Elasticsearch聽vs聽PredictionIO

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Elasticsearch
Elasticsearch

9.1K
6.1K
+ 1
1.6K
PredictionIO
PredictionIO

47
54
+ 1
4
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Elasticsearch vs PredictionIO: What are the differences?

Elasticsearch: 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); PredictionIO: Open Source Machine Learning Server. PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Elasticsearch belongs to "Search as a Service" category of the tech stack, while PredictionIO can be primarily classified under "Machine Learning Tools".

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, PredictionIO provides the following key features:

  • Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.
  • Customize the modularized open codebase to fulfill any unique prediction requirement.
  • Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.

"Powerful api" is the top reason why over 310 developers like Elasticsearch, while over 3 developers mention "Predict Future" as the leading cause for choosing PredictionIO.

Elasticsearch and PredictionIO are both open source tools. It seems that Elasticsearch with 41.9K GitHub stars and 14K forks on GitHub has more adoption than PredictionIO with 11.8K GitHub stars and 1.92K GitHub forks.

Uber Technologies, Udemy, and DigitalOcean are some of the popular companies that use Elasticsearch, whereas PredictionIO is used by 500 Startups, Betaout, and Tokopedia. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 937 developers stacks; compared to PredictionIO, which is listed in 5 company stacks and 5 developer stacks.

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 PredictionIO?

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.
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      What are some alternatives to Elasticsearch and PredictionIO?
      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 PredictionIO
      Tim Specht
      Tim Specht
      鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 70.2K 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
      Principal Software Engineer at Tophatter | 16 upvotes 373.3K 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.

      See more
      Interest over time
      Reviews of Elasticsearch and PredictionIO
      Review ofPredictionIOPredictionIO

      PredictionIO is an open source Machine Learning Server. It empowers programmers and data engineers to build smart applications. Machine Learning is about attempting to teach computers to predict future, or otherwise unknown events, by applying computer science or statistics techniques to analyze existing data. It can be seen as a transformation from existing data to improve insights about the unknown. PredictionIO is useful for any web and mobile apps. Here are some examples of smart features you can build with PredictionIO: predict user behaviors; offer personalized video, news, deals, ads and job openings; help users to discover interesting events, documents, apps and restaurants; provide impressive match-making services, etc.

      How developers use Elasticsearch and PredictionIO
      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.

      Avatar of Dolls Kill
      Dolls Kill uses PredictionIOPredictionIO

      We run eCommerce product recommendations using predictionIO machine learning techniques

      How much does Elasticsearch cost?
      How much does PredictionIO cost?
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