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Goodbye Sense - Welcome Alternatives?

I only recently noticed that Sense, the Chrome Plugin for Elasticsearch has been pulled from the app store by its creator. There are quite strong opinions in this thread and I would like to have Sense as a Chrome plugin as well. But I am also totally fine with Elasticsearch as a company trying to monetize some of its products so that is maybe something we just have to accept. What is interesting is that it isn't even possible to fork the project and keep developing it as there is no explicit license in the repo. I guess there is a lesson buried somewhere in here.

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An Alternative to the Twitter River - Index Tweets in Elasticsearch with Logstash

For some time now I've been using the Elasticsearch Twitter river for streaming conference tweets to Elasticsearch. The river runs on an Elasticsearch node, tracks the Twitter streaming API for keywords and directly indexes the documents in Elasticsearch. As the rivers are about to be deprecated it is time to move on to the recommended replacement: Logstash.

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A Tour Through elasticsearch-kopf

When I needed a plugin to display the cluster state of Elasticsearch or needed some insight into the indices I normally reached for the classic plugin elasticsearch-head. As it is recommended a lot and seems to be the unofficial successor I recently took a more detailed look at elasticsearch-kopf. And I like it.

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See Your Solr Cache Sizes: Eclipse Memory Analyzer

Solr uses different caches to prevent too much IO access and calculations during requests. When indexing doesn't happen too frequently you can get huge performance gains by employing those caches. Depending on the structure of your index data and the size of the caches they can become rather large and use a substantial part of your heap memory. In this post I would like to show how you can use the Eclipse Memory Analyzer to see how much space your caches are really using in memory.

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What Is Special About This? Significant Terms in Elasticseach

I have been using Elasticsearch a few times now for doing analytics of twitter data for conferences. Popular hashtags and mentions that can be extraced using facets can show what is hot at a conference. But you can go even further and see what makes each hashtag special. In this post I would like to show you the significant terms aggregation that is available with Elasticsearch 1.1. I am using the tweets of last years Devoxx as those contain enough documents to play around.

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