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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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

Advice on Cassandra and Druid
Vinay Mehta
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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Replies (4)
Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 132K views

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

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By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

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Alex Peake

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

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Pros of Cassandra
Pros of Druid
  • 119
  • 97
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 52
  • 26
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
  • 15
    Real Time Aggregations
  • 6
    Batch and Real-Time Ingestion
  • 4
  • 3
  • 2
    Combining stream and historical analytics
  • 1

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Cons of Cassandra
Cons of Druid
  • 3
    Reliability of replication
  • 1
  • 1
  • 3
    Limited sql support
  • 2
    Joins are not supported well
  • 1

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What is Cassandra?

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.

What is Druid?

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.

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Dec 22 2021 at 5:41AM


MySQLKafkaApache Spark+6
What are some alternatives to Cassandra and Druid?
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
See all alternatives