Recent Posts by Vineeth Mohan

Vineeth Mohan is a certified Elasticsearch trainer and author of "Elasticsearch Blueprints" book.

Are you looking for full-text search and highlight capability on .PDF, .doc, or .epub files that you have in your system? In this tutorial, we show you how with the mapper-attachment-plugin

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So far we have seen the grouping of documents based on various aggregations like term, date histogram, and more. In this post we are going to see the limitations of the date_histogram aggregation in certain cases, and how to overcome that by employing elasticsearch scripting.

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In the previous post we saw how to set up, configure, and index network traffic data using packetbeat, logstash and elasticsearch. 

In this post we will see how to visualize the data with the help of Kibana.

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In many cases we want to use inputs from different databases which are not natively supported by Elasticsearch. In this post we show how to migrate data from a MySql database to Elasticsearch via Logstash.

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When dealing with a large number of parallel operations in elasticsearch, such as search requests or bulk indexing operations, you may encounter thread pool related errors. Here we explain thread pools and discuss a typical search request thread pool error that might be faced, as well as how to handle such issues.

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In the last article we explained how the simple moving average pipeline aggregation worked and how to analyze the trends of the stock values of a firm with it. In this post, we discuss more moving average aggregation models and their differences in detail.  We use the same data set as in the previous post.  

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We have seen numerous pipeline aggregations in previous posts. Here we discuss another pipeline aggregation called the moving average aggregation and its significance, as well as its application in real-life scenarios.

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In this last installment of the pipeline aggregation blog series, we introduce the remaining class of aggregations, which includes the stats, extended stats, and the percentile aggregations. These aggregations are used to get a range of statistics like the average, percentiles, mean square values, and more for the data we have.

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In previous installments of the pipeline aggregation blog series, we discussed ready-made aggregations which are ready to be used with the query. In this post, we explore pipeline aggregations which make use of scripts for its operations, and thus provides flexibility for the user to handle the field data.

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In this installment of the pipeline aggregations series, we introduce three important pipeline aggregations: Sum Buckets, Derivative Sum, and Cumulative Sum Aggregations. These aggregations provide us with easy ways to get required results by using the results of other aggregations.

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