Performance and experimenting against cached warehouse tables without reload. Rather than replication, our out-of-process cache that survives Spark JVM restarts, allows for fine tuning No Cache -> Tachyon: in-memory distributed file system, with HDFS backup, resilience through lineage.Unmanageable contention and delayed execution while maximizing cluster utilization (dynamic scheduling) Spark Job servers, mixed Hive and Shark queries (ELT), and establish priority queues: no more More mature than YARN, allows us to separate productionįrom experimentation workloads, co-locates legacy Hadoop MR jobs, multiple Shark servers (Jaws), multiple NO resource manager - > Mesos: multiple workloads from multiple frameworks can co-exist and fairlyĬonsume the cluster resources (policy based).Yields 4-20x performance improvement, ELT script base migration required minimal effort (same familiar Hive -> Shark: interactive queries on large datasets have become reasonable requests (in-memory caching.Submission, shell for quick prototyping and testing, ideal for our iterative algorithms Operational cost, machine learning primitives, simpler programming model (Scala, Python, Java), faster job Hadoop -> Spark: faster distributed computing engine leveraging in-memory computation at a much lower.
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