Today when we create a Hive table, it is a common technique to partition the table across different values and ranges to improve query performance and reduce maintenance cost. However, Hive can not access a single table directly using a single query with the data of this Hive table across different mediums of storage and … Continued
Tag: alluxio engineering
Alluxio provides a unified namespace where you can mount multiple different storage systems and access them through the same API. To serve the file system requests to operate on all the files and directories in this namespace, Alluxio masters must handle the file system metadata at a scale of all mounted systems combined. We are … Continued
As part of the Alluxio 2.0 release, we have moved our RPC framework from Apache Thrift to gRPC. In this article, we will talk about the reasons behind this change as well as some lessons we learned along the way. Alluxio is an open-source distributed virtual file system, acting as the data access layer that enables bigdata and … Continued
Testing distributed systems at scale is typically a costly yet necessary process. At Alluxio we take testing very seriously as organizations across the world rely on our technology, therefore, a problem we want to solve is how to test at scale without breaking the bank. In this blog we are going to show how the maintainers of the Alluxio open source project build and test our system at scale cost-effectively using public cloud infrastructure. We test with the most popular frameworks, such as Spark and Hive, and pervasive storage systems, such as HDFS and S3. Using Amazon AWS EC2, we are able to test 1000+ worker clusters, at a cost of about $16 per hour.
Testing distributed systems at scale is typically a costly yet necessary process. At Alluxio we take testing very seriously as organizations across the world rely on our technology, therefore, a problem we want to solve is how to test at scale without breaking the bank. In this blog we are going to show how the … Continued
Small (kilobyte-sized) objects are the bane of highly scalable cloud object stores. Larger (at least megabytesized) objects not only improve performance, but also result in orders of magnitude lower cost, due to the current operation-based pricing model of commodity cloud object stores. For example, in Amazon S3’s current pricing scheme, uploading 1GiB data by issuing … Continued
Organizations commonly use Apache Spark to gain actionable insight from their large amounts of data. Often, these analytics are in the form of data processing pipelines, where there are a series of processing stages, and each stage performs a particular function, and the output of one stage is the input of the next stage. There … Continued