landing_page/qdrant-landing/content/documentation/migrate-to-qdrant/from-elasticsearch.md

title, weight, partition

title	weight	partition
From Elasticsearch	20	ecosystem

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 + kNN scoring, you'll need to reconstruct this in Qdrant using sparse vectors (for BM25-like behavior) alongside dense vectors. The migration tool transfers the dense vectors; you'll need to generate sparse vectors separately if you want hybrid search in Qdrant.

Qdrant supports native hybrid search with Reciprocal Rank Fusion (RRF) to combine dense and sparse results.

Gotchas

• Nested documents: Elasticsearch nested documents need to be flattened or restructured for Qdrant's payload model.
• Score normalization: Elasticsearch _score values are not comparable to Qdrant scores. Use rank-based metrics (recall@k, Spearman correlation) rather than raw score comparison when verifying your migration.
• BM25 is not migrated: The migration tool transfers vectors and document fields. If you relied on Elasticsearch's BM25 scoring, you'll need to set up sparse vectors in Qdrant separately.

Next Steps

After migration, verify your data arrived correctly with the Migration Verification Guide.