Kristian Aune

Head of Customer Success, Vespa

29 Mar 2019

In last month’s Vespa update, we mentioned Boolean Field Type, Environment Variables, and Advanced Search Core Tuning. 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 Oath Ads Platforms. Thanks to feedback and contributions from the community, Vespa continues to grow.

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

Tensor update

Easily update individual tensor cells. Add, remove, and modify cell is now supported. This enables high throughput and continuous updates as tensor values can be updated without writing the full tensor.

Advanced Query Trace

Query tracing now includes matching and ranking execution information from content nodes –
Query Explain,  is useful for performance optimization.

Search coverage in access log

Search coverage is now available in the access log. This enables operators to track the fraction of queries that are degraded with lower coverage. Vespa has features to gracefully reduce query coverage in overload situations and now it’s easier to track this. Search coverage is a useful signal to reconfigure or increase the capacity for the application. Explore the access log documentation to learn more.

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

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

01 Oct 2019

In the August Vespa product update, we mentioned BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export. 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 updates with you:

Tensor Float Support

Tensors now supports float cell values, for example tensor<float>(key{}, x[100]). Using the 32 bits float type cuts memory footprint in half compared to the 64 bits double, and can increase ranking performance up to 30%. Vespa’s TensorFlow and ONNX integration now converts to float tensors for higher performance. Read more.

Reduced Memory Use for Text Attributes 

Attributes in Vespa are fields stored in columnar form in memory for access during ranking and grouping. From Vespa 7.102, the enum store used to hold attribute data uses a set of smaller buffers instead of one large. This typically cuts static memory usage by 5%, but more importantly reduces peak memory usage (during background compaction) by 30%.

Prometheus Monitoring Support

Integrating with the Prometheus open-source monitoring solution is now easy to do
using the new interface to Vespa metrics.
Read more.

Query Dispatch Integrated in Container

The Vespa query flow is optimized for multi-phase evaluation over a large set of search nodes. Since Vespa-7-109.10, the dispatch function is integrated into the Vespa Container process which simplifies the architecture with one less service to manage. Read more.

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

05 Nov 2019

In the September Vespa product update, we mentioned Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container.

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

Nearest Neighbor and Tensor Ranking

Tensors are native to Vespa. We compared elastic.co to vespa.ai testing nearest neighbor ranking using dense tensor dot product. The result of an out-of-the-box configuration demonstrated that Vespa performed 5 times faster than Elastic. View the test results.

Optimized JSON Tensor Feed Format

A tensor is a data type used for advanced ranking and recommendation use cases in Vespa. This month, we released an optimized tensor format, enabling a more than 10x improvement in feed rate. Read more.

Matched Elements in Complex Multi-value Fields 

Vespa is used in many use cases with structured data – documents can have arrays of structs or maps. Such arrays and maps can grow large, and often only the entries matching the query are relevant. You can now use the recently released matched-elements-only setting to return matches only. This increases performance and simplifies front-end code.

Large Weighted Set Update Performance

Weighted sets in documents are used to store a large number of elements used in ranking. Such sets are often updated at high volume, in real-time, enabling online big data serving. Vespa-7.129 includes a performance optimization for updating large sets. E.g. a set with 10K elements, without fast-search, is 86.5% faster to update.

Datadog Monitoring Support

Vespa is often used in large scale mission-critical applications. For easy integration into dashboards,
Vespa is now in Datadog’s integrations-extras GitHub repository.
Existing Datadog users will now find it easy to monitor Vespa.
Read more.

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.