Introduction to Elasticsearch for Business Intelligence and Application Insights
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The document is an introduction to Elasticsearch presented by Sean Donnelly, focusing on its use for business intelligence and application insights. It outlines the functionalities of Elasticsearch, including its search capabilities, the Elastic Stack, and its applications beyond search, while detailing specific use cases at Clark. Key aspects such as document indexing and retrieval, as well as goals for search performance, are discussed, emphasizing the importance of speed and full-text capabilities.
![Searching for Documents
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…
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Introduction to Elasticsearch for Business Intelligence and Application Insights
- 1. Introduction to Elasticsearch for Business Intelligence and Application Insights Presented by Sean Donnelly at Data Works MD – May 29th 2019
- 2. @DonSeannelly http://donseannelly.com https://github.com/donseannelly Sean Donnelly Software Architect at Cyber4All https://clark.centerCLARK Cyber4All https://towson.edu/cyber4all
- 3. Outline • What we will cover • Introduction to Elasticsearch • Fundamentals of Storage and Retrieval in Elasticsearch • Why we are using it at CLARK • The Elastic Stack • Use Beyond Search • What we will not cover • Clusters, Nodes, Scaling, Sharding and other nitty-gritty details for getting to know Elasticsearch • Details on standing up the Elastic Stack
- 4. What is Elasticsearch? RESTful search and analytics engine
- 5. What does it do? • Uses Lucene indices under the hood • Provides built-in tokenizers and analyzers • Provides a score for the relevancy of each result to the query • Provides binary matching via filters • Supports pagination out of the box
- 6. PUT meetups/_doc/1 { ”name" : ”Data Works MD", "date" : "2019-05-29T18:30:00" } Indexing a Document
- 7. Getting a Document GET meetups/_doc/1 { "_index" : ”meetups", "_type" : "_doc", "_id" : ”1", "_version" : 1, "_seq_no" : 10, "_primary_term" : 1, "found": true, "_source" : { ”name" : ”Data Works MD", "date" : "2019-05-29T18:30:00” } }
- 8. Searching for Documents GET meetups/_search?q=name:Data Works MD … "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 1.3862944, "hits" : [ { "_index" : ”meetups", "_type" : "_doc", "_id" : ”1", "_score" : 1.3862944, "_source" : { ”name" : ”Data Works MD", "date" : "2019-05-29T18:30:00” } } ] }
- 9. Elasticsearch at CLARK • Goals for search: • Fast • Full text • Word stemming, stop words, fuzziness • We already had documents formatted in MongoDB • Our searchable data is read-only in the application
- 12. Ingest Store, Search, Analyze Visualize Custom Self Hosted Hosting
- 13. Key Use Cases • Application Search • Application Performance Monitoring • have our response times slowed significantly? • Logging • explore the logs that we rarely (never) read • Machine Learning • Alert me if there is an unusual increase in abandoned shopping carts • Alert me if there is an unusual change in HTTP status codes for a resource
- 14. Thanks!
Editor's Notes
- #3: I am an Elasticsearch enthusiast
- #5: Allows you to index your data and make it searchable
- #6: Fast – inverted indices, finite state transducers, BKD trees for numeric and geo data, column store for analytics Prototype to Production easily – interact with one node the way you would with 1000
- #10: Any action taken on it is actually creating a new document
- #13: This modular approach allows you to build fantastic, custom data pipelines for ingestion. Integrate your existing pipeline directly into Elasticsearch Ingest message queues into Logstash Ship filebeats to logstash or directly to elasticsearch