Apache Ignite聽vs聽Elasticsearch

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Apache Ignite

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Advice on Apache Ignite and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza | 5 upvotes 路 250.6K views
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AlgoliaAlgoliaElasticsearchElasticsearch
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FirebaseFirebase

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 | 8 upvotes 路 182.6K 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 Ignite
Pros of Elasticsearch
  • 4
    Written in java. runs on jvm
  • 4
    Free
  • 3
    Load balancing
  • 3
    Multiple client language support
  • 3
    Sql query support in cluster wide
  • 3
    Rest interface
  • 3
    High Avaliability
  • 2
    Better Documentation
  • 2
    Easy to use
  • 1
    Distributed compute
  • 1
    Distributed Locking
  • 322
    Powerful api
  • 314
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    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
    Easy to scale
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Reliable
  • 2
    Nosql DB
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Great piece of software
  • 1
    Ecosystem
  • 1
    Scalability
  • 1
    Not stable
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Easy to get hot data
  • 1
    Open
  • 0
    Community

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Cons of Apache Ignite
Cons of Elasticsearch
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    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 4
      Hard to keep stable at large scale

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    What is Apache Ignite?

    It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

    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).

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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

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    What are some alternatives to Apache Ignite and Elasticsearch?
    Redis
    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Hazelcast
    With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
    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.
    See all alternatives