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docs: Add Qdrant search performance enhancement with filters

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qdrant-landing/content/documentation/faq/database-optimization.md
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@@ -12,8 +12,8 @@ The primary source of memory usage vector data. There are several ways to addres
- Configure [Quantization](../../guides/quantization/) to reduce the memory usage of vectors.
- Configure on-disk vector storage
The choice of the approach depends on your requirements.
Read more about [configuring the optimal](../../tutorials/optimize/) use of Qdrant.
The choice of the approach depends on your requirements.
Read more about [configuring the optimal](../../tutorials/optimize/) use of Qdrant.
### How do you choose machine configuration?
@@ -22,7 +22,7 @@ There are two main scenarios of Qdrant usage in terms of resource consumption:
- **Performance-optimized** -- when you need to serve vector search as fast (many) as possible. In this case, you need to have as much vector data in RAM as possible. Use our [calculator](https://cloud.qdrant.io/calculator) to estimate the required RAM.
- **Storage-optimized** -- when you need to store many vectors and minimize costs by compromising some search speed. In this case, pay attention to the disk speed instead. More about it in the article about [Memory Consumption](../../../articles/memory-consumption/).
### I configured on-disk vector storage, but memory usage is still high. Why?
### I configured on-disk vector storage, but memory usage is still high. Why?
Firstly, memory usage metrics as reported by `top` or `htop` may be misleading. They are not showing the minimal amount of memory required to run the service.
If the RSS memory usage is 10 GB, it doesn't mean that it won't work on a machine with 8 GB of RAM.
@@ -34,7 +34,6 @@ As a result, the Qdrant process might use more memory than the minimum required
If you want to limit the memory usage of the service, we recommend using [limits in Docker](https://docs.docker.com/config/containers/resource_constraints/#memory) or Kubernetes.
### My requests are very slow or time out. What should I do?
There are several possible reasons for that:
@@ -43,11 +42,10 @@ There are several possible reasons for that:
- **Usage of on-disk vector storage with slow disks** -- If you're using on-disk vector storage, ensure you have fast enough disks. We recommend using local SSDs with at least 50k IOPS. Read more about the influence of the disk speed on the search latency in the article about [Memory Consumption](../../../articles/memory-consumption/).
- **Large limit or non-optimal query parameters** -- A large limit or offset might lead to significant performance degradation. Please pay close attention to the query/collection parameters that significantly diverge from the defaults. They might be the reason for the performance issues.
### How can I optimize optimizers configuration settings for better accuracy and speed, especially for large-scale collections?
To optimize optimizers config in Qdrant for better accuracy and speed, consider the following:
- For low memory footprint with high-speed search, utilize vector quantization with disk storage for vectors and in-memory quantized vectors. Configure memmap_threshold and always_ram accordingly.
- To prioritize high precision with a low memory footprint, enable on-disk vectors and HNSW index. Adjust HNSW parameters for precision while considering disk IOPS.
- For high precision with high-speed search, focus on keeping data in RAM. Utilize quantization with re-scoring and adjust search-time parameters like hnsw_ef for accuracy and speed balance.
@@ -56,4 +54,18 @@ To optimize optimizers config in Qdrant for better accuracy and speed, consider
- Fine-tune quantization parameters such as quantile for scalar quantization to optimize search precision and memory usage.
- Adjust storage modes to balance between memory footprint and search speed, considering factors like disk reads and storage type (RAM, SSD, or HDD).
Read more about [optimizing](../../guides/optimize/), and [quantization](../../guides/quantization/) in Qdrant.
Read more about [optimizing](../../guides/optimize/), and [quantization](../../guides/quantization/) in Qdrant.
### How can Qdrant I enhance search performance when applying filters to their queries?
To improve search performance with filters in Qdrant:
- **Utilize Payload Index:** Implement payload indexes for fields used in filtering conditions. This enhances point retrieval speed based on filter criteria.
- **Consider Full-text Index:** For string payloads, utilize full-text indexes for word or phrase presence filtering, adjusting tokenization parameters as needed.
- **Parameterized Integer Index:** Opt for parameterized integer indexes for specific range filter requirements, balancing between lookup and range operations.
- **Leverage Vector Index:** Employ HNSW for dense vector indexing, adjusting parameters like `m` and `ef_construct` for improved search accuracy and efficiency.
- **Sparse Vector Index:** For sparse vectors, ensure efficient indexing and search mechanisms. Configure memory or disk storage based on segment characteristics and query patterns.
- **Filtrable Index Extension:** Enhance HNSW graph with additional edges based on payload values to optimize search efficiency with varying filter stringency.
By strategically combining these indexing techniques, you can address performance issues and achieve faster search results with applied filters in Qdrant.
Read more about [indexing](../../concepts/indexing/) in Qdrant.