The idea of predicate pushdown allows for certain parts of SQL queries (the predicates) to be “pushed” down to where the data lives. This optimization can reduce query processing time by filtering data that much earlier. Depending on compute framework, predicate pushdown can also optimize your query by filtering data before it is transferred over … Continued
If one disk fails on a given Alluxio worker, the Alluxio client on the application side will detect from either the failure or a timeout, then the client will check other workers serving the same data block since one block may have multiple replicas cached in Alluxio space and go to other workers to continue … Continued
Alluxio is available via Docker. You can create a cluster of Alluxio within a Kubernetes cluster. Given that we do have these containers, you can either use a daemon set or a replica set within a Kubernetes cluster to create an alluxio cluster itself and have it co-located within your other nodes that may be … Continued
While adding a higher-bandwidth dedicated circuit will help, Alluxio data orchestration addresses the hybrid problem by making the data local to the compute nodes.
Problem If you have hundreds of external tables defined in Hive, what is the easist way to change those references to point to new locations? That is a fairly normal challenge for those that want to integrate Alluxio into their stack. A typical setup that we will see is that users will have Spark-SQL or … Continued
Alluxio is a data orchestration system which provides data locality with intelligent multi-tiering. The replication parameters are easily configured and once done, Alluxio handles replication transparently to the requesting compute framework. As always, there’s no changes required by the end user, it’s transparent: In the above diagram, data is stored in RAM, SSD, or HDD. … Continued