The document provides a comprehensive overview of full-text search solutions using Apache Solr, covering various aspects such as architecture, indexing strategies, and client libraries. It highlights features like spell checking, faceted search, and advanced querying capabilities along with performance tuning techniques. Additionally, it discusses data ingestion methods, text processing, and real-life applications of Solr to enhance search functionalities.

1. Full text search for lazy guys
STARRING APACHE SOLR

2. Agenda
• Introduction
• FTS solutions
• FTS patterns
• Apache Solr
• Architecture
• Client libraries
• data treatment pipeline
• Index modeling
• ingestion
• Searching
• Demo 1
• Solr in clustered environment
• Architecture
• Idexing
• quering
• Demo 2
• Advanced Solr
• Cool features overview
• Performance tuning
• Q&A sessions

3. FTS solutions attributes
1. Search by content of documents rather than by attributes
2. Read-oriented
3. Flexible data structure
4. 1 dedicated tailored index used further for search
5. index contains unique terms and their position in all documents
6. Indexer takes into account language-specific nuances like stop words, stemming,
shingling (word-grams, common-grams)

4. FTS architectures
Id
Price
Weight
Description
RDBMS
FTS
FTS server
Index

5. FTS usage patterns
1. Spell checking
2. Full text search
3. Highlighting

6. FTS usage patterns
1. Suggestions
2. Faceted search
3. Paging

7. Market leaders

8. FTS scope

9. Q&A

10. Solr
• True open source (under Apache) full text search engine
• Built over Lucene
• Multi-language support
• Rich document parsing (rtf, pdf, …)
• Various client APIs
• Versatile query language
• Scalable
• Full of additional features 

11. Well-known Solr users
and many others in https://wiki.apache.org/solr/PublicServers

12. Architecture

13. Client access
1. Main REST API
• Common operations
• Schema API
• Rebalance/collection API
• Search API
• Faceted API
2. Native JAVA client SolrJ
3. Client bindings like Ruby, .Net, Python, PHP, Scala – see
https://wiki.apache.org/solr/IntegratingSolr +
https://wiki.apache.org/solr/SolPython
4. Parallel SQL (via REST and JDBC)

14. Inverted index

15. Index modeling
Choose Solr mode:
1. Schema
2. Schema-less
Define field attributes:
1. Indexed (query, sort, facet, group by, provide query suggestions for, execute
function)
2. Stored – all fields which are intended to be shown in a response
3. Mandatory
4. Data type
5. Multivalued
6. Copy field (calculated)
Choose a field for UniqueIdentifier

16. Field data types
1. Dates
2. Strings
3. Numeric
4. Guid
5. Spatial
6. Boolean
7. Currency and etc

17. Real life-schema

18. Text processing
Intended to mitigate differences between terms to provide perfect search

19. Text processing
Set of filters to get desired results

20. Text processing
Set of filters to get desired results

21. Ingestion

22. Transaction management
1. Solr doesn’t expose immediately new data as well as not remove deleted
2. Commit/rollback should be issued
Commit types:
1. Soft
Data indexed in memory
1. Hard
It moves data to hard-drive
Risks:
1. Commits are slow
2. Many simultaneous commits could lead to Solr exceptions (too many commits)
<h2>HTTP ERROR: 503</h2>
<pre>Error opening new searcher. exceeded limit of maxWarmingSearchers=2, try again later.</pre>
3. Commit command works on instance level – not on user one

23. Transaction log
Intention:
1. recovery/durability
2. Nearly-Real-Time (NRT) update
3. Replication for Solr cloud
4. Atomic document update, in-place update (syntax is different)
5. Optimistic concurrency
Transaction log could be enabled in solrconfig.xml
<updateLog>
<str name="dir">${solr.ulog.dir:}</str>
</updateLog>
Atomic update example:
{"id":"mydoc",
"price":{"set":99},
"popularity":{"inc":20},
"categories":{"add":["toys","games"]},
"promo_ids":{"remove":"a123x"},
"tags":{"remove":["free_to_try"," on_sale"]}
}

24. Data modification Rest API
Rest API accepts:
1. Json objects
2. Xml-update
3. CSV
Solr UPDATE = UPSERT if schema.xml has <UniqueIdentifier>

25. Data modification Rest API
curl http://192.168.77.65:8983/solr/single-core/update?commit=true -H 'Content-type:application/json' -d '
[
{"id" : "3",
"internal_name":"post 2",
},
{"id" : “1",
"internal_name":"post 1",
}
]‘
Data.xml
<add>
<doc>
<field name='id'>8</field>
<field name='internal_name'>test1</field>
<doc>
<doc>
<field name='id'>9</field>
<field name='internal_name'>test6</field>
<doc>
</add>
curl -X POST 'http://192.168.77.65:8983/solr/single-core/update?commit=true&wt=json' -H 'Content-Type:text/xml' -d @data.xml
Delete.xml
<delete>
<id>11604</id>
<id>:11603</id>
</delete>
Delete_with_query.xml
<delete>
<query>id:[1 TO 85]</query>
</delete>

26. Post utility
1. Java-written utility
2. Intended to load files
3. Works extremely fast
4. Loads csv, json
5. Loads files by mask of file-by-file
bin/post -c http://localhost:8983/cloud tags*.json
ISSUE: doesn’t work with Solr Cloud

27. Data import handler
1. Solr loads data itself
2. DIH could access to JDBC, ATOM/RSS, HTTP, XML, SMTP datasource
3. Delta approach could be implemented (statements for new, updated and deleted
data)
4. Loading progress could be tracked
5. Various transformation could be done inside (regexp, conversion, javascript)
6. Own datasource loaders could be implemented via Java
7. Web console to run/monitor/modify

28. Data import handler
How to implement:
1. Create data config
<dataConfig>
<dataSource name="jdbc" driver="org.postgresql.Driver"
url="jdbc:postgresql://localhost/db"
user="admin" readOnly="true" autoCommit="false" />
<document>
<entity name="artist" dataSource="jdbc" pk="id"
query="select *from artist a"
transformer="DateFormatTransformer"
>
<field column="id" name="id"/>
<field column="department_code" name="department_code"/>
<field column="department_name" name="department_name"/>
<field column = "begin_date" dateTimeFormat="yyyy-MM-dd" />
</entity>
</document>
</dataConfig>
2. Publish in solrconfig.xml
<requestHandler name="/jdbc"
class="org.apache.solr.handler.dataimport.DataImportHandler ">
<lst name=“default">
<str name="jdbc.xml</str>
</lst>
</requestHandler>
DIH could be started via REST call
curl http://localhost:8983/cloud/jdbc -F command=full-import

29. Data import handler
In process:
<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="initArgs">
<lst name="defaults">
<str name="config">jdbc.xml</str>
</lst>
</lst>
<str name="status">busy</str>
<str name="importResponse">A command is still running...</str>
<lst name="statusMessages">
<str name="Time Elapsed">0:1:15.460</str>
<str name="Total Requests made to DataSource">39547</str>
<str name="Total Rows Fetched">59319</str>
<str name="Total Documents Processed">19772</ str>
<str name="Total Documents Skipped">0</str>
<str name="Full Dump Started">2010-10-03 14:28:00</str>
</lst>
<str name="WARNING">This response format is experimental. It is likely to change in the future.</ str>
</response>

30. Data import handler
After import:
<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="initArgs">
<lst name="defaults">
<str name="config">jdbc.xml</str>
</lst>
</lst>
<str name="status">idle</str>
<str name="importResponse"/>
<lst name="statusMessages">
<str name="Total Requests made to DataSource">2118645</str>
<str name="Total Rows Fetched">3177966</str>
<str name="Total Documents Skipped">0</str>
<str name="Full Dump Started">2010-10-03 14:28:00</str>
<str name="">Indexing completed. Added/Updated: 1059322 documents. Deleted 0
documents.</str>
<str name="Committed">2010-10-03 14:55:20</str>
<str name="Optimized">2010-10-03 14:55:20</str>
<str name="Total Documents Processed">1059322</str>
<str name="Time taken ">0:27:20.325</str>
</lst>
<str name="WARNING">This response format is experimental. It is likely to change in the
future.</str>
</response>

31. Search

32. Search

33. Search typesFuzzy
Developer~ Developer~1 Developer~4
It matches developer, developers, development and etc.
Proximity
“solr search developer”~ “solr search developer”~1
It matches: solr search developer, solr senior developer
Wildcard
Deal* Com*n C??t
Need *xed? Add ReversedWildcardFilterFactory.
Range
[1 TO25] {23 TO50} {23 TO90]

34. Search characteristics
1. Similarity
2. Term frequency
Similarity could be changed via boosting:
q=title:(solr for developers)^2.5 AND description:(professional)
q=title:(java)^0.5 AND description:(professional)^3

35. Search result customization
Field list
/query?=&fl=id, genre /query?=&fl=*,score
Sort
/query?=&fl=id, name&sort=date, score desc
Paging
select?q=*:*&sort=id&fl=id&rows=5&start=5
Transformers
[docid] [shard]
Debuging
/query?=&fl=id&debug=true
Format
/query?=&fl=id&wt=json /query?=&fl=id&wt=xml

36. Search queries examples
Parameter style
curl "http://localhost:8983/cloud/ query?q=heroy&fq=inStock:true"
JSON API
$ curl http://localhost:8983/cloud/query -d '
{
query:"hero"
"filter" : "inStock:true"
}'
Response
{
"responseHeader":{
"status":0,
"QTime":2,
"params":{
"json":"n{n query:"hero" "filter" : "inStock:true" n}"}},
"response":{"numFound":1,"start":0,"docs":[
{
"id":"book3",
"author":"Brandon Sanderson",
"author_s":"Brandon Sanderson",
"title":["The Hero of Aages"],
"series_s":"Mistborn",
"sequence_i":3,
"genre_s":"fantasy",
"_version_":1486581355536973824
}]
}
}

37. Q&A

38. SolrCloud
ALEX 2 turn!!!!

39. Advanced Solr
1. Streaming language
Special language tailored mostly for Solr Cloud, parallel processing, map-reduce style
approach. The idea is to process and return big datasets.
Commands like: search, jdbc, intersect, parallel, or, and
2. Parallel query
JDBC/REST to process data in SQL style. Works on many Solr nodes in MPP style.
curl --data-urlencode 'stmt=SELECT to, count(*) FROM collection4 GROUP BY to ORDER BY count(*) desc LIMIT 10'
http://localhost:8983/solr/cloud/sql
3. Graph functions
Graph traversal, aggregations, cycle detection, export to GraphML format
4. Spatial queries
There is field datatype Location. It permits to deal with spatial conditions like filtering by distance (circle,
square, sphere)
and etc.
&q=*:*&fq=(state:"FL" AND city:"Jacksonville")&sort=geodist()+asc
5. Spellchecking
It could be based on a current index, another index, file or using word breaks. Many options what to
return: most similar,
more popular etc
http://localhost:8983/solr/cloud/spell?df=text&spellcheck.q=delll+ultra+sharp&spellcheck=true
6. Suggestions
http://localhost:8983/solr/cloud/a_term_suggest?q=sma&wt=json
7. Highlighter
Marks fragments in found document
http://localhost:8983/solr/cloud/select?hl=on&q=apple
8. Facets
Arrangement of search results into categories based on indexed terms with statistics. Could be done by
values, range, dates, interval, heatmap

40. Performance tuning Cache
Be aware of Solr cache types:
1. Filter cache
Holds unordered document identifiers associated with filter queries that have
been executed (only if fq query parameter is used)
2. Query result cache
Holds ordered document identifiers resulting from queries that have been
executed
3. Document cache
Holds Lucene document instances for access to fields marked as stored
Identify most suitable cache class
1. LRUCache – last recently used are evicted first, track time
2. FastLRUCache – the same but works in separate thread
3. LFUCache – least frequently used are evicted first, track usage count
Play with auto-warm
<filterCache class="solr.FastLRUCache" size="512“ initialSize=“100" autowarmCount=“10"/>
Be aware how auto-warm works internally – doesn’t delete data, repopulated
completely

41. Performance tuning Memory
Care about OS memory for disk caching
Estimate properly Java heap size for Solr – use
https://svn.apache.org/repos/asf/lucene/dev/tags/lucene_solr_4_2_0/dev -tools/size-estimator-lucene-solr.xls

42. Performance tuning Schema design
1. Try to decrease number of stored fields mark as indexed only
2. If fields are used only to be returned in search results – use stored only

43. Performance tuning Ingestion
1. Use bulk sending data rather than per-document
2. If you use SolrJ use ConcurentUpdateSolrServer class
3. Disable ID uniqueness checking
4. Identify proper mergeFactor + maxSegments for Lucene segment merge
5. Issue OPTIMIZE after huge bulk loadings
6. If you use DIH try to not use transformers – pass them to DB level in SQL
7. Configure AUTOCOMMIT properly

44. Performance tuning Search
1. Choose appropriate query parser based on use case
2. Use Solr pagination to return data without waiting for a long time
3. If you return huge data set use Solr cursors rather than pagination
4. Use fq clause to speed up queries with one equal condition – time for scoring
isn’t used + results are put in cache
5. If you have a lot of stored fields but queries don’t show all of them use field lazy
loading
<enableLazyFieldLoading>true</enableLazyFieldLoading>
6. Use shingling to make phrasal search faster
<filter class="solr.ShingleFilterFactory“ maxShingleSize="2" outputUnigrams="true"/>
<filter class="solr.CommonGramsQueryFilterFactory“ words="commongrams.txt" ignoreCase="true""/>

45. Q&A

46. THANKYOU
AND
WE ARE HIRING!
Alexander Tokarev
Senior Developer, DataArt
atokarev@dataart.com