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Druid vs Oracle: What are the differences?
Introduction
In this markdown, we will be discussing the key differences between Druid and Oracle.
Data Model: Druid is a columnar data store that is optimized for time-series data and provides fast aggregations. It uses a denormalized, column-oriented storage format and requires pre-aggregated data ingestion. On the other hand, Oracle is a relational database management system (RDBMS) that follows a row-oriented approach. It supports complex data models with support for joins, constraints, and normalization.
Scalability: Druid is designed to scale horizontally by adding more nodes to the cluster, allowing for high volume and high-speed data ingestion. It can handle large amounts of data and supports real-time data ingestion. Oracle, on the other hand, can also scale horizontally but requires additional configuration and management. It is better suited for traditional transactional workloads.
Processing Speed: Druid is built to provide near real-time analytics and fast query response times. Its architecture and indexing structure enable quick aggregations on large volumes of data. Oracle, while capable of handling analytical workloads, may not provide the same level of performance as Druid for large-scale data analytics.
Query Language: Druid uses a SQL-like query language called Druid Query Language (DQL), which is specifically designed for time-series data. It provides functions for aggregations, filtering, and advanced analytics. Oracle, on the other hand, supports SQL, PL/SQL, and other procedural languages. It provides a broader range of features for complex queries and supports a wide range of data types.
Data Consistency: Druid offers eventual consistency for queries, which means that the data may not always reflect real-time updates immediately. This is because Druid is optimized for speed rather than strict consistency. Oracle, being a traditional RDBMS, provides strong consistency guarantees by enforcing ACID properties for transactions.
Cost: Druid is an open-source project and is available for free. Its underlying infrastructure can be deployed on commodity hardware or cloud infrastructure, reducing the overall cost of ownership. Oracle, on the other hand, is a commercial product that comes with licensing costs. Additionally, the hardware requirements for running Oracle may be higher, resulting in higher infrastructure costs.
In Summary, Druid and Oracle differ in their data models, scalability, processing speed, query language, data consistency, and cost. While Druid excels in time-series data analytics and performance, Oracle provides a broader range of features for complex queries and supports strong consistency for transactions.
We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.
We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.
In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.
MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.
Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.
Pros of Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP5
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
Cons of Oracle
- Expensive14
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What is Druid?
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