> > 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 >
