Re: Facets in Lucene 4.7.2
Hi, Thanks again! This time, I have indexed data with the following specs. I run into 40 seconds for the FastTaxonomyFacetCounts to create all the facets. Is this as per your measurements? Subsequent runs fare much better probably because of the Windows file system cache. How can I speed this up? I believe there was a CategoryListCache earlier. Is there any cache or other implementation that I can use? Secondly, I had a general question. If I extrapolate these numbers for a billion documents, my search and facet number may probably be unusable in a real time scenario. What are the strategies employed when you deal with such large scale? I am new to Lucene so please also direct me to the relevant info sources. Thanks! Corpus: Count: 20M, Size: 51GB Index: Size (w/o Facets): 19GB, Size (w/Facets): 20.12GB Creation Time (w/o Facets): 3.46hrs, Creation Time (w/Facets): 3.49hrs Search Performance: With 29055 hits (5 terms in query): Query Execution: 8 seconds Facet counts execution: 40-45 seconds With 4.22M hits (2 terms in query): Query Execution: 3 seconds Facet counts execution: 42-46 seconds With 15.1M hits (1 term in query): Query Execution: 2 seconds Facet counts execution: 45-53 seconds With 6183 hits (5 different values for the same 5 terms): (Without Flushing Windows File Cache on Next run) Query Execution: 11 seconds Facet counts execution: 1 second With 4.9M hits (1 different value for the 1 term): (Without Flushing Windows File Cache on Next run) Query Execution: 2 seconds Facet counts execution: 3 seconds --- Thanks n Regards, Sandeep Ramesh Khanzode On Monday, June 16, 2014 8:11 PM, Shai Erera ser...@gmail.com wrote: Hi 1.] Is there any API that gives me the count of a specific dimension from FacetCollector in response to a search query. Currently, I use the getTopChildren() with some value and then check the FacetResult object for the actual number of dimensions hit along with their occurrences. Also, the getSpecificValue() does not work without a path attribute to the API. To get the value of the dimension itself, you should call getTopChildren(1, dim). Note that getSpecificValue does not allow to pass only the dimension, and getTopChildren requires topN to be 0. Passing 1 is a hack, but I'm not sure we should specifically support getting the aggregated value of just the dimension ... once you get that, the FacetResult.value tells you the aggregated count. 2.] Can I find the MAX or MIN value of a Numeric type field written to the index? Depends how you index them. If you index the field as a numeric field (e.g. LongField), I believe you can use NumericUtils.getMaxLong. If it's a DocValues field, I don't know of a built-in function that does it, but this thread has a demo code: http://www.gossamer-threads.com/lists/lucene/java-user/195594. 3.] I am trying to compare and contrast Lucene Facets with Elastic Search. I could determine that ES does search time faceting and dynamically returns the response without any prior faceting during indexing time. Is index time lag is not my concern, can I assume that, in general, performance-wise Lucene facets would be faster? I will start by saying that I don't know much about how ES facets work. We have some committers who know both how Lucene and ES facets work, so they can comment on that. But I personally don't think there's no index-time decision when it comes to faceting. Well .. not unless you're faceting on arbitrary terms. Otherwise, you already make decision such as indexing the field as not tokenized/analyzed/lowercased/doc-values etc. Note that Lucene facets also support non-taxonomy based faceting option, using the DocValues fields. Look at SortedSetDocValuesFacetField. This too can be perceived as an index-time decision though... And there are some built-in dynamic faceting capabilities too, like range facets (LongRangeFacetCounts), which can work on any NumericDocValuesField, as well as any ValueSource (such as Expressions). I cannot compare ES facets to Lucene's in terms of performance, as I haven't benchmarked them yet. 4.] I index a semi-large-ish corpus of 20M files across 50GB. If I do not use IndexWriter.commit(), I get standard files like cfe/cfs/si in the index directory. However, if I do use the commit(), then as I understand it, the state is persisted to the disk. But this time, there are additional file extensions like doc/pos/tim/tip/dvd/dvm, etc. I am not sure about this difference and its cause. The information of the doc/tim/tip etc. is buffered in memory (controlled by ramBufferSizeMB) and when they are flushed (on commit or when the RAM buffer fills up), those files materialize on disk. When you call
Re: Facets in Lucene 4.7.2
Hi 40 seconds for faceted search is ... crazy. Also, note how the times don't differ much even though the number of hits is much higher (29K vs 15.1M) ... That, w/ that you say that subsequent queries are much faster (few seconds) suggests that something is seriously messed up w/ your environment. Maybe it's a faulty disk? E.g. after the file system cache is warm, you no longer hit the disk? In general, the more hits you have, the more expensive is faceted search. It's also true for scoring as well (i.e. even without facets). There's just more work to determine the top results (docs, facets...). With facets, you can use sampling (see RandomSamplingFacetsCollector), but I would do that only after you verify that collecting 15M docs is very expensive for you, even when the file system cache is hot. I've never seen those numbers before, therefore it's difficult for me to relate to them. There's a caching mechanism for facets, through CachedOrdinalsReader. But I wouldn't go there until you verify that your IO system is good (try another machine, OS, disk ...)., and that the 40s times are truly from the faceting code. Shai On Tue, Jun 17, 2014 at 4:21 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks again! This time, I have indexed data with the following specs. I run into 40 seconds for the FastTaxonomyFacetCounts to create all the facets. Is this as per your measurements? Subsequent runs fare much better probably because of the Windows file system cache. How can I speed this up? I believe there was a CategoryListCache earlier. Is there any cache or other implementation that I can use? Secondly, I had a general question. If I extrapolate these numbers for a billion documents, my search and facet number may probably be unusable in a real time scenario. What are the strategies employed when you deal with such large scale? I am new to Lucene so please also direct me to the relevant info sources. Thanks! Corpus: Count: 20M, Size: 51GB Index: Size (w/o Facets): 19GB, Size (w/Facets): 20.12GB Creation Time (w/o Facets): 3.46hrs, Creation Time (w/Facets): 3.49hrs Search Performance: With 29055 hits (5 terms in query): Query Execution: 8 seconds Facet counts execution: 40-45 seconds With 4.22M hits (2 terms in query): Query Execution: 3 seconds Facet counts execution: 42-46 seconds With 15.1M hits (1 term in query): Query Execution: 2 seconds Facet counts execution: 45-53 seconds With 6183 hits (5 different values for the same 5 terms): (Without Flushing Windows File Cache on Next run) Query Execution: 11 seconds Facet counts execution: 1 second With 4.9M hits (1 different value for the 1 term): (Without Flushing Windows File Cache on Next run) Query Execution: 2 seconds Facet counts execution: 3 seconds --- Thanks n Regards, Sandeep Ramesh Khanzode On Monday, June 16, 2014 8:11 PM, Shai Erera ser...@gmail.com wrote: Hi 1.] Is there any API that gives me the count of a specific dimension from FacetCollector in response to a search query. Currently, I use the getTopChildren() with some value and then check the FacetResult object for the actual number of dimensions hit along with their occurrences. Also, the getSpecificValue() does not work without a path attribute to the API. To get the value of the dimension itself, you should call getTopChildren(1, dim). Note that getSpecificValue does not allow to pass only the dimension, and getTopChildren requires topN to be 0. Passing 1 is a hack, but I'm not sure we should specifically support getting the aggregated value of just the dimension ... once you get that, the FacetResult.value tells you the aggregated count. 2.] Can I find the MAX or MIN value of a Numeric type field written to the index? Depends how you index them. If you index the field as a numeric field (e.g. LongField), I believe you can use NumericUtils.getMaxLong. If it's a DocValues field, I don't know of a built-in function that does it, but this thread has a demo code: http://www.gossamer-threads.com/lists/lucene/java-user/195594. 3.] I am trying to compare and contrast Lucene Facets with Elastic Search. I could determine that ES does search time faceting and dynamically returns the response without any prior faceting during indexing time. Is index time lag is not my concern, can I assume that, in general, performance-wise Lucene facets would be faster? I will start by saying that I don't know much about how ES facets work. We have some committers who know both how Lucene and ES facets work, so they can comment on that. But I personally don't think there's no index-time decision when it comes to faceting. Well ..
Re: Facets in Lucene 4.7.2
Hi, Thanks for your response. It does sound pretty bad which is why I am not sure whether there is an issue with the code, the index, the searcher, or just the machine, as you say. I will try with another machine just to make sure and post the results. Meanwhile, can you tell me if there is anything wrong in the below measurement? Or is the API usage or the pattern incorrect? I used a tool called RAMMap to clean the Windows cache. If I do not, the results are very fast as I mentioned already. If I do, then the total time is 40s. Can you please provide any pointers on what could be wrong? I will be checking on a Linux box anyway. = System.out.println(1. Start Date: + new Date()); TopDocs topDocs = FacetsCollector.search(searcher, query, 100, fc); System.out.println(1. End Date: + new Date()); // Above part takes approx 2-12 seconds depending on the query System.out.println(2. Start Date: + new Date()); ListFacetResult results = new ArrayListFacetResult(); Facets facets = new FastTaxonomyFacetCounts(taxoReader, config, fc); System.out.println(2. End Date: + new Date()); // Above part takes approx 40-53 seconds depending on the query for the first time on Windows System.out.println(3. Start Date: + new Date()); results.add(facets.getTopChildren(1000, F1)); results.add(facets.getTopChildren(1000, F2)); results.add(facets.getTopChildren(1000, F3)); results.add(facets.getTopChildren(1000, F4)); results.add(facets.getTopChildren(1000, F5)); results.add(facets.getTopChildren(1000, F6)); results.add(facets.getTopChildren(1000, F7)); System.out.println(3. End Date: + new Date()); // Above part takes approx less than 1 second = --- Thanks n Regards, Sandeep Ramesh Khanzode On Tuesday, June 17, 2014 10:15 PM, Shai Erera ser...@gmail.com wrote: Hi 40 seconds for faceted search is ... crazy. Also, note how the times don't differ much even though the number of hits is much higher (29K vs 15.1M) ... That, w/ that you say that subsequent queries are much faster (few seconds) suggests that something is seriously messed up w/ your environment. Maybe it's a faulty disk? E.g. after the file system cache is warm, you no longer hit the disk? In general, the more hits you have, the more expensive is faceted search. It's also true for scoring as well (i.e. even without facets). There's just more work to determine the top results (docs, facets...). With facets, you can use sampling (see RandomSamplingFacetsCollector), but I would do that only after you verify that collecting 15M docs is very expensive for you, even when the file system cache is hot. I've never seen those numbers before, therefore it's difficult for me to relate to them. There's a caching mechanism for facets, through CachedOrdinalsReader. But I wouldn't go there until you verify that your IO system is good (try another machine, OS, disk ...)., and that the 40s times are truly from the faceting code. Shai On Tue, Jun 17, 2014 at 4:21 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks again! This time, I have indexed data with the following specs. I run into 40 seconds for the FastTaxonomyFacetCounts to create all the facets. Is this as per your measurements? Subsequent runs fare much better probably because of the Windows file system cache. How can I speed this up? I believe there was a CategoryListCache earlier. Is there any cache or other implementation that I can use? Secondly, I had a general question. If I extrapolate these numbers for a billion documents, my search and facet number may probably be unusable in a real time scenario. What are the strategies employed when you deal with such large scale? I am new to Lucene so please also direct me to the relevant info sources. Thanks! Corpus: Count: 20M, Size: 51GB Index: Size (w/o Facets): 19GB, Size (w/Facets): 20.12GB Creation Time (w/o Facets): 3.46hrs, Creation Time (w/Facets): 3.49hrs Search Performance: With 29055 hits (5 terms in query): Query Execution: 8 seconds Facet counts execution: 40-45 seconds With 4.22M hits (2 terms in query): Query Execution: 3 seconds Facet counts execution: 42-46 seconds With 15.1M hits (1 term in query): Query Execution: 2 seconds Facet counts execution: 45-53 seconds With 6183 hits (5 different values for the same 5 terms): (Without Flushing Windows File Cache on Next run) Query Execution: 11 seconds Facet counts execution: 1 second With 4.9M hits (1 different value for the 1 term): (Without Flushing Windows File Cache on Next run) Query Execution: 2 seconds Facet counts execution: 3 seconds
Re: Facets in Lucene 4.7.2
Nothing suspicious ... code looks fine. The call to FastTaxoFacetCounts actually computes the counts ... that's the expensive part of faceted search. How big is your taxonomy (number categories)? Is it hierarchical (i.e. are your dimensions flat, or deep like A/1/2/3/)? What does your FacetsConfig look like? Still, well maybe if your taxonomy is huge (hundreds of millions of categories), I don't think you can intentionally mess up something that much to end up w/ 40-45s response times! Shai On Tue, Jun 17, 2014 at 8:51 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks for your response. It does sound pretty bad which is why I am not sure whether there is an issue with the code, the index, the searcher, or just the machine, as you say. I will try with another machine just to make sure and post the results. Meanwhile, can you tell me if there is anything wrong in the below measurement? Or is the API usage or the pattern incorrect? I used a tool called RAMMap to clean the Windows cache. If I do not, the results are very fast as I mentioned already. If I do, then the total time is 40s. Can you please provide any pointers on what could be wrong? I will be checking on a Linux box anyway. = System.out.println(1. Start Date: + new Date()); TopDocs topDocs = FacetsCollector.search(searcher, query, 100, fc); System.out.println(1. End Date: + new Date()); // Above part takes approx 2-12 seconds depending on the query System.out.println(2. Start Date: + new Date()); ListFacetResult results = new ArrayListFacetResult(); Facets facets = new FastTaxonomyFacetCounts(taxoReader, config, fc); System.out.println(2. End Date: + new Date()); // Above part takes approx 40-53 seconds depending on the query for the first time on Windows System.out.println(3. Start Date: + new Date()); results.add(facets.getTopChildren(1000, F1)); results.add(facets.getTopChildren(1000, F2)); results.add(facets.getTopChildren(1000, F3)); results.add(facets.getTopChildren(1000, F4)); results.add(facets.getTopChildren(1000, F5)); results.add(facets.getTopChildren(1000, F6)); results.add(facets.getTopChildren(1000, F7)); System.out.println(3. End Date: + new Date()); // Above part takes approx less than 1 second = --- Thanks n Regards, Sandeep Ramesh Khanzode On Tuesday, June 17, 2014 10:15 PM, Shai Erera ser...@gmail.com wrote: Hi 40 seconds for faceted search is ... crazy. Also, note how the times don't differ much even though the number of hits is much higher (29K vs 15.1M) ... That, w/ that you say that subsequent queries are much faster (few seconds) suggests that something is seriously messed up w/ your environment. Maybe it's a faulty disk? E.g. after the file system cache is warm, you no longer hit the disk? In general, the more hits you have, the more expensive is faceted search. It's also true for scoring as well (i.e. even without facets). There's just more work to determine the top results (docs, facets...). With facets, you can use sampling (see RandomSamplingFacetsCollector), but I would do that only after you verify that collecting 15M docs is very expensive for you, even when the file system cache is hot. I've never seen those numbers before, therefore it's difficult for me to relate to them. There's a caching mechanism for facets, through CachedOrdinalsReader. But I wouldn't go there until you verify that your IO system is good (try another machine, OS, disk ...)., and that the 40s times are truly from the faceting code. Shai On Tue, Jun 17, 2014 at 4:21 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks again! This time, I have indexed data with the following specs. I run into 40 seconds for the FastTaxonomyFacetCounts to create all the facets. Is this as per your measurements? Subsequent runs fare much better probably because of the Windows file system cache. How can I speed this up? I believe there was a CategoryListCache earlier. Is there any cache or other implementation that I can use? Secondly, I had a general question. If I extrapolate these numbers for a billion documents, my search and facet number may probably be unusable in a real time scenario. What are the strategies employed when you deal with such large scale? I am new to Lucene so please also direct me to the relevant info sources. Thanks! Corpus: Count: 20M, Size: 51GB Index: Size (w/o Facets): 19GB, Size (w/Facets): 20.12GB Creation Time (w/o Facets): 3.46hrs, Creation Time (w/Facets): 3.49hrs Search Performance: With 29055 hits (5 terms in query): Query Execution: 8 seconds Facet counts execution: 40-45 seconds With 4.22M hits (2 terms in query):
Re: Facets in Lucene 4.7.2
If I am counting correctly, the $facets field in the index shows a count of approx. 28k. That does not sound like much, I guess. All my facets are flat and the FacetsConfig only defines a couple of them to be multi-valued. Let me know if I am not counting the taxonomy size correctly. The taxoReader.getSize() also shows this count. I will check on a Linux box to make sure. Thanks, --- Thanks n Regards, Sandeep Ramesh Khanzode On Tuesday, June 17, 2014 11:28 PM, Shai Erera ser...@gmail.com wrote: Nothing suspicious ... code looks fine. The call to FastTaxoFacetCounts actually computes the counts ... that's the expensive part of faceted search. How big is your taxonomy (number categories)? Is it hierarchical (i.e. are your dimensions flat, or deep like A/1/2/3/)? What does your FacetsConfig look like? Still, well maybe if your taxonomy is huge (hundreds of millions of categories), I don't think you can intentionally mess up something that much to end up w/ 40-45s response times! Shai On Tue, Jun 17, 2014 at 8:51 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks for your response. It does sound pretty bad which is why I am not sure whether there is an issue with the code, the index, the searcher, or just the machine, as you say. I will try with another machine just to make sure and post the results. Meanwhile, can you tell me if there is anything wrong in the below measurement? Or is the API usage or the pattern incorrect? I used a tool called RAMMap to clean the Windows cache. If I do not, the results are very fast as I mentioned already. If I do, then the total time is 40s. Can you please provide any pointers on what could be wrong? I will be checking on a Linux box anyway. = System.out.println(1. Start Date: + new Date()); TopDocs topDocs = FacetsCollector.search(searcher, query, 100, fc); System.out.println(1. End Date: + new Date()); // Above part takes approx 2-12 seconds depending on the query System.out.println(2. Start Date: + new Date()); ListFacetResult results = new ArrayListFacetResult(); Facets facets = new FastTaxonomyFacetCounts(taxoReader, config, fc); System.out.println(2. End Date: + new Date()); // Above part takes approx 40-53 seconds depending on the query for the first time on Windows System.out.println(3. Start Date: + new Date()); results.add(facets.getTopChildren(1000, F1)); results.add(facets.getTopChildren(1000, F2)); results.add(facets.getTopChildren(1000, F3)); results.add(facets.getTopChildren(1000, F4)); results.add(facets.getTopChildren(1000, F5)); results.add(facets.getTopChildren(1000, F6)); results.add(facets.getTopChildren(1000, F7)); System.out.println(3. End Date: + new Date()); // Above part takes approx less than 1 second = --- Thanks n Regards, Sandeep Ramesh Khanzode On Tuesday, June 17, 2014 10:15 PM, Shai Erera ser...@gmail.com wrote: Hi 40 seconds for faceted search is ... crazy. Also, note how the times don't differ much even though the number of hits is much higher (29K vs 15.1M) ... That, w/ that you say that subsequent queries are much faster (few seconds) suggests that something is seriously messed up w/ your environment. Maybe it's a faulty disk? E.g. after the file system cache is warm, you no longer hit the disk? In general, the more hits you have, the more expensive is faceted search. It's also true for scoring as well (i.e. even without facets). There's just more work to determine the top results (docs, facets...). With facets, you can use sampling (see RandomSamplingFacetsCollector), but I would do that only after you verify that collecting 15M docs is very expensive for you, even when the file system cache is hot. I've never seen those numbers before, therefore it's difficult for me to relate to them. There's a caching mechanism for facets, through CachedOrdinalsReader. But I wouldn't go there until you verify that your IO system is good (try another machine, OS, disk ...)., and that the 40s times are truly from the faceting code. Shai On Tue, Jun 17, 2014 at 4:21 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks again! This time, I have indexed data with the following specs. I run into 40 seconds for the FastTaxonomyFacetCounts to create all the facets. Is this as per your measurements? Subsequent runs fare much better probably because of the Windows file system cache. How can I speed this up? I believe there was a CategoryListCache earlier. Is there any cache or other implementation that I can use? Secondly, I had a general question. If I extrapolate these numbers for a billion documents, my search and facet number may probably be unusable in a real time scenario. What are the strategies employed when you deal
Re: Facets in Lucene 4.7.2
You can get the size of the taxonomy by calling taxoReader.getSize(). What does the 28K of the $facets field denote - the number of terms (drill-down)? If so, that sounds like your taxonomy is of that size. And indeed, this is a tiny taxonomy ... How many facets do you record per document? This also affects the amount of IO that's done during search, as we traverse the BinaryDocValues field, reading the categories of each document. Shai On Tue, Jun 17, 2014 at 9:32 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: If I am counting correctly, the $facets field in the index shows a count of approx. 28k. That does not sound like much, I guess. All my facets are flat and the FacetsConfig only defines a couple of them to be multi-valued. Let me know if I am not counting the taxonomy size correctly. The taxoReader.getSize() also shows this count. I will check on a Linux box to make sure. Thanks, --- Thanks n Regards, Sandeep Ramesh Khanzode On Tuesday, June 17, 2014 11:28 PM, Shai Erera ser...@gmail.com wrote: Nothing suspicious ... code looks fine. The call to FastTaxoFacetCounts actually computes the counts ... that's the expensive part of faceted search. How big is your taxonomy (number categories)? Is it hierarchical (i.e. are your dimensions flat, or deep like A/1/2/3/)? What does your FacetsConfig look like? Still, well maybe if your taxonomy is huge (hundreds of millions of categories), I don't think you can intentionally mess up something that much to end up w/ 40-45s response times! Shai On Tue, Jun 17, 2014 at 8:51 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, Thanks for your response. It does sound pretty bad which is why I am not sure whether there is an issue with the code, the index, the searcher, or just the machine, as you say. I will try with another machine just to make sure and post the results. Meanwhile, can you tell me if there is anything wrong in the below measurement? Or is the API usage or the pattern incorrect? I used a tool called RAMMap to clean the Windows cache. If I do not, the results are very fast as I mentioned already. If I do, then the total time is 40s. Can you please provide any pointers on what could be wrong? I will be checking on a Linux box anyway. = System.out.println(1. Start Date: + new Date()); TopDocs topDocs = FacetsCollector.search(searcher, query, 100, fc); System.out.println(1. End Date: + new Date()); // Above part takes approx 2-12 seconds depending on the query System.out.println(2. Start Date: + new Date()); ListFacetResult results = new ArrayListFacetResult(); Facets facets = new FastTaxonomyFacetCounts(taxoReader, config, fc); System.out.println(2. End Date: + new Date()); // Above part takes approx 40-53 seconds depending on the query for the first time on Windows System.out.println(3. Start Date: + new Date()); results.add(facets.getTopChildren(1000, F1)); results.add(facets.getTopChildren(1000, F2)); results.add(facets.getTopChildren(1000, F3)); results.add(facets.getTopChildren(1000, F4)); results.add(facets.getTopChildren(1000, F5)); results.add(facets.getTopChildren(1000, F6)); results.add(facets.getTopChildren(1000, F7)); System.out.println(3. End Date: + new Date()); // Above part takes approx less than 1 second = --- Thanks n Regards, Sandeep Ramesh Khanzode On Tuesday, June 17, 2014 10:15 PM, Shai Erera ser...@gmail.com wrote: Hi 40 seconds for faceted search is ... crazy. Also, note how the times don't differ much even though the number of hits is much higher (29K vs 15.1M) ... That, w/ that you say that subsequent queries are much faster (few seconds) suggests that something is seriously messed up w/ your environment. Maybe it's a faulty disk? E.g. after the file system cache is warm, you no longer hit the disk? In general, the more hits you have, the more expensive is faceted search. It's also true for scoring as well (i.e. even without facets). There's just more work to determine the top results (docs, facets...). With facets, you can use sampling (see RandomSamplingFacetsCollector), but I would do that only after you verify that collecting 15M docs is very expensive for you, even when the file system cache is hot. I've never seen those numbers before, therefore it's difficult for me to relate to them. There's a caching mechanism for facets, through CachedOrdinalsReader. But I wouldn't go there until you verify that your IO system is good (try another machine, OS, disk ...)., and that the 40s times are truly from the faceting code. Shai On Tue, Jun 17, 2014 at 4:21 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi,
Re: Facets in Lucene 4.7.2
Hi Shai, Thanks for the response. Appreciated! I understand that this particular use case has to be handled in a different way. Can you please help me with the below questions? 1.] Is there any API that gives me the count of a specific dimension from FacetCollector in response to a search query. Currently, I use the getTopChildren() with some value and then check the FacetResult object for the actual number of dimensions hit along with their occurrences. Also, the getSpecificValue() does not work without a path attribute to the API. 2.] Can I find the MAX or MIN value of a Numeric type field written to the index? 3.] I am trying to compare and contrast Lucene Facets with Elastic Search. I could determine that ES does search time faceting and dynamically returns the response without any prior faceting during indexing time. Is index time lag is not my concern, can I assume that, in general, performance-wise Lucene facets would be faster? 4.] I index a semi-large-ish corpus of 20M files across 50GB. If I do not use IndexWriter.commit(), I get standard files like cfe/cfs/si in the index directory. However, if I do use the commit(), then as I understand it, the state is persisted to the disk. But this time, there are additional file extensions like doc/pos/tim/tip/dvd/dvm, etc. I am not sure about this difference and its cause. 5.] Does the RAMBufferSizeMB() control the commit intervals, so that when the limit is reached across all writing threads, the contents are flushed to disk periodically? Appreciate your response to the above queries. Thanks again, --- Thanks n Regards, Sandeep Ramesh Khanzode On Sunday, June 15, 2014 10:40 AM, Shai Erera ser...@gmail.com wrote: Hi Currently there's now way to add e.g. terms to already indexed documents, you have to re-index them. The only updatable field type Lucene offers currently are DocValues fields. If the list of markers/flags is fixed in your case, and you can map them to an integer, I think you could use a NumericDocValues field, which supports field-level updates. Once you do that, you can then: * Count on this field pretty easily. You will need to write a Facets implementation, but otherwise it's very easy. * Filter queries: you will need to write a Filter which returns a DocIdSet of the documents that belong to one category (e.g. Financially Relevant). Here you might want to consider caching the result of the Filter, by using CachingWrapperFilter. It's not the best approach, updatable Terms would better suit your usecase, however we don't offer them yet and it will be a while until we do (and IF we do). You should also benchmark that approach vs re-indexing the documents since the current implementation of updatable doc-values fields isn't optimized for a few document updates between index reopens. See here: http://shaierera.blogspot.com/2014/04/benchmarking-updatable-docvalues.html Shai On Fri, Jun 13, 2014 at 10:19 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi Shai, Thanks so much for the clear explanation. I agree on the first question. Taxonomy Writer with a separate index would probably be my approach too. For the second question: I am a little new to the Facets API so I will try to figure out the approach that you outlined below. However, the scenario is such: Assume a document corpus that is indexed. For a user query, a document is returned and selected by the user for editing as part of some use case/workflow. That document is now marked as either historically interesting or not, financially relevant, specific to media or entertainment domain, etc. by the user. So, essentially the user is flagging the document with certain markers. Another set of users could possibly want to query on these markers. So, lets say, a second user comes along, and wants to see the top documents belonging to one category, say, agriculture or farming. Since these markers are run time activities, how can I use the facets on them? So, I was envisioning facets as the various markers. But, if I constantly re-index or update the documents whenever a marker changes, I believe it would not be very efficient. Is there anything, facets or otherwise, in Lucene that can help me solve this use case? Please let me know. And, thanks! --- Thanks n Regards, Sandeep Ramesh Khanzode On Friday, June 13, 2014 9:51 PM, Shai Erera ser...@gmail.com wrote: Hi You can check the demo code here: https://svn.apache.org/repos/asf/lucene/dev/branches/lucene_solr_4_8/lucene/demo/src/java/org/apache/lucene/demo/facet/ . This code is updated with each release, so you always get a working code examples, even when the API changes. If you don't mind managing the sidecar index, which I agree isn't such a big deal, then yes - the taxonomy index currently performs the fastest. I plan to explore porting the taxonomy-based approach from BinaryDocValues to the
Re: Facets in Lucene 4.7.2
Correction on [4] below. I do get doc/pos/tim/tip/dvd/dvm files in either ase. What I meant was the number of those files appear different in both cases. Also, does commit() stop the world and behave serially to flush the contents? --- Thanks n Regards, Sandeep Ramesh Khanzode On Monday, June 16, 2014 7:10 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.INVALID wrote: Hi Shai, Thanks for the response. Appreciated! I understand that this particular use case has to be handled in a different way. Can you please help me with the below questions? 1.] Is there any API that gives me the count of a specific dimension from FacetCollector in response to a search query. Currently, I use the getTopChildren() with some value and then check the FacetResult object for the actual number of dimensions hit along with their occurrences. Also, the getSpecificValue() does not work without a path attribute to the API. 2.] Can I find the MAX or MIN value of a Numeric type field written to the index? 3.] I am trying to compare and contrast Lucene Facets with Elastic Search. I could determine that ES does search time faceting and dynamically returns the response without any prior faceting during indexing time. Is index time lag is not my concern, can I assume that, in general, performance-wise Lucene facets would be faster? 4.] I index a semi-large-ish corpus of 20M files across 50GB. If I do not use IndexWriter.commit(), I get standard files like cfe/cfs/si in the index directory. However, if I do use the commit(), then as I understand it, the state is persisted to the disk. But this time, there are additional file extensions like doc/pos/tim/tip/dvd/dvm, etc. I am not sure about this difference and its cause. 5.] Does the RAMBufferSizeMB() control the commit intervals, so that when the limit is reached across all writing threads, the contents are flushed to disk periodically? Appreciate your response to the above queries. Thanks again, --- Thanks n Regards, Sandeep Ramesh Khanzode On Sunday, June 15, 2014 10:40 AM, Shai Erera ser...@gmail.com wrote: Hi Currently there's now way to add e.g. terms to already indexed documents, you have to re-index them. The only updatable field type Lucene offers currently are DocValues fields. If the list of markers/flags is fixed in your case, and you can map them to an integer, I think you could use a NumericDocValues field, which supports field-level updates. Once you do that, you can then: * Count on this field pretty easily. You will need to write a Facets implementation, but otherwise it's very easy. * Filter queries: you will need to write a Filter which returns a DocIdSet of the documents that belong to one category (e.g. Financially Relevant). Here you might want to consider caching the result of the Filter, by using CachingWrapperFilter. It's not the best approach, updatable Terms would better suit your usecase, however we don't offer them yet and it will be a while until we do (and IF we do). You should also benchmark that approach vs re-indexing the documents since the current implementation of updatable doc-values fields isn't optimized for a few document updates between index reopens. See here: http://shaierera.blogspot.com/2014/04/benchmarking-updatable-docvalues.html Shai On Fri, Jun 13, 2014 at 10:19 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi Shai, Thanks so much for the clear explanation. I agree on the first question. Taxonomy Writer with a separate index would probably be my approach too. For the second question: I am a little new to the Facets API so I will try to figure out the approach that you outlined below. However, the scenario is such: Assume a document corpus that is indexed. For a user query, a document is returned and selected by the user for editing as part of some use case/workflow. That document is now marked as either historically interesting or not, financially relevant, specific to media or entertainment domain, etc. by the user. So, essentially the user is flagging the document with certain markers. Another set of users could possibly want to query on these markers. So, lets say, a second user comes along, and wants to see the top documents belonging to one category, say, agriculture or farming. Since these markers are run time activities, how can I use the facets on them? So, I was envisioning facets as the various markers. But, if I constantly re-index or update the documents whenever a marker changes, I believe it would not be very efficient. Is there anything, facets or otherwise, in Lucene that can help me solve this use case? Please let me know. And, thanks! --- Thanks n Regards, Sandeep Ramesh Khanzode On Friday, June 13, 2014 9:51 PM, Shai Erera ser...@gmail.com wrote: Hi You can check the demo code here:
Re: Facets in Lucene 4.7.2
Hi Currently there's now way to add e.g. terms to already indexed documents, you have to re-index them. The only updatable field type Lucene offers currently are DocValues fields. If the list of markers/flags is fixed in your case, and you can map them to an integer, I think you could use a NumericDocValues field, which supports field-level updates. Once you do that, you can then: * Count on this field pretty easily. You will need to write a Facets implementation, but otherwise it's very easy. * Filter queries: you will need to write a Filter which returns a DocIdSet of the documents that belong to one category (e.g. Financially Relevant). Here you might want to consider caching the result of the Filter, by using CachingWrapperFilter. It's not the best approach, updatable Terms would better suit your usecase, however we don't offer them yet and it will be a while until we do (and IF we do). You should also benchmark that approach vs re-indexing the documents since the current implementation of updatable doc-values fields isn't optimized for a few document updates between index reopens. See here: http://shaierera.blogspot.com/2014/04/benchmarking-updatable-docvalues.html Shai On Fri, Jun 13, 2014 at 10:19 PM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi Shai, Thanks so much for the clear explanation. I agree on the first question. Taxonomy Writer with a separate index would probably be my approach too. For the second question: I am a little new to the Facets API so I will try to figure out the approach that you outlined below. However, the scenario is such: Assume a document corpus that is indexed. For a user query, a document is returned and selected by the user for editing as part of some use case/workflow. That document is now marked as either historically interesting or not, financially relevant, specific to media or entertainment domain, etc. by the user. So, essentially the user is flagging the document with certain markers. Another set of users could possibly want to query on these markers. So, lets say, a second user comes along, and wants to see the top documents belonging to one category, say, agriculture or farming. Since these markers are run time activities, how can I use the facets on them? So, I was envisioning facets as the various markers. But, if I constantly re-index or update the documents whenever a marker changes, I believe it would not be very efficient. Is there anything, facets or otherwise, in Lucene that can help me solve this use case? Please let me know. And, thanks! --- Thanks n Regards, Sandeep Ramesh Khanzode On Friday, June 13, 2014 9:51 PM, Shai Erera ser...@gmail.com wrote: Hi You can check the demo code here: https://svn.apache.org/repos/asf/lucene/dev/branches/lucene_solr_4_8/lucene/demo/src/java/org/apache/lucene/demo/facet/ . This code is updated with each release, so you always get a working code examples, even when the API changes. If you don't mind managing the sidecar index, which I agree isn't such a big deal, then yes - the taxonomy index currently performs the fastest. I plan to explore porting the taxonomy-based approach from BinaryDocValues to the new SortedNumericDocValues (coming out in 4.9) since it might perform even faster. I didn't quite get the marker/flag facet. Can you give an example? For instance, if you can model that as a NumericDocValuesField added to documents (w/ the different markers/flags translated to numbers), then you can use Lucene's updatable numeric DocValues and write a custom Facets to aggregate on that NumericDocValues field. Shai On Fri, Jun 13, 2014 at 11:48 AM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, I am evaluating Lucene Facets for a project. Since there is a lot of change in 4.7.2 for Facets, I am relying on UTs for reference. Please let me know if there are other sources of information. I have a couple of questions: 1.] All categories in my application are flat, not hierarchical. But, it seems from a few sources, that even that notwithstanding, you would want to use a Taxonomy based index for performance reasons. It is faster but uses more RAM. Or is the deterrent to use it is the fact that it is a separate data structure. If one could do with the life-cycle management of the extra index, should we go ahead with the taxonomy index for better performance across tens of millions of documents? Another note to add is that I do not see a scenario wherein I would want to re-index my collection over and over again or, in other words, the changes would be spread over time. 2.] I need a type of dynamic facet that allows me to add a flag or marker to the document at runtime since it will change/update every time a user modifies or adds to the list of markers. Is this possible to do with the current implementation? Since I believe, that currently all
Facets in Lucene 4.7.2
Hi, I am evaluating Lucene Facets for a project. Since there is a lot of change in 4.7.2 for Facets, I am relying on UTs for reference. Please let me know if there are other sources of information. I have a couple of questions: 1.] All categories in my application are flat, not hierarchical. But, it seems from a few sources, that even that notwithstanding, you would want to use a Taxonomy based index for performance reasons. It is faster but uses more RAM. Or is the deterrent to use it is the fact that it is a separate data structure. If one could do with the life-cycle management of the extra index, should we go ahead with the taxonomy index for better performance across tens of millions of documents? Another note to add is that I do not see a scenario wherein I would want to re-index my collection over and over again or, in other words, the changes would be spread over time. 2.] I need a type of dynamic facet that allows me to add a flag or marker to the document at runtime since it will change/update every time a user modifies or adds to the list of markers. Is this possible to do with the current implementation? Since I believe, that currently all faceting is done at indexing time. --- Thanks n Regards, Sandeep Ramesh Khanzode
Re: Facets in Lucene 4.7.2
Hi You can check the demo code here: https://svn.apache.org/repos/asf/lucene/dev/branches/lucene_solr_4_8/lucene/demo/src/java/org/apache/lucene/demo/facet/. This code is updated with each release, so you always get a working code examples, even when the API changes. If you don't mind managing the sidecar index, which I agree isn't such a big deal, then yes - the taxonomy index currently performs the fastest. I plan to explore porting the taxonomy-based approach from BinaryDocValues to the new SortedNumericDocValues (coming out in 4.9) since it might perform even faster. I didn't quite get the marker/flag facet. Can you give an example? For instance, if you can model that as a NumericDocValuesField added to documents (w/ the different markers/flags translated to numbers), then you can use Lucene's updatable numeric DocValues and write a custom Facets to aggregate on that NumericDocValues field. Shai On Fri, Jun 13, 2014 at 11:48 AM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, I am evaluating Lucene Facets for a project. Since there is a lot of change in 4.7.2 for Facets, I am relying on UTs for reference. Please let me know if there are other sources of information. I have a couple of questions: 1.] All categories in my application are flat, not hierarchical. But, it seems from a few sources, that even that notwithstanding, you would want to use a Taxonomy based index for performance reasons. It is faster but uses more RAM. Or is the deterrent to use it is the fact that it is a separate data structure. If one could do with the life-cycle management of the extra index, should we go ahead with the taxonomy index for better performance across tens of millions of documents? Another note to add is that I do not see a scenario wherein I would want to re-index my collection over and over again or, in other words, the changes would be spread over time. 2.] I need a type of dynamic facet that allows me to add a flag or marker to the document at runtime since it will change/update every time a user modifies or adds to the list of markers. Is this possible to do with the current implementation? Since I believe, that currently all faceting is done at indexing time. --- Thanks n Regards, Sandeep Ramesh Khanzode
Re: Facets in Lucene 4.7.2
Hi Shai, Thanks so much for the clear explanation. I agree on the first question. Taxonomy Writer with a separate index would probably be my approach too. For the second question: I am a little new to the Facets API so I will try to figure out the approach that you outlined below. However, the scenario is such: Assume a document corpus that is indexed. For a user query, a document is returned and selected by the user for editing as part of some use case/workflow. That document is now marked as either historically interesting or not, financially relevant, specific to media or entertainment domain, etc. by the user. So, essentially the user is flagging the document with certain markers. Another set of users could possibly want to query on these markers. So, lets say, a second user comes along, and wants to see the top documents belonging to one category, say, agriculture or farming. Since these markers are run time activities, how can I use the facets on them? So, I was envisioning facets as the various markers. But, if I constantly re-index or update the documents whenever a marker changes, I believe it would not be very efficient. Is there anything, facets or otherwise, in Lucene that can help me solve this use case? Please let me know. And, thanks! --- Thanks n Regards, Sandeep Ramesh Khanzode On Friday, June 13, 2014 9:51 PM, Shai Erera ser...@gmail.com wrote: Hi You can check the demo code here: https://svn.apache.org/repos/asf/lucene/dev/branches/lucene_solr_4_8/lucene/demo/src/java/org/apache/lucene/demo/facet/. This code is updated with each release, so you always get a working code examples, even when the API changes. If you don't mind managing the sidecar index, which I agree isn't such a big deal, then yes - the taxonomy index currently performs the fastest. I plan to explore porting the taxonomy-based approach from BinaryDocValues to the new SortedNumericDocValues (coming out in 4.9) since it might perform even faster. I didn't quite get the marker/flag facet. Can you give an example? For instance, if you can model that as a NumericDocValuesField added to documents (w/ the different markers/flags translated to numbers), then you can use Lucene's updatable numeric DocValues and write a custom Facets to aggregate on that NumericDocValues field. Shai On Fri, Jun 13, 2014 at 11:48 AM, Sandeep Khanzode sandeep_khanz...@yahoo.com.invalid wrote: Hi, I am evaluating Lucene Facets for a project. Since there is a lot of change in 4.7.2 for Facets, I am relying on UTs for reference. Please let me know if there are other sources of information. I have a couple of questions: 1.] All categories in my application are flat, not hierarchical. But, it seems from a few sources, that even that notwithstanding, you would want to use a Taxonomy based index for performance reasons. It is faster but uses more RAM. Or is the deterrent to use it is the fact that it is a separate data structure. If one could do with the life-cycle management of the extra index, should we go ahead with the taxonomy index for better performance across tens of millions of documents? Another note to add is that I do not see a scenario wherein I would want to re-index my collection over and over again or, in other words, the changes would be spread over time. 2.] I need a type of dynamic facet that allows me to add a flag or marker to the document at runtime since it will change/update every time a user modifies or adds to the list of markers. Is this possible to do with the current implementation? Since I believe, that currently all faceting is done at indexing time. --- Thanks n Regards, Sandeep Ramesh Khanzode
