News For Dev-ops

Last time, we wrote a coroutine function that cached the result of another coroutine , but it only cached successful calls. It didn't cache failures, so if the inner coroutine fails, the outer one will simply try again the next time it is called. But what if we want to call the inner coroutine only once and cache the failure, too? We now have three states: Never tried, tried with success (and cache the success result), and tried with failure (and cache the failure). We can represent that as a variant with three types, using std:: monostate to represent the "never tried" state.¹ struct Widget : WidgetT<Widget> { std::variant<std::monostate, winrt::Thing, std::exception_ptr> m_thing; wil::unique_event m_busy{ wil::EventOptions::Signaled }; // auto-reset, initially signaled IAsyncOperation<winrt::Thing> GetThingAsync() { auto lifetime = get_strong(); co_await winrt::resume_on_signal(m_busy.get()); auto not_busy ...

Amazon Redshift now allows you to get started with Amazon Redshift Serverless with a lower data warehouse base capacity configuration of 4 Redshift Processing Units (RPUs) in the Asia Pacific (Hong Kong), Asia Pacific (Seoul), Canada (Central), Europe (London), South America (Sao Paulo), AWS GovCloud (US-East), and AWS GovCloud (US-West) regions. Amazon Redshift Serverless measures data warehouse capacity in RPUs. 1 RPU provides you 16 GB of memory. You pay only for the duration of workloads you run in RPU-hours on a per-second basis. Previously, the minimum base capacity required to run Amazon Redshift Serverless was 8 RPUs. You can start using Amazon Redshift Serverless for as low as $1.50 per hour and pay only for the compute capacity your data warehouse consumes when it is active. For predictable workloads, Amazon Redshift Serverless capacity reservations with 1-year and 3-year terms provide additional price-performance benefits. Amazon Redshift Serverless enables users to run...

Amazon Connect Customer now supports tag-based access controls for the agent login/logout report, enabling you to apply granular access controls to meet compliance and regulatory requirements for data access. Contact center administrators can use resource tags to control who has access to view login and logout information for specific agents in their contact center. For example, you can tag agents with Department:Customer Service to only enable the Customer Service team manager to see login/logout information for these agents. Amazon Connect Customer agent login/logout report is available in all AWS commercial and AWS GovCloud (US-West) regions where Amazon Connect Customer is offered. To learn more about dashboards, see the . To learn more about Amazon Connect Customer, the AWS cloud-based contact center, please visit the Amazon Connect Customer website . Post Updated on May 26, 2026 at 10:04PM

Today, AWS announces that Amazon Aurora MySQL-Compatible Edition now supports integration with Kiro Powers , enabling developers to build Aurora MySQL-backed applications faster with AI agent assistance. Kiro Powers is a repository of curated and pre-packaged Model Context Protocol (MCP) servers, steering files, and hooks that have been validated by Kiro partners to accelerate specialized software development and deployment. This integration bundles direct database connectivity with Aurora MySQL best practices, providing developers with instant expertise in Aurora MySQL operations and schema design through natural language interactions. With this integration, developers can perform both data plane operations (database queries, table creation, schema management) and control plane operations (cluster creation and management) through conversational commands instead of complex syntax. The Kiro agent dynamically loads task-specific guidance for Aurora MySQL Serverless scaling, migration f...

AWS Glue now offers large and memory-optimized workers in the AWS Europe (Spain) Region, giving customers in this region more power to handle complex data processing workloads. The new additions include two general compute workers (G.12X and G.16X) as well as four memory-optimized workers (R.1X, R.2X, R.4X, and R.8X). With these options, you can now tackle more complex transforms, aggregations, joins, and queries while processing higher volumes of data quickly using AWS Glue. The G.12X and G.16X workers extend the existing G worker lineup with additional compute, memory, and storage which makes them ideal for large, resource-intensive workloads. The R-series workers (R.1X, R.2X, R.4X, and R.8X) offer double the memory of their G counterparts, making them well-suited for memory-intensive Spark operations such as caching, shuffling, and aggregating. You can select any of these worker types through AWS Glue Studio, using notebooks or Visual ETL , or programmatically via the Glue Job AP...

AWS Deadline Cloud now supports browsing job attachment files directly within the Deadline Cloud monitor. AWS Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated graphics and visual effects, for films, television, broadcasting, web content, and design. The Deadline Cloud job attachments feature simplifies managing input and output files for render jobs by handling uploads and downloads through Amazon S3. Previously, you could download all outputs for a job but couldn't easily see which specific files were attached as inputs or generated as outputs. With this new browsing capability, you can now view all attached files for a given job directly from the monitor in the browser or desktop application, clearly organized by inputs and outputs. You can also download individual files as needed, making it easy to retrieve specific assets without downloading everything. For more information, please visit the Deadline Cloud pro...