Casting the result of palloc etc. to the intended type is more per
project style anyway.
(The fact that cpluspluscheck doesn't notice these problems is
because it doesn't expand any macros, which seems like a troubling
shortcoming. Don't have a good idea about improving that.)
Back-patch to v13, which is as far as the patch applies cleanly;
doesn't seem worth working harder.
David Geier
Discussion: https://postgr.es/m/aa5d88a3-71f4-3455-11cf-82de0372c941@gmail.com
We have various requirements when using a dlist_head to keep track of the
number of items in the list. This, traditionally, has been done by
maintaining a counter variable in the calling code. Here we tidy this up
by adding "dclist", which is very similar to dlist but also keeps track of
the number of items stored in the list.
Callers may use the new dclist_count() function when they need to know how
many items are stored. Obtaining the count is an O(1) operation.
For simplicity reasons, dclist and dlist both use dlist_node as their node
type and dlist_iter/dlist_mutable_iter as their iterator type. dclists
have all of the same functionality as dlists except there is no function
named dclist_delete(). To remove an item from a list dclist_delete_from()
must be used. This requires knowing which dclist the given item is stored
in.
Additionally, here we also convert some dlists where additional code
exists to keep track of the number of items stored and to make these use
dclists instead.
Author: David Rowley
Reviewed-by: Bharath Rupireddy, Aleksander Alekseev
Discussion: https://postgr.es/m/CAApHDvrtVxr+FXEX0VbViCFKDGxA3tWDgw9oFewNXCJMmwLjLg@mail.gmail.com
In a similar effort to f01592f91, here we mostly rename shadowed local
variables to remove the warnings produced when compiling with
-Wshadow=compatible-local.
This fixes 63 warnings and leaves just 5.
Author: Justin Pryzby, David Rowley
Reviewed-by: Justin Pryzby
Discussion https://postgr.es/m/20220817145434.GC26426%40telsasoft.com
PostgreSQL contains the implementation of the red-black tree. The red-black
tree is the ordered data structure, and one of its advantages is the ability
to do inequality searches. This commit adds rbt_find_less() and
rbt_find_great() functions implementing these searches. While these searches
aren't yet used in the core code, they might be useful for extensions.
Discussion: https://postgr.es/m/CAGRrpzYE8-7GCoaPjOiL9T_HY605MRax-2jgTtLq236uksZ1Sw%40mail.gmail.com
Author: Steve Chavez, Alexander Korotkov
Reviewed-by: Alexander Korotkov
Get rid of the separate "FATAL" log level, as it was applied
so inconsistently as to be meaningless. This mostly involves
s/pg_log_fatal/pg_log_error/g.
Create a macro pg_fatal() to handle the common use-case of
pg_log_error() immediately followed by exit(1). Various
modules had already invented either this or equivalent macros;
standardize on pg_fatal() and apply it where possible.
Invent the ability to add "detail" and "hint" messages to a
frontend message, much as we have long had in the backend.
Except where rewording was needed to convert existing coding
to detail/hint style, I have (mostly) resisted the temptation
to change existing message wording.
Patch by me. Design and patch reviewed at various stages by
Robert Haas, Kyotaro Horiguchi, Peter Eisentraut and
Daniel Gustafsson.
Discussion: https://postgr.es/m/1363732.1636496441@sss.pgh.pa.us
The previous coding of dshash_seq_next(), on the first call, accessed
status->hash_table->size_log2 without holding a partition lock and without
guaranteeing that ensure_valid_bucket_pointers() had ever been called.
That oversight turns out to not have immediately visible effects, because
bucket 0 is always in partition 0, and ensure_valid_bucket_pointers() was
called after acquiring the partition lock. However,
PARTITION_FOR_BUCKET_INDEX() with a size_log2 of 0 ends up triggering formally
undefined behaviour.
Simplify by accessing partition 0, without using PARTITION_FOR_BUCKET_INDEX().
While at it, remove dshash_get_current(), there is no convincing use
case. Also polish a few comments.
Author: Andres Freund <andres@anarazel.de>
Reviewed-By: Thomas Munro <thomas.munro@gmail.com>
Discussion: https://postgr.es/m/CA+hUKGL9hY_VY=+oUK+Gc1iSRx-Ls5qeYJ6q=dQVZnT3R63Taw@mail.gmail.com
Add ability to scan all entries sequentially to dshash. The interface is
similar but a bit different both from that of dynahash and simple dshash
search functions. The most significant differences is that dshash's interfac
always needs a call to dshash_seq_term when scan ends. Another is
locking. Dshash holds partition lock when returning an entry,
dshash_seq_next() also holds lock when returning an entry but callers
shouldn't release it, since the lock is essential to continue a scan. The
seqscan interface allows entry deletion while a scan is in progress using
dshash_delete_current().
Reviewed-By: Andres Freund <andres@anarazel.de>
Author: Kyotaro Horiguchi <horikyoga.ntt@gmail.com>
The code does not expect sh_error() to return, but the patch
that made this header usable in frontend didn't get that memo.
While here, plaster unlikely() on the tests that decide whether
to invoke sh_error(), and add our standard copyright notice.
Noted by Andres Freund. Back-patch to v13 where this frontend
support came in.
Discussion: https://postgr.es/m/0D54435C-1199-4361-9D74-2FBDCF8EA164@anarazel.de
I just got burnt by trying to use pg_malloc instead of pg_malloc0
with this. Save the next hacker some time by not leaving this
API detail undocumented.
This fixes a bug in simplehash.h which caused an incorrect size mask to be
used when the hash table grew to SH_MAX_SIZE (2^32). The code was
incorrectly setting the size mask to 0 when the hash tables reached the
maximum possible number of buckets. This would result always trying to
use the 0th bucket causing an infinite loop of trying to grow the hash
table due to there being too many collisions.
Seemingly it's not that common for simplehash tables to ever grow this big
as this bug dates back to v10 and nobody seems to have noticed it before.
However, probably the most likely place that people would notice it would
be doing a large in-memory Hash Aggregate with something close to at least
2^31 groups.
After this fix, the code now works correctly with up to within 98% of 2^32
groups and will fail with the following error when trying to insert any
more items into the hash table:
ERROR: hash table size exceeded
However, the work_mem (or hash_mem_multiplier in newer versions) settings
will generally cause Hash Aggregates to spill to disk long before reaching
that many groups. The minimal test case I did took a work_mem setting of
over 192GB to hit the bug.
simplehash hash tables are used in a few other places such as Bitmap Index
Scans, however, again the size that the hash table can become there is
also limited to work_mem and it would take a relation of around 16TB
(2^31) pages and a very large work_mem setting to hit this. With smaller
work_mem values the table would become lossy and never grow large enough
to hit the problem.
Author: Yura Sokolov
Reviewed-by: David Rowley, Ranier Vilela
Discussion: https://postgr.es/m/b1f7f32737c3438136f64b26f4852b96@postgrespro.ru
Backpatch-through: 10, where simplehash.h was added
Also "make reformat-dat-files".
The only change worthy of note is that pgindent messed up the formatting
of launcher.c's struct LogicalRepWorkerId, which led me to notice that
that struct wasn't used at all anymore, so I just took it out.
Comment fixes are applied on HEAD, and documentation improvements are
applied on back-branches where needed.
Author: Justin Pryzby
Discussion: https://postgr.es/m/20210408164008.GJ6592@telsasoft.com
Backpatch-through: 9.6
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This removes "Add Result Cache executor node". It seems that something
weird is going on with the tracking of cache hits and misses as
highlighted by many buildfarm animals. It's not yet clear what the
problem is as other parts of the plan indicate that the cache did work
correctly, it's just the hits and misses that were being reported as 0.
This is especially a bad time to have the buildfarm so broken, so
reverting before too many more animals go red.
Discussion: https://postgr.es/m/CAApHDvq_hydhfovm4=izgWs+C5HqEeRScjMbOgbpC-jRAeK3Yw@mail.gmail.com
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
Previously simplehash.h only exposed a method to perform a hash table
delete using the hash table key. This meant that the delete function had
to perform a hash lookup in order to find the entry to delete. Here we
add a new function so that users of simplehash.h can perform a hash delete
directly using the entry pointer, thus saving the hash lookup.
An upcoming patch that uses simplehash.h already has performed the hash
lookup so already has the entry pointer. This change will allow the
code in that patch to perform the hash delete without the code in
simplehash.h having to perform an additional hash lookup.
Author: David Rowley
Reviewed-by: Andres Freund
Discussion: https://postgr.es/m/CAApHDvqFLXXge153WmPsjke5VGOSt7Ez0yD0c7eBXLfmWxs3Kw@mail.gmail.com
When reading the code it's not obvious when one should prefer dynahash
over simplehash and vice-versa, so, for programmer-friendliness, add
comments to inform that decision.
Show sample simplehash method signatures.
Author: James Coleman <jtc331@gmail.com>
Discussion: https://postgr.es/m/CAAaqYe_dOF39gAJ8rL-a3YO3Qo96MHMRQ2whFjK5ZcU6YvMQSA%40mail.gmail.com
Includes some manual cleanup of places that pgindent messed up,
most of which weren't per project style anyway.
Notably, it seems some people didn't absorb the style rules of
commit c9d297751, because there were a bunch of new occurrences
of function calls with a newline just after the left paren, all
with faulty expectations about how the rest of the call would get
indented.
First pass of modifying various places that obtain the next power of 2 of
a number and make them use the new functions added in pg_bitutils.h
instead.
This also removes the _hash_log2() function. There are no longer any
callers in core. Other users can swap their _hash_log2(n) call to make use
of pg_ceil_log2_32(n).
Author: David Fetter, with some minor adjustments by me
Reviewed-by: John Naylor, Jesse Zhang
Discussion: https://postgr.es/m/20200114173553.GE32763%40fetter.org
Commit 48995040d5 removed the largest
barrier to use of simplehash in frontend code, but there's one more
problem: it uses elog(ERROR, ...) or elog(LOG, ...) in a couple of
places. Work around that by changing those to pg_log_error() and
pg_log_info() when FRONTEND is defined.
Patch by me, reviewed by Andres Freund.
Discussion: http://postgr.es/m/CA+Tgmob8oyh02NrZW=xCScB+5GyJ-jVowE3+TWTUmPF=FsGWTA@mail.gmail.com
If the SH_RAW_ALLOCATOR is defined, it will be used to allocate bytes
for the hash table, and no dependencies on MemoryContext will exist.
This means, in particular, that the SH_CREATE function will not take
a MemoryContext argument.
Patch by me, reviewed by Andres Freund.
Discussion: http://postgr.es/m/CA+Tgmob8oyh02NrZW=xCScB+5GyJ-jVowE3+TWTUmPF=FsGWTA@mail.gmail.com
There's plenty places in frontend code that could benefit from a
string buffer implementation. Some because it yields simpler and
faster code, and some others because of the desire to share code
between backend and frontend.
While there is a string buffer implementation available to frontend
code, libpq's PQExpBuffer, it is clunkier than stringinfo, it
introduces a libpq dependency, doesn't allow for sharing between
frontend and backend code, and has a higher API/ABI stability
requirement due to being exposed via libpq.
Therefore it seems best to just making StringInfo being usable by
frontend code. There's not much to do for that, except for rewriting
two subsequent elog/ereport calls into others types of error
reporting, and deciding on a maximum string length.
For the maximum string size I decided to privately define MaxAllocSize
to the same value as used in the backend. It seems likely that we'll
want to reconsider this for both backend and frontend code in the not
too far away future.
For now I've left stringinfo.h in lib/, rather than common/, to reduce
the likelihood of unnecessary breakage. We could alternatively decide
to provide a redirecting stringinfo.h in lib/, or just not provide
compatibility.
Author: Andres Freund
Reviewed-By: Kyotaro Horiguchi, Daniel Gustafsson
Discussion: https://postgr.es/m/20190920051857.2fhnvhvx4qdddviz@alap3.anarazel.de
Add _lookup_hash and _insert_hash functions for callers that have
already calculated the hash value of the key.
The immediate use case is for hash algorithms that write to disk in
partitions. The hash value can be calculated once, used to perform a
lookup, used to select the partition, then written to the partition
along with the tuple. When the tuple is read back, the hash value does
not need to be recalculated.
Author: Jeff Davis
Reviewed-by: Andres Freund
Discussion: https://postgr.es/m/48abe675e1330f0c264ab2fe0d4ff23eb244f9ef.camel%40j-davis.com
The set is implemented as a B-tree, with a compact representation at leaf
items, using Simple-8b algorithm, so that clusters of nearby values use
less memory.
The IntegerSet isn't used for anything yet, aside from the test code, but
we have two patches in the works that would benefit from this: A patch to
allow GiST vacuum to delete empty pages, and a patch to reduce heap
VACUUM's memory usage, by storing the list of dead TIDs more efficiently
and lifting the 1 GB limit on its size.
This includes a unit test module, in src/test/modules/test_integerset.
It can be used to verify correctness, as a regression test, but if you run
it manully, it can also print memory usage and execution time of some of
the tests.
Author: Heikki Linnakangas, Andrey Borodin
Reviewed-by: Julien Rouhaud
Discussion: https://www.postgresql.org/message-id/b5e82599-1966-5783-733c-1a947ddb729f@iki.fi
The "rb" prefix is used by Ruby, so that our existing code results
in name collisions that break plruby. We discussed ways to prevent
that by adjusting dynamic linker options, but it seems that at best
we'd move the pain to other cases. Renaming to avoid the collision
is the only portable fix anyway. Fortunately, our rbtree code is
not (yet?) widely used --- in core, there's only a single usage
in GIN --- so it seems likely that we can get away with a rename.
I chose to do this basically as s/rb/rbt/g, except for places where
there already was a "t" after "rb". The patch could have been made
smaller by only touching linker-visible symbols, but it would have
resulted in oddly inconsistent-looking code. Better to make it look
like "rbt" was the plan all along.
Back-patch to v10. The rbtree.c code exists back to 9.5, but
rb_iterate() which is the actual immediate source of pain was added
in v10, so it seems like changing the names before that would have
more risk than benefit.
Per report from Pavel Raiskup.
Discussion: https://postgr.es/m/4738198.8KVIIDhgEB@nb.usersys.redhat.com
Fix reference to non-existent file in comment.
Add SH_ prefix to the EMPTY and IN_USE tokens, to reduce likelihood of
collisions with unrelated macros.
Add include guards around the function definitions that are not
"parameterized", so the header can be used again in the same translation
unit.
Undefine SH_EQUAL macro where other "parameter" macros are undefined, for
the same reason.
Author: Thomas Munro
Reviewed-by: Tom Lane
Discussion: https://postgr.es/m/CAEepm%3D1LdXZ3mMTM8tHt_b%3DK1kREit%3Dp8sikesak%3DkzHHM07Nw%40mail.gmail.com
A Bloom filter is a space-efficient, probabilistic data structure that
can be used to test set membership. Callers will sometimes incur false
positives, but never false negatives. The rate of false positives is a
function of the total number of elements and the amount of memory
available for the Bloom filter.
Two classic applications of Bloom filters are cache filtering, and data
synchronization testing. Any user of Bloom filters must accept the
possibility of false positives as a cost worth paying for the benefit in
space efficiency.
This commit adds a test harness extension module, test_bloomfilter. It
can be used to get a sense of how the Bloom filter implementation
performs under varying conditions.
This is infrastructure for the upcoming "heapallindexed" amcheck patch,
which verifies the consistency of a heap relation against one of its
indexes.
Author: Peter Geoghegan
Reviewed-By: Andrey Borodin, Michael Paquier, Thomas Munro, Andres Freund
Discussion: https://postgr.es/m/CAH2-Wzm5VmG7cu1N-H=nnS57wZThoSDQU+F5dewx3o84M+jY=g@mail.gmail.com
For consistency with other code that deals in numbers of buckets, the
macro BUCKETS_PER_PARTITION should produce a value of type size_t.
Also, fix a mention of an obsolete proposed name for dshash.c that
appeared in a comment.
Author: Thomas Munro, based on an observation from Amit Kapila
Discussion: https://postgr.es/m/CAA4eK1%2BBOp5aaW3aHEkg5Bptf8Ga_BkBnmA-%3DXcAXShs0yCiYQ%40mail.gmail.com
Other header files should never #include postgres.h (nor postgres_fe.h,
nor c.h), per project policy. Also, there's no need for any backend .c
file to explicitly include elog.h or palloc.h, because postgres.h pulls
those in already.
Extracted from a larger patch by Kyotaro Horiguchi. The rest of the
removals he suggests require more study, but these are no-brainers.
Discussion: https://postgr.es/m/20180215.200447.209320006.horiguchi.kyotaro@lab.ntt.co.jp
In cases where simplehash tables where filled with either a lot of
conflicting hash-values, or values that hash to consecutive
values (i.e. build "chains") the growth heuristics in
d4c62a6b62 could trigger rather
explosively.
To fix that, address some of the reasons (see previous commit) of why
the growth heuristics where needed, and only allow growth when the
table isn't too empty. While that means there's a few cases of bad
input that can be slower, that seems a lot better than running very
quickly out of memory.
Author: Tomas Vondra and Andres Freund, with additional input by
Thomas Munro, Tom Lane Todd A. Cook
Reported-By: Todd A. Cook, Tomas Vondra, Thomas Munro
Discussion: https://postgr.es/m/20171127185700.1470.20362@wrigleys.postgresql.org
Backpatch: 10, where simplehash was introduced
In a lot of the places having appendBinaryStringInfo() maintain a
trailing NUL byte wasn't actually meaningful, e.g. when appending an
integer which can contain 0 in one of its bytes.
Removing this yields some small speedup, but more importantly will be
more consistent when providing faster variants of pq_sendint etc.
Author: Andres Freund
Discussion: https://postgr.es/m/20170914063418.sckdzgjfrsbekae4@alap3.anarazel.de
This code isn't used, and there's no clear reason why anybody would ever
want to use it. These traversal mechanisms don't yield a visitation order
that is semantically meaningful for any external purpose, nor are they
any faster or simpler than the left-to-right or right-to-left traversals.
(In fact, some rough testing suggests they are slower :-(.) Moreover,
these mechanisms are impossible to test in any arm's-length fashion; doing
so requires knowledge of the red-black tree's internal implementation.
Hence, let's just jettison them.
Discussion: https://postgr.es/m/17735.1505003111@sss.pgh.pa.us
Peter Eisentraut