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Search Engine Technologies ( E-Commerce Search, Natural Language Processing, Solr, Lucene, Elasticsearch, etc)
19.4K views | +0 today
Search Engines, Architecture, Information Retrieval , Solr , Lucene , ElasticSearch , Natural Language Processing, Rank, Relevance, etc
Curated by
Carlos Sponchiado (Sponch)
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We invite you to take a moment of your day to acquaint yourself with the best monitoring tools for Elasticsearch performance.
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In one of my previous posts on elasticsearch, i shared my understanding of elasticsearch configurations and best practices. That was mostly from an indexing perspective. There are several tweaks one can use to optimise query performance as well. Improving querying time can be even more challenging than trying to improve indexing times. Lets see why querying is more of a challenge: Queries can go on while index is getting updatedDifferent queries would need different strategies for optimisationsThere are far more configurations that impact query performance:Query syntax/clauses usedIndex schemaElasticsearch configurationsRAM, CPU, Network, IOAnd there are times when you need to fire 2 or more queries in succession to get certain results back from ES. I have had one such scenario recently where i needed to fire 3 queries to ES and make sure that the response times where always less then a second. The 3 queries in question were related to each other in a sense that query 2 uses output of query 1 and query 3 uses output from query 2. For my use case, one of the queries was simple, while others two were more complex as they had aggregations, stats, filters etc. As outlined above, there are several things that can prevent an optimal response time. Also, to safely say that a desired response time has been achieved, one needs to test and test right. A poor testing method would lead to misleading performance statistics. Below are details of my testing methodology and tweaks that led to sub second response times for 3 queries. ElasticSearch Cluster and Indexes5 Machines in the cluster5 Shards per index250 GB EBS volume on each machine to hold indexesIndexes are stored as compressedNo indexing takes place while testing (my use case asks for indexing in batch once a day)3 indexesIndex A: with 24+ million records (used in 1st query)All integer fields.4 fields.Index B: with 90+ million records (used in 2nd query)All integers3 fieldsIndex C: with 340K records (used in 3rd query)String, Integer and Date fieldsonly few fields used in querying.Different machine types:to hold ES indexes: m3.large to c3.4xlargeRAMDifferent sizes for tests, starting from 4GB to 15GB given to ES instance.Via Alex Kantone
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Globalization at Netflix Having launched the Netflix service globally in January, we now support search in 190 countries. We currentl
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We take a look at how to implement autocomplete using Elasticsearch and nGrams in this post. Read on for more information.
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As Ops/DevOps, we are usually more concerned about cluster health and the data inside. This article is a simple step-by-step guide to check Elasticsearch, cluster, nodes, shards, indices, documents, and more.
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In this session we will explore elasticsearch, specifically, how to handle huge amount of data with it, how to effectively search it, and last, use facet
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search operators that work with each Google search service
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With Big Data, it is now possible to harvest user event data, such as search logs and click logs, for the purpose of computing user-based search accuracy metrics. These metrics can determine how well |
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Last week, a colleague asked me a really simple question about segments merging in Solr. After discussing the answer for some minutes while playing aroun
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Search and big data analytics have evolved significantly over the last few years, and organizations are increasingly using these technologies to meet their mission-critical needs. At the beginn
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Amazon Search: The Joy of Ranking Products: Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales
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In their book "Relevant Search", Doug Turnbull and John Berryman focus on the challenge of providing search results by balancing the needs and intents of the user. Using Elasticsearch and Solr, relevance engineers can constantly tune the needs of the business vs. the needs of the user.
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The premier Elasticsearch, Logstash, and Kibana conference for the Elastic data platform.
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f you've ever tried to write in a language that isn't your own, you are probably well aware of at least two things: first, writing requires much more time..
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Lucidworks Senior Software Engineer and Solr Committer Steve Rowe explains how to increase relevance using Solr with Apache OpenNLP. |
