Weaviate v1.15 Release with Etienne Dilocker and Dirk Kulawiak
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Weaviate Podcast #24. Weaviate v1.15 Release! Thank you so much for checking out the Weaviate podcast -- here is a summary of what is new in Weaviate 1.15:  1. Cloud-native backups – allows you to configure your environment to create backups – of selected classes or the whole database – straight into AWS S3, GCS or local filesystem 2. Reduced memory usage - we found new ways to optimize memory usage, reducing RAM usage by 10-30%. 3. Better control over Garbage Collector – with the introduction of GOMEMLIMIT we gained more control over the garbage collector, which significantly reduced the chances of OOM kills for your Weaviate setups. 4. Faster imports for ordered data – by extending the Binary Search Tree structure with a self-balancing Red-black tree, we were able to speed up imports from O(n) to O(log n) 5. More efficient filtered aggregations – thanks to optimization to a library reading binary data, filtered aggregations are now 10-20 faster and require a lot less memory. 6. Two new distance metrics – with the addition of Hamming and Manhattan distance metrics, you can choose the metric (or a combination of) to best suit your data and use case. 7. Two new Weaviate modules – with the Summarization module, you can summarize any text on the fly, while with the HuggingFace module, you can use compatible transformers from the HuggingFace 8. Other improvements and bug fixes – it goes without saying that with every Weaviate release, we strive to make Weaviate more stable – through bug fixes – and more efficient – through many optimizations.  Please check out this awesome blog post from Sebastian Witalec and the team describing these further - https://weaviate.io/blog/2022/09/Weav...).