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Apache Druid VS SQLite

Compare Apache Druid VS SQLite and see what are their differences

Apache Druid logo Apache Druid

Fast column-oriented distributed data store

SQLite logo SQLite

SQLite Home Page
  • Apache Druid Landing page
    Landing page //
    2023-10-07
  • SQLite Landing page
    Landing page //
    2023-10-21

Apache Druid features and specs

  • Real-Time Data Ingestion
    Apache Druid supports real-time data ingestion, which allows users to immediately query and analyze freshly ingested data, making it ideal for applications that require up-to-the-minute insights.
  • High Performance
    Druid is designed to provide fast query performance, especially for OLAP (Online Analytical Processing) queries. Its architecture leverages techniques like indexing, compression, and shard-based parallel processing to deliver quick results, even on large data sets.
  • Scalability
    Druid's architecture allows it to scale horizontally, supporting both large amounts of data and numerous concurrent queries. This makes it suitable for systems that need to handle high scalability requirements.
  • Flexible Data Exploration
    It supports complex queries, including group-bys, filters, and aggregations, which are essential for exploratory data analysis. Users can perform a wide range of data slicing and dicing operations.
  • Rich Multi-Tenancy Support
    Druid supports multi-tenancy, enabling different user groups to access and query the database simultaneously without performance degradation, thus accommodating diverse data analytics requirements within the same system.

Possible disadvantages of Apache Druid

  • Complex Setup and Configuration
    Setting up and configuring Apache Druid can be complex and resource-intensive. It requires a good understanding of its architecture and components, which may pose a steep learning curve for beginners.
  • Resource Heavy
    Druid can be resource-intensive, often requiring significant CPU, memory, and disk resources, especially when handling large scale data and high query loads. This can result in increased infrastructure costs.
  • Limited Transactional Support
    Druid is not designed for transactional workloads and lacks full ACID compliance. It is optimized for read-heavy analytical queries rather than write-heavy transactional operations.
  • Complexity in Handling Updates
    Updating or deleting existing records in Druid is not straightforward and often involves re-indexing data. This can complicate use cases where mutable data is a common requirement.
  • Limited Tooling and Ecosystem
    Compared to more established databases and analytical engines, Druid's ecosystem and available tooling for development, monitoring, and management might be less extensive, potentially requiring custom solutions.

SQLite features and specs

  • Zero Configuration
    SQLite does not require any server setup or configuration, allowing for easy integration and deployment in applications.
  • Lightweight
    It is extremely lightweight, with a small footprint, making it ideal for embedded systems and mobile applications.
  • Self-Contained
    SQLite is self-contained, meaning it has minimal external dependencies, which simplifies its distribution and usage.
  • File-Based Storage
    Data is stored in a single file, which makes it easy to manage and transfer databases as simple files.
  • ACID Compliance
    SQLite supports Atomicity, Consistency, Isolation, and Durability (ACID) properties, ensuring reliable transactions.
  • Cross-Platform
    SQLite is available on numerous platforms, including Windows, MacOS, Linux, iOS, and Android, providing a broad compatibility range.
  • Public Domain
    SQLite operates under the public domain, allowing for unrestricted use in commercial and non-commercial applications.

Possible disadvantages of SQLite

  • Limited Scalability
    SQLite is not designed to handle high levels of concurrency and large-scale databases, making it less suitable for large, high-traffic applications.
  • Write Performance
    Write operations can be slower compared to server-based databases, especially under heavy write loads.
  • Lack of Certain Features
    SQLite lacks some advanced features offered by other RDBMS like stored procedures, user-defined functions, and full-text search indexing.
  • Security
    As SQLite is file-based, it might lack some of the security features present in server-based databases, such as sophisticated access control.
  • Concurrency
    SQLite uses a locking mechanism to control access to the database, which can lead to contention and performance bottlenecks in highly concurrent environments.
  • Backup and Restore
    While it's straightforward to copy SQLite database files, it lacks the advanced backup and restore features found in more complex RDBMS.

Analysis of SQLite

Overall verdict

  • SQLite is an excellent choice for a variety of use cases, particularly where ease of use, scalability for smaller applications, and integration simplicity are prioritized. Its robust feature set and extensive community support make it a reliable option for many developers.

Why this product is good

  • SQLite is highly regarded for its efficiency, simplicity, and portability. It is a self-contained, serverless database engine that requires no configuration, making it easy to integrate into applications. Its zero-configuration system and minimal setup offer a lightweight solution that supports complex queries with ACID compliance. SQLite is also used widely due to its high reliability and performance, and it is included by default in several programming environments.

Recommended for

  • Small to medium-sized applications
  • Embedded devices and IoT applications
  • Mobile applications
  • Testing and prototyping
  • Internal or standalone tools and applications
  • Education and learning environments

Apache Druid videos

An introduction to Apache Druid

More videos:

  • Review - Building a Real-Time Analytics Stack with Apache Kafka and Apache Druid

SQLite videos

SQLite | What, Why , Where

More videos:

  • Review - W20 PROG1442 3.3 UWP sqLite Review
  • Tutorial - How To Create SQLite Databases From Scratch For Beginners - Full Tutorial

Category Popularity

0-100% (relative to Apache Druid and SQLite)
Databases
11 11%
89% 89
Big Data
100 100%
0% 0
Relational Databases
10 10%
90% 90
NoSQL Databases
4 4%
96% 96

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 Druid and SQLite

Apache Druid Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
“When you're dealing with highly concurrent environments, you really need an architecture that’s designed for that CPU efficiency to get the most performance out of the smallest hardware footprint—which is another reason why folks like to use Apache Druid,” says David Wang, VP of Product and Corporate Marketing at Imply. (Imply offers Druid as a service.)
Source: embeddable.com
Apache Druid vs. Time-Series Databases
Druid is a real-time analytics database that not only incorporates architecture designs from TSDBs such as time-based partitioning and fast aggregation, but also includes ideas from search systems and data warehouses, making it a great fit for all types of event-driven data. Druid is fundamentally an OLAP engine at heart, albeit one designed for more modern, event-driven...
Source: imply.io

SQLite Reviews

We have no reviews of SQLite yet.
Be the first one to post

Social recommendations and mentions

Based on our record, SQLite should be more popular than Apache Druid. It has been mentiond 18 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 Druid mentions (10)

  • Why You Shouldn’t Invest In Vector Databases?
    Regarding the storage aspect of vector databases, it is noteworthy that indexing techniques take precedence over the choice of underlying storage. In fact, many databases have the capability to incorporate indexing modules directly, enabling efficient vector search. Existing OLAP databases that are designed for real-time analytics and utilizing columnar storage, such as ClickHouse, Apache Pinot, and Apache Druid,... - Source: dev.to / about 2 months ago
  • How to choose the right type of database
    Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence. - Source: dev.to / over 1 year ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in... - Source: dev.to / over 1 year ago
  • Analysing Github Stars - Extracting and analyzing data from Github using Apache NiFi®, Apache Kafka® and Apache Druid®
    Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s... - Source: dev.to / over 2 years ago
  • Apache Druid® - an enterprise architect's overview
    Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications. - Source: dev.to / over 2 years ago
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SQLite mentions (18)

  • Can I have my Lightroom catalogue pointing at two sources...?
    Yes. A Lightroom catalog file is, after all, just a SQLite database. (Srsly, make a copy of your catalog file, rename it whatever.sqlite and use your favorite SQLite GUI to rip it open and look at the tables and fields). It's just storing the pathame to the RAW file for that file's record in the database. Source: about 2 years ago
  • Building a database to search Excel files
    I use visidata with a playback script I recorded to open the sheet to a specific Excel tab, add a column, save the sheet as a csv file. Then I have a sqlite script that takes the csv file and puts it in a database, partitioned by monthYear. Source: about 2 years ago
  • Saw this on my friends Snapchat story, this hurts my heart
    Use the most-used database in the world: https://sqlite.org/index.html. Source: over 2 years ago
  • "Managing" a SQLite Database with J (Part 2)
    With this in mind, I wrote a few versions of this post, but I hated them all. Then I realized that jodliterate PDF documents mostly do what I want. So, instead of rewriting MirrorXref.pdf, I will make a few comments about jodliterate group documents in general. If you're interested in using SQLite with J, download the self-contained GitHub files MirrorXref.ijs and MirrorXref.pdf and have a look. - Source: dev.to / almost 3 years ago
  • "Managing" a SQLite Database with J (Part 1)
    SQLite, by many estimates, is the most widely deployed SQL database system on Earth. It's everywhere. It's in your phone, your laptop, your cameras, your car, your cloud, and your breakfast cereal. SQLite's global triumph is a gratifying testament to the virtues of technical excellence and the philosophy of "less is more.". - Source: dev.to / almost 3 years ago
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What are some alternatives?

When comparing Apache Druid and SQLite, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

MySQL - The world's most popular open source database

Splunk - Splunk's operational intelligence platform helps unearth intelligent insights from machine data.

Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.