Elasticsearch聽vs聽rasa NLU

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Elasticsearch
Elasticsearch

8.8K
5.9K
+ 1
1.6K
rasa NLU
rasa NLU

22
28
+ 1
7
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Elasticsearch vs rasa NLU: What are the differences?

Developers describe Elasticsearch as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). On the other hand, rasa NLU is detailed as "Open source, drop-in replacement for NLP tools like wit.ai". rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

Elasticsearch can be classified as a tool in the "Search as a Service" category, while rasa NLU is grouped under "NLP / Sentiment Analysis".

Some of the features offered by Elasticsearch are:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

On the other hand, rasa NLU provides the following key features:

  • open source
  • python
  • NLP

Elasticsearch and rasa NLU are both open source tools. Elasticsearch with 41.9K GitHub stars and 14K forks on GitHub appears to be more popular than rasa NLU with 5.6K GitHub stars and 1.66K GitHub forks.

What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

What is rasa NLU?

rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
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Jobs that mention Elasticsearch and rasa NLU as a desired skillset
PinterestPinterest
San Francisco, CA; Palo Alto, CA
PinterestPinterest
San Francisco, CA; Palo Alto, CA
PinterestPinterest
San Francisco, CA; Palo Alto, CA
PinterestPinterest
San Francisco, CA; Palo Alto, CA
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    What are some alternatives to Elasticsearch and rasa NLU?
    Solr
    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
    Lucene
    Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Algolia
    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
    Splunk
    Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
    See all alternatives
    Decisions about Elasticsearch and rasa NLU
    Tim Specht
    Tim Specht
    鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 51.8K views
    atDubsmashDubsmash
    Memcached
    Memcached
    Algolia
    Algolia
    Elasticsearch
    Elasticsearch
    #SearchAsAService

    Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

    Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

    Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

    #SearchAsAService

    See more
    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail | 16 upvotes 265K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
    Memcached
    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

    I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

    For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

    Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

    Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

    Future improvements / technology decisions included:

    Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

    As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

    One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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    Reviews of Elasticsearch and rasa NLU
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    How developers use Elasticsearch and rasa NLU
    Avatar of imgur
    imgur uses ElasticsearchElasticsearch

    Elasticsearch is the engine that powers search on the site. From a high level perspective, it鈥檚 a Lucene wrapper that exposes Lucene鈥檚 features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

    Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

    Avatar of Instacart
    Instacart uses ElasticsearchElasticsearch

    The very first version of the search was just a Postgres database query. It wasn鈥檛 terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn鈥檛 come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

    Avatar of AngeloR
    AngeloR uses ElasticsearchElasticsearch

    We use ElasticSearch for

    • Session Logs
    • Analytics
    • Leaderboards

    We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

    The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

    Avatar of Brandon Adams
    Brandon Adams uses ElasticsearchElasticsearch

    Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

    Avatar of Ana Phi Sancho
    Ana Phi Sancho uses ElasticsearchElasticsearch

    Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

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