analyze.c

来自「PostgreSQL 8.1.4的源码 适用于Linux下的开源数据库系统」· C语言 代码 · 共 2,242 行 · 第 1/5 页

		eqfunc = oprfuncid(func_operator);
		ReleaseSysCache(func_operator);
	}
	if (!OidIsValid(eqfunc))
		return false;

	/* Is there a "<" operator with suitable semantics? */
	func_operator = ordering_oper(attr->atttypid, true);
	if (func_operator != NULL)
	{
		ltopr = oprid(func_operator);
		ReleaseSysCache(func_operator);
	}

	/* Save the operator info for compute_stats routines */
	mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
	mystats->eqopr = eqopr;
	mystats->eqfunc = eqfunc;
	mystats->ltopr = ltopr;
	stats->extra_data = mystats;

	/*
	 * Determine which standard statistics algorithm to use
	 */
	if (OidIsValid(ltopr))
	{
		/* Seems to be a scalar datatype */
		stats->compute_stats = compute_scalar_stats;
		/*--------------------
		 * The following choice of minrows is based on the paper
		 * "Random sampling for histogram construction: how much is enough?"
		 * by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
		 * Proceedings of ACM SIGMOD International Conference on Management
		 * of Data, 1998, Pages 436-447.  Their Corollary 1 to Theorem 5
		 * says that for table size n, histogram size k, maximum relative
		 * error in bin size f, and error probability gamma, the minimum
		 * random sample size is
		 *		r = 4 * k * ln(2*n/gamma) / f^2
		 * Taking f = 0.5, gamma = 0.01, n = 1 million rows, we obtain
		 *		r = 305.82 * k
		 * Note that because of the log function, the dependence on n is
		 * quite weak; even at n = 1 billion, a 300*k sample gives <= 0.59
		 * bin size error with probability 0.99.  So there's no real need to
		 * scale for n, which is a good thing because we don't necessarily
		 * know it at this point.
		 *--------------------
		 */
		stats->minrows = 300 * attr->attstattarget;
	}
	else
	{
		/* Can't do much but the minimal stuff */
		stats->compute_stats = compute_minimal_stats;
		/* Might as well use the same minrows as above */
		stats->minrows = 300 * attr->attstattarget;
	}

	return true;
}

/*
 *	compute_minimal_stats() -- compute minimal column statistics
 *
 *	We use this when we can find only an "=" operator for the datatype.
 *
 *	We determine the fraction of non-null rows, the average width, the
 *	most common values, and the (estimated) number of distinct values.
 *
 *	The most common values are determined by brute force: we keep a list
 *	of previously seen values, ordered by number of times seen, as we scan
 *	the samples.  A newly seen value is inserted just after the last
 *	multiply-seen value, causing the bottommost (oldest) singly-seen value
 *	to drop off the list.  The accuracy of this method, and also its cost,
 *	depend mainly on the length of the list we are willing to keep.
 */
static void
compute_minimal_stats(VacAttrStatsP stats,
					  AnalyzeAttrFetchFunc fetchfunc,
					  int samplerows,
					  double totalrows)
{
	int			i;
	int			null_cnt = 0;
	int			nonnull_cnt = 0;
	int			toowide_cnt = 0;
	double		total_width = 0;
	bool		is_varlena = (!stats->attr->attbyval &&
							  stats->attr->attlen == -1);
	bool		is_varwidth = (!stats->attr->attbyval &&
							   stats->attr->attlen < 0);
	FmgrInfo	f_cmpeq;
	typedef struct
	{
		Datum		value;
		int			count;
	} TrackItem;
	TrackItem  *track;
	int			track_cnt,
				track_max;
	int			num_mcv = stats->attr->attstattarget;
	StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;

	/*
	 * We track up to 2*n values for an n-element MCV list; but at least 10
	 */
	track_max = 2 * num_mcv;
	if (track_max < 10)
		track_max = 10;
	track = (TrackItem *) palloc(track_max * sizeof(TrackItem));
	track_cnt = 0;

	fmgr_info(mystats->eqfunc, &f_cmpeq);

	for (i = 0; i < samplerows; i++)
	{
		Datum		value;
		bool		isnull;
		bool		match;
		int			firstcount1,
					j;

		vacuum_delay_point();

		value = fetchfunc(stats, i, &isnull);

		/* Check for null/nonnull */
		if (isnull)
		{
			null_cnt++;
			continue;
		}
		nonnull_cnt++;

		/*
		 * If it's a variable-width field, add up widths for average width
		 * calculation.  Note that if the value is toasted, we use the toasted
		 * width.  We don't bother with this calculation if it's a fixed-width
		 * type.
		 */
		if (is_varlena)
		{
			total_width += VARSIZE(DatumGetPointer(value));

			/*
			 * If the value is toasted, we want to detoast it just once to
			 * avoid repeated detoastings and resultant excess memory usage
			 * during the comparisons.	Also, check to see if the value is
			 * excessively wide, and if so don't detoast at all --- just
			 * ignore the value.
			 */
			if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
			{
				toowide_cnt++;
				continue;
			}
			value = PointerGetDatum(PG_DETOAST_DATUM(value));
		}
		else if (is_varwidth)
		{
			/* must be cstring */
			total_width += strlen(DatumGetCString(value)) + 1;
		}

		/*
		 * See if the value matches anything we're already tracking.
		 */
		match = false;
		firstcount1 = track_cnt;
		for (j = 0; j < track_cnt; j++)
		{
			if (DatumGetBool(FunctionCall2(&f_cmpeq, value, track[j].value)))
			{
				match = true;
				break;
			}
			if (j < firstcount1 && track[j].count == 1)
				firstcount1 = j;
		}

		if (match)
		{
			/* Found a match */
			track[j].count++;
			/* This value may now need to "bubble up" in the track list */
			while (j > 0 && track[j].count > track[j - 1].count)
			{
				swapDatum(track[j].value, track[j - 1].value);
				swapInt(track[j].count, track[j - 1].count);
				j--;
			}
		}
		else
		{
			/* No match.  Insert at head of count-1 list */
			if (track_cnt < track_max)
				track_cnt++;
			for (j = track_cnt - 1; j > firstcount1; j--)
			{
				track[j].value = track[j - 1].value;
				track[j].count = track[j - 1].count;
			}
			if (firstcount1 < track_cnt)
			{
				track[firstcount1].value = value;
				track[firstcount1].count = 1;
			}
		}
	}

	/* We can only compute real stats if we found some non-null values. */
	if (nonnull_cnt > 0)
	{
		int			nmultiple,
					summultiple;

		stats->stats_valid = true;
		/* Do the simple null-frac and width stats */
		stats->stanullfrac = (double) null_cnt / (double) samplerows;
		if (is_varwidth)
			stats->stawidth = total_width / (double) nonnull_cnt;
		else
			stats->stawidth = stats->attrtype->typlen;

		/* Count the number of values we found multiple times */
		summultiple = 0;
		for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
		{
			if (track[nmultiple].count == 1)
				break;
			summultiple += track[nmultiple].count;
		}

		if (nmultiple == 0)
		{
			/* If we found no repeated values, assume it's a unique column */
			stats->stadistinct = -1.0;
		}
		else if (track_cnt < track_max && toowide_cnt == 0 &&
				 nmultiple == track_cnt)
		{
			/*
			 * Our track list includes every value in the sample, and every
			 * value appeared more than once.  Assume the column has just
			 * these values.
			 */
			stats->stadistinct = track_cnt;
		}
		else
		{
			/*----------
			 * Estimate the number of distinct values using the estimator
			 * proposed by Haas and Stokes in IBM Research Report RJ 10025:
			 *		n*d / (n - f1 + f1*n/N)
			 * where f1 is the number of distinct values that occurred
			 * exactly once in our sample of n rows (from a total of N),
			 * and d is the total number of distinct values in the sample.
			 * This is their Duj1 estimator; the other estimators they
			 * recommend are considerably more complex, and are numerically
			 * very unstable when n is much smaller than N.
			 *
			 * We assume (not very reliably!) that all the multiply-occurring
			 * values are reflected in the final track[] list, and the other
			 * nonnull values all appeared but once.  (XXX this usually
			 * results in a drastic overestimate of ndistinct.	Can we do
			 * any better?)
			 *----------
			 */
			int			f1 = nonnull_cnt - summultiple;
			int			d = f1 + nmultiple;
			double		numer,
						denom,
						stadistinct;

			numer = (double) samplerows *(double) d;

			denom = (double) (samplerows - f1) +
				(double) f1 *(double) samplerows / totalrows;

			stadistinct = numer / denom;
			/* Clamp to sane range in case of roundoff error */
			if (stadistinct < (double) d)
				stadistinct = (double) d;
			if (stadistinct > totalrows)
				stadistinct = totalrows;
			stats->stadistinct = floor(stadistinct + 0.5);
		}

		/*
		 * If we estimated the number of distinct values at more than 10% of
		 * the total row count (a very arbitrary limit), then assume that
		 * stadistinct should scale with the row count rather than be a fixed
		 * value.
		 */
		if (stats->stadistinct > 0.1 * totalrows)
			stats->stadistinct = -(stats->stadistinct / totalrows);

		/*
		 * Decide how many values are worth storing as most-common values. If
		 * we are able to generate a complete MCV list (all the values in the
		 * sample will fit, and we think these are all the ones in the table),
		 * then do so.	Otherwise, store only those values that are
		 * significantly more common than the (estimated) average. We set the
		 * threshold rather arbitrarily at 25% more than average, with at
		 * least 2 instances in the sample.
		 */
		if (track_cnt < track_max && toowide_cnt == 0 &&
			stats->stadistinct > 0 &&
			track_cnt <= num_mcv)
		{
			/* Track list includes all values seen, and all will fit */
			num_mcv = track_cnt;
		}
		else
		{
			double		ndistinct = stats->stadistinct;
			double		avgcount,
						mincount;

			if (ndistinct < 0)
				ndistinct = -ndistinct * totalrows;
			/* estimate # of occurrences in sample of a typical value */
			avgcount = (double) samplerows / ndistinct;
			/* set minimum threshold count to store a value */
			mincount = avgcount * 1.25;
			if (mincount < 2)
				mincount = 2;
			if (num_mcv > track_cnt)
				num_mcv = track_cnt;
			for (i = 0; i < num_mcv; i++)
			{
				if (track[i].count < mincount)
				{
					num_mcv = i;
					break;
				}
			}
		}

		/* Generate MCV slot entry */
		if (num_mcv > 0)
		{
			MemoryContext old_context;
			Datum	   *mcv_values;
			float4	   *mcv_freqs;

			/* Must copy the target values into anl_context */
			old_context = MemoryContextSwitchTo(stats->anl_context);
			mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
			mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
			for (i = 0; i < num_mcv; i++)
			{
				mcv_values[i] = datumCopy(track[i].value,
										  stats->attr->attbyval,
										  stats->attr->attlen);
				mcv_freqs[i] = (double) track[i].count / (double) samplerows;
			}
			MemoryContextSwitchTo(old_context);

			stats->stakind[0] = STATISTIC_KIND_MCV;
			stats->staop[0] = mystats->eqopr;
			stats->stanumbers[0] = mcv_freqs;
			stats->numnumbers[0] = num_mcv;
			stats->stavalues[0] = mcv_values;
			stats->numvalues[0] = num_mcv;
		}
	}
	else if (null_cnt > 0)
	{
		/* We found only nulls; assume the column is entirely null */
		stats->stats_valid = true;
		stats->stanullfrac = 1.0;
		if (is_varwidth)
			stats->stawidth = 0;	/* "unknown" */
		else
			stats->stawidth = stats->attrtype->typlen;
		stats->stadistinct = 0.0;		/* "unknown" */
	}

	/* We don't need to bother cleaning up any of our temporary palloc's */
}


/*
 *	compute_scalar_stats() -- compute column statistics
 *
 *	We use this when we can find "=" and "<" operators for the datatype.
 *
 *	We determine the fraction of non-null rows, the average width, the
 *	most common values, the (estimated) number of distinct values, the
 *	distribution histogram, and the correlation of physical to logical order.
 *
 *	The desired stats can be determined fairly easily after sorting the
 *	data values into order.
 */
static void
compute_scalar_stats(VacAttrStatsP stats,
					 AnalyzeAttrFetchFunc fetchfunc,
					 int samplerows,
					 double totalrows)
{
	int			i;
	int			null_cnt = 0;
	int			nonnull_cnt = 0;
	int			toowide_cnt = 0;
	double		total_width = 0;
	bool		is_varlena = (!stats->attr->attbyval &&
							  stats->attr->attlen == -1);
	bool		is_varwidth = (!stats->attr->attbyval &&
							   stats->attr->attlen < 0);
	double		corr_xysum;
	RegProcedure cmpFn;
	SortFunctionKind cmpFnKind;
	FmgrInfo	f_cmpfn;
	ScalarItem *values;
	int			values_cnt = 0;
	int		   *tupnoLink;
	ScalarMCVItem *track;
	int			track_cnt = 0;
	int			num_mcv = stats->attr->attstattarget;
	int			num_bins = stats->attr->attstattarget;
	StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;

	values = (ScalarItem *) palloc(samplerows * sizeof(ScalarItem));
	tupnoLink = (int *) palloc(samplerows * sizeof(int));
	track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));

	SelectSortFunction(mystats->ltopr, &cmpFn, &cmpFnKind);
	fmgr_info(cmpFn, &f_cmpfn);

	/* Initial scan to find sortable values */
	for (i = 0; i < samplerows; i++)
	{
		Datum		value;
		bool		isnull;

		vacuum_delay_point();

		value = fetchfunc(stats, i, &isnull);

		/* Check for null/nonnull */
		if (isnull)
		{
			null_cnt++;
			continue;
		}
		nonnull_cnt++;

		/*
		 * If it's a variable-width field, add up widths for average width