Apache Ignite vs Elasticsearch

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

Apache Ignite

105
164
+ 1
32
Elasticsearch

34.7K
26.4K
+ 1
1.6K
Add tool

Apache Ignite vs Elasticsearch: What are the differences?

Introduction

Apache Ignite and Elasticsearch are both popular open-source distributed platforms used for data storage and analysis. Despite some similarities, there are key differences between the two that make them suitable for different use cases. This article will highlight and explain the main differences between Apache Ignite and Elasticsearch.

  1. Data Processing and Analytics: Apache Ignite is primarily designed as an in-memory data processing platform, whereas Elasticsearch is optimized for search and analytics on distributed data. Ignite uses distributed in-memory computing techniques to provide real-time analytics and processing capabilities, making it suitable for use cases that require low-latency data access and real-time processing. Elasticsearch, on the other hand, leverages its distributed search and indexing capabilities, making it more suitable for use cases that need powerful full-text searching and analysis of structured and unstructured data.

  2. Data Model and Query Language: Another key difference lies in the data model and query language used by these platforms. Apache Ignite supports various data models, including key-value, SQL, and compute grid, allowing users to choose the most appropriate model for their specific needs. It also supports SQL queries, making it easier for users familiar with SQL to interact with the data. In contrast, Elasticsearch uses a document-oriented data model and a query language called Elasticsearch Query DSL. This query language is specifically designed for full-text searching and provides features like faceted search, filtering, and relevance scoring.

  3. Scalability and Fault Tolerance: Both Apache Ignite and Elasticsearch are designed to be highly scalable and fault-tolerant. However, the underlying mechanisms differ. Apache Ignite achieves scalability by distributing data and computation across a cluster of nodes, allowing it to handle large amounts of data and processing tasks. It also provides data replication and backup mechanisms to ensure fault tolerance. Elasticsearch, on the other hand, achieves scalability by sharding data across multiple nodes and using a distributed architecture. It also provides automated failover and replication mechanisms to ensure high availability and fault tolerance.

  4. Data Indexing and Search Capabilities: One of the key strengths of Elasticsearch lies in its powerful and efficient search capabilities. It uses inverted indexes to index and search data, making it fast and efficient for full-text and structured searches. It also provides advanced features like relevance scoring, fuzzy matching, and aggregations. Apache Ignite, on the other hand, does not provide built-in search capabilities like Elasticsearch. While it can perform basic queries on in-memory data using SQL, it may not be as efficient or feature-rich as Elasticsearch for complex search use cases.

  5. Integration and Ecosystem: Apache Ignite is designed to integrate well with existing databases and data sources. It provides various connectors and integrations for popular databases like MySQL, Oracle, and PostgreSQL. It also integrates with other Apache projects like Hadoop and Spark, allowing users to leverage their existing ecosystem. Elasticsearch, on the other hand, is part of the Elastic Stack, which includes various complementary tools like Logstash and Kibana. These tools provide end-to-end data ingestion, processing, and visualization capabilities, making it a comprehensive solution for data analytics and search.

  6. Data Durability and Persistence: Apache Ignite provides durable memory-based storage, allowing it to survive node restarts and failures. It also supports disk-based persistence, which can be used to store larger datasets that do not fit entirely in memory. Elasticsearch, on the other hand, provides durability and persistence through its distributed storage model. It shards and replicates data across multiple nodes, ensuring data availability even in the face of node failures. It also provides data snapshot and restore capabilities for backup and recovery purposes.

In summary, Apache Ignite and Elasticsearch are both powerful distributed platforms, but they have key differences in terms of data processing and analytics capabilities, data model and query language, scalability and fault tolerance mechanisms, search capabilities, integration and ecosystem, and data durability and persistence. The choice between the two depends on the specific use case and requirements of the project.

Advice on Apache Ignite and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 369.1K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
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!

See more
Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 274.1K views
Recommends
on
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.

See more
Mike Endale
Recommends
on
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.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Ignite
Pros of Elasticsearch
  • 4
    Multiple client language support
  • 4
    Written in java. runs on jvm
  • 4
    Free
  • 4
    High Avaliability
  • 3
    Load balancing
  • 3
    Sql query support in cluster wide
  • 3
    Rest interface
  • 2
    Easy to use
  • 2
    Distributed compute
  • 2
    Better Documentation
  • 1
    Distributed Locking
  • 326
    Powerful api
  • 315
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    Near real-time search
  • 97
    Free
  • 84
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Highly Available
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 3
    Easy to scale
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Potato
  • 2
    Nosql DB
  • 2
    Document Store
  • 1
    Not stable
  • 1
    Scalability
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Easy to get hot data
  • 0
    Community

Sign up to add or upvote prosMake informed product decisions

Cons of Apache Ignite
Cons of Elasticsearch
    Be the first to leave a con
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 4
      Hard to keep stable at large scale

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    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).

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

    Jobs that mention Apache Ignite and Elasticsearch as a desired skillset
    LaunchDarkly
    Oakland, California, United States
    What companies use Apache Ignite?
    What companies use Elasticsearch?
    See which teams inside your own company are using Apache Ignite or Elasticsearch.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Apache Ignite?
    What tools integrate with Elasticsearch?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
    12
    5150
    GitHubPythonReact+42
    49
    40691
    GitHubPythonNode.js+47
    54
    72280
    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