Cassandra vs Druid

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Druid

Stacks376

Followers867

Votes32

Cassandra vs Druid: What are the differences?

Cassandra and Druid are both distributed database systems designed for handling large-scale data. Here are some key differences between Cassandra and Druid:

Data Model and Querying: Cassandra is a NoSQL database that follows a wide-column data model. It is optimized for write-heavy workloads and offers efficient data writes and horizontal scalability. Cassandra's query language, CQL (Cassandra Query Language), allows basic CRUD operations and simple queries. On the other hand, Druid is a specialized database designed for real-time analytics and data exploration. It follows a column-oriented data model and is specifically built for fast analytical queries on large datasets. Druid's query language supports advanced OLAP-style queries with sub-second response times, making it ideal for interactive data analysis.
Data Ingestion and Processing: Cassandra is well-suited for ingesting high volumes of data and providing real-time data storage and retrieval. It can handle continuous data streams and is commonly used in applications where high write throughput is essential. Druid, on the other hand, is optimized for bulk data ingestion and batch processing. It is often used with real-time data streams but is specifically designed to handle large data sets and provide fast analytical capabilities for complex queries.
Data Partitioning and Distribution: Cassandra uses a distributed architecture with a peer-to-peer model, where data is partitioned across multiple nodes in a ring-like structure. Each node is responsible for a range of data, ensuring horizontal scalability and fault tolerance. In contrast, Druid follows a distributed ingestion model, where data is partitioned into segments across multiple nodes based on time intervals. This design allows Druid to efficiently manage time-based data and support fast time-series queries.
Use Cases: Cassandra is commonly used in applications that require high availability, low latency data access, and scaling for write-intensive workloads. It is a popular choice for use cases like real-time analytics, logging, and time-series data storage. Druid, on the other hand, is specifically built for use cases that involve complex analytical queries, such as business intelligence, ad-hoc reporting, and exploratory data analysis. It excels in scenarios where sub-second response times for large datasets are critical.
Data Consistency and Replication: Cassandra provides tunable consistency levels, allowing users to balance between data consistency and availability based on their application requirements. It supports multi-data center replication for high availability and disaster recovery. In contrast, Druid provides eventual consistency, focusing on providing fast query responses over strict consistency. It leverages data segments and historical nodes to efficiently replicate data across the cluster.

In summary, Cassandra is a distributed NoSQL database optimized for write-heavy workloads and real-time data storage, while Druid is a specialized analytical database designed for fast interactive querying and data exploration on large datasets.

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Advice on Cassandra, Druid

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Comments

Detailed Comparison

Cassandra	Druid
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 376
Followers 3.5K	Followers 867
Votes 507	Votes 32
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	Pros 15 Real Time Aggregations 6 Batch and Real-Time Ingestion 5 OLAP 3 OLAP + OLTP 2 Combining stream and historical analytics Cons 3 Limited sql support 2 Joins are not supported well 1 Complexity
Integrations
No integrations available	Zookeeper

What are some alternatives to Cassandra, Druid?

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.

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.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Data Model and Querying: Cassandra is a NoSQL database that follows a wide-column data model. It is optimized for write-heavy workloads and offers efficient data writes and horizontal scalability. Cassandra's query language, CQL (Cassandra Query Language), allows basic CRUD operations and simple queries. On the other hand, Druid is a specialized database designed for real-time analytics and data exploration. It follows a column-oriented data model and is specifically built for fast analytical queries on large datasets. Druid's query language supports advanced OLAP-style queries with sub-second response times, making it ideal for interactive data analysis.
Data Ingestion and Processing: Cassandra is well-suited for ingesting high volumes of data and providing real-time data storage and retrieval. It can handle continuous data streams and is commonly used in applications where high write throughput is essential. Druid, on the other hand, is optimized for bulk data ingestion and batch processing. It is often used with real-time data streams but is specifically designed to handle large data sets and provide fast analytical capabilities for complex queries.
Data Partitioning and Distribution: Cassandra uses a distributed architecture with a peer-to-peer model, where data is partitioned across multiple nodes in a ring-like structure. Each node is responsible for a range of data, ensuring horizontal scalability and fault tolerance. In contrast, Druid follows a distributed ingestion model, where data is partitioned into segments across multiple nodes based on time intervals. This design allows Druid to efficiently manage time-based data and support fast time-series queries.
Use Cases: Cassandra is commonly used in applications that require high availability, low latency data access, and scaling for write-intensive workloads. It is a popular choice for use cases like real-time analytics, logging, and time-series data storage. Druid, on the other hand, is specifically built for use cases that involve complex analytical queries, such as business intelligence, ad-hoc reporting, and exploratory data analysis. It excels in scenarios where sub-second response times for large datasets are critical.
Data Consistency and Replication: Cassandra provides tunable consistency levels, allowing users to balance between data consistency and availability based on their application requirements. It supports multi-data center replication for high availability and disaster recovery. In contrast, Druid provides eventual consistency, focusing on providing fast query responses over strict consistency. It leverages data segments and historical nodes to efficiently replicate data across the cluster.

Cassandra vs Druid