@clearstorydata.com> > wrote: >>> >>> For example, if you're looking to scale out to 1000 concurrent requests, >>> this is 1000 concurrent Spark jobs. This would require a cluster with 1000 >>> cores. >> >> >> This doesn't make sense. A Spark Job is a driver/DAGScheduler concept >> without any 1:1 correspondence between Worker cores and Jobs. Cores are >> used to run Tasks, not Jobs. So, yes, a 1000 core cluster can run at most >> 1000 simultaneous Tasks, but that doesn't really tell you anything about how >> many Jobs are or can be concurrently tracked by the DAGScheduler, which will >> be apportioning the Tasks from those concurrent Jobs across the available >> Executor cores. >> >> On Thu, Mar 10, 2016 at 2:00 PM, Chris Fregly <ch...@fregly.com> wrote: >>> >>> Good stuff, Evan. Looks like this is utilizing the in-memory >>> capabilities of FiloDB which is pretty cool. looking forward to the webcast >>> as I don't know much about FiloDB. >>> >>> My personal thoughts here are to removed Spark from the user >>> request/response hot path. >>> >>> I can't tell you how many times i've had to unroll that architecture at >>> clients - and replace with a real database like Cassandra, ElasticSearch, >>> HBase, MySql. >>> >>> Unfortunately, Spark - and Spark Streaming, especially - lead you to >>> believe that Spark could be used as an application server. This is not a >>> good use case for Spark. >>> >>> Remember that every job that is launched by Spark requires 1 CPU core, >>> some memory, and an available Executor JVM to provide the CPU and memory. >>> >>> Yes, you can horizontally scale this because of the distributed nature of >>> Spark, however it is not an efficient scaling strategy. >>> >>> For example, if you're looking to scale out to 1000 concurrent requests, >>> this is 1000 concurrent Spark jobs. This would require a cluster with 1000 >>> cores. this is just not cost effective. >>> >>> Use Spark for what it's good for - ad-hoc, interactive, and iterative >>> (machine learning, graph) analytics. Use an application server for what >>> it's good - managing a large amount of concurrent requests. And use a >>> database for what it's good for - storing/retrieving data. >>> >>> And any serious production deployment will need failover, throttling, >>> back pressure, auto-scaling, and service discovery. >>> >>> While Spark supports these to varying levels of production-readiness, >>> Spark is a batch-oriented system and not meant to be put on the user >>> request/response hot path. >>> >>> For the failover, throttling, back pressure, autoscaling that i mentioned >>> above, it's worth checking out the suite of Netflix OSS - particularly >>> Hystrix, Eureka, Zuul, Karyon, etc: http://netflix.github.io/ >>> >>> Here's my github project that incorporates a lot of these: >>> https://github.com/cfregly/fluxcapacitor >>> >>> Here's a netflix Skunkworks github project that packages these up in >>> Docker images: https://github.com/Netflix-Skunkworks/zerotodocker >>> >>> >>> On Thu, Mar 10, 2016 at 1:40 PM, velvia.github <velvia.git...@gmail.com> >>> wrote: >>>> >>>> Hi, >>>> >>>> I just wrote a blog post which might be really useful to you -- I have >>>> just >>>> benchmarked being able to achieve 700 queries per second in Spark. So, >>>> yes, >>>> web speed SQL queries are definitely possible. Read my new blog post: >>>> >>>> http://velvia.github.io/Spark-Concurrent-Fast-Queries/ >>>> >>>> and feel free to email me (at vel...@gmail.com) if you would like to >>>> follow >>>> up. >>>> >>>> -Evan >>>> >>>> >>>> >>>> >>>> -- >>>> View this message in context: >>>> http://apache-spark-user-list.1001560.n3.nabble.com/Can-we-use-spark-inside-a-web-service-tp26426p26451.html >>>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>>> >>>> --------------------------------------------------------------------- >>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>>> For additional commands, e-mail: user-h...@spark.apache.org >>>> >>> >>> >>> >>> -- >>> >>> Chris Fregly >>> Principal Data Solutions Engineer >>> IBM Spark Technology Center, San Francisco, CA >>> http://spark.tc | http://advancedspark.com >> >> > > > > -- > > Chris Fregly > Principal Data Solutions Engineer > IBM Spark Technology Center, San Francisco, CA > http://spark.tc | http://advancedspark.com --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
