The document discusses the challenges and techniques related to working with deeply nested documents in Apache Solr, including their indexing, querying, and faceting. It emphasizes the importance of retaining the hierarchical context of data, using examples such as blog posts and comments to illustrate the necessity of nested structures. Additionally, the document outlines various methods for indexing and querying these nested documents, highlighting specific commands and considerations for effective data management.

1. Working with deeply nested documents in
Apache Solr
Anshum Gupta
Apache Lucene/Solr PMC member & committer
IBM Watson

2. 2
Anshum Gupta
• Apache Lucene/Solr committer and PMC member
• Search guy @ IBM Watson.
• Interested in search and related stuff.
• Apache Lucene since 2006 and Solr since 2010.

3. 3
Agenda
• Hierarchical Data/Nested Documents
• Indexing Nested Documents
• Querying Nested Documents
• Faceting on Nested Documents
Thanks to my fellow IBMer Alisa Zhila for working on this with me!

4. Hierarchical Documents

5. 5
• Social media comments, Email threads,
Annotated data - AI
• Relationship between documents
• Possibility to flatten
Need for nested data
EXAMPLE: Blog Post with Comments
Peter Navarro outlines the Trump economic plan
Tyler Cowen, September 27, 2016 at 3:07am
Trump proposes eliminating America’s $500 billion
trade deficit through a combination of increased
exports and reduced imports.
1 Ray Lopez September 27, 2016 at 3:21 am
I’ll be the first to say this, but the analysis is flawed.
{negative}
2 Brian Donohue September 27, 2016 at 9:20 am
The math checks out. Solid.
{positive}
examples from http://marginalrevolution.com

6. 6
• Can not flatten, need to retain context
• Relationship between documents
• Get all 'positive comments' to 'posts about
Trump' -- IMPOSSIBLE!!!
Nested Documents
EXAMPLE: Data Flattening
Title: Peter Navarro outlines the Trump economic plan
Author: Tyler Cowen
Date: September 27, 2016 at 3:07am
Body: Trump proposes eliminating America’s $500 billion
trade deficit through a combination of increased exports and
reduced imports.
Comment_authors: [Ray Lopez, Brian Donohue]
Comment_dates: [September 27, 2016 at 3:21 am,
September 27, 2016 at 9:20 am]
Comment_texts: ["I’ll be the first to say this, but the analysis is
flawed.", "The math checks out. Solid."]
Comment_sentiments: [negative, positive]

7. 7
• Can not flatten, need to retain context
• Relationship between documents
• Get all 'positive comments' to 'posts about
Trump' -- POSSIBLE!!! (stay tuned)
Nested Documents
EXAMPLE: Hierarchical Documents
Type: Post
Title: Peter Navarro outlines the Trump economic plan
Author: Tyler Cowen
Date: September 27, 2016 at 3:07am
Body: Trump proposes eliminating America’s $500 billion
trade deficit through a combination of increased exports and
reduced imports.
Type: Comment
Author: Ray Lopez
Date: September 27, 2016 at 3:21 am
Text: I’ll be the first to say this, but the analysis is flawed.
Sentiment: negative
Type: Comment
Author: Brian Donohue
Date: September 27, 2016 at 9:20 am
Text: The math checks out. Solid.
Sentiment: positive

8. 8
• Blog Post Data with Comments and Replies
from http://marginalrevolution.com (cured)
• 2 posts, 2-3 comments per post, 0-3 replies
per comment
• Extracted keywords & sentiment data
• 4 levels of "nesting"
• Too big to show on slides
• Data + Scripts + Demo Queries:
• https://github.com/alisa-ipn/solr-
revolution-2016-nested-demo
Running Example

9. Indexing Nested Documents

10. 10
• Nested XML
• JSON Documents
• Add _childDocument_ tags for all children
• Pre-process field names to FQNs
• Lose information, or add that as meta-data during pre-processing
• JSON Document endpoint (6x only) - /update/json/docs
• Field name mappings
• Child Document splitting - Enhanced support coming soon.
Sending Documents to Solr

11. 11
solr-6.2.1$ bin/post -c demo-xml ./data/example-data.xml
Sending Documents to Solr: Nested XML
<add>
<doc>
<field name="type">post</field>
<field name="author"> "Alex Tabarrok"</field>
<field name="title">"The Irony of Hillary Clinton’s Data Analytics"</
field>
<field name="body">"Barack Obama’s campaign adopted data but
Hillary Clinton’s campaign has been molded by data from birth."</field>
<field name="id">"12015-24204"</field>
<doc>
<field name="type">comment</field>
<field name="author">"Todd"</field>
<field name="text">"Clinton got out data-ed and out organized in
2008 by Obama. She seems at least to learn over time, and apply the
lessons learned to the real world."</field>
<field name="sentiment">"positive"</field>
<field name="id">"29798-24171"</field>
<doc>
<field name="type">reply</field>
<field name="author">"The Other Jim"</field>
<field name="text">"No, she lost because (1) she is thoroughly
detested person and (2) the DNC decided Obama should therefore
win."</field>
<field name="sentiment">"negative"</field>
<field name="id">"29798-21232"</field>
</doc>
</doc>
</doc>
</add>

12. 12
• Add _childDocument_ tags for all children
• Pre-process field names to FQNs
• Lose information, or add that as meta-data during pre-processing
solr-6.2.1$ bin/post -c demo-solr-json ./data/small-example-data-solr.json -format solr
Sending Documents to Solr: JSON Documents
[{ "path": "1.posts",
"id": "28711",
"author": "Alex Tabarrok",
"title": "The Irony of Hillary Clinton’s Data Analytics",
"body": "Barack Obama’s campaign adopted data but Hillary Clinton’s campaign
has been molded by data from birth.",
"_childDocuments_": [
{
"path": "2.posts.comments",
"id": "28711-19237",
"author": "Todd",
"text": "Clinton got out data-ed and out organized in 2008 by Obama. She
seems at least to learn over time, and apply the lessons learned to the real world.",
"sentiment": "positive",
"_childDocuments_": [
{
"path": "3.posts.comments.replies",
"author": "The Other Jim",
"id": "28711-12444",
"sentiment": "negative",
"text": "No, she lost because (1) she is thoroughly detested person and
(2) the DNC decided Obama should therefore win."
}]}]}]

13. 13
• JSON Document endpoint (6x only) - /update/json/docs
• Field name mappings
• Child Document splitting - Enhanced support coming soon.
solr-6.2.1$ curl 'http://localhost:8983/solr/gettingstarted/update/json/docs?
split=/|/posts|/posts/comments|/posts/comments/replies&commit=true' --data-
binary @small-example-data.json -H ‘Content-type:application/json'
NOTE: All documents must contain a unique ID.
Sending Documents to Solr: JSON Endpoint

14. 14
• Update Request Processors don’t work with nested documents
• Example:
• UUID update processor does not auto-add an id for a child document.
• Workaround:
• Take responsibility at the client layer to handle the computation for nested
documents.
• Change the update processor in Solr to handle nested documents.
Update Processors and Nested Documents

15. 15
• The entire block needs reindexing
• Forgot to add a meta-data field that might be useful? Complete reindex
• Store everything in Solr IF
• it’s too expensive to reconstruct the doc from original data source
• No access to data anymore e.g. streaming data
Re-Indexing Your Documents

16. 16
• Various ways to index nested documents
• Need to re-index entire block
Nested Document Indexing Summary

17. Let’s ask some interesting questions

18. 18
{
"path":["4.posts.comments.replies.keywords"],
"text":["Trump"]},
{
"path":["3.posts.comments.keywords"],
"text":["Trump"]},
{
"path":["2.posts.keywords"],
"text":["Trump"]},
{
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is funnier."],
"path":["3.posts.comments.replies"]},
{
"text":["Trump proposes eliminating America’s $500 billion trade deficit through a combination of increased exports and reduced imports."],
"path":["1.posts"]},
{
"text":["Hillary was impressive, for sure, and Trump spent time spluttering and floundering, but he was actually able to find his feet and score some points."],
"path":["2.posts.comments"]}
Easy question first
Find all documents that mention Trump
q=text:Trump

19. 19
{
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is funnier."],
"path":["3.posts.comments.replies"]},
{
"text":["Hillary was impressive, for sure, and Trump spent time spluttering and floundering, but he was actually able to find his feet and score some points."],
"path":["2.posts.comments"]},
{
"text":["No one goes to Clinton rallies while tens of thousands line up to see Trump, data-mining leads to a fantasy view of the World."],
"path":["2.posts.comments"]}
Returning certain types of documents
Find all comments and replies that mention Trump
q=(path:2.posts.comments OR path:3.posts.comments.replies) AND text:Trump
Recipe:
At the data pre-processing stage, add a field that indicates document type
and also its path in the hierarchy (-- stay tuned):

20. 20
{
"path":["3.posts.comments.keywords"],
"sentiment":["positive"],
"text":["Hillary"]},
{
"path":["4.posts.comments.replies.keywords"],
"sentiment":["negative"],
"text":["Hillary"]},
{
"path":["2.posts.keywords"],
"text":["Hillary"]}
Returning similar type from different level
Find all keywords that are Hillary
q=path:*.keywords AND text:Hillary
Recipe:
Use wild-cards in the field that stores the hierarchy path

21. Cross-Level Querying

22. 22
{
"path":["3.posts.comments.keywords"],
"sentiment":["positive"],
"text":["Hillary"]},
{
"path":["4.posts.comments.replies.keywords"],
"sentiment":["negative"],
"text":["Hillary"]},
{
"path":["2.posts.keywords"],
"text":["Hillary"]}
Recap so far...
Find all keywords that are Hillary
q=path:*.keywords AND text:Hillary
We're querying precisely for documents
which we provide a search condition for
Query
Level 3
Result
Level 3
Query
Level 4
Result
Level 4
Query
Level 2
Result
Level 2

23. 23
Returning parents by querying children:
Block Join Parent Query
Find all comments whose keywords detected positive sentiment towards Hillary
q={!parent which="path:2.posts.comments"}path:3.posts.comments.keywords AND text:Hillary AND sentiment:positive
Query
Level 3
Result
Level 2
{
"author":["Brian Donohue"],
"text":["Hillary was impressive, for sure, and Trump spent time spluttering and floundering,
but he was actually able to find his feet and score some points."],
"path":["2.posts.comments"]},
{
"author":["Todd"],
"text":["Clinton got out data-ed and out organized in 2008 by Obama. She seems at least to
learn over time, and apply the lessons learned to the real world."],
"path":["2.posts.comments"]}

24. 24
{
"sentiment":["negative"],
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is funnier."],
"path":["3.posts.comments.replies"]},
{
"sentiment":["neutral"],
"text":["So then I guess he will also eliminate the current account surplus? What will happen to U.S.
asset values?"],
"path":["3.posts.comments.replies"]},
{
"sentiment":["positive"],
"text":["Agreed why spend time data-mining for a fantasy view of the world , when instead you can see
a fantasy in person?"],
"path":["3.posts.comments.replies"]}
Returning children by querying parents:
Block Join Child Query
Find replies to negative comments
q={!child of="path:2.posts.comments"}path:2.posts.comments AND sentiment:negative&fq=path:3.posts.comments.replies
Query
Level 2
Result
Level 3
Block Join Child Query + Filter Query
A bit counterintuitive and non-symmetrical to the BJPQ

25. 25
Returning all document's descendants
Block Join Child Query
Find all descendants of negative comments
q={!child of="path:2.posts.comments"}path:2.posts.comments AND sentiment:negative
Query
Level 2
Results
Level 3
Results
Level 4
{
"path":["4.posts.comments.replies.keywords"],
"id":"17413-13550",
"text":["Trump"]},
{
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is funnier."],
"path":["3.posts.comments.replies"],
"id":"17413-66188"},
{
"path":["3.posts.comments.keywords"],
"id":"12413-12487",
"text":["Hillary"]},
{
"text":["Agreed why spend time data-mining for a fantasy view of the world , when instead you can see
a fantasy in person?"],
"path":["3.posts.comments.replies"],
"id":"12413-10998"}
Issue: no grouping by parent
What if we want to bring the whole sub-structure?

26. 26
Find all negative comments and return them with all their descendants
q=path:2.posts.comments AND sentiment:negative&fl=*,[child parentFilter=path:2.*]
Query
Level 2
Result
Level 2
sub-
hierarchy
Returning document with all descendants:
ChildDocTransformer
{
"sentiment":["negative"],
"text":["I’ll be the first to say this, but the analysis is flawed."],
"path":["2.posts.comments"],
"_childDocuments_":[
{
"path":["4.posts.comments.replies.keywords"],
"text":["Trump"]},
{
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is funnier."],
"path":["3.posts.comments.replies"]},
{
"path":["4.posts.comments.replies.keywords"],
"text":["U.S."]},
{
"text":["So then I guess he will also eliminate the current account surplus? What
will happen to U.S. asset values?"],
"path":["3.posts.comments.replies"]}
]
},
...
Issue: the "sub-hierarchy" is flat

27. • Returns all descendant documents along with the queried document
• flattens the sub-hierarchy
• Workarounds:
• Reconstruct the document using path ("path":["3.posts.comments.replies"])
information in case you want the entire subtree (result post-processing)
• use childFilter in case you want a specific level
27
“This transformer returns all descendant documents of each parent document matching your query in
a flat list nested inside the matching parent document." (ChildDocTransformer cwiki)
Returning document with all descendants:
ChildDocTransformer

28. 28
Find all negative comments and return them with all replies to them
q=path:2.posts.comments AND sentiment:negative&fl=*,[child parentFilter=path:2.*
childFilter=path:3.posts.comments.replies]
{
"sentiment":["negative"],
"text":["I’ll be the first to say this, but the analysis is flawed."],
"path":["2.posts.comments"],
"_childDocuments_":[
{
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is
funnier."],
"path":["3.posts.comments.replies"]},
{
"text":["So then I guess he will also eliminate the current account surplus? What
will happen to U.S. asset values?"],
"path":["3.posts.comments.replies"]}
]
},
...
Returning document with specific descendants:
ChildDocTransformer + childFilter
Query
Level 2:comments
Result
Level 2:comments
+ Level 3:replies

29. 29
Find all negative comments and return them with all their descendants that mention Trump
q=path:2.posts.comments AND sentiment:negative&fl=*,[child parentFilter=path:2.* childFilter=text:Trump]
{
"sentiment":["negative"],
"text":["I’ll be the first to say this, but the analysis is flawed."],
"path":["2.posts.comments"],
"_childDocuments_":[
{
"path":["4.posts.comments.replies.keywords"],
"text":["Trump"]},
{
"text":["LOL. I enjoyed Trump during last night’s stand-up bit, but this is
funnier."],
"path":["3.posts.comments.replies"]}
]
},
...
Returning document with queried descendants:
ChildDocTransformer + childFilter
Query
Level 2:comments
Result
Level 2:comments
+ sub-levels
Issue: cannot use boolean expressions in childFilter query

30. 30
Cross-Level Querying Mechanisms:
• Block Join Parent Query
• Block Join Children Query
• ChildDocTransformer
Good points:
• overlapping & complementary features
• good capabilities of querying direct ancestors/descendants
• possible to query on siblings of different type
Drawbacks:
• need for data-preprocessing for better querying flexibility
• limited support of querying over non-directly related branches (overcome with graphs?)
• flattening nested data (additional post-processing is needed for reconstruction)
Nested Document Querying Summary

31. Faceting on Nested Documents

32. 32
• Solr allows faceting on nested documents!
• Two mechanisms for faceting:
• Faceting with JSON Facet API (since Solr 5.3)
• Block Join Faceting (since Solr 5.5)
Faceting on Nested Documents

33. 33
q=path:2.posts.comments AND sentiment:positive&
json.facet={
most_liked_authors : {
type: terms,
field: author,
domain: { blockParent : "path:1.posts"}
}}
Faceting on parents by descendants
JSON Facet API: Parent Domain
Count authors of the posts that received positive comments
"most_liked_authors":{
"buckets":[
{
"val":"Alex Tabarrok",
"count":1},
{
"val":"Tyler Cowen",
"count":1}
]
}
Query
Level 2
Facet
Level 1

34. 34
Faceting on descendants by ancestors
JSON Facet API: Child Domain
Distribution of keywords that appear in comments and replies by the top-level posts
Query
Level 1
Facet
Descendant
Levels
"top_keywords":{
"buckets":[{
"val":"hillary",
"count":4,
"counts_by_posts":2},
{
"val":"trump",
"count":3,
"counts_by_posts":2},
{
"val":"dnc",
"count":1,
"counts_by_posts":1},
{
"val":"obama",
"count":2,
"counts_by_posts":1},
{
"val":"u.s",
"count":1,
"counts_by_posts":1}
]}

35. 35
q=path:1.posts&rows=0&
json.facet={
filter_by_child_type :{
type:query,
q:"path:*comments*keywords",
domain: { blockChildren : "path:1.posts" },
facet:{
top_keywords : {
type: terms,
field: text,
sort: "counts_by_posts desc",
facet: {
counts_by_posts: "unique(_root_)"
}}}}}
Faceting on descendants by ancestors
JSON Facet API: Child Domain
Distribution of keywords that appear in comments and replies by the top-level posts
Query
Level 1
Facet
Descendant
Levels

36. 36
Faceting on descendants by top-level ancestor
JSON Facet API: Child Domain
Distribution of keywords that appear in comments and replies by the top-level posts
Query
Level 1
Facet
Descendant
Levels
Issue: only the top-ancestor gets the unique "_root_" field by default
q=path:1.posts&rows=0&
json.facet={
filter_by_child_type :{
type:query,
q:"path:*comments*keywords",
domain: { blockChildren : "path:1.posts" },
facet:{
top_keywords : {
type: terms,
field: text,
sort: "counts_by_posts desc",
facet: {
counts_by_posts: "unique(_root_)"
}}}}}

37. 37
q=path:2.posts.comments&rows=0&
json.facet={
filter_by_child_type :{
type:query,
q:"path:*comments*keywords",
domain: { blockChildren : "path:2.posts.comments" },
facet:{
top_keywords : {
type: terms,
field: text,
sort: "counts_by_comments desc",
facet: {
counts_by_comments: "unique(2.posts.comments-id)"
}}}}}
Faceting on descendants by intermediate ancestors
JSON Facet API: Child Domain + unique fields
Distribution of keywords that appear in comments and replies by the comments
Query
Level 2
Facet
Descendant
Levels
At pre-processing, introduce unique fields for each level

38. 38
Faceting on descendants by intermediate ancestors
JSON Facet API: Child Domain + unique fields
Distribution of keywords that appear in comments and replies by the comments
Query
Level 2
Facet
Descendant
Levels
"top_keywords":{
"buckets":[{
"val":"Hillary",
"count":4,
"counts_by_comments":3},
{
"val":"Trump",
"count":3,
"counts_by_comments":3},
{
"val":"DNC",
"count":1,
"counts_by_comments":1},
{
"val":"Obama",
"count":2,
"counts_by_comments":1},
{
"val":"U.S.",
"count":1,
"counts_by_comments":1}
]}

39. Now let's try the same using Block Join Faceting

40. 40
• Experimental Feature
• Needs to be turned on explicitly in solrconfig.xml
More info: https://cwiki.apache.org/confluence/display/solr/BlockJoin+Faceting
Block Join Faceting

41. 41
bjqfacet?q={!parent which=path:2.posts.comments}
path:*.comments*keywords&rows=0&facet=true&child.facet.field=text
Faceting on descendants by ancestors #2:
Block Join Faceting on Children Domain
Distribution of keywords that appear in comments and replies by the comments
"facet_fields":{
"text":[
"dnc",1,
"hillary",3,
"obama",1,
"trump",3,
"u.s",1
]
}
Query
Level 2
Facet
Descendant
Levels
bjqfacet request handler instead of query

42. 42
Output Comparison
Block Join Facet JSON Facet API
"facet_fields":{
"text":[
"dnc",1,
"hillary",3,
"obama",1,
"trump",3,
"u.s",1
]
}
"top_keywords":{
"buckets":[{
"val":"Hillary",
"count":4,
"counts_by_comments":3},
{
"val":"Trump",
"count":3,
"counts_by_comments":3},
{
"val":"DNC",
"count":1,
"counts_by_comments":1},
{
"val":"Obama",
"count":2,
"counts_by_comments":1},
{
"val":"U.S.",
"count":1,
"counts_by_comments":1}
]}
Distribution of keywords that appear in comments and replies by the comments

43. 43
Output Comparison
Block Join Facet JSON Facet API
"facet_fields":{
"text":[
"dnc",1,
"hillary",3,
"obama",1,
"trump",3,
"u.s",1
]
}
"top_keywords":{
"buckets":[{
"val":"Hillary",
"count":4,
"counts_by_comments":3},
{
"val":"Trump",
"count":3,
"counts_by_comments":3},
{
"val":"DNC",
"count":1,
"counts_by_comments":1},
...
Distribution of keywords that appear in comments and replies by the comments
Output is sorted in alphabetical
order. It cannot be changed
facet:{
top_keywords : {
...
sort: "counts_by_comments desc"
}}}

44. 44
JSON Facet API:
• Experimental - but more mature
• More developed and established feature
• bulky JSON syntax
• faceting on children by non-top level ancestors requires introducing unique branch
identifiers similar to "_root_" on each level
Block Join Facet:
• Experimental feature
• Lacks controls: sorting, limit...
• traditional query-style syntax
• proper handling of faceting on children by non-top level ancestors
Hierarchical Faceting Summary

45. 45
• Returning hierarchical structure
• JSON facet rollups is in the works - SOLR-8998
• Graph query might replace a lot of functionalities of cross-level querying - No
distributed support right now.
• There’s more but the community would love to have more people involved!
Community Roadmap

46. Thank you!
Anshum Gupta anshum@apache.org | @anshumgupta
https://github.com/alisa-ipn/solr-revolution-2016-nested-demo