Tag: caching

Whitepaper: MOMO – Accelerating Ad Hoc Analysis with Spark SQL and Alluxio

From our friends at MOMO The hadoop ecosystem makes many distributed system/algorithms easier to use and generally lowers the cost of operations. However, enterprises and vendors are never satisfied with that, so higher performance becomes the next issue. We considered several options to address our performance needs and focused our efforts on Alluxio, which improves performance … Continued

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Lenovo Case Study: Analytics on Data from Multiple Locations and Eliminating ETL

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Lenovo is an Alluxio customer with a common problem and use case in the world of data analytics. They have petabytes of data in multiple data centers in different geographic locations. Analyzing it requires an ETL process to get all of the data in the right place. This is both slow, because data has to be transferred across the network, and costly because multiple copies of the data need to be stored. Freshness and quality of the data can also suffer as the data is also potentially out of date and incomplete because regulatory issues prevent certain data from being transferred.

Apache Spark DataFrame caching with Alluxio

Many organizations deploy Alluxio together with Spark for performance gains and data manageability benefits. Qunar recently deployed Alluxio in production, and their Spark streaming jobs sped up by 15x on average and up to 300x during peak times. They noticed that some Spark jobs would slow down or would not finish, but with Alluxio, those jobs could finish quickly. In this blog post, we investigate how Alluxio helps Spark be more effective. Alluxio increases performance of Spark jobs, helps Spark jobs perform more predictably, and enables multiple Spark jobs to share the same data from memory.

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Using Alluxio to Improve the Performance and Consistency of HDFS Clusters

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Alluxio is the world’s first memory-speed virtual distributed storage system that bridges applications and underlying storage systems, providing unified data access orders of magnitudes faster than existing solutions. The Hadoop Distributed File System (HDFS) is a distributed file system for storing large volumes of data. HDFS popularized the paradigm of bringing computation to data and the co-located compute and storage architecture.
In this blog, we highlight two key benefits Alluxio brings to a compute cluster co-located with HDFS.