'Avoiding Data Silos in Presto in Meta: From Raptor to RaptorX' debugging presto
Having compute/storage collocated, Raptor clusters can support low-latency high-throughput query workloads. However, the flip side of collocation is also significant.The size of a Raptor cluster is typically decided by how much data needs to be stored. As the tables grow, more worker nodes are needed due to the collocated compute/storage, which also creates challenges to repurpose these machines for other uses even when the cluster is idle.
Caching as a feature in the filesystem layer, is part of the presto-hive connector, thus the architecture of a RaptorX cluster is similar to other warehouse presto clusters, reducing operational overhead.The fundamental difference between Raptor and Raptor X is how the local SSDs on workers are used. In RaptorX, Presto workers useto cache file data locally.
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