Locking tuples
--------------

Locking tuples is not as easy as locking tables or other database objects.
The problem is that transactions might want to lock large numbers of tuples at
any one time, so it's not possible to keep the locks objects in shared memory.
To work around this limitation, we use a two-level mechanism.  The first level
is implemented by storing locking information in the tuple header: a tuple is
marked as locked by setting the current transaction's XID as its XMAX, and
setting additional infomask bits to distinguish this case from the more normal
case of having deleted the tuple.  When multiple transactions concurrently
lock a tuple, a MultiXact is used; see below.  This mechanism can accommodate
arbitrarily large numbers of tuples being locked simultaneously.

When it is necessary to wait for a tuple-level lock to be released, the basic
delay is provided by XactLockTableWait or MultiXactIdWait on the contents of
the tuple's XMAX.  However, that mechanism will release all waiters
concurrently, so there would be a race condition as to which waiter gets the
tuple, potentially leading to indefinite starvation of some waiters.  The
possibility of share-locking makes the problem much worse --- a steady stream
of share-lockers can easily block an exclusive locker forever.  To provide
more reliable semantics about who gets a tuple-level lock first, we use the
standard lock manager, which implements the second level mentioned above.  The
protocol for waiting for a tuple-level lock is really

     LockTuple()
     XactLockTableWait()
     mark tuple as locked by me
     UnlockTuple()

When there are multiple waiters, arbitration of who is to get the lock next
is provided by LockTuple().  However, at most one tuple-level lock will
be held or awaited per backend at any time, so we don't risk overflow
of the lock table.  Note that incoming share-lockers are required to
do LockTuple as well, if there is any conflict, to ensure that they don't
starve out waiting exclusive-lockers.  However, if there is not any active
conflict for a tuple, we don't incur any extra overhead.

We make an exception to the above rule for those lockers that already hold
some lock on a tuple and attempt to acquire a stronger one on it.  In that
case, we skip the LockTuple() call even when there are conflicts, provided
that the target tuple is being locked, updated or deleted by multiple sessions
concurrently.  Failing to skip the lock would risk a deadlock, e.g., between a
session that was first to record its weaker lock in the tuple header and would
be waiting on the LockTuple() call to upgrade to the stronger lock level, and
another session that has already done LockTuple() and is waiting for the first
session transaction to release its tuple header-level lock.

We provide four levels of tuple locking strength: SELECT FOR UPDATE obtains an
exclusive lock which prevents any kind of modification of the tuple. This is
the lock level that is implicitly taken by DELETE operations, and also by
UPDATE operations if they modify any of the tuple's key fields. SELECT FOR NO
KEY UPDATE likewise obtains an exclusive lock, but only prevents tuple removal
and modifications which might alter the tuple's key. This is the lock that is
implicitly taken by UPDATE operations which leave all key fields unchanged.
SELECT FOR SHARE obtains a shared lock which prevents any kind of tuple
modification. Finally, SELECT FOR KEY SHARE obtains a shared lock which only
prevents tuple removal and modifications of key fields. This lock level is
just strong enough to implement RI checks, i.e. it ensures that tuples do not
go away from under a check, without blocking transactions that want to update
the tuple without changing its key.

The conflict table is:

                  UPDATE       NO KEY UPDATE    SHARE        KEY SHARE
UPDATE           conflict        conflict      conflict      conflict
NO KEY UPDATE    conflict        conflict      conflict
SHARE            conflict        conflict
KEY SHARE        conflict

When there is a single locker in a tuple, we can just store the locking info
in the tuple itself.  We do this by storing the locker's Xid in XMAX, and
setting infomask bits specifying the locking strength.  See "Infomask Bits"
below for details on the bit patterns we use.

MultiXacts
----------

A tuple header provides very limited space for storing information about tuple
locking and updates: there is room only for a single Xid and a small number of
infomask bits.  Whenever we need to store more than one lock, we replace the
first locker's Xid with a new MultiXactId.  Each MultiXact provides extended
locking data; it comprises an array of Xids plus some flags bits for each one.
The flags are currently used to store the locking strength of each member
transaction.  (The flags also distinguish a pure locker from an updater.)

In earlier PostgreSQL releases, a MultiXact always meant that the tuple was
locked in shared mode by multiple transactions.  This is no longer the case; a
MultiXact may contain an update or delete Xid.  (Keep in mind that tuple locks
in a transaction do not conflict with other tuple locks in the same
transaction, so it's possible to have otherwise conflicting locks in a
MultiXact if they belong to the same transaction).

Note that each lock is attributed to the subtransaction that acquires it.
This means that a subtransaction that aborts is seen as though it releases the
locks it acquired; concurrent transactions can then proceed without having to
wait for the main transaction to finish.  It also means that a subtransaction
can upgrade to a stronger lock level than an earlier transaction had, and if
the subxact aborts, the earlier, weaker lock is kept.

The possibility of having an update within a MultiXact means that they must
persist across crashes and restarts: a future reader of the tuple needs to
figure out whether the update committed or aborted.  So we have a requirement
that pg_multixact needs to retain pages of its data until we're certain that
the MultiXacts in them are no longer of interest.

VACUUM is in charge of removing old MultiXacts at the time of tuple freezing.
The lower bound used by vacuum (that is, the value below which all multixacts
are removed) is stored as pg_class.relminmxid for each table; the minimum of
all such values is stored in pg_database.datminmxid.  The minimum across
all databases, in turn, is recorded in checkpoint records, and CHECKPOINT
removes pg_multixact/ segments older than that value once the checkpoint
record has been flushed.

Infomask Bits
-------------

The following infomask bits are applicable:

- HEAP_XMAX_INVALID
  Any tuple with this bit set does not have a valid value stored in XMAX.

- HEAP_XMAX_IS_MULTI
  This bit is set if the tuple's Xmax is a MultiXactId (as opposed to a
  regular TransactionId).

- HEAP_XMAX_LOCK_ONLY
  This bit is set when the XMAX is a locker only; that is, if it's a
  multixact, it does not contain an update among its members.  It's set when
  the XMAX is a plain Xid that locked the tuple, as well.

- HEAP_XMAX_KEYSHR_LOCK
- HEAP_XMAX_SHR_LOCK
- HEAP_XMAX_EXCL_LOCK
  These bits indicate the strength of the lock acquired; they are useful when
  the XMAX is not a MultiXactId.  If it's a multi, the info is to be found in
  the member flags.  If HEAP_XMAX_IS_MULTI is not set and HEAP_XMAX_LOCK_ONLY
  is set, then one of these *must* be set as well.

  Note that HEAP_XMAX_EXCL_LOCK does not distinguish FOR NO KEY UPDATE from
  FOR UPDATE; this is implemented by the HEAP_KEYS_UPDATED bit.

- HEAP_KEYS_UPDATED
  This bit lives in t_infomask2.  If set, indicates that the operation(s) done
  by the XMAX compromise the tuple key, such as a SELECT FOR UPDATE, an UPDATE
  that modifies the columns of the key, or a DELETE.  It's set regardless of
  whether the XMAX is a TransactionId or a MultiXactId.

We currently never set the HEAP_XMAX_COMMITTED when the HEAP_XMAX_IS_MULTI bit
is set.

Locking to write inplace-updated tables
---------------------------------------

If IsInplaceUpdateRelation() returns true for a table, the table is a system
catalog that receives systable_inplace_update_begin() calls.  Preparing a
heap_update() of these tables follows additional locking rules, to ensure we
don't lose the effects of an inplace update.  In particular, consider a moment
when a backend has fetched the old tuple to modify, not yet having called
heap_update().  Another backend's inplace update starting then can't conclude
until the heap_update() places its new tuple in a buffer.  We enforce that
using locktags as follows.  While DDL code is the main audience, the executor
follows these rules to make e.g. "MERGE INTO pg_class" safer.  Locking rules
are per-catalog:

  pg_class systable_inplace_update_begin() callers: before the call, acquire a
  lock on the relation in mode ShareUpdateExclusiveLock or stricter.  If the
  update targets a row of RELKIND_INDEX (but not RELKIND_PARTITIONED_INDEX),
  that lock must be on the table.  Locking the index rel is not necessary.
  (This allows VACUUM to overwrite per-index pg_class while holding a lock on
  the table alone.) systable_inplace_update_begin() acquires and releases
  LOCKTAG_TUPLE in InplaceUpdateTupleLock, an alias for ExclusiveLock, on each
  tuple it overwrites.

  pg_class heap_update() callers: before copying the tuple to modify, take a
  lock on the tuple, a ShareUpdateExclusiveLock on the relation, or a
  ShareRowExclusiveLock or stricter on the relation.

  SearchSysCacheLocked1() is one convenient way to acquire the tuple lock.
  Most heap_update() callers already hold a suitable lock on the relation for
  other reasons and can skip the tuple lock.  If you do acquire the tuple
  lock, release it immediately after the update.


  pg_database: before copying the tuple to modify, all updaters of pg_database
  rows acquire LOCKTAG_TUPLE.  (Few updaters acquire LOCKTAG_OBJECT on the
  database OID, so it wasn't worth extending that as a second option.)

Ideally, DDL might want to perform permissions checks before LockTuple(), as
we do with RangeVarGetRelidExtended() callbacks.  We typically don't bother.
LOCKTAG_TUPLE acquirers release it after each row, so the potential
inconvenience is lower.

Reading inplace-updated columns
-------------------------------

Inplace updates create an exception to the rule that tuple data won't change
under a reader holding a pin.  A reader of a heap_fetch() result tuple may
witness a torn read.  Current inplace-updated fields are aligned and are no
wider than four bytes, and current readers don't need consistency across
fields.  Hence, they get by with just fetching each field once.  XXX such a
caller may also read a value that has not reached WAL; see
systable_inplace_update_finish().
