O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Using Elasticsearch and Kibana

Video Description

Scalable Search and Analytics for Document Data

About This Video

  • How search works, and the role that inverted indices and relevance scoring play
  • The tf-idf algorithm and the intuition behind term frequency, inverse document frequency and field length
  • Horizontal scaling using sharding and replication
  • Powerful querying functionality including a query-DSL
  • Using REST APIs - from browser as well as from cURL
  • Kibana for exploring data and finding insights
  • Support for CRUD operations - Create, Retrieve, Update and Delete
  • Aggregations - metrics, bucketing and nested aggs
  • Python client usage

In Detail

Elasticsearch wears two hats: It is both a powerful search engine built atop Apache Lucene, as well as a serious data warehousing/BI technology. This course will help you use the power of ES in both contexts: -

ES as search engine technology and ES as data warehouse/OLAP technology.

Table of Contents

  1. Chapter 1 : Introduction
    1. You, This Course and Us 00:02:23
  2. Chapter 2 : Introducing Elasticsearch
    1. Course Outline 00:03:01
    2. A Brief History of Search 00:07:52
    3. Steps in Search 00:08:15
    4. Inverted Index 00:06:13
    5. Using the Inverted Index 00:05:20
    6. Lucene 00:07:20
    7. Elasticsearch Introduced 00:05:38
    8. Installing ES 00:08:43
    9. Clusters and Nodes 00:05:43
    10. Indices and Documents 00:08:27
    11. Cluster Health 00:07:01
  3. Chapter 3 : CRUD Operations in Elasticsearch
    1. Curl 00:07:20
    2. Create Index 00:08:15
    3. Create Document 00:08:21
    4. Retrieve Documents 00:05:23
    5. Update Documents 00:08:19
    6. Script Elements 00:04:41
    7. Delete 00:04:35
    8. mGet 00:04:40
    9. The Bulk API 00:09:06
    10. Bulk Loading 00:09:06
  4. Chapter 4 : The Query DSL (Domain-Specific Language)
    1. Search Recap 00:04:21
    2. Random Data Gen 00:05:20
    3. Contexts 00:05:52
    4. Contexts 00:05:57
    5. Query Params 00:07:15
    6. Request Body 00:09:03
    7. Source Filtering 00:08:32
    8. Full Text Search_Match 00:04:10
    9. Full Text Search_MatchPhrasePrefix 00:07:14
    10. Relevance 00:08:10
    11. TfIdf 00:06:06
    12. Common Terms 00:06:17
    13. Boolean Compound Queries 00:06:43
    14. Term Queries Boosting Terms 00:04:43
    15. Filters 00:06:02
    16. Wildcards 00:06:10
  5. Chapter 5 : Aggregations
    1. Types Of Aggregations 00:03:59
    2. Metric Aggregations 00:07:13
    3. Cardinality Aggregations 00:09:07
    4. Bucketing Aggregations 00:05:32
    5. Bucketing Aggregations_2 00:06:10
    6. Multilevel Nested Aggregations 00:05:13
    7. FilterBucketAggs 00:06:44
  6. Chapter 6 : Elasticsearch and Python
    1. Pythonsetup 00:08:33
    2. Create Index 00:04:59
    3. Documents 00:05:07
    4. Search_Count 00:04:40
  7. Chapter 7 : Kibana
    1. Kibana_elk 00:04:27
    2. Kibana_Install 00:02:48
    3. Mapping 00:07:52
    4. Loading Logs 00:06:38
    5. Discovery 00:06:49
    6. Visualize 00:07:00
    7. Timelion 00:08:01
    8. Dashboard 00:03:50
    9. Anaconda and Pip 00:09:00