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https://issues.apache.org/jira/browse/LUCENE-1458?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12649569#action_12649569
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Marvin Humphrey commented on LUCENE-1458:
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> Take a large Jira instance, where the app itself is also
> consuming alot of RAM, doing alot of its own IO, etc., where perhaps
> searching is done infrequently enough relative to other operations
> that the OS may no longer think the pages you hit for the terms index
> are hot enough to keep around.
Search responsiveness is already compromised in such a situation, because we
can all but guarantee that the posting list files have already been evicted
from cache. If the box has enough RAM for the large JIRA instance including
the Lucene index, search responsiveness won't be a problem. As soon as you
start running a little short on RAM, though, there's no way to stop infrequent
searches from being sluggish.
Nevertheless, the terms index isn't that big in comparison to, say, the size
of a posting list for a common term, so the cost of re-heating it isn't
astronomical in the grand scheme of things.
> Similarly, when a BG merge is burning through data, or say backup kicks off
> and moves many GB, or the simple act of iterating through a big postings
> list, the OS will gleefully evict my terms index or norms in order to
> populate its IO cache with data it will need again for a very long time.
When that background merge finishes, the new files will be hot. So, if we
open a new IndexReader right away and that IndexReader uses mmap() to get at
the file data, new segments be responsive right away.
Even better, any IO caches for old segments used by the previous IndexReader
may still be warm. All of this without having to decompress a bunch of stream
data into per-process data structures at IndexReader startup.
The terms index could indeed get evicted some of the time on busy systems, but
the point is that the system IO cache usually works in our favor, even under
load.
As far as backup daemons blowing up everybody's cache, that's stupid,
pathological behavior: <http://kerneltrap.org/node/3000#comment-8573>. Such
apps ought to be calling madvise(ptr, len, MADV_SEQUENTIAL) so that the kernel
knows it can recycle the cache pages as soon as they're cleared.
>> But hey, we can simplify even further! How about dispensing with the index
>> file? We can just divide the main dictionary file into blocks and binary
>> search on that.
>
> I'm not convinced this'll be a win in practice. You are now paying an
> even higher overhead cost for each "check" of your binary search,
> especially with something like pulsing which inlines more stuff into
> the terms dict. I agree it's simpler, but I think that's trumped by
> the performance hit.
I'm persuaded that we shouldn't do away with the terms index. Even if we're
operating on a dedicated search box with gobs of RAM, loading entire cache
pages when we only care about the first few bytes of each is poor use of
memory bandwidth. And, just in case the cache does get blown, we'd like to
keep the cost of rewarming down.
Nathan Kurz and I brainstormed this subject in a phone call this morning, and
we came up with a three-file lexicon index design:
* A file which is a solid stack of 64-bit file pointers into the lexicon
index term data. Term data UTF-8 byte length can be determined by
subtracting the current pointer from the next one (or the file length at
the end).
* A file which is contains solid UTF-8 term content. (No string lengths, no
file pointers, just character data.)
* A file which is a solid stack of 64-bit file pointers into the primary
lexicon.
Since the integers are already expanded and the raw UTF-8 data can be compared
as-is, those files can be memory-mapped and used as-is for binary search.
> In Lucene java, the concurrency model we are aiming for is a single JVM
> sharing a single instance of IndexReader.
When I mentioned this to Nate, he remarked that we're using the OS kernel like
you're using the JVM.
We don't keep a single IndexReader around, but we do keep the bulk of its data
cached so that we can just slap a cheap wrapper around it.
> I do agree, if fork() is the basis of your concurrency model then sharing
> pages becomes critical. However, modern OSs implement copy-on-write sharing
> of VM pages after a fork, so that's another good path to sharing?
Lucy/KS can't enforce that, and we wouldn't want to. It's very convenient to
be able to launch a cheap search process.
> Have you tried any actual tests swapping these approaches in as your
> terms index impl?
No -- changing something like this requires a lot of coding, so it's better to
do thought experiments first to winnow down the options.
> Tests of fully hot and fully cold ends of the
> spectrum would be interesting, but also tests where a big segment
> merge or a backup is running in the background...
>> That doesn't meet the design goals of bringing the cost of opening/warming
>> an IndexReader down to near-zero and sharing backing buffers among
>> multiple forks.
>
> That's a nice goal. Our biggest cost in Lucene is warming the FieldCache, used
> for sorting, function queries, etc.
Exactly. It would be nice to add a plug-in indexing component that writes sort
caches to files that can be memory mapped at IndexReader startup. There would
be multiple files: both a solid array of 32-bit integers mapping document
number to sort order, and the field cache values. Such a component would
allow us to move the time it takes to read in a sort cache from
IndexReader-startup-time to index-time.
Hmm, maybe we can conflate this with a column-stride field writer and require
that sort fields have a fixed width?
> In my approach here, the blob is opaque to the terms dict reader: it
> simply seeks to the right spot in the tis file, and then asks the
> codec to decode the entry. TermsDictReader is entirely unaware of
> what/how is stored there.
Sounds good. Basically, a hash lookup.
In KS, the relevant IndexReader methods no longer take a Term object. (In
fact, there IS no Term object any more -- KinoSearch::Index::Term has been
removed.) Instead, they take a string field and a generic "Obj".
Lexicon*
SegReader_lexicon(SegReader *self, const CharBuf *field, Obj *term)
{
return (Lexicon*)LexReader_Lexicon(self->lex_reader, field, term);
}
I suppose we genericize this by adding a TermsDictReader/LexReader argument to
the IndexReader constructor? That way, someone can supply a custom subclass
that knows how to decode custom dictionary files.
> Further steps towards flexible indexing
> ---------------------------------------
>
> Key: LUCENE-1458
> URL: https://issues.apache.org/jira/browse/LUCENE-1458
> Project: Lucene - Java
> Issue Type: New Feature
> Components: Index
> Affects Versions: 2.9
> Reporter: Michael McCandless
> Assignee: Michael McCandless
> Priority: Minor
> Fix For: 2.9
>
> Attachments: LUCENE-1458.patch, LUCENE-1458.patch, LUCENE-1458.patch
>
>
> I attached a very rough checkpoint of my current patch, to get early
> feedback. All tests pass, though back compat tests don't pass due to
> changes to package-private APIs plus certain bugs in tests that
> happened to work (eg call TermPostions.nextPosition() too many times,
> which the new API asserts against).
> [Aside: I think, when we commit changes to package-private APIs such
> that back-compat tests don't pass, we could go back, make a branch on
> the back-compat tag, commit changes to the tests to use the new
> package private APIs on that branch, then fix nightly build to use the
> tip of that branch?o]
> There's still plenty to do before this is committable! This is a
> rather large change:
> * Switches to a new more efficient terms dict format. This still
> uses tii/tis files, but the tii only stores term & long offset
> (not a TermInfo). At seek points, tis encodes term & freq/prox
> offsets absolutely instead of with deltas delta. Also, tis/tii
> are structured by field, so we don't have to record field number
> in every term.
> .
> On first 1 M docs of Wikipedia, tii file is 36% smaller (0.99 MB
> -> 0.64 MB) and tis file is 9% smaller (75.5 MB -> 68.5 MB).
> .
> RAM usage when loading terms dict index is significantly less
> since we only load an array of offsets and an array of String (no
> more TermInfo array). It should be faster to init too.
> .
> This part is basically done.
> * Introduces modular reader codec that strongly decouples terms dict
> from docs/positions readers. EG there is no more TermInfo used
> when reading the new format.
> .
> There's nice symmetry now between reading & writing in the codec
> chain -- the current docs/prox format is captured in:
> {code}
> FormatPostingsTermsDictWriter/Reader
> FormatPostingsDocsWriter/Reader (.frq file) and
> FormatPostingsPositionsWriter/Reader (.prx file).
> {code}
> This part is basically done.
> * Introduces a new "flex" API for iterating through the fields,
> terms, docs and positions:
> {code}
> FieldProducer -> TermsEnum -> DocsEnum -> PostingsEnum
> {code}
> This replaces TermEnum/Docs/Positions. SegmentReader emulates the
> old API on top of the new API to keep back-compat.
>
> Next steps:
> * Plug in new codecs (pulsing, pfor) to exercise the modularity /
> fix any hidden assumptions.
> * Expose new API out of IndexReader, deprecate old API but emulate
> old API on top of new one, switch all core/contrib users to the
> new API.
> * Maybe switch to AttributeSources as the base class for TermsEnum,
> DocsEnum, PostingsEnum -- this would give readers API flexibility
> (not just index-file-format flexibility). EG if someone wanted
> to store payload at the term-doc level instead of
> term-doc-position level, you could just add a new attribute.
> * Test performance & iterate.
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