The document discusses the advantages of using Apache Lucene and Solr for e-commerce search engines over traditional RDBMS, emphasizing full-text search capabilities, scalability, and flexibility for handling high volumes of queries and documents. It outlines specific scenarios for using full-text search and RDBMS, highlighting the importance of search relevancy and user experience in e-commerce applications. Practical examples and text retrieval basics are also provided, along with an analysis of Lucene vs Solr features and usage.

1. E-commerce Search Engine with
Apache Lucene/Solr
A sightseeing with few practical examples
By Vincenzo D’Amore
v.damore@gmail.com
@VincenzoDAmore

2. eCommerce Search
- Why not an RDBMS
- Characteristics
- Scenarios
- Applications
- Text Retrieval Basics
- Lucene vs Solr
- Hands on

3. Why not an RDBMS
AKA eCommerce and
Search? Start with why

4. Why not an
RDBMS
Use the right tool
for the job.

5. (NoSQL) Full Text Search vs RDBMS
NoSQL
- Documents
- Denormalization
RDBMS
- Tables/Records
- Normalization

6. (NoSQL) Full Text Search vs RDBMS
- Cluster-friendly
- Optimistic locking
- Schema-less (almost)
- Scale vertically
- ACID transactions

7. (NoSQL) Full Text Search vs RDBMS
- Text Analysis/Stemming
- Full text search scored
- Faceting/Categorization
- Non-text data
manipulation

8. When to use a full text search engine
1. High volume of documents to be searched and/or
faceted/categorized
2. High volume of interactive text-based queries
3. Demand for very flexible full text search querying
4. Demand for highly relevant search results

9. When to use a RDBMS
1. Demands for many different record types
2. Non-text data manipulation
3. Secure transaction processing

10. eCommerce Search
characteristics
Scalable and fast to users
requests

11. eCommerce Search characteristics
Scalable and fast to users requests
- Thousands of concurrent users
- Millions of queries per day (with
peaks during Xmas
and Black Friday)
- Average response under few ms

12. eCommerce Search
characteristics
Flexible marketing
requirements

13. eCommerce Search characteristics
Flexible marketing requirements
- Promoted products should appear first
- Best sellers products should appear first
- Fresh products should appear first (freshness?)
- Hot keywords and curated search
- Everything should appear first (OMG & WTH)

14. eCommerce Search
characteristics
Users can't buy if they
can't find it

15. eCommerce Search characteristics
Users can't buy if they can't find it
- Search and discovery is mission critical
- Products descriptions and metadata
are poorly written and often don't fits
users requests

16. eCommerce Search characteristics
Users can't buy if they can't find it
- Users don't know how to spell
bluettoth, blu tooh, blutooh, bluetoot,
bluetooh, blue toot, blue tooh, blue tooth
=> bluetooth

17. eCommerce Search characteristics
Users can't buy if they can't find it
- Users don't know how to spell
hawey, uawei, huwaei, huwei, wawei,
hawuei, huawai, hawei, huwawei,
huwavei, huwawei, huawey, hauwei,
hawuei, hawei, hawawei, huawe
=> huawei

18. eCommerce Search characteristics
Users can't buy if they can't find it
- Users don't know how to spell
tapi rulan, tapisrulant, tapis rulant,
tapi roulant, tapiroulant, tapisroulant
=> tapis roulant

19. eCommerce Search characteristics
Common search documents vs eCommerce documents
VS

20. eCommerce Search
Scenarios
How an online store
typically look like

21. eCommerce Search Scenarios
How an online store typically look like
- Thousands, millions and even billions of products
- Lots of meta-data in text form

22. eCommerce Search Scenarios
How an online store typically look like
- Tricky product names & manufacturer names
- star trek, star wars (w/ or w/o space?)
- ÖKOKombi

23. eCommerce Search Scenarios
How an online store typically look like
- Word-level ambiguities in product
Names
- Gulliver
- Portatile
- Sacco
- WD Desktop
- Reflex

24. eCommerce
Search
Applications
A list of the most popular
eCommerce Search
Applications

25. Applications
Search suggest drop-down list (aka autocomplete)

26. Applications
Typo tolerance aka Spellchecker aka “forse cercavi”

27. Applications
Typo tolerance aka Spellchecker aka “forse cercavi”

28. Applications
Instant search
aka search as you type
aka incremental search

29. Applications
Filters and Facets (Refiners)

30. Applications
More like this - Related products and articles

31. Applications
Spatial search

32. Applications
Zero results page

33. Text Retrieval Basics
A list of few basic concepts, principles
of Text Retrieval

34. Text Retrieval Basics
What is Information retrieval (IR)
- Information retrieval is the science of searching for
information in a document, searching for documents
themselves, and also searching for metadata that
describe data, and for databases of texts, images or
sounds.

35. Text Retrieval Basics
What is text retrieval (TR)
- Collection of documents exists
- The user submit a query to express the information
need
- The search engine returns documents relevant to the
user’s query.

36. Text Retrieval Basics
What is Relevance
- the quality of results returned from a query,
encompassing both what documents are found, and
their relative ranking (the order that they are returned
to the user.)
- Measure of the effectiveness of communication
- Trying also to satisfy the marketing requests

37. Text Retrieval Basics
Access Mode: Push vs Pull

38. Text Retrieval Basics
Pull mode: Querying vs Browsing
Internet
Directory
VS

39. Text Retrieval Basics
Measure of relevance: what is Precision/Recall?
TRUE NEGATIVESFALSE NEGATIVES
TRUE
POSITIVES
FALSE
POSITIVES
Precision = Recall =
Selected or retrieved elements
How many items are
relevant?
How many relevant items are
selected?
Relevant elements
┏━━━━━━━━━━━┓
A B
C D A
A B A
C
A

40. Text Retrieval Basics
Measures
- Effectiveness or accuracy
- System centered
- User centered

41. Text Retrieval Basics
Measures
- Efficiency
- Retrieval time
- Indexing time
- Index size

42. Text Retrieval Basics
Measures
- Usability

43. Text Retrieval Basics
Understanding and improving search relevancy can
often feel like a never ending journey.

44. Lucene vs Solr
Apache Lucene and Solr

45. What is Apache Lucene
- Java-based indexing and search technology, as well as spellchecking,
hit highlighting and advanced analysis/tokenization capabilities.
- Many Lucene-based projects: Solr, Elasticsearch, Hadoop, Nutch, etc.
Lucene vs Solr

46. What is Apache Solr
- Solr (pronounced "solar") is an open source enterprise search platform.
Its major features include full-text search, hit highlighting, faceted
search, real-time indexing, dynamic clustering, database integration,
NoSQL features and rich document (e.g., Word, PDF) handling.
Providing distributed search and index replication, Solr is designed for
scalability and fault tolerance.
Lucene vs Solr

47. Lucene vs Solr - Create SynonymGraphFilterFactory
Map<String, String> args = new HashMap<>();
args.put("synonyms", "synonyms.txt");
args.put("ignoreCase", Boolean.toString(true));
args.put("expand", Boolean.toString(true));
SynonymGraphFilterFactory syf = new
SynonymGraphFilterFactory(args);
ResourceLoader rl = new
FilesystemResourceLoader(Paths.get("."),
this.getClassLoader());
syf.inform(rl);

48. Lucene vs Solr - Apply SynonymGraphFilterFactory
StringBuilder sb = new StringBuilder();
try (Tokenizer wt = new WhitespaceTokenizer()) {
wt.setReader(new StringReader(input));
try (TokenStream syn = localSyf.create(wt)) {
syn.reset();
CharTermAttribute term = syn.addAttribute(CharTermAttribute.class);
if (syn.incrementToken()) {
sb.append(term.toString());
while (syn.incrementToken()) {
sb.append(" ");
sb.append(term.toString());
}
}
}
}

49. Lucene vs Solr - Solr SynonymGraphFilterFactory
<fieldType name="text_general" class="solr.TextField" positionIncrementGap="100"
multiValued="true">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" words="stopwords.txt" ignoreCase="true"/>
<filter class="solr.SynonymGraphFilterFactory" expand="true" ignoreCase="true"
synonyms="synonyms.txt"/>
<filter class="solr.FlattenGraphFilterFactory"/>
<filter class="solr.RemoveDuplicatesTokenFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" words="stopwords.txt" ignoreCase="true"/>
<filter class="solr.SynonymGraphFilterFactory" expand="true" ignoreCase="true"
synonyms="synonyms.txt"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>

50. Lucene vs Solr
Go ahead with Solr
Should I use Lucene
or Solr?
Cool!
Are you
Twitter?
YES
NO

51. Lucene vs Solr
IndexIndexer Searcher Results
Tokenizer Tokenizer
Docs
Query
Simple Solr Architecture

52. Hands on Lucene &
Solr
A practical example of what Lucene & Solr
is

53. Hands on Lucene & Solr
Examples in the presentation
https://github.com/freedev/lucene-example
Apache Lucene/Solr Books
- Apache Solr Reference Guide
- Lucene in Action
- Relevant Search
- Apache Solr Search Patterns