Tag: locality

Tech Talk: Accelerating Spark with Kubernetes

Kubernetes is widely used across enterprises to orchestrate computation. And while Kubernetes helps improve flexibility and portability for computation in public/hybrid cloud environments across infrastructure providers, running data-intensive workloads can be challenging.

When it comes to efficiently moving data closer to Spark or Presto frameworks, co-locating data with these frameworks and accessing data from multiple or remote clouds is hard to do. That’s where Alluxio, an open source data orchestration platform, can help.

Alluxio enables data locality with your Spark and Presto workloads for faster performance and better data accessibility in Kubernetes. It also provides portability across storage providers.

In this on demand tech talk we’ll give a quick overview of Alluxio and the use cases it powers for Spark/Presto in Kubernetes. We’ll show you how to set up Alluxio and Spark/Presto to run in Kubernetes as well.

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Community Office Hour: Running Spark & Alluxio in Kubernetes

by Bin Fan & Adit Madan

The data orchestration layer bridging the gap between data locality with improved performance and data accessibility for analytics workloads in Kubernetes, and enables portability across storage providers.
An overview of Alluxio and the cloud use case with Spark in Kubernetes. Learn how to set up Alluxio and Spark to run in Kubernetes.

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Hybrid Environments for Data Analytics is a Possibility

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As the data ecosystem becomes massively complex and more and more disaggregated, data analysts and end users have trouble adapting and working with hybrid environments. The proliferation of compute applications along with storage mediums leads to a hybrid model that we are just not accustomed to.
With this disaggregated system data engineers now come across a multitude of problems that they must overcome in order to get meaningful insights.

Summertime themed In-Memory Computing extravaganza! (cross-post)

New York Meetup *

[Talk 1] A “how-to” presentation for building a real-time alerting, analytics and reporting system (at scale). With Denis Magda, vice president of the Apache Ignite PMC and director of product management at GridGain Systems. And Viktor Gamov, developer advocate at Confluent.
[Talk 2] Using In-Memory technology for real time analytics. With Andy Rivenes is a Product Manager at Oracle for Database In-Memory.
[Talk 3] Feeding data to the Kubernetes beast: bringing data locality to your containerized big data workloads. With Bin Fan, founding engineer of Alluxio, Inc. and PMC member of Alluxio open source project.

Top 5 Performance Tuning Tips for Presto caching using Alluxio

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Presto is an open source distributed SQL engine widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Alluxio is an open-source distributed file system that provides a unified data access layer at in-memory speed. The combination of Presto and Alluxio is getting more popular in many companies like JD, NetEase to leverage Alluxio as distributed caching tier on top of slow or remote storage for the hot data to query, avoiding reading data repeatedly from the cloud. In general, Presto doesn’t include a distributed caching tier and Alluxio enables caching of files and objects that the Presto query engine needs.

Top 10 Tips for Making the Spark + Alluxio Stack Blazing Fast

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The Apache Spark + Alluxio stack is getting quite popular particularly for the unification of data access across S3 and HDFS. In addition, compute and storage are increasingly being separated causing larger latencies for queries. Alluxio is leveraged as compute-side virtual storage to improve performance. But to get the best performance, like any technology stack, you need to follow the best practices. This article provides the top 10 tips for performance tuning for real-world workloads when running Spark on Alluxio with data locality giving the most bang for the buck.

Data Location Awareness: Optimize Performance and Lower Cost with Tiered Locality

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Caching frequently used data in memory is not a new computing technique, however it is a concept that Alluxio has taken to the next level with the ability to aggregate data from multiple storage systems in a unified pool of memory. Alluxio capabilities extend further to intelligently managing the data within that virtual data layer. Tiered locality uses awareness of network topology and configurable policies to manage data placement for performance and cost optimizations. This feature is particularly useful with cloud deployments across multiple availability zones. It can also be useful for cost savings in environments where cross-zone or cross-location traffic is more expensive than intra-zone data traffic.