Kristian Aune

Head of Customer Success, Vespa

21 Aug 2019

In the recent Vespa product update, we mentioned Large Machine Learning Models, Multithreaded Disk Index Fusion, Ideal State Optimizations, and Feeding Improvements. Largely developed by Yahoo engineers, Vespa is an open source big data processing and serving engine. It’s in use by many products, such as Yahoo News, Yahoo Sports, Yahoo Finance, and the Verizon Media Ad Platform. Thanks to feedback and contributions from the community, Vespa continues to grow.

This month, we’re excited to share the following feature updates with you:

BM25 Rank Feature

The BM25 rank feature implements the Okapi BM25 ranking function and is a great candidate to use in a first phase ranking function when you’re ranking text documents. Read more.

Searchable Reference Attribute

A reference attribute field can be searched using the document id of the parent document-type instance as query term, making it easy to find all children for a parent document. Learn more.

Tensor in Summary Features

A tensor can now be returned in summary features.
This makes rank tuning easier and can be used in custom Searchers when generating result sets.
Read more.

Metrics Export

To export metrics out of Vespa, you can now use the new node metric interface. Aliasing metric names is possible and metrics are assigned to a namespace. This simplifies integration with monitoring products like CloudWatch and Prometheus. Learn more about this update.

We welcome your contributions and feedback (tweet or email) about any of these new features or future improvements you’d like to request.

Kristian Aune

Head of Customer Success, Vespa

19 Dec 2019

In the November Vespa product update, we mentioned Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance and Datadog Monitoring Support.

Today, we’re excited to share the following updates:

Improved ONNX Support

Vespa has added more operations to its ONNX model API, such as GEneral Matrix to Matrix Multiplication (GEMM) –
see list of supported opsets.
Vespa has also improved support for PyTorch through ONNX,
see the pytorch_test.py example.

New Rank Feature attributeMatch().maxWeight

attributeMatch(name).maxWeight was added in Vespa-7.135.5. The value is  the maximum weight of the attribute keys matched in a weighted set attribute.

Free Lists for Attribute Multivalue Mapping

Since Vespa-7.141.8, multivalue attributes uses a free list to improve performance. This reduces CPU (no compaction jobs) and approximately 10% memory. This primarily benefits applications with a high update rate to such attributes.

Faster Updates for Out-of-Sync Documents

Vespa handles replica consistency using bucket checksums. Updating documents can be cheaper than putting a new document, due to less updates to posting lists. For updates to documents in inconsistent buckets, a GET-UPDATE is now used instead of a GET-PUT whenever the document to update is consistent across replicas. This is the common case when only a subset of the documents in the bucket are out of sync. This is useful for applications with high update rates, updating multi-value fields with large sets. Explore details here.

ZooKeeper 3.5.6

Vespa now uses Apache ZooKeeper 3.5.6 and can encrypt communication between ZooKeeper servers.

About Vespa: Largely developed by Yahoo engineers, Vespa is an open source big data processing and serving engine. It’s in use by many products, such as Yahoo News, Yahoo Sports, Yahoo Finance, and the Verizon Media Ad Platform. Thanks to feedback and contributions from the community, Vespa continues to grow.

We welcome your contributions and feedback (tweet or email) about any of these new features or future improvements you’d like to request.