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mirror of https://github.com/postgres/postgres.git synced 2025-04-20 00:42:27 +03:00

Fix typos and grammar in code comments

Several mistakes have piled in the code comments over the time,
including incorrect grammar, function names and simple typos.  This
commit takes care of a portion of these.

No backpatch is done as this is only cosmetic.

Author: Justin Pryzby
Discussion: https://postgr.es/m/20210924215827.GS831@telsasoft.com
This commit is contained in:
Michael Paquier 2021-09-27 14:21:28 +09:00
parent 895267a326
commit e767ddcd35
8 changed files with 29 additions and 29 deletions

View File

@ -1480,7 +1480,7 @@ validateConnectbyTupleDesc(TupleDesc td, bool show_branch, bool show_serial)
"fifth column must be type %s", "fifth column must be type %s",
format_type_be(INT4OID)))); format_type_be(INT4OID))));
/* check that the type of the fifth column is INT4 */ /* check that the type of the fourth column is INT4 */
if (!show_branch && show_serial && if (!show_branch && show_serial &&
TupleDescAttr(td, 3)->atttypid != INT4OID) TupleDescAttr(td, 3)->atttypid != INT4OID)
ereport(ERROR, ereport(ERROR,

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@ -213,14 +213,14 @@ CreateStatistics(CreateStatsStmt *stmt)
* Convert the expression list to a simple array of attnums, but also keep * Convert the expression list to a simple array of attnums, but also keep
* a list of more complex expressions. While at it, enforce some * a list of more complex expressions. While at it, enforce some
* constraints - we don't allow extended statistics on system attributes, * constraints - we don't allow extended statistics on system attributes,
* and we require the data type to have less-than operator. * and we require the data type to have a less-than operator.
* *
* There are many ways how to "mask" a simple attribute refenrece as an * There are many ways to "mask" a simple attribute reference as an
* expression, for example "(a+0)" etc. We can't possibly detect all of * expression, for example "(a+0)" etc. We can't possibly detect all of
* them, but we handle at least the simple case with attribute in parens. * them, but we handle at least the simple case with the attribute in
* There'll always be a way around this, if the user is determined (like * parens. There'll always be a way around this, if the user is determined
* the "(a+0)" example), but this makes it somewhat consistent with how * (like the "(a+0)" example), but this makes it somewhat consistent with
* indexes treat attributes/expressions. * how indexes treat attributes/expressions.
*/ */
foreach(cell, stmt->exprs) foreach(cell, stmt->exprs)
{ {

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@ -14,11 +14,11 @@
*/ */
/* /*
* INTERFACE ROUTINES * INTERFACE ROUTINES
* ExecTableFuncscan scans a function. * ExecTableFuncScan scans a function.
* ExecFunctionNext retrieve next tuple in sequential order. * ExecFunctionNext retrieve next tuple in sequential order.
* ExecInitTableFuncscan creates and initializes a TableFuncscan node. * ExecInitTableFuncScan creates and initializes a TableFuncscan node.
* ExecEndTableFuncscan releases any storage allocated. * ExecEndTableFuncScan releases any storage allocated.
* ExecReScanTableFuncscan rescans the function * ExecReScanTableFuncScan rescans the function
*/ */
#include "postgres.h" #include "postgres.h"
@ -46,7 +46,7 @@ static void tfuncLoadRows(TableFuncScanState *tstate, ExprContext *econtext);
/* ---------------------------------------------------------------- /* ----------------------------------------------------------------
* TableFuncNext * TableFuncNext
* *
* This is a workhorse for ExecTableFuncscan * This is a workhorse for ExecTableFuncScan
* ---------------------------------------------------------------- * ----------------------------------------------------------------
*/ */
static TupleTableSlot * static TupleTableSlot *
@ -84,7 +84,7 @@ TableFuncRecheck(TableFuncScanState *node, TupleTableSlot *slot)
} }
/* ---------------------------------------------------------------- /* ----------------------------------------------------------------
* ExecTableFuncscan(node) * ExecTableFuncScan(node)
* *
* Scans the function sequentially and returns the next qualifying * Scans the function sequentially and returns the next qualifying
* tuple. * tuple.
@ -103,7 +103,7 @@ ExecTableFuncScan(PlanState *pstate)
} }
/* ---------------------------------------------------------------- /* ----------------------------------------------------------------
* ExecInitTableFuncscan * ExecInitTableFuncScan
* ---------------------------------------------------------------- * ----------------------------------------------------------------
*/ */
TableFuncScanState * TableFuncScanState *
@ -205,7 +205,7 @@ ExecInitTableFuncScan(TableFuncScan *node, EState *estate, int eflags)
} }
/* ---------------------------------------------------------------- /* ----------------------------------------------------------------
* ExecEndTableFuncscan * ExecEndTableFuncScan
* *
* frees any storage allocated through C routines. * frees any storage allocated through C routines.
* ---------------------------------------------------------------- * ----------------------------------------------------------------
@ -234,7 +234,7 @@ ExecEndTableFuncScan(TableFuncScanState *node)
} }
/* ---------------------------------------------------------------- /* ----------------------------------------------------------------
* ExecReScanTableFuncscan * ExecReScanTableFuncScan
* *
* Rescans the relation. * Rescans the relation.
* ---------------------------------------------------------------- * ----------------------------------------------------------------

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@ -105,7 +105,7 @@ compare_path_costs(Path *path1, Path *path2, CostSelector criterion)
} }
/* /*
* compare_path_fractional_costs * compare_fractional_path_costs
* Return -1, 0, or +1 according as path1 is cheaper, the same cost, * Return -1, 0, or +1 according as path1 is cheaper, the same cost,
* or more expensive than path2 for fetching the specified fraction * or more expensive than path2 for fetching the specified fraction
* of the total tuples. * of the total tuples.

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@ -12,7 +12,7 @@ hopefully improving the estimates and producing better plans.
Types of statistics Types of statistics
------------------- -------------------
There are currently two kinds of extended statistics: There are currently several kinds of extended statistics:
(a) ndistinct coefficients (a) ndistinct coefficients
@ -73,8 +73,8 @@ it will do if:
When the above conditions are met, clauselist_selectivity() first attempts to When the above conditions are met, clauselist_selectivity() first attempts to
pass the clause list off to the extended statistics selectivity estimation pass the clause list off to the extended statistics selectivity estimation
function. This functions may not find any clauses which is can perform any function. This function may not find any clauses which it can perform any
estimations on. In such cases these clauses are simply ignored. When actual estimations on. In such cases, these clauses are simply ignored. When actual
estimation work is performed in these functions they're expected to mark which estimation work is performed in these functions they're expected to mark which
clauses they've performed estimations for so that any other function clauses they've performed estimations for so that any other function
performing estimations knows which clauses are to be skipped. performing estimations knows which clauses are to be skipped.

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@ -2,7 +2,7 @@ MCV lists
========= =========
Multivariate MCV (most-common values) lists are a straightforward extension of Multivariate MCV (most-common values) lists are a straightforward extension of
regular MCV list, tracking most frequent combinations of values for a group of regular MCV lists, tracking most frequent combinations of values for a group of
attributes. attributes.
This works particularly well for columns with a small number of distinct values, This works particularly well for columns with a small number of distinct values,
@ -18,7 +18,7 @@ Estimates of some clauses (e.g. equality) based on MCV lists are more accurate
than when using histograms. than when using histograms.
Also, MCV lists don't necessarily require sorting of the values (the fact that Also, MCV lists don't necessarily require sorting of the values (the fact that
we use sorting when building them is implementation detail), but even more we use sorting when building them is an implementation detail), but even more
importantly the ordering is not built into the approximation (while histograms importantly the ordering is not built into the approximation (while histograms
are built on ordering). So MCV lists work well even for attributes where the are built on ordering). So MCV lists work well even for attributes where the
ordering of the data type is disconnected from the meaning of the data. For ordering of the data type is disconnected from the meaning of the data. For
@ -53,7 +53,7 @@ Hashed MCV (not yet implemented)
Regular MCV lists have to include actual values for each item, so if those items Regular MCV lists have to include actual values for each item, so if those items
are large the list may be quite large. This is especially true for multivariate are large the list may be quite large. This is especially true for multivariate
MCV lists, although the current implementation partially mitigates this by MCV lists, although the current implementation partially mitigates this by
performing de-duplicating the values before storing them on disk. de-duplicating the values before storing them on disk.
It's possible to only store hashes (32-bit values) instead of the actual values, It's possible to only store hashes (32-bit values) instead of the actual values,
significantly reducing the space requirements. Obviously, this would only make significantly reducing the space requirements. Obviously, this would only make
@ -77,7 +77,7 @@ to select the columns from pg_stats. The data is encoded as anyarrays, and
all the items have the same data type, so anyarray provides a simple way to all the items have the same data type, so anyarray provides a simple way to
get a text representation. get a text representation.
With multivariate MCV lists the columns may use different data types, making With multivariate MCV lists, the columns may use different data types, making
it impossible to use anyarrays. It might be possible to produce a similar it impossible to use anyarrays. It might be possible to produce a similar
array-like representation, but that would complicate further processing and array-like representation, but that would complicate further processing and
analysis of the MCV list. analysis of the MCV list.

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@ -699,11 +699,11 @@ examine_expression(Node *expr, int stattarget)
} }
/* /*
* Using 'vacatts' of size 'nvacatts' as input data, return a newly built * Using 'vacatts' of size 'nvacatts' as input data, return a newly-built
* VacAttrStats array which includes only the items corresponding to * VacAttrStats array which includes only the items corresponding to
* attributes indicated by 'stxkeys'. If we don't have all of the per column * attributes indicated by 'attrs'. If we don't have all of the per-column
* stats available to compute the extended stats, then we return NULL to indicate * stats available to compute the extended stats, then we return NULL to
* to the caller that the stats should not be built. * indicate to the caller that the stats should not be built.
*/ */
static VacAttrStats ** static VacAttrStats **
lookup_var_attr_stats(Relation rel, Bitmapset *attrs, List *exprs, lookup_var_attr_stats(Relation rel, Bitmapset *attrs, List *exprs,

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@ -825,7 +825,7 @@ pglz_decompress(const char *source, int32 slen, char *dest,
/* ---------- /* ----------
* pglz_max_compressed_size - * pglz_maximum_compressed_size -
* *
* Calculate the maximum compressed size for a given amount of raw data. * Calculate the maximum compressed size for a given amount of raw data.
* Return the maximum size, or total compressed size if maximum size is * Return the maximum size, or total compressed size if maximum size is