Elasticsearch

Autocomplete using Elasticsearch

Reading Time: 2 minutes You would have seen in a movie data store like IMDB, Whenever a user enters ‘g’, the search bar suggests him that you might be looking for gone girl or all the movies that have ‘g’ in them. This is what an Autocomplete or word completion is and it has become an essential part of any application. Autocomplete speeds up human-computer interaction by predicting the Continue Reading

Meetup: Stream Processing Using Spark & Kafka

Reading Time: < 1 minute Knoldus organized a Meetup on Friday, 9 September 2016. Topics which were covered in this meetup are: Overview of Spark Streaming. Fault-tolerance Semantics & Performance Tuning. Spark Streaming Integration with  Kafka. Meetup code sample available here Real time stream processing engine application code available here

Building Analytics Engine Using Akka, Kafka & ElasticSearch

Reading Time: 5 minutes In this blog , I will share my experience on building scalable, distributed and fault-tolerant  Analytics engine using Scala, Akka, Play, Kafka and ElasticSearch. I would like to take you through the journey of  building an analytics engine which was primarily used for text analysis. The inputs were structured, unstructured and semi-structured data and we were doing a lot of data crunching using it. The Analytics Continue Reading

Introduction to Elasticsearch in Scala

Reading Time: 2 minutes ElasticSearch is a real-time distributed search and analytics engine built on top of Apache Lucene. It is used for full-text search, structured search and analytics. Lucene is just a library and to leverage its power you need to use Java. Integrating Lucene directly with your application is a very complex task. Elastic Search uses the indexing and searching capabilities of Lucene but hides the complexities Continue Reading

How to tokenize your search by N-Grams using Elastic Search in Scala?

Reading Time: 2 minutes N–Grams can be used to search big data with compound words. German language is famous and referred for combining several small words into one massive compound word in order to capture precise or complex meanings. N-Grams are the fragments in which a word is broken, and as more number of fragments relevant to data, the more fragments will match.N-Grams has its length of fragment as Continue Reading

Implementing full text search with Couchbase and harnessing the power of Couchbase full text search (CBFT)

Reading Time: 5 minutes Hey Folks.! In this blog we are going to be introduced to the Couchbase Full text search. In my recent blog ,we talked about how we can user ElasticSearch for the full text search and how we can connect it with Couchbase so that our data gets copied in real time and we can search on it too. But what if we do not want Continue Reading

Working with Nested Aggregation of Elasticsearch

Reading Time: 2 minutes First of all we need to understand aggregation in ElasticSearch.In Elasticsearch an aggregation can be seen as a unit of work that builds analytic information over a set of documents.It is a powerful tool for build complex summaries of the data. There are many different types of aggregations, each with its own purpose and output. To better understand these types, it is often easier to break Continue Reading

Transfering Data from Couchbase to the ElasticSearch (Transport-Couchbase Plugin)

Reading Time: 3 minutes If we want to transfer the data persisted in your Couchbase to ElasticSearch and use the power of inverted indexing of Elastic Search along with Couchbase. Then we can do this in easy steps. Couchbase provides us a plugin for the Elastic search that makes your ElasticSearch node appear like a Couchbase Server node. After installation you can use the Cross-Datacenter Replication (XDCR) feature of Continue Reading