--- title: Rehearse a migration against a golden graph summary: Fingerprint a graph before a migration runs, declare what the migration may change, and catch a bug that node counts and intended-change checks both miss. level: Advanced category: Data quality features: [COUNT subqueries, COLLECT subqueries, EXISTS subqueries, "CALL () {}", list comprehensions, keys(), dynamic property access, MERGE … ON CREATE SET, FOREACH] browser: full dataset: A synthetic company graph of 90 organizations, 68 CEOs, 10 industry categories, and 26 cities in 7 countries; 201 nodes and 676 relationships (26 KB) source: Synthetic, generated with a fixed seed for this recipe. The graph's shape and the migration follow the examples project's 08-migration-regression, whose golden data is not reproduced here license: Apache-2.0 --- # Rehearse a migration against a golden graph *A migration can keep every node count and every intended change right and still destroy data. Fingerprint the graph before the migration runs, write down what it is allowed to change, and compare afterwards. Here a probe on each organization's relationships catches a bug that deletes 352 city links across 61 organizations, and the fingerprint puts the links back.* The graph is a company directory: `(:Organization)-[:HAS_CEO]->(:Person)`, `(:Organization)-[:HAS_CATEGORY]->(:IndustryCategory)`, and `(:Organization)-[:IN_CITY]->(:City)-[:IN_COUNTRY]->(:Country)`. The migration renames `HAS_CEO` to `LED_BY`, adds an `industry` property, drops summaries from organizations without a CEO, and removes duplicate city links. One of its four statements has a bug. ```mermaid flowchart LR accTitle: Data model: the company directory; the migration renames HAS_CEO to LED_BY and adds industry accDescr { An Organization is linked to its chief executive, a Person, by HAS_CEO, which the migration renames to LED_BY; the migration also adds an industry property to Organization. Each Organization has HAS_CATEGORY to an IndustryCategory and IN_CITY to a City, and each City is IN_COUNTRY a Country. } Org(("Organization
id · name · founded · employees
isPublic · summary · industry")) -- "HAS_CEO → LED_BY" --> Person(("Person
id · name")) Org -- "HAS_CATEGORY" --> Category(("IndustryCategory
id · name")) Org -- "IN_CITY" --> City(("City
id · name")) City -- "IN_COUNTRY" --> Country(("Country
id · name")) class Org l1 class Person l2 class Category l3 class City l4 class Country l6 ``` The companion runs the migration on a copy of a committed golden file and diffs the two files from Python. Here everything happens in one database: the fingerprint is stored in the graph itself, on `Probe` and `Fingerprint` nodes that the migration never matches. ```mermaid flowchart TB accTitle: The rehearsal, in one database accDescr { The golden graph is fingerprinted with Probe and Fingerprint nodes, then the migration runs with its bug and the verdict is FAIL. The diff shows which probes failed and who lost relationships; the graph is restored from the fingerprint and the fixed dedupe runs, and the verdict is PASS. Last, the scaffolding is deleted; every step runs in one database. } subgraph Break[" "] direction LR Golden[("Golden graph")] --> Fingerprint["Fingerprint
Probe and Fingerprint nodes"] Fingerprint --> Migrate["Run the migration
with its bug"] Migrate --> Fail("Verdict: FAIL") end subgraph Mend[" "] direction LR Diff["Which probes failed
who lost relationships"] --> Repair["Restore from the fingerprint
and run the fixed dedupe"] Repair --> Pass("Verdict: PASS") Pass --> Clean["Delete the scaffolding"] end Break --> Mend style Break fill:none,stroke:none style Mend fill:none,stroke:none class Fail bad class Pass good ``` ## Load the golden graph Every node has an `id`, and uniqueness constraints keep them unique. The fingerprint identifies relationships by their target's `id`. ```cypher cell=schema setup CREATE CONSTRAINT org_id IF NOT EXISTS FOR (o:Organization) REQUIRE o.id IS UNIQUE; CREATE CONSTRAINT person_id IF NOT EXISTS FOR (p:Person) REQUIRE p.id IS UNIQUE; CREATE CONSTRAINT category_id IF NOT EXISTS FOR (c:IndustryCategory) REQUIRE c.id IS UNIQUE; CREATE CONSTRAINT city_id IF NOT EXISTS FOR (c:City) REQUIRE c.id IS UNIQUE; CREATE CONSTRAINT country_id IF NOT EXISTS FOR (c:Country) REQUIRE c.id IS UNIQUE ``` ```table (no rows) · constraints added: 5 ``` Rows for organizations without a summary have no `summary` key, so `r.summary` is null and `CREATE` sets no property. ```cypher cell=load setup params=golden UNWIND $countries AS r CREATE (:Country {id: r.id, name: r.name}); UNWIND $cities AS r MATCH (k:Country {id: r.country}) CREATE (:City {id: r.id, name: r.name})-[:IN_COUNTRY]->(k); UNWIND $categories AS r CREATE (:IndustryCategory {id: r.id, name: r.name}); UNWIND $organizations AS r CREATE (:Organization {id: r.id, name: r.name, founded: r.founded, employees: r.employees, isPublic: r.isPublic, summary: r.summary}); UNWIND $organizations AS r WITH r WHERE r.ceo IS NOT NULL MATCH (o:Organization {id: r.id}) CREATE (o)-[:HAS_CEO]->(:Person {id: r.ceo.id, name: r.ceo.name}); UNWIND $organizations AS r MATCH (o:Organization {id: r.id}) UNWIND r.categories AS id MATCH (c:IndustryCategory {id: id}) CREATE (o)-[:HAS_CATEGORY]->(c); UNWIND $organizations AS r MATCH (o:Organization {id: r.id}) UNWIND r.cities AS id MATCH (c:City {id: id}) CREATE (o)-[:IN_CITY]->(c); RETURN COUNT { MATCH (:Organization) } AS organizations, COUNT { MATCH (:Person) } AS ceos, COUNT { MATCH (:City) } AS cities, COUNT { MATCH (:Organization)-[:IN_CITY]->(:City) } AS city_links ``` ```table | organizations | ceos | cities | city_links | |---------------|------|--------|------------| | 90 | 68 | 26 | 438 | 1 row · nodes created: 201, relationships created: 676, properties set: 718, labels added: 201 ``` One organization, Nimbus Labs, as it is before anything changes: every node it links to, and every city in the directory, linked or not. The same statement runs again after the migration, so the two results can be compared. ```json data=victim { "org": "org-11" } ``` ```cypher cell=nimbus params=victim MATCH (o:Organization {id: $org}), (x) WHERE x:City OR EXISTS { (o)-->(x) } OPTIONAL MATCH (o)-[r]->(x) RETURN o, r, x ORDER BY x.id ``` ```gram // 29 rows [ | (n53:Organization {employees: 80, founded: 2013, id: "org-11", isPublic: false, name: "Nimbus Labs"}), (n53)-[r282:IN_CITY]->(n11), (n11:City {id: "austin", name: "Austin"})] [ | (n53), (n53)-[r291:IN_CITY]->(n30), (n30:City {id: "bangalore", name: "Bangalore"})] [ | (n53), (n53)-[r290:IN_CITY]->(n18), (n18:City {id: "berlin", name: "Berlin"})] [ | (n53), (n53)-[r280:IN_CITY]->(n9), (n9:City {id: "boston", name: "Boston"})] [ | (n53), (n53)-[r281:IN_CITY]->(n17), (n17:City {id: "cambridge", name: "Cambridge"})] [ | (n53), (n53)-[r293:IN_CITY]->(n12), (n12:City {id: "chicago", name: "Chicago"})] [ | (n53), (n53)-[r271:IN_CITY]->(n13), (n13:City {id: "denver", name: "Denver"})] [ | (n53), (n53)-[r288:IN_CITY]->(n16), (n16:City {id: "edinburgh", name: "Edinburgh"})] [ | (n53), (n53)-[r111:HAS_CATEGORY]->(n42), (n42:IndustryCategory {id: "edtech", name: "Education Technology Companies"})] [ | (n53), (n53)-[r289:IN_CITY]->(n20), (n20:City {id: "hamburg", name: "Hamburg"})] [ | (n53), (n53)-[r269:IN_CITY]->(n31), (n31:City {id: "hyderabad", name: "Hyderabad"})] [ | (n53), (n53)-[r270:IN_CITY]->(n14), (n14:City {id: "london", name: "London"})] [ | (n53), (n53)-[r292:IN_CITY]->(n22), (n22:City {id: "lyon", name: "Lyon"})] [ | (n53), (n53)-[r294:IN_CITY]->(n15), (n15:City {id: "manchester", name: "Manchester"})] [ | (n53), (n53)-[r276:IN_CITY]->(n25), (n25:City {id: "montreal", name: "Montreal"})] [ | (n53), (n53)-[r275:IN_CITY]->(n19), (n19:City {id: "munich", name: "Munich"})] [ | (n53), (n53)-[r272:IN_CITY]->(n8), (n8:City {id: "new-york", name: "New York"})] [ | (n53), (n53)-[r287:IN_CITY]->(n29), (n29:City {id: "osaka", name: "Osaka"})] [ | (n53), (n53)-[r285:IN_CITY]->(n21), (n21:City {id: "paris", name: "Paris"})] [ | (n53), (n53)-[r31:HAS_CEO]->(n138), (n138:Person {id: "person-06", name: "Kavya Ueda"})] [ | (n53), (n53)-[r278:IN_CITY]->(n32), (n32:City {id: "pune", name: "Pune"})] [ | (n53), (n53)-[r112:HAS_CATEGORY]->(n35), (n35:IndustryCategory {id: "robotics", name: "Robotics Companies"})] [ | (n53), (n53)-[r284:IN_CITY]->(n7), (n7:City {id: "san-francisco", name: "San Francisco"})] [ | (n53), (n53)-[r279:IN_CITY]->(n10), (n10:City {id: "seattle", name: "Seattle"})] [ | (n53), (n53)-[r273:IN_CITY]->(n28), (n28:City {id: "tokyo", name: "Tokyo"})] [ | (n53), (n53)-[r286:IN_CITY]->(n24), (n24:City {id: "toronto", name: "Toronto"})] [ | (n53), (n53)-[r277:IN_CITY]->(n23), (n23:City {id: "toulouse", name: "Toulouse"})] [ | (n53), (n53)-[r283:IN_CITY]->(n26), (n26:City {id: "vancouver", name: "Vancouver"})] [ | (n53), (n53)-[r274:IN_CITY]->(n27), (n27:City {id: "waterloo", name: "Waterloo"})] ``` ## Fingerprint it before anything changes The migration's contract is written down before it runs. Its one structural change is a relationship rename, passed as a map from old type to new type: ```json data=intent { "renamed": { "HAS_CEO": "LED_BY" } } ``` The fingerprint has two parts: - **Count probes**: one `Probe` node per label and per relationship type, holding the count now (`before`) and the count the migration must leave (`expected`). Counts stay the same, except that a renamed type drops to 0 and its new name inherits the old count. `$renamed[type]` looks the type up in the map, and is `null` for a type that is not renamed. - **Per-organization fingerprints**: one `Fingerprint` node per organization, holding its property keys, its properties as `key=value` strings, and its relationships as `TYPE:targetId` strings. `expected` is the relationship list with the rename applied: the relationships the organization must have afterwards. ```cypher cell=fingerprint params=intent MATCH (n) WITH labels(n)[0] AS label, count(*) AS n CREATE (:Probe {kind: 'nodes', key: label, name: 'nodes :' + label, before: n, expected: n}); MATCH ()-[r]->() WITH type(r) AS type, count(*) AS n CREATE (:Probe {kind: 'rels', key: type, name: 'rels :' + type, before: n, expected: CASE WHEN $renamed[type] IS NULL THEN n ELSE 0 END}); UNWIND keys($renamed) AS old MATCH (p:Probe {kind: 'rels', key: old}) MERGE (q:Probe {kind: 'rels', key: $renamed[old]}) ON CREATE SET q.name = 'rels :' + $renamed[old], q.before = 0, q.expected = 0 SET q.expected = q.expected + p.before; MATCH (o:Organization) WITH o, COLLECT { MATCH (o)-[r]->(x) RETURN type(r) + ':' + x.id } AS rels CREATE (:Fingerprint { org: o.id, name: o.name, propKeys: keys(o), props: [k IN keys(o) | k + '=' + toString(o[k])], rels: rels, expected: [e IN rels | coalesce($renamed[split(e, ':')[0]], split(e, ':')[0]) + ':' + split(e, ':')[1]], hadCeo: EXISTS { (o)-[:HAS_CEO]->() }, hadCategory: EXISTS { (o)-[:HAS_CATEGORY]->() } }); MATCH (p:Probe) RETURN p.name AS probe, p.before AS before, p.expected AS expected ORDER BY probe ``` ```table | probe | before | expected | |---------------------------|--------|----------| | "nodes :City" | 26 | 26 | | "nodes :Country" | 7 | 7 | | "nodes :IndustryCategory" | 10 | 10 | | "nodes :Organization" | 90 | 90 | | "nodes :Person" | 68 | 68 | | "rels :HAS_CATEGORY" | 144 | 144 | | "rels :HAS_CEO" | 68 | 0 | | "rels :IN_CITY" | 438 | 438 | | "rels :IN_COUNTRY" | 26 | 26 | | "rels :LED_BY" | 0 | 68 | 10 rows · nodes created: 100, properties set: 771, labels added: 100 ``` `LED_BY` does not exist yet, so its probe starts at 0 and expects the 68 `HAS_CEO` relationships. ## Run the migration These are the four statements the migration's author wants to ship. Step 4 has a bug. Try to spot it before the probes do. ```cypher cell=migrate // 1. Rename HAS_CEO -> LED_BY (relationship types are immutable: copy + delete). MATCH (o:Organization)-[r:HAS_CEO]->(p:Person) CREATE (o)-[:LED_BY]->(p) DELETE r; // 2. Backfill a denormalised primary industry (alphabetically first category). MATCH (o:Organization) WHERE EXISTS { (o)-[:HAS_CATEGORY]->() } SET o.industry = COLLECT { MATCH (o)-[:HAS_CATEGORY]->(c) RETURN c.name ORDER BY c.name LIMIT 1 }[0]; // 3. Summaries are only shown on company pages with a CEO; drop the rest. MATCH (o:Organization) WHERE NOT EXISTS { (o)-[:LED_BY]->() } REMOVE o.summary; // 4. Dedupe parallel IN_CITY edges (same org, same city). MATCH (o:Organization)-[r:IN_CITY]->(c:City) WITH o, collect(r) AS rels WHERE size(rels) > 1 FOREACH (r IN tail(rels) | DELETE r) ``` ```table (no rows) · relationships created: 68, relationships deleted: 420, properties set: 95 ``` The counters are the first hint: 68 relationships were created for the rename, but far more than 68 were deleted. ## Ask for a verdict One read turns the fingerprint into a pass or fail, the way a CI gate would. It recounts every probe, compares each organization's relationships with its `expected` list, and checks every property change against the contract: `industry` may appear on an organization with a category (and must), `summary` may disappear from an organization without a CEO, and nothing else may change. ```cypher cell=verdict CALL () { MATCH (p:Probe) WITH p, CASE p.kind WHEN 'nodes' THEN COUNT { MATCH (n) WHERE p.key IN labels(n) } ELSE COUNT { MATCH ()-[r]->() WHERE type(r) = p.key } END AS after WITH p, after WHERE after <> p.expected RETURN collect(p.name) AS failing_probes } CALL () { MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH f, COLLECT { MATCH (o)-[r]->(x) RETURN type(r) + ':' + x.id } AS now WITH f, now WHERE any(e IN f.expected WHERE NOT e IN now) OR any(e IN now WHERE NOT e IN f.expected) RETURN count(f) AS orgs_with_changed_relationships } CALL () { MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH f, o, [k IN keys(o) | k + '=' + toString(o[k])] AS now WITH f, o, now WHERE any(e IN f.props WHERE NOT e IN now AND NOT (e STARTS WITH 'summary=' AND NOT f.hadCeo)) OR any(k IN keys(o) WHERE NOT k IN f.propKeys AND NOT (k = 'industry' AND f.hadCategory)) OR (f.hadCategory AND o.industry IS NULL) RETURN count(f) AS orgs_with_unexpected_properties } RETURN CASE WHEN size(failing_probes) = 0 AND orgs_with_changed_relationships = 0 AND orgs_with_unexpected_properties = 0 THEN 'PASS' ELSE 'FAIL' END AS verdict, failing_probes, orgs_with_changed_relationships, orgs_with_unexpected_properties ``` ```table | verdict | failing_probes | orgs_with_changed_relationships | orgs_with_unexpected_properties | |---------|-------------------|---------------------------------|---------------------------------| | "FAIL" | ["rels :IN_CITY"] | 61 | 0 | 1 row ``` > **Note** > Each subquery repeats its new variables in a second `WITH` (`WITH p, after`) > before filtering. Inside a `CALL (…) {}` subquery, Relate currently > rejects a `WHERE` that reads a variable its own `WITH` just introduced > (`UndefinedVariable`). The same `WITH … WHERE` works outside the subquery. ## Which probes failed The same recount, probe by probe. Every node count matches, and so does the rename: `HAS_CEO` went to 0 and `LED_BY` to 68. A check that stopped at node counts would pass. `IN_CITY` did not survive. ```cypher cell=probes MATCH (p:Probe) WITH p, CASE p.kind WHEN 'nodes' THEN COUNT { MATCH (n) WHERE p.key IN labels(n) } ELSE COUNT { MATCH ()-[r]->() WHERE type(r) = p.key } END AS after RETURN p.name AS probe, p.before AS before, after, p.expected AS expected, after = p.expected AS ok ORDER BY probe ``` ```table | probe | before | after | expected | ok | |---------------------------|--------|-------|----------|-------| | "nodes :City" | 26 | 26 | 26 | true | | "nodes :Country" | 7 | 7 | 7 | true | | "nodes :IndustryCategory" | 10 | 10 | 10 | true | | "nodes :Organization" | 90 | 90 | 90 | true | | "nodes :Person" | 68 | 68 | 68 | true | | "rels :HAS_CATEGORY" | 144 | 144 | 144 | true | | "rels :HAS_CEO" | 68 | 0 | 0 | true | | "rels :IN_CITY" | 438 | 86 | 438 | false | | "rels :IN_COUNTRY" | 26 | 26 | 26 | true | | "rels :LED_BY" | 0 | 68 | 68 | true | 10 rows ``` ## The intended changes happened Property changes, grouped, each checked against the contract. Only the two intended changes appear, both allowed. ```cypher cell=properties MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH f, o, [k IN keys(o) | k + '=' + toString(o[k])] AS now UNWIND [k IN keys(o) WHERE NOT k IN f.propKeys | {change: k + ' added', allowed: k = 'industry' AND f.hadCategory}] + [k IN f.propKeys WHERE NOT k IN keys(o) | {change: k + ' removed', allowed: k = 'summary' AND NOT f.hadCeo}] + [e IN f.props WHERE split(e, '=')[0] IN keys(o) AND NOT e IN now | {change: split(e, '=')[0] + ' changed', allowed: false}] AS c RETURN c.change AS change, c.allowed AS allowed, count(*) AS organizations ORDER BY change ``` ```table | change | allowed | organizations | |-------------------|---------|---------------| | "industry added" | true | 85 | | "summary removed" | true | 10 | 2 rows ``` ## Who lost relationships For each organization, `lost` is what its `expected` list has and the graph no longer does, and `gained` is the reverse. The ten largest losses: ```cypher cell=victims MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH f, COLLECT { MATCH (o)-[r]->(x) RETURN type(r) + ':' + x.id } AS now WITH f, [e IN f.expected WHERE NOT e IN now] AS lost, [e IN now WHERE NOT e IN f.expected] AS gained WHERE size(lost) + size(gained) > 0 RETURN f.name AS organization, size(lost) AS lost, size(gained) AS gained, COLLECT { UNWIND lost AS e RETURN DISTINCT split(e, ':')[0] AS type ORDER BY type } AS lost_types ORDER BY lost DESC, organization LIMIT 10 ``` ```table | organization | lost | gained | lost_types | |--------------------|------|--------|-------------| | "Nimbus Labs" | 25 | 0 | ["IN_CITY"] | | "Cinder Vision" | 22 | 0 | ["IN_CITY"] | | "Fathom Analytics" | 19 | 0 | ["IN_CITY"] | | "Meridian AI" | 19 | 0 | ["IN_CITY"] | | "Cobalt Analytics" | 18 | 0 | ["IN_CITY"] | | "Lumen Robotics" | 18 | 0 | ["IN_CITY"] | | "Keel AI" | 16 | 0 | ["IN_CITY"] | | "Brindle AI" | 14 | 0 | ["IN_CITY"] | | "Meridian Vision" | 14 | 0 | ["IN_CITY"] | | "Yarrow Vision" | 13 | 0 | ["IN_CITY"] | 10 rows ``` Every loss is an `IN_CITY` link, and nothing was gained. The histogram of city links per organization shows the pattern: every organization that was in two or more cities is now in exactly one. ```cypher cell=histogram MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH size([e IN f.rels WHERE e STARTS WITH 'IN_CITY:']) AS before, COUNT { MATCH (o)-[:IN_CITY]->() } AS after UNWIND [['before', before], ['after', after]] AS pair WITH pair[0] AS phase, CASE WHEN pair[1] = 0 THEN '0' WHEN pair[1] = 1 THEN '1' WHEN pair[1] <= 5 THEN '2-5' WHEN pair[1] <= 20 THEN '6-20' ELSE '>20' END AS cities RETURN cities, sum(CASE WHEN phase = 'before' THEN 1 ELSE 0 END) AS before, sum(CASE WHEN phase = 'after' THEN 1 ELSE 0 END) AS after ORDER BY cities ``` ```table | cities | before | after | |--------|--------|-------| | "0" | 4 | 4 | | "1" | 25 | 86 | | "2-5" | 45 | 0 | | "6-20" | 14 | 0 | | ">20" | 2 | 0 | 5 rows ``` Nimbus Labs had an office in all 26 cities. The statement from before the migration, run again: one city link is left. ```cypher cell=victim params=victim MATCH (o:Organization {id: $org}), (x) WHERE x:City OR EXISTS { (o)-->(x) } OPTIONAL MATCH (o)-[r]->(x) RETURN o, r, x ORDER BY x.id ``` ```gram // 29 rows [{r: null} | (n53:Organization {employees: 80, founded: 2013, id: "org-11", industry: "Education Technology Companies", isPublic: false, name: "Nimbus Labs"}), (n11:City {id: "austin", name: "Austin"})] [{r: null} | (n53), (n30:City {id: "bangalore", name: "Bangalore"})] [{r: null} | (n53), (n18:City {id: "berlin", name: "Berlin"})] [{r: null} | (n53), (n9:City {id: "boston", name: "Boston"})] [{r: null} | (n53), (n17:City {id: "cambridge", name: "Cambridge"})] [{r: null} | (n53), (n12:City {id: "chicago", name: "Chicago"})] [{r: null} | (n53), (n13:City {id: "denver", name: "Denver"})] [{r: null} | (n53), (n16:City {id: "edinburgh", name: "Edinburgh"})] [ | (n53), (n53)-[r111:HAS_CATEGORY]->(n42), (n42:IndustryCategory {id: "edtech", name: "Education Technology Companies"})] [{r: null} | (n53), (n20:City {id: "hamburg", name: "Hamburg"})] [{r: null} | (n53), (n31:City {id: "hyderabad", name: "Hyderabad"})] [{r: null} | (n53), (n14:City {id: "london", name: "London"})] [{r: null} | (n53), (n22:City {id: "lyon", name: "Lyon"})] [{r: null} | (n53), (n15:City {id: "manchester", name: "Manchester"})] [{r: null} | (n53), (n25:City {id: "montreal", name: "Montreal"})] [{r: null} | (n53), (n19:City {id: "munich", name: "Munich"})] [{r: null} | (n53), (n8:City {id: "new-york", name: "New York"})] [{r: null} | (n53), (n29:City {id: "osaka", name: "Osaka"})] [{r: null} | (n53), (n21:City {id: "paris", name: "Paris"})] [ | (n53), (n53)-[r681:LED_BY]->(n138), (n138:Person {id: "person-06", name: "Kavya Ueda"})] [{r: null} | (n53), (n32:City {id: "pune", name: "Pune"})] [ | (n53), (n53)-[r112:HAS_CATEGORY]->(n35), (n35:IndustryCategory {id: "robotics", name: "Robotics Companies"})] [ | (n53), (n53)-[r284:IN_CITY]->(n7), (n7:City {id: "san-francisco", name: "San Francisco"})] [{r: null} | (n53), (n10:City {id: "seattle", name: "Seattle"})] [{r: null} | (n53), (n28:City {id: "tokyo", name: "Tokyo"})] [{r: null} | (n53), (n24:City {id: "toronto", name: "Toronto"})] [{r: null} | (n53), (n23:City {id: "toulouse", name: "Toulouse"})] [{r: null} | (n53), (n26:City {id: "vancouver", name: "Vancouver"})] [{r: null} | (n53), (n27:City {id: "waterloo", name: "Waterloo"})] ``` The two results side by side, nodes matched by their `id`: the rename removed `HAS_CEO` and added `LED_BY`, step 2 changed the organization, and step 4 removed 25 of the 26 city links. The cities themselves are still there. The graph diff compares what cell `nimbus` returned with what cell `victim` returned, as recorded. A node is matched by its `id` property, and a relationship by its type and the `id` of the node at each end; element ids are not compared, since a store need not keep them from one run to the next. Nodes: 1 changed, 29 unchanged. Relationships: 26 removed, 1 added, 3 unchanged. − marks what was removed, + what was added and ~ what changed; an unchanged element carries no mark. | Change | Element | What changed | | --- | --- | --- | | ~ changed | Nimbus Labs (:Organization, id "org-11") | industry added: "Education Technology Companies" | | − removed | Nimbus Labs -[:HAS_CEO]-> Kavya Ueda | - | | − removed | Nimbus Labs -[:IN_CITY]-> Austin | - | | − removed | Nimbus Labs -[:IN_CITY]-> Bangalore | - | | − removed | Nimbus Labs -[:IN_CITY]-> Berlin | - | | − removed | Nimbus Labs -[:IN_CITY]-> Boston | - | | − removed | Nimbus Labs -[:IN_CITY]-> Cambridge | - | | − removed | Nimbus Labs -[:IN_CITY]-> Chicago | - | | − removed | Nimbus Labs -[:IN_CITY]-> Denver | - | | − removed | Nimbus Labs -[:IN_CITY]-> Edinburgh | - | | − removed | Nimbus Labs -[:IN_CITY]-> Hamburg | - | | − removed | Nimbus Labs -[:IN_CITY]-> Hyderabad | - | | − removed | Nimbus Labs -[:IN_CITY]-> London | - | | − removed | Nimbus Labs -[:IN_CITY]-> Lyon | - | | − removed | Nimbus Labs -[:IN_CITY]-> Manchester | - | | − removed | Nimbus Labs -[:IN_CITY]-> Montreal | - | | − removed | Nimbus Labs -[:IN_CITY]-> Munich | - | | − removed | Nimbus Labs -[:IN_CITY]-> New York | - | | − removed | Nimbus Labs -[:IN_CITY]-> Osaka | - | | − removed | Nimbus Labs -[:IN_CITY]-> Paris | - | | − removed | Nimbus Labs -[:IN_CITY]-> Pune | - | | − removed | Nimbus Labs -[:IN_CITY]-> Seattle | - | | − removed | Nimbus Labs -[:IN_CITY]-> Tokyo | - | | − removed | Nimbus Labs -[:IN_CITY]-> Toronto | - | | − removed | Nimbus Labs -[:IN_CITY]-> Toulouse | - | | − removed | Nimbus Labs -[:IN_CITY]-> Vancouver | - | | − removed | Nimbus Labs -[:IN_CITY]-> Waterloo | - | | + added | Nimbus Labs -[:LED_BY]-> Kavya Ueda | - | Live: the diff is drawn in a graph view beside the table of changes, and Compare runs the page's cells again, from the first to the after cell and with any edit you made to them, on a separate in-memory database in your browser, apart from the page's database, then draws the new diff; choosing a row of the table marks its element in the view. ![What changed between cells nimbus and victim, as recorded](https://relatedb.ai/cookbook/migration-rehearsal/element-graph-diff-1-light.svg) ## The bug Step 4 groups the city links by organization only, so `tail(rels)` deletes all but one link per organization, not all but one per city. The intended statement groups by organization and city: ```diff MATCH (o:Organization)-[r:IN_CITY]->(c:City) -WITH o, collect(r) AS rels +WITH o, c, collect(r) AS rels WHERE size(rels) > 1 FOREACH (r IN tail(rels) | DELETE r); ``` ## Repair from the fingerprint The companion fixes the migration and reruns it on a fresh copy of the golden file. A single database cannot rewind, but the fingerprint recorded every city link, so the lost ones can be put back. The victims query showed that `IN_CITY` is the only type that lost anything. `IN_CITY` links carry no properties, so a recreated link matches the lost one in everything but its internal id. ```cypher cell=restore MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) UNWIND f.rels AS e WITH o, split(e, ':') AS part WHERE part[0] = 'IN_CITY' MATCH (c:City {id: part[1]}) WHERE NOT EXISTS { (o)-[:IN_CITY]->(c) } CREATE (o)-[:IN_CITY]->(c) ``` ```table (no rows) · relationships created: 352 ``` Now the fixed step 4. The golden graph has no duplicate city links, so it deletes nothing, which is what a dedupe of clean data should do: ```cypher cell=fixed MATCH (o:Organization)-[r:IN_CITY]->(c:City) WITH o, c, collect(r) AS rels WHERE size(rels) > 1 FOREACH (r IN tail(rels) | DELETE r) ``` ```table (no rows) ``` Steps 1 to 3 already did what the contract asks, so they need no rerun. The verdict, unchanged: ```cypher cell=verdict-again CALL () { MATCH (p:Probe) WITH p, CASE p.kind WHEN 'nodes' THEN COUNT { MATCH (n) WHERE p.key IN labels(n) } ELSE COUNT { MATCH ()-[r]->() WHERE type(r) = p.key } END AS after WITH p, after WHERE after <> p.expected RETURN collect(p.name) AS failing_probes } CALL () { MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH f, COLLECT { MATCH (o)-[r]->(x) RETURN type(r) + ':' + x.id } AS now WITH f, now WHERE any(e IN f.expected WHERE NOT e IN now) OR any(e IN now WHERE NOT e IN f.expected) RETURN count(f) AS orgs_with_changed_relationships } CALL () { MATCH (f:Fingerprint) MATCH (o:Organization {id: f.org}) WITH f, o, [k IN keys(o) | k + '=' + toString(o[k])] AS now WITH f, o, now WHERE any(e IN f.props WHERE NOT e IN now AND NOT (e STARTS WITH 'summary=' AND NOT f.hadCeo)) OR any(k IN keys(o) WHERE NOT k IN f.propKeys AND NOT (k = 'industry' AND f.hadCategory)) OR (f.hadCategory AND o.industry IS NULL) RETURN count(f) AS orgs_with_unexpected_properties } RETURN CASE WHEN size(failing_probes) = 0 AND orgs_with_changed_relationships = 0 AND orgs_with_unexpected_properties = 0 THEN 'PASS' ELSE 'FAIL' END AS verdict, failing_probes, orgs_with_changed_relationships, orgs_with_unexpected_properties ``` ```table | verdict | failing_probes | orgs_with_changed_relationships | orgs_with_unexpected_properties | |---------|----------------|---------------------------------|---------------------------------| | "PASS" | [] | 0 | 0 | 1 row ``` Nimbus Labs is back in all 26 cities: ```cypher cell=victim-restored params=victim MATCH (o:Organization {id: $org}), (x) WHERE x:City OR EXISTS { (o)-->(x) } OPTIONAL MATCH (o)-[r]->(x) RETURN o, r, x ORDER BY x.id ``` ```gram // 29 rows [ | (n53:Organization {employees: 80, founded: 2013, id: "org-11", industry: "Education Technology Companies", isPublic: false, name: "Nimbus Labs"}), (n53)-[r776:IN_CITY]->(n11), (n11:City {id: "austin", name: "Austin"})] [ | (n53), (n53)-[r768:IN_CITY]->(n30), (n30:City {id: "bangalore", name: "Bangalore"})] [ | (n53), (n53)-[r769:IN_CITY]->(n18), (n18:City {id: "berlin", name: "Berlin"})] [ | (n53), (n53)-[r778:IN_CITY]->(n9), (n9:City {id: "boston", name: "Boston"})] [ | (n53), (n53)-[r777:IN_CITY]->(n17), (n17:City {id: "cambridge", name: "Cambridge"})] [ | (n53), (n53)-[r766:IN_CITY]->(n12), (n12:City {id: "chicago", name: "Chicago"})] [ | (n53), (n53)-[r787:IN_CITY]->(n13), (n13:City {id: "denver", name: "Denver"})] [ | (n53), (n53)-[r771:IN_CITY]->(n16), (n16:City {id: "edinburgh", name: "Edinburgh"})] [ | (n53), (n53)-[r111:HAS_CATEGORY]->(n42), (n42:IndustryCategory {id: "edtech", name: "Education Technology Companies"})] [ | (n53), (n53)-[r770:IN_CITY]->(n20), (n20:City {id: "hamburg", name: "Hamburg"})] [ | (n53), (n53)-[r789:IN_CITY]->(n31), (n31:City {id: "hyderabad", name: "Hyderabad"})] [ | (n53), (n53)-[r788:IN_CITY]->(n14), (n14:City {id: "london", name: "London"})] [ | (n53), (n53)-[r767:IN_CITY]->(n22), (n22:City {id: "lyon", name: "Lyon"})] [ | (n53), (n53)-[r765:IN_CITY]->(n15), (n15:City {id: "manchester", name: "Manchester"})] [ | (n53), (n53)-[r782:IN_CITY]->(n25), (n25:City {id: "montreal", name: "Montreal"})] [ | (n53), (n53)-[r783:IN_CITY]->(n19), (n19:City {id: "munich", name: "Munich"})] [ | (n53), (n53)-[r786:IN_CITY]->(n8), (n8:City {id: "new-york", name: "New York"})] [ | (n53), (n53)-[r772:IN_CITY]->(n29), (n29:City {id: "osaka", name: "Osaka"})] [ | (n53), (n53)-[r774:IN_CITY]->(n21), (n21:City {id: "paris", name: "Paris"})] [ | (n53), (n53)-[r681:LED_BY]->(n138), (n138:Person {id: "person-06", name: "Kavya Ueda"})] [ | (n53), (n53)-[r780:IN_CITY]->(n32), (n32:City {id: "pune", name: "Pune"})] [ | (n53), (n53)-[r112:HAS_CATEGORY]->(n35), (n35:IndustryCategory {id: "robotics", name: "Robotics Companies"})] [ | (n53), (n53)-[r284:IN_CITY]->(n7), (n7:City {id: "san-francisco", name: "San Francisco"})] [ | (n53), (n53)-[r779:IN_CITY]->(n10), (n10:City {id: "seattle", name: "Seattle"})] [ | (n53), (n53)-[r785:IN_CITY]->(n28), (n28:City {id: "tokyo", name: "Tokyo"})] [ | (n53), (n53)-[r773:IN_CITY]->(n24), (n24:City {id: "toronto", name: "Toronto"})] [ | (n53), (n53)-[r781:IN_CITY]->(n23), (n23:City {id: "toulouse", name: "Toulouse"})] [ | (n53), (n53)-[r775:IN_CITY]->(n26), (n26:City {id: "vancouver", name: "Vancouver"})] [ | (n53), (n53)-[r784:IN_CITY]->(n27), (n27:City {id: "waterloo", name: "Waterloo"})] ``` ## Clean up The probes and fingerprints were scaffolding for the rehearsal. Delete them before the graph is used for anything else: ```cypher cell=cleanup MATCH (n) WHERE n:Probe OR n:Fingerprint DELETE n ``` ```table (no rows) · nodes deleted: 100 ``` ## Data The dataset the steps above load. It is a JSON object whose keys are the query parameters.
golden (26 KB) ```json data=golden { "countries": [ {"id": "us", "name": "United States"}, {"id": "uk", "name": "United Kingdom"}, {"id": "de", "name": "Germany"}, {"id": "fr", "name": "France"}, {"id": "ca", "name": "Canada"}, {"id": "jp", "name": "Japan"}, {"id": "in", "name": "India"} ], "cities": [ {"id": "san-francisco", "name": "San Francisco", "country": "us"}, {"id": "new-york", "name": "New York", "country": "us"}, {"id": "boston", "name": "Boston", "country": "us"}, {"id": "seattle", "name": "Seattle", "country": "us"}, {"id": "austin", "name": "Austin", "country": "us"}, {"id": "chicago", "name": "Chicago", "country": "us"}, {"id": "denver", "name": "Denver", "country": "us"}, {"id": "london", "name": "London", "country": "uk"}, {"id": "manchester", "name": "Manchester", "country": "uk"}, {"id": "edinburgh", "name": "Edinburgh", "country": "uk"}, {"id": "cambridge", "name": "Cambridge", "country": "uk"}, {"id": "berlin", "name": "Berlin", "country": "de"}, {"id": "munich", "name": "Munich", "country": "de"}, {"id": "hamburg", "name": "Hamburg", "country": "de"}, {"id": "paris", "name": "Paris", "country": "fr"}, {"id": "lyon", "name": "Lyon", "country": "fr"}, {"id": "toulouse", "name": "Toulouse", "country": "fr"}, {"id": "toronto", "name": "Toronto", "country": "ca"}, {"id": "montreal", "name": "Montreal", "country": "ca"}, {"id": "vancouver", "name": "Vancouver", "country": "ca"}, {"id": "waterloo", "name": "Waterloo", "country": "ca"}, {"id": "tokyo", "name": "Tokyo", "country": "jp"}, {"id": "osaka", "name": "Osaka", "country": "jp"}, {"id": "bangalore", "name": "Bangalore", "country": "in"}, {"id": "hyderabad", "name": "Hyderabad", "country": "in"}, {"id": "pune", "name": "Pune", "country": "in"} ], "categories": [ {"id": "ai", "name": "Artificial Intelligence Companies"}, {"id": "vision", "name": "Computer Vision Companies"}, {"id": "robotics", "name": "Robotics Companies"}, {"id": "nlp", "name": "Natural Language Processing Companies"}, {"id": "chips", "name": "Semiconductor Companies"}, {"id": "health", "name": "Healthcare Technology Companies"}, {"id": "fintech", "name": "Financial Technology Companies"}, {"id": "security", "name": "Cybersecurity Companies"}, {"id": "analytics", "name": "Data Analytics Companies"}, {"id": "edtech", "name": "Education Technology Companies"} ], "organizations": [ {"id": "org-01", "name": "Drift AI", "founded": 2022, "employees": 300, "isPublic": false, "categories": ["edtech"], "cities": ["cambridge"]}, {"id": "org-02", "name": "Quarry Systems", "founded": 2019, "employees": 150, "isPublic": true, "categories": ["analytics"], "cities": ["new-york", "vancouver"]}, {"id": "org-03", "name": "Estuary Vision", "founded": 2003, "employees": 4000, "isPublic": true, "ceo": {"id": "person-01", "name": "Kavya Varga"}, "categories": ["ai", "nlp", "robotics"], "cities": ["osaka", "new-york"]}, {"id": "org-04", "name": "Juniper Robotics", "founded": 2018, "employees": 15000, "isPublic": true, "summary": "Juniper Robotics builds edge chips for logistics firms.", "categories": ["health", "robotics"], "cities": ["paris", "vancouver", "osaka", "london", "hyderabad"]}, {"id": "org-05", "name": "Lumen Systems", "founded": 2015, "employees": 4000, "isPublic": false, "summary": "Lumen Systems builds edge chips for retailers.", "ceo": {"id": "person-02", "name": "Zofia Ueda"}, "categories": ["analytics", "health"], "cities": ["seattle", "manchester", "san-francisco"]}, {"id": "org-06", "name": "Alder Robotics", "founded": 2008, "employees": 650, "isPublic": false, "ceo": {"id": "person-03", "name": "Xiu Quinn"}, "categories": [], "cities": ["waterloo", "osaka", "montreal", "toulouse", "denver"]}, {"id": "org-07", "name": "Dunlin Analytics", "founded": 2016, "employees": 12, "isPublic": true, "categories": ["health"], "cities": ["cambridge", "toronto", "waterloo"]}, {"id": "org-08", "name": "Meridian Systems", "founded": 2006, "employees": 4000, "isPublic": false, "summary": "Meridian Systems builds threat hunting for schools.", "ceo": {"id": "person-04", "name": "Omar Haddad"}, "categories": ["analytics", "fintech"], "cities": ["lyon"]}, {"id": "org-09", "name": "Zephyr Labs", "founded": 2019, "employees": 25, "isPublic": false, "summary": "Zephyr Labs builds fraud detection for insurers.", "categories": ["ai", "fintech", "nlp"], "cities": ["pune", "london", "toronto", "chicago"]}, {"id": "org-10", "name": "Gantry AI", "founded": 2004, "employees": 80, "isPublic": false, "summary": "Gantry AI builds sensor dashboards for retailers.", "ceo": {"id": "person-05", "name": "Kavya Jensen"}, "categories": ["fintech", "nlp"], "cities": ["munich", "toronto", "manchester", "lyon", "cambridge"]}, {"id": "org-11", "name": "Nimbus Labs", "founded": 2013, "employees": 80, "isPublic": false, "ceo": {"id": "person-06", "name": "Kavya Ueda"}, "categories": ["edtech", "robotics"], "cities": ["hyderabad", "london", "denver", "new-york", "tokyo", "waterloo", "munich", "montreal", "toulouse", "pune", "seattle", "boston", "cambridge", "austin", "vancouver", "san-francisco", "paris", "toronto", "osaka", "edinburgh", "hamburg", "berlin", "bangalore", "lyon", "chicago", "manchester"]}, {"id": "org-12", "name": "Cinder Analytics", "founded": 1995, "employees": 25, "isPublic": false, "summary": "Cinder Analytics builds edge chips for hospitals.", "ceo": {"id": "person-07", "name": "Amara Moreau"}, "categories": ["ai", "fintech", "robotics"], "cities": ["san-francisco"]}, {"id": "org-13", "name": "Juniper Labs", "founded": 2008, "employees": 1200, "isPublic": false, "summary": "Juniper Labs builds forecasting models for logistics firms.", "ceo": {"id": "person-08", "name": "Rosa Dimitrov"}, "categories": ["chips", "health", "security"], "cities": ["toronto", "hamburg"]}, {"id": "org-14", "name": "Tessel Systems", "founded": 2007, "employees": 25, "isPublic": true, "summary": "Tessel Systems builds sensor dashboards for utilities.", "ceo": {"id": "person-09", "name": "Zofia Dimitrov"}, "categories": ["security"], "cities": ["edinburgh", "london", "cambridge"]}, {"id": "org-15", "name": "Harbor AI", "founded": 2007, "employees": 12, "isPublic": true, "summary": "Harbor AI builds forecasting models for banks.", "categories": ["analytics"], "cities": ["seattle", "pune", "boston", "lyon", "vancouver"]}, {"id": "org-16", "name": "Meridian Labs", "founded": 1998, "employees": 40, "isPublic": false, "summary": "Meridian Labs builds inspection cameras for retailers.", "ceo": {"id": "person-10", "name": "Jonas Eze"}, "categories": ["ai", "chips", "edtech"], "cities": ["toronto"]}, {"id": "org-17", "name": "Cobalt Labs", "founded": 1996, "employees": 150, "isPublic": false, "categories": ["analytics", "nlp"], "cities": ["london"]}, {"id": "org-18", "name": "Basalt Vision", "founded": 2001, "employees": 1200, "isPublic": false, "ceo": {"id": "person-11", "name": "Quentin Eze"}, "categories": ["analytics", "chips"], "cities": ["hamburg", "bangalore", "tokyo", "seattle"]}, {"id": "org-19", "name": "Arbor Analytics", "founded": 2018, "employees": 300, "isPublic": false, "ceo": {"id": "person-12", "name": "Goran Fontaine"}, "categories": ["fintech", "security"], "cities": ["lyon", "tokyo", "pune", "toulouse", "boston", "munich", "cambridge", "waterloo", "vancouver", "hamburg", "edinburgh", "austin", "london"]}, {"id": "org-20", "name": "Meridian Analytics", "founded": 2017, "employees": 80, "isPublic": true, "summary": "Meridian Analytics builds sensor dashboards for retailers.", "ceo": {"id": "person-13", "name": "Chen Petrov"}, "categories": ["ai"], "cities": ["san-francisco", "seattle"]}, {"id": "org-21", "name": "Quarry Vision", "founded": 2000, "employees": 4000, "isPublic": false, "summary": "Quarry Vision builds inspection cameras for schools.", "ceo": {"id": "person-14", "name": "Tomas Jensen"}, "categories": ["chips", "health", "security"], "cities": ["austin", "vancouver", "paris", "berlin", "manchester", "edinburgh", "chicago", "munich", "waterloo", "san-francisco", "seattle"]}, {"id": "org-22", "name": "Basalt Systems", "founded": 2001, "employees": 650, "isPublic": false, "ceo": {"id": "person-15", "name": "Hana Gupta"}, "categories": ["edtech"], "cities": ["seattle", "chicago", "manchester", "paris", "osaka", "hyderabad", "montreal", "london", "munich", "edinburgh", "hamburg", "toulouse", "lyon"]}, {"id": "org-23", "name": "Sable Labs", "founded": 2007, "employees": 12, "isPublic": true, "summary": "Sable Labs builds threat hunting for factories.", "categories": ["health"], "cities": ["bangalore", "paris"]}, {"id": "org-24", "name": "Orchid Labs", "founded": 2002, "employees": 25, "isPublic": true, "summary": "Orchid Labs builds warehouse robots for utilities.", "ceo": {"id": "person-16", "name": "Priya Gupta"}, "categories": [], "cities": ["cambridge", "new-york", "hamburg"]}, {"id": "org-25", "name": "Alder Vision", "founded": 2010, "employees": 12, "isPublic": false, "summary": "Alder Vision builds threat hunting for hospitals.", "ceo": {"id": "person-17", "name": "Goran Gupta"}, "categories": ["chips", "robotics"], "cities": []}, {"id": "org-26", "name": "Arbor Systems", "founded": 2017, "employees": 650, "isPublic": false, "ceo": {"id": "person-18", "name": "Chen Weber"}, "categories": ["ai", "chips", "health"], "cities": ["denver"]}, {"id": "org-27", "name": "Harbor Labs", "founded": 2006, "employees": 150, "isPublic": true, "summary": "Harbor Labs builds forecasting models for hospitals.", "categories": ["chips", "robotics"], "cities": ["vancouver", "paris"]}, {"id": "org-28", "name": "Tessel Vision", "founded": 1998, "employees": 80, "isPublic": false, "ceo": {"id": "person-19", "name": "Goran Moreau"}, "categories": ["ai", "chips"], "cities": ["bangalore", "manchester", "toulouse"]}, {"id": "org-29", "name": "Cobalt AI", "founded": 2016, "employees": 650, "isPublic": false, "ceo": {"id": "person-20", "name": "Goran Eze"}, "categories": ["security"], "cities": ["boston"]}, {"id": "org-30", "name": "Orchid Systems", "founded": 2018, "employees": 25, "isPublic": true, "summary": "Orchid Systems builds sensor dashboards for banks.", "ceo": {"id": "person-21", "name": "Rosa Varga"}, "categories": ["fintech"], "cities": ["austin", "lyon", "tokyo", "hyderabad", "berlin"]}, {"id": "org-31", "name": "Dunlin Labs", "founded": 2007, "employees": 80, "isPublic": true, "summary": "Dunlin Labs builds sensor dashboards for schools.", "ceo": {"id": "person-22", "name": "Quentin Gupta"}, "categories": ["fintech"], "cities": ["london"]}, {"id": "org-32", "name": "Wren Labs", "founded": 2005, "employees": 1200, "isPublic": false, "summary": "Wren Labs builds inspection cameras for retailers.", "categories": ["analytics", "robotics", "security"], "cities": ["cambridge", "denver", "osaka"]}, {"id": "org-33", "name": "Lumen Robotics", "founded": 2005, "employees": 150, "isPublic": false, "ceo": {"id": "person-23", "name": "Yusuf Abara"}, "categories": ["vision"], "cities": ["san-francisco", "toulouse", "cambridge", "paris", "boston", "hyderabad", "vancouver", "new-york", "berlin", "montreal", "austin", "london", "tokyo", "bangalore", "hamburg", "denver", "toronto", "manchester", "chicago"]}, {"id": "org-34", "name": "Arbor Robotics", "founded": 2005, "employees": 300, "isPublic": true, "categories": ["nlp", "vision"], "cities": ["montreal", "lyon", "cambridge", "toulouse", "chicago"]}, {"id": "org-35", "name": "Wren Systems", "founded": 1998, "employees": 15000, "isPublic": false, "categories": ["nlp", "security"], "cities": ["toronto"]}, {"id": "org-36", "name": "Lumen Vision", "founded": 2006, "employees": 12, "isPublic": false, "summary": "Lumen Vision builds clinical triage software for hospitals.", "categories": ["analytics"], "cities": ["toronto", "seattle"]}, {"id": "org-37", "name": "Wren AI", "founded": 2007, "employees": 25, "isPublic": true, "categories": ["fintech"], "cities": ["chicago", "edinburgh"]}, {"id": "org-38", "name": "Ember AI", "founded": 2004, "employees": 12, "isPublic": false, "ceo": {"id": "person-24", "name": "Bruno Varga"}, "categories": ["ai", "robotics", "security"], "cities": ["edinburgh", "montreal", "pune"]}, {"id": "org-39", "name": "Basalt Labs", "founded": 1997, "employees": 1200, "isPublic": false, "ceo": {"id": "person-25", "name": "Tomas Castillo"}, "categories": ["chips", "security"], "cities": ["waterloo", "hamburg", "vancouver", "cambridge", "berlin", "boston", "toronto", "lyon"]}, {"id": "org-40", "name": "Basalt AI", "founded": 2022, "employees": 650, "isPublic": false, "summary": "Basalt AI builds inspection cameras for insurers.", "ceo": {"id": "person-26", "name": "Chen Eze"}, "categories": ["fintech", "nlp"], "cities": ["toulouse"]}, {"id": "org-41", "name": "Brindle Analytics", "founded": 1999, "employees": 12, "isPublic": true, "ceo": {"id": "person-27", "name": "Yusuf Ishikawa"}, "categories": ["ai", "chips", "vision"], "cities": ["vancouver", "berlin", "boston", "toulouse"]}, {"id": "org-42", "name": "Juniper Vision", "founded": 2011, "employees": 1200, "isPublic": true, "summary": "Juniper Vision builds sensor dashboards for banks.", "categories": [], "cities": []}, {"id": "org-43", "name": "Yarrow AI", "founded": 2001, "employees": 15000, "isPublic": false, "ceo": {"id": "person-28", "name": "Tomas Fontaine"}, "categories": ["health", "nlp"], "cities": ["osaka", "chicago", "munich", "boston", "berlin"]}, {"id": "org-44", "name": "Fathom Systems", "founded": 2022, "employees": 300, "isPublic": true, "summary": "Fathom Systems builds forecasting models for utilities.", "ceo": {"id": "person-29", "name": "Freya Lindqvist"}, "categories": ["nlp"], "cities": ["pune", "tokyo"]}, {"id": "org-45", "name": "Quarry Robotics", "founded": 2019, "employees": 40, "isPublic": false, "ceo": {"id": "person-30", "name": "Ines Rahman"}, "categories": ["fintech"], "cities": ["hamburg", "boston"]}, {"id": "org-46", "name": "Meridian AI", "founded": 2015, "employees": 150, "isPublic": false, "ceo": {"id": "person-31", "name": "Dalia Silva"}, "categories": ["edtech", "nlp"], "cities": ["austin", "toronto", "montreal", "berlin", "chicago", "san-francisco", "edinburgh", "manchester", "waterloo", "lyon", "toulouse", "denver", "new-york", "osaka", "munich", "pune", "london", "boston", "bangalore", "cambridge"]}, {"id": "org-47", "name": "Orchid AI", "founded": 2020, "employees": 12, "isPublic": false, "summary": "Orchid AI builds tutoring assistants for schools.", "ceo": {"id": "person-32", "name": "Chen Fontaine"}, "categories": ["vision"], "cities": ["toronto", "pune", "san-francisco", "lyon", "edinburgh"]}, {"id": "org-48", "name": "Umber Vision", "founded": 2017, "employees": 650, "isPublic": true, "ceo": {"id": "person-33", "name": "Tomas Quinn"}, "categories": ["analytics"], "cities": ["osaka", "hamburg"]}, {"id": "org-49", "name": "Pylon AI", "founded": 2015, "employees": 1200, "isPublic": false, "ceo": {"id": "person-34", "name": "Priya Castillo"}, "categories": ["chips"], "cities": ["hyderabad"]}, {"id": "org-50", "name": "Juniper Analytics", "founded": 2003, "employees": 4000, "isPublic": true, "ceo": {"id": "person-35", "name": "Amara Dimitrov"}, "categories": ["analytics", "chips"], "cities": ["bangalore"]}, {"id": "org-51", "name": "Keel AI", "founded": 2019, "employees": 80, "isPublic": true, "summary": "Keel AI builds sensor dashboards for schools.", "ceo": {"id": "person-36", "name": "Hana Dimitrov"}, "categories": ["vision"], "cities": ["tokyo", "chicago", "vancouver", "lyon", "munich", "new-york", "hyderabad", "pune", "seattle", "london", "boston", "toronto", "manchester", "denver", "osaka", "berlin", "toulouse"]}, {"id": "org-52", "name": "Keel Vision", "founded": 2000, "employees": 150, "isPublic": true, "summary": "Keel Vision builds forecasting models for logistics firms.", "categories": ["analytics", "chips"], "cities": ["waterloo", "manchester", "berlin", "chicago", "new-york"]}, {"id": "org-53", "name": "Vesper Systems", "founded": 2003, "employees": 80, "isPublic": false, "summary": "Vesper Systems builds clinical triage software for factories.", "ceo": {"id": "person-37", "name": "Bruno Lindqvist"}, "categories": ["edtech"], "cities": ["osaka"]}, {"id": "org-54", "name": "Drift Analytics", "founded": 2018, "employees": 40, "isPublic": false, "categories": ["edtech", "fintech", "vision"], "cities": ["chicago"]}, {"id": "org-55", "name": "Cobalt Analytics", "founded": 2001, "employees": 40, "isPublic": false, "summary": "Cobalt Analytics builds inspection cameras for retailers.", "ceo": {"id": "person-38", "name": "Amara Eze"}, "categories": ["edtech", "vision"], "cities": ["vancouver", "munich", "new-york", "edinburgh", "seattle", "hamburg", "san-francisco", "paris", "toronto", "berlin", "osaka", "montreal", "london", "manchester", "cambridge", "tokyo", "austin", "bangalore", "hyderabad"]}, {"id": "org-56", "name": "Gantry Analytics", "founded": 2001, "employees": 150, "isPublic": false, "categories": ["analytics", "nlp"], "cities": ["tokyo", "hyderabad", "hamburg", "pune", "austin"]}, {"id": "org-57", "name": "Yarrow Systems", "founded": 2001, "employees": 40, "isPublic": false, "ceo": {"id": "person-39", "name": "Nadia Moreau"}, "categories": ["ai", "nlp", "robotics"], "cities": ["montreal", "toronto", "lyon", "manchester", "new-york", "vancouver", "edinburgh", "hyderabad", "london", "munich", "berlin", "chicago"]}, {"id": "org-58", "name": "Keel Labs", "founded": 2007, "employees": 650, "isPublic": false, "summary": "Keel Labs builds inspection cameras for factories.", "categories": ["nlp"], "cities": ["tokyo"]}, {"id": "org-59", "name": "Alder AI", "founded": 1999, "employees": 12, "isPublic": false, "ceo": {"id": "person-40", "name": "Tomas Dimitrov"}, "categories": ["nlp"], "cities": ["toulouse"]}, {"id": "org-60", "name": "Ionic Analytics", "founded": 2018, "employees": 650, "isPublic": false, "summary": "Ionic Analytics builds clinical triage software for retailers.", "ceo": {"id": "person-41", "name": "Dalia Okafor"}, "categories": [], "cities": ["edinburgh"]}, {"id": "org-61", "name": "Vesper Analytics", "founded": 1996, "employees": 4000, "isPublic": false, "ceo": {"id": "person-42", "name": "Viktor Okafor"}, "categories": ["chips"], "cities": []}, {"id": "org-62", "name": "Cinder Vision", "founded": 2007, "employees": 40, "isPublic": false, "ceo": {"id": "person-43", "name": "Omar Castillo"}, "categories": ["ai", "chips"], "cities": ["bangalore", "pune", "london", "toronto", "munich", "boston", "lyon", "seattle", "hyderabad", "vancouver", "waterloo", "san-francisco", "tokyo", "denver", "berlin", "osaka", "new-york", "manchester", "paris", "hamburg", "edinburgh", "austin", "cambridge"]}, {"id": "org-63", "name": "Dunlin AI", "founded": 1996, "employees": 300, "isPublic": false, "categories": ["nlp"], "cities": ["edinburgh"]}, {"id": "org-64", "name": "Fathom Vision", "founded": 1998, "employees": 300, "isPublic": false, "categories": ["health", "vision"], "cities": ["boston", "manchester", "hamburg", "chicago", "austin"]}, {"id": "org-65", "name": "Cinder Robotics", "founded": 1999, "employees": 40, "isPublic": false, "summary": "Cinder Robotics builds tutoring assistants for hospitals.", "ceo": {"id": "person-44", "name": "Emeka Silva"}, "categories": ["ai", "health"], "cities": ["vancouver", "waterloo", "san-francisco"]}, {"id": "org-66", "name": "Rill Robotics", "founded": 2017, "employees": 1200, "isPublic": false, "summary": "Rill Robotics builds tutoring assistants for hospitals.", "ceo": {"id": "person-45", "name": "Emeka Dimitrov"}, "categories": ["nlp"], "cities": []}, {"id": "org-67", "name": "Rill Systems", "founded": 2005, "employees": 650, "isPublic": false, "summary": "Rill Systems builds fraud detection for schools.", "ceo": {"id": "person-46", "name": "Yusuf Petrov"}, "categories": ["vision"], "cities": ["berlin", "denver", "london", "bangalore", "hamburg"]}, {"id": "org-68", "name": "Keel Systems", "founded": 2012, "employees": 80, "isPublic": false, "ceo": {"id": "person-47", "name": "Rosa Okafor"}, "categories": ["ai"], "cities": ["munich", "bangalore", "hamburg", "denver", "hyderabad"]}, {"id": "org-69", "name": "Drift Robotics", "founded": 2014, "employees": 25, "isPublic": false, "categories": ["nlp", "robotics"], "cities": ["tokyo"]}, {"id": "org-70", "name": "Dunlin Robotics", "founded": 1994, "employees": 1200, "isPublic": false, "ceo": {"id": "person-48", "name": "Priya Tanaka"}, "categories": ["robotics", "vision"], "cities": ["toronto", "seattle", "toulouse"]}, {"id": "org-71", "name": "Fathom Analytics", "founded": 1999, "employees": 12, "isPublic": false, "ceo": {"id": "person-49", "name": "Rosa Silva"}, "categories": ["chips", "edtech"], "cities": ["chicago", "cambridge", "london", "seattle", "hyderabad", "boston", "toronto", "paris", "lyon", "montreal", "berlin", "new-york", "toulouse", "vancouver", "pune", "munich", "tokyo", "austin", "edinburgh", "bangalore"]}, {"id": "org-72", "name": "Rill Analytics", "founded": 2021, "employees": 15000, "isPublic": false, "ceo": {"id": "person-50", "name": "Bruno Quinn"}, "categories": ["chips", "security"], "cities": ["london"]}, {"id": "org-73", "name": "Vesper AI", "founded": 2009, "employees": 80, "isPublic": true, "summary": "Vesper AI builds clinical triage software for schools.", "ceo": {"id": "person-51", "name": "Mei Castillo"}, "categories": ["fintech", "nlp"], "cities": ["bangalore", "london", "denver"]}, {"id": "org-74", "name": "Nimbus Systems", "founded": 2020, "employees": 15000, "isPublic": false, "ceo": {"id": "person-52", "name": "Kavya Lindqvist"}, "categories": ["edtech", "health", "nlp"], "cities": ["tokyo", "london", "toulouse"]}, {"id": "org-75", "name": "Pylon Labs", "founded": 2001, "employees": 80, "isPublic": false, "summary": "Pylon Labs builds speech tools for hospitals.", "ceo": {"id": "person-53", "name": "Amara Gupta"}, "categories": ["security"], "cities": ["montreal", "toulouse", "seattle", "london", "boston"]}, {"id": "org-76", "name": "Yarrow Vision", "founded": 2003, "employees": 300, "isPublic": false, "summary": "Yarrow Vision builds clinical triage software for banks.", "ceo": {"id": "person-54", "name": "Jonas Berg"}, "categories": ["chips", "security"], "cities": ["boston", "edinburgh", "toulouse", "denver", "pune", "tokyo", "montreal", "osaka", "lyon", "munich", "hyderabad", "bangalore", "cambridge", "vancouver"]}, {"id": "org-77", "name": "Wren Robotics", "founded": 2019, "employees": 4000, "isPublic": false, "summary": "Wren Robotics builds sensor dashboards for factories.", "ceo": {"id": "person-55", "name": "Bruno Okafor"}, "categories": ["fintech"], "cities": ["toronto"]}, {"id": "org-78", "name": "Harbor Analytics", "founded": 2010, "employees": 25, "isPublic": false, "ceo": {"id": "person-56", "name": "Uma Ueda"}, "categories": [], "cities": ["berlin", "lyon", "chicago", "pune", "vancouver"]}, {"id": "org-79", "name": "Orchid Analytics", "founded": 2018, "employees": 15000, "isPublic": false, "summary": "Orchid Analytics builds tutoring assistants for factories.", "ceo": {"id": "person-57", "name": "Omar Berg"}, "categories": ["edtech"], "cities": ["new-york"]}, {"id": "org-80", "name": "Meridian Vision", "founded": 2016, "employees": 1200, "isPublic": false, "summary": "Meridian Vision builds inspection cameras for insurers.", "ceo": {"id": "person-58", "name": "Uma Petrov"}, "categories": ["chips"], "cities": ["montreal", "cambridge", "waterloo", "bangalore", "lyon", "tokyo", "new-york", "austin", "chicago", "toulouse", "paris", "osaka", "vancouver", "munich", "seattle"]}, {"id": "org-81", "name": "Quarry AI", "founded": 2015, "employees": 650, "isPublic": false, "ceo": {"id": "person-59", "name": "Freya Dimitrov"}, "categories": ["chips"], "cities": ["austin", "london", "osaka", "munich"]}, {"id": "org-82", "name": "Umber Systems", "founded": 2010, "employees": 40, "isPublic": true, "ceo": {"id": "person-60", "name": "Emeka Berg"}, "categories": ["security"], "cities": ["new-york", "toulouse"]}, {"id": "org-83", "name": "Umber Robotics", "founded": 2002, "employees": 4000, "isPublic": false, "summary": "Umber Robotics builds sensor dashboards for insurers.", "ceo": {"id": "person-61", "name": "Emeka Novak"}, "categories": ["ai", "edtech"], "cities": ["chicago", "vancouver", "waterloo", "osaka", "edinburgh", "hyderabad", "san-francisco", "munich", "bangalore", "lyon"]}, {"id": "org-84", "name": "Alder Labs", "founded": 2006, "employees": 650, "isPublic": false, "ceo": {"id": "person-62", "name": "Ines Jensen"}, "categories": ["edtech"], "cities": ["toulouse", "bangalore", "berlin", "boston", "edinburgh"]}, {"id": "org-85", "name": "Yarrow Labs", "founded": 2020, "employees": 12, "isPublic": false, "summary": "Yarrow Labs builds tutoring assistants for hospitals.", "ceo": {"id": "person-63", "name": "Sami Lindqvist"}, "categories": ["fintech", "vision"], "cities": ["london", "paris"]}, {"id": "org-86", "name": "Rill Labs", "founded": 2010, "employees": 150, "isPublic": false, "summary": "Rill Labs builds forecasting models for retailers.", "ceo": {"id": "person-64", "name": "Jonas Weber"}, "categories": ["security"], "cities": ["chicago", "cambridge", "manchester"]}, {"id": "org-87", "name": "Ember Robotics", "founded": 2016, "employees": 4000, "isPublic": false, "summary": "Ember Robotics builds edge chips for hospitals.", "ceo": {"id": "person-65", "name": "Priya Berg"}, "categories": ["chips", "edtech"], "cities": ["cambridge"]}, {"id": "org-88", "name": "Brindle AI", "founded": 2016, "employees": 12, "isPublic": false, "summary": "Brindle AI builds warehouse robots for schools.", "ceo": {"id": "person-66", "name": "Jonas Lindqvist"}, "categories": ["robotics"], "cities": ["toronto", "san-francisco", "manchester", "paris", "hyderabad", "berlin", "chicago", "lyon", "munich", "seattle", "new-york", "montreal", "osaka", "austin", "denver"]}, {"id": "org-89", "name": "Meridian Robotics", "founded": 2008, "employees": 150, "isPublic": false, "summary": "Meridian Robotics builds speech tools for retailers.", "ceo": {"id": "person-67", "name": "Ines Kowalski"}, "categories": ["fintech"], "cities": ["chicago"]}, {"id": "org-90", "name": "Ionic AI", "founded": 2002, "employees": 12, "isPublic": false, "summary": "Ionic AI builds threat hunting for retailers.", "ceo": {"id": "person-68", "name": "Dalia Eze"}, "categories": ["health"], "cities": ["boston"]} ] } ```