We started playing with Ignite back Hadoop, hive and spark services, and looking to move to it as our default for deployment going forward, still early but so far its been pretty nice and excited for the flexibility it will provide for our particular use cases.
Would say in general its worth looking into if your data workloads are: a) mix of read/write, or heavy write at times b) want write/read access to data from services/apps outside of your spark workloads (old Hadoop jobs, custom apps, etc) c) have strings of spark jobs that could benefit from caching your data across them (think similar usage to tachyon) d) you have sparksql queries that could benefit from indexing and mutability (see pt (a) about mix read/write) If your data is read exclusive and very batch oriented, and your workloads are strictly spark based, benefits will be less and ignite would probably act as more of a tachyon replacement as many of the other features outside of RDD caching wont be leveraged. -----Original Message----- From: unk1102 [mailto:umesh.ka...@gmail.com] Sent: Tuesday, January 5, 2016 10:15 AM To: user@spark.apache.org Subject: Spark on Apache Ingnite? Hi has anybody tried and had success with Spark on Apache Ignite seems promising? https://ignite.apache.org/ -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-on-Apache-Ingnite- tp25884.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 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
