Migrate from Elasticsearch to Qdrant
What You Need from Elasticsearch
- Elasticsearch URL — the HTTP endpoint
- Index name — the index containing your vectors
- Credentials — username/password or API key
Concept Mapping
| Elasticsearch | Qdrant | Notes |
|---|---|---|
| Index | Collection | One-to-one mapping |
| Document | Point | Each document becomes a point |
dense_vector field | Vector | Mapped automatically |
| Document fields | Payload | Non-vector fields become payload |
cosine | Cosine | ES returns 1 - cosine_distance; Qdrant returns cosine similarity directly |
l2_norm | Euclid | Direct mapping |
dot_product | Dot | Direct mapping |
Run the Migration
docker run --net=host --rm -it registry.cloud.qdrant.io/library/qdrant-migration elasticsearch \
--elasticsearch.url 'https://your-es-host:9200' \
--elasticsearch.index 'your-index' \
--elasticsearch.username 'elastic' \
--elasticsearch.password 'your-password' \
--qdrant.url 'https://your-instance.cloud.qdrant.io:6334' \
--qdrant.api-key 'your-qdrant-api-key' \
--qdrant.collection 'your-collection'
Using API Key Authentication
docker run --net=host --rm -it registry.cloud.qdrant.io/library/qdrant-migration elasticsearch \
--elasticsearch.url 'https://your-es-host:9200' \
--elasticsearch.index 'your-index' \
--elasticsearch.api-key 'your-es-api-key' \
--qdrant.url 'https://your-instance.cloud.qdrant.io:6334' \
--qdrant.api-key 'your-qdrant-api-key' \
--qdrant.collection 'your-collection'
All Elasticsearch-Specific Flags
| Flag | Required | Description |
|---|---|---|
--elasticsearch.url | Yes | Elasticsearch HTTP endpoint |
--elasticsearch.index | Yes | Index to migrate |
--elasticsearch.username | No | Username for basic auth |
--elasticsearch.password | No | Password for basic auth |
--elasticsearch.api-key | No | API key for authentication |
--elasticsearch.insecure-skip-verify | No | Skip TLS certificate verification |
Qdrant-Side Options
| Flag | Default | Description |
|---|---|---|
--qdrant.id-field | __id__ | Payload field name for original Elasticsearch document IDs |
Hybrid Search Considerations
If your Elasticsearch setup uses hybrid BM25 and kNN scoring, you can reconstruct this in Qdrant with BM25 on a full-text index alongside dense vectors. The migration tool transfers the dense vectors and document fields, so you don’t need to generate embeddings for BM25. Create a full-text index with scoring enabled on the text fields you want to search.
Qdrant supports native hybrid search with Reciprocal Rank Fusion (RRF) to combine dense and BM25 results.
Next Steps
After migration, verify your data arrived correctly with the Migration Verification Guide.
Ready to try Qdrant?