AlloyDB for PostgreSQL supports a 1 virtual central processing unit (vCPU) configuration with 8GB of memory, which is suitable for development and sandbox environments

## Feature AlloyDB for PostgreSQL supports a [1 virtual central processing unit](https://cloud.google.com/alloydb/docs/cluster-create) (vCPU) configuration with 8GB of memory, which is suitable for development and sandbox environments. For information about 1 vCPU supported regions and limitations, see [Considerations when using 1 vCPU](https://cloud.google.com/alloydb/docs/cluster-create#considerations-one-vcpu). This feature is generally available ([GA](https://cloud.google.com/products#product-launch-stages)). ## Feature AlloyDB supports [AI-assisted troubleshooting](https://cloud.google.com/alloydb/docs/monitor-troubleshoot-with-ai) that helps you resolve complex database performance issues like [slow queries](https://cloud.google.com/alloydb/docs/troubleshoot/slow-queries-ai) and [high load](https://cloud.google.com/alloydb/docs/troubleshoot/high-database-load-ai). AI-assisted troubleshooting is available in [Preview](https://cloud.google.com/products#product-launch-stages). ## Feature AlloyDB for PostgreSQL supports parameterized secure views, which provide a secure interface for application developers by improving data security and row access control while using SQL. This feature is in ([Preview](https://cloud.google.com/products#product-launch-stages)). For more information, see [Parameterized secure views overview](https://cloud.google.com/alloydb/docs/parameterized-secure-views-overview). ## Feature AlloyDB AI natural language ([Preview](https://cloud.google.com/products#product-launch-stages)) delivers secure and accurate responses for application end user natural language questions. For more information, see [AlloyDB AI natural language overview](https://cloud.google.com/alloydb/docs/ai/natural-language-overview). ## Feature [AlloyDB AI query engine that builds on model endpoint management](https://cloud.google.com/alloydb/docs/ai/model-endpoint-overview#overview), and adds support for AI operators and Vertex AI multimodal and ranking models is available in ([Preview](https://cloud.google.com/products#product-launch-stages)). You can combine natural language phrases with SQL queries, like ai.if() for filters and joins, ai.rank() for ordering using ranking models, and ai.generate() for generating summaries of your data, and generate multimodal embeddings. ## Announcement The `alloydb_scann` extension is updated to include the following vector search improvements. These features are generally available ([GA](https://cloud.google.com/products#product-launch-stages)): * Inline filtering enables the execution of vector search and filter evaluation through the combined use of vector and secondary indexes. For more information, see "Inline filtering" in the documentation for [AlloyDB PostgreSQL](https://cloud.google.com/alloydb/docs/ai/filtered-vector-search-overview#inline-filtering) and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/ai/filtered-vector-search-overview#inline-filtering) and [16.3.0](https://cloud..google.com/alloydb/omni/16.3.0/docs/ai/filtered-vector-search-overview#inline-filtering). * You can let AlloyDB automatically create multiple parallel workers during index creation when the dataset grows, leading to faster build times. For more information, see "Build indexes in parallel" in the documentation for [AlloyDB PostgreSQL](https://cloud.google.com/alloydb/docs/ai/store-index-query-vectors) and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/ai/store-index-query-vectors?resource=scann) and [16.3.0](https://cloud.google.com/alloydb/omni/15.7.1/docs/ai/store-index-query-vectors?resource=scann). * A distribution histogram is available in the `pg_stat_ann_indexes` view, which helps you understand the distribution of vectors between partitions of your ScaNN index. For more information, including recommendations about tuning the `distributionpercentile` metric, see "Tuning metrics" in the documentation for [AlloyDB PostgreSQL](https://cloud.google.com/alloydb/docs/reference/vector-index-metrics#tuning-metrics), and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/reference/vector-index-metrics) and [16.3.0](https://cloud.google.com/alloydb/omni/16.3.0/docs/reference/vector-index-metrics). * You can use a query recall evaluator to find the recall for a vector query for a given configuration, and to tune your parameters to achieve the desired vector query recall results for different vector indexes. For more information, see "Measure vector query recall" in the documentation for [AlloyDB PostgreSQL](https://cloud.google.com/alloydb/docs/ai/measure-vector-query-recall), and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/ai/measure-vector-query-recall) and [16.3.0](https://cloud.google.com/alloydb/omni/16.3.0/docs/ai/measure-vector-query-recall). ## Announcement The `alloydb_scann` extension is updated to include the following vector search improvements in ([Preview](https://cloud.google.com/products#product-launch-stages)): * You can enable auto-maintenance for your ScaNN index and let incrementally manage the index such that when your dataset grows, AlloyDB splits large outlier partitions, and tries to provide better QPS and search results. For more information, see "Maintain indexes automatically" in the documentation for [AlloyDB PostgreSQL](https://cloud.google.com/alloydb/docs/ai/maintain-vector-indexes) and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/ai/maintain-vector-indexes) and [16.3.0](https://cloud.google.com/alloydb/omni/16.3.0/docs/ai/maintain-vector-indexes). * Adaptive filtering for ScaNN significantly improves the speed of filtered vector searches. Adaptive filtering automatically selects the most efficient filtering method at runtime. For more information, see "Filtered vector search" and "Adaptive filtering" in the documentation for [AlloyDB for PostgreSQL](https://cloud.google.com/alloydb/docs/ai/filtered-vector-search-overview) and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/ai/filtered-vector-search-overview#inline-filtering) and [16.3.0](https://cloud..google.com/alloydb/omni/16.3.0/docs/ai/filtered-vector-search-overview#inline-filtering). * You can enable index auto maintenance and adaptive inline filtering together using the `scann.enable_preview_features` Grand Unified Configuration (GUC) parameters. For more information, see "AlloyDB flags" for [AlloyDB for PostgreSQL](https://cloud.google.com/alloydb/docs/reference/alloydb-flags) and AlloyDB Omni [15.7.1](https://cloud.google.com/alloydb/omni/15.7.1/docs/reference/alloydb-flags) and [16.3.0](https://cloud.google.com/alloydb/omni/16.3.0/docs/reference/alloydb-flags). ## Feature AlloyDB supports [C4A Arm VMs](https://cloud.google.com/compute/docs/general-purpose-machines#c4a%5Fseries) on Google's custom-built Axiom processors. C4A VMs are available as predefined configurations from 1, 4, 8, 16, 32, 48, 64, and 72 vCPUs, up to 576 GB of DDR5 memory. C4A machines are available in limited regions. This feature is in [Preview](https://cloud.google.com/productsproduct-launch-stages). For more information, see [Considerations when using the 1 vCPU machine type](https://cloud.google.com/alloydb/docs/cluster-create#considerations-c4a). ## Feature AlloyDB now supports managed connection pooling in [Preview](https://cloud.google.com/productsproduct-launch-stages). You can use managed connection pooling on your instances to improve the reliability, scalability, and performance of your workloads by optimizing resource utilization. For more information, see [Configure managed connection pooling](https://cloud.google.com/alloydb/docs/configure-managed-connection-pooling).