Amazon CloudWatch Logs Insights now supports saved queries with parameters

Amazon CloudWatch Logs Insights saved queries now support parameters, allowing you to pass values to reusable query templates with placeholders. This eliminates the need to maintain multiple copies of nearly identical queries that differ only in specific values such as log levels, service names, or time intervals. You can define up to 20 parameters in a query, with each parameter supporting optional default values. For example, you can create a single template to query logs by severity level (such as ERROR or WARN) and pass different service names each time you run it. To execute a query with parameters, invoke it using the query name prefixed with $ and pass your parameter values, such as $ErrorsByService(logLevel=”ERROR”, serviceName=”OrderEntry”). You can also use multiple saved queries with parameters together for complex log analysis, significantly reducing query maintenance overhead while improving reusability. Saved queries with parameters are available in all commercial AWS regions. You can create and use saved queries with parameters using the Amazon CloudWatch console, AWS Command Line Interface (AWS CLI), AWS Cloud Development Kit (AWS CDK), and AWS SDKs. To learn more, see the Amazon CloudWatch Logs documentation.
Quelle: aws.amazon.com

Aurora DSQL launches connector that simplifies building PHP applications

Today we are announcing the release of the Aurora DSQL Connector for PHP (PDO_PGSQL) that makes it easy to build PHP applications on Aurora DSQL. The PHP Connector streamlines authentication and eliminates security risks associated with traditional user-generated passwords by automatically generating tokens for each connection, ensuring valid tokens are always used while maintaining full compatibility with existing PDO_PGSQL features. The connector handles IAM token generation, SSL configuration, and connection pooling, enabling customers to scale from simple scripts to production workloads without changing their authentication approach. It also provides opt-in optimistic concurrency control (OCC) retry with exponential backoff, custom IAM credential providers, and AWS profile support, making it easier to develop client retry logic and manage AWS credentials. To get started, visit the Connectors for Aurora DSQL documentation page. For code examples, visit our GitHub page for the PHP connector. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.    
Quelle: aws.amazon.com

Amazon Redshift introduces key performance optimization for Top-K queries

Amazon Redshift further optimizes the processing of top-k queries (queries with ORDER BY and LIMIT clauses) by intelligently skipping irrelevant data blocks to return results faster, dramatically reducing the amount of data processed. This optimization reorders and efficiently adjusts the data blocks to be read based on the ORDER BY column’s min/max values, maintaining only the K most qualifying rows in memory. When the ORDER BY column is sorted or partially sorted, Amazon Redshift now processes only the minimal data blocks needed rather than scanning entire tables, eliminating unnecessary I/O and compute overhead.
This enhancement particularly benefits top-k queries when the data permanently stores in descending order (ORDER BY … DESC LIMIT K) on large tables where qualifying rows are appended at the end of the data storage. Common examples include:

Finding the k most recent orders from millions or billions of transactions
Retrieving top-k best performing products or k worst performing products (top-k in descending order) from your sales catalog containing hundreds of thousands stock keeping units (SKUs) and millions or billions of sales transactions associated with all product SKUs in your sales catalog
Finding the top-k most recent or top-k oldest (top k in descending order) prompts inferred by a foundational large language model (LLM) out of billions of prompts.

With this new optimization, top-k query performance improves dramatically. This optimization for top-k queries is now available in Amazon Redshift at no additional cost starting with patch release P199 across all AWS regions where Amazon Redshift is available. This optimization automatically applies to eligible queries without requiring any query rewrites or configuration changes.
Quelle: aws.amazon.com

Amazon OpenSearch Serverless now supports Derived Source for storage optimization

Amazon OpenSearch Serverless introduces support for Derived Source, a new feature that can help reduce the amount of storage required for your OpenSearch Service collections. With derived source support, you can skip storing source fields and dynamically derive them when required. 
With Derived Source, OpenSearch Serverless reconstructs the _source field on the fly using the values already stored in the index, eliminating the need to maintain a separate copy of the original document. This can significantly reduce storage consumption, particularly for time-series and log analytics collections where documents contain many indexed fields. You can enable derived source at the index level when creating or updating index mappings.
Derived Source support is available today in all AWS Regions where Amazon OpenSearch Serverless is supported. For more information, see the Amazon OpenSearch Serverless documentation.
Quelle: aws.amazon.com