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Jack Ye
fd177e0aea
The tab now runs write, read, curate, derive, train before it reaches the management side: Ingestion, Semantic Search, Data Curation, Feature Engineering, Training, then Management and Operations. Indexing joins Operations, where the index maintenance page already was, and gains it as Build & Repair. Ingestion gains a Create Table page. It was a section inside the overview, so the one thing a new reader needs first was the one thing the sidebar did not show. Data Curation is new: Aggregate, Sampling, Join, Windowing. The SQL reference answers what the syntax is; these answer why you would reach for it, framed as the questions curation actually asks -- what do I have, what can I afford, what else do I know, which one do I keep. Each links to the reference for the complete grammar rather than repeating it. They carry the things a reference page would not: that count(*) minus count(col) is your coverage, that sampling both sides of a join gives a percent rather than ten, that a window ordered by a value you recompute moves rows between train and test, and that a one percent sample tells you about the head and nothing about the tail.
92 lines
2.7 KiB
Plaintext
92 lines
2.7 KiB
Plaintext
---
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title: "Joining datasets"
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sidebarTitle: "Join"
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description: "Bringing labels, metadata and scores together with the rows they describe."
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icon: link
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keywords: ["join", "labels", "metadata", "enrichment", "namespace"]
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---
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<Badge color="red">Enterprise</Badge> Joins are SQL, and
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[SQL needs a deployment](/basics/apis).
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Curation usually means combining things that were produced separately — images
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in one table, human labels in another, model scores in a third. A join is how
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they become one dataset.
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## Attaching labels {#labels}
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```sql
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SELECT i.id, i.embedding, l.label
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FROM images AS i
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JOIN labels AS l ON l.image_id = i.id
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```
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An inner join keeps only rows that matched, which for curation is often exactly
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what you want: unlabelled rows are not training data.
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When you need to *see* what did not match, use a left join and filter:
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```sql
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SELECT i.id
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FROM images AS i
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LEFT JOIN labels AS l ON l.image_id = i.id
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WHERE l.image_id IS NULL
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```
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That is your unlabelled backlog, which is the thing worth counting before you
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plan an annotation round.
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## Across namespaces {#namespaces}
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Tables in different [namespaces](/management/namespace) join by their dotted
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paths — the catalog is one catalog, so there is nothing to federate:
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```sql
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SELECT i.id, u.team
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FROM prod.media.images AS i
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JOIN prod.reference.uploaders AS u ON u.id = i.uploader_id
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```
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## Joining a search result {#search}
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Full-text search is a table function, so its result is a relation and joins like
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any other. This is how you ask which categories a query matches, rather than
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just which rows:
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```sql
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SELECT c.name, count(*) AS hits
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FROM fts('documents', 'puppy') AS m
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JOIN categories AS c ON c.id = m.category_id
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GROUP BY c.name
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ORDER BY hits DESC
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```
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<Note>
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Vector search has no SQL form, so a similarity result cannot be joined this way.
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Run the search in an SDK, then join over its output — or write the result to a
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table first.
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</Note>
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## Making it fast {#performance}
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Two things dominate join cost on a lakehouse table.
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**Index the join key.** A [scalar index](/indexing/scalar-index) on the key lets
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the planner look rows up instead of scanning for them. Check with
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[`EXPLAIN`](/sql/explain): an indexed key shows a `ScalarIndexQuery`, an
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unindexed one shows a `LanceScan` feeding the join.
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**Do not carry wide columns through.** Select the columns you need before
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joining, not after. Embeddings and blobs are the expensive part of a multimodal
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table, and a join can materialize them many more times than the result has rows.
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```sql
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SELECT i.id, l.label
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FROM (SELECT id, uploader_id FROM images) AS i
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JOIN labels AS l ON l.image_id = i.id
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```
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## Reference {#reference}
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The join types and their semantics are in [Joins](/sql/join).
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