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Update README-SSI. Add a section to describe the "dangerous structure" that

Browse Source 
SSI is based on, as well as the optimizations about relative commit times
and read-only transactions. Plus a bunch of other misc fixes and
improvements.

Dan Ports
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Heikki Linnakangas 2011-06-16 21:12:56 +03:00
parent f3008c31f0
commit 78475b0eca
1 changed files with 116 additions and 86 deletions
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@ -51,13 +51,13 @@ if a transaction can be shown to always do the right thing when it is
run alone (before or after any other transaction), it will always do
the right thing in any mix of concurrent serializable transactions.
Where conflicts with other transactions would result in an
inconsistent state within the database, or an inconsistent view of
inconsistent state within the database or an inconsistent view of
the data, a serializable transaction will block or roll back to
prevent the anomaly. The SQL standard provides a specific SQLSTATE
for errors generated when a transaction rolls back for this reason,
so that transactions can be retried automatically.
Before version 9.1 PostgreSQL did not support a full serializable
Before version 9.1, PostgreSQL did not support a full serializable
isolation level. A request for serializable transaction isolation
actually provided snapshot isolation. This has well known anomalies
which can allow data corruption or inconsistent views of the data
@ -77,7 +77,7 @@ Serializable Isolation Implementation Strategies
Techniques for implementing full serializable isolation have been
published and in use in many database products for decades. The
primary technique which has been used is Strict 2 Phase Locking
primary technique which has been used is Strict Two-Phase Locking
(S2PL), which operates by blocking writes against data which has been
read by concurrent transactions and blocking any access (read or
write) against data which has been written by concurrent
@ -114,52 +114,88 @@ the serializable transaction isolation level, the results are
required to be consistent with some serial (one-at-a-time) execution
of the transactions [1]. How is that order determined in each?
S2PL locks rows used by the transaction in a way which blocks
conflicting access, so that at the moment of a successful commit it
is certain that no conflicting access has occurred. Some transactions
may have blocked, essentially being partially serialized with the
committing transaction, to allow this. Some transactions may have
been rolled back, due to cycles in the blocking. But with S2PL,
transactions can always be viewed as having occurred serially, in the
order of successful commit.
In S2PL, each transaction locks any data it accesses. It holds the
locks until committing, preventing other transactions from making
conflicting accesses to the same data in the interim. Some
transactions may have to be rolled back to prevent deadlock. But
successful transactions can always be viewed as having occurred
sequentially, in the order they committed.
With snapshot isolation, reads never block writes, nor vice versa, so
there is much less actual serialization. The order in which
transactions appear to have executed is determined by something more
subtle than in S2PL: read/write dependencies. If a transaction
attempts to read data which is not visible to it because the
transaction which wrote it (or will later write it) is concurrent
(one of them was running when the other acquired its snapshot), then
the reading transaction appears to have executed first, regardless of
the actual sequence of transaction starts or commits (since it sees a
database state prior to that in which the other transaction leaves
it). If one transaction has both rw-dependencies in (meaning that a
concurrent transaction attempts to read data it writes) and out
(meaning it attempts to read data a concurrent transaction writes),
and a couple other conditions are met, there can appear to be a cycle
in execution order of the transactions. This is when the anomalies
occur.
more concurrency is possible. The order in which transactions appear
to have executed is determined by something more subtle than in S2PL:
read/write dependencies. If a transaction reads data, it appears to
execute after the transaction that wrote the data it is reading.
Similarly, if it updates data, it appears to execute after the
transaction that wrote the previous version. These dependencies, which
we call "wr-dependencies" and "ww-dependencies", are consistent with
the commit order, because the first transaction must have committed
before the second starts. However, there can also be dependencies
between two *concurrent* transactions, i.e. where one was running when
the other acquired its snapshot. These "rw-conflicts" occur when one
transaction attempts to read data which is not visible to it because
the transaction which wrote it (or will later write it) is
concurrent. The reading transaction appears to have executed first,
regardless of the actual sequence of transaction starts or commits,
because it sees a database state prior to that in which the other
transaction leaves it.
SSI works by watching for the conditions mentioned above, and rolling
back a transaction when needed to prevent any anomaly. The apparent
order of execution will always be consistent with any actual
serialization (i.e., a transaction which run by itself can always be
considered to have run after any transactions committed before it
started and before any transacton which starts after it commits); but
among concurrent transactions it will appear that the transaction on
the read side of a rw-dependency executed before the transaction on
the write side.
Anomalies occur when a cycle is created in the graph of dependencies:
when a dependency or series of dependencies causes transaction A to
appear to have executed before transaction B, but another series of
dependencies causes B to appear before A. If that's the case, then
the results can't be consistent with any serial execution of the
transactions.
SSI Algorithm
-------------
Serializable transaction in PostgreSQL are implemented using
Serializable Snapshot Isolation (SSI), based on the work of Cahill
et al. Fundamentally, this allows snapshot isolation to run as it
has, while monitoring for conditions which could create a serialization
anomaly.
SSI is based on the observation [2] that each snapshot isolation
anomaly corresponds to a cycle that contains a "dangerous structure"
of two adjacent rw-conflict edges:
Tin ------> Tpivot ------> Tout
rw rw
SSI works by watching for this dangerous structure, and rolling
back a transaction when needed to prevent any anomaly. This means it
only needs to track rw-conflicts between concurrent transactions, not
wr- and ww-dependencies. It also means there is a risk of false
positives, because not every dangerous structure corresponds to an
actual serialization failure.
The PostgreSQL implementation uses two additional optimizations:
* Tout must commit before any other transaction in the cycle
(see proof of Theorem 2.1 of [2]). We only roll back a transaction
if Tout commits before Tpivot and Tin.
* if Tin is read-only, there can only be an anomaly if Tout committed
before Tin takes its snapshot. This optimization is an original
one. Proof:
- Because there is a cycle, there must be some transaction T0 that
precedes Tin in the serial order. (T0 might be the same as Tout).
- The dependency between T0 and Tin can't be a rw-conflict,
because Tin was read-only, so it must be a wr-dependency.
Those can only occur if T0 committed before Tin started.
- Because Tout must commit before any other transaction in the
cycle, it must commit before T0 commits -- and thus before Tin
starts.
PostgreSQL Implementation
-------------------------
The implementation of serializable transactions for PostgreSQL is
accomplished through Serializable Snapshot Isolation (SSI), based on
the work of Cahill, et al. Fundamentally, this allows snapshot
isolation to run as it has, while monitoring for conditions which
could create a serialization anomaly.
* Since this technique is based on Snapshot Isolation (SI), those
areas in PostgreSQL which don't use SI can't be brought under SSI.
This includes system tables, temporary tables, sequences, hint bit
@ -180,7 +216,7 @@ lock or to use SELECT FOR SHARE or SELECT FOR UPDATE.
* Those who want to continue to use snapshot isolation without
the additional protections of SSI (and the associated costs of
enforcing those protections), can use the REPEATABLE READ transaction
isolation level. This level will retain its legacy behavior, which
isolation level. This level retains its legacy behavior, which
is identical to the old SERIALIZABLE implementation and fully
consistent with the standard's requirements for the REPEATABLE READ
transaction isolation level.
@ -236,7 +272,7 @@ in PostgreSQL, but tailored to the needs of SIREAD predicate locking,
are used. These refer to physical objects actually accessed in the
course of executing the query, to model the predicates through
inference. Anyone interested in this subject should review the
Hellerstein, Stonebraker and Hamilton paper[2], along with the
Hellerstein, Stonebraker and Hamilton paper [3], along with the
locking papers referenced from that and the Cahill papers.
Because the SIREAD locks don't block, traditional locking techniques
@ -273,6 +309,15 @@ transaction already holds a write lock on any tuple representing the
row, since a rw-dependency would also create a ww-dependency which
has more aggressive enforcement and will thus prevent any anomaly.
* Modifying a heap tuple creates a rw-conflict with any transaction
that holds a SIREAD lock on that tuple, or on the page or relation
that contains it.
* Inserting a new tuple creates a rw-conflict with any transaction
holding a SIREAD lock on the entire relation. It doesn't conflict with
page-level locks, because page-level locks are only used to aggregate
tuple locks. Unlike index page locks, they don't lock "gaps" on the page.
Index AM implementations
------------------------
@ -296,13 +341,13 @@ need not generate a conflict, although an update which "moves" a row
into the scan must generate a conflict. While correctness allows
false positives, they should be minimized for performance reasons.
Several optimizations are possible:
Several optimizations are possible, though not all implemented yet:
* An index scan which is just finding the right position for an
index insertion or deletion need not acquire a predicate lock.
index insertion or deletion needs not acquire a predicate lock.
* An index scan which is comparing for equality on the entire key
for a unique index need not acquire a predicate lock as long as a key
for a unique index needs not acquire a predicate lock as long as a key
is found corresponding to a visible tuple which has not been modified
by another transaction -- there are no "between or around" gaps to
cover.
@ -317,10 +362,10 @@ x = 1 AND x = 2), then no predicate lock is needed.
Other index AM implementation considerations:
* If a btree search discovers that no root page has yet been
created, a predicate lock on the index relation is required;
otherwise btree searches must get to the leaf level to determine
which tuples match, so predicate locks go there.
* B-tree index searches acquire predicate locks only on the
index *leaf* pages needed to lock the appropriate index range. If,
however, a search discovers that no root page has yet been created, a
predicate lock on the index relation is required.
* GiST searches can determine that there are no matches at any
level of the index, so there must be a predicate lock at each index
@ -346,11 +391,6 @@ to be added from scratch.
2. The existing in-memory lock structures were not suitable for
tracking SIREAD locks.
* The database products used for the prototype
implementations for the papers used update-in-place with a rollback
log for their MVCC implementations, while PostgreSQL leaves the old
version of a row in place and adds a new tuple to represent the row
at a new location.
* In PostgreSQL, tuple level locks are not held in RAM for
any length of time; lock information is written to the tuples
involved in the transactions.
@ -450,18 +490,19 @@ there can't be a rw-conflict from T3 to T0.
o In both cases, we didn't need the T1 -> T3 edge.
* Predicate locking in PostgreSQL will start at the tuple level
when possible, with automatic conversion of multiple fine-grained
locks to coarser granularity as need to avoid resource exhaustion.
The amount of memory used for these structures will be configurable,
to balance RAM usage against SIREAD lock granularity.
* Predicate locking in PostgreSQL starts at the tuple level
when possible. Multiple fine-grained locks are promoted to a single
coarser-granularity lock as needed to avoid resource exhaustion. The
amount of memory used for these structures is configurable, to balance
RAM usage against SIREAD lock granularity.
* A process-local copy of locks held by a process and the coarser
covering locks with counts, are kept to support granularity promotion
decisions with low CPU and locking overhead.
* Each backend keeps a process-local table of the locks it holds.
To support granularity promotion decisions with low CPU and locking
overhead, this table also includes the coarser covering locks and the
number of finer-granularity locks they cover.
* Conflicts will be identified by looking for predicate locks
when tuples are written and looking at the MVCC information when
* Conflicts are identified by looking for predicate locks
when tuples are written, and by looking at the MVCC information when
tuples are read. There is no matching between two RAM-based locks.
* Because write locks are stored in the heap tuples rather than a
@ -493,12 +534,12 @@ to be READ ONLY.)
o We can more aggressively clean up conflicts, predicate
locks, and SSI transaction information.
* Allow a READ ONLY transaction to "opt out" of SSI if there are
* We allow a READ ONLY transaction to "opt out" of SSI if there are
no READ WRITE transactions which could cause the READ ONLY
transaction to ever become part of a "dangerous structure" of
overlapping transaction dependencies.
* Allow the user to request that a READ ONLY transaction wait
* We allow the user to request that a READ ONLY transaction wait
until the conditions are right for it to start in the "opt out" state
described above. We add a DEFERRABLE state to transactions, which is
specified and maintained in a way similar to READ ONLY. It is
@ -538,12 +579,6 @@ address it?
replication solutions, like Postgres-R, Slony, pgpool, HS/SR, etc.
This is related to the "WAL file replay" issue.
* Weak-memory-ordering machines. Make sure that shared memory
access which involves visibility across multiple transactions uses
locks as needed to avoid problems. On the other hand, ensure that we
really need volatile where we're using it.
http://archives.postgresql.org/pgsql-committers/2008-06/msg00228.php
* UNIQUE btree search for equality on all columns. Since a search
of a UNIQUE index using equality tests on all columns will lock the
heap tuple if an entry is found, it appears that there is no need to
@ -551,15 +586,6 @@ get a predicate lock on the index in that case. A predicate lock is
still needed for such a search if a matching index entry which points
to a visible tuple is not found.
* Planner index probes. To avoid problems with data skew at the
ends of an index which have historically caused bad plans, the
planner now probes the end of an index to see what the maximum or
minimum value is when a query appears to be requesting a range of
data outside what statistics shows is present. These planner checks
don't require predicate locking, but there's currently no easy way to
avoid it. What can we do to avoid predicate locking for such planner
activity?
* Minimize touching of shared memory. Should lists in shared
memory push entries which have just been returned to the front of the
available list, so they will be popped back off soon and some memory
@ -573,13 +599,17 @@ Footnotes
[1] http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt
Search for serial execution to find the relevant section.
[2] http://db.cs.berkeley.edu/papers/fntdb07-architecture.pdf
Joseph M. Hellerstein, Michael Stonebraker and James Hamilton. 2007.
[2] A. Fekete et al. Making Snapshot Isolation Serializable. In ACM
Transactions on Database Systems 30:2, Jun. 2005.
http://dx.doi.org/10.1145/1071610.1071615
[3] Joseph M. Hellerstein, Michael Stonebraker and James Hamilton. 2007.
Architecture of a Database System. Foundations and Trends(R) in
Databases Vol. 1, No. 2 (2007) 141-259.
http://db.cs.berkeley.edu/papers/fntdb07-architecture.pdf
Of particular interest:
* 6.1 A Note on ACID
* 6.2 A Brief Review of Serializability
* 6.3 Locking and Latching
* 6.3.1 Transaction Isolation Levels
* 6.5.3 Next-Key Locking: Physical Surrogates for Logical
* 6.5.3 Next-Key Locking: Physical Surrogates for Logical Properties