-C Two\sJS\sfile\srenames\swhich\sgot\sinadvertently\sundone\swhile\ssetting\sup\s[9062b31174618c0e].\sCosmetic\scleanups\sin\sext/wasm/dist.make.
-D 2023-01-29T06:01:32.206
+C Additional\stweaks\sto\sthe\senhancement\sat\s[609fbb94b8f01d67]\sto\sfurther\sreduce\nthe\scost\sestimate\sfor\sconstructing\san\sautomatic\sindex\son\san\sephemeral\stable,\nin\sorder\sto\sresolve\sthe\sperformance\sproblem\sdescribed\sby\n[forum:/forumpost/1d571c0296|forum\spost\s1d571c0296].
+D 2023-01-30T20:44:54.399
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# Remove this line to create a well-formed Fossil manifest.
1, 1, 1, 1;
} {xyz}
+# 2023-01-30
+# Tests case for the query planner performance issue reported by
+# https://sqlite.org/forum/forumpost/1d571c0296
+#
+# The fix was to adjust the cost of computing an automatic index for
+# ephemeral tables, to help ensure that they are generated if they are
+# needed. The test case below only looks at the query plan. But 12x
+# improved performance has been verified by populating the "raw" table
+# with 100K rows of random data and running actual speed tests.
+#
+do_test where-30.1 {
+ unset -nocomplain res
+ set res {}
+ db eval {CREATE TABLE raw(country,date,total,delta, UNIQUE(country,date));}
+ db eval {
+ EXPLAIN QUERY PLAN
+ WITH
+ -- Find the country and min/max date
+ init(country, date, fin) AS (SELECT country, min(date), max(date)
+ FROM raw WHERE total > 0 GROUP BY country),
+
+ -- Generate the date stream for each country
+ src(country, date) AS (SELECT raw.country, raw.date
+ FROM raw JOIN init i on raw.country = i.country AND raw.date > i.date
+ ORDER BY raw.country, raw.date),
+
+ -- Generate the x & y for each entry in the country/date stream
+ vals(country, date, x, y) AS (SELECT src.country, src.date,
+ julianday(raw.date) - julianday(src.date), log(delta+1)
+ FROM src JOIN raw on raw.country = src.country
+ AND raw.date > date(src.date,'-7 days')
+ AND raw.date <= src.date AND delta >= 0),
+
+ -- Accumulate the data we need
+ sums(country, date, x2, x, n, xy, y) AS (SELECT country, date,
+ sum(x*x*1.0), sum(x*1.0), sum(1.0), sum(x*y*1.0), sum(y*1.0)
+ FROM vals GROUP BY 1, 2),
+
+ -- use these to calculate to divisor for the inverse matrix
+ mult(country, date, m) AS (SELECT country, date, 1.0/(x2 * n - x * x)
+ FROM sums),
+
+ -- Build the inverse matrix
+ inv(country, date, a,b,c,d) AS (SELECT mult.country, mult.date, n * m,
+ -x * m, -x * m, x2 * m
+ FROM mult JOIN sums on sums.country=mult.country
+ AND mult.date=sums.date),
+
+ -- Calculate the coefficients for the least squares fit
+ fit(country, date, a, b) AS (SELECT inv.country, inv.date,
+ a * xy + b * y, c * xy + d * y
+ FROM inv
+ JOIN mult on mult.country = inv.country AND mult.date = inv.date
+ JOIN sums on sums.country = mult.country AND sums.date = mult.date
+ )
+ SELECT *, nFin/nPrev - 1 AS growth, log(2)/log(nFin/nPrev) AS doubling
+ FROM (SELECT f.*, exp(b) - 1 AS nFin, exp(a* (-1) + b) - 1 AS nPrev
+ FROM fit f JOIN init i on i.country = f.country
+ AND f.date <= date(i.fin,'-3 days'))
+ WHERE nPrev > 0 AND nFin > 0;
+ } {
+ if {$parent!=0} continue
+ if {![string match SCAN* $detail]} continue
+ lappend res SCAN
+ }
+ set res
+} {SCAN}
+# ^^^^^^-- there should only be one top-level table scan in the query plan.
+
finish_test