Splink does not normally score every possible record pair. Its prediction flow first uses SQL blocking rules to select candidate pairs, then evaluates configured comparisons and applies model-derived weights to produce a match_weight and match_probability. DuckDB executes the SQL for the selected backend, but there is no single fixed query or physical plan: both depend on the Splink version, settings, input tables, data and runtime.
What happens when Splink predicts a match?
Ordinary predict() is best understood as a pipeline with separate candidate-generation and scoring stages. A useful conceptual outline is: generate candidate pairs, evaluate comparison expressions, combine their evidence using model parameters and any enabled term-frequency adjustments, then return scores. This describes the behavior, not the exact SQL emitted by any particular installation.
- Generate candidates: Splink applies prediction blocking rules, which are SQL expressions over left (
l) and right (r) records. A pair qualifies if it satisfies at least one rule; if multiple rules find the same pair, Splink deduplicates it. See the official blocking tutorial. - Evaluate comparisons: For each candidate, configured Comparisons evaluate fields or expressions and assign categorical outcomes. These outcomes are commonly represented in
gamma_-prefixed comparison-vector columns. - Calculate evidence: The model uses a prior match probability and estimated
manduprobabilities for comparison outcomes among matches and nonmatches. If enabled, term-frequency adjustments account for how common a value is. Intermediate contributions may appear inmw_-prefixed columns, and adjustments intf_-prefixed columns. Splink documentsgamma_,mw_andtf_as default prefixes in its prediction tutorial. - Return scores: Splink combines the configured contributions into
match_weightandmatch_probability. Optionalthreshold_match_weightorthreshold_match_probabilitysettings can filter results.
The comparison vector is evidence, not a probability: a gamma_ value encodes a comparison outcome. The later model calculation turns outcomes into weighted evidence and a final score. The parameter-estimation tutorial explains that m and u values represent outcome probabilities among matches and nonmatches; they can be estimated from unlabeled records, while labels can improve estimation.
Does Splink compare every row with every other row?
Usually, no: prediction blocking rules limit scoring to pairs that satisfy at least one configured predicate. Blocking is a trade-off. A restrictive rule set can reduce work but exclude true matches before scoring; a broader or complementary set can preserve more plausible candidates at the cost of creating more pairs. A high model score cannot recover a true pair that blocking never generated.
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The settings guide says an empty or omitted prediction blocking-rule list causes a Cartesian comparison. That means comparing every possible pair, with pair count growing quadratically as the number of records increases; Splink warns this is generally intractable at large scale. Rules such as l.first_name = r.first_name can restrict candidates, and combined predicates can make that restriction more selective. The exact settings behavior is described in the settings guide.
Splink’s blocking tutorial suggests about 20 million comparisons as a practical target for DuckDB on a modest laptop, and notes that more powerful machines may handle a billion or more. These are contextual documentation guidelines, not benchmark results or guarantees; feasibility depends on hardware, backend, rule design and data distribution.
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What DuckDB executes—and what cannot be inferred from it
DuckDB executes the SQL associated with the selected Splink backend. Its execution documentation describes vectorized query processing: operators work on vectors, and the documented default STANDARD_VECTOR_SIZE is 2048 tuples. That is an engine-level default, not a claim that every Splink operation processes exactly 2048 rows at a time.
Neither vectorization nor the conceptual pipeline identifies the exact joins, projections, filters, temporary relations or physical operators in a particular prediction. Those details depend on the Splink release, settings, tables, data and runtime. The DuckDB execution-format documentation explains vectors, but it is not a query plan for a Splink job. The official Splink tutorials also include illustrative timings, not a generally applicable measured speed or accuracy figure for Splink on DuckDB.
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Other prediction APIs can behave differently from ordinary blocking-based predict(): Splink provides options to score a known pair or Cartesian products supplied explicitly. Do not treat those routes as evidence that normal prediction always compares all rows.
How to choose between blocking, Cartesian comparison and chunking
| Approach | What it does | Main trade-off |
|---|---|---|
| Blocking rules | Generates pairs satisfying any configured rule and deduplicates pairs found by multiple rules. | Reduces scoring workload, but rules can exclude true matches. Candidate recall and workload depend on the data and predicates. |
| Cartesian comparison | Compares all possible pairs; an empty or omitted prediction blocking-rule list can trigger this behavior under the settings guide. | Can include candidates that blocking would miss, but pair count grows quadratically and can become intractable. |
| Chunked prediction | Processes a grid of left- and right-side chunks serially using the documented helper. | Lowers peak materialization and adds progress reporting, but does not remove the total computation. The guide says processing all chunks yields the same result as one prediction call. |
Blocking-rule quality is data-dependent. A common or placeholder value can make a block unexpectedly large, so inspect comparison counts and the largest blocks before scaling up. Splink’s blocking tutorial describes analysis tools for those checks. Multiple complementary rules may help preserve candidates, but review their marginal candidate counts after deduplication rather than assuming each rule adds useful coverage.
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How to inspect the scores and manage intermediate output
For diagnosis, keep intermediate calculation columns so you can examine comparison outcomes, partial weights and term-frequency adjustments. Splink’s prediction tutorial notes that disabling retention can make computations faster, trading inspectability for runtime. The appropriate choice depends on whether the job is being debugged or optimized.
There is no universal emitted SQL statement to copy as “what Splink runs.” To inspect a reproducible implementation, pin the Splink and DuckDB versions, record the model settings and input schema, then capture the generated SQL or DuckDB EXPLAIN output for that prediction. Without those specifics, an exact query plan is not established.
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