{ "license": "CC-BY-NC-4.0; derived from the MerRec dataset, Copyright Mercari, Inc.; see LICENSE-MERREC.md in this directory", "name": "merrec-agent-session", "recorded_at_utc": "2026-10-15T15:01:09Z", "method": "Statements written in order by an AI agent (Claude, in Claude Code) on 2026-10-12 against a freshly started MerRec scale server over Arrow Flight SQL, each written after reading the previous response, following the GPU UDTF discovery guide. On 2026-10-15 the same statements were replayed in the same order against a freshly started server built from commit d4b48c569, with both source Parquet files in the operating-system page cache; elapsed times, query ids, execution details and rows are from that replay. Result rows of steps 1, 3, 4 and 5 are identical to the 2026-10-12 session; step 2 returns the descriptor in its current shape. The probes were sent after the session to record the planning-time errors for two wrong column names.", "server": { "config": "fixture/demo/merrec/scripts/scale/server.toml", "execution_mode": "native_required", "binary_sha256": "f11352d8a2bb6873bf466b15698bbfb67d797a55d1a1dccbfb6713ca0e422969" }, "dataset": "MerRec, 1,268,092,738 events, May through October 2023", "steps": [ { "step": 1, "intent": "Which graph functions can rank brands around a seed?", "sql": "SELECT function_name, summary\nFROM gpu_list_functions()\nWHERE provider = 'cugraph' AND (function_name LIKE '%pagerank%' OR function_name LIKE '%jaccard%')\nORDER BY function_name", "status": "ok", "elapsed_s": 0.0041826339438557625, "source_cache_resident_before": { "items.parquet": 1.0, "events.parquet": 1.0 }, "query_id": "ba9821ca-b67b-4254-8140-2ec28403e297", "rows": [ { "function_name": "cugraph_jaccard", "summary": "Compare explicit vertex pairs by dividing the size of their shared-neighbor intersection by the size of their neighbor union." }, { "function_name": "cugraph_pagerank", "summary": "Rank vertices by the stationary probability of a damped random walk that follows outgoing edges." }, { "function_name": "cugraph_personalized_pagerank", "summary": "Rank vertices with PageRank while biasing random-walk restarts toward explicitly weighted personalization vertices." } ] }, { "step": 2, "intent": "What does personalized PageRank take, and what does it return?", "sql": "SELECT signature, relation_roles_json, result_schemas_json\nFROM gpu_describe_function('cugraph_personalized_pagerank')", "status": "ok", "elapsed_s": 0.0027022899594157934, "source_cache_resident_before": { "items.parquet": 1.0, "events.parquet": 1.0 }, "query_id": "21a19e2a-2b3b-4143-b71d-bc023511c27f", "rows": [ { "signature": "cugraph_personalized_pagerank(edges => (relation subquery), directed => value, renumber => value, construction_policy => value, personalization => (relation subquery), alpha => value, epsilon => value, max_iterations => value)", "relation_roles_json": "[{\"name\":\"edges\",\"required\":true,\"description\":\"edge relation with canonical src and dst columns, plus optional weight and edge_id columns\",\"fields\":[{\"name\":\"src\",\"required\":true,\"type\":\"vertex_id\",\"data_types\":[\"Int32\",\"Int64\",\"Utf8\",\"LargeUtf8\",\"Utf8View\"],\"description\":\"source vertex identifier\"},{\"name\":\"dst\",\"required\":true,\"type\":\"vertex_id\",\"data_types\":[\"Int32\",\"Int64\",\"Utf8\",\"LargeUtf8\",\"Utf8View\"],\"description\":\"destination vertex identifier\"},{\"name\":\"weight\",\"required\":false,\"type\":\"numeric\",\"data_types\":[\"Float32\",\"Float64\"],\"description\":\"optional edge weight\"},{\"name\":\"edge_id\",\"required\":false,\"type\":\"integer_edge_id\",\"data_types\":[\"Int32\",\"Int64\"],\"description\":\"optional edge identifier\"}],\"vertex_id_contract\":{\"numeric_endpoint_types\":[\"Int32\",\"Int64\"],\"mapped_numeric_endpoint_types\":[],\"logical_string_endpoint_types\":[\"Utf8\",\"LargeUtf8\",\"Utf8View\"],\"logical_string_output_type\":\"Utf8\",\"native_logical_mapping_type\":\"Int64\",\"edge_id_supported_for_logical_string\":false,\"call_specific_authority\":\"gpu_validate_call\"}},{\"name\":\"personalization\",\"required\":true,\"description\":\"personalization relation with vertex and numeric value columns\",\"fields\":[{\"name\":\"vertex\",\"required\":true,\"type\":\"vertex_id\",\"data_types\":[\"Int32\",\"Int64\",\"Utf8\",\"LargeUtf8\",\"Utf8View\"],\"description\":\"personalization vertex identifier\"},{\"name\":\"value\",\"required\":true,\"type\":\"numeric\",\"data_types\":[\"Float32\",\"Float64\"],\"description\":\"personalization value\"}]}]", "result_schemas_json": "{\"default_schema\":\"default\",\"schemas\":{\"default\":{\"fields\":[{\"name\":\"vertex\",\"data_type\":\"Int64|Utf8\",\"nullable\":false},{\"name\":\"value\",\"data_type\":\"Float64\",\"nullable\":false}]}},\"call_specific_authority\":\"gpu_validate_call\",\"note\":\"Generic registry schemas; run gpu_validate_call for the concrete table-specific output schema.\",\"field_descriptions\":[{\"name\":\"vertex\",\"description\":\"Vertex receiving the PageRank score.\"},{\"name\":\"value\",\"description\":\"PageRank score for the vertex.\"}]}" } ] }, { "step": 3, "intent": "Which vertex is Barbie? Look up the brand id to seed from", "sql": "SELECT brand_id, COUNT(*) AS items\nFROM merrec_items\nWHERE brand_name = 'Barbie'\nGROUP BY brand_id\nORDER BY items DESC", "status": "ok", "elapsed_s": 0.5186802430544049, "source_cache_resident_before": { "items.parquet": 1.0, "events.parquet": 1.0 }, "query_id": "4addcf9e-6958-44f0-8785-ba682b563b8e", "rows": [ { "brand_id": 2595, "items": 221444 } ] }, { "step": 4, "intent": "Before spending GPU time: will the whole query run on the GPU?", "sql": "EXPLAIN GPU\nWITH likes AS (\n SELECT DISTINCT -(e.user_id + 1) AS src, CAST(i.brand_id AS BIGINT) AS dst,\n e.event_time >= TIMESTAMP '2023-07-01 00:00:00' AS after_release\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND e.event_time >= TIMESTAMP '2023-05-01 00:00:00'\n AND e.event_time < TIMESTAMP '2023-09-01 00:00:00'\n), lift_before AS (\n SELECT p.vertex, p.value / r.value AS lift\n FROM cugraph_personalized_pagerank(\n edges => (SELECT src, dst FROM likes WHERE NOT after_release),\n personalization => (SELECT 2595 AS vertex, 1.0 AS value),\n directed => false) p\n JOIN cugraph_pagerank(\n edges => (SELECT src, dst FROM likes WHERE NOT after_release), directed => false) r\n ON r.vertex = p.vertex\n), lift_after AS (\n SELECT p.vertex, p.value / r.value AS lift\n FROM cugraph_personalized_pagerank(\n edges => (SELECT src, dst FROM likes WHERE after_release),\n personalization => (SELECT 2595 AS vertex, 1.0 AS value),\n directed => false) p\n JOIN cugraph_pagerank(\n edges => (SELECT src, dst FROM likes WHERE after_release), directed => false) r\n ON r.vertex = p.vertex\n), fans AS (\n SELECT dst AS brand_id,\n SUM(CASE WHEN after_release THEN 0 ELSE 1 END) AS fans_before,\n SUM(CASE WHEN after_release THEN 1 ELSE 0 END) AS fans_after\n FROM likes GROUP BY dst\n), brands AS (\n SELECT CAST(brand_id AS BIGINT) AS brand_id, MIN(brand_name) AS brand_name\n FROM merrec_items WHERE brand_id <> 0 GROUP BY brand_id\n)\nSELECT b.brand_name, f.fans_before, f.fans_after,\n lb.lift AS lift_before, la.lift AS lift_after, la.lift / lb.lift AS lift_change\nFROM lift_after la\nJOIN lift_before lb ON lb.vertex = la.vertex\nJOIN fans f ON f.brand_id = la.vertex\nJOIN brands b ON b.brand_id = la.vertex\nWHERE la.vertex > 0 AND la.vertex <> 2595\n AND f.fans_before >= 1000 AND f.fans_after >= 1000\nORDER BY lift_change DESC, b.brand_name\nLIMIT 25", "status": "ok", "elapsed_s": 0.08838822110556066, "source_cache_resident_before": { "items.parquet": 1.0, "events.parquet": 1.0 }, "query_id": "ca241ffd-f4db-4e1f-bdc5-0974272abf1d", "summary": { "gpu_path": "native", "gpu_fragments": 15, "cpu_boundaries": 0, "outcome": "accepted_replaced" }, "candidates": [ { "candidate_family": "sort_fetch_hash_join_input", "outcome": "selected" }, { "candidate_family": "projected_global_hash_join_aggregate", "outcome": "selected" }, { "candidate_family": "other_literal_input", "outcome": "selected" }, { "candidate_family": "projected_global_hash_join_aggregate", "outcome": "selected" }, { "candidate_family": "projected_global_hash_join_aggregate", "outcome": "selected" }, { "candidate_family": "other_literal_input", "outcome": "selected" }, { "candidate_family": "projected_global_hash_join_aggregate", "outcome": "selected" } ] }, { "step": 5, "intent": "Run it: which brands moved toward Barbie's audience after the July release?", "sql": "WITH likes AS (\n SELECT DISTINCT -(e.user_id + 1) AS src, CAST(i.brand_id AS BIGINT) AS dst,\n e.event_time >= TIMESTAMP '2023-07-01 00:00:00' AS after_release\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND e.event_time >= TIMESTAMP '2023-05-01 00:00:00'\n AND e.event_time < TIMESTAMP '2023-09-01 00:00:00'\n), lift_before AS (\n SELECT p.vertex, p.value / r.value AS lift\n FROM cugraph_personalized_pagerank(\n edges => (SELECT src, dst FROM likes WHERE NOT after_release),\n personalization => (SELECT 2595 AS vertex, 1.0 AS value),\n directed => false) p\n JOIN cugraph_pagerank(\n edges => (SELECT src, dst FROM likes WHERE NOT after_release), directed => false) r\n ON r.vertex = p.vertex\n), lift_after AS (\n SELECT p.vertex, p.value / r.value AS lift\n FROM cugraph_personalized_pagerank(\n edges => (SELECT src, dst FROM likes WHERE after_release),\n personalization => (SELECT 2595 AS vertex, 1.0 AS value),\n directed => false) p\n JOIN cugraph_pagerank(\n edges => (SELECT src, dst FROM likes WHERE after_release), directed => false) r\n ON r.vertex = p.vertex\n), fans AS (\n SELECT dst AS brand_id,\n SUM(CASE WHEN after_release THEN 0 ELSE 1 END) AS fans_before,\n SUM(CASE WHEN after_release THEN 1 ELSE 0 END) AS fans_after\n FROM likes GROUP BY dst\n), brands AS (\n SELECT CAST(brand_id AS BIGINT) AS brand_id, MIN(brand_name) AS brand_name\n FROM merrec_items WHERE brand_id <> 0 GROUP BY brand_id\n)\nSELECT b.brand_name, f.fans_before, f.fans_after,\n lb.lift AS lift_before, la.lift AS lift_after, la.lift / lb.lift AS lift_change\nFROM lift_after la\nJOIN lift_before lb ON lb.vertex = la.vertex\nJOIN fans f ON f.brand_id = la.vertex\nJOIN brands b ON b.brand_id = la.vertex\nWHERE la.vertex > 0 AND la.vertex <> 2595\n AND f.fans_before >= 1000 AND f.fans_after >= 1000\nORDER BY lift_change DESC, b.brand_name\nLIMIT 25;", "status": "ok", "elapsed_s": 3.3178140989039093, "source_cache_resident_before": { "items.parquet": 1.0, "events.parquet": 1.0 }, "query_id": "4c00f57a-0cc8-44a3-b57a-0312dd4fbfa7", "execution": { "post_native_execution_path": "native", "native_plan_fragment_count": "11", "host_datafusion_boundary_count": "0", "peak_gib": 29.7 }, "rows": [ { "brand_name": "NYX", "fans_before": 2781, "fans_after": 4253, "lift_before": 0.9702536288473614, "lift_after": 2.069575818786982, "lift_change": 2.1330255896549337 }, { "brand_name": "Amc", "fans_before": 2654, "fans_after": 2815, "lift_before": 0.974835772628121, "lift_after": 1.820914719301097, "lift_change": 1.8679194695450891 }, { "brand_name": "Primark", "fans_before": 1966, "fans_after": 2987, "lift_before": 1.100701129377499, "lift_after": 2.0405481221730764, "lift_change": 1.853862113621258 }, { "brand_name": "ALDO", "fans_before": 4571, "fans_after": 6379, "lift_before": 0.8514795429073221, "lift_after": 1.502451737505666, "lift_change": 1.7645188895268598 }, { "brand_name": "Taylor Swift", "fans_before": 15577, "fans_after": 18514, "lift_before": 0.6494634413140877, "lift_after": 0.9581264043999292, "lift_change": 1.4752584109450568 }, { "brand_name": "Stoney Clover Lane", "fans_before": 5156, "fans_after": 5420, "lift_before": 1.0601656087864544, "lift_after": 1.5021175630610628, "lift_change": 1.416870676252647 }, { "brand_name": "Stanley", "fans_before": 16209, "fans_after": 16642, "lift_before": 0.49297024461696226, "lift_after": 0.6880479858617968, "lift_change": 1.3957190994284248 }, { "brand_name": "Dunkin'", "fans_before": 1580, "fans_after": 1459, "lift_before": 0.7278208437624923, "lift_after": 1.010969176346272, "lift_change": 1.3890357565469487 }, { "brand_name": "Henri Bendel", "fans_before": 1133, "fans_after": 1358, "lift_before": 0.6493267043168143, "lift_after": 0.8548874439419886, "lift_change": 1.3165752128452102 }, { "brand_name": "Dutch Bros", "fans_before": 1035, "fans_after": 1086, "lift_before": 0.5347161436898036, "lift_after": 0.7002860869880118, "lift_change": 1.30964081644458 }, { "brand_name": "Fresh", "fans_before": 1136, "fans_after": 1325, "lift_before": 1.3315948349120303, "lift_after": 1.739209278059506, "lift_change": 1.306109961123726 }, { "brand_name": "Starbucks", "fans_before": 26096, "fans_after": 29130, "lift_before": 0.7560988611686769, "lift_after": 0.9780395291461631, "lift_change": 1.2935339270772608 }, { "brand_name": "BOGG BAG", "fans_before": 4578, "fans_after": 3496, "lift_before": 0.521001595171577, "lift_after": 0.672753935740426, "lift_change": 1.2912703952832885 }, { "brand_name": "Farm Rio", "fans_before": 2782, "fans_after": 3044, "lift_before": 0.5037218722608601, "lift_after": 0.643431593797627, "lift_change": 1.2773548841738922 }, { "brand_name": "Alice + Olivia", "fans_before": 1279, "fans_after": 1511, "lift_before": 0.6314239613818571, "lift_after": 0.8051047705091445, "lift_change": 1.2750621131754185 }, { "brand_name": "HAUS LABORATORIES", "fans_before": 1111, "fans_after": 1361, "lift_before": 0.7191215607652373, "lift_after": 0.9158310521539136, "lift_change": 1.2735413622967338 }, { "brand_name": "Interscope Records", "fans_before": 1630, "fans_after": 2218, "lift_before": 0.9198744687519776, "lift_after": 1.169039965503048, "lift_change": 1.27086902095361 }, { "brand_name": "MacKenzie-Childs", "fans_before": 1270, "fans_after": 1758, "lift_before": 0.5764184248206329, "lift_after": 0.7286124379092185, "lift_change": 1.2640339144883244 }, { "brand_name": "ZARA", "fans_before": 38166, "fans_after": 48545, "lift_before": 0.6506154623226323, "lift_after": 0.820134820739725, "lift_change": 1.2605523050619263 }, { "brand_name": "Bombshell Sportswear", "fans_before": 1113, "fans_after": 1285, "lift_before": 0.5058789068498597, "lift_after": 0.6375117200629787, "lift_change": 1.2602061707470766 }, { "brand_name": "OPI", "fans_before": 2406, "fans_after": 3063, "lift_before": 0.7159357090239905, "lift_after": 0.8989143480968065, "lift_change": 1.2555797074604145 }, { "brand_name": "Dagne Dover", "fans_before": 1823, "fans_after": 2390, "lift_before": 0.4315092577405609, "lift_after": 0.5392955031503286, "lift_change": 1.2497889523254972 }, { "brand_name": "Rothy's", "fans_before": 4272, "fans_after": 4756, "lift_before": 0.44114877255991186, "lift_after": 0.5506279056182595, "lift_change": 1.2481682821490325 }, { "brand_name": "Brahmin", "fans_before": 4044, "fans_after": 4290, "lift_before": 0.4785214128868047, "lift_after": 0.5964184233767231, "lift_change": 1.2463777112473904 }, { "brand_name": "Golden Goose", "fans_before": 2855, "fans_after": 3436, "lift_before": 0.3848386153784122, "lift_after": 0.4752099059134255, "lift_change": 1.234829060608045 } ] } ], "probes": [ { "label": "personalization columns named after the source table", "sql": "SELECT vertex, value\nFROM cugraph_personalized_pagerank(\n edges => (SELECT DISTINCT -(e.user_id + 1) AS src, CAST(i.brand_id AS BIGINT) AS dst\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND e.event_time >= TIMESTAMP '2023-07-01 00:00:00' AND e.event_time < TIMESTAMP '2023-09-01 00:00:00'),\n personalization => (SELECT 2595 AS brand_id, 1.0 AS weight),\n directed => false)\nORDER BY value DESC LIMIT 5", "status": "error", "elapsed_s": 0.0024795071221888065, "error": "Flight returned invalid argument error, with message: cuGraph side-input child is missing canonical column 'vertex'" }, { "label": "output column named by guess", "sql": "SELECT vertex, score\nFROM cugraph_personalized_pagerank(\n edges => (SELECT DISTINCT -(e.user_id + 1) AS src, CAST(i.brand_id AS BIGINT) AS dst\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND e.event_time >= TIMESTAMP '2023-07-01 00:00:00' AND e.event_time < TIMESTAMP '2023-09-01 00:00:00'),\n personalization => (SELECT 2595 AS vertex, 1.0 AS value),\n directed => false)\nORDER BY score DESC LIMIT 5", "status": "error", "elapsed_s": 0.002592553850263357, "error": "Flight returned invalid argument error, with message: No field named score. Valid fields are vertex, value." } ] }