Author: Jason Hunter

When using Amazon DynamoDB, you sometimes want to perform bulk operations against all the items in a table, which has historically required custom coding. The open source Bulk Executor for DynamoDB simplifies bulk tasks like these. You can use this feature to invoke commands like count, find, delete, or update. No coding is required, even when running at large scale. In this post, we explore the built-in capabilities of Bulk Executor and show you how to install and use it for common bulk operations against your DynamoDB tables.

In this post, we show an algorithm to efficiently compare two Amazon DynamoDB tables and find the differences between their items. We provide an example where two tables, each containing approximately half a billion items, are compared in less than 7 minutes, for less than $10.

Part 1 of this series showed how to rate-limit calls to Amazon DynamoDB by using Python Boto3 event hooks. In this post, I expand on the concept and show how to rate-limit calls in a distributed environment, where you want a maximum allowed rate across the full set of clients but can’t use direct client-to-client communication.

In this post, I present a technique where a Python script making calls to Amazon DynamoDB can rate limit its consumption of read and write capacity units. The technique uses Boto3 event hooks to apply the rate limiting without having to modify the client code performing the read and write calls.

Amazon’s Finance Technologies Tax team (FinTech Tax) manages mission-critical services for tax computation, deduction, remittance, and reporting across global jurisdictions. The Application processes billions of transactions annually across multiple international marketplaces. In this post, we show how the team implemented tiered tax withholding using Amazon DynamoDB transactions and conditional writes.

In this post we demonstrate how to enforce client-side timestamp-based write sequence order on Amazon DynamoDB. The goal is to ensure items with lower timestamps don’t overwrite items with higher timestamps, even if the requests are received out of order by the database.

Amazon DynamoDB enables you to back up your table data continuously by using point-in-time recovery (PITR). When you enable PITR, DynamoDB backs up your table data automatically with per-second granularity. PITR helps protect you against accidental writes and deletes. For example, if a test script accidentally writes to a production DynamoDB table, or someone mistakenly […]

Amazon DynamoDB continuously sends metrics about its behavior to Amazon CloudWatch. Something I’ve heard customers ask for is how to get a count of successful requests of each operation type (for example, how many GetItem or DeleteItem calls were made) in order to better understand usage and costs. In this post, I show you how to retrieve this metric.

Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale. DynamoDB recently launched a new feature: Incremental export to Amazon Simple Storage Service (Amazon S3). You can use incremental exports to update your downstream systems regularly using only the changed data. You no longer need to do a […]

Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multi-Region, multi-active, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications. In 2020, DynamoDB introduced a feature to export DynamoDB table data to Amazon Simple Storage Service (Amazon S3) with […]