Solr vs. ElasticSearch

Transcription

1 Solr vs. ElasticSearch Case by Case comparison Alexandre Rafalovitch

2 Solr vs. ElasticSearch Case by Case comparison Alexandre Rafalovitch This book is for sale at This version was published on This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do Alexandre Rafalovitch

3 Tweet This Book! Please help Alexandre Rafalovitch by spreading the word about this book on Twitter! The suggested hashtag for this book is #SolrVsES. Find out what other people are saying about the book by clicking on this link to search for this hashtag on Twitter:

4 Also By Alexandre Rafalovitch Solr clients - cross-sectional study

5 Alexandre is currently working for the United Nations; however, the views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

6 Contents Introduction Political level Philosophy level Kitchen Sink vs. Components Clients Technical level Simple example Cases The case of the Palantír The case of file-sizes The case of mangled names The case of PDF processing The case of client ecosystem The case of index size The case of dynamic fields The case of schemaless mode The case of Castle in the Cloud The case of a Russian doll The case of mismatched socks The case of stacking barels The case of committed flusher Complex example

7 Introduction Comparisons between Solr and ElasticSearch are common and popular. However, they focus on the feature checklists and are very shallow. This book will look at the real search use-cases (searching multiple fields, dynamic fields, data import, nested documents, etc) and see how the solutions are represented in Solr s and ElasticSearch s paradigms. Some usability aspects will also be discussed (such as minimal configuration, dynamic field types, JSON handling).

8 Political level Should this be Solr vs. ElasticSearch. Or LucidWorks vs. ElasticSearch? Or even Cloudera vs. ElasticSearch?

9 Philosophy level Kitchen Sink vs. Components Solr is a Kitchen Sink. Elastic Search is small core + plugins. But what does ES small initial size mean for common operations? Clients

10 Technical level

11 Simple example 1. Download 2. Unpack 3. Start 4. Run the first example (does ES ship with one?) 5. Check it on command line 6. Check it in web browser (flash development-only) Onboarding experience - a separate category or discuss here?

12 Cases The case of the Palantír Administration console Discuss difference between Open Source and commercial ES suggests registration after 7 days. How? Why? Also shares data with central server (phones home) The production cost of Marvel (ES Console) is fairly low ($500/year). However, that s not what it s about. Most likely it is more of a signal. If your company cannot figure out a way to give ES $500 for a useful piece of software (life without Marven/Sense is hard), what chances are your company will be willing to pay significantly higher fees for ES other software/services? And, in that case, how much does ES wants to keep you as a non-paying customer. They still do of course, to grow the ecosystem and community. But, they probably don t mind if you suffer a bit without a useful tool like a console. Whereas, Solr bundles the Admin console with the distribution and it is featured prominently in tutorials and troubleshooting guides. One can rely on users having access to the console. With ElasticSearch, not so much. The case of file-sizes Javadoc, etc The case of mangled names The case of PDF processing The case of client ecosystem The case of index size store all by default, ES document field vs. index field vs???

13 Cases 7 The case of dynamic fields ES equivalent? Templates? The case of schemaless mode How to use, how to modify the sequence (e.g. date formats) The case of Castle in the Cloud The case of a Russian doll nested documents, finding parents, finding children, finding combined ES has pretend nested documents and real nested documents. The case of mismatched socks Diferent schemas overlayed on the same index How does it work with atomic update? The case of stacking barels Multi-collection grouping aliases with rolling membership (e.g. for monthly logs collection) The case of committed flusher Solr has hard/soft commit, reopen/not-reopen searcher (4 states, not all make sense) ElasticSearch has refresh (soft commit with reopened searcher) and flush (hard commit with searcher not reopened)

14 Complex example