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Solr vs TensorFlow: What are the differences?

Developers describe Solr as "An open source enterprise search server based on Lucene search library, with XML/HTTP and JSON APIs, hit highlighting, faceted search, caching, replication etc". 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. On the other hand, TensorFlow is detailed as "Open Source Software Library for Machine Intelligence". TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Solr belongs to "Search Engines" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".

"Powerful" is the top reason why over 33 developers like Solr, while over 15 developers mention "High Performance" as the leading cause for choosing TensorFlow.

Uber Technologies, 9GAG, and VSCO are some of the popular companies that use TensorFlow, whereas Solr is used by Slack, SendGrid, and Bodybuilding.com. TensorFlow has a broader approval, being mentioned in 195 company stacks & 126 developers stacks; compared to Solr, which is listed in 139 company stacks and 42 developer stacks.

- No public GitHub repository available -

What is 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.

What is TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
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    What are some alternatives to Solr and TensorFlow?
    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.
    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.
    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).
    Sphinx
    It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.
    Apache Solr
    It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.
    See all alternatives
    Decisions about Solr and TensorFlow
    StackShare Editors
    StackShare Editors
    Lucene
    Lucene
    Solr
    Solr

    "Slack provides two strategies for searching: Recent and Relevant. Recent search finds the messages that match all terms and presents them in reverse chronological order. If a user is trying to recall something that just happened, Recent is a useful presentation of the results.

    Relevant search relaxes the age constraint and takes into account the Lucene score of the document — how well it matches the query terms (Solr powers search at Slack). Used about 17% of the time, Relevant search performed slightly worse than Recent according to the search quality metrics we measured: the number of clicks per search and the click-through rate of the search results in the top several positions. We recognized that Relevant search could benefit from using the user’s interaction history with channels and other users — their ‘work graph’."

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    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 6 upvotes · 403.9K views
    atUber TechnologiesUber Technologies
    PyTorch
    PyTorch
    Keras
    Keras
    TensorFlow
    TensorFlow

    Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

    At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

    TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

    Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

    https://eng.uber.com/horovod/

    (Direct GitHub repo: https://github.com/uber/horovod)

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    Interest over time
    Reviews of Solr and TensorFlow
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    How developers use Solr and TensorFlow
    Avatar of Kang Hyeon Ku
    Kang Hyeon Ku uses SolrSolr

    elastic search 와 함께 유명한 검색 엔진 오픈 소스 중 하나 이다. 처음 설정할 것이 많은데, 어플리케이션의 이해가 없다면 잦은 수정이 필요하다. Solr Client 로 제어 할 수 없고 Server 에서 설정해 줘야하는 것들이 있어 서버 설정하는 부분이 중요하다. 서버 설정만 잘 되있다면, Client 쪽 소스는 별게 없다.

    중요한 건 형태소 분석기....

    Avatar of The Independent
    The Independent uses SolrSolr

    Full text search is provided by a SOLR cluster. This is done on Master/Slave replication with Varnish as a cache.

    Avatar of Eliana Abraham
    Eliana Abraham uses TensorFlowTensorFlow

    Machine Learning in EECS 445

    Avatar of Blue Kangaroo
    Blue Kangaroo uses SolrSolr

    Personalized search engine (for content-based filtering)

    Avatar of Satoru Ishikawa
    Satoru Ishikawa uses SolrSolr

    某プロプライエタリなWebアプリを動かすのに必要だった(インストールと構築のみ)

    Avatar of SAP Hybris
    SAP Hybris uses SolrSolr

    standard hybris-commerce implementation

    Avatar of Taylor Host
    Taylor Host uses TensorFlowTensorFlow

    Pilot integration for retraining.

    How much does Solr cost?
    How much does TensorFlow cost?
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