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Cassandra vs Sequelize: What are the differences?

Cassandra vs Sequelize: Key Differences

Cassandra and Sequelize are two popular databases used in web development. While Cassandra is a NoSQL database designed for handling large amounts of distributed data, Sequelize is an Object-Relational Mapping (ORM) library for Node.js that supports SQL databases.

  1. Data Model: Cassandra follows a schema-agnostic model, allowing for the flexibility of adding or modifying columns without affecting existing data. On the other hand, Sequelize follows a strict schema model where the structure of the database is predefined, and any changes require migrations or manual alterations.

  2. Query Language: Cassandra uses Query Language (CQL), a SQL-like language for data manipulation and querying. Sequelize, on the other hand, relies on SQL queries to interact with the database. This means that Cassandra provides a native way of handling NoSQL interactions, while Sequelize relies on traditional SQL query syntax.

  3. Scaling: Cassandra is designed to scale horizontally, meaning that it can efficiently handle large amounts of data across multiple servers. It achieves this through its distributed architecture and replication strategy. Sequelize, being an ORM, relies on the underlying SQL database for scaling options, which typically involves vertical scaling by increasing hardware resources.

  4. Data Consistency: Cassandra is known for its eventual consistency model, where data updates are not immediately propagated to all nodes. Instead, updates are eventually replicated across all nodes, resulting in lower latency but potentially introducing stale data. Sequelize, on the other hand, typically relies on the ACID properties of SQL databases, ensuring strong data consistency across all operations.

  5. Performance: Cassandra is optimized for write-heavy workloads, making it ideal for applications that require high write throughput. Its decentralized architecture and data distribution strategy allow for efficient write scalability. Sequelize, being an ORM that relies on traditional SQL databases, may not be as performant as Cassandra for write-intensive applications.

  6. Database Support: Cassandra is a standalone database that does not rely on any external database engine. Sequelize, on the other hand, is an ORM library that supports various SQL databases such as MySQL, PostgreSQL, and SQLite. This means that Sequelize can be used with a wide range of existing SQL databases, while Cassandra requires its own infrastructure to be set up.

In Summary, Cassandra is a schema-agnostic NoSQL database optimized for write-heavy workloads and distributed architectures, while Sequelize is an ORM library for SQL databases with stricter schemas, stronger data consistency, and support for various SQL databases.

Advice on Cassandra and Sequelize
Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 437.2K views
Needs advice
on
CassandraCassandraDruidDruid
and
TimescaleDBTimescaleDB

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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Replies (1)
Recommends
on
MongoDBMongoDB

Hi Umair, Did you try MongoDB. We are using MongoDB on a production environment and collecting data from devices like your scenario. We have a MongoDB cluster with three replicas. Data from devices are being written to the master node and real-time dashboard UI is using the secondary nodes for read operations. With this setup write operations are not affected by read operations too.

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Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

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)
Recommends
on
ScyllaDBScyllaDB

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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Alex Peake
Recommends
on
CassandraCassandra

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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Recommends
on
ScyllaDBScyllaDB

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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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 148.8K views
Recommends
on
CassandraCassandra

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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Pros of Cassandra
Pros of Sequelize
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
  • 42
    Good ORM for node.js
  • 31
    Easy setup
  • 21
    Support MySQL & MariaDB, PostgreSQL, MSSQL, Sqlite
  • 14
    Open source
  • 13
    Free
  • 12
    Promise Based
  • 4
    Recommend for mongoose users
  • 3
    Typescript
  • 3
    Atrocious documentation, buggy, issues closed by bots

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Cons of Cassandra
Cons of Sequelize
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
  • 30
    Docs are awful
  • 10
    Relations can be confusing

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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 Sequelize?

Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.

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What are some alternatives to Cassandra and Sequelize?
HBase
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.
Hadoop
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
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.
Couchbase
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