Although I haven't work explicitly with either, they do seem to differ in design and consequently in usage scenarios.
Ignite is claimed to be a pure in-memory distributed database. With Ignite, updating existing keys is something that is self-managed comparing with Tachyon. In Tachyon once a value is created for a given key, becomes immutable, so you either delete and insert again, or need to manage/update the tachyon keys yourself. Also, Tachyon's resilience design is based on the underlying file system (typically hadoop), which means that if a node goes down, to recover the lost data, it would need first to have been persisted on the corresponding file partition. With Ignite, there is no master dependency like with Tachyon, and my understanding is that API calls will depend on master's availability in Tachyon. I believe Ignite has some options for replication which would be more aligned with the in-memory datastore. If you are looking for persisting some RDD's output into an in-memory store and query it outside of Spark, on the paper Ignite sounds like a better solution. Since you are asking about Ignite benefits that was the focus of my response. Tachyon has its own benefits like the community support and the Spark lineage persistency integration. If you are doing batch based processing and want to persist fast Spark RDDs, Tachyon is your friend. Hope this helps. Tnks, Rod -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-on-Apache-Ingnite-tp25884p25933.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
