Implementing this solution to distribute objects and requests across multiple prefixes involves changes to your data ingress or data egress applications. Also, if supported request rates are exceeded, it’s a best practice to distribute objects and requests across multiple prefixes. It does this while it scales in the background to handle the increased request rate. For certain workloads that need a sudden increase in the request rate for objects in a prefix, Amazon S3 might return 503 Slow Down errors, also known as S3 throttling. Instead, as the request rate for a prefix increases gradually, Amazon S3 automatically scales to handle the increased request rate. The resources for this request rate aren’t automatically assigned when a prefix is created. Increase Amazon S3 performance and throughputĪmazon S3 supports a request rate of 3,500 PUT/COPY/POST/DELETE or 5,500 GET/HEAD requests per second per prefix in a bucket. We include an example implementation of an S3 access point with Apache Iceberg later in this post. Apache Iceberg supports access points to perform S3 operations by specifying a mapping of bucket to access points. During a planned or unplanned regional traffic disruption, failover controls let you control failover between buckets in different Regions and accounts within minutes. With Amazon S3 multi-Region access point failover controls, you can route all S3 data request traffic through a single global endpoint and directly control the shift of S3 data request traffic between Regions at any time. With S3 data residing in multiple Regions, you can use an S3 multi-Region access point as a solution to access the data from the backup Region. Still, to make your data lake workloads highly available in an unlikely outage situation, you can replicate your S3 data to another AWS Region as a backup. Amazon S3 is designed for 99.999999999% (11 9’s) of durability, S3 Standard is designed for 99.99% availability, and Standard – IA is designed for 99.9% availability. Implement business continuityĪmazon S3 gives any developer access to the same highly scalable, reliable, fast, inexpensive data storage infrastructure that Amazon uses to run its own global network of web sites. The example notebook in this post shows an example implementation of S3 object tagging and lifecycle rules for Apache Iceberg tables to optimize storage cost. This is expected to be used in combination with Amazon S3 delete tagging, so objects are tagged and removed using an Amazon S3 lifecycle policy. When the catalog property s3.delete-enabled is set to false, the objects are not hard-deleted from Amazon S3. With the s3.delete.tags config property in Iceberg, objects are tagged with the configured key-value pairs before deletion. Iceberg also let you configure a tag-based object lifecycle policy at the bucket level to transition objects to different Amazon S3 tiers. From an Apache Iceberg perspective, it supports custom Amazon S3 object tags that can be added to S3 objects while writing and deleting into the table. Amazon S3 deletes expired objects on your behalf.Īmazon S3 uses object tagging to categorize storage where each tag is a key-value pair. Expiration actions – These actions define when objects expire.Transition actions – These actions define when objects transition to another storage class for example, Amazon S3 Standard to Amazon S3 Glacier.An Amazon S3 Lifecycle configuration is a set of rules that define actions that Amazon S3 applies to a group of objects. You can use Amazon S3 Lifecycle configurations and Amazon S3 object tagging with Apache Iceberg tables to optimize the cost of your overall data lake storage. One of the major advantages of building modern data lakes on Amazon S3 is it offers lower cost without compromising on performance. In this post, we show you how to improve operational efficiencies of your Apache Iceberg tables built on Amazon S3 data lake and Amazon EMR big data platform. Some of the important non-functional use cases for an S3 data lake that organizations are focusing on include storage cost optimizations, capabilities for disaster recovery and business continuity, cross-account and multi-Region access to the data lake, and handling increased Amazon S3 request rates. When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. Apache Iceberg is an open table format for large datasets in Amazon Simple Storage Service (Amazon S3) and provides fast query performance over large tables, atomic commits, concurrent writes, and SQL-compatible table evolution.
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