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| import pathlib | |
| from .utils import * | |
| from .assertions import assert_http_ok | |
| N_PEERS = 3 | |
| N_SHARDS = 4 | |
| N_REPLICA = 2 | |
| def test_collection_shard_update(tmp_path: pathlib.Path): | |
| assert_project_root() | |
| peer_dirs = make_peer_folders(tmp_path, N_PEERS) | |
| # Gathers REST API uris | |
| peer_api_uris = [] | |
| # Start bootstrap | |
| (bootstrap_api_uri, bootstrap_uri) = start_first_peer( | |
| peer_dirs[0], "peer_0_0.log") | |
| peer_api_uris.append(bootstrap_api_uri) | |
| # Wait for leader | |
| leader = wait_peer_added(bootstrap_api_uri) | |
| # Start other peers | |
| for i in range(1, len(peer_dirs)): | |
| peer_api_uris.append(start_peer( | |
| peer_dirs[i], f"peer_0_{i}.log", bootstrap_uri)) | |
| # Wait for cluster | |
| wait_for_uniform_cluster_status(peer_api_uris, leader) | |
| # Check that there are no collections on all peers | |
| for uri in peer_api_uris: | |
| r = requests.get(f"{uri}/collections") | |
| assert_http_ok(r) | |
| assert len(r.json()["result"]["collections"]) == 0 | |
| # Create collection in first peer | |
| r = requests.put( | |
| f"{peer_api_uris[0]}/collections/test_collection", json={ | |
| "vectors": { | |
| "image": { | |
| "size": 4, | |
| "distance": "Dot" | |
| }, | |
| "text": { | |
| "size": 4, | |
| "distance": "Cosine" | |
| } | |
| }, | |
| "shard_number": N_SHARDS, | |
| "replication_factor": N_REPLICA, | |
| }) | |
| assert_http_ok(r) | |
| # Check that it exists on all peers | |
| wait_collection_exists_and_active_on_all_peers(collection_name="test_collection", peer_api_uris=peer_api_uris) | |
| # Check collection's cluster info | |
| collection_cluster_info = get_collection_cluster_info(peer_api_uris[0], "test_collection") | |
| assert collection_cluster_info["shard_count"] == N_SHARDS | |
| # Create request with missing named vectors in first peer's collection | |
| r = requests.put( | |
| f"{peer_api_uris[0]}/collections/test_collection/points?wait=true", json={ | |
| "points": [ | |
| { | |
| "id": 1, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74], | |
| "text": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| }, | |
| { | |
| "id": 2, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| }, | |
| { | |
| "id": 3, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74], | |
| "text": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| }, | |
| { | |
| "id": 4, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74], | |
| "text": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| } | |
| ] | |
| }) | |
| assert_http_ok(r) | |
| # Create malformed points in first peer's collection | |
| r = requests.put( | |
| f"{peer_api_uris[0]}/collections/test_collection/points?wait=true", json={ | |
| "points": [ | |
| { | |
| "id": 1, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74], | |
| "text": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| }, | |
| { | |
| "id": 2, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76] | |
| } | |
| }, | |
| { | |
| "id": 3, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74], | |
| "text": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| }, | |
| { | |
| "id": 4, | |
| "vector": { | |
| "image": [0.05, 0.61, 0.76, 0.74], | |
| "text": [0.05, 0.61, 0.76, 0.74] | |
| } | |
| } | |
| ] | |
| }) | |
| assert r.status_code == 400 | |
| error = r.json()["status"]["error"] | |
| assert error.__contains__("Wrong input: 1 out of 2 shards failed to apply operation") | |
| # Update requests are applied on the local shard and propagated to the remote shards in parallel. | |
| # These operations may complete (or fail) in arbitrary order, and if request fails, Qdrant returns | |
| # error message of the first failed operation. | |
| # Local and remote operations return different errors, so we check for both. | |
| assert error.__contains__("Wrong input: Vector dimension error: expected dim") or error.__contains__("Wrong input: InvalidArgument") | |