Oreilly - Using Elasticsearch and Kibana
by Loonycorn | Released February 2018 | ISBN: 9781789134438
Scalable Search and Analytics for Document DataAbout This VideoHow search works, and the role that inverted indices and relevance scoring playThe tf-idf algorithm and the intuition behind term frequency, inverse document frequency and field lengthHorizontal scaling using sharding and replicationPowerful querying functionality including a query-DSLUsing REST APIs - from browser as well as from cURLKibana for exploring data and finding insightsSupport for CRUD operations - Create, Retrieve, Update and DeleteAggregations - metrics, bucketing and nested aggsPython client usageIn DetailElasticsearch 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. Show and hide more
- Chapter 1 : Introduction
- You, This Course and Us 00:02:23
- Chapter 2 : Introducing Elasticsearch
- Course Outline 00:03:01
- A Brief History of Search 00:07:52
- Steps in Search 00:08:15
- Inverted Index 00:06:13
- Using the Inverted Index 00:05:20
- Lucene 00:07:20
- Elasticsearch Introduced 00:05:38
- Installing ES 00:08:43
- Clusters and Nodes 00:05:43
- Indices and Documents 00:08:27
- Cluster Health 00:07:01
- Chapter 3 : CRUD Operations in Elasticsearch
- Curl 00:07:20
- Create Index 00:08:15
- Create Document 00:08:21
- Retrieve Documents 00:05:23
- Update Documents 00:08:19
- Script Elements 00:04:41
- Delete 00:04:35
- mGet 00:04:40
- The Bulk API 00:09:06
- Bulk Loading 00:09:06
- Chapter 4 : The Query DSL (Domain-Specific Language)
- Search Recap 00:04:21
- Random Data Gen 00:05:20
- Contexts 00:05:52
- Contexts 00:05:57
- Query Params 00:07:15
- Request Body 00:09:03
- Source Filtering 00:08:32
- Full Text Search_Match 00:04:10
- Full Text Search_MatchPhrasePrefix 00:07:14
- Relevance 00:08:10
- TfIdf 00:06:06
- Common Terms 00:06:17
- Boolean Compound Queries 00:06:43
- Term Queries Boosting Terms 00:04:43
- Filters 00:06:02
- Wildcards 00:06:10
- Chapter 5 : Aggregations
- Types Of Aggregations 00:03:59
- Metric Aggregations 00:07:13
- Cardinality Aggregations 00:09:07
- Bucketing Aggregations 00:05:32
- Bucketing Aggregations_2 00:06:10
- Multilevel Nested Aggregations 00:05:13
- FilterBucketAggs 00:06:44
- Chapter 6 : Elasticsearch and Python
- Pythonsetup 00:08:33
- Create Index 00:04:59
- Documents 00:05:07
- Search_Count 00:04:40
- Chapter 7 : Kibana
- Kibana_elk 00:04:27
- Kibana_Install 00:02:48
- Mapping 00:07:52
- Loading Logs 00:06:38
- Discovery 00:06:49
- Visualize 00:07:00
- Timelion 00:08:01
- Dashboard 00:03:50
- Anaconda and Pip 00:09:00
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