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Apache Doris VS Apache Ignite

Compare Apache Doris VS Apache Ignite and see what are their differences

Apache Doris logo Apache Doris

Apache Doris is an open-source real-time data warehouse for big data analytics.

Apache Ignite logo Apache Ignite

high-performance, integrated and distributed in-memory platform for computing and transacting on...
  • Apache Doris Apache Doris
    Apache Doris //
    2024-01-10
  • Apache Ignite Landing page
    Landing page //
    2023-07-08

Apache Doris features and specs

  • High Performance
    Apache Doris is designed to deliver high query performance, especially for aggregate queries, due to its columnar storage and vectorized execution engine.
  • Real-time Analytics
    Supports real-time data analytics with low latency, thanks to its efficient data ingestion processes and real-time data update capabilities.
  • Unified Analytics
    Provides a unified platform that supports both real-time and batch data processing, offering flexibility for different analytical workloads.
  • Ease of Use
    Features a SQL-like interface, which makes it accessible for users familiar with SQL, reducing the learning curve.
  • Scalability
    Can scale out horizontally, allowing it to handle increasing volumes of data and user queries by adding more nodes to the cluster.

Possible disadvantages of Apache Doris

  • Ecosystem Integration
    While improving, the ecosystem isn't as mature as older database management systems, which might pose integration challenges with certain tools.
  • Community Support
    Being a relatively newer project, it may not have as large a community or as extensive third-party support as more established databases.
  • Complexity in Setup
    Initial setup and configuration can be complex, especially for users not already familiar with similar distributed systems.
  • Limited Use Cases
    Optimized specifically for online analytical processing (OLAP), it may not be suitable for all types of databases or transactional use cases.
  • Features Maturity
    Some features may lack the maturity and robustness found in more mature and widely adopted database systems, requiring careful evaluation based on project needs.

Apache Ignite features and specs

  • In-Memory Data Grid
    Apache Ignite provides a robust in-memory data grid that can drastically improve data access speeds by storing data in memory across distributed nodes.
  • Scalability
    The system is designed to scale horizontally, allowing users to add more nodes to handle increased loads, thereby ensuring high availability and performance.
  • Distributed Compute Capabilities
    Ignite supports parallel execution of tasks across cluster nodes, which is beneficial for complex computations and real-time processing.
  • Persistence
    Although primarily in-memory, Ignite offers a durable and transactional Persistence layer that ensures data can be persisted on disk, providing a hybrid in-memory and persistent storage solution.
  • SQL Queries
    Ignite offers support for ANSI-99 SQL, which allows users to execute complex SQL queries across distributed datasets easily.
  • Integration
    It integrates well with existing Hadoop and Spark setups, allowing users to enhance their existing data pipelines with Ignite’s capabilities.
  • Fault Tolerance
    Apache Ignite includes built-in mechanisms for recovery and ensures that data copies are maintained across nodes for resilience against node failures.

Possible disadvantages of Apache Ignite

  • Complexity
    Apache Ignite can be complex to set up and manage, especially when configuring a large, distributed system with multiple nodes.
  • Resource Intensive
    Running an in-memory data grid like Ignite requires significant memory resources, which can increase operational costs.
  • Learning Curve
    Due to its comprehensive features and distributed nature, there is a steep learning curve associated with effectively utilizing Ignite.
  • Configuration Overhead
    There is substantial configuration overhead involved to optimize performance and ensure proper cluster management.
  • Community Support
    Although it has active development, the community support might not be as robust compared to other more mature solutions, possibly leading to challenges in finding solutions to niche issues.
  • YARN Dependence
    For those looking to integrate with Hadoop, Ignite's optimal performance is sometimes reliant on Hadoop YARN, which can introduce additional complexity.

Apache Doris videos

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Apache Ignite videos

Best Practices for a Microservices Architecture on Apache Ignite

More videos:

  • Review - Apache Ignite + GridGain powering up banks and financial institutions with distributed systems

Category Popularity

0-100% (relative to Apache Doris and Apache Ignite)
Databases
44 44%
56% 56
Relational Databases
73 73%
27% 27
NoSQL Databases
0 0%
100% 100
Data Warehousing
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Doris and Apache Ignite

Apache Doris Reviews

Log analysis: Elasticsearch vs Apache Doris
If you are looking for an efficient log analytic solution, Apache Doris is friendly to anyone equipped with SQL knowledge; if you find friction with the ELK stack, try Apache Doris provides better schema-free support, enables faster data writing and queries, and brings much less storage burden.

Apache Ignite Reviews

We have no reviews of Apache Ignite yet.
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Social recommendations and mentions

Based on our record, Apache Doris should be more popular than Apache Ignite. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Doris mentions (6)

  • Evolution of Data Sharding Towards Automation and Flexibility
    Like in many databases, Apache Doris shards data into partitions, and then a partition is further divided into buckets. Partitions are typically defined by time or other continuous values. This allows query engines to quickly locate the target data during queries by pruning irrelevant data ranges. - Source: dev.to / 9 months ago
  • Steps to industry-leading query speed: evolution of the Apache Doris execution engine
    What makes a modern database system? The three key modules are query optimizer, execution engine, and storage engine. Among them, the role of execution engine to the DBMS is like the chef to a restaurant. This article focuses on the execution engine of the Apache Doris data warehouse, explaining the secret to its high performance. - Source: dev.to / 10 months ago
  • Apache Doris for log and time series data analysis in NetEase, why not Elasticsearch and InfluxDB?
    For most people looking for a log management and analytics solution, Elasticsearch is the go-to choice. The same applies to InfluxDB for time series data analysis. These were exactly the choices of NetEase, one of the world's highest-yielding game companies but more than that. As NetEase expands its business horizons, the logs and time series data it receives explode, and problems like surging storage costs and... - Source: dev.to / 11 months ago
  • Multi-tenant workload isolation in Apache Doris: a better balance between isolation and utilization
    This is an in-depth introduction to the workload isolation capabilities of Apache Doris. But first of all, why and when do you need workload isolation? If you relate to any of the following situations, read on and you will end up with a solution:. - Source: dev.to / 12 months ago
  • SQL Convertor for Easy Migration from Presto, Trino, ClickHouse, and Hive to Apache Doris
    Apache Doris is an all-in-one data platform that is capable of real-time reporting, ad-hoc queries, data lakehousing, log management and analysis, and batch data processing. As more and more companies have been replacing their component-heavy data architecture with Apache Doris, there is an increasing need for a more convenient data migration solution. That's why the Doris SQL Convertor is made. - Source: dev.to / about 1 year ago
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Apache Ignite mentions (3)

  • API Caching: Techniques for Better Performance
    Apache Ignite — Free and open-source, Apache Ignite is a horizontally scalable key-value cache store system with a robust multi-model database that powers APIs to compute distributed data. Ignite provides a security system that can authenticate users' credentials on the server. It can also be used for system workload acceleration, real-time data processing, analytics, and as a graph-centric programming model. - Source: dev.to / 8 months ago
  • Ask HN: P2P Databases?
    Ignite works as you describe: https://ignite.apache.org/ I wouldn't really recommend this approach, I would think more in terms of subscriptions and topics and less of a 'database'. - Source: Hacker News / over 3 years ago
  • .NET and Apache Ignite: Testing Cache and SQL API features — Part I
    Last days, I started using Apache Ignite as a cache strategy for some applications. Apache Ignite is an open-source In-Memory Data Grid, distributed database, caching, and high-performance computing platform. Source: over 3 years ago

What are some alternatives?

When comparing Apache Doris and Apache Ignite, you can also consider the following products

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

StarRocks - StarRocks offers the next generation of real-time SQL engines for enterprise-scale analytics. Learn how we make it easy to deliver real-time analytics.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

memcached - High-performance, distributed memory object caching system