The slightly verbose slides accompanying my introductory ElasticSearch talk at Utrecht.rb These slides are outdated, see https://speakerdeck.com/timonv/youre-not-using-elasticsearch

1. You’re not using
ElasticSearch?
Timon Vonk (@timonvonk)

2. About me
•
CTO @ Tolq
•
Freelance hacker and consultant
•
Used ElasticSearch as a solution for Translation
Memory, text author recommendation and as a
spam filter.

3. What is it?
•
Full text search engine based on Apache Lucene
•
Cloud in mind, built for speeed
•
Easy to use JSON api
•
A *lot* of room for custom solutions

4. Why use it (vs solr)?
•
Cloud based setup out of the box. Setting this up is
super easy. That means automagic replication,
sharding and bonus mapreduce.
•
Indexes happen in a few seconds (vs minutes in solr)
•
Again, easy Json API
•
Nosql, mappings can be generated on the fly
•
Well scriptable and customisable for fancy
aggregation or dynamic analysing

5. O M G W H AT I S
LUCENE
•
Java library for doing full text searches by
Apache
•
Just a library, by no means a solution in itself
(although you can)
•
More in depth, the search works via a terms
indexing algorithm. Scoring is not only based on
occurrence, but also on uniqueness

6. Making a query
•
You send json to a _search endpoint on either an index, with maybe
a type
This is a basic full text search with a response:
!
GET localhost:9200/example/peanuts/_search
{ ‘query’: { text: { ‘my_field’: ’many search terms’ }}}
{ took: 5, timed_out: false,
_shards: { total: 5, successful: 5, failed: 0 },
hits: [
{ _index: “example”,
_type: “peanuts”,
_score: 0.9,
_source: { …data }
}
]
}
}

7. Other types of queries
•
Terms, full text, boolean, fuzzy, geolocation and
lots more variants
•
You can also do filters to narrow down results

8. Analysing
•
Before data is indexed or queries are made its
analysed
•
At this point, terms are scored
•
But before that, the data is normalised. This
usually includes stemming and stop word
removal. With support for over 30 languages.
Woot!
•
You can create your own analysers.

9. Facet me awesome
•
Facets allow you to do aggregation over your
search.
{ “query”: … }
{ “facets”: {
“my_facets”: {
“terms”: { “name”: “utrechtrb” }
}
}
}
Facets will be deprecated in 1.0 in favour of ‘aggregations’

10. Ruby libraries
•
Good old Tire
deprecated
•
Stretcher!
•
Or just a json client

11. H o w To l q u s e s
ElasticSearch
•
We use ElasticSearch as a Translation Memory.
In our case, that means we suggest other
relevant translations while a translator is
translating.
•
By storing original texts and dynamically
adjusting the analyser to the correct languages,
we can suggest similar translations.

12. Tolq code was here

13. Tolq code was here

14. Tolq code was here

15. Also, it works great for
text search!
Thanks!