Re: strange problem of PForDelta decoder
Li Li <fancyerii <at> gmail.com>
2011-01-01 17:33:57 GMT
I sent a mail to MG4J group and Sebastiano Vigna recommended the paper
Reducing query latencies in web search using fine-grained parallelism.
World Wide Web, 12(4):441-460, 2009.
I read it roughly. But there are some questions
it first coalesces all disk reads in a single process, and then
distributes the index data among
the parallel threads. So when the index server receives a query, it
loads from storage (disk or cache)
the required posting lists, initializes the query execution, and then
spawns lightweight threads, one per core.
Each thread receives an equal-sized subset of document IDs to scan,
together covering the entire index
partition. All threads execute the same code on the same query, but
with private data structures.
The only writable shared data structure is a heap of the top-scoring
hits, protected by a lock. At the end of the threads'
execution, the heap contains the highest-scoring hits in the entire
index partition, which is then transmitted to the query
integrator as usual. Since the index contains skip-lists that permit
near-random-access to any document ID, and since
hits are generally distributed evenly in the index, our measurements
show that all threads complete their work with
little variability, within 1-2% of each other.
I have some questions
1.Since the index contains skip-lists that permit
near-random-access to any document ID
skip list can be near random access?(especially when it's in hard disk)
2.For "or query", it's easy. e.g. we search "search" and "engine",
we using one main thread to get postings the
the 2 terms and divide their postings into 2 groups(e.g. even docIds
and odd docIds) and using 2 threads to score
them and finally merge it(or using locked priority queue)
but for "and query", we usally don't decode all the docIDs
because we can skip many documents. especially when
searching low frequent terms with high frequent terms. only a small
number of docIDs of high frequent term is decoded.
btw. I think or query is often much slower than and query. If we can
parallel or query well, it's also very useful.
On Dec 31, 2010, at 7:25 AM, Li Li wrote:
> Which one is used in MG4J to support multithreads searching? Are
2010/12/31 Li Li <fancyerii <at> gmail.com>:
> is there anyone familiar with MG4J(http://mg4j.dsi.unimi.it/)
> it says Multithreading. Indices can be queried and scored concurrently.
> maybe we can learn something from it.
> 2010/12/31 Li Li <fancyerii <at> gmail.com>:
>> 2 means search a term need seek many times for tis(if it's not cached in tii)
>> 2010/12/31 Li Li <fancyerii <at> gmail.com>:
>>> searching multi segments is a alternative solution but it has some
>>> 1. idf is not global?(I am not familiar with its implementation) maybe
>>> it's easy to solve it by share global idf
>>> 2. each segments will has it's own tii and tis files, which may make
>>> search slower(that's why optimization of
>>> index is neccessary)
>>> 3. one term's docList is distributed in many files rather than one.
>>> more than one frq files means
>>> hard disk must seek different tracks, it's time consuming. if there is
>>> only one segment, the are likely
>>> stored in a single track.
>>> 2010/12/31 Earwin Burrfoot <earwin <at> gmail.com>:
>>>>>>>until we fix Lucene to run a single search concurrently (which we
>>>>>>>badly need to do).
>>>>> I am interested in this idea.(I have posted it before) do you have some
>>>>> resources such as papers or tech articles about it?
>>>>> I have tried but it need to modify index format dramatically and we use
>>>>> solr distributed search to relieve the problem of response time. so finally
>>>>> give it up.
>>>>> lucene4's index format is more flexible that it supports customed codecs
>>>>> and it's now on development, I think it's good time to take it into
>>>>> that let it support multithread searching for a single query.
>>>>> I have a naive solution. dividing docList into many groups
>>>>> e.g grouping docIds by it's even or odd
>>>>> term1 df1=4 docList = 0 4 8 10
>>>>> term1 df2=4 docList = 1 3 9 11
>>>>> term2 df1=4 docList = 0 6 8 12
>>>>> term2 df2=4 docList = 3 9 11 15
>>>>> then we can use 2 threads to search topN docs on even group and odd group
>>>>> and finally merge their results into a single on just like solr
>>>>> distributed search.
>>>>> But it's better than solr distributed search.
>>>>> First, it's in a single process and data communication between
>>>>> threads is much
>>>>> faster than network.
>>>>> Second, each threads process the same number of documents.For solr
>>>>> search, one shard may process 7 documents and another shard may 1 document
>>>>> Even if we can make each shard have the same document number. we can not
>>>>> make it uniformly for each term.
>>>>> e.g. shard1 has doc1 doc2
>>>>> shard2 has doc3 doc4
>>>>> but term1 may only occur in doc1 and doc2
>>>>> while term2 may only occur in doc3 and doc4
>>>>> we may modify it
>>>>> shard1 doc1 doc3
>>>>> shard2 doc2 doc4
>>>>> it's good for term1 and term2
>>>>> but term3 may occur in doc1 and doc3...
>>>>> So I think it's fine-grained distributed in index while solr
>>>>> distributed search is coarse-
>>>> This is just crazy :)
>>>> The simple way is just to search different segments in parallel.
>>>> BalancedSegmentMergePolicy makes sure you have roughly even-sized
>>>> large segments (and small ones don't count, they're small!).
>>>> If you're bound on squeezing out that extra millisecond (and making
>>>> your life miserable along the way), you can search a single segment
>>>> with multiple threads (by dividing it in even chunks, and then doing
>>>> skipTo to position your iterators to the beginning of each chunk).
>>>> First approach is really easy to implement. Second one is harder, but
>>>> still doesn't require you to cook the number of CPU cores available
>>>> into your index!
>>>> It's the law of diminishing returns at play here. You're most likely
>>>> to search in parallel over mostly memory-resident index
>>>> (RAMDir/mmap/filesys cache - doesn't matter), as most of IO subsystems
>>>> tend to slow down considerably on parallel sequential reads, so you
>>>> already have pretty decent speed.
>>>> Searching different segments in parallel (with BSMP) makes you several
>>>> times faster.
>>>> Searching in parallel within a segment requires some weird hacks, but
>>>> has maybe a few percent advantage over previous solution.
>>>> Sharding posting lists requires a great deal of weird hacks, makes
>>>> index machine-bound, and boosts speed by another couple of percent.
>>>> Sounds worthless.
>>>> Kirill Zakharenko/Кирилл Захаренко (earwin <at> gmail.com)
>>>> Phone: +7 (495) 683-567-4
>>>> ICQ: 104465785
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