y.I am encounterd by a question about the execution
> of BooleanQuery:although,BooleanQuery#rewrite has done some works to
> remove duplicate FILTER,SHOULD clauses.however still the same term query
> can been executed the several times.
>
> I copy the test code in the Tes
,SHOULD clauses.however still the same term query
can been executed the several times.
I copy the test code in the TestBooleanQuery to approve my assumption.
Unit Test Code as follows:
BooleanQuery.Builder qBuilder = new BooleanQuery.Builder();
qBuilder = new BooleanQuery.Builder
ast 5x compared to old code.
> Is there any thoughts on why term frequency calls on PostingsEnum are that
> slow ?
>
>
>
> *Thanks and Regards,*
> *Vimal Jain*
>
>
> On Wed, Jun 21, 2023 at 1:43 PM Adrien Grand wrote:
>
> > As far as your performance problem i
I did profiling of new code and found that below api call is most time
consuming :-
org.apache.lucene.index.PostingsEnum#freq
If i comment out this call and instead use some random integer for testing
purpose, then perf is at least 5x compared to old code.
Is there any thoughts on why term
mal Jain wrote:
> >
> > > Thanks Adrien for quick response.
> > > Yes , i am replacing disjuncts across multiple fields with single
> custom
> > > term query over merged field.
> > > Can you please provide more details on what do you mean by dynamic
esponse.
> > Yes , i am replacing disjuncts across multiple fields with single custom
> > term query over merged field.
> > Can you please provide more details on what do you mean by dynamic
> pruning
> > in context of custom term query ?
> >
> > On Tue,
.co/blog/faster-retrieval-of-top-hits-in-elasticsearch-with-block-max-wand>
a few years ago if you're curious.
On Tue, Jun 20, 2023 at 6:58 PM Vimal Jain wrote:
> Thanks Adrien for quick response.
> Yes , i am replacing disjuncts across multiple fields with single custom
> te
Thanks Adrien for quick response.
Yes , i am replacing disjuncts across multiple fields with single custom
term query over merged field.
Can you please provide more details on what do you mean by dynamic pruning
in context of custom term query ?
On Tue, 20 Jun, 2023, 9:45 pm Adrien Grand, wrote
Intuitively replacing a disjunction across multiple fields with a single
term query should always be faster.
You're saying that you're storing the type of token as part of the term
frequency. This doesn't sound like something that would play well with
dynamic pruning, so I wonder
Ok , sorry , I realized that I need to provide more context.
So we used to create a lucene query which consisted of custom term queries
for different fields and based on the type of field , we used to assign a
boost that would be used in scoring.
Now we want to get rid off different fields and
i,
> > I want to understand if fetching the term frequency of a term during
> > scoring is relatively cpu bound operation ?
> > Context - I am storing custom term frequency during indexing and later
> > using it for scoring during query execution time ( in Scorer'
Note - i am using lucene 7.7.3
*Thanks and Regards,*
*Vimal Jain*
On Tue, Jun 20, 2023 at 12:26 PM Vimal Jain wrote:
> Hi,
> I want to understand if fetching the term frequency of a term during
> scoring is relatively cpu bound operation ?
> Context - I am storing custom term freq
Hi,
I want to understand if fetching the term frequency of a term during
scoring is relatively cpu bound operation ?
Context - I am storing custom term frequency during indexing and later
using it for scoring during query execution time ( in Scorer's score()
method ). I noticed a performance
Hi Lucene users,
I recently came across SynonymQuery and found out that it only supports
single-term synonyms (since it accepts a list of Term which will be
considered as synonyms). We have some multi-term synonyms like "internet
device" <-> "wifi router" or "dns&q
Oh yes, I also use Spring Cache which works fine and I don't have to store
products in Lucene making index smaller and faster.
On Fri, 23 Sept 2022, 19:26 Stephane Passignat,
wrote:
> Hi
>
> I would don't store the original value. That's "just" an index. But store
> the value of your db identifi
Well, my bad is that I used wrong word. I'm not storing but just goving
keywords to analyzer. That was my mistake in writing. So far I don't index
exotic letters, but just normalized.
Additionally I put in index something like "Prod_3443" which is a product
ID for situation when specific product is
Good point!
For now I'll leave it normalized. Every search term coming from frontend is
stored and also its counter updated which will help me after some time to
see trends and to decide to change the logic or not.
P.S. Here is the funny part: in Croatian "pišanje" means peeing
Hi
I would don't store the original value. That's "just" an index. But store the
value of your db identifiers, because I think you'll want it at some point. (I
made the same kind of feature on top of datanucleus)
I use to have tech id in my db. Even more since I started to use jdo jpa some
20
I think it depends how precise you want to make the search. If you
want to enable diacritic-sensitive search in order to avoid confusions
when users actually are able to enter the diacritics, you can index
both ways (ascii-folded and not folded) and not normalize the query
terms. Or you can just fo
Hi Stephane!
Actually, I have excactly that kind of conversion, but I didn't mention as
my mail was long enough whithout it :)
My main concern it should I let Lucene index original keywords or not.
Considering what you wrote, I guess your answer would be to store only
converted values without exot
Hello,
The way I did it took me some time and I almost sure it's applicable to all
languages.
I normalized the words. Replacing letters or group of letters by another
approaching one.
In french e é è ê ai ei sound a bit the same, and for someone who write
mistakes having to use the right lett
Hi!
I'm using Hibernate Search / Lucene to index my entities in Spring Boot
aplication.
One thing I'm not sure is how to handle Croatian specific letters.
Croatian language has few additional letters "*č* *Č* *ć* *Ć* *đ* *Đ* *š*
*Š* *ž* *Ž*".
Letters "*đ* *Đ*" are commonly replaced with "*dj* *DJ
I am using Lucene 8.6.3 in an application which searches a library of
technical documentation. I have implemented synonym matching which works for
single word replacements, but does not match when one of the synonyms has
two or more words. My attempts to support multi-term synonyms are failing
Hi,
at first i think i missed pointing out my problem exactly. What i wanna do is
run a normal query on my index. After that i wanna change the frequencies of
some important terms to another number and i know neither the new frequency nor
the term which frequency i update at index creation. As
applicable.
If you want to have "per document" scoring factors (not per term), you can also
use additional DocValues fields with per-document factors and you can use a
function query (e.g. using expressions module) to modify the score.
Uwe
-
Uwe Schindler
Achterdiek 19,
.@protonmail.com.invalid wrote:
>
> > Hello,
> > for some Queries i need to calcuate the score mostly like the normal
> > score, but for some documents certain terms are assigned a Frequency given
> > by me and the score should be calculated with these new term frequencies.
>
be calculated with these new term frequencies.
> After some research, it seems i have to implement a custom Query, custom
> Weight and Custom Scorer for this. I wanted to ask if I'm overlooking a
> simpler solution or if this is the way to go.
> Thanks,
> Marcel
--
Adrien
Hello,
for some Queries i need to calcuate the score mostly like the normal score, but
for some documents certain terms are assigned a Frequency given by me and the
score should be calculated with these new term frequencies. After some
research, it seems i have to implement a custom Query
to each of the candidate docs
> >> >
> >> > Simple example of the query:
> >> > - query terms ("aaa", "bbb")
> >> > - indexed docs with terms:
> >> > docId 0 has terms ("aaa", "bbb")
> >>
date docs
>> >
>> > Simple example of the query:
>> > - query terms ("aaa", "bbb")
>> > - indexed docs with terms:
>> > docId 0 has terms ("aaa", "bbb")
>> > docId 1 has terms ("aaa", &q
idates = 1
> > - simple scoring function score(docId) = docId + 10
> > The query first builds a count array [2, 1], because docId 0 contains two
> > matching terms and docId 1 contains 1 matching term.
> > Then it picks docId 0 as the candidate subset.
> > Then i
ple scoring function score(docId) = docId + 10
> The query first builds a count array [2, 1], because docId 0 contains two
> matching terms and docId 1 contains 1 matching term.
> Then it picks docId 0 as the candidate subset.
> Then it applies the scoring function, returning a score of
2, 1], because docId 0 contains two
matching terms and docId 1 contains 1 matching term.
Then it picks docId 0 as the candidate subset.
Then it applies the scoring function, returning a score of 10 for docId 0.
The main bottleneck right now is doing the initial counting, i.e. the part
that ret
e e.g.
string and numeric values from the original document, but not schema level
information like whether offsets/positions are indexed into postings and
term vectors for each field, or not. That would be safe, if you are trying
to avoid the cost of retrieving the full values for all fields from your
ba
Regards,
Albert
> From: "Michael McCandless"
> To: "java-user" , "albert macsweeny"
>
> Sent: Monday, 29 June, 2020 15:23:43
> Subject: Re: Adding fields with same field type complains that they have
> different term vector settings
> Hi A
field results in the following exception
>
> java.lang.IllegalArgumentException: all instances of a given field name
> must have the same term vectors settings (storeTermVectorPositions changed
> for field="f1")
> at
> org.apache.lucene.index.TermVectorsC
e for the same field
results in the following exception
java.lang.IllegalArgumentException: all instances of a given field name must
have the same term vectors settings (storeTermVectorPositions changed for
field="f1")
at
org.apache.lucene.index.TermVectorsConsume
Hi Tommaso, thanks for the input and links! I'll add your paper to my
literature review.
So far I've seen very promising results from modifying the TermInSetQuery.
It was pretty simple to keep a map of `doc id -> matched term count` and
then only evaluate the exact similarity on the
On Wed, 2020-06-24 at 13:46 -0400, Alex K wrote:
> My implementation isn't specific to any particular dataset or access
> pattern (i.e. infinite vs. subset).
Without a clearly defined use case, I would say that the sequential
scan approach is not the right one: As these things goes, someone will
hi Alex,
I had worked on a similar problem directly on Lucene (within Anserini
toolkit) using LSH fingerprints of tokenized feature vector values.
You can find code at [1] and some information on the Anserini documentation
page [2] and in a short preprint [3].
As a side note my current thinking is
Hi Toke. Indeed a nice coincidence. It's an interesting and fun problem
space!
My implementation isn't specific to any particular dataset or access
pattern (i.e. infinite vs. subset).
So far the plugin supports exact L1, L2, Jaccard, Hamming, and Angular
similarities with LSH variants for all but
> tricks
> > >> > for my use case.
> > >> >
> > >> > An example use case for the plugin is reverse image search. A user
> can
> > >> > store vectors representing images and run a nearest-neighbors query
> to
> > >> > retr
On Tue, 2020-06-23 at 09:50 -0400, Alex K wrote:
> I'm working on an Elasticsearch plugin (using Lucene internally) that
> allows users to index numerical vectors and run exact and approximate
> k-nearest-neighbors similarity queries.
Quite a coincidence. I'm looking into the same thing :-)
> 1
elastiknn.klibisz.com/
> >> >
> >> > The main method for indexing the vectors is based on Locality Sensitive
> >> > Hashing <https://en.wikipedia.org/wiki/Locality-sensitive_hashing>.
> >> > The general pattern is:
> >> >
>
>
>> > The main method for indexing the vectors is based on Locality Sensitive
>> > Hashing <https://en.wikipedia.org/wiki/Locality-sensitive_hashing>.
>> > The general pattern is:
>> >
>> >1. When indexing a vector, apply a hash function to it, prod
g a
> set
> >of discrete hashes. Usually there are anywhere from 100 to 1000
> hashes.
> >Similar vectors are more likely to share hashes (i.e., similar vectors
> >produce hash collisions).
> >2. Convert each hash to a byte array and store the byte arr
, producing a set
>of discrete hashes. Usually there are anywhere from 100 to 1000 hashes.
>Similar vectors are more likely to share hashes (i.e., similar vectors
>produce hash collisions).
>2. Convert each hash to a byte array and store the byte array as a
>Lucene
discrete hashes. Usually there are anywhere from 100 to 1000 hashes.
Similar vectors are more likely to share hashes (i.e., similar vectors
produce hash collisions).
2. Convert each hash to a byte array and store the byte array as a
Lucene Term at a specific field.
3. Store the comp
e the contents of this
communication to others.
--
发件人：Akanksha
日 期：2020年01月20日 20:55:23
收件人：
主 题：Lucene Query parser term Length
Hello Everyone
I am working with Lucene 4.7.1
When parsing query using Lucene query parser. If query l
Hello Everyone
I am working with Lucene 4.7.1
When parsing query using Lucene query parser. If query length is greater
than 255 bytes, it returns query with space appended after every 255 bytes.
Which is causing further issues in my project.
Can you please let me know why the term (parsed query
Thanks Ameer!
Was thinking about few ideas. Thought something like tapping into Codec
extension to store multi-IDF values in 2 files, namely an IDF Meta-file & a
IDF Data-file
IDF Meta-file holds List of {UserId, Terms-Data-File-Offset} pairs for each
Term, encoded via ForUtil.
IDF Data-
; dceccarel...@bloomberg.net> wrote:
>
> > Hi Ravi,
> > Can you give more details on how you store an entity into lucene? what is
> > a doc type?
> > what fields do you have?
> >
> > Cheers
> >
> > From: java-user@lucene.apache.org At: 12/03/19 1
give more details on how you store an entity into lucene? what is
> a doc type?
> what fields do you have?
>
> Cheers
>
> From: java-user@lucene.apache.org At: 12/03/19 12:50:40To:
> java-user@lucene.apache.org
> Subject: Multi-IDF for a single term possible?
>
>
Hi Ravi,
Can you give more details on how you store an entity into lucene? what is a doc
type?
what fields do you have?
Cheers
From: java-user@lucene.apache.org At: 12/03/19 12:50:40To:
java-user@lucene.apache.org
Subject: Multi-IDF for a single term possible?
Hello,
We are using TF-IDF
it is enough to give each its own field.
On Tue, Dec 3, 2019 at 7:57 AM Adrien Grand wrote:
> Is there any reason why you are not storing each DOC_TYPE in its own index?
>
> On Tue, Dec 3, 2019 at 1:50 PM Ravikumar Govindarajan
> wrote:
> >
> > Hello,
> >
> > We are using TF-IDF for scoring (Ye
Is there any reason why you are not storing each DOC_TYPE in its own index?
On Tue, Dec 3, 2019 at 1:50 PM Ravikumar Govindarajan
wrote:
>
> Hello,
>
> We are using TF-IDF for scoring (Yet to migrate to BM25). Different
> entities (DOC_TYPES) are crunched & stored together in a single index.
>
>
Hello,
We are using TF-IDF for scoring (Yet to migrate to BM25). Different
entities (DOC_TYPES) are crunched & stored together in a single index.
When it comes to IDF, I find that there is a single value computed across
documents & stored as part of TermStats, whereas our documents are not
homoge
Can you create a scoring scenario that counts the number of fields in
which a term occurs and rank by that (descending) with some kind of
post-filtering?
On Fri, Apr 19, 2019 at 11:24 AM Valentin Popov wrote:
>
> Hi,
> I trying find the way, to search all docs has equals term on
static approaches?
> I would index an auxiliary field which has binary values (0/1 or
> "T"/"F") representing "has equals term on different fields"
> so that you can filtering out the docs (maybe by constant score query).
>
> Tomoko
>
> 2019年4月20
Hi,
I'm not sure there are better ways to meet your requirement by
querying, but how about considering static approaches?
I would index an auxiliary field which has binary values (0/1 or
"T"/"F") representing "has equals term on different fields"
so that you c
Hi,
I trying find the way, to search all docs has equals term on different
fields. Like
doc1 {"foo":"master", "bar":"master"}
doc2 {"foo":"test", "bar":"master"}
As result should be doc1 only.
Right now, I'm ge
on to the following problem:
>
> I’m trying to implement suggestions for PhraseQuery. Let’s say we have a
> PhraseQuery in the form "com.example". I would like to find all the terms
> that are right after the `example` term. From the implementation of the
> PhraseQuery I was
Hello,
I’m having difficulty finding a solution to the following problem:
I’m trying to implement suggestions for PhraseQuery. Let’s say we have a
PhraseQuery in the form "com.example". I would like to find all the terms that
are right after the `example` term. From the implementat
You could use the PostingsEnum API, advance to your document, then call
freq()?
I believe there is also a function query based on the term doc freq.
Mike McCandless
http://blog.mikemccandless.com
On Fri, Nov 17, 2017 at 11:37 AM, Ahmet Arslan
wrote:
> Hi,
>
> I am also intersted
Hi,
I am also intersted into the answer to this question.
I wonder whether term freq. function query would work here.
Ahmet
On Friday, November 17, 2017, 10:32:23 AM GMT+3, Dwaipayan Roy
wrote:
Hi,
I want to get the term frequency of a given term t in a given document with
lucene
Hi,
I want to get the term frequency of a given term t in a given document with
lucene docid say d.
Formally, I need a function say f() that takes two arguments: 1.
lucene-docid d, 2. term t, and returns the number of time t occurs in d.
I know of one solution, that is, traversing the whole
Hi,
I am traversing posting list of a given term/word using the following code. I
am accessing/processing term frequency and document length.
Term term = new Term(field, word);
PostingsEnum postingsEnum = MultiFields.getTermDocsEnum(reader, field,
term.bytes());
if (postingsEnum == null) return
of 50m.
>
> I am looking to reduce search/sort time to 10ms. I have 4g of RAM for the
> java process which is more than sufficient.
>
> Any suggestions greatly appreciated.
>
> Thanks,
> sc
>
>
>
> --
> View this message in context:
> http://lucene.472066.
radius of 50m.
I am looking to reduce search/sort time to 10ms. I have 4g of RAM for the
java process which is more than sufficient.
Any suggestions greatly appreciated.
Thanks,
sc
--
View this message in context:
http://lucene.472066.n3.nabble.com/Term-Dictionary-taking-up-lots-of-memory
can support about 5k qps
> @ p95 9ms, which is a great improvement from the RPT strategy we had been
> using. Once again, thanks for your help.
>
> Best,
> Tom Hirschfeld
>
> On Thu, May 18, 2017 at 4:22 AM, Uwe Schindler wrote:
>
> > Hi,
> > Are you sure that the ter
Once again, thanks for your help.
Best,
Tom Hirschfeld
On Thu, May 18, 2017 at 4:22 AM, Uwe Schindler wrote:
> Hi,
> Are you sure that the term index is the problem? Even with huge indexes
> you never need 65 good of heap! That's impossible.
> Are you sure that your problem is not
is only a match if the tokens are exactly
> the same bytes. There are never done any substring matches, just simple
> comparison of bytes.
>
> To have more fuzzier matches, you have to do text analysis right. This
> includes splitting of tokens (Tokenizer), but also term "normali
term "normalization" (TokenFilters).
One example is lowercasing (to allow case insensitive matching), but also
stemming might be done, or conversion to phonetic codes (to allow phonetic
matches). The output of the tokens does not necessarily need to be "human
readable" an
;
> I need to use lucene to create a mapping file based on text searching and I
> found there is a following problem. Let take a term 'abcd' which is mapped
> to node 'abcd-2' whereas node 'abcd' exists. I found the issue is lucene is
> searching the ter
on that.
I need to use lucene to create a mapping file based on text searching and I
found there is a following problem. Let take a term 'abcd' which is mapped
to node 'abcd-2' whereas node 'abcd' exists. I found the issue is lucene is
searching the term and finds it
following problem. Let take a term 'abcd' which is mapped
to node 'abcd-2' whereas node 'abcd' exists. I found the issue is lucene is
searching the term and finds it in both nodes 'abcd' and 'abcd-2' and gives
the same score. My question is: how to modify
to issue with memory on our leaf servers as the term
> dictionary for the entire index is being loaded into heap space. If we
> allocate > 65g heap space, our queries return relatively quickly (10s -100s
> of ms), but if we drop below ~65g heap space on the leaf nodes, query time
> dr
Hi,
Are you sure that the term index is the problem? Even with huge indexes you
never need 65 good of heap! That's impossible.
Are you sure that your problem is not something else?:
- too large heap? Heaps greater than 31 gigs are bad by default. Lucene needs
only few heap, although you
That sounds like a fun amount of terms!
Note that Lucene does not load all terms into memory; only the "prefix
trie", stored as an FST (
http://blog.mikemccandless.com/2010/12/using-finite-state-transducers-in.html),
mapping term prefixes to on-disk blocks of terms. FSTs are very co
Hirschfeld a
écrit :
> Hey!
>
> I am working on a lucene based service for reverse geocoding. We have a
> large index with lots of unique terms (550 million) and it appears that
> we're running into issue with memory on our leaf servers as the term
> dictionary for the entire ind
Hey!
I am working on a lucene based service for reverse geocoding. We have a
large index with lots of unique terms (550 million) and it appears that
we're running into issue with memory on our leaf servers as the term
dictionary for the entire index is being loaded into heap space. If we
all
Hey!
I am working on a lucene based service for reverse geocoding. We have a
large index with lots of unique terms (550 million) and it appears that
we're running into issue with memory on our leaf servers as the term
dictionary for the entire index is being loaded into heap space. If we
all
ent: Monday 1st May 2017 12:33
> To: java-user@lucene.apache.org; solr-user
> Subject: RE: Term no longer matches if PositionLengthAttr is set to two
>
> Hello again, apologies for cross-posting and having to get back to this
> unsolved problem.
>
> Initially i thought this
-
> From:Markus Jelsma
> Sent: Tuesday 25th April 2017 13:40
> To: java-user@lucene.apache.org
> Subject: Term no longer matches if PositionLengthAttr is set to two
>
> Hello,
>
> We have a decompounder and recently implemented the PositionLengthAttribute
> in it and
query
time seems to be a problem.
Any thoughts on this issue? Is it a bug? Do i not understand
PositionLengthAttribute? Why does it affect term/document matching? At query
time but not at index time?
Many thanks,
Markus
---
Ahh I see.
Term vectors are actually an inverted index for a single document, and they
also have the same postings API as the whole index (including
TermsEnum.totalTermFreq), but that method likely always returns -1 for term
vectors because it's not implemented? Maybe Lucene's def
I think you want to use the TermsEnum.totalTermFreq method?
Mike McCandless
http://blog.mikemccandless.com
On Sun, Apr 16, 2017 at 11:36 AM, Manjula Wijewickrema
wrote:
> Hi,
>
> Is there any way to get the total count of terms in the Term Frequency
> Vector (tvf)? I need to c
Hi,
Is there any way to get the total count of terms in the Term Frequency
Vector (tvf)? I need to calculate the Normalized term frequency of each
term in my tvf. I know how to obtain the length of the tvf, but it doesn't
work since I need to count duplicate occurrences as well.
H
See "termfreq" in the function query section of the reference guide.
Best,
Erick
On Thu, Apr 6, 2017 at 1:02 AM, Manjula Wijewickrema
wrote:
> Hi,
>
> I have a document collection with hundreds of documents. I need to do know
> the term frequency for a given query term in
Hi,
I have a document collection with hundreds of documents. I need to do know
the term frequency for a given query term in each document. I know that
'hit.score' will give me the Lucene score for each document (and it
includes term frequency as well). But I need to call only term freq
I have code as part of LUCENE-5318 that counts terms that cooccur within a
window of where your query terms appear. This makes a really useful query term
recommender, and the math is dirt simple.
INPUT
Doc1: quick brown fox jumps over the lazy dog
Doc2: quick green fox leaps over the lazy dog
bitsch-punkt
| xmlns:tg = "http://www.turnguard.com/turnguard#";
| blockchain : https://onename.com/turnguard
2017-03-10 11:49 GMT+01:00 Dawid Weiss :
> Or you could encode those term/ ngram frequencies one FST and then
> reuse it. This would be memory-saving and fairly fast (~compar
er to make it faster to get ngram counts.
>
> Mike McCandless
>
> http://blog.mikemccandless.com
>
> On Thu, Mar 9, 2017 at 3:22 PM, Jürgen Jakobitsch <
> juergen.jakobit...@semantic-web.com> wrote:
>
>> hi,
>>
>> i'd like to ask users for their experien
Or you could encode those term/ ngram frequencies one FST and then
reuse it. This would be memory-saving and fairly fast (~comparable to
a hash table).
Dawid
On Fri, Mar 10, 2017 at 11:41 AM, Michael McCandless
wrote:
> Yes, this is a reasonable way to use Lucene (to see terms statistics acr
h the fastest way to access
> the term dictionary.
>
> what i want to do is to implement some algorithms to find phrases (e.g.
> mutual rank ratio [1])
> (and other statistics on term distribution, generally: corpus related
> stuff)
>
> the idea would be to do statistics on n
hi,
i'd like to ask users for their experiences with the fastest way to access
the term dictionary.
what i want to do is to implement some algorithms to find phrases (e.g.
mutual rank ratio [1])
(and other statistics on term distribution, generally: corpus related stuff)
the idea would be
anec a écrit :
>> Lucene beginner here, please excuse me if I’m asking anything obvious.
>>
>> In Lucene 6, LongField and IntField were renamed to LegacyLongField
>> and LegacyIntField, deprecated with a JavaDoc suggestion to use
>> LongPoint and IntPoint classes ins
th a JavaDoc suggestion to use LongPoint and
> IntPoint classes instead.
>
> However, it seems impossible to build a term vocabulary (=enumerate all
> distinct values) of these XPoint fields.
>
> As a third option, one can add a field of class NumericDocValuesField. I
> tried har
Lucene beginner here, please excuse me if I’m asking anything obvious.
In Lucene 6, LongField and IntField were renamed to LegacyLongField and
LegacyIntField, deprecated with a JavaDoc suggestion to use LongPoint and
IntPoint classes instead.
However, it seems impossible to build a term
ring ID = "_id" and ... KEY =
>> > "5836962b0293a47b09d345f1". Minimises the risk of typos.
>> >
>> > And use RAMDirectory. Means your program doesn't leave junk on my disk
>> if
>> > I run it, and also means it starts with an empty
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