Streamlining Search Indexing using Elastic Search and Spark

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Everyone who has maintained a search cluster knows the pain of keeping our on-line update code and offline reindexing pipelines in sync. Subtle bugs can pop up when our data is indexed differently depending on the context. By using Spark & Spark Streaming we can reuse the same indexing code between contexts and even take advantage reduce overhead by talking directly to the correct indexing node.

Sometimes we need to use search data as part of our distributed map reduce jobs. We will illustrate how to use Elastic Search as side data source with Spark.

We will also illustrate both of these tasks in two real examples using the Twitter firehose. In the first we will index tweets in a geospatial context and in the second we will use the same index to determine the top hashtags per region.



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About Holden Karau

Holden is a transgender Canadian open source developer with a focus on Apache Spark, Airflow, Kubeflow, and related "big data" tools. She is the co-author of Learning Spark, High Performance Spark, and Kubeflow for Machine Learning. She is a committer and PMC on Apache Spark. She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal.