[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16863160#comment-16863160
]
Nazerke Seidan edited comment on SOLR-13047 at 6/13/19 2:56 PM:
[~ctargett], closed the PR #659.
was (Author: snazerke):
[~ctargett], closed the PR #660.
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
> Fix For: 8.2
>
> Time Spent: 1h
> Remaining Estimate: 0h
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Commented] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16863160#comment-16863160
]
Nazerke Seidan commented on SOLR-13047:
---
[~ctargett], closed the PR #660.
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
> Fix For: 8.2
>
> Time Spent: 1h
> Remaining Estimate: 0h
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16818793#comment-16818793
]
Nazerke Seidan edited comment on SOLR-13047 at 4/16/19 10:50 AM:
-
Regarding the implementation details, are the math expressions limited to
metrics such as sum, count, max, min and avg?
I came up with the following implementation ideas:
This is a constructor how it looks like in this stream:
facet2DStream(String collection, ModifiableSolrParams params, Bucket x, Bucket
y, String dimensions, Metric metric).
The basic idea is that first I will apply a count metric on the given buckets.
Then I will internally sort the buckets in descending order. Then I will get
the tuples while the x and y values are not equal in the dimensions.
Any suggestions?
was (Author: snazerke):
Regarding the implementation details, are the math expressions limited to
metrics such as sum, count, max, min and avg?
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16818793#comment-16818793
]
Nazerke Seidan edited comment on SOLR-13047 at 4/16/19 9:00 AM:
Regarding the implementation details, are the math expressions limited to
metrics such as sum, count, max, min and avg?
was (Author: snazerke):
Regarding the math expressions, is it limited to metrics?
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Commented] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16818793#comment-16818793
]
Nazerke Seidan commented on SOLR-13047:
---
Regarding the math expressions, is it limited to metrics?
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Issue Comment Deleted] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Nazerke Seidan updated SOLR-13047:
--
Comment: was deleted
(was: Regarding the implementation details, are the math expressions limited to
metrics such as count(*) , sum(col), max(col), min(col) and avg(col)? Why do we
need count? as a parameter in the facet2D? Obviously, we are returning 300
diseases containing 10 symptoms for each disease.)
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16818790#comment-16818790
]
Nazerke Seidan edited comment on SOLR-13047 at 4/16/19 8:57 AM:
Regarding the implementation details, are the math expressions limited to
metrics such as count(*) , sum(col), max(col), min(col) and avg(col)? Why do we
need count? as a parameter in the facet2D? Obviously, we are returning 300
diseases containing 10 symptoms for each disease.
was (Author: snazerke):
Regarding the implementation details, are the math expressions limited to
metrics such as count(*), sum(col), max(col), min(col) and avg(col)? Why do we
need count(*) as a parameter in the facet2D? Obviously, we are returning 300
diseases containing 10 symptoms for each disease.
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Commented] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16818790#comment-16818790
]
Nazerke Seidan commented on SOLR-13047:
---
Regarding the implementation details, are the math expressions limited to
metrics such as count(*), sum(col), max(col), min(col) and avg(col)? Why do we
need count(*) as a parameter in the facet2D? Obviously, we are returning 300
diseases containing 10 symptoms for each disease.
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression
[
https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16818790#comment-16818790
]
Nazerke Seidan edited comment on SOLR-13047 at 4/16/19 8:56 AM:
Regarding the implementation details, are the math expressions limited to
metrics such as count(*), sum(col), max(col), min(col) and avg(col)? Why do we
need count(*) as a parameter in the facet2D? Obviously, we are returning 300
diseases containing 10 symptoms for each disease.
was (Author: snazerke):
Regarding the implementation details, are the math expressions limited to
metrics such as count(*), sum(col), max(col), min(col) and avg(col)? Why do we
need count(*) as a parameter in the facet2D? Obviously, we are returning 300
diseases containing 10 symptoms for each disease.
> Add facet2D Streaming Expression
>
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
>Reporter: Joel Bernstein
>Assignee: Joel Bernstein
>Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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[jira] [Commented] (SOLR-13391) Add variance and standard deviation stream evaluators
[ https://issues.apache.org/jira/browse/SOLR-13391?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16814745#comment-16814745 ] Nazerke Seidan commented on SOLR-13391: --- [~joel.bernstein], I have ready code on this to the pull request. What I used were StatUtils.variance(data) and Math.sqrt(StatUtils.variance(data)) instead of StatUtils.mean(data) in the MeanEvaluator.java. > Add variance and standard deviation stream evaluators > - > > Key: SOLR-13391 > URL: https://issues.apache.org/jira/browse/SOLR-13391 > Project: Solr > Issue Type: Improvement > Security Level: Public(Default Security Level. Issues are Public) > Components: streaming expressions > Reporter: Nazerke Seidan >Priority: Minor > Labels: pull-request-available > > It seems variance and standard deviation stream evaluators are not supported > by any of the solr version. For example, > let(echo="m,v,sd", arr=array(1,3,3), m=mean(a), v=var(a), > sd=stddev(a)) > So far, only the mean function is implemented. I think it is useful to have > var and sttdev functions separately as a stream evaluator. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
[jira] [Created] (SOLR-13391) Add variance and standard deviation stream evaluators
Nazerke Seidan created SOLR-13391: - Summary: Add variance and standard deviation stream evaluators Key: SOLR-13391 URL: https://issues.apache.org/jira/browse/SOLR-13391 Project: Solr Issue Type: Improvement Security Level: Public (Default Security Level. Issues are Public) Components: streaming expressions Reporter: Nazerke Seidan It seems variance and standard deviation stream evaluators are not supported by any of the solr version. For example, let(echo="m,v,sd", arr=array(1,3,3), m=mean(a), v=var(a), sd=stddev(a)) So far, only the mean function is implemented. I think it is useful to have var and sttdev functions separately as a stream evaluator. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
Re: Query about solr volunteers to mentor: GSoC 19
Hi David, Thank you for your response! Well, actually I have recently started learning Solr, thus I have not explored it much. I am currently focusing on *streaming expressions in SolrJ *and its use cases. It would be much useful if there were enough streaming expressions examples using not only curl tool but also solrj and others. I think from your interest list, *UnifiedHighlighter *seems interesting for me. Also, this highlighter does not support surround parser that I have read. It might be useful to implement this, but I have no clue about its implementation complexity. Also, adding multi-highlighting feature can be useful. Other than that, I was thinking of a *feature* to solr admin ui for modifying the solrconfig.xml without any command line tools. But I am not sure about the security stuff. --Nazerke On Mon, Mar 4, 2019 at 10:31 PM David Smiley wrote: > BTW another topic is the migration of Solr's admin UI to a more modern > Angular JS -- or something like that -- I haven't been following that very > closely. I'm definitely not the right mentor for that but perhaps someone > here could mentor if you choose to pick that up. > > ~ David Smiley > Freelance Apache Lucene/Solr Search Consultant/Developer > http://www.linkedin.com/in/davidwsmiley > > > On Mon, Mar 4, 2019 at 4:16 PM David Smiley > wrote: > >> Hello Nazerke, >> >> Thanks for your interest and proactively reaching out to us! >> >> I am interested in being a mentor provided the topic interests me. >> Topics of interest to me: >> * spatial >>ex: any open issue, such as: >> https://issues.apache.org/jira/browse/SOLR-4242 >> * highlighting >>ex: any open issue, esp. relating to the UnifiedHighlighter >> * test infrastructure utilities >> * benchmarking automation >> * the build: migrate from Ant to Gradle >> * refactorings related to technical debt >> >> And perhaps others might interest me if you propose something specific. I >> know you commented on SOLR-10329 but I'd rather not mentor for that. >> >> Depending on the scope of some issue(s) there might be multiple actual >> things to work on, perhaps ideally in the same subject area. >> >> What do you think? >> >> ~ David >> >> On Mon, Mar 4, 2019 at 11:35 AM Nazerke Seidan >> wrote: >> >>> Hi All, >>> >>> I am a final year CS BSc student, interested in participating GSoC'19 by >>> contributing to Apache Solr project. I was wondering if there are any >>> volunteers from Solr community to mentor GSoC'19 project. I would like to >>> discuss about potential topics. >>> >>> >>> Many thanks, >>> >>> Nazerke >>> >> -- >> Lucene/Solr Search Committer (PMC), Developer, Author, Speaker >> LinkedIn: http://linkedin.com/in/davidwsmiley | Book: >> http://www.solrenterprisesearchserver.com >> >
[jira] [Commented] (SOLR-7229) Allow DIH to handle attachments as separate documents
[ https://issues.apache.org/jira/browse/SOLR-7229?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16783547#comment-16783547 ] Nazerke Seidan commented on SOLR-7229: -- Hi Tim, I was wondering whether this project is still open or not? I would like to participate in GSoC'19 by contributing to solr community. > Allow DIH to handle attachments as separate documents > - > > Key: SOLR-7229 > URL: https://issues.apache.org/jira/browse/SOLR-7229 > Project: Solr > Issue Type: Improvement >Reporter: Tim Allison >Assignee: Alexandre Rafalovitch >Priority: Minor > Labels: gsoc2017 > > With Tika 1.7's RecursiveParserWrapper, it is possible to maintain metadata > of individual attachments/embedded documents. Tika's default handling was to > maintain the metadata of the container document and concatenate the contents > of all embedded files. With SOLR-7189, we added the legacy behavior. > It might be handy, for example, to be able to send an MSG file through DIH > and treat the container email as well each attachment as separate (child?) > documents, or send a zip of jpeg files and correctly index the geo locations > for each image file. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
[jira] [Commented] (SOLR-10329) Rebuild Solr examples
[ https://issues.apache.org/jira/browse/SOLR-10329?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16783533#comment-16783533 ] Nazerke Seidan commented on SOLR-10329: --- Hi Alexandre, I was wondering whether this project is still open or not? I would like to participate in GSoC'19 by contributing to solr community. Many thanks! > Rebuild Solr examples > - > > Key: SOLR-10329 > URL: https://issues.apache.org/jira/browse/SOLR-10329 > Project: Solr > Issue Type: Wish > Components: examples >Reporter: Alexandre Rafalovitch >Priority: Major > Labels: gsoc2017 > > Apache Solr ships with a number of examples. They evolved from a kitchen sync > example and are rather large. When new Solr features are added, they are > often shoehorned into the most appropriate example and sometimes are not > represented at all. > Often, for new users, it is hard to tell what part of example is relevant, > what part is default and what part is demonstrating something completely > different. > It would take significant (and very appreciated) effort to review all the > examples and rebuild them to provide clean way to showcase best practices > around base and most recent features. > Specific issues are around kitchen sync vs. minimal examples, better approach > to "schemaless" mode and creating examples and datasets that allow to create > both "hello world" and more-advanced tutorials. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
Query about solr volunteers to mentor: GSoC 19
Hi All, I am a final year CS BSc student, interested in participating GSoC'19 by contributing to Apache Solr project. I was wondering if there are any volunteers from Solr community to mentor GSoC'19 project. I would like to discuss about potential topics. Many thanks, Nazerke
