It is critical for Alluxio to be able to store and serve the metadata of all files and directories from all mounted external storage both at scale and at speed. This talk shares our design, implementation, and optimization of Alluxio metadata service (master node) to address the scalability challenges.
ADVANCED ANALYTICS & AI ON REMOTE DATA FOR HYBRID AND MULTI-CLOUD
Open Source Data Orchestration for the Cloud
Looking for more compute capacity with remote data?
See how “Zero-Copy” hybrid bursting helps
How the Development Bank of Singapore solves on-prem compute capacity challenges with “zero-copy” cloud bursting
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
Announcing Alluxio 2.0! Learn more about the release >
powered by alluxio
What’s Happening
Alluxio, the developer of open source cloud data orchestration software, today announced the availability of Alluxio Structured Data Service (SDS) featuring a data Catalog Service and Transformation Service, two new major architectural components of its Data Orchestration Platform. Data engineers, architects and developers can now spend less resources storing data and more time delivering data to analytical compute engines.
Alluxio, the developer of open source cloud data orchestration software, today announced the availability of Alluxio Structured Data Service (SDS) featuring a data Catalog Service and Transformation Service, two new major architectural components of its Data Orchestration Platform. Data engineers, architects and developers can now spend less resources storing data and more time delivering data to analytical compute engines.
With this release comes the General Availability (GA) of Alluxio Structured Data Services (SDS), the subsystem of Alluxio responsible for managing and transforming structured data, such as databases, tables, and partitions.
Accessing data to run analytic workloads in Spark across data centers and/or clouds can be challenging. Additionally, network I/O can bottleneck Spark jobs that … Continued
This whitepaper details how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data … Continued