Summary:
Scripted and removed all trailing spaces and converted all tabs to
spaces.
Also fixed other lint errors.
All lint errors from this point of time should be taken seriously.
Test Plan: make all check
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb
Differential Revision: https://reviews.facebook.net/D7059
Summary:
The leveldb API is enhanced to support different compression algorithms at
different levels.
This adds the option min_level_to_compress to db_bench that specifies
the minimum level for which compression should be done when
compression is enabled. This can be used to disable compression for levels
0 and 1 which are likely to suffer from stalls because of the CPU load
for memtable flushes and (L0,L1) compaction. Level 0 is special as it
gets frequent memtable flushes. Level 1 is special as it frequently
gets all:all file compactions between it and level 0. But all other levels
could be the same. For any level N where N > 1, the rate of sequential
IO for that level should be the same. The last level is the
exception because it might not be full and because files from it are
not read to compact with the next larger level.
The same amount of time will be spent doing compaction at any
level N excluding N=0, 1 or the last level. By this standard all
of those levels should use the same compression. The difference is that
the loss (using more disk space) from a faster compression algorithm
is less significant for N=2 than for N=3. So we might be willing to
trade disk space for faster write rates with no compression
for L0 and L1, snappy for L2, zlib for L3. Using a faster compression
algorithm for the mid levels also allows us to reclaim some cpu
without trading off much loss in disk space overhead.
Also note that little is to be gained by compressing levels 0 and 1. For
a 4-level tree they account for 10% of the data. For a 5-level tree they
account for 1% of the data.
With compression enabled:
* memtable flush rate is ~18MB/second
* (L0,L1) compaction rate is ~30MB/second
With compression enabled but min_level_to_compress=2
* memtable flush rate is ~320MB/second
* (L0,L1) compaction rate is ~560MB/second
This practicaly takes the same code from https://reviews.facebook.net/D6225
but makes the leveldb api more general purpose with a few additional
lines of code.
Test Plan: make check
Differential Revision: https://reviews.facebook.net/D6261
In particular, we add a new FilterPolicy class. An instance
of this class can be supplied in Options when opening a
database. If supplied, the instance is used to generate
summaries of keys (e.g., a bloom filter) which are placed in
sstables. These summaries are consulted by DB::Get() so we
can avoid reading sstable blocks that are guaranteed to not
contain the key we are looking for.
This change provides one implementation of FilterPolicy
based on bloom filters.
Other changes:
- Updated version number to 1.4.
- Some build tweaks.
- C binding for CompactRange.
- A few more benchmarks: deleteseq, deleterandom, readmissing, seekrandom.
- Minor .gitignore update.
- Replace raw slice comparison with a call to user comparator.
Added test for custom comparators.
- Fix end of namespace comments.
- Fixed bug in picking inputs for a level-0 compaction.
When finding overlapping files, the covered range may expand
as files are added to the input set. We now correctly expand
the range when this happens instead of continuing to use the
old range. For example, suppose L0 contains files with the
following ranges:
F1: a .. d
F2: c .. g
F3: f .. j
and the initial compaction target is F3. We used to search
for range f..j which yielded {F2,F3}. However we now expand
the range as soon as another file is added. In this case,
when F2 is added, we expand the range to c..j and restart the
search. That picks up file F1 as well.
This change fixes a bug related to deleted keys showing up
incorrectly after a compaction as described in Issue 44.
(Sync with upstream @25072954)
Abhishek Kona