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125 lines
3.6 KiB
Markdown
125 lines
3.6 KiB
Markdown
---
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title: Bulk Upload Vectors
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weight: 13
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---
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# Bulk upload a large number of vectors
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Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that.
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The first important detail about data uploading is that the bottleneck is usually located on the client side, not on the server side.
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This means that if you are uploading a large dataset, you should prefer a high-performance client library.
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We recommend using our [Rust client library](https://github.com/qdrant/rust-client) for this purpose, as it is the fastest client library available for Qdrant.
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If you are not using Rust, you might want to consider parallelizing your upload process.
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## Disable indexing during upload
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In case you are doing an initial upload of a large dataset, you might want to disable indexing during upload.
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It will enable to avoid unnecessary indexing of vectors, which will be overwritten by the next batch.
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To disable indexing during upload, set `indexing_threshold` to `0`:
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```http
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PUT /collections/{collection_name}
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{
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"vectors": {
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"size": 768,
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"distance": "Cosine"
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},
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"optimizers_config": {
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"indexing_threshold": 0
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}
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}
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```
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```python
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from qdrant_client import QdrantClient, models
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client = QdrantClient("localhost", port=6333)
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client.recreate_collection(
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collection_name="{collection_name}",
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vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
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optimizers_config=models.OptimizersConfigDiff(
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indexing_threshold=0,
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),
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)
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```
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After upload is done, you can enable indexing by setting `indexing_threshold` to a desired value (default is 20000):
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```http
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PATCH /collections/{collection_name}
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{
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"optimizers_config": {
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"indexing_threshold": 20000
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}
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}
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```
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```python
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from qdrant_client import QdrantClient, models
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client = QdrantClient("localhost", port=6333)
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client.update_collection(
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collection_name="{collection_name}",
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optimizer_config=models.OptimizersConfigDiff(
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indexing_threshold=20000
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)
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)
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```
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## Upload directly to disk
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When the vectors you upload do not all fit in RAM, you likely want to use
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[memmap](../../concepts/storage/#configuring-memmap-storage)
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support.
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During collection
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[creation](../../concepts/collections/#create-collection),
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memmaps may be enabled on a per-vector basis using the `on_disk` parameter. This
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will store vector data directly on disk at all times. It is suitable for
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ingesting a large amount of data, essential for the billion scale benchmark.
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Using `memmap_threshold_kb` is not recommended in this case. It would require
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the [optimizer](../../concepts/optimizer/) to constantly
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transform in-memory segments into memmap segments on disk. This process is
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slower, and the optimizer can be a bottleneck when ingesting a large amount of
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data.
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Read more about this in
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[Configuring Memmap Storage](../../concepts/storage/#configuring-memmap-storage).
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## Parallel upload into multiple shards
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In Qdrant, each collection is split into shards. Each shard has a separate Write-Ahead-Log (WAL), which is responsible for ordering operations.
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By creating multiple shards, you can parallelize upload of a large dataset. From 2 to 4 shards per one machine is a reasonable number.
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```http
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PUT /collections/{collection_name}
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{
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"vectors": {
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"size": 768,
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"distance": "Cosine"
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},
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"shard_number": 2
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}
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```
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```python
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from qdrant_client import QdrantClient, models
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client = QdrantClient("localhost", port=6333)
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client.recreate_collection(
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collection_name="{collection_name}",
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vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
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shard_number=2,
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)
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``` |