Search Analytics Component: Presented by Steven Bower, Bloomberg L.P.
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This document describes Bloomberg's development of a search analytics component for Solr. It was created by their search team to enable complex calculations and aggregations on numerical time-series data. Key features include statistical and mathematical expressions to facet and analyze data, supporting int, long, float, date and string fields. Examples show calculating a weighted average and variance. Future plans include multi-shard support and filtering result sets based on calculated statistics.
Search Analytics Component: Presented by Steven Bower, Bloomberg L.P.
- 2. Search Analytics Component Steven Bower ©2014 Bloomberg L.P.
- 3. Bloomberg • Largest provider of financial news and information • Our strength is quickly and accurately delivering data, news and analytics • Creating high performance and accurate information retrieval systems is core to our strength
- 4. Bloomberg Search Team • Search infrastructure • Develop and support search as a service platform • Support for other search applications within the company • Consultancy • Provide design consultancy/support to application teams • Promote search best practices/standardization throughout the company • Machine learning • Develop machine learning techniques to improve relevancy • Create natural language processors to answer questions • Unified search • Create information retrieval tools to organize and connect the vast and varied datasets provided to our clients
- 6. Our Approach • Use Search/Solr as it provides flexible search/filtering over large, fast moving, result sets • Initially used StatsComponent, but quickly ran into limitations • Wanted to push the bounds of analytics capabilities in Solr/Lucene • Needed a pluggable framework to perform complex calculations/aggregations on numerical time-series data • DocValues provided high performance columnar access to fields in the index (without un-inversion cost)
- 7. DocValues • DocValues provide high performance columnar access to fields in the index • No un-inversion cost • Increased storage footprint • Helps achieve NRT • Values live off-heap in memory map
- 8. Analytics Component • New component from the ground up • Designed/Implemented by the Bloomberg Search Team over summer of 2013 • Initial implementation was built using DocValues API directly, but moved to FieldCache • Refactored existing faceting implementation to support analytics • Created simple prefix notation for statistical expressions • Available as a Solr Contrib module in Solr 5.x or patches for 4.8+ on SOLR-5302
- 9. Features • Flexible/Extendable framework for adding additional statistics/faceting • Supports Multiple Analytics Requests per query execution • Multiple statistic calculations per request • Multiple facets per request • Each request can facet statistics over different fields and ranges
- 10. Features - Faceting • Field Faceting • Support for int, long, float, double, date, string fields • Support for multi-value fields • Support for limit, offset and mincount • Support for sorting of stats-facets by any statistic (i.e. sort by mean) • Range faceting • Numeric types and dates • Dynamically calculate range/gap based on calculated statistics • Support for query faceting of stats • Use calculated statistics to generate facet queries
- 11. Features – Map Operators • Basic Math • neg(<expr>) • add(<expr>,...) • mult(<expr>,...) • div(<expr>,<expr>) • pow(<expr>,<expr>) • log(<expr>,<expr>) • Constants • const_num(<number>) • const_date(<date>) • const_str(<string>) • Date Math • date_math(<date expr>,<date op>,...) • String operations • rev(<expr>) • concat(<expr>,...) • Field • <field> • Missing Values • miss(<expr>,<value>)
- 12. Features – Reduction Operators • Statistical • min(<expr>) • max(<expr>) • sum(<expr>) • count(<expr>) • miss(<expr>) • unique(<expr>) • Complex • sumofsquares(<expr>) • mean(<expr>) • stddev(<expr>) • median(<expr>) • percentile(<expr>)
- 13. Examples • Weighted Average • Calculate weighted average of field_a with field_b as the weight div( mean( mult(field_a, field_b) ), sum(field_b) ) • Variance • Calculate the variance of field_a pow( stddev(field_a), const_num(2) )
- 14. Examples • T-Score • Calculate a t-score where ## is the value and all values in your sample are stored in field_a. div( add( const_num(##), neg( mean(field_a) ) ), div( stddev(field_a), pow( count(field_a), const_num(.5) ) ) )
- 15. How We Use It • Segment, aggregate and analyze financial data quickly • Aggregate time series data across multiple fields to render charts • Created flexible diagnostic tools/ visualizations to analyze Solr performance
- 16. Future Plans • Multi-shard support • Pivot Facet Support • Statistics on Multi-value fields • To support unique() • Filter result set based upon calculated statistics • Generalize facet implementation
- 17. Links and Questions? Analytics Component h"ps://issues.apache.org/jira/browse/SOLR-‐5302 More About Bloomberg h"p://www.bloomberglabs.com/