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davidmyriel
2025-01-20 11:45:54 -05:00
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qdrant-landing/content/blog/qdrant-1.13.x.md
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@@ -34,7 +34,7 @@ This new feature reduces indexing times, making it a game-changer for projects w
Our custom implementation of GPU-accelerated HNSW indexing **is built entirely in-house**. Unlike solutions that depend on third-party libraries, our approach is vendor-agnostic, meaning it works seamlessly with any modern GPU that supports **Vulkan API**. This ensures broad compatibility and flexibility for a wide range of systems.
*Qdrant's new AMD Docker Image running on SteamDeck with an AMD Van Gogh GPU:*
*Here is a picture of us, running Qdrant with GPU support on a SteamDeck (AMD Van Gogh GPU):*
{{< figure src="/blog/qdrant-1.13.x/gpu-test.jpg" alt="Qdrant on SteamDeck" caption="Qdrant on SteamDeck with AMD GPU" >}}
@@ -46,12 +46,12 @@ This experiment didn't require any changes to the codebase, and everything worke
**Qdrant doesn't require high-end GPUs** to achieve significant performance improvements. Let's take a look at some benchmark results for common GPU machines:
| **Configuration** | **With GPU (s)** | **Without GPU (s)** | **Price per Instance (USD/hour)** |
|-------------------------------|-------------------|----------------------|------------------------------------|
| 8 vCPU / AMD Radeon Pro V520 | 33.066 | 94.733 | $0.54 |
| 8 vCPU / Nvidia T4 | 18.801 | 97.709 | $0.51 |
| 8 vCPU / Nvidia L4 | 12.389 | 99.944 | $0.85 |
| 4 vCPU / Nvidia T4 | 19.333 | 221.933 | $0.38 |
| **Configuration** | **With GPU (s)** | **Without GPU (s)** | **Price per Instance (USD/hour)** | **Price per Instance (USD/month)** |
|------------------------------|------------------|---------------------|-----------------------------------|------------------------------------|
| 8 vCPU / AMD Radeon Pro V520 | 33.066 | 94.733 | $0.54 | $394.20 |
| 8 vCPU / Nvidia T4 | 18.801 | 97.709 | $0.51 | $372.30 |
| 8 vCPU / Nvidia L4 | 12.389 | 99.944 | $0.85 | $620.50 |
| 4 vCPU / Nvidia T4 | 19.333 | 221.933 | $0.38 | $277.40 |
**Additional Benefits:**
@@ -84,7 +84,7 @@ This was particularly challenging given that vector data has very high entropy,
**How We Did It:**
To implement this feature, we not only had to modify the code in Qdrant itself but also introduce changes to the upstream [**tar-rs**](https://github.com/alexcrichton/tar-rs/pulls?q=is%3Apr+author%3Axzfc+is%3Aclosed) library, a Rust library for working with tar archives.
To implement this feature, we not only had to modify the code in Qdrant itself but also introduce changes to the upstream [**tar-rs**](https://github.com/alexcrichton/tar-rs/pulls?q=is%3Apr+author%3Axzfc+is%3Aclosed) library, a Rust library used to work with tar archives.
The introduction of streaming support finally brings tar (short for “tape archive”), a format historically designed for tape streamers, back to its original purpose.
@@ -232,21 +232,15 @@ client.CreateCollection(context.Background(), &qdrant.CreateCollection{
![hnsw-graph-compression](/blog/qdrant-1.13.x/image_3.png)
We’re always looking for ways to make your search experience faster and more efficient. That’s why we are introducing a new optimization method for our HNSW graph technology: [**Delta Encoding**](https://en.wikipedia.org/wiki/Delta_encoding). This improvement makes your searches lighter on memory without sacrificing speed.
We’re always looking for ways to make your search experience faster and more efficient.
That’s why we are introducing a new optimization method for our HNSW graph technology: [**Delta Encoding**](https://en.wikipedia.org/wiki/Delta_encoding).
This improvement makes your searches lighter on memory without sacrificing speed.
**Delta Encoding** is a clever way to compress data by storing only the differences (or “deltas”) between values. It’s commonly used in search engines (*for the classical inverted index*) to save space and improve performance. We’ve now [**adapted this technique**](https://github.com/qdrant/qdrant/pull/5487) for the HNSW graph structure that powers Qdrant’s search.
> With **Delta Encoding**, the memory needed to store your data’s graph structure can be reduced by up to 30%.
In contrast with traditional compression algorithms, like gzip or lz4, **Delta Encoding** requires very little CPU overhead for decompression, which makes it a perfect fit for the HNSW graph links.
The best part? This optimization doesn’t slow down your searches. You’ll experience the same lightning-fast results you’re used to, but with a smaller memory footprint.
### How Delta Encoding Works
Unlike traditional compression methods like gzip, which can be resource-intensive to decompress, **Delta Encoding** is designed to be lightweight. It works seamlessly in the background, minimizing the strain on your system while keeping performance at its peak.
1. Imagine your data is represented as a series of connected points (a graph).
2. **Delta Encoding** compresses this graph by storing only the necessary information about the differences between points.
3. The result is a smaller, more efficient structure that’s quick to access.
> Our experiments didn't observe any measurable performance degradation. However, the memory footprint of the HNSW graph was **reduced by up to 30%**.
*For more general info, read about [**Indexing and Data Structures in Qdrant**](/documentation/concepts/indexing/)*
@@ -388,7 +382,7 @@ client.Scroll(context.Background(), &qdrant.ScrollPoints{
},
})
```
This feature makes it easier to manage and query collections with heterogeneous data. It give you more flexibility and control over your vector search workflows.
This feature makes it easier to manage and query collections with heterogeneous data. It will give you more flexibility and control over your vector search workflows.
*To dive deeper into filtering by named vectors, check out the [**Filtering Documentation**](/documentation/concepts/filtering/#has-vector)*