Checking bloom filters of sstables to compute max purgeable timestamp
for compaction is expensive in terms of CPU time. We can avoid
calculating it if we're not about to GC any tombstone.
This patch changes compacting functions to accept a function instead
of ready value for max_purgeable.
I verified that bloom filter operations no longer appear on flame
graphs during compaction-heavy workload (without tombstones).
Refs #1322.
This patch uses the composite_marker to add inclusiveness information
to the prefixes of a range tombstone.
Signed-off-by: Duarte Nunes <duarte@scylladb.com>
Since Scylla now supports proper range tombstones, the code for
reading ranges from sstables and converting them to overlapping
tombstones is no longer necessary, and is, in fact, wasteful as
the internal representation converts overlapping tombstones back to
ranges.
Signed-off-by: Duarte Nunes <duarte@scylladb.com>
This patch changes the type of the mutation partition's row_tombstones
to be a range_tombstone_list, so that they are now represented as a
set of disjoint ranges. All of its usages are updated accordingly.
Fixes#1155
Signed-off-by: Duarte Nunes <duarte@scylladb.com>
The first erase_and_dispose(), which removes rows between last
position and beginning of the next range, can invalidate end()
iterator of the range. Fix by looking up end after erasing.
mutation_partition::range() was split into lower_bound() and
upper_bound() to allow for that.
This affects for example queries with descending order where the
selected clustering range is empty and falls before all rows.
Exposed by f15c380a4f, which is now
calling do_compact() during query.
Reproduced by dtest paging_test.py:TestPagingData.static_columns_paging_test
"Currently data query digest includes cells and tombstones which may have
expired or be covered by higher-level tombstones. This causes digest
mismatch between replicas if some elements are compacted on one of the
nodes and not on others. This mismatch triggers read-repair which doesn't
resolve because mutations received by mutation queries are not differing,
they are compacted already.
The fix adds compacting step before writing and digesting query results by
reusing the algorithm used by mutation query. This is not the most optimal
way to fix this. The compaction step could be folded with the query writing,
there is redundancy in both steps. However such change carries more risk,
and thus was postponed.
perf_simple_query test (cassandra-stress-like partitions) shows regression
from 83k to 77k (7%) ops/s.
Fixes #1165."
Currently data query digest includes cells and tombstones which may have
expired or be covered by higher-level tombstones. This causes digest
mismatch between replicas if some elements are compacted on one of the
nodes and not on others. This mismatch triggers read-repair which doesn't
resolve because mutations received by mutation queries are not differing,
they are compacted already.
The fix adds compacting step before writing and digesting query results by
reusing the algorithm used by mutation query. This is not the most optimal
way to fix this. The compaction step could be folded with the query writing,
there is redundancy in both steps. However such change carries more risk,
and thus was postponed.
perf_simple_query test (cassandra-stress-like partitions) shows regression
from 83k to 77k (7%) ops/s.
Fixes#1165.
We cannot leave partially applied mutation behind when the write
fails. It may fail if memory allocation fails in the middle of
apply(). This for example would violate write atomicity, readers
should either see the whole write or none at all.
This fix makes apply() revert partially applied data upon failure, by
the means of ReversiblyMergeable concept. In a nut shell the idea is
to store old state in the source mutation as we apply it and swap back
in case of exception. At cell level this swapping is inexpensive, just
rewiring pointers. For this to work, the source mutation needs to be
brought into mutable form, so frozen mutations need to be unfrozen. In
practice this doesn't increase amount of cell allocations in the
memtable apply path because incoming data will usually be newer and we
will have to copy it into LSA anyway. There are extra allocations
though for the data structures which holds cells.
I didn't see significant change in performance of:
build/release/tests/perf/perf_simple_query -c1 -m1G --write --duration 13
The score fluctuates around ~77k ops/s.
Fixes#283.
Currently only "set" storage could store empty cells, but not the
"vector" one because there empty cell has the meaning of being
missing. To implement rolback, we need to be able to distinguish empty
cells from missing ones. Solve by making vector storage use a bitmap
for presence checking instead of emptiness. This adds 4 bytes to
vector storage.
The query result footprint for cassandra-stress mutation as reported
by tests/memory-footprint increased by 18% from 285 B to 337 B.
perf_simple_query shows slight regression in throughput (-8%):
build/release/tests/perf/perf_simple_query -c4 -m1G --partitions 100000
Before: ~433k tps
After: ~400k tps
Seems like boost::intrusive::set<>::comp() is not accessible on some
versions of boost. Replace by the equivalent
boost::intrusive::set<>::key_comp().
Fixes#858.
Message-Id: <1454326483-29780-1-git-send-email-avi@scylladb.com>
Schema is tracked in memtable and cache per-entry. Entries are
upgraded lazily on access. Incoming mutations are upgraded to table's
current schema on given shard.
Mutating nodes need to keep schema_ptr alive in case schema version is
requested by target node.
do_compact() wasn't removing an empty row that is covered by a
tombstone. As a result, an empty partition could be written to a
sstable. To solve this problem, let's make trim_rows remove a
row that is considered to be empty. A row is empty if it has no
tombstone, no marker and no cells.
Signed-off-by: Raphael S. Carvalho <raphaelsc@scylladb.com>
The default move assignment operator calls boost::intrusive::set's move
assignment operator, which leaks, because it does not believe it owns
the data.
Fix by providing a custom implementation.
"This series add code for computing mutation_partition difference.
For mutations A and B:
diffA = A.difference(B);
diffB = B.difference(A);
AB = A.apply(B);
diffA is the minimal mutation that when applied to B makes it equal
to AB and diffB is the minimal mutation that applied to A results in AB.
Fixes #430."
