Filtered search benchmarks (#112)

* new: add filtered search bench draft

* new: update filtered search benchmark post

* new: render article

* refactoring: add newlines

* new: add filtered search bench file structure, add chart

* fix: fix plot preselect

* fix: fix canvas id

* debug: debug js

* debug: add benchmark filter search script to header

* fix: fix filter search plot selection

* new: update benchmark data

* refactoring: remove commented code

* fix: fix bench data

* new: fix labels order

* new: sort dataset labels

* new: add some text

* fix: fix descriptions

* debug: fix mapping not allowed yaml parsing error

* new: update bench data

* fix: fix dataset name

* new: update bench data

* fix: fix qdrant kw small vocab

* text for the articles

* upd text

* fix: fix typos

* new: replace bars with scatter plots

* fix: fix method calls

* debug: add initializer

* fix: replace debug const with variable

* new: remove data based split, remain only one plot

* fix: remove geo data for milvus

* new: remove range data for weaviate

* new: remove int 2048 for milvus, as it crashes at the moment

* upd graphs

* link

---------

Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com>

qdrant-landing/content/benchmarks/_index.md

@@ -6,6 +6,7 @@ keywords:
   - ANN Benchmark
   - Qdrant vs Milvus
   - Qdrant vs Weaviate
+  - Qdrant vs Redis
   - Qdrant vs ElasticSearch
   - benchmark
   - performance

qdrant-landing/content/benchmarks/benchmarks-intro.md

@@ -19,8 +19,8 @@ So in our benchmarks, we **focus on the relative numbers**, so it is possible to
 The list will be updated:
 
 * Upload & Search speed on single node - [Benchmark](/benchmarks/single-node-speed-benchmark/)
+* Filtered search benchmark - [Benchmark](/benchmarks/#filtered-search-benchmark)
 * Memory consumption benchmark - TBD
-* Filtered search benchmark - TBD
 * Cluster mode benchmark - TBD
 
 Some of our experiment design decisions are described at [F.A.Q Section](/benchmarks/#benchmarks-faq).

qdrant-landing/content/benchmarks/filtered-search-benchmark.md

@@ -0,0 +1,25 @@
+---
+draft: false
+id: 5
+title: 
+description: 
+
+filter_data: /benchmarks/filter-result-2023-02-03.json
+date: 2023-02-13
+weight: 4
+---
+
+
+## Filtered Results
+
+As you can see from the charts, there are three main patterns:
+
+- **Speed boost** - for some engines/queries, the filtered search is faster than the unfiltered one. It might happen if the filter is restrictive enough, to completely avoid the usage of the vector index.
+
+- **Speed downturn** - some engines struggle to keep high RPS, it might be related to the requirement of building a filtering mask for the dataset, as described above.
+
+- **Accuracy collapse** - some engines are loosing accuracy dramatically under some filters. It is related to the fact that the HNSW graph becomes disconnected, and the search becomes unreliable.
+
+Qdrant avoids all these problems and also benefits from the speed boost, as it implements an advanced [query planning strategy](/documentation/search/#query-planning).
+
+<aside role="status">The Filtering Benchmark is all about changes in performance between filter and un-filtered queries. Please refer to the search benchmark for absolute speed comparison.</aside>

qdrant-landing/content/benchmarks/filtered-search-intro.md

@@ -0,0 +1,34 @@
+---
+draft: false
+id: 4
+title: Filtered search benchmark
+description: 
+
+date: 2023-02-13
+weight: 3
+---
+
+# Filtered search benchmark
+
+Applying filters to search results brings a whole new level of complexity.
+It is no longer enough to apply one algorithm to plain data. With filtering, it becomes a matter of the _cross-integration_ of the different indices.
+
+To measure how well different engines perform in this scenario, we have prepared a set of **Filtered ANN Benchmark Datasets** -
+ https://github.com/qdrant/ann-filtering-benchmark-datasets
+
+
+It is similar to the ones used in the [ann-benchmarks project](https://github.com/erikbern/ann-benchmarks/) but enriched with payload metadata and pre-generated filtering requests. It includes synthetic and real-world datasets with various filters, from keywords to geo-spatial queries.
+
+### Why filtering is not trivial?
+
+Not many ANN algorithms are compatible with filtering.
+HNSW is one of the few of them, but search engines approach its integration in different ways:
+
+- Some use **post-filtering**, which applies filters after ANN search. It doesn't scale well as it either loses results or requires many candidates on the first stage.
+- Others use **pre-filtering**, which requires a binary mask of the whole dataset to be passed into the ANN algorithm. It is also not scalable, as the mask size grows linearly with the dataset size.
+
+On top of it, there is also a problem with search accuracy. 
+It appears if too many vectors are filtered out, so the HNSW graph becomes disconnected.
+
+Qdrant uses a different approach, not requiring pre- or post-filtering while addressing the accuracy problem.
+Read more about the Qdrant approach in our [Filtrable HNSW](/articles/filtrable-hnsw/) article.