[jira] [Commented] (LUCENE-5159) compressed diskdv sorted/sortedset termdictionaries

2013-08-09 Thread Michael McCandless (JIRA)

[ 
https://issues.apache.org/jira/browse/LUCENE-5159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13735496#comment-13735496
 ] 

Michael McCandless commented on LUCENE-5159:


+1, patch looks great.  I love the new test case!

 compressed diskdv sorted/sortedset termdictionaries
 ---

 Key: LUCENE-5159
 URL: https://issues.apache.org/jira/browse/LUCENE-5159
 Project: Lucene - Core
  Issue Type: Improvement
  Components: core/index
Reporter: Robert Muir
 Attachments: LUCENE-5159.patch, LUCENE-5159.patch, LUCENE-5159.patch


 Sorted/SortedSet give you ordinal(s) per document, but them separately have a 
 term dictionary of all the values.
 You can do a few operations on these:
 * ord - term lookup (e.g. retrieving facet labels)
 * term - ord lookup (reverse lookup: e.g. fieldcacherangefilter)
 * get a term enumerator (e.g. merging, ordinalmap construction)
 The current implementation for diskdv was the simplest thing that can 
 possibly work: under the hood it just makes a binary DV for these (treating 
 ordinals as document ids). When the terms are fixed length, you can address a 
 term directly with multiplication. When they are variable length though, we 
 have to store a packed ints structure in RAM.
 This variable length case is overkill and chews up a lot of RAM if you have 
 many unique values. It also chews up a lot of disk since all the values are 
 just concatenated (no sharing).

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[jira] [Commented] (LUCENE-5159) compressed diskdv sorted/sortedset termdictionaries

2013-08-09 Thread ASF subversion and git services (JIRA)

[ 
https://issues.apache.org/jira/browse/LUCENE-5159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13735596#comment-13735596
 ] 

ASF subversion and git services commented on LUCENE-5159:
-

Commit 1512543 from [~rcmuir] in branch 'dev/trunk'
[ https://svn.apache.org/r1512543 ]

LUCENE-5159: prefix-code the sorted/sortedset value dictionaries in DiskDV

 compressed diskdv sorted/sortedset termdictionaries
 ---

 Key: LUCENE-5159
 URL: https://issues.apache.org/jira/browse/LUCENE-5159
 Project: Lucene - Core
  Issue Type: Improvement
  Components: core/index
Reporter: Robert Muir
 Attachments: LUCENE-5159.patch, LUCENE-5159.patch, LUCENE-5159.patch


 Sorted/SortedSet give you ordinal(s) per document, but them separately have a 
 term dictionary of all the values.
 You can do a few operations on these:
 * ord - term lookup (e.g. retrieving facet labels)
 * term - ord lookup (reverse lookup: e.g. fieldcacherangefilter)
 * get a term enumerator (e.g. merging, ordinalmap construction)
 The current implementation for diskdv was the simplest thing that can 
 possibly work: under the hood it just makes a binary DV for these (treating 
 ordinals as document ids). When the terms are fixed length, you can address a 
 term directly with multiplication. When they are variable length though, we 
 have to store a packed ints structure in RAM.
 This variable length case is overkill and chews up a lot of RAM if you have 
 many unique values. It also chews up a lot of disk since all the values are 
 just concatenated (no sharing).

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[jira] [Commented] (LUCENE-5159) compressed diskdv sorted/sortedset termdictionaries

2013-08-09 Thread ASF subversion and git services (JIRA)

[ 
https://issues.apache.org/jira/browse/LUCENE-5159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13735651#comment-13735651
 ] 

ASF subversion and git services commented on LUCENE-5159:
-

Commit 1512548 from [~rcmuir] in branch 'dev/branches/branch_4x'
[ https://svn.apache.org/r1512548 ]

LUCENE-5159: prefix-code the sorted/sortedset value dictionaries in DiskDV

 compressed diskdv sorted/sortedset termdictionaries
 ---

 Key: LUCENE-5159
 URL: https://issues.apache.org/jira/browse/LUCENE-5159
 Project: Lucene - Core
  Issue Type: Improvement
  Components: core/index
Reporter: Robert Muir
 Attachments: LUCENE-5159.patch, LUCENE-5159.patch, LUCENE-5159.patch


 Sorted/SortedSet give you ordinal(s) per document, but them separately have a 
 term dictionary of all the values.
 You can do a few operations on these:
 * ord - term lookup (e.g. retrieving facet labels)
 * term - ord lookup (reverse lookup: e.g. fieldcacherangefilter)
 * get a term enumerator (e.g. merging, ordinalmap construction)
 The current implementation for diskdv was the simplest thing that can 
 possibly work: under the hood it just makes a binary DV for these (treating 
 ordinals as document ids). When the terms are fixed length, you can address a 
 term directly with multiplication. When they are variable length though, we 
 have to store a packed ints structure in RAM.
 This variable length case is overkill and chews up a lot of RAM if you have 
 many unique values. It also chews up a lot of disk since all the values are 
 just concatenated (no sharing).

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[jira] [Commented] (LUCENE-5159) compressed diskdv sorted/sortedset termdictionaries

2013-08-08 Thread Michael McCandless (JIRA)

[ 
https://issues.apache.org/jira/browse/LUCENE-5159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13733787#comment-13733787
 ] 

Michael McCandless commented on LUCENE-5159:


+1, patch looks great.

 compressed diskdv sorted/sortedset termdictionaries
 ---

 Key: LUCENE-5159
 URL: https://issues.apache.org/jira/browse/LUCENE-5159
 Project: Lucene - Core
  Issue Type: Improvement
  Components: core/index
Reporter: Robert Muir
 Attachments: LUCENE-5159.patch, LUCENE-5159.patch


 Sorted/SortedSet give you ordinal(s) per document, but them separately have a 
 term dictionary of all the values.
 You can do a few operations on these:
 * ord - term lookup (e.g. retrieving facet labels)
 * term - ord lookup (reverse lookup: e.g. fieldcacherangefilter)
 * get a term enumerator (e.g. merging, ordinalmap construction)
 The current implementation for diskdv was the simplest thing that can 
 possibly work: under the hood it just makes a binary DV for these (treating 
 ordinals as document ids). When the terms are fixed length, you can address a 
 term directly with multiplication. When they are variable length though, we 
 have to store a packed ints structure in RAM.
 This variable length case is overkill and chews up a lot of RAM if you have 
 many unique values. It also chews up a lot of disk since all the values are 
 just concatenated (no sharing).

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