As users put more and more workloads onto Solr, performance and scalability become even more critical. In this talk, we will cover our high-level strategy to work with the community to improve them -- including a new microbenchmark framework -- and how you can make use of it to improve the performance of your own system.

1. Apache Solr Performance and Scalability
Effort Update 2017/7 Palo Alto
Michael Sun wsun@cloudera.com

2. Scaling Solr in a Reliable Way - Challenges
● Scientific Side
○ Velocity, Volume, Variety
○ Designed to Address a Large Spectrum of Use Cases
○ Dealing with Partial Failures
○ Dependencies
○ ...

3. Scaling Solr in a Reliable Way - Challenges
● Scientific Side
● Human Side
○ Leaky Abstractions
○ Fast Moving Code Base
○ Hard to Test
○ Knowledge Gap for Operation Engineers

4. High Level Approach - Tactics
1. Improvement on the ‘Scientific Side’
2. Regression Prevention
3. Investigations - Uncover Issues that Really Matters
4. Field Enablement - Tuning Guide for Production, Capacity Planning
Guide, Design Tutorial etc.
5. Automation - Solr Benchmark Framework

5. High Level Approach - Tactics cont.
1. Optimization
2. Load Test/Scalability Study
3. Lead Large Scale Study (>1k nodes)

6. Solr Benchmark Framework - Challenges
1. Lack of Standardization. Hard to Collaborate.
2. Lack of Instrumentations.
3. Little Code Reuse.
4. Most Benchmarks Address a Small Set of Use Cases.

7. Solr Benchmark Framework - Tactics
1. A standardized, extensible object model
2. A layer of reusable components
3. A set of standardized instruments
4. A set of benchmarks for common use cases
● inc. Solr and Solr dependencies

8. Solr Benchmark Framework - Object Model

9. Solr Benchmark Framework - Result
1. Solr Nightly Performance Result
a. Both Solr and Solr dependencies
b. https://michael-sun-github.github.io/index.html (the front page is under construction)
2. Solr Benchmarks https://issues.apache.org/jira/browse/SOLR-10317