Optimizing Query Performance by Decoupling Presto and Hive Data Warehouse

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Alluxio, as a data orchestration layer provides the physical data independence, for Presto to interact with the data more efficiently. In addition to caching for IO acceleration, Alluxio also provides a catalog service to abstract the metadata in the Hive Metastore, and transformations to expose the data in compute-optimized way. In this talk, we describe some of the challenges of using Presto with Hive, and introduce Alluxio data orchestration for solving those challenges.

What’s new in Alluxio 2: from seamless operations to structured data management

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Alluxio 2.0 expands the system in three major directions including improving the operability of the system, having more advanced data management, as well as re-architecting the system to be able to scale to 1 billion + file. The system is now cloud native on AWS, Google Cloud, and allow users to enable native deployment with K8s. The new advanced data management enables data migration and replication from diff storage systems.

Testing Distributed System at Scale for the Cost of a Large Pizza on AWS

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Building distributed systems is no small feat. Software testing is just one of many critical practices that engineers who build these systems need to utilize to ensure the quality and usability of their software. For distributed systems, scaling out testing frameworks to ensure that enterprises who run our in highly distributed environments is a complicated (and expensive task!)

Running Presto with Alluxio on Amazon EMR

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Many organizations are leveraging EMR to run big data analytics on public cloud. However, reading and writing data to S3 directly can result in slow and inconsistent performance. Alluxio is a data orchestration layer for the cloud, and in this use case it caches data for S3, ensuring high and predictable performance as well as reduced network traffic.

Improving Data Locality for Spark Jobs on Kubernetes Using Alluxio

Alluxio Community Office Hour *

One important performance optimization in Apache Spark is to schedule tasks on nodes with HDFS data nodes locally serving the task input data. However, more users are running Apache Spark natively on Kubernetes where HDFS is not an option. This office hour describes the concept and dataflow with respect to using the stack of Spark/Alluxio in Kubernetes with enhanced data locality even the storage service is outside or remote.

Improving Memory Utilization of Spark Jobs Using Alluxio

Alluxio Community Office Hour *

This office hour shares a demo and compares two approaches, caching data directly in-memory into the Spark JVM versus storing data off-heap via an in-memory storage service like Alluxio

Accelerating Hive with Alluxio on S3

Alluxio Community Office Hour *

Hear about Bazaarvoice’s use case leveraging Apache Spark, Hive, and Alluxio on S3. And learn how to set up Hive with Alluxio so that Hive jobs can seamlessly read/write to S3.