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Apache Doris VS DuckDB

Compare Apache Doris VS DuckDB 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.

DuckDB logo DuckDB

DuckDB is an in-process SQL OLAP database management system
  • Apache Doris Apache Doris
    Apache Doris //
    2024-01-10
  • DuckDB Landing page
    Landing page //
    2023-06-18

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.

DuckDB features and specs

  • Lightweight
    DuckDB is a lightweight database that is easy to install and use without requiring a separate server process.
  • In-Memory Processing
    It supports efficient in-memory execution, which makes it suitable for analytical queries that require quick data processing.
  • Columnar Storage
    DuckDB uses a columnar storage format that optimizes for analytical workloads by improving read performance for large datasets.
  • Integration with Data Science Tools
    The database integrates well with popular data science tools and libraries such as Pandas, R, and Jupyter Notebooks.
  • SQL Support
    DuckDB offers full support for SQL, allowing users to leverage their existing SQL knowledge without having to learn new query languages.
  • Open Source
    DuckDB is open-source, enabling users to inspect the code, contribute to its development, and use it without licensing costs.

Possible disadvantages of DuckDB

  • Limited Scalability
    DuckDB is optimized for single-node operations, which may not be suitable for scaling out to large, distributed data workloads.
  • Relatively New
    As a newer database system, DuckDB might lack some features and optimizations found in more mature database systems.
  • Lack of Advanced Features
    DuckDB may not support some advanced database management features like complex transactions and user permissions found in other database systems.
  • Community and Support
    Being a less mature project, it might not have as large a community or extensive documentation and support as other established database systems.
  • Limited Distributed Processing
    DuckDB currently focuses more on local data processing and may not be the best choice for applications needing distributed computing capabilities.

Apache Doris videos

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DuckDB videos

DuckDB An Embeddable Analytical Database

More videos:

  • Review - DuckDB: Hi-performance SQL queries on pandas dataframe (Python)
  • Review - DuckDB An Embeddable Analytical Database

Category Popularity

0-100% (relative to Apache Doris and DuckDB)
Databases
52 52%
48% 48
Relational Databases
68 68%
32% 32
Big Data
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 DuckDB

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.

DuckDB Reviews

We have no reviews of DuckDB yet.
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Social recommendations and mentions

Based on our record, DuckDB should be more popular than Apache Doris. It has been mentiond 37 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 (8)

  • Doris x Gravitino: Unified Metadata Management for Modern Lakehouse Architecture
    This article provides an in-depth introduction to deep integration between Apache Doris and Apache Gravitino, building a modern lakehouse architecture based on Iceberg REST Catalog. Through Gravitino's unified metadata management and dynamic credential vending capabilities, we achieve efficient and secure access to Iceberg data stored on S3. - Source: dev.to / 5 days ago
  • Gravitino 0.5.0: Expanding the horizon to Apache Spark, non-tabular data, and more!
    Tagging onto our Real-Time Analytics support, we are now also supporting Apache Doris in this release. Doris is a high-performance, real-time analytical data warehouse that is known for its speed and ease of use. By adding a Doris catalog, engineers implementing Gravitino will now have more flexibility in their cataloging options for their analytical workloads. (Issue #1339, visit jdbc-doris-catalog for... - Source: dev.to / about 2 months ago
  • 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 / about 1 year 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 / about 1 year 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 / about 1 year ago
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DuckDB mentions (37)

  • From OLTP to OLAP: Streaming Databases into MotherDuck with Estuary
    DuckDB is an open source analytical database designed with a clear goal: to make complex queries fast and simple without heavy infrastructure. Instead of being a traditional client-server database, DuckDB is embedded. It runs inside the host process, which reduces overhead and makes it easy to integrate directly into applications, notebooks, or scripts. Several features stand out:. - Source: dev.to / 8 days ago
  • DuckDB + Iceberg: The ultimate synergy
    Apache Iceberg and DuckDB have established themselves as key players in data architecture landscape. With DuckDB 1.4's native support for Iceberg writes, combined with Apache Polaris and MinIO, this promising stack offers efficiency, scalability, and flexibility. - Source: dev.to / 14 days ago
  • DuckDB on AWS Lambda: The Easy Way with Layers
    It seemed like the perfect opportunity to explore DuckDB, an in-process analytical database known for its efficiency and simplicity. - Source: dev.to / 23 days ago
  • From Go to Rust: Supercharging Our ClickHouse UDFs with Alloy
    While our Go-based implementation has served us well, we've been exploring whether Rustโ€”with its rapidly maturing Ethereum ecosystemโ€”could take us even further. The potential benefits are compelling: better performance, enhanced safety, and improved portability that could make it easier to bring these UDFs to other analytical engines like DataFusion or DuckDB. - Source: dev.to / 3 months ago
  • Show HN: TextQuery โ€“ Query CSV, JSON, XLSX Files with SQL
    Have you seen duckdb? https://duckdb.org/ It's basically what you're building, but more low-level. Really cool, to be honest -- serves the same market too. Do you have any significant differentiator, other than charts? - Source: Hacker News / 5 months ago
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What are some alternatives?

When comparing Apache Doris and DuckDB, 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.

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.

Apache Arrow - Apache Arrow is a cross-language development platform for in-memory data.

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

Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.

Greenplum Database - Greenplum Database is an open source parallel data warehousing platform.