tuplesort.c

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/*-------------------------------------------------------------------------
 *
 * tuplesort.c
 *    Generalized tuple sorting routines.
 *
 * This module provides a generalized facility for tuple sorting, which can be
 * applied to different kinds of sortable objects.  Implementation of
 * the particular sorting variants is given in tuplesortvariants.c.
 * This module works efficiently for both small and large amounts
 * of data.  Small amounts are sorted in-memory.  Large amounts are
 * sorted using temporary files and a standard external sort
 * algorithm.
 *
 * See Knuth, volume 3, for more than you want to know about external
 * sorting algorithms.  The algorithm we use is a balanced k-way merge.
 * Before PostgreSQL 15, we used the polyphase merge algorithm (Knuth's
 * Algorithm 5.4.2D), but with modern hardware, a straightforward balanced
 * merge is better.  Knuth is assuming that tape drives are expensive
 * beasts, and in particular that there will always be many more runs than
 * tape drives.  The polyphase merge algorithm was good at keeping all the
 * tape drives busy, but in our implementation a "tape drive" doesn't cost
 * much more than a few Kb of memory buffers, so we can afford to have
 * lots of them.  In particular, if we can have as many tape drives as
 * sorted runs, we can eliminate any repeated I/O at all.
 *
 * Historically, we divided the input into sorted runs using replacement
 * selection, in the form of a priority tree implemented as a heap
 * (essentially Knuth's Algorithm 5.2.3H), but now we always use quicksort
 * or radix sort for run generation.
 *
 * The approximate amount of memory allowed for any one sort operation
 * is specified in kilobytes by the caller (most pass work_mem).  Initially,
 * we absorb tuples and simply store them in an unsorted array as long as
 * we haven't exceeded workMem.  If we reach the end of the input without
 * exceeding workMem, we sort the array in memory and subsequently return
 * tuples just by scanning the tuple array sequentially.  If we do exceed
 * workMem, we begin to emit tuples into sorted runs in temporary tapes.
 * When tuples are dumped in batch after in-memory sorting, we begin a new run
 * with a new output tape.  If we reach the max number of tapes, we write
 * subsequent runs on the existing tapes in a round-robin fashion.  We will
 * need multiple merge passes to finish the merge in that case.  After the
 * end of the input is reached, we dump out remaining tuples in memory into
 * a final run, then merge the runs.
 *
 * When merging runs, we use a heap containing just the frontmost tuple from
 * each source run; we repeatedly output the smallest tuple and replace it
 * with the next tuple from its source tape (if any).  When the heap empties,
 * the merge is complete.  The basic merge algorithm thus needs very little
 * memory --- only M tuples for an M-way merge, and M is constrained to a
 * small number.  However, we can still make good use of our full workMem
 * allocation by pre-reading additional blocks from each source tape.  Without
 * prereading, our access pattern to the temporary file would be very erratic;
 * on average we'd read one block from each of M source tapes during the same
 * time that we're writing M blocks to the output tape, so there is no
 * sequentiality of access at all, defeating the read-ahead methods used by
 * most Unix kernels.  Worse, the output tape gets written into a very random
 * sequence of blocks of the temp file, ensuring that things will be even
 * worse when it comes time to read that tape.  A straightforward merge pass
 * thus ends up doing a lot of waiting for disk seeks.  We can improve matters
 * by prereading from each source tape sequentially, loading about workMem/M
 * bytes from each tape in turn, and making the sequential blocks immediately
 * available for reuse.  This approach helps to localize both read and write
 * accesses.  The pre-reading is handled by logtape.c, we just tell it how
 * much memory to use for the buffers.
 *
 * In the current code we determine the number of input tapes M on the basis
 * of workMem: we want workMem/M to be large enough that we read a fair
 * amount of data each time we read from a tape, so as to maintain the
 * locality of access described above.  Nonetheless, with large workMem we
 * can have many tapes.  The logical "tapes" are implemented by logtape.c,
 * which avoids space wastage by recycling disk space as soon as each block
 * is read from its "tape".
 *
 * When the caller requests random access to the sort result, we form
 * the final sorted run on a logical tape which is then "frozen", so
 * that we can access it randomly.  When the caller does not need random
 * access, we return from tuplesort_performsort() as soon as we are down
 * to one run per logical tape.  The final merge is then performed
 * on-the-fly as the caller repeatedly calls tuplesort_getXXX; this
 * saves one cycle of writing all the data out to disk and reading it in.
 *
 * This module supports parallel sorting.  Parallel sorts involve coordination
 * among one or more worker processes, and a leader process, each with its own
 * tuplesort state.  The leader process (or, more accurately, the
 * Tuplesortstate associated with a leader process) creates a full tapeset
 * consisting of worker tapes with one run to merge; a run for every
 * worker process.  This is then merged.  Worker processes are guaranteed to
 * produce exactly one output run from their partial input.
 *
 *
 * Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group
 * Portions Copyright (c) 1994, Regents of the University of California
 *
 * IDENTIFICATION
 *    src/backend/utils/sort/tuplesort.c
 *
 *-------------------------------------------------------------------------
 */
 
#include "postgres.h"
 
#include <limits.h>
 
#include "commands/tablespace.h"
#include "miscadmin.h"
#include "pg_trace.h"
#include "storage/shmem.h"
#include "utils/guc.h"
#include "utils/memutils.h"
#include "utils/pg_rusage.h"
#include "utils/tuplesort.h"
 
/*
 * Initial size of memtuples array.  This must be more than
 * ALLOCSET_SEPARATE_THRESHOLD; see comments in grow_memtuples().  Clamp at
 * 1024 elements to avoid excessive reallocs.
 */
#define INITIAL_MEMTUPSIZE Max(1024, \
    ALLOCSET_SEPARATE_THRESHOLD / sizeof(SortTuple) + 1)
 
/* GUC variables */
bool        trace_sort = false;
 
#ifdef DEBUG_BOUNDED_SORT
bool        optimize_bounded_sort = true;
#endif
 
 
/*
 * During merge, we use a pre-allocated set of fixed-size slots to hold
 * tuples.  To avoid palloc/pfree overhead.
 *
 * Merge doesn't require a lot of memory, so we can afford to waste some,
 * by using gratuitously-sized slots.  If a tuple is larger than 1 kB, the
 * palloc() overhead is not significant anymore.
 *
 * 'nextfree' is valid when this chunk is in the free list.  When in use, the
 * slot holds a tuple.
 */
#define SLAB_SLOT_SIZE 1024
 
typedef union SlabSlot
{
    union SlabSlot *nextfree;
    char        buffer[SLAB_SLOT_SIZE];
} SlabSlot;
 
/*
 * Possible states of a Tuplesort object.  These denote the states that
 * persist between calls of Tuplesort routines.
 */
typedef enum
{
    TSS_INITIAL,                /* Loading tuples; still within memory limit */
    TSS_BOUNDED,                /* Loading tuples into bounded-size heap */
    TSS_BUILDRUNS,              /* Loading tuples; writing to tape */
    TSS_SORTEDINMEM,            /* Sort completed entirely in memory */
    TSS_SORTEDONTAPE,           /* Sort completed, final run is on tape */
    TSS_FINALMERGE,             /* Performing final merge on-the-fly */
} TupSortStatus;
 
/*
 * Parameters for calculation of number of tapes to use --- see inittapes()
 * and tuplesort_merge_order().
 *
 * In this calculation we assume that each tape will cost us about 1 blocks
 * worth of buffer space.  This ignores the overhead of all the other data
 * structures needed for each tape, but it's probably close enough.
 *
 * MERGE_BUFFER_SIZE is how much buffer space we'd like to allocate for each
 * input tape, for pre-reading (see discussion at top of file).  This is *in
 * addition to* the 1 block already included in TAPE_BUFFER_OVERHEAD.
 */
#define MINORDER        6       /* minimum merge order */
#define MAXORDER        500     /* maximum merge order */
#define TAPE_BUFFER_OVERHEAD        BLCKSZ
#define MERGE_BUFFER_SIZE           (BLCKSZ * 32)
 
 
/*
 * Private state of a Tuplesort operation.
 */
struct Tuplesortstate
{
    TuplesortPublic base;
    TupSortStatus status;       /* enumerated value as shown above */
    bool        bounded;        /* did caller specify a maximum number of
                                 * tuples to return? */
    bool        boundUsed;      /* true if we made use of a bounded heap */
    int         bound;          /* if bounded, the maximum number of tuples */
    int64       tupleMem;       /* memory consumed by individual tuples.
                                 * storing this separately from what we track
                                 * in availMem allows us to subtract the
                                 * memory consumed by all tuples when dumping
                                 * tuples to tape */
    int64       availMem;       /* remaining memory available, in bytes */
    int64       allowedMem;     /* total memory allowed, in bytes */
    int         maxTapes;       /* max number of input tapes to merge in each
                                 * pass */
    int64       maxSpace;       /* maximum amount of space occupied among sort
                                 * of groups, either in-memory or on-disk */
    bool        isMaxSpaceDisk; /* true when maxSpace tracks on-disk space,
                                 * false means in-memory */
    TupSortStatus maxSpaceStatus;   /* sort status when maxSpace was reached */
    LogicalTapeSet *tapeset;    /* logtape.c object for tapes in a temp file */
 
    /*
     * This array holds the tuples now in sort memory.  If we are in state
     * INITIAL, the tuples are in no particular order; if we are in state
     * SORTEDINMEM, the tuples are in final sorted order; in states BUILDRUNS
     * and FINALMERGE, the tuples are organized in "heap" order per Algorithm
     * H.  In state SORTEDONTAPE, the array is not used.
     */
    SortTuple  *memtuples;      /* array of SortTuple structs */
    int         memtupcount;    /* number of tuples currently present */
    int         memtupsize;     /* allocated length of memtuples array */
    bool        growmemtuples;  /* memtuples' growth still underway? */
 
    /*
     * Memory for tuples is sometimes allocated using a simple slab allocator,
     * rather than with palloc().  Currently, we switch to slab allocation
     * when we start merging.  Merging only needs to keep a small, fixed
     * number of tuples in memory at any time, so we can avoid the
     * palloc/pfree overhead by recycling a fixed number of fixed-size slots
     * to hold the tuples.
     *
     * For the slab, we use one large allocation, divided into SLAB_SLOT_SIZE
     * slots.  The allocation is sized to have one slot per tape, plus one
     * additional slot.  We need that many slots to hold all the tuples kept
     * in the heap during merge, plus the one we have last returned from the
     * sort, with tuplesort_gettuple.
     *
     * Initially, all the slots are kept in a linked list of free slots.  When
     * a tuple is read from a tape, it is put to the next available slot, if
     * it fits.  If the tuple is larger than SLAB_SLOT_SIZE, it is palloc'd
     * instead.
     *
     * When we're done processing a tuple, we return the slot back to the free
     * list, or pfree() if it was palloc'd.  We know that a tuple was
     * allocated from the slab, if its pointer value is between
     * slabMemoryBegin and -End.
     *
     * When the slab allocator is used, the USEMEM/LACKMEM mechanism of
     * tracking memory usage is not used.
     */
    bool        slabAllocatorUsed;
 
    char       *slabMemoryBegin;    /* beginning of slab memory arena */
    char       *slabMemoryEnd;  /* end of slab memory arena */
    SlabSlot   *slabFreeHead;   /* head of free list */
 
    /* Memory used for input and output tape buffers. */
    size_t      tape_buffer_mem;
 
    /*
     * When we return a tuple to the caller in tuplesort_gettuple_XXX, that
     * came from a tape (that is, in TSS_SORTEDONTAPE or TSS_FINALMERGE
     * modes), we remember the tuple in 'lastReturnedTuple', so that we can
     * recycle the memory on next gettuple call.
     */
    void       *lastReturnedTuple;
 
    /*
     * While building initial runs, this is the current output run number.
     * Afterwards, it is the number of initial runs we made.
     */
    int         currentRun;
 
    /*
     * Logical tapes, for merging.
     *
     * The initial runs are written in the output tapes.  In each merge pass,
     * the output tapes of the previous pass become the input tapes, and new
     * output tapes are created as needed.  When nInputTapes equals
     * nInputRuns, there is only one merge pass left.
     */
    LogicalTape **inputTapes;
    int         nInputTapes;
    int         nInputRuns;
 
    LogicalTape **outputTapes;
    int         nOutputTapes;
    int         nOutputRuns;
 
    LogicalTape *destTape;      /* current output tape */
 
    /*
     * These variables are used after completion of sorting to keep track of
     * the next tuple to return.  (In the tape case, the tape's current read
     * position is also critical state.)
     */
    LogicalTape *result_tape;   /* actual tape of finished output */
    int         current;        /* array index (only used if SORTEDINMEM) */
    bool        eof_reached;    /* reached EOF (needed for cursors) */
 
    /* markpos_xxx holds marked position for mark and restore */
    int64       markpos_block;  /* tape block# (only used if SORTEDONTAPE) */
    int         markpos_offset; /* saved "current", or offset in tape block */
    bool        markpos_eof;    /* saved "eof_reached" */
 
    /*
     * These variables are used during parallel sorting.
     *
     * worker is our worker identifier.  Follows the general convention that
     * -1 value relates to a leader tuplesort, and values >= 0 worker
     * tuplesorts. (-1 can also be a serial tuplesort.)
     *
     * shared is mutable shared memory state, which is used to coordinate
     * parallel sorts.
     *
     * nParticipants is the number of worker Tuplesortstates known by the
     * leader to have actually been launched, which implies that they must
     * finish a run that the leader needs to merge.  Typically includes a
     * worker state held by the leader process itself.  Set in the leader
     * Tuplesortstate only.
     */
    int         worker;
    Sharedsort *shared;
    int         nParticipants;
 
    /*
     * Additional state for managing "abbreviated key" sortsupport routines
     * (which currently may be used by all cases except the hash index case).
     * Tracks the intervals at which the optimization's effectiveness is
     * tested.
     */
    int64       abbrevNext;     /* Tuple # at which to next check
                                 * applicability */
 
    /*
     * Resource snapshot for time of sort start.
     */
    PGRUsage    ru_start;
};
 
/*
 * Private mutable state of tuplesort-parallel-operation.  This is allocated
 * in shared memory.
 */
struct Sharedsort
{
    /* mutex protects all fields prior to tapes */
    slock_t     mutex;
 
    /*
     * currentWorker generates ordinal identifier numbers for parallel sort
     * workers.  These start from 0, and are always gapless.
     *
     * Workers increment workersFinished to indicate having finished.  If this
     * is equal to state.nParticipants within the leader, leader is ready to
     * merge worker runs.
     */
    int         currentWorker;
    int         workersFinished;
 
    /* Temporary file space */
    SharedFileSet fileset;
 
    /* Size of tapes flexible array */
    int         nTapes;
 
    /*
     * Tapes array used by workers to report back information needed by the
     * leader to concatenate all worker tapes into one for merging
     */
    TapeShare   tapes[FLEXIBLE_ARRAY_MEMBER];
};
 
/*
 * Is the given tuple allocated from the slab memory arena?
 */
#define IS_SLAB_SLOT(state, tuple) \
    ((char *) (tuple) >= (state)->slabMemoryBegin && \
     (char *) (tuple) < (state)->slabMemoryEnd)
 
/*
 * Return the given tuple to the slab memory free list, or free it
 * if it was palloc'd.
 */
#define RELEASE_SLAB_SLOT(state, tuple) \
    do { \
        SlabSlot *buf = (SlabSlot *) tuple; \
        \
        if (IS_SLAB_SLOT((state), buf)) \
        { \
            buf->nextfree = (state)->slabFreeHead; \
            (state)->slabFreeHead = buf; \
        } else \
            pfree(buf); \
    } while(0)
 
#define REMOVEABBREV(state,stup,count)  ((*(state)->base.removeabbrev) (state, stup, count))
#define COMPARETUP(state,a,b)   ((*(state)->base.comparetup) (a, b, state))
#define WRITETUP(state,tape,stup)   ((*(state)->base.writetup) (state, tape, stup))
#define READTUP(state,stup,tape,len) ((*(state)->base.readtup) (state, stup, tape, len))
#define FREESTATE(state)    ((state)->base.freestate ? (*(state)->base.freestate) (state) : (void) 0)
#define LACKMEM(state)      ((state)->availMem < 0 && !(state)->slabAllocatorUsed)
#define USEMEM(state,amt)   ((state)->availMem -= (amt))
#define FREEMEM(state,amt)  ((state)->availMem += (amt))
#define SERIAL(state)       ((state)->shared == NULL)
#define WORKER(state)       ((state)->shared && (state)->worker != -1)
#define LEADER(state)       ((state)->shared && (state)->worker == -1)
 
/*
 * NOTES about on-tape representation of tuples:
 *
 * We require the first "unsigned int" of a stored tuple to be the total size
 * on-tape of the tuple, including itself (so it is never zero; an all-zero
 * unsigned int is used to delimit runs).  The remainder of the stored tuple
 * may or may not match the in-memory representation of the tuple ---
 * any conversion needed is the job of the writetup and readtup routines.
 *
 * If state->sortopt contains TUPLESORT_RANDOMACCESS, then the stored
 * representation of the tuple must be followed by another "unsigned int" that
 * is a copy of the length --- so the total tape space used is actually
 * sizeof(unsigned int) more than the stored length value.  This allows
 * read-backwards.  When the random access flag was not specified, the
 * write/read routines may omit the extra length word.
 *
 * writetup is expected to write both length words as well as the tuple
 * data.  When readtup is called, the tape is positioned just after the
 * front length word; readtup must read the tuple data and advance past
 * the back length word (if present).
 *
 * The write/read routines can make use of the tuple description data
 * stored in the Tuplesortstate record, if needed.  They are also expected
 * to adjust state->availMem by the amount of memory space (not tape space!)
 * released or consumed.  There is no error return from either writetup
 * or readtup; they should ereport() on failure.
 *
 *
 * NOTES about memory consumption calculations:
 *
 * We count space allocated for tuples against the workMem limit, plus
 * the space used by the variable-size memtuples array.  Fixed-size space
 * is not counted; it's small enough to not be interesting.
 *
 * Note that we count actual space used (as shown by GetMemoryChunkSpace)
 * rather than the originally-requested size.  This is important since
 * palloc can add substantial overhead.  It's not a complete answer since
 * we won't count any wasted space in palloc allocation blocks, but it's
 * a lot better than what we were doing before 7.3.  As of 9.6, a
 * separate memory context is used for caller passed tuples.  Resetting
 * it at certain key increments significantly ameliorates fragmentation.
 * readtup routines use the slab allocator (they cannot use
 * the reset context because it gets deleted at the point that merging
 * begins).
 */
 
 
static void tuplesort_begin_batch(Tuplesortstate *state);
static bool consider_abort_common(Tuplesortstate *state);
static void inittapes(Tuplesortstate *state, bool mergeruns);
static void inittapestate(Tuplesortstate *state, int maxTapes);
static void selectnewtape(Tuplesortstate *state);
static void init_slab_allocator(Tuplesortstate *state, int numSlots);
static void mergeruns(Tuplesortstate *state);
static void mergeonerun(Tuplesortstate *state);
static void beginmerge(Tuplesortstate *state);
static bool mergereadnext(Tuplesortstate *state, LogicalTape *srcTape, SortTuple *stup);
static void dumptuples(Tuplesortstate *state, bool alltuples);
static void make_bounded_heap(Tuplesortstate *state);
static void sort_bounded_heap(Tuplesortstate *state);
static void tuplesort_sort_memtuples(Tuplesortstate *state);
static void tuplesort_heap_insert(Tuplesortstate *state, SortTuple *tuple);
static void tuplesort_heap_replace_top(Tuplesortstate *state, SortTuple *tuple);
static void tuplesort_heap_delete_top(Tuplesortstate *state);
static void reversedirection(Tuplesortstate *state);
static unsigned int getlen(LogicalTape *tape, bool eofOK);
static void markrunend(LogicalTape *tape);
static int  worker_get_identifier(Tuplesortstate *state);
static void worker_freeze_result_tape(Tuplesortstate *state);
static void worker_nomergeruns(Tuplesortstate *state);
static void leader_takeover_tapes(Tuplesortstate *state);
static void free_sort_tuple(Tuplesortstate *state, SortTuple *stup);
static void tuplesort_free(Tuplesortstate *state);
static void tuplesort_updatemax(Tuplesortstate *state);
 
 
/*
 * Special versions of qsort just for SortTuple objects.  qsort_tuple() sorts
 * any variant of SortTuples, using the appropriate comparetup function.
 * qsort_ssup() is specialized for the case where the comparetup function
 * reduces to ApplySortComparator(), that is single-key MinimalTuple sorts
 * and Datum sorts.
 */
 
#define ST_SORT qsort_tuple
#define ST_ELEMENT_TYPE SortTuple
#define ST_COMPARE_RUNTIME_POINTER
#define ST_COMPARE_ARG_TYPE Tuplesortstate
#define ST_CHECK_FOR_INTERRUPTS
#define ST_SCOPE static
#define ST_DECLARE
#define ST_DEFINE
#include "lib/sort_template.h"
 
#define ST_SORT qsort_ssup
#define ST_ELEMENT_TYPE SortTuple
#define ST_COMPARE(a, b, ssup) \
    ApplySortComparator((a)->datum1, (a)->isnull1, \
                        (b)->datum1, (b)->isnull1, (ssup))
#define ST_COMPARE_ARG_TYPE SortSupportData
#define ST_CHECK_FOR_INTERRUPTS
#define ST_SCOPE static
#define ST_DEFINE
#include "lib/sort_template.h"
 
/* state for radix sort */
typedef struct RadixSortInfo
{
    union
    {
        size_t      count;
        size_t      offset;
    };
    size_t      next_offset;
} RadixSortInfo;
 
/*
 * Threshold below which qsort_tuple() is generally faster than a radix sort.
 */
#define QSORT_THRESHOLD 40
 
 
/*
 *      tuplesort_begin_xxx
 *
 * Initialize for a tuple sort operation.
 *
 * After calling tuplesort_begin, the caller should call tuplesort_putXXX
 * zero or more times, then call tuplesort_performsort when all the tuples
 * have been supplied.  After performsort, retrieve the tuples in sorted
 * order by calling tuplesort_getXXX until it returns false/NULL.  (If random
 * access was requested, rescan, markpos, and restorepos can also be called.)
 * Call tuplesort_end to terminate the operation and release memory/disk space.
 *
 * Each variant of tuplesort_begin has a workMem parameter specifying the
 * maximum number of kilobytes of RAM to use before spilling data to disk.
 * (The normal value of this parameter is work_mem, but some callers use
 * other values.)  Each variant also has a sortopt which is a bitmask of
 * sort options.  See TUPLESORT_* definitions in tuplesort.h
 */
 
Tuplesortstate *
tuplesort_begin_common(int workMem, SortCoordinate coordinate, int sortopt)
{
    Tuplesortstate *state;
    MemoryContext maincontext;
    MemoryContext sortcontext;
    MemoryContext oldcontext;
 
    /* See leader_takeover_tapes() remarks on random access support */
    if (coordinate && (sortopt & TUPLESORT_RANDOMACCESS))
        elog(ERROR, "random access disallowed under parallel sort");
 
    /*
     * Memory context surviving tuplesort_reset.  This memory context holds
     * data which is useful to keep while sorting multiple similar batches.
     */
    maincontext = AllocSetContextCreate(CurrentMemoryContext,
                                        "TupleSort main",
                                        ALLOCSET_DEFAULT_SIZES);
 
    /*
     * Create a working memory context for one sort operation.  The content of
     * this context is deleted by tuplesort_reset.
     */
    sortcontext = AllocSetContextCreate(maincontext,
                                        "TupleSort sort",
                                        ALLOCSET_DEFAULT_SIZES);
 
    /*
     * Additionally a working memory context for tuples is setup in
     * tuplesort_begin_batch.
     */
 
    /*
     * Make the Tuplesortstate within the per-sortstate context.  This way, we
     * don't need a separate pfree() operation for it at shutdown.
     */
    oldcontext = MemoryContextSwitchTo(maincontext);
 
    state = palloc0_object(Tuplesortstate);
 
    if (trace_sort)
        pg_rusage_init(&state->ru_start);
 
    state->base.sortopt = sortopt;
    state->base.tuples = true;
    state->abbrevNext = 10;
 
    /*
     * workMem is forced to be at least 64KB, the current minimum valid value
     * for the work_mem GUC.  This is a defense against parallel sort callers
     * that divide out memory among many workers in a way that leaves each
     * with very little memory.
     */
    state->allowedMem = Max(workMem, 64) * (int64) 1024;
    state->base.sortcontext = sortcontext;
    state->base.maincontext = maincontext;
 
    state->memtupsize = INITIAL_MEMTUPSIZE;
    state->memtuples = NULL;
 
    /*
     * After all of the other non-parallel-related state, we setup all of the
     * state needed for each batch.
     */
    tuplesort_begin_batch(state);
 
    /*
     * Initialize parallel-related state based on coordination information
     * from caller
     */
    if (!coordinate)
    {
        /* Serial sort */
        state->shared = NULL;
        state->worker = -1;
        state->nParticipants = -1;
    }
    else if (coordinate->isWorker)
    {
        /* Parallel worker produces exactly one final run from all input */
        state->shared = coordinate->sharedsort;
        state->worker = worker_get_identifier(state);
        state->nParticipants = -1;
    }
    else
    {
        /* Parallel leader state only used for final merge */
        state->shared = coordinate->sharedsort;
        state->worker = -1;
        state->nParticipants = coordinate->nParticipants;
        Assert(state->nParticipants >= 1);
    }
 
    MemoryContextSwitchTo(oldcontext);
 
    return state;
}
 
/*
 *      tuplesort_begin_batch
 *
 * Setup, or reset, all state need for processing a new set of tuples with this
 * sort state. Called both from tuplesort_begin_common (the first time sorting
 * with this sort state) and tuplesort_reset (for subsequent usages).
 */
static void
tuplesort_begin_batch(Tuplesortstate *state)
{
    MemoryContext oldcontext;
 
    oldcontext = MemoryContextSwitchTo(state->base.maincontext);
 
    /*
     * Caller tuple (e.g. IndexTuple) memory context.
     *
     * A dedicated child context used exclusively for caller passed tuples
     * eases memory management.  Resetting at key points reduces
     * fragmentation. Note that the memtuples array of SortTuples is allocated
     * in the parent context, not this context, because there is no need to
     * free memtuples early.  For bounded sorts, tuples may be pfreed in any
     * order, so we use a regular aset.c context so that it can make use of
     * free'd memory.  When the sort is not bounded, we make use of a bump.c
     * context as this keeps allocations more compact with less wastage.
     * Allocations are also slightly more CPU efficient.
     */
    if (TupleSortUseBumpTupleCxt(state->base.sortopt))
        state->base.tuplecontext = BumpContextCreate(state->base.sortcontext,
                                                     "Caller tuples",
                                                     ALLOCSET_DEFAULT_SIZES);
    else
        state->base.tuplecontext = AllocSetContextCreate(state->base.sortcontext,
                                                         "Caller tuples",
                                                         ALLOCSET_DEFAULT_SIZES);
 
 
    state->status = TSS_INITIAL;
    state->bounded = false;
    state->boundUsed = false;
 
    state->availMem = state->allowedMem;
 
    state->tapeset = NULL;
 
    state->memtupcount = 0;
 
    state->growmemtuples = true;
    state->slabAllocatorUsed = false;
    if (state->memtuples != NULL && state->memtupsize != INITIAL_MEMTUPSIZE)
    {
        pfree(state->memtuples);
        state->memtuples = NULL;
        state->memtupsize = INITIAL_MEMTUPSIZE;
    }
    if (state->memtuples == NULL)
    {
        state->memtuples = (SortTuple *) palloc(state->memtupsize * sizeof(SortTuple));
        USEMEM(state, GetMemoryChunkSpace(state->memtuples));
    }
 
    /* workMem must be large enough for the minimal memtuples array */
    if (LACKMEM(state))
        elog(ERROR, "insufficient memory allowed for sort");
 
    state->currentRun = 0;
 
    /*
     * Tape variables (inputTapes, outputTapes, etc.) will be initialized by
     * inittapes(), if needed.
     */
 
    state->result_tape = NULL;  /* flag that result tape has not been formed */
 
    MemoryContextSwitchTo(oldcontext);
}
 
/*
 * tuplesort_set_bound
 *
 *  Advise tuplesort that at most the first N result tuples are required.
 *
 * Must be called before inserting any tuples.  (Actually, we could allow it
 * as long as the sort hasn't spilled to disk, but there seems no need for
 * delayed calls at the moment.)
 *
 * This is a hint only. The tuplesort may still return more tuples than
 * requested.  Parallel leader tuplesorts will always ignore the hint.
 */
void
tuplesort_set_bound(Tuplesortstate *state, int64 bound)
{
    /* Assert we're called before loading any tuples */
    Assert(state->status == TSS_INITIAL && state->memtupcount == 0);
    /* Assert we allow bounded sorts */
    Assert(state->base.sortopt & TUPLESORT_ALLOWBOUNDED);
    /* Can't set the bound twice, either */
    Assert(!state->bounded);
    /* Also, this shouldn't be called in a parallel worker */
    Assert(!WORKER(state));
 
    /* Parallel leader allows but ignores hint */
    if (LEADER(state))
        return;
 
#ifdef DEBUG_BOUNDED_SORT
    /* Honor GUC setting that disables the feature (for easy testing) */
    if (!optimize_bounded_sort)
        return;
#endif
 
    /* We want to be able to compute bound * 2, so limit the setting */
    if (bound > (int64) (INT_MAX / 2))
        return;
 
    state->bounded = true;
    state->bound = (int) bound;
 
    /*
     * Bounded sorts are not an effective target for abbreviated key
     * optimization.  Disable by setting state to be consistent with no
     * abbreviation support.
     */
    state->base.sortKeys->abbrev_converter = NULL;
    if (state->base.sortKeys->abbrev_full_comparator)
        state->base.sortKeys->comparator = state->base.sortKeys->abbrev_full_comparator;
 
    /* Not strictly necessary, but be tidy */
    state->base.sortKeys->abbrev_abort = NULL;
    state->base.sortKeys->abbrev_full_comparator = NULL;
}
 
/*
 * tuplesort_used_bound
 *
 * Allow callers to find out if the sort state was able to use a bound.
 */
bool
tuplesort_used_bound(Tuplesortstate *state)
{
    return state->boundUsed;
}
 
/*
 * tuplesort_free
 *
 *  Internal routine for freeing resources of tuplesort.
 */
static void
tuplesort_free(Tuplesortstate *state)
{
    /* context swap probably not needed, but let's be safe */
    MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
    int64       spaceUsed;
 
    if (state->tapeset)
        spaceUsed = LogicalTapeSetBlocks(state->tapeset);
    else
        spaceUsed = (state->allowedMem - state->availMem + 1023) / 1024;
 
    /*
     * Delete temporary "tape" files, if any.
     *
     * We don't bother to destroy the individual tapes here. They will go away
     * with the sortcontext.  (In TSS_FINALMERGE state, we have closed
     * finished tapes already.)
     */
    if (state->tapeset)
        LogicalTapeSetClose(state->tapeset);
 
    if (trace_sort)
    {
        if (state->tapeset)
            elog(LOG, "%s of worker %d ended, %" PRId64 " disk blocks used: %s",
                 SERIAL(state) ? "external sort" : "parallel external sort",
                 state->worker, spaceUsed, pg_rusage_show(&state->ru_start));
        else
            elog(LOG, "%s of worker %d ended, %" PRId64 " KB used: %s",
                 SERIAL(state) ? "internal sort" : "unperformed parallel sort",
                 state->worker, spaceUsed, pg_rusage_show(&state->ru_start));
    }
 
    TRACE_POSTGRESQL_SORT_DONE(state->tapeset != NULL, spaceUsed);
 
    FREESTATE(state);
    MemoryContextSwitchTo(oldcontext);
 
    /*
     * Free the per-sort memory context, thereby releasing all working memory.
     */
    MemoryContextReset(state->base.sortcontext);
}
 
/*
 * tuplesort_end
 *
 *  Release resources and clean up.
 *
 * NOTE: after calling this, any pointers returned by tuplesort_getXXX are
 * pointing to garbage.  Be careful not to attempt to use or free such
 * pointers afterwards!
 */
void
tuplesort_end(Tuplesortstate *state)
{
    tuplesort_free(state);
 
    /*
     * Free the main memory context, including the Tuplesortstate struct
     * itself.
     */
    MemoryContextDelete(state->base.maincontext);
}
 
/*
 * tuplesort_updatemax
 *
 *  Update maximum resource usage statistics.
 */
static void
tuplesort_updatemax(Tuplesortstate *state)
{
    int64       spaceUsed;
    bool        isSpaceDisk;
 
    /*
     * Note: it might seem we should provide both memory and disk usage for a
     * disk-based sort.  However, the current code doesn't track memory space
     * accurately once we have begun to return tuples to the caller (since we
     * don't account for pfree's the caller is expected to do), so we cannot
     * rely on availMem in a disk sort.  This does not seem worth the overhead
     * to fix.  Is it worth creating an API for the memory context code to
     * tell us how much is actually used in sortcontext?
     */
    if (state->tapeset)
    {
        isSpaceDisk = true;
        spaceUsed = LogicalTapeSetBlocks(state->tapeset) * BLCKSZ;
    }
    else
    {
        isSpaceDisk = false;
        spaceUsed = state->allowedMem - state->availMem;
    }
 
    /*
     * Sort evicts data to the disk when it wasn't able to fit that data into
     * main memory.  This is why we assume space used on the disk to be more
     * important for tracking resource usage than space used in memory. Note
     * that the amount of space occupied by some tupleset on the disk might be
     * less than amount of space occupied by the same tupleset in memory due
     * to more compact representation.
     */
    if ((isSpaceDisk && !state->isMaxSpaceDisk) ||
        (isSpaceDisk == state->isMaxSpaceDisk && spaceUsed > state->maxSpace))
    {
        state->maxSpace = spaceUsed;
        state->isMaxSpaceDisk = isSpaceDisk;
        state->maxSpaceStatus = state->status;
    }
}
 
/*
 * tuplesort_reset
 *
 *  Reset the tuplesort.  Reset all the data in the tuplesort, but leave the
 *  meta-information in.  After tuplesort_reset, tuplesort is ready to start
 *  a new sort.  This allows avoiding recreation of tuple sort states (and
 *  save resources) when sorting multiple small batches.
 */
void
tuplesort_reset(Tuplesortstate *state)
{
    tuplesort_updatemax(state);
    tuplesort_free(state);
 
    /*
     * After we've freed up per-batch memory, re-setup all of the state common
     * to both the first batch and any subsequent batch.
     */
    tuplesort_begin_batch(state);
 
    state->lastReturnedTuple = NULL;
    state->slabMemoryBegin = NULL;
    state->slabMemoryEnd = NULL;
    state->slabFreeHead = NULL;
}
 
/*
 * Grow the memtuples[] array, if possible within our memory constraint.  We
 * must not exceed INT_MAX tuples in memory or the caller-provided memory
 * limit.  Return true if we were able to enlarge the array, false if not.
 *
 * Normally, at each increment we double the size of the array.  When doing
 * that would exceed a limit, we attempt one last, smaller increase (and then
 * clear the growmemtuples flag so we don't try any more).  That allows us to
 * use memory as fully as permitted; sticking to the pure doubling rule could
 * result in almost half going unused.  Because availMem moves around with
 * tuple addition/removal, we need some rule to prevent making repeated small
 * increases in memtupsize, which would just be useless thrashing.  The
 * growmemtuples flag accomplishes that and also prevents useless
 * recalculations in this function.
 */
static bool
grow_memtuples(Tuplesortstate *state)
{
    int         newmemtupsize;
    int         memtupsize = state->memtupsize;
    int64       memNowUsed = state->allowedMem - state->availMem;
 
    /* Forget it if we've already maxed out memtuples, per comment above */
    if (!state->growmemtuples)
        return false;
 
    /* Select new value of memtupsize */
    if (memNowUsed <= state->availMem)
    {
        /*
         * We've used no more than half of allowedMem; double our usage,
         * clamping at INT_MAX tuples.
         */
        if (memtupsize < INT_MAX / 2)
            newmemtupsize = memtupsize * 2;
        else
        {
            newmemtupsize = INT_MAX;
            state->growmemtuples = false;
        }
    }
    else
    {
        /*
         * This will be the last increment of memtupsize.  Abandon doubling
         * strategy and instead increase as much as we safely can.
         *
         * To stay within allowedMem, we can't increase memtupsize by more
         * than availMem / sizeof(SortTuple) elements.  In practice, we want
         * to increase it by considerably less, because we need to leave some
         * space for the tuples to which the new array slots will refer.  We
         * assume the new tuples will be about the same size as the tuples
         * we've already seen, and thus we can extrapolate from the space
         * consumption so far to estimate an appropriate new size for the
         * memtuples array.  The optimal value might be higher or lower than
         * this estimate, but it's hard to know that in advance.  We again
         * clamp at INT_MAX tuples.
         *
         * This calculation is safe against enlarging the array so much that
         * LACKMEM becomes true, because the memory currently used includes
         * the present array; thus, there would be enough allowedMem for the
         * new array elements even if no other memory were currently used.
         *
         * We do the arithmetic in float8, because otherwise the product of
         * memtupsize and allowedMem could overflow.  Any inaccuracy in the
         * result should be insignificant; but even if we computed a
         * completely insane result, the checks below will prevent anything
         * really bad from happening.
         */
        double      grow_ratio;
 
        grow_ratio = (double) state->allowedMem / (double) memNowUsed;
        if (memtupsize * grow_ratio < INT_MAX)
            newmemtupsize = (int) (memtupsize * grow_ratio);
        else
            newmemtupsize = INT_MAX;
 
        /* We won't make any further enlargement attempts */
        state->growmemtuples = false;
    }
 
    /* Must enlarge array by at least one element, else report failure */
    if (newmemtupsize <= memtupsize)
        goto noalloc;
 
    /*
     * On a 32-bit machine, allowedMem could exceed MaxAllocHugeSize.  Clamp
     * to ensure our request won't be rejected.  Note that we can easily
     * exhaust address space before facing this outcome.  (This is presently
     * impossible due to guc.c's MAX_KILOBYTES limitation on work_mem, but
     * don't rely on that at this distance.)
     */
    if ((Size) newmemtupsize >= MaxAllocHugeSize / sizeof(SortTuple))
    {
        newmemtupsize = (int) (MaxAllocHugeSize / sizeof(SortTuple));
        state->growmemtuples = false;   /* can't grow any more */
    }
 
    /*
     * We need to be sure that we do not cause LACKMEM to become true, else
     * the space management algorithm will go nuts.  The code above should
     * never generate a dangerous request, but to be safe, check explicitly
     * that the array growth fits within availMem.  (We could still cause
     * LACKMEM if the memory chunk overhead associated with the memtuples
     * array were to increase.  That shouldn't happen because we chose the
     * initial array size large enough to ensure that palloc will be treating
     * both old and new arrays as separate chunks.  But we'll check LACKMEM
     * explicitly below just in case.)
     */
    if (state->availMem < (int64) ((newmemtupsize - memtupsize) * sizeof(SortTuple)))
        goto noalloc;
 
    /* OK, do it */
    FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
    state->memtupsize = newmemtupsize;
    state->memtuples = (SortTuple *)
        repalloc_huge(state->memtuples,
                      state->memtupsize * sizeof(SortTuple));
    USEMEM(state, GetMemoryChunkSpace(state->memtuples));
    if (LACKMEM(state))
        elog(ERROR, "unexpected out-of-memory situation in tuplesort");
    return true;
 
noalloc:
    /* If for any reason we didn't realloc, shut off future attempts */
    state->growmemtuples = false;
    return false;
}
 
/*
 * Shared code for tuple and datum cases.
 */
void
tuplesort_puttuple_common(Tuplesortstate *state, SortTuple *tuple,
                          bool useAbbrev, Size tuplen)
{
    MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
 
    Assert(!LEADER(state));
 
    /* account for the memory used for this tuple */
    USEMEM(state, tuplen);
    state->tupleMem += tuplen;
 
    if (!useAbbrev)
    {
        /*
         * Leave ordinary Datum representation, or NULL value.  If there is a
         * converter it won't expect NULL values, and cost model is not
         * required to account for NULL, so in that case we avoid calling
         * converter and just set datum1 to zeroed representation (to be
         * consistent, and to support cheap inequality tests for NULL
         * abbreviated keys).
         */
    }
    else if (!consider_abort_common(state))
    {
        /* Store abbreviated key representation */
        tuple->datum1 = state->base.sortKeys->abbrev_converter(tuple->datum1,
                                                               state->base.sortKeys);
    }
    else
    {
        /*
         * Set state to be consistent with never trying abbreviation.
         *
         * Alter datum1 representation in already-copied tuples, so as to
         * ensure a consistent representation (current tuple was just
         * handled).  It does not matter if some dumped tuples are already
         * sorted on tape, since serialized tuples lack abbreviated keys
         * (TSS_BUILDRUNS state prevents control reaching here in any case).
         */
        REMOVEABBREV(state, state->memtuples, state->memtupcount);
    }
 
    switch (state->status)
    {
        case TSS_INITIAL:
 
            /*
             * Save the tuple into the unsorted array.  First, grow the array
             * as needed.  Note that we try to grow the array when there is
             * still one free slot remaining --- if we fail, there'll still be
             * room to store the incoming tuple, and then we'll switch to
             * tape-based operation.
             */
            if (state->memtupcount >= state->memtupsize - 1)
            {
                (void) grow_memtuples(state);
                Assert(state->memtupcount < state->memtupsize);
            }
            state->memtuples[state->memtupcount++] = *tuple;
 
            /*
             * Check if it's time to switch over to a bounded heapsort. We do
             * so if the input tuple count exceeds twice the desired tuple
             * count (this is a heuristic for where heapsort becomes cheaper
             * than a quicksort), or if we've just filled workMem and have
             * enough tuples to meet the bound.
             *
             * Note that once we enter TSS_BOUNDED state we will always try to
             * complete the sort that way.  In the worst case, if later input
             * tuples are larger than earlier ones, this might cause us to
             * exceed workMem significantly.
             */
            if (state->bounded &&
                (state->memtupcount > state->bound * 2 ||
                 (state->memtupcount > state->bound && LACKMEM(state))))
            {
                if (trace_sort)
                    elog(LOG, "switching to bounded heapsort at %d tuples: %s",
                         state->memtupcount,
                         pg_rusage_show(&state->ru_start));
                make_bounded_heap(state);
                MemoryContextSwitchTo(oldcontext);
                return;
            }
 
            /*
             * Done if we still fit in available memory and have array slots.
             */
            if (state->memtupcount < state->memtupsize && !LACKMEM(state))
            {
                MemoryContextSwitchTo(oldcontext);
                return;
            }
 
            /*
             * Nope; time to switch to tape-based operation.
             */
            inittapes(state, true);
 
            /*
             * Dump all tuples.
             */
            dumptuples(state, false);
            break;
 
        case TSS_BOUNDED:
 
            /*
             * We don't want to grow the array here, so check whether the new
             * tuple can be discarded before putting it in.  This should be a
             * good speed optimization, too, since when there are many more
             * input tuples than the bound, most input tuples can be discarded
             * with just this one comparison.  Note that because we currently
             * have the sort direction reversed, we must check for <= not >=.
             */
            if (COMPARETUP(state, tuple, &state->memtuples[0]) <= 0)
            {
                /* new tuple <= top of the heap, so we can discard it */
                free_sort_tuple(state, tuple);
                CHECK_FOR_INTERRUPTS();
            }
            else
            {
                /* discard top of heap, replacing it with the new tuple */
                free_sort_tuple(state, &state->memtuples[0]);
                tuplesort_heap_replace_top(state, tuple);
            }
            break;
 
        case TSS_BUILDRUNS:
 
            /*
             * Save the tuple into the unsorted array (there must be space)
             */
            state->memtuples[state->memtupcount++] = *tuple;
 
            /*
             * If we are over the memory limit, dump all tuples.
             */
            dumptuples(state, false);
            break;
 
        default:
            elog(ERROR, "invalid tuplesort state");
            break;
    }
    MemoryContextSwitchTo(oldcontext);
}
 
static bool
consider_abort_common(Tuplesortstate *state)
{
    Assert(state->base.sortKeys[0].abbrev_converter != NULL);
    Assert(state->base.sortKeys[0].abbrev_abort != NULL);
    Assert(state->base.sortKeys[0].abbrev_full_comparator != NULL);
 
    /*
     * Check effectiveness of abbreviation optimization.  Consider aborting
     * when still within memory limit.
     */
    if (state->status == TSS_INITIAL &&
        state->memtupcount >= state->abbrevNext)
    {
        state->abbrevNext *= 2;
 
        /*
         * Check opclass-supplied abbreviation abort routine.  It may indicate
         * that abbreviation should not proceed.
         */
        if (!state->base.sortKeys->abbrev_abort(state->memtupcount,
                                                state->base.sortKeys))
            return false;
 
        /*
         * Finally, restore authoritative comparator, and indicate that
         * abbreviation is not in play by setting abbrev_converter to NULL
         */
        state->base.sortKeys[0].comparator = state->base.sortKeys[0].abbrev_full_comparator;
        state->base.sortKeys[0].abbrev_converter = NULL;
        /* Not strictly necessary, but be tidy */
        state->base.sortKeys[0].abbrev_abort = NULL;
        state->base.sortKeys[0].abbrev_full_comparator = NULL;
 
        /* Give up - expect original pass-by-value representation */
        return true;
    }
 
    return false;
}
 
/*
 * All tuples have been provided; finish the sort.
 */
void
tuplesort_performsort(Tuplesortstate *state)
{
    MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
 
    if (trace_sort)
        elog(LOG, "performsort of worker %d starting: %s",
             state->worker, pg_rusage_show(&state->ru_start));
 
    switch (state->status)
    {
        case TSS_INITIAL:
 
            /*
             * We were able to accumulate all the tuples within the allowed
             * amount of memory, or leader to take over worker tapes
             */
            if (SERIAL(state))
            {
                /* Sort in memory and we're done */
                tuplesort_sort_memtuples(state);
                state->status = TSS_SORTEDINMEM;
            }
            else if (WORKER(state))
            {
                /*
                 * Parallel workers must still dump out tuples to tape.  No
                 * merge is required to produce single output run, though.
                 */
                inittapes(state, false);
                dumptuples(state, true);
                worker_nomergeruns(state);
                state->status = TSS_SORTEDONTAPE;
            }
            else
            {
                /*
                 * Leader will take over worker tapes and merge worker runs.
                 * Note that mergeruns sets the correct state->status.
                 */
                leader_takeover_tapes(state);
                mergeruns(state);
            }
            state->current = 0;
            state->eof_reached = false;
            state->markpos_block = 0L;
            state->markpos_offset = 0;
            state->markpos_eof = false;
            break;
 
        case TSS_BOUNDED:
 
            /*
             * We were able to accumulate all the tuples required for output
             * in memory, using a heap to eliminate excess tuples.  Now we
             * have to transform the heap to a properly-sorted array. Note
             * that sort_bounded_heap sets the correct state->status.
             */
            sort_bounded_heap(state);
            state->current = 0;
            state->eof_reached = false;
            state->markpos_offset = 0;
            state->markpos_eof = false;
            break;
 
        case TSS_BUILDRUNS:
 
            /*
             * Finish tape-based sort.  First, flush all tuples remaining in
             * memory out to tape; then merge until we have a single remaining
             * run (or, if !randomAccess and !WORKER(), one run per tape).
             * Note that mergeruns sets the correct state->status.
             */
            dumptuples(state, true);
            mergeruns(state);
            state->eof_reached = false;
            state->markpos_block = 0L;
            state->markpos_offset = 0;
            state->markpos_eof = false;
            break;
 
        default:
            elog(ERROR, "invalid tuplesort state");
            break;
    }
 
    if (trace_sort)
    {
        if (state->status == TSS_FINALMERGE)
            elog(LOG, "performsort of worker %d done (except %d-way final merge): %s",
                 state->worker, state->nInputTapes,
                 pg_rusage_show(&state->ru_start));
        else
            elog(LOG, "performsort of worker %d done: %s",
                 state->worker, pg_rusage_show(&state->ru_start));
    }
 
    MemoryContextSwitchTo(oldcontext);
}
 
/*
 * Internal routine to fetch the next tuple in either forward or back
 * direction into *stup.  Returns false if no more tuples.
 * Returned tuple belongs to tuplesort memory context, and must not be freed
 * by caller.  Note that fetched tuple is stored in memory that may be
 * recycled by any future fetch.
 */
bool
tuplesort_gettuple_common(Tuplesortstate *state, bool forward,
                          SortTuple *stup)
{
    unsigned int tuplen;
    size_t      nmoved;
 
    Assert(!WORKER(state));
 
    switch (state->status)
    {
        case TSS_SORTEDINMEM:
            Assert(forward || state->base.sortopt & TUPLESORT_RANDOMACCESS);
            Assert(!state->slabAllocatorUsed);
            if (forward)
            {
                if (state->current < state->memtupcount)
                {
                    *stup = state->memtuples[state->current++];
                    return true;
                }
                state->eof_reached = true;
 
                /*
                 * Complain if caller tries to retrieve more tuples than
                 * originally asked for in a bounded sort.  This is because
                 * returning EOF here might be the wrong thing.
                 */
                if (state->bounded && state->current >= state->bound)
                    elog(ERROR, "retrieved too many tuples in a bounded sort");
 
                return false;
            }
            else
            {
                if (state->current <= 0)
                    return false;
 
                /*
                 * if all tuples are fetched already then we return last
                 * tuple, else - tuple before last returned.
                 */
                if (state->eof_reached)
                    state->eof_reached = false;
                else
                {
                    state->current--;   /* last returned tuple */
                    if (state->current <= 0)
                        return false;
                }
                *stup = state->memtuples[state->current - 1];
                return true;
            }
            break;
 
        case TSS_SORTEDONTAPE:
            Assert(forward || state->base.sortopt & TUPLESORT_RANDOMACCESS);
            Assert(state->slabAllocatorUsed);
 
            /*
             * The slot that held the tuple that we returned in previous
             * gettuple call can now be reused.
             */
            if (state->lastReturnedTuple)
            {
                RELEASE_SLAB_SLOT(state, state->lastReturnedTuple);
                state->lastReturnedTuple = NULL;
            }
 
            if (forward)
            {
                if (state->eof_reached)
                    return false;
 
                if ((tuplen = getlen(state->result_tape, true)) != 0)
                {
                    READTUP(state, stup, state->result_tape, tuplen);
 
                    /*
                     * Remember the tuple we return, so that we can recycle
                     * its memory on next call.  (This can be NULL, in the
                     * !state->tuples case).
                     */
                    state->lastReturnedTuple = stup->tuple;
 
                    return true;
                }
                else
                {
                    state->eof_reached = true;
                    return false;
                }
            }
 
            /*
             * Backward.
             *
             * if all tuples are fetched already then we return last tuple,
             * else - tuple before last returned.
             */
            if (state->eof_reached)
            {
                /*
                 * Seek position is pointing just past the zero tuplen at the
                 * end of file; back up to fetch last tuple's ending length
                 * word.  If seek fails we must have a completely empty file.
                 */
                nmoved = LogicalTapeBackspace(state->result_tape,
                                              2 * sizeof(unsigned int));
                if (nmoved == 0)
                    return false;
                else if (nmoved != 2 * sizeof(unsigned int))
                    elog(ERROR, "unexpected tape position");
                state->eof_reached = false;
            }
            else
            {
                /*
                 * Back up and fetch previously-returned tuple's ending length
                 * word.  If seek fails, assume we are at start of file.
                 */
                nmoved = LogicalTapeBackspace(state->result_tape,
                                              sizeof(unsigned int));
                if (nmoved == 0)
                    return false;
                else if (nmoved != sizeof(unsigned int))
                    elog(ERROR, "unexpected tape position");
                tuplen = getlen(state->result_tape, false);
 
                /*
                 * Back up to get ending length word of tuple before it.
                 */
                nmoved = LogicalTapeBackspace(state->result_tape,
                                              tuplen + 2 * sizeof(unsigned int));
                if (nmoved == tuplen + sizeof(unsigned int))
                {
                    /*
                     * We backed up over the previous tuple, but there was no
                     * ending length word before it.  That means that the prev
                     * tuple is the first tuple in the file.  It is now the
                     * next to read in forward direction (not obviously right,
                     * but that is what in-memory case does).
                     */
                    return false;
                }
                else if (nmoved != tuplen + 2 * sizeof(unsigned int))
                    elog(ERROR, "bogus tuple length in backward scan");
            }
 
            tuplen = getlen(state->result_tape, false);
 
            /*
             * Now we have the length of the prior tuple, back up and read it.
             * Note: READTUP expects we are positioned after the initial
             * length word of the tuple, so back up to that point.
             */
            nmoved = LogicalTapeBackspace(state->result_tape,
                                          tuplen);
            if (nmoved != tuplen)
                elog(ERROR, "bogus tuple length in backward scan");
            READTUP(state, stup, state->result_tape, tuplen);
 
            /*
             * Remember the tuple we return, so that we can recycle its memory
             * on next call. (This can be NULL, in the Datum case).
             */
            state->lastReturnedTuple = stup->tuple;
 
            return true;
 
        case TSS_FINALMERGE:
            Assert(forward);
            /* We are managing memory ourselves, with the slab allocator. */
            Assert(state->slabAllocatorUsed);
 
            /*
             * The slab slot holding the tuple that we returned in previous
             * gettuple call can now be reused.
             */
            if (state->lastReturnedTuple)
            {
                RELEASE_SLAB_SLOT(state, state->lastReturnedTuple);
                state->lastReturnedTuple = NULL;
            }
 
            /*
             * This code should match the inner loop of mergeonerun().
             */
            if (state->memtupcount > 0)
            {
                int         srcTapeIndex = state->memtuples[0].srctape;
                LogicalTape *srcTape = state->inputTapes[srcTapeIndex];
                SortTuple   newtup;
 
                *stup = state->memtuples[0];
 
                /*
                 * Remember the tuple we return, so that we can recycle its
                 * memory on next call. (This can be NULL, in the Datum case).
                 */
                state->lastReturnedTuple = stup->tuple;
 
                /*
                 * Pull next tuple from tape, and replace the returned tuple
                 * at top of the heap with it.
                 */
                if (!mergereadnext(state, srcTape, &newtup))
                {
                    /*
                     * If no more data, we've reached end of run on this tape.
                     * Remove the top node from the heap.
                     */
                    tuplesort_heap_delete_top(state);
                    state->nInputRuns--;
 
                    /*
                     * Close the tape.  It'd go away at the end of the sort
                     * anyway, but better to release the memory early.
                     */
                    LogicalTapeClose(srcTape);
                    return true;
                }
                newtup.srctape = srcTapeIndex;
                tuplesort_heap_replace_top(state, &newtup);
                return true;
            }
            return false;
 
        default:
            elog(ERROR, "invalid tuplesort state");
            return false;       /* keep compiler quiet */
    }
}
 
 
/*
 * Advance over N tuples in either forward or back direction,
 * without returning any data.  N==0 is a no-op.
 * Returns true if successful, false if ran out of tuples.
 */
bool
tuplesort_skiptuples(Tuplesortstate *state, int64 ntuples, bool forward)
{
    MemoryContext oldcontext;
 
    /*
     * We don't actually support backwards skip yet, because no callers need
     * it.  The API is designed to allow for that later, though.
     */
    Assert(forward);
    Assert(ntuples >= 0);
    Assert(!WORKER(state));
 
    switch (state->status)
    {
        case TSS_SORTEDINMEM:
            if (state->memtupcount - state->current >= ntuples)
            {
                state->current += ntuples;
                return true;
            }
            state->current = state->memtupcount;
            state->eof_reached = true;
 
            /*
             * Complain if caller tries to retrieve more tuples than
             * originally asked for in a bounded sort.  This is because
             * returning EOF here might be the wrong thing.
             */
            if (state->bounded && state->current >= state->bound)
                elog(ERROR, "retrieved too many tuples in a bounded sort");
 
            return false;
 
        case TSS_SORTEDONTAPE:
        case TSS_FINALMERGE:
 
            /*
             * We could probably optimize these cases better, but for now it's
             * not worth the trouble.
             */
            oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
            while (ntuples-- > 0)
            {
                SortTuple   stup;
 
                if (!tuplesort_gettuple_common(state, forward, &stup))
                {
                    MemoryContextSwitchTo(oldcontext);
                    return false;
                }
                CHECK_FOR_INTERRUPTS();
            }
            MemoryContextSwitchTo(oldcontext);
            return true;
 
        default:
            elog(ERROR, "invalid tuplesort state");
            return false;       /* keep compiler quiet */
    }
}
 
/*
 * tuplesort_merge_order - report merge order we'll use for given memory
 * (note: "merge order" just means the number of input tapes in the merge).
 *
 * This is exported for use by the planner.  allowedMem is in bytes.
 */
int
tuplesort_merge_order(int64 allowedMem)
{
    int         mOrder;
 
    /*----------
     * In the merge phase, we need buffer space for each input and output tape.
     * Each pass in the balanced merge algorithm reads from M input tapes, and
     * writes to N output tapes.  Each tape consumes TAPE_BUFFER_OVERHEAD bytes
     * of memory.  In addition to that, we want MERGE_BUFFER_SIZE workspace per
     * input tape.
     *
     * totalMem = M * (TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE) +
     *            N * TAPE_BUFFER_OVERHEAD
     *
     * Except for the last and next-to-last merge passes, where there can be
     * fewer tapes left to process, M = N.  We choose M so that we have the
     * desired amount of memory available for the input buffers
     * (TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE), given the total memory
     * available for the tape buffers (allowedMem).
     *
     * Note: you might be thinking we need to account for the memtuples[]
     * array in this calculation, but we effectively treat that as part of the
     * MERGE_BUFFER_SIZE workspace.
     *----------
     */
    mOrder = allowedMem /
        (2 * TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE);
 
    /*
     * Even in minimum memory, use at least a MINORDER merge.  On the other
     * hand, even when we have lots of memory, do not use more than a MAXORDER
     * merge.  Tapes are pretty cheap, but they're not entirely free.  Each
     * additional tape reduces the amount of memory available to build runs,
     * which in turn can cause the same sort to need more runs, which makes
     * merging slower even if it can still be done in a single pass.  Also,
     * high order merges are quite slow due to CPU cache effects; it can be
     * faster to pay the I/O cost of a multi-pass merge than to perform a
     * single merge pass across many hundreds of tapes.
     */
    mOrder = Max(mOrder, MINORDER);
    mOrder = Min(mOrder, MAXORDER);
 
    return mOrder;
}
 
/*
 * Helper function to calculate how much memory to allocate for the read buffer
 * of each input tape in a merge pass.
 *
 * 'avail_mem' is the amount of memory available for the buffers of all the
 *      tapes, both input and output.
 * 'nInputTapes' and 'nInputRuns' are the number of input tapes and runs.
 * 'maxOutputTapes' is the max. number of output tapes we should produce.
 */
static int64
merge_read_buffer_size(int64 avail_mem, int nInputTapes, int nInputRuns,
                       int maxOutputTapes)
{
    int         nOutputRuns;
    int         nOutputTapes;
 
    /*
     * How many output tapes will we produce in this pass?
     *
     * This is nInputRuns / nInputTapes, rounded up.
     */
    nOutputRuns = (nInputRuns + nInputTapes - 1) / nInputTapes;
 
    nOutputTapes = Min(nOutputRuns, maxOutputTapes);
 
    /*
     * Each output tape consumes TAPE_BUFFER_OVERHEAD bytes of memory.  All
     * remaining memory is divided evenly between the input tapes.
     *
     * This also follows from the formula in tuplesort_merge_order, but here
     * we derive the input buffer size from the amount of memory available,
     * and M and N.
     */
    return Max((avail_mem - TAPE_BUFFER_OVERHEAD * nOutputTapes) / nInputTapes, 0);
}
 
/*
 * inittapes - initialize for tape sorting.
 *
 * This is called only if we have found we won't sort in memory.
 */
static void
inittapes(Tuplesortstate *state, bool mergeruns)
{
    Assert(!LEADER(state));
 
    if (mergeruns)
    {
        /* Compute number of input tapes to use when merging */
        state->maxTapes = tuplesort_merge_order(state->allowedMem);
    }
    else
    {
        /* Workers can sometimes produce single run, output without merge */
        Assert(WORKER(state));
        state->maxTapes = MINORDER;
    }
 
    if (trace_sort)
        elog(LOG, "worker %d switching to external sort with %d tapes: %s",
             state->worker, state->maxTapes, pg_rusage_show(&state->ru_start));
 
    /* Create the tape set */
    inittapestate(state, state->maxTapes);
    state->tapeset =
        LogicalTapeSetCreate(false,
                             state->shared ? &state->shared->fileset : NULL,
                             state->worker);
 
    state->currentRun = 0;
 
    /*
     * Initialize logical tape arrays.
     */
    state->inputTapes = NULL;
    state->nInputTapes = 0;
    state->nInputRuns = 0;
 
    state->outputTapes = palloc0(state->maxTapes * sizeof(LogicalTape *));
    state->nOutputTapes = 0;
    state->nOutputRuns = 0;
 
    state->status = TSS_BUILDRUNS;
 
    selectnewtape(state);
}
 
/*
 * inittapestate - initialize generic tape management state
 */
static void
inittapestate(Tuplesortstate *state, int maxTapes)
{
    int64       tapeSpace;
 
    /*
     * Decrease availMem to reflect the space needed for tape buffers; but
     * don't decrease it to the point that we have no room for tuples. (That
     * case is only likely to occur if sorting pass-by-value Datums; in all
     * other scenarios the memtuples[] array is unlikely to occupy more than
     * half of allowedMem.  In the pass-by-value case it's not important to
     * account for tuple space, so we don't care if LACKMEM becomes
     * inaccurate.)
     */
    tapeSpace = (int64) maxTapes * TAPE_BUFFER_OVERHEAD;
 
    if (tapeSpace + GetMemoryChunkSpace(state->memtuples) < state->allowedMem)
        USEMEM(state, tapeSpace);
 
    /*
     * Make sure that the temp file(s) underlying the tape set are created in
     * suitable temp tablespaces.  For parallel sorts, this should have been
     * called already, but it doesn't matter if it is called a second time.
     */
    PrepareTempTablespaces();
}
 
/*
 * selectnewtape -- select next tape to output to.
 *
 * This is called after finishing a run when we know another run
 * must be started.  This is used both when building the initial
 * runs, and during merge passes.
 */
static void
selectnewtape(Tuplesortstate *state)
{
    /*
     * At the beginning of each merge pass, nOutputTapes and nOutputRuns are
     * both zero.  On each call, we create a new output tape to hold the next
     * run, until maxTapes is reached.  After that, we assign new runs to the
     * existing tapes in a round robin fashion.
     */
    if (state->nOutputTapes < state->maxTapes)
    {
        /* Create a new tape to hold the next run */
        Assert(state->outputTapes[state->nOutputRuns] == NULL);
        Assert(state->nOutputRuns == state->nOutputTapes);
        state->destTape = LogicalTapeCreate(state->tapeset);
        state->outputTapes[state->nOutputTapes] = state->destTape;
        state->nOutputTapes++;
        state->nOutputRuns++;
    }
    else
    {
        /*
         * We have reached the max number of tapes.  Append to an existing
         * tape.
         */
        state->destTape = state->outputTapes[state->nOutputRuns % state->nOutputTapes];
        state->nOutputRuns++;
    }
}
 
/*
 * Initialize the slab allocation arena, for the given number of slots.
 */
static void
init_slab_allocator(Tuplesortstate *state, int numSlots)
{
    if (numSlots > 0)
    {
        char       *p;
        int         i;
 
        state->slabMemoryBegin = palloc(numSlots * SLAB_SLOT_SIZE);
        state->slabMemoryEnd = state->slabMemoryBegin +
            numSlots * SLAB_SLOT_SIZE;
        state->slabFreeHead = (SlabSlot *) state->slabMemoryBegin;
        USEMEM(state, numSlots * SLAB_SLOT_SIZE);
 
        p = state->slabMemoryBegin;
        for (i = 0; i < numSlots - 1; i++)
        {
            ((SlabSlot *) p)->nextfree = (SlabSlot *) (p + SLAB_SLOT_SIZE);
            p += SLAB_SLOT_SIZE;
        }
        ((SlabSlot *) p)->nextfree = NULL;
    }
    else
    {
        state->slabMemoryBegin = state->slabMemoryEnd = NULL;
        state->slabFreeHead = NULL;
    }
    state->slabAllocatorUsed = true;
}
 
/*
 * mergeruns -- merge all the completed initial runs.
 *
 * This implements the Balanced k-Way Merge Algorithm.  All input data has
 * already been written to initial runs on tape (see dumptuples).
 */
static void
mergeruns(Tuplesortstate *state)
{
    int         tapenum;
 
    Assert(state->status == TSS_BUILDRUNS);
    Assert(state->memtupcount == 0);
 
    if (state->base.sortKeys != NULL && state->base.sortKeys->abbrev_converter != NULL)
    {
        /*
         * If there are multiple runs to be merged, when we go to read back
         * tuples from disk, abbreviated keys will not have been stored, and
         * we don't care to regenerate them.  Disable abbreviation from this
         * point on.
         */
        state->base.sortKeys->abbrev_converter = NULL;
        state->base.sortKeys->comparator = state->base.sortKeys->abbrev_full_comparator;
 
        /* Not strictly necessary, but be tidy */
        state->base.sortKeys->abbrev_abort = NULL;
        state->base.sortKeys->abbrev_full_comparator = NULL;
    }
 
    /*
     * Reset tuple memory.  We've freed all the tuples that we previously
     * allocated.  We will use the slab allocator from now on.
     */
    MemoryContextResetOnly(state->base.tuplecontext);
 
    /*
     * We no longer need a large memtuples array.  (We will allocate a smaller
     * one for the heap later.)
     */
    FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
    pfree(state->memtuples);
    state->memtuples = NULL;
 
    /*
     * Initialize the slab allocator.  We need one slab slot per input tape,
     * for the tuples in the heap, plus one to hold the tuple last returned
     * from tuplesort_gettuple.  (If we're sorting pass-by-val Datums,
     * however, we don't need to do allocate anything.)
     *
     * In a multi-pass merge, we could shrink this allocation for the last
     * merge pass, if it has fewer tapes than previous passes, but we don't
     * bother.
     *
     * From this point on, we no longer use the USEMEM()/LACKMEM() mechanism
     * to track memory usage of individual tuples.
     */
    if (state->base.tuples)
        init_slab_allocator(state, state->nOutputTapes + 1);
    else
        init_slab_allocator(state, 0);
 
    /*
     * Allocate a new 'memtuples' array, for the heap.  It will hold one tuple
     * from each input tape.
     *
     * We could shrink this, too, between passes in a multi-pass merge, but we
     * don't bother.  (The initial input tapes are still in outputTapes.  The
     * number of input tapes will not increase between passes.)
     */
    state->memtupsize = state->nOutputTapes;
    state->memtuples = (SortTuple *) MemoryContextAlloc(state->base.maincontext,
                                                        state->nOutputTapes * sizeof(SortTuple));
    USEMEM(state, GetMemoryChunkSpace(state->memtuples));
 
    /*
     * Use all the remaining memory we have available for tape buffers among
     * all the input tapes.  At the beginning of each merge pass, we will
     * divide this memory between the input and output tapes in the pass.
     */
    state->tape_buffer_mem = state->availMem;
    USEMEM(state, state->tape_buffer_mem);
    if (trace_sort)
        elog(LOG, "worker %d using %zu KB of memory for tape buffers",
             state->worker, state->tape_buffer_mem / 1024);
 
    for (;;)
    {
        /*
         * On the first iteration, or if we have read all the runs from the
         * input tapes in a multi-pass merge, it's time to start a new pass.
         * Rewind all the output tapes, and make them inputs for the next
         * pass.
         */
        if (state->nInputRuns == 0)
        {
            int64       input_buffer_size;
 
            /* Close the old, emptied, input tapes */
            if (state->nInputTapes > 0)
            {
                for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
                    LogicalTapeClose(state->inputTapes[tapenum]);
                pfree(state->inputTapes);
            }
 
            /* Previous pass's outputs become next pass's inputs. */
            state->inputTapes = state->outputTapes;
            state->nInputTapes = state->nOutputTapes;
            state->nInputRuns = state->nOutputRuns;
 
            /*
             * Reset output tape variables.  The actual LogicalTapes will be
             * created as needed, here we only allocate the array to hold
             * them.
             */
            state->outputTapes = palloc0(state->nInputTapes * sizeof(LogicalTape *));
            state->nOutputTapes = 0;
            state->nOutputRuns = 0;
 
            /*
             * Redistribute the memory allocated for tape buffers, among the
             * new input and output tapes.
             */
            input_buffer_size = merge_read_buffer_size(state->tape_buffer_mem,
                                                       state->nInputTapes,
                                                       state->nInputRuns,
                                                       state->maxTapes);
 
            if (trace_sort)
                elog(LOG, "starting merge pass of %d input runs on %d tapes, " INT64_FORMAT " KB of memory for each input tape: %s",
                     state->nInputRuns, state->nInputTapes, input_buffer_size / 1024,
                     pg_rusage_show(&state->ru_start));
 
            /* Prepare the new input tapes for merge pass. */
            for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
                LogicalTapeRewindForRead(state->inputTapes[tapenum], input_buffer_size);
 
            /*
             * If there's just one run left on each input tape, then only one
             * merge pass remains.  If we don't have to produce a materialized
             * sorted tape, we can stop at this point and do the final merge
             * on-the-fly.
             */
            if ((state->base.sortopt & TUPLESORT_RANDOMACCESS) == 0
                && state->nInputRuns <= state->nInputTapes
                && !WORKER(state))
            {
                /* Tell logtape.c we won't be writing anymore */
                LogicalTapeSetForgetFreeSpace(state->tapeset);
                /* Initialize for the final merge pass */
                beginmerge(state);
                state->status = TSS_FINALMERGE;
                return;
            }
        }
 
        /* Select an output tape */
        selectnewtape(state);
 
        /* Merge one run from each input tape. */
        mergeonerun(state);
 
        /*
         * If the input tapes are empty, and we output only one output run,
         * we're done.  The current output tape contains the final result.
         */
        if (state->nInputRuns == 0 && state->nOutputRuns <= 1)
            break;
    }
 
    /*
     * Done.  The result is on a single run on a single tape.
     */
    state->result_tape = state->outputTapes[0];
    if (!WORKER(state))
        LogicalTapeFreeze(state->result_tape, NULL);
    else
        worker_freeze_result_tape(state);
    state->status = TSS_SORTEDONTAPE;
 
    /* Close all the now-empty input tapes, to release their read buffers. */
    for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
        LogicalTapeClose(state->inputTapes[tapenum]);
}
 
/*
 * Merge one run from each input tape.
 */
static void
mergeonerun(Tuplesortstate *state)
{
    int         srcTapeIndex;
    LogicalTape *srcTape;
 
    /*
     * Start the merge by loading one tuple from each active source tape into
     * the heap.
     */
    beginmerge(state);
 
    Assert(state->slabAllocatorUsed);
 
    /*
     * Execute merge by repeatedly extracting lowest tuple in heap, writing it
     * out, and replacing it with next tuple from same tape (if there is
     * another one).
     */
    while (state->memtupcount > 0)
    {
        SortTuple   stup;
 
        /* write the tuple to destTape */
        srcTapeIndex = state->memtuples[0].srctape;
        srcTape = state->inputTapes[srcTapeIndex];
        WRITETUP(state, state->destTape, &state->memtuples[0]);
 
        /* recycle the slot of the tuple we just wrote out, for the next read */
        if (state->memtuples[0].tuple)
            RELEASE_SLAB_SLOT(state, state->memtuples[0].tuple);
 
        /*
         * pull next tuple from the tape, and replace the written-out tuple in
         * the heap with it.
         */
        if (mergereadnext(state, srcTape, &stup))
        {
            stup.srctape = srcTapeIndex;
            tuplesort_heap_replace_top(state, &stup);
        }
        else
        {
            tuplesort_heap_delete_top(state);
            state->nInputRuns--;
        }
    }
 
    /*
     * When the heap empties, we're done.  Write an end-of-run marker on the
     * output tape.
     */
    markrunend(state->destTape);
}
 
/*
 * beginmerge - initialize for a merge pass
 *
 * Fill the merge heap with the first tuple from each input tape.
 */
static void
beginmerge(Tuplesortstate *state)
{
    int         activeTapes;
    int         srcTapeIndex;
 
    /* Heap should be empty here */
    Assert(state->memtupcount == 0);
 
    activeTapes = Min(state->nInputTapes, state->nInputRuns);
 
    for (srcTapeIndex = 0; srcTapeIndex < activeTapes; srcTapeIndex++)
    {
        SortTuple   tup;
 
        if (mergereadnext(state, state->inputTapes[srcTapeIndex], &tup))
        {
            tup.srctape = srcTapeIndex;
            tuplesort_heap_insert(state, &tup);
        }
    }
}
 
/*
 * mergereadnext - read next tuple from one merge input tape
 *
 * Returns false on EOF.
 */
static bool
mergereadnext(Tuplesortstate *state, LogicalTape *srcTape, SortTuple *stup)
{
    unsigned int tuplen;
 
    /* read next tuple, if any */
    if ((tuplen = getlen(srcTape, true)) == 0)
        return false;
    READTUP(state, stup, srcTape, tuplen);
 
    return true;
}
 
/*
 * dumptuples - remove tuples from memtuples and write initial run to tape
 *
 * When alltuples = true, dump everything currently in memory.  (This case is
 * only used at end of input data.)
 */
static void
dumptuples(Tuplesortstate *state, bool alltuples)
{
    int         memtupwrite;
    int         i;
 
    /*
     * Nothing to do if we still fit in available memory and have array slots,
     * unless this is the final call during initial run generation.
     */
    if (state->memtupcount < state->memtupsize && !LACKMEM(state) &&
        !alltuples)
        return;
 
    /*
     * Final call might require no sorting, in rare cases where we just so
     * happen to have previously LACKMEM()'d at the point where exactly all
     * remaining tuples are loaded into memory, just before input was
     * exhausted.  In general, short final runs are quite possible, but avoid
     * creating a completely empty run.  In a worker, though, we must produce
     * at least one tape, even if it's empty.
     */
    if (state->memtupcount == 0 && state->currentRun > 0)
        return;
 
    Assert(state->status == TSS_BUILDRUNS);
 
    /*
     * It seems unlikely that this limit will ever be exceeded, but take no
     * chances
     */
    if (state->currentRun == INT_MAX)
        ereport(ERROR,
                (errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
                 errmsg("cannot have more than %d runs for an external sort",
                        INT_MAX)));
 
    if (state->currentRun > 0)
        selectnewtape(state);
 
    state->currentRun++;
 
    if (trace_sort)
        elog(LOG, "worker %d starting quicksort of run %d: %s",
             state->worker, state->currentRun,
             pg_rusage_show(&state->ru_start));
 
    /*
     * Sort all tuples accumulated within the allowed amount of memory for
     * this run.
     */
    tuplesort_sort_memtuples(state);
 
    if (trace_sort)
        elog(LOG, "worker %d finished quicksort of run %d: %s",
             state->worker, state->currentRun,
             pg_rusage_show(&state->ru_start));
 
    memtupwrite = state->memtupcount;
    for (i = 0; i < memtupwrite; i++)
    {
        SortTuple  *stup = &state->memtuples[i];
 
        WRITETUP(state, state->destTape, stup);
    }
 
    state->memtupcount = 0;
 
    /*
     * Reset tuple memory.  We've freed all of the tuples that we previously
     * allocated.  It's important to avoid fragmentation when there is a stark
     * change in the sizes of incoming tuples.  In bounded sorts,
     * fragmentation due to AllocSetFree's bucketing by size class might be
     * particularly bad if this step wasn't taken.
     */
    MemoryContextReset(state->base.tuplecontext);
 
    /*
     * Now update the memory accounting to subtract the memory used by the
     * tuple.
     */
    FREEMEM(state, state->tupleMem);
    state->tupleMem = 0;
 
    markrunend(state->destTape);
 
    if (trace_sort)
        elog(LOG, "worker %d finished writing run %d to tape %d: %s",
             state->worker, state->currentRun, (state->currentRun - 1) % state->nOutputTapes + 1,
             pg_rusage_show(&state->ru_start));
}
 
/*
 * tuplesort_rescan     - rewind and replay the scan
 */
void
tuplesort_rescan(Tuplesortstate *state)
{
    MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
 
    Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
 
    switch (state->status)
    {
        case TSS_SORTEDINMEM:
            state->current = 0;
            state->eof_reached = false;
            state->markpos_offset = 0;
            state->markpos_eof = false;
            break;
        case TSS_SORTEDONTAPE:
            LogicalTapeRewindForRead(state->result_tape, 0);
            state->eof_reached = false;
            state->markpos_block = 0L;
            state->markpos_offset = 0;
            state->markpos_eof = false;
            break;
        default:
            elog(ERROR, "invalid tuplesort state");
            break;
    }
 
    MemoryContextSwitchTo(oldcontext);
}
 
/*
 * tuplesort_markpos    - saves current position in the merged sort file
 */
void
tuplesort_markpos(Tuplesortstate *state)
{
    MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
 
    Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
 
    switch (state->status)
    {
        case TSS_SORTEDINMEM:
            state->markpos_offset = state->current;
            state->markpos_eof = state->eof_reached;
            break;
        case TSS_SORTEDONTAPE:
            LogicalTapeTell(state->result_tape,
                            &state->markpos_block,
                            &state->markpos_offset);
            state->markpos_eof = state->eof_reached;
            break;
        default:
            elog(ERROR, "invalid tuplesort state");
            break;
    }
 
    MemoryContextSwitchTo(oldcontext);
}
 
/*
 * tuplesort_restorepos - restores current position in merged sort file to
 *                        last saved position
 */
void
tuplesort_restorepos(Tuplesortstate *state)
{
    MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
 
    Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
 
    switch (state->status)
    {
        case TSS_SORTEDINMEM:
            state->current = state->markpos_offset;
            state->eof_reached = state->markpos_eof;
            break;
        case TSS_SORTEDONTAPE:
            LogicalTapeSeek(state->result_tape,
                            state->markpos_block,
                            state->markpos_offset);
            state->eof_reached = state->markpos_eof;
            break;
        default:
            elog(ERROR, "invalid tuplesort state");
            break;
    }
 
    MemoryContextSwitchTo(oldcontext);
}
 
/*
 * tuplesort_get_stats - extract summary statistics
 *
 * This can be called after tuplesort_performsort() finishes to obtain
 * printable summary information about how the sort was performed.
 */
void
tuplesort_get_stats(Tuplesortstate *state,
                    TuplesortInstrumentation *stats)
{
    /*
     * Note: it might seem we should provide both memory and disk usage for a
     * disk-based sort.  However, the current code doesn't track memory space
     * accurately once we have begun to return tuples to the caller (since we
     * don't account for pfree's the caller is expected to do), so we cannot
     * rely on availMem in a disk sort.  This does not seem worth the overhead
     * to fix.  Is it worth creating an API for the memory context code to
     * tell us how much is actually used in sortcontext?
     */
    tuplesort_updatemax(state);
 
    if (state->isMaxSpaceDisk)
        stats->spaceType = SORT_SPACE_TYPE_DISK;
    else
        stats->spaceType = SORT_SPACE_TYPE_MEMORY;
    stats->spaceUsed = (state->maxSpace + 1023) / 1024;
 
    switch (state->maxSpaceStatus)
    {
        case TSS_SORTEDINMEM:
            if (state->boundUsed)
                stats->sortMethod = SORT_TYPE_TOP_N_HEAPSORT;
            else
                stats->sortMethod = SORT_TYPE_QUICKSORT;
            break;
        case TSS_SORTEDONTAPE:
            stats->sortMethod = SORT_TYPE_EXTERNAL_SORT;
            break;
        case TSS_FINALMERGE:
            stats->sortMethod = SORT_TYPE_EXTERNAL_MERGE;
            break;
        default:
            stats->sortMethod = SORT_TYPE_STILL_IN_PROGRESS;
            break;
    }
}
 
/*
 * Convert TuplesortMethod to a string.
 */
const char *
tuplesort_method_name(TuplesortMethod m)
{
    switch (m)
    {
        case SORT_TYPE_STILL_IN_PROGRESS:
            return "still in progress";
        case SORT_TYPE_TOP_N_HEAPSORT:
            return "top-N heapsort";
        case SORT_TYPE_QUICKSORT:
            return "quicksort";
        case SORT_TYPE_EXTERNAL_SORT:
            return "external sort";
        case SORT_TYPE_EXTERNAL_MERGE:
            return "external merge";
    }
 
    return "unknown";
}
 
/*
 * Convert TuplesortSpaceType to a string.
 */
const char *
tuplesort_space_type_name(TuplesortSpaceType t)
{
    Assert(t == SORT_SPACE_TYPE_DISK || t == SORT_SPACE_TYPE_MEMORY);
    return t == SORT_SPACE_TYPE_DISK ? "Disk" : "Memory";
}
 
 
/*
 * Heap manipulation routines, per Knuth's Algorithm 5.2.3H.
 */
 
/*
 * Convert the existing unordered array of SortTuples to a bounded heap,
 * discarding all but the smallest "state->bound" tuples.
 *
 * When working with a bounded heap, we want to keep the largest entry
 * at the root (array entry zero), instead of the smallest as in the normal
 * sort case.  This allows us to discard the largest entry cheaply.
 * Therefore, we temporarily reverse the sort direction.
 */
static void
make_bounded_heap(Tuplesortstate *state)
{
    int         tupcount = state->memtupcount;
    int         i;
 
    Assert(state->status == TSS_INITIAL);
    Assert(state->bounded);
    Assert(tupcount >= state->bound);
    Assert(SERIAL(state));
 
    /* Reverse sort direction so largest entry will be at root */
    reversedirection(state);
 
    state->memtupcount = 0;     /* make the heap empty */
    for (i = 0; i < tupcount; i++)
    {
        if (state->memtupcount < state->bound)
        {
            /* Insert next tuple into heap */
            /* Must copy source tuple to avoid possible overwrite */
            SortTuple   stup = state->memtuples[i];
 
            tuplesort_heap_insert(state, &stup);
        }
        else
        {
            /*
             * The heap is full.  Replace the largest entry with the new
             * tuple, or just discard it, if it's larger than anything already
             * in the heap.
             */
            if (COMPARETUP(state, &state->memtuples[i], &state->memtuples[0]) <= 0)
            {
                free_sort_tuple(state, &state->memtuples[i]);
                CHECK_FOR_INTERRUPTS();
            }
            else
                tuplesort_heap_replace_top(state, &state->memtuples[i]);
        }
    }
 
    Assert(state->memtupcount == state->bound);
    state->status = TSS_BOUNDED;
}
 
/*
 * Convert the bounded heap to a properly-sorted array
 */
static void
sort_bounded_heap(Tuplesortstate *state)
{
    int         tupcount = state->memtupcount;
 
    Assert(state->status == TSS_BOUNDED);
    Assert(state->bounded);
    Assert(tupcount == state->bound);
    Assert(SERIAL(state));
 
    /*
     * We can unheapify in place because each delete-top call will remove the
     * largest entry, which we can promptly store in the newly freed slot at
     * the end.  Once we're down to a single-entry heap, we're done.
     */
    while (state->memtupcount > 1)
    {
        SortTuple   stup = state->memtuples[0];
 
        /* this sifts-up the next-largest entry and decreases memtupcount */
        tuplesort_heap_delete_top(state);
        state->memtuples[state->memtupcount] = stup;
    }
    state->memtupcount = tupcount;
 
    /*
     * Reverse sort direction back to the original state.  This is not
     * actually necessary but seems like a good idea for tidiness.
     */
    reversedirection(state);
 
    state->status = TSS_SORTEDINMEM;
    state->boundUsed = true;
}
 
 
/* radix sort routines */
 
/*
 * Retrieve byte from datum, indexed by 'level': 0 for MSB, 7 for LSB
 */
static inline uint8
current_byte(Datum key, int level)
{
    int         shift = (sizeof(Datum) - 1 - level) * BITS_PER_BYTE;
 
    return (key >> shift) & 0xFF;
}
 
/*
 * Normalize datum such that unsigned comparison is order-preserving,
 * taking ASC/DESC into account as well.
 */
static inline Datum
normalize_datum(Datum orig, SortSupport ssup)
{
    Datum       norm_datum1;
 
    if (ssup->comparator == ssup_datum_signed_cmp)
    {
        norm_datum1 = orig + ((uint64) PG_INT64_MAX) + 1;
    }
    else if (ssup->comparator == ssup_datum_int32_cmp)
    {
        /*
         * First truncate to uint32. Technically, we don't need to do this,
         * but it forces the upper half of the datum to be zero regardless of
         * sign.
         */
        uint32      u32 = DatumGetUInt32(orig) + ((uint32) PG_INT32_MAX) + 1;
 
        norm_datum1 = UInt32GetDatum(u32);
    }
    else
    {
        Assert(ssup->comparator == ssup_datum_unsigned_cmp);
        norm_datum1 = orig;
    }
 
    if (ssup->ssup_reverse)
        norm_datum1 = ~norm_datum1;
 
    return norm_datum1;
}
 
/*
 * radix_sort_recursive
 *
 * Radix sort by (pass-by-value) datum1, diverting to qsort_tuple()
 * for tiebreaks.
 *
 * This is a modification of
 * ska_byte_sort() from https://github.com/skarupke/ska_sort
 * The original copyright notice follows:
 *
 * Copyright Malte Skarupke 2016.
 * Distributed under the Boost Software License, Version 1.0.
 *
 * Boost Software License - Version 1.0 - August 17th, 2003
 *
 * Permission is hereby granted, free of charge, to any person or organization
 * obtaining a copy of the software and accompanying documentation covered by
 * this license (the "Software") to use, reproduce, display, distribute,
 * execute, and transmit the Software, and to prepare derivative works of the
 * Software, and to permit third-parties to whom the Software is furnished to
 * do so, all subject to the following:
 *
 * The copyright notices in the Software and this entire statement, including
 * the above license grant, this restriction and the following disclaimer,
 * must be included in all copies of the Software, in whole or in part, and
 * all derivative works of the Software, unless such copies or derivative
 * works are solely in the form of machine-executable object code generated by
 * a source language processor.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
 * SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
 * FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
 * ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 * DEALINGS IN THE SOFTWARE.
 */
static void
radix_sort_recursive(SortTuple *begin, size_t n_elems, int level, Tuplesortstate *state)
{
    RadixSortInfo partitions[256] = {0};
    uint8       remaining_partitions[256];
    size_t      total = 0;
    int         num_partitions = 0;
    int         num_remaining;
    SortSupport ssup = &state->base.sortKeys[0];
    size_t      start_offset = 0;
    SortTuple  *partition_begin = begin;
 
    /* count number of occurrences of each byte */
    for (SortTuple *st = begin; st < begin + n_elems; st++)
    {
        uint8       this_partition;
 
        /* extract the byte for this level from the normalized datum */
        this_partition = current_byte(normalize_datum(st->datum1, ssup),
                                      level);
 
        /* save it for the permutation step */
        st->curbyte = this_partition;
 
        partitions[this_partition].count++;
 
        CHECK_FOR_INTERRUPTS();
    }
 
    /* compute partition offsets */
    for (int i = 0; i < 256; i++)
    {
        size_t      count = partitions[i].count;
 
        if (count != 0)
        {
            partitions[i].offset = total;
            total += count;
            remaining_partitions[num_partitions] = i;
            num_partitions++;
        }
        partitions[i].next_offset = total;
    }
 
    /*
     * Swap tuples to correct partition.
     *
     * In traditional American flag sort, a swap sends the current element to
     * the correct partition, but the array pointer only advances if the
     * partner of the swap happens to be an element that belongs in the
     * current partition. That only requires one pass through the array, but
     * the disadvantage is we don't know if the pointer can advance until the
     * swap completes. Here lies the most interesting innovation from the
     * upstream ska_byte_sort: After initiating the swap, we immediately
     * proceed to the next element. This makes better use of CPU pipelining,
     * but also means that we will often need multiple iterations of this
     * loop. ska_byte_sort() maintains a separate list of which partitions
     * haven't finished, which is updated every loop iteration. Here we simply
     * check each partition during every iteration.
     *
     * If we started with a single partition, there is nothing to do. If a
     * previous loop iteration results in only one partition that hasn't been
     * counted as sorted, we know it's actually sorted and can exit the loop.
     */
    num_remaining = num_partitions;
    while (num_remaining > 1)
    {
        /* start the count over */
        num_remaining = num_partitions;
 
        for (int i = 0; i < num_partitions; i++)
        {
            uint8       idx = remaining_partitions[i];
 
            for (SortTuple *st = begin + partitions[idx].offset;
                 st < begin + partitions[idx].next_offset;
                 st++)
            {
                size_t      offset = partitions[st->curbyte].offset++;
                SortTuple   tmp;
 
                /* swap current tuple with destination position */
                Assert(offset < n_elems);
                tmp = *st;
                *st = begin[offset];
                begin[offset] = tmp;
 
                CHECK_FOR_INTERRUPTS();
            };
 
            /* Is this partition sorted? */
            if (partitions[idx].offset == partitions[idx].next_offset)
                num_remaining--;
        }
    }
 
    /* recurse */
    for (uint8 *rp = remaining_partitions;
         rp < remaining_partitions + num_partitions;
         rp++)
    {
        size_t      end_offset = partitions[*rp].next_offset;
        SortTuple  *partition_end = begin + end_offset;
        size_t      num_elements = end_offset - start_offset;
 
        if (num_elements > 1)
        {
            if (level < sizeof(Datum) - 1)
            {
                if (num_elements < QSORT_THRESHOLD)
                {
                    qsort_tuple(partition_begin,
                                num_elements,
                                state->base.comparetup,
                                state);
                }
                else
                {
                    radix_sort_recursive(partition_begin,
                                         num_elements,
                                         level + 1,
                                         state);
                }
            }
            else if (state->base.onlyKey == NULL)
            {
                /*
                 * We've finished radix sort on all bytes of the pass-by-value
                 * datum (possibly abbreviated), now sort using the tiebreak
                 * comparator.
                 */
                qsort_tuple(partition_begin,
                            num_elements,
                            state->base.comparetup_tiebreak,
                            state);
            }
        }
 
        start_offset = end_offset;
        partition_begin = partition_end;
    }
}
 
/*
 * Entry point for radix_sort_recursive
 *
 * Partition tuples by isnull1, then sort both partitions, using
 * radix sort on the NOT NULL partition if it's large enough.
 */
static void
radix_sort_tuple(SortTuple *data, size_t n, Tuplesortstate *state)
{
    bool        nulls_first = state->base.sortKeys[0].ssup_nulls_first;
    SortTuple  *null_start;
    SortTuple  *not_null_start;
    size_t      d1 = 0,
                d2,
                null_count,
                not_null_count;
 
    /*
     * Find the first NOT NULL if NULLS FIRST, or first NULL if NULLS LAST.
     * This also serves as a quick check for the common case where all tuples
     * are NOT NULL in the first sort key.
     */
    while (d1 < n && data[d1].isnull1 == nulls_first)
    {
        d1++;
        CHECK_FOR_INTERRUPTS();
    }
 
    /*
     * If we have more than one tuple left after the quick check, partition
     * the remainder using branchless cyclic permutation, based on
     * https://orlp.net/blog/branchless-lomuto-partitioning/
     */
    Assert(n > 0);
    if (d1 < n - 1)
    {
        size_t      i = d1,
                    j = d1;
        SortTuple   tmp = data[d1]; /* create gap at front */
 
        while (j < n - 1)
        {
            /* gap is at j, move i's element to gap */
            data[j] = data[i];
            /* advance j to the first unknown element */
            j += 1;
            /* move the first unknown element back to i */
            data[i] = data[j];
            /* advance i if this element belongs in the left partition */
            i += (data[i].isnull1 == nulls_first);
 
            CHECK_FOR_INTERRUPTS();
        }
 
        /* place gap between left and right partitions */
        data[j] = data[i];
        /* restore the saved element */
        data[i] = tmp;
        /* assign it to the correct partition */
        i += (data[i].isnull1 == nulls_first);
 
        /* d1 is now the number of elements in the left partition */
        d1 = i;
    }
 
    d2 = n - d1;
 
    /* set pointers and counts for each partition */
    if (nulls_first)
    {
        null_start = data;
        null_count = d1;
        not_null_start = data + d1;
        not_null_count = d2;
    }
    else
    {
        not_null_start = data;
        not_null_count = d1;
        null_start = data + d1;
        null_count = d2;
    }
 
    for (SortTuple *st = null_start;
         st < null_start + null_count;
         st++)
        Assert(st->isnull1 == true);
    for (SortTuple *st = not_null_start;
         st < not_null_start + not_null_count;
         st++)
        Assert(st->isnull1 == false);
 
    /*
     * Sort the NULL partition using tiebreak comparator, if necessary.
     */
    if (state->base.onlyKey == NULL && null_count > 1)
    {
        qsort_tuple(null_start,
                    null_count,
                    state->base.comparetup_tiebreak,
                    state);
    }
 
    /*
     * Sort the NOT NULL partition, using radix sort if large enough,
     * otherwise fall back to quicksort.
     */
    if (not_null_count < QSORT_THRESHOLD)
    {
        qsort_tuple(not_null_start,
                    not_null_count,
                    state->base.comparetup,
                    state);
    }
    else
    {
        bool        presorted = true;
 
        for (SortTuple *st = not_null_start + 1;
             st < not_null_start + not_null_count;
             st++)
        {
            if (COMPARETUP(state, st - 1, st) > 0)
            {
                presorted = false;
                break;
            }
 
            CHECK_FOR_INTERRUPTS();
        }
 
        if (presorted)
            return;
        else
        {
            radix_sort_recursive(not_null_start,
                                 not_null_count,
                                 0,
                                 state);
        }
    }
}
 
/* Verify in-memory sort using standard comparator. */
static void
verify_memtuples_sorted(Tuplesortstate *state)
{
#ifdef USE_ASSERT_CHECKING
    for (SortTuple *st = state->memtuples + 1;
         st < state->memtuples + state->memtupcount;
         st++)
        Assert(COMPARETUP(state, st - 1, st) <= 0);
#endif
}
 
/*
 * Sort all memtuples using specialized routines.
 *
 * Quicksort or radix sort is used for small in-memory sorts,
 * and external sort runs.
 */
static void
tuplesort_sort_memtuples(Tuplesortstate *state)
{
    Assert(!LEADER(state));
 
    if (state->memtupcount > 1)
    {
        /*
         * Do we have the leading column's value or abbreviation in datum1?
         */
        if (state->base.haveDatum1 && state->base.sortKeys)
        {
            SortSupport ssup = &state->base.sortKeys[0];
 
            /* Does it compare as an integer? */
            if (state->memtupcount >= QSORT_THRESHOLD &&
                (ssup->comparator == ssup_datum_unsigned_cmp ||
                 ssup->comparator == ssup_datum_signed_cmp ||
                 ssup->comparator == ssup_datum_int32_cmp))
            {
                radix_sort_tuple(state->memtuples,
                                 state->memtupcount,
                                 state);
                verify_memtuples_sorted(state);
                return;
            }
        }
 
        /* Can we use the single-key sort function? */
        if (state->base.onlyKey != NULL)
        {
            qsort_ssup(state->memtuples, state->memtupcount,
                       state->base.onlyKey);
        }
        else
        {
            qsort_tuple(state->memtuples,
                        state->memtupcount,
                        state->base.comparetup,
                        state);
        }
    }
}
 
/*
 * Insert a new tuple into an empty or existing heap, maintaining the
 * heap invariant.  Caller is responsible for ensuring there's room.
 *
 * Note: For some callers, tuple points to a memtuples[] entry above the
 * end of the heap.  This is safe as long as it's not immediately adjacent
 * to the end of the heap (ie, in the [memtupcount] array entry) --- if it
 * is, it might get overwritten before being moved into the heap!
 */
static void
tuplesort_heap_insert(Tuplesortstate *state, SortTuple *tuple)
{
    SortTuple  *memtuples;
    int         j;
 
    memtuples = state->memtuples;
    Assert(state->memtupcount < state->memtupsize);
 
    CHECK_FOR_INTERRUPTS();
 
    /*
     * Sift-up the new entry, per Knuth 5.2.3 exercise 16. Note that Knuth is
     * using 1-based array indexes, not 0-based.
     */
    j = state->memtupcount++;
    while (j > 0)
    {
        int         i = (j - 1) >> 1;
 
        if (COMPARETUP(state, tuple, &memtuples[i]) >= 0)
            break;
        memtuples[j] = memtuples[i];
        j = i;
    }
    memtuples[j] = *tuple;
}
 
/*
 * Remove the tuple at state->memtuples[0] from the heap.  Decrement
 * memtupcount, and sift up to maintain the heap invariant.
 *
 * The caller has already free'd the tuple the top node points to,
 * if necessary.
 */
static void
tuplesort_heap_delete_top(Tuplesortstate *state)
{
    SortTuple  *memtuples = state->memtuples;
    SortTuple  *tuple;
 
    if (--state->memtupcount <= 0)
        return;
 
    /*
     * Remove the last tuple in the heap, and re-insert it, by replacing the
     * current top node with it.
     */
    tuple = &memtuples[state->memtupcount];
    tuplesort_heap_replace_top(state, tuple);
}
 
/*
 * Replace the tuple at state->memtuples[0] with a new tuple.  Sift up to
 * maintain the heap invariant.
 *
 * This corresponds to Knuth's "sift-up" algorithm (Algorithm 5.2.3H,
 * Heapsort, steps H3-H8).
 */
static void
tuplesort_heap_replace_top(Tuplesortstate *state, SortTuple *tuple)
{
    SortTuple  *memtuples = state->memtuples;
    unsigned int i,
                n;
 
    Assert(state->memtupcount >= 1);
 
    CHECK_FOR_INTERRUPTS();
 
    /*
     * state->memtupcount is "int", but we use "unsigned int" for i, j, n.
     * This prevents overflow in the "2 * i + 1" calculation, since at the top
     * of the loop we must have i < n <= INT_MAX <= UINT_MAX/2.
     */
    n = state->memtupcount;
    i = 0;                      /* i is where the "hole" is */
    for (;;)
    {
        unsigned int j = 2 * i + 1;
 
        if (j >= n)
            break;
        if (j + 1 < n &&
            COMPARETUP(state, &memtuples[j], &memtuples[j + 1]) > 0)
            j++;
        if (COMPARETUP(state, tuple, &memtuples[j]) <= 0)
            break;
        memtuples[i] = memtuples[j];
        i = j;
    }
    memtuples[i] = *tuple;
}
 
/*
 * Function to reverse the sort direction from its current state
 *
 * It is not safe to call this when performing hash tuplesorts
 */
static void
reversedirection(Tuplesortstate *state)
{
    SortSupport sortKey = state->base.sortKeys;
    int         nkey;
 
    for (nkey = 0; nkey < state->base.nKeys; nkey++, sortKey++)
    {
        sortKey->ssup_reverse = !sortKey->ssup_reverse;
        sortKey->ssup_nulls_first = !sortKey->ssup_nulls_first;
    }
}
 
 
/*
 * Tape interface routines
 */
 
static unsigned int
getlen(LogicalTape *tape, bool eofOK)
{
    unsigned int len;
 
    if (LogicalTapeRead(tape,
                        &len, sizeof(len)) != sizeof(len))
        elog(ERROR, "unexpected end of tape");
    if (len == 0 && !eofOK)
        elog(ERROR, "unexpected end of data");
    return len;
}
 
static void
markrunend(LogicalTape *tape)
{
    unsigned int len = 0;
 
    LogicalTapeWrite(tape, &len, sizeof(len));
}
 
/*
 * Get memory for tuple from within READTUP() routine.
 *
 * We use next free slot from the slab allocator, or palloc() if the tuple
 * is too large for that.
 */
void *
tuplesort_readtup_alloc(Tuplesortstate *state, Size tuplen)
{
    SlabSlot   *buf;
 
    /*
     * We pre-allocate enough slots in the slab arena that we should never run
     * out.
     */
    Assert(state->slabFreeHead);
 
    if (tuplen > SLAB_SLOT_SIZE || !state->slabFreeHead)
        return MemoryContextAlloc(state->base.sortcontext, tuplen);
    else
    {
        buf = state->slabFreeHead;
        /* Reuse this slot */
        state->slabFreeHead = buf->nextfree;
 
        return buf;
    }
}
 
 
/*
 * Parallel sort routines
 */
 
/*
 * tuplesort_estimate_shared - estimate required shared memory allocation
 *
 * nWorkers is an estimate of the number of workers (it's the number that
 * will be requested).
 */
Size
tuplesort_estimate_shared(int nWorkers)
{
    Size        tapesSize;
 
    Assert(nWorkers > 0);
 
    /* Make sure that BufFile shared state is MAXALIGN'd */
    tapesSize = mul_size(sizeof(TapeShare), nWorkers);
    tapesSize = MAXALIGN(add_size(tapesSize, offsetof(Sharedsort, tapes)));
 
    return tapesSize;
}
 
/*
 * tuplesort_initialize_shared - initialize shared tuplesort state
 *
 * Must be called from leader process before workers are launched, to
 * establish state needed up-front for worker tuplesortstates.  nWorkers
 * should match the argument passed to tuplesort_estimate_shared().
 */
void
tuplesort_initialize_shared(Sharedsort *shared, int nWorkers, dsm_segment *seg)
{
    int         i;
 
    Assert(nWorkers > 0);
 
    SpinLockInit(&shared->mutex);
    shared->currentWorker = 0;
    shared->workersFinished = 0;
    SharedFileSetInit(&shared->fileset, seg);
    shared->nTapes = nWorkers;
    for (i = 0; i < nWorkers; i++)
    {
        shared->tapes[i].firstblocknumber = 0L;
    }
}
 
/*
 * tuplesort_attach_shared - attach to shared tuplesort state
 *
 * Must be called by all worker processes.
 */
void
tuplesort_attach_shared(Sharedsort *shared, dsm_segment *seg)
{
    /* Attach to SharedFileSet */
    SharedFileSetAttach(&shared->fileset, seg);
}
 
/*
 * worker_get_identifier - Assign and return ordinal identifier for worker
 *
 * The order in which these are assigned is not well defined, and should not
 * matter; worker numbers across parallel sort participants need only be
 * distinct and gapless.  logtape.c requires this.
 *
 * Note that the identifiers assigned from here have no relation to
 * ParallelWorkerNumber number, to avoid making any assumption about
 * caller's requirements.  However, we do follow the ParallelWorkerNumber
 * convention of representing a non-worker with worker number -1.  This
 * includes the leader, as well as serial Tuplesort processes.
 */
static int
worker_get_identifier(Tuplesortstate *state)
{
    Sharedsort *shared = state->shared;
    int         worker;
 
    Assert(WORKER(state));
 
    SpinLockAcquire(&shared->mutex);
    worker = shared->currentWorker++;
    SpinLockRelease(&shared->mutex);
 
    return worker;
}
 
/*
 * worker_freeze_result_tape - freeze worker's result tape for leader
 *
 * This is called by workers just after the result tape has been determined,
 * instead of calling LogicalTapeFreeze() directly.  They do so because
 * workers require a few additional steps over similar serial
 * TSS_SORTEDONTAPE external sort cases, which also happen here.  The extra
 * steps are around freeing now unneeded resources, and representing to
 * leader that worker's input run is available for its merge.
 *
 * There should only be one final output run for each worker, which consists
 * of all tuples that were originally input into worker.
 */
static void
worker_freeze_result_tape(Tuplesortstate *state)
{
    Sharedsort *shared = state->shared;
    TapeShare   output;
 
    Assert(WORKER(state));
    Assert(state->result_tape != NULL);
    Assert(state->memtupcount == 0);
 
    /*
     * Free most remaining memory, in case caller is sensitive to our holding
     * on to it.  memtuples may not be a tiny merge heap at this point.
     */
    pfree(state->memtuples);
    /* Be tidy */
    state->memtuples = NULL;
    state->memtupsize = 0;
 
    /*
     * Parallel worker requires result tape metadata, which is to be stored in
     * shared memory for leader
     */
    LogicalTapeFreeze(state->result_tape, &output);
 
    /* Store properties of output tape, and update finished worker count */
    SpinLockAcquire(&shared->mutex);
    shared->tapes[state->worker] = output;
    shared->workersFinished++;
    SpinLockRelease(&shared->mutex);
}
 
/*
 * worker_nomergeruns - dump memtuples in worker, without merging
 *
 * This called as an alternative to mergeruns() with a worker when no
 * merging is required.
 */
static void
worker_nomergeruns(Tuplesortstate *state)
{
    Assert(WORKER(state));
    Assert(state->result_tape == NULL);
    Assert(state->nOutputRuns == 1);
 
    state->result_tape = state->destTape;
    worker_freeze_result_tape(state);
}
 
/*
 * leader_takeover_tapes - create tapeset for leader from worker tapes
 *
 * So far, leader Tuplesortstate has performed no actual sorting.  By now, all
 * sorting has occurred in workers, all of which must have already returned
 * from tuplesort_performsort().
 *
 * When this returns, leader process is left in a state that is virtually
 * indistinguishable from it having generated runs as a serial external sort
 * might have.
 */
static void
leader_takeover_tapes(Tuplesortstate *state)
{
    Sharedsort *shared = state->shared;
    int         nParticipants = state->nParticipants;
    int         workersFinished;
    int         j;
 
    Assert(LEADER(state));
    Assert(nParticipants >= 1);
 
    SpinLockAcquire(&shared->mutex);
    workersFinished = shared->workersFinished;
    SpinLockRelease(&shared->mutex);
 
    if (nParticipants != workersFinished)
        elog(ERROR, "cannot take over tapes before all workers finish");
 
    /*
     * Create the tapeset from worker tapes, including a leader-owned tape at
     * the end.  Parallel workers are far more expensive than logical tapes,
     * so the number of tapes allocated here should never be excessive.
     */
    inittapestate(state, nParticipants);
    state->tapeset = LogicalTapeSetCreate(false, &shared->fileset, -1);
 
    /*
     * Set currentRun to reflect the number of runs we will merge (it's not
     * used for anything, this is just pro forma)
     */
    state->currentRun = nParticipants;
 
    /*
     * Initialize the state to look the same as after building the initial
     * runs.
     *
     * There will always be exactly 1 run per worker, and exactly one input
     * tape per run, because workers always output exactly 1 run, even when
     * there were no input tuples for workers to sort.
     */
    state->inputTapes = NULL;
    state->nInputTapes = 0;
    state->nInputRuns = 0;
 
    state->outputTapes = palloc0(nParticipants * sizeof(LogicalTape *));
    state->nOutputTapes = nParticipants;
    state->nOutputRuns = nParticipants;
 
    for (j = 0; j < nParticipants; j++)
    {
        state->outputTapes[j] = LogicalTapeImport(state->tapeset, j, &shared->tapes[j]);
    }
 
    state->status = TSS_BUILDRUNS;
}
 
/*
 * Convenience routine to free a tuple previously loaded into sort memory
 */
static void
free_sort_tuple(Tuplesortstate *state, SortTuple *stup)
{
    if (stup->tuple)
    {
        FREEMEM(state, GetMemoryChunkSpace(stup->tuple));
        pfree(stup->tuple);
        stup->tuple = NULL;
    }
}
 
int
ssup_datum_unsigned_cmp(Datum x, Datum y, SortSupport ssup)
{
    if (x < y)
        return -1;
    else if (x > y)
        return 1;
    else
        return 0;
}
 
int
ssup_datum_signed_cmp(Datum x, Datum y, SortSupport ssup)
{
    int64       xx = DatumGetInt64(x);
    int64       yy = DatumGetInt64(y);
 
    if (xx < yy)
        return -1;
    else if (xx > yy)
        return 1;
    else
        return 0;
}
 
int
ssup_datum_int32_cmp(Datum x, Datum y, SortSupport ssup)
{
    int32       xx = DatumGetInt32(x);
    int32       yy = DatumGetInt32(y);
 
    if (xx < yy)
        return -1;
    else if (xx > yy)
        return 1;
    else
        return 0;
}