Spark Jobs
Apache Spark can power a wide variety of data analysis jobs. In Fusion, Spark jobs are especially useful for generating recommendations.
Spark job subtypes
For the Spark job type, the available subtypes are listed below.
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A Spark SQL aggregation job where user-defined parameters are injected into a built-in SQL template at runtime.
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The Custom Python job provides user the ability to run Python code via Fusion. This job supports Python 3.6+ code.
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Run a custom Scala script as a Fusion job.
| See Additional Spark jobs for more information. |
Spark job configuration
Spark jobs can be created and modified using the Fusion UI or the Spark Jobs API. They can be scheduled using the Fusion UI or the Spark Jobs API.
For the complete list of configuration parameters for all Spark job subtypes, see the Jobs Configuration Reference.
Machine learning jobs
Fusion provides these job types to perform machine learning tasks.
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Lucidworks offers free training to help you get started with Fusion. Check out the Machine Learning in Fusion course, which focuses on the basics about machine learning jobs in Fusion:
Visit the LucidAcademy to see the full training catalog. |
Signals analysis
These jobs analyze a collection of signals in order to perform query rewriting, signals aggregation, or experiment analysis.
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Estimate ground truth queries using click signals and query signals, with document relevance per query determined using a click/skip formula.
Query rewriting
These jobs produce data that can be used for query rewriting or to inform updates to the synonyms.txt file.
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Perform head/tail analysis of queries from collections of raw or aggregated signals, to identify underperforming queries and the reasons. This information is valuable for improving overall conversions, Solr configurations, auto-suggest, product catalogs, and SEO/SEM strategies, in order to improve conversion rates.
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Identify multi-word phrases in signals.
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Use this job to generate pairs of synonyms and pairs of similar queries. Two words are considered potential synonyms when they are used in a similar context in similar queries.
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Token and Phrase Spell Correction
Detect misspellings in queries or documents using the numbers of occurrences of words and phrases.
Signals aggregation
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A Spark SQL aggregation job where user-defined parameters are injected into a built-in SQL template at runtime.
Experiment analysis
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Calculate relevance metrics (nDCG and so on) by replaying ground truth queries against catalog data using variants from an experiment.
Collaborative recommenders
These jobs analyze signals and generate matrices used to provide collaborative recommendations.
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Use this job when you want to compute user recommendations or item similarities using a Bayesian Personalized Ranking (BPR) recommender algorithm.
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Query-to-Query Session-Based Similarity jobs
This recommender is based on co-occurrence of queries in the context of clicked documents and sessions. It is useful when your data shows that users tend to search for similar items in a single search session. This method of generating query-to-query recommendations is faster and more reliable than the Query-to-Query Similarity recommender job, and is session-based unlike the similar queries previously generated as part of the Synonym Detection job.
Content-based recommenders
Content-based recommenders create matrices of similar items based on their content.
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Use this job when you want to compute item similarities based on their content, such as product descriptions.
Content analysis
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Use this job when you already have clusters or well-defined document categories, and you want to discover and attach keywords to see representative words within those existing clusters. (If you want to create new clusters, use the Document Clustering job.)
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The Document Clustering job uses an unsupervised machine learning algorithm to group documents into clusters based on similarities in their content. You can enable more efficient document exploration by using these clusters as facets, high-level summaries or themes, or to recommend other documents from the same cluster. The job can automatically group similar documents in all kinds of content, such as clinical trials, legal documents, book reviews, blogs, scientific papers, and products.
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This job analyzes how your existing documents are categorized and produces a classification model that can be used to predict the categories of new documents at index time.
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Use this job when you want to find outliers from a set of documents and attach labels for each outlier group.
Data ingest
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The Parallel Bulk Loader (PBL) job enables bulk ingestion of structured and semi-structured data from big data systems, NoSQL databases, and common file formats like Parquet and Avro.