Practical Machine Learning for Smarter Search with Spark+Solr
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Practical Machine Learning for Smarter Search with Spark+Solr
- 1. Practical Machine Learning for Smarter Search with Solr and Spark Jake Mannix @pbrane Lead Data Engineer, Lucidworks
- 2. $ whoami Now: Lucidworks, Office of the CTO: applied ML / data engineering R&D Previously: • Allen Institute for AI: Semantic Search on academic research publications • Twitter: account search, user interest modeling, content recommendations • LinkedIn: profile search, generic entity-to-entity recommender systems Prehistory: • other software companies, algebraic topology, particle cosmology
- 3. • Why Spark and Solr for Data Engineering? • Quick intro to Solr • Quick intro to Spark • Example: ManyNewsgroups • data exploration • clustering: unsupervised ML • classification: supervised ML • recommender: collaborative filtering + content-based • search ranking Overview
- 4. Practical Data Science with Spark and Solr Why does Solr need Spark? Why does Spark need Solr?
- 5. Why do data engineering with Solr and Spark? Solr Spark • Data exploration and visualization • Easy ingestion and feature selection • Powerful ranking features • Quick and dirty classification and clustering • Simple operation and scaling • Stats and math built in • General purpose batch/streaming compute engine Whole collection analysis! • Fast, large scale iterative algorithms • Advanced machine learning: MLLib, Mahout, Deep Learning4j • Lots of integrations with other big data systems
- 6. Why does Spark need Solr? Typical Hadoop / Spark data-engineering task, start with some data on HDFS: $ hdfs dfs -ls /user/jake/mail/lucene-solr-user/2015 … -rw-r--r-- 1 jake staff 6304388 Feb 4 18:22 part-00001.lzo -rw-r--r-- 1 jake staff 7977085 Feb 4 18:22 part-00002.lzo -rw-r--r-- 1 jake staff 7210817 Feb 4 18:22 part-00003.lzo -rw-r--r-- 1 jake staff 1215048 Feb 4 18:22 part-00004.lzo Now what? What’s in these files?
- 7. Solr gives you: • random access data store • full-text search • fast aggregate statistics • just starting out: no HDFS / S3 necessary! • world-class multilingual text analytics: • no more: tokens = str.toLowerCase().split(“s+“) • relevancy / ranking • realtime HTTP service layer
- 8. • Apache Lucene • Grouping and Joins • Stats, expressions, transformations and more • Lang. Detection • Extensible • Massive Scale/Fault tolerance Solr Key Features • Full text search (Info Retr.) • Facets/Guided Nav galore! • Lots of data types • Spelling, auto-complete, highlighting • Cursors • More Like This • De-duplication
- 9. Why Spark for Solr? • Spark-shell: a Big Data REPL with all your fave JVM libs! • Build the index in parallel very, very quickly! • Aggregations • Boosts, stats, iterative global computations • Offline compute to update index with additional info (e.g. PageRank, popularity) • Whole corpus analytics and ML: clustering, classification, CF, rankers • General-purpose distributed computation • Joins with other storage (Cassandra, HDFS, DB, HBase)
- 10. Spark Key Features • General purpose, high powered cluster computing system • Modern, faster alternative to MapReduce • 3x faster w/ 10x less hardware for Terasort • Great for iterative algorithms • APIs for Java, Scala, Python and R • Rich set of add-on libraries for machine learning, graph processing, integrations with SQL and other systems • Deploys: Standalone, Hadoop YARN, Mesos, AWS, Docker, …
- 11. • Initial exploration of ASF mailing-list archives • Index it into Solr • Explore a bit deeper: unsupervised Spark ML • Exploit labels: predictive analytics Example: Many NewsGroups
- 12. • Initial exploration of ASF mailing-list archives • index into Solr: just need to turn your records into json • facet: • fields with low cardinality or with sensible ranges • document size histogram • projects, authors, dates • find: broken fields, automated content, expected data missing, errors • now: load into a spark RDD via SolrRDD: Many NewsGroups: Initial Exploration
- 13. • cleanup/filtering via spark DataFrame operations: • create thread groups: Many NewsGroups: Initial Exploration
- 14. • try other text analyzers: (no more str.split(“w+”)! ) Many NewsGroups: Initial Exploration ref: Lucidworks blog on LuceneTextAnalyzer by Steve Rowe
- 15. • Unsupervised machine learning: • clustering documents with KMeans • extract topics with Latent Dirichlet Allocation • learn word vectors with Word2Vec Many NewsGroups: Exploratory Data Science
- 16. • Vectorize and run KMeans: Many NewsGroups: Exploratory Data Science
- 17. • Build topic models with LDA: Many NewsGroups: Exploratory Data Science
- 18. • Build word vector representations with Word2Vec: Many NewsGroups: Exploratory Data Science
- 19. • Now for some real Data Science: Many NewsGroups: Supervised Learning
- 20. • What else could you do? • Try other classification algs, cross-validate to pick! • Recommender Systems • content-based: • mail-thread as “item”, head msgs grouped by replier as “user” profile • search query of users against items to recommend • collaborative-filtering: • users replying to a head msg “rate” them +-tively • train a Spark ML ALS RecSys model • Train search rankers in click logs Many NewsGroups: Next steps?
- 21. Resources • spark-solr: https://github.com/Lucidworks/spark-solr • Company: http://www.lucidworks.com • Our blog: http://www.lucidworks.com/blog • Apache Solr: http://lucene.apache.org/solr • Apache Spark: http://spark.apache.org • Fusion: http://www.lucidworks.com/products/fusion • Twitter: @pbrane
Hinweis der Redaktion
- I live at the intersection of information retrieval and machine learning, and the scalable / distributed systems engineering to enable them
- Two open source Apache projects: Solr is a fantastically scalable, production-grade search engine Spark is a high-performance, flexible distributed computation engine
- Download one? These aren’t big, but sometimes each chunk *is* big. These are moderately human-readable (after decompress), so you could “hdfs cat” them, but often it’s binary: parquet/avro/thrift/protobufs. Maybe you can “hdfs cat” them, too… but you want to explore, tinker, see what ugly rough edges there are.
- No HDFS means no NameNode SPOF, no need for HA NN, Ops to go with that.
- faceting does global reads / stats, but is “just counting”
- overview: buildVectorizer runs word count, filters out ultra-rare and too freq words, builds a dictionary with IDF values in it vectorize: applies the vectorizer as a UDF to make a vector field buildKMeansModel: runs kMeans over the vectors
- next slide: w2v - sooooooo much code! ;P
- this model may be applied to data you have that doesn’t have labels it may also be serialized, and loaded up into a service layer (plug for Fusion) Remember: just index these modified DataFrames back into Solr and take a peek at them!