tpch

package
v0.18.15 Latest Latest
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 Go to latest Published: Sep 3, 2026 License: AGPL-3.0 Imports: 14 Imported by: 0

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Index

Constants

View Source 
const (
	ModeOrderedTotal   = "ordered-total"
	ModeMultisetKeySeq = "multiset+keyseq"
)

Comparison modes. Both are order-sensitive; they differ in how they stay immune to the order of TIED rows, which SQL does not pin down.

View Source 
const (
	// KindGroundTruth: every entry came from an external reference engine.
	// This file can fail Wadjet for being wrong.
	KindGroundTruth = "ground-truth"
	// KindRegression: every entry came from Wadjet itself. This file can
	// only say "the answer changed", never "the answer is right".
	KindRegression = "regression"
)

File kinds. The distinction is the whole point of the provenance stamp: a fingerprint produced by Wadjet is regression detection, never correctness.

View Source 
const (
	MoneyPrecision = 15
	MoneyScale     = 2
)

MoneyPrecision and MoneyScale are the specification's DECIMAL(15,2).

View Source 
const SelfEngine = "wadjet"

SelfEngine stamps a Wadjet-produced entry. It is refused in a ground-truth file by name.

Variables

View Source 
var AllTables = map[string]parquet.Schema{
	"region":   RegionSchema,
	"nation":   NationSchema,
	"supplier": SupplierSchema,
	"part":     PartSchema,
	"partsupp": PartSuppSchema,
	"customer": CustomerSchema,
	"orders":   OrdersSchema,
	"lineitem": LineItemSchema,
}

AllTables maps table names to their schemas.

View Source 
var AllTablesDecimal = buildDecimalSchemas()

AllTablesDecimal is AllTables with the eight monetary columns declared DECIMAL(15,2). It is BUILT by rewriting the FLOAT64 set rather than written out again, so the two can never drift apart in any other column: a column added to schema.go appears in both.

View Source 
var CustomerSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "c_custkey", Type: parquet.TypeInt32},
		{Name: "c_name", Type: parquet.TypeString},
		{Name: "c_address", Type: parquet.TypeString},
		{Name: "c_nationkey", Type: parquet.TypeInt32},
		{Name: "c_phone", Type: parquet.TypeString},
		{Name: "c_acctbal", Type: parquet.TypeFloat64},
		{Name: "c_mktsegment", Type: parquet.TypeString},
		{Name: "c_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var ErrUnpopulated = errors.New("fingerprint file has no entries")

ErrUnpopulated reports a well-formed file that carries no entries yet — the SF100 gate is not active. Callers distinguish it from a malformed file so a benchmark run says "gate not active, generate it" rather than "corrupt".

View Source 
var LineItemSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "l_orderkey", Type: parquet.TypeInt32},
		{Name: "l_partkey", Type: parquet.TypeInt32},
		{Name: "l_suppkey", Type: parquet.TypeInt32},
		{Name: "l_linenumber", Type: parquet.TypeInt32},
		{Name: "l_quantity", Type: parquet.TypeFloat64},
		{Name: "l_extendedprice", Type: parquet.TypeFloat64},
		{Name: "l_discount", Type: parquet.TypeFloat64},
		{Name: "l_tax", Type: parquet.TypeFloat64},
		{Name: "l_returnflag", Type: parquet.TypeString},
		{Name: "l_linestatus", Type: parquet.TypeString},
		{Name: "l_shipdate", Type: parquet.TypeString},
		{Name: "l_commitdate", Type: parquet.TypeString},
		{Name: "l_receiptdate", Type: parquet.TypeString},
		{Name: "l_shipinstruct", Type: parquet.TypeString},
		{Name: "l_shipmode", Type: parquet.TypeString},
		{Name: "l_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var MoneyColumns = map[string]bool{
	"s_acctbal":       true,
	"p_retailprice":   true,
	"ps_supplycost":   true,
	"c_acctbal":       true,
	"o_totalprice":    true,
	"l_extendedprice": true,
	"l_discount":      true,
	"l_tax":           true,
}

MoneyColumns are the eight columns TPC-H v3 §1.3 declares DECIMAL(15,2).

View Source 
var NationSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "n_nationkey", Type: parquet.TypeInt32},
		{Name: "n_name", Type: parquet.TypeString},
		{Name: "n_regionkey", Type: parquet.TypeInt32},
		{Name: "n_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var OrdersSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "o_orderkey", Type: parquet.TypeInt32},
		{Name: "o_custkey", Type: parquet.TypeInt32},
		{Name: "o_orderstatus", Type: parquet.TypeString},
		{Name: "o_totalprice", Type: parquet.TypeFloat64},
		{Name: "o_orderdate", Type: parquet.TypeString},
		{Name: "o_orderpriority", Type: parquet.TypeString},
		{Name: "o_clerk", Type: parquet.TypeString},
		{Name: "o_shippriority", Type: parquet.TypeInt32},
		{Name: "o_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var PartSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "p_partkey", Type: parquet.TypeInt32},
		{Name: "p_name", Type: parquet.TypeString},
		{Name: "p_mfgr", Type: parquet.TypeString},
		{Name: "p_brand", Type: parquet.TypeString},
		{Name: "p_type", Type: parquet.TypeString},
		{Name: "p_size", Type: parquet.TypeInt32},
		{Name: "p_container", Type: parquet.TypeString},
		{Name: "p_retailprice", Type: parquet.TypeFloat64},
		{Name: "p_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var PartSuppSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "ps_partkey", Type: parquet.TypeInt32},
		{Name: "ps_suppkey", Type: parquet.TypeInt32},
		{Name: "ps_availqty", Type: parquet.TypeInt32},
		{Name: "ps_supplycost", Type: parquet.TypeFloat64},
		{Name: "ps_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var RegionSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "r_regionkey", Type: parquet.TypeInt32},
		{Name: "r_name", Type: parquet.TypeString},
		{Name: "r_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var SupplierSchema = parquet.Schema{
	Columns: []parquet.Column{
		{Name: "s_suppkey", Type: parquet.TypeInt32},
		{Name: "s_name", Type: parquet.TypeString},
		{Name: "s_address", Type: parquet.TypeString},
		{Name: "s_nationkey", Type: parquet.TypeInt32},
		{Name: "s_phone", Type: parquet.TypeString},
		{Name: "s_acctbal", Type: parquet.TypeFloat64},
		{Name: "s_comment", Type: parquet.TypeString, Nullable: true},
	},
}
View Source 
var TPCHQueries = map[int]QueryDef{

	1: {
		Name: "Pricing Summary Report",
		SQL: `SELECT
			l_returnflag,
			l_linestatus,
			SUM(l_quantity) as sum_qty,
			SUM(l_extendedprice) as sum_base_price,
			SUM(l_extendedprice * (1 - l_discount)) as sum_disc_price,
			SUM(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
			AVG(l_quantity) as avg_qty,
			AVG(l_extendedprice) as avg_price,
			AVG(l_discount) as avg_disc,
			COUNT(*) as count_order
		FROM lineitem
		WHERE l_shipdate <= '1998-09-02'
		GROUP BY l_returnflag, l_linestatus
		ORDER BY l_returnflag, l_linestatus`,
	},

	2: {
		Name: "Minimum Cost Supplier",
		SQL: `SELECT
			s_acctbal, s_name, n_name, p_partkey, p_mfgr,
			s_address, s_phone, s_comment
		FROM part
		JOIN partsupp ON p_partkey = ps_partkey
		JOIN supplier ON s_suppkey = ps_suppkey
		JOIN nation ON s_nationkey = n_nationkey
		JOIN region ON n_regionkey = r_regionkey
		WHERE r_name = 'EUROPE'
			AND p_size = 15
			AND p_type LIKE '%BRASS'
			AND ps_supplycost = (
				SELECT MIN(ps_supplycost)
				FROM partsupp
				JOIN supplier ON s_suppkey = ps_suppkey
				JOIN nation ON s_nationkey = n_nationkey
				JOIN region ON n_regionkey = r_regionkey
				WHERE ps_partkey = p_partkey
					AND r_name = 'EUROPE'
			)
		ORDER BY s_acctbal DESC, n_name, s_name, p_partkey
		LIMIT 100`,
	},

	3: {
		Name: "Shipping Priority",
		SQL: `SELECT
			l_orderkey,
			SUM(l_extendedprice * (1 - l_discount)) as revenue,
			o_orderdate,
			o_shippriority
		FROM customer
		JOIN orders ON c_custkey = o_custkey
		JOIN lineitem ON l_orderkey = o_orderkey
		WHERE c_mktsegment = 'BUILDING'
			AND o_orderdate < '1995-03-15'
			AND l_shipdate > '1995-03-15'
		GROUP BY l_orderkey, o_orderdate, o_shippriority
		ORDER BY revenue DESC, o_orderdate
		LIMIT 10`,
	},

	4: {
		Name: "Order Priority Checking",
		SQL: `SELECT
			o_orderpriority,
			COUNT(*) as order_count
		FROM orders
		WHERE o_orderdate >= '1993-07-01'
			AND o_orderdate < '1993-10-01'
			AND EXISTS (
				SELECT 1 FROM lineitem
				WHERE l_orderkey = o_orderkey
					AND l_commitdate < l_receiptdate
			)
		GROUP BY o_orderpriority
		ORDER BY o_orderpriority`,
	},

	5: {
		Name: "Local Supplier Volume",
		SQL: `SELECT
			n_name,
			SUM(l_extendedprice * (1 - l_discount)) as revenue
		FROM customer
		JOIN orders ON c_custkey = o_custkey
		JOIN lineitem ON l_orderkey = o_orderkey
		JOIN supplier ON l_suppkey = s_suppkey
		JOIN nation ON s_nationkey = n_nationkey
		JOIN region ON n_regionkey = r_regionkey
		WHERE c_nationkey = s_nationkey
			AND r_name = 'ASIA'
			AND o_orderdate >= '1994-01-01'
			AND o_orderdate < '1995-01-01'
		GROUP BY n_name
		ORDER BY revenue DESC`,
	},

	6: {
		Name: "Forecasting Revenue Change",
		SQL: `SELECT
			SUM(l_extendedprice * l_discount) as revenue
		FROM lineitem
		WHERE l_shipdate >= '1994-01-01'
			AND l_shipdate < '1995-01-01'
			AND l_discount >= 0.05
			AND l_discount <= 0.07
			AND l_quantity < 24`,
	},

	7: {
		Name: "Volume Shipping",
		SQL: `SELECT
			n1.n_name as supp_nation,
			n2.n_name as cust_nation,
			SUBSTR(l_shipdate, 1, 4) as l_year,
			SUM(l_extendedprice * (1 - l_discount)) as revenue
		FROM supplier
		JOIN lineitem ON s_suppkey = l_suppkey
		JOIN orders ON o_orderkey = l_orderkey
		JOIN customer ON c_custkey = o_custkey
		JOIN nation n1 ON s_nationkey = n1.n_nationkey
		JOIN nation n2 ON c_nationkey = n2.n_nationkey
		WHERE ((n1.n_name = 'FRANCE' AND n2.n_name = 'GERMANY')
			OR (n1.n_name = 'GERMANY' AND n2.n_name = 'FRANCE'))
			AND l_shipdate >= '1995-01-01'
			AND l_shipdate <= '1996-12-31'
		GROUP BY n1.n_name, n2.n_name, SUBSTR(l_shipdate, 1, 4)
		ORDER BY supp_nation, cust_nation, l_year`,
	},

	8: {
		Name: "National Market Share",
		SQL: `SELECT
			SUBSTR(o_orderdate, 1, 4) as o_year,
			SUM(CASE WHEN n2.n_name = 'BRAZIL' THEN l_extendedprice * (1 - l_discount) ELSE 0 END) as brazil_revenue,
			SUM(l_extendedprice * (1 - l_discount)) as total_revenue
		FROM part
		JOIN lineitem ON p_partkey = l_partkey
		JOIN supplier ON s_suppkey = l_suppkey
		JOIN orders ON o_orderkey = l_orderkey
		JOIN customer ON c_custkey = o_custkey
		JOIN nation n1 ON c_nationkey = n1.n_nationkey
		JOIN region ON n1.n_regionkey = r_regionkey
		JOIN nation n2 ON s_nationkey = n2.n_nationkey
		WHERE r_name = 'AMERICA'
			AND o_orderdate >= '1995-01-01'
			AND o_orderdate <= '1996-12-31'
			AND p_type = 'ECONOMY ANODIZED STEEL'
		GROUP BY SUBSTR(o_orderdate, 1, 4)
		ORDER BY o_year`,
	},

	9: {
		Name: "Product Type Profit Measure",
		SQL: `SELECT
			n_name as nation,
			SUBSTR(o_orderdate, 1, 4) as o_year,
			SUM(l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity) as sum_profit
		FROM part
		JOIN lineitem ON p_partkey = l_partkey
		JOIN supplier ON s_suppkey = l_suppkey
		JOIN partsupp ON ps_suppkey = l_suppkey AND ps_partkey = l_partkey
		JOIN orders ON o_orderkey = l_orderkey
		JOIN nation ON s_nationkey = n_nationkey
		WHERE p_name LIKE '%green%'
		GROUP BY n_name, SUBSTR(o_orderdate, 1, 4)
		ORDER BY nation, o_year DESC`,
	},

	10: {
		Name: "Returned Item Reporting",
		SQL: `SELECT
			c_custkey, c_name,
			SUM(l_extendedprice * (1 - l_discount)) as revenue,
			c_acctbal, n_name, c_address, c_phone, c_comment
		FROM customer
		JOIN orders ON c_custkey = o_custkey
		JOIN lineitem ON l_orderkey = o_orderkey
		JOIN nation ON c_nationkey = n_nationkey
		WHERE o_orderdate >= '1993-10-01'
			AND o_orderdate < '1994-01-01'
			AND l_returnflag = 'R'
		GROUP BY c_custkey, c_name, c_acctbal, c_phone, n_name, c_address, c_comment
		ORDER BY revenue DESC
		LIMIT 20`,
	},

	11: {
		Name: "Important Stock Identification",
		SQL: `SELECT
			ps_partkey,
			SUM(ps_supplycost * ps_availqty) as value
		FROM partsupp
		JOIN supplier ON ps_suppkey = s_suppkey
		JOIN nation ON s_nationkey = n_nationkey
		WHERE n_name = 'GERMANY'
		GROUP BY ps_partkey
		HAVING SUM(ps_supplycost * ps_availqty) > (
			SELECT SUM(ps_supplycost * ps_availqty) * 0.0001
			FROM partsupp
			JOIN supplier ON ps_suppkey = s_suppkey
			JOIN nation ON s_nationkey = n_nationkey
			WHERE n_name = 'GERMANY'
		)
		ORDER BY value DESC`,
	},

	12: {
		Name: "Shipping Modes and Order Priority",
		SQL: `SELECT
			l_shipmode,
			SUM(CASE WHEN o_orderpriority = '1-URGENT' OR o_orderpriority = '2-HIGH' THEN 1 ELSE 0 END) as high_line_count,
			SUM(CASE WHEN o_orderpriority != '1-URGENT' AND o_orderpriority != '2-HIGH' THEN 1 ELSE 0 END) as low_line_count
		FROM orders
		JOIN lineitem ON o_orderkey = l_orderkey
		WHERE l_shipmode IN ('MAIL', 'SHIP')
			AND l_commitdate < l_receiptdate
			AND l_shipdate < l_commitdate
			AND l_receiptdate >= '1994-01-01'
			AND l_receiptdate < '1995-01-01'
		GROUP BY l_shipmode
		ORDER BY l_shipmode`,
	},

	13: {
		Name: "Customer Distribution",
		SQL: `SELECT
			c_custkey,
			COUNT(o_orderkey) as c_count
		FROM customer
		LEFT JOIN orders ON c_custkey = o_custkey AND o_comment NOT LIKE '%special%requests%'
		GROUP BY c_custkey
		ORDER BY c_count DESC, c_custkey
		LIMIT 100`,
	},

	14: {
		Name: "Promotion Effect",
		SQL: `SELECT
			SUM(CASE WHEN p_type LIKE 'PROMO%' THEN l_extendedprice * (1 - l_discount) ELSE 0 END) as promo_revenue,
			SUM(l_extendedprice * (1 - l_discount)) as total_revenue
		FROM lineitem
		JOIN part ON l_partkey = p_partkey
		WHERE l_shipdate >= '1995-09-01'
			AND l_shipdate < '1995-10-01'`,
	},

	15: {
		Name: "Top Supplier",
		SQL: `WITH revenue AS (
			SELECT
				l_suppkey as supplier_no,
				SUM(l_extendedprice * (1 - l_discount)) as total_revenue
			FROM lineitem
			WHERE l_shipdate >= '1996-01-01'
				AND l_shipdate < '1996-04-01'
			GROUP BY l_suppkey
		)
		SELECT s_suppkey, s_name, s_address, s_phone, total_revenue
		FROM supplier
		JOIN revenue ON s_suppkey = supplier_no
		WHERE total_revenue = (
			SELECT MAX(total_revenue) FROM revenue
		)
		ORDER BY s_suppkey`,
	},

	16: {
		Name: "Parts/Supplier Relationship",
		SQL: `SELECT
			p_brand, p_type, p_size,
			COUNT(DISTINCT ps_suppkey) as supplier_cnt
		FROM partsupp
		JOIN part ON p_partkey = ps_partkey
		WHERE p_brand != 'Brand#45'
			AND p_type NOT LIKE 'MEDIUM POLISHED%'
			AND p_size IN (49, 14, 23, 45, 19, 3, 36, 9)
		GROUP BY p_brand, p_type, p_size
		ORDER BY supplier_cnt DESC, p_brand, p_type, p_size`,
	},

	17: {
		Name: "Small-Quantity-Order Revenue",
		SQL: `SELECT
			SUM(l_extendedprice) / 7.0 as avg_yearly
		FROM lineitem
		JOIN part ON p_partkey = l_partkey
		WHERE p_brand = 'Brand#23'
			AND p_container = 'MED BOX'
			AND l_quantity < (
				SELECT 0.2 * AVG(l_quantity)
				FROM lineitem
				WHERE l_partkey = p_partkey
			)`,
	},

	18: {
		Name: "Large Volume Customer",
		SQL: `SELECT
			c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice,
			SUM(l_quantity) as total_qty
		FROM customer
		JOIN orders ON c_custkey = o_custkey
		JOIN lineitem ON o_orderkey = l_orderkey
		WHERE o_orderkey IN (
			SELECT l_orderkey
			FROM lineitem
			GROUP BY l_orderkey
			HAVING SUM(l_quantity) > 300
		)
		GROUP BY c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice
		ORDER BY o_totalprice DESC, o_orderdate
		LIMIT 100`,
	},

	19: {
		Name: "Discounted Revenue",
		SQL: `SELECT
			SUM(l_extendedprice * (1 - l_discount)) as revenue
		FROM lineitem
		JOIN part ON p_partkey = l_partkey
		WHERE (
			p_brand = 'Brand#12'
			AND p_container IN ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG')
			AND l_quantity >= 1 AND l_quantity <= 11
			AND p_size >= 1 AND p_size <= 5
			AND l_shipmode IN ('AIR', 'REG AIR')
			AND l_shipinstruct = 'DELIVER IN PERSON'
		) OR (
			p_brand = 'Brand#23'
			AND p_container IN ('MED BAG', 'MED BOX', 'MED PACK', 'MED PKG')
			AND l_quantity >= 10 AND l_quantity <= 20
			AND p_size >= 1 AND p_size <= 10
			AND l_shipmode IN ('AIR', 'REG AIR')
			AND l_shipinstruct = 'DELIVER IN PERSON'
		) OR (
			p_brand = 'Brand#34'
			AND p_container IN ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG')
			AND l_quantity >= 20 AND l_quantity <= 30
			AND p_size >= 1 AND p_size <= 15
			AND l_shipmode IN ('AIR', 'REG AIR')
			AND l_shipinstruct = 'DELIVER IN PERSON'
		)`,
	},

	20: {
		Name: "Potential Part Promotion",
		SQL: `SELECT s_name, s_address
		FROM supplier
		JOIN nation ON s_nationkey = n_nationkey
		WHERE n_name = 'CANADA'
			AND s_suppkey IN (
				SELECT ps_suppkey
				FROM partsupp
				WHERE ps_partkey IN (
					SELECT p_partkey FROM part WHERE p_name LIKE 'forest%'
				)
				AND ps_availqty > (
					SELECT 0.5 * SUM(l_quantity)
					FROM lineitem
					WHERE l_partkey = ps_partkey
						AND l_suppkey = ps_suppkey
						AND l_shipdate >= '1994-01-01'
						AND l_shipdate < '1995-01-01'
				)
			)
		ORDER BY s_name`,
	},

	21: {
		Name: "Suppliers Who Kept Orders Waiting",
		SQL: `SELECT s_name, COUNT(*) as numwait
		FROM supplier
		JOIN lineitem l1 ON s_suppkey = l1.l_suppkey
		JOIN orders ON o_orderkey = l1.l_orderkey
		JOIN nation ON s_nationkey = n_nationkey
		WHERE o_orderstatus = 'F'
			AND l1.l_receiptdate > l1.l_commitdate
			AND n_name = 'SAUDI ARABIA'
			AND EXISTS (
				SELECT 1 FROM lineitem l2
				WHERE l2.l_orderkey = l1.l_orderkey
					AND l2.l_suppkey != l1.l_suppkey
			)
			AND NOT EXISTS (
				SELECT 1 FROM lineitem l3
				WHERE l3.l_orderkey = l1.l_orderkey
					AND l3.l_suppkey != l1.l_suppkey
					AND l3.l_receiptdate > l3.l_commitdate
			)
		GROUP BY s_name
		ORDER BY numwait DESC, s_name
		LIMIT 100`,
	},

	22: {
		Name: "Global Sales Opportunity",
		SQL: `SELECT
			SUBSTR(c_phone, 1, 2) as cntrycode,
			COUNT(*) as numcust,
			SUM(c_acctbal) as totacctbal
		FROM customer
		WHERE SUBSTR(c_phone, 1, 2) IN ('13', '31', '23', '29', '30', '18', '17')
			AND c_acctbal > (
				SELECT AVG(c_acctbal)
				FROM customer
				WHERE c_acctbal > 0.00
					AND SUBSTR(c_phone, 1, 2) IN ('13', '31', '23', '29', '30', '18', '17')
			)
			AND NOT EXISTS (
				SELECT 1 FROM orders WHERE o_custkey = c_custkey
			)
		GROUP BY SUBSTR(c_phone, 1, 2)
		ORDER BY cntrycode`,
	},
}

Functions

func Generate 

func Generate(sf ScaleFactor) map[string][]map[string]any

Generate produces all TPC-H tables at the given scale factor in the FLOAT64 fixture. Returns a map of table name → rows. Deterministic for a given SF.

func GenerateChunked 

func GenerateChunked(sf ScaleFactor, chunkSize int, emit func(table string, rows []map[string]any) error) error

GenerateChunked streams TPC-H data for all tables, calling emit with chunks of rows. Memory usage is bounded to O(chunkSize) regardless of scale factor.

func GenerateChunkedFor added in v0.18.5 

func GenerateChunkedFor(sf ScaleFactor, chunkSize int, f Fixture, emit func(table string, rows []map[string]any) error) error

GenerateChunkedFor is GenerateChunked for a chosen fixture.

func GenerateFor added in v0.18.5 

func GenerateFor(sf ScaleFactor, f Fixture) map[string][]map[string]any

GenerateFor is Generate for a chosen fixture. The RNG draws are identical in both, so the two fixtures hold the SAME VALUES and differ only in the carrier of the eight monetary columns (see schema_decimal.go).

func MarshalFingerprintFile 

func MarshalFingerprintFile(f *FingerprintFile) ([]byte, error)

MarshalFingerprintFile renders f for storage.

func MoneyText added in v0.18.5 

func MoneyText(cents int64) string

MoneyText renders cents as exact DECIMAL(15,2) text.

func QueryName 

func QueryName(n int) string

QueryName renders the corpus name for a query number.

func SchemaFor added in v0.18.5 

func SchemaFor(f Fixture, table string) parquet.Schema

SchemaFor returns one table's schema for the fixture.

func TablesFor added in v0.18.5 

func TablesFor(f Fixture) map[string]parquet.Schema

TablesFor returns the table schemas for the fixture.

Types

type FPEntry 

type FPEntry struct {
	// Engine names what produced this entry. A ground-truth file requires a
	// reference engine here; "wadjet" is refused by name.
	Engine        string `json:"engine"`
	EngineVersion string `json:"engine_version"`
	// Dataset names the bytes the engine read, so an entry can be traced to
	// the data the benchmark reads (they must be the same parquet).
	Dataset    string `json:"dataset"`
	CapturedAt string `json:"captured_at"`

	Mode string `json:"mode"`
	// Tiebreak names the columns appended to the query's ORDER BY to make it
	// total (ModeOrderedTotal only). Empty when the canonical order was
	// already total.
	Tiebreak string `json:"tiebreak,omitempty"`
	// OrderKeys are the projected ORDER BY key columns whose SEQUENCE is
	// digested (ModeMultisetKeySeq only).
	OrderKeys []string `json:"order_keys,omitempty"`

	Columns  []string `json:"columns"`
	RowCount int      `json:"row_count"`
	// Fine/Coarse digest the rows at 6 and 4 significant float digits —
	// positionally under ModeOrderedTotal, as a multiset under
	// ModeMultisetKeySeq.
	Fine   string `json:"fine"`
	Coarse string `json:"coarse"`
	// KeyFine/KeyCoarse digest the ORDER BY key sequence
	// (ModeMultisetKeySeq only).
	KeyFine   string `json:"key_fine,omitempty"`
	KeyCoarse string `json:"key_coarse,omitempty"`

	Why string `json:"why,omitempty"`
}

FPEntry is one query's stored signature. It carries no result values: the row count and column names are structural, everything else is a digest.

func NewEntry 

func NewEntry(engine, version, dataset, capturedAt string, q FPQuery, sig Signature) FPEntry

NewEntry stamps a computed signature into a storable entry. engine names what produced it — a reference engine for a ground-truth file, SelfEngine for a regression file. Nothing else in the repo writes an entry, so an unstamped or mis-stamped entry means a hand edit, which ParseFingerprintFile refuses.

func (FPEntry) Match 

func (e FPEntry) Match(got Signature) (bool, string)

Match reports whether got is the same answer as the stored entry. The detail string names the first property that differs.

type FPQuery 

type FPQuery struct {
	Num  int
	Name string // "q03"
	SQL  string
	Mode string
	// Tiebreak lists the columns appended to the trailing ORDER BY, "" when
	// the canonical order was already total.
	Tiebreak string
	// OrderKeys are the ORDER BY key columns digested positionally under
	// ModeMultisetKeySeq.
	OrderKeys []string
	Why       string
}

FPQuery is one correctness variant: the SQL the gate runs (which is NOT the SQL the benchmark times) plus how its answer is compared.

func CorrectnessQueries 

func CorrectnessQueries(sf ScaleFactor) []FPQuery

CorrectnessQueries returns the 22 correctness variants for scale factor sf, in query order. These are NOT the queries the benchmark times: GetQuery returns that text unmodified, and this function may append a tiebreaker to its trailing ORDER BY. Both read the same tables and mean the same thing; only the order of tied rows differs, which is exactly what SQL leaves open and what a positional digest cannot be allowed to guess at.

type FingerprintFile 

type FingerprintFile struct {
	Version   int                `json:"version"`
	Kind      string             `json:"kind"`
	Scale     string             `json:"scale"`
	Generator string             `json:"generator"`
	Note      string             `json:"note"`
	Queries   map[string]FPEntry `json:"queries"`
}

FingerprintFile is the on-disk shape: a provenance header plus one entry per correctness query.

func GroundTruthSF001 

func GroundTruthSF001() (*FingerprintFile, error)

GroundTruthSF001 returns the SF0.01 sibling: the same correctness variants captured from DuckDB over the committed benchmarks/tpch/duckdb-data fixtures. It exists so this apparatus is exercised at a scale CI can run — the ordered-total comparison, the tiebreakers, and the cross-engine cell rendering are all proven there rather than first meeting reality on a 280 GiB bucket.

func GroundTruthSF100 

func GroundTruthSF100() (*FingerprintFile, error)

GroundTruthSF100 returns the committed SF100 ground-truth file, embedded so a benchmark binary carries it (the SF100 run happens on an EC2 host with no checkout). It returns ErrUnpopulated while the file has no entries — the gate is then inactive and the caller must say so out loud rather than reporting a pass.

func ParseFingerprintFile 

func ParseFingerprintFile(data []byte, wantKind string) (*FingerprintFile, error)

ParseFingerprintFile parses and validates a fingerprint file.

The provenance check is the loud one. An entry that cannot be traced to an external reference engine is the failure mode this whole file exists to prevent: Wadjet's own answer frozen as the expectation, against which a CORRECT engine fails and a WRONG engine passes. A Wadjet-produced file is legitimate — as regression detection — but it must say so in its kind, and a ground-truth load refuses it by name.

func (*FingerprintFile) CheckCoversCorpus 

func (f *FingerprintFile) CheckCoversCorpus(corpus []FPQuery) error

CheckCoversCorpus requires the file and the corpus to name the same queries and each entry to carry the mode and tiebreaker its variant declares. A corpus query with no entry would pass unchecked; a stale entry hides that a variant changed under it, which would make the digest describe SQL nobody runs any more.

type Fixture added in v0.18.5 

type Fixture int

Fixture selects the carrier of the eight monetary columns. 

const (
	// FloatFixture is the FLOAT64 schema — the default, and the one the
	// published benchmark numbers are measured on.
	FloatFixture Fixture = iota
	// DecimalFixture is the spec-conformant DECIMAL(15,2) schema.
	DecimalFixture
)

func FixtureFromEnv added in v0.18.5 

func FixtureFromEnv() Fixture

FixtureFromEnv reads TPCH_DECIMAL. Anything but "1"/"true"/"yes" is the FLOAT64 default, so an unset or misspelled variable can never silently swap the published benchmark's fixture.

It is read by the tests whose fixture is a CHOICE — the PostgreSQL oracle (both arms) and the wire corpus. The tests that hold a banked expectation — TestTPCHQueries, the baseline files, the DuckDB float gate — take FloatFixture explicitly and are unaffected by the variable, because their stored answers are the float schema's. The decimal gates name DecimalFixture explicitly for the same reason.

func (Fixture) String added in v0.18.5 

func (f Fixture) String() string

type QueryDef 

type QueryDef struct {
	Name string
	SQL  string
}

QueryDef holds a TPC-H query definition.

func GetQuery 

func GetQuery(qNum int, sf ScaleFactor) QueryDef

GetQuery returns the query definition for the given query number, adjusted for the scale factor. Q11 uses FRACTION = 0.0001/SF per the TPC-H spec.

type ScaleFactor 

type ScaleFactor float64

ScaleFactor controls data volume. SF=0.01 is ~10MB, SF=1 is ~1GB. 

const (
	SF001 ScaleFactor = 0.01
	SF01  ScaleFactor = 0.1
	SF1   ScaleFactor = 1.0
	SF10  ScaleFactor = 10.0
	SF100 ScaleFactor = 100.0
)

func (ScaleFactor) RowCounts 

func (sf ScaleFactor) RowCounts() TableCounts

RowCounts returns the row counts for each table at the given scale factor.

type Signature 

type Signature struct {
	Mode string
	// Rows is the row-content fingerprint; Keys is the ORDER BY key-sequence
	// fingerprint, populated only under ModeMultisetKeySeq.
	Rows oracle.Fingerprint
	Keys oracle.Fingerprint
	// Columns is the result's column list, in order.
	Columns []string
}

Signature is a computed (not yet stored) result signature.

func SignatureOf 

func SignatureOf(res *oracle.Result, q FPQuery) Signature

SignatureOf digests one result under q's comparison mode.

func (Signature) String 

func (s Signature) String() string

type TableCounts 

type TableCounts struct {
	Region, Nation, Supplier, Part, PartSupp, Customer, Orders, LineItem int
}

Source Files

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