Streamlining Search Indexing using Elastic Search and Spark - Databricks

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.

About Holden Karau

Holden Karau is transgender Canadian, Apache Spark committer, and co-author of Learning Spark & High Performance Spark. When not in San Francisco working as a software development engineer at IBM's Spark Technology Center, Holden talks internationally on Spark and holds office hours at coffee shops at home and abroad. She makes frequent contributions to Spark, specializing in PySpark and Machine Learning. Prior to IBM, she worked on a variety of distributed and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science.