Alluxio’s distributed systems experts explore today’s data access challenges and open source data orchestration solutions for modernizing your data platform. … Continued
Alluxio Community Day VI
OCTOBER 12, 9:00 AM – 11 AM PDT | VIRTUAL EVENT
Join fellow Alluxio community users live to learn how they are solving complex data access challenges. Hear Alluxio users from Facebook, Princeton, Apache Hudi, Zendesk, and Uber.
Alluxio 2.6 is now available! Better Performance for AI/ML Workloads plus Improved Operating Metrics Visibility.
Explore today’s data access challenges and open source data orchestration solutions for modernizing your platform.
We’re hiring! Join our team and build the future of data orchestration. See open positions >
Alluxio enables compute
Data locality
Bring your data close to compute.
Make your data local to compute workloads for Spark caching, Presto caching, Hive caching and more.
Data Accessibility
Make your data accessible.
No matter if it sits on-prem or in the cloud, HDFS or S3, make your files and objects accessible in many different ways.
Data On-Demand
Make your data as elastic as compute.
Effortlessly orchestrate your data for compute in any cloud, even if data is spread across multiple clouds.
“zero-copy” burst user spotlight: walmart
Why Walmart chose Alluxio’s “Zero-Copy” burst solution:
- No requirement to persist data into the cloud
- Improved query performance and no network hops on recurrent queries
- Lower costs without the need for creating data copies
See more on how Alluxio powers Walmart’s “zero-copy” burst solution in their presentation >
Featured Use Cases and Deployments
Managing data copies/app changes when bursting compute to cloud?
Zero-copy hybrid bursting with no app changes to intelligently make remote data accessible in the public cloud.
Expanding compute capacity across geo-distributed data centers?
Zero-copy bursting across data centers for Presto, Spark, and Hive with no app changes on data stored in HDFS.
Interact with Alluxio in any stack
Pick a compute. Pick a storage. Alluxio just works.
-- Pointing Table location to Alluxio
CREATE SCHEMA hive.web
WITH (location = 'alluxio://master:port/my-table/‘)
// Using Alluxio as input and output for RDD
scala> sc.textFile("alluxio://master:19998/Input")
scala> rdd.saveAsTextFile("alluxio://master:19998/Output")
// Using Alluxio as input and output for Dataframe
scala> df = sqlContext.read.parquet("alluxio://master:19998/Input.parquet")
scala> df.write.parquet("alluxio://master:19998/Output.parquet”)
-- Pointing Table location to Alluxio
hive> CREATE TABLE u_user (
userid INT,
age INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '|'
LOCATION 'alluxio://master:port/table_data';
Create and Query table stored in Alluxio
hbase(main):001:0> create 'test', 'cf'
hbase(main):002:0> list ‘test'
# Accessing Alluxio after mounting Alluxio service to local file system
$ ls /mnt/alluxio_mount
$ cat /mnt/alluxio_mount/mydata.txt
ALLUXIO
$ ./bin/alluxio fs mount \
--option aws.accessKeyId=<AWS_ACCESS_KEY_ID> \
--option aws.secretKey=<AWS_SECRET_KEY_ID> \
alluxio://master:port/s3 s3a://<S3_BUCKET>/<S3_DIRECTORY>
$ ./bin/alluxio fs mount \
alluxio://master:port/hdfs hdfs://namenode:port/dir/
$ ./bin/alluxio fs mount \
--option
fs.azure.account.key.<AZURE_ACCOUNT>.blob.core.windows.net=<AZURE_ACCESS_KEY> \
alluxio://master:port/azure
wasb://<AZURE_CONTAINER>@<AZURE_ACCOUNT>.blob.core.windows.net/<AZURE_DIRECTORY>/
$ ./bin/alluxio fs mount \
--option fs.gcs.accessKeyId=<GCS_ACCESS_KEY_ID> \
--option fs.gcs.secretAccessKey=<GCS_SECRET_ACCESS_KEY> \
alluxio://master:port/gcs gs://<GCS_BUCKET>/<GCS_DIRECTORY>
$ ./bin/alluxio fs mount \
--option aws.accessKeyId=<AWS_ACCESS_KEY_ID> \
--option aws.secretKey=<AWS_SECRET_KEY_ID> \
--option alluxio.underfs.s3.endpoint=http://<rgw-hostname>:<rgw-port> \
--option alluxio.underfs.s3.disable.dns.buckets=true \
alluxio://master:port/ceph s3a://<S3_BUCKET>/<S3_DIRECTORY>
$ ./bin/alluxio fs mount alluxio://master:port/nfs /mnt/nfs
powered by alluxio
What’s Happening
Alluxio 2.6 significantly improves the performance of data-intensive AI/ML workloads across any storage, and also improves the general maintainability and visibility of Alluxio clusters, especially for large-scale deployments. We have taken the feedback and contributions from the community and introduced features which simplify deployment, introduce new data management capabilities, optimize performance, and provide enhanced visibility into system behavior.
Today’s analytics workloads demand real-time access to expansive amounts of data. This session demonstrates how Alluxio’s data orchestration platform, running on Intel Optane persistent … Continued
Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to … Continued
This whitepaper details how to evaluate Alluxio’s data orchestration platform as a distributed cache for Apache Spark in a public cloud or on-premises. We … Continued
Data processing is increasingly making use of NVIDIA computing for massive parallelism. Advancements in accelerated compute mean that access to storage must also be quicker, whether in analytics, artificial intelligence (AI), or machine learning (ML) pipelines.