Apache Solr聽vs聽Elasticsearch

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Apache Solr

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Apache Solr vs Elasticsearch: What are the differences?

Apache Solr: An open source search platform. 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; 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).

Apache Solr belongs to "Search Engines" category of the tech stack, while Elasticsearch can be primarily classified under "Search as a Service".

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

Advice on Apache Solr and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza | 5 upvotes 路 173.3K views
Needs advice
on
FirebaseFirebaseElasticsearchElasticsearch
and
AlgoliaAlgolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit | 7 upvotes 路 131.8K views
Recommends
AlgoliaAlgolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Apache Solr
Pros of Elasticsearch
    Be the first to leave a pro
    • 321
      Powerful api
    • 311
      Great search engine
    • 231
      Open source
    • 213
      Restful
    • 200
      Near real-time search
    • 96
      Free
    • 83
      Search everything
    • 54
      Easy to get started
    • 45
      Analytics
    • 26
      Distributed
    • 6
      Fast search
    • 5
      More than a search engine
    • 3
      Great docs
    • 3
      Awesome, great tool
    • 3
      Easy to scale
    • 2
      Document Store
    • 2
      Nosql DB
    • 2
      Great piece of software
    • 2
      Great customer support
    • 2
      Intuitive API
    • 2
      Fast
    • 2
      Easy setup
    • 2
      Highly Available
    • 1
      Not stable
    • 1
      Scalability
    • 1
      Open
    • 1
      Reliable
    • 1
      Github
    • 1
      Elaticsearch
    • 1
      Actively developing
    • 1
      Responsive maintainers on GitHub
    • 1
      Ecosystem
    • 1
      Easy to get hot data
    • 1
      Potato
    • 0
      Community

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    Cons of Apache Solr
    Cons of Elasticsearch
      Be the first to leave a con
      • 6
        Resource hungry
      • 6
        Diffecult to get started
      • 5
        Expensive
      • 3
        Hard to keep stable at large scale

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      What is 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.

      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).

      Need advice about which tool to choose?Ask the StackShare community!

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      What companies use Elasticsearch?
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      Blog Posts

      May 21 2019 at 12:20AM

      Elastic

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      GitHubPythonNode.js+47
      50
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      What are some alternatives to Apache Solr and Elasticsearch?
      Splunk
      It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
      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.
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      Azure Search
      Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.
      See all alternatives