Cassandra vs Microsoft SQL Server

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Cassandra vs Microsoft SQL Server: What are the differences?

Introduction

Cassandra and Microsoft SQL Server are both popular database management systems, but they have key differences in terms of architecture, scalability, query language, and data model.

  1. Scalability: One of the major differences is in terms of scalability. Cassandra is designed to be highly scalable and can easily handle huge amounts of data and high write loads. It follows a masterless architecture where all nodes are equal, allowing for horizontal scaling. On the other hand, SQL Server follows a master-slave architecture and is typically limited in scalability compared to Cassandra.

  2. Architecture: Cassandra is a distributed database system that is designed to handle big data workloads. It follows a peer-to-peer architecture where data is evenly distributed across multiple nodes. This architecture allows for fault tolerance and high availability. SQL Server, on the other hand, follows a client-server architecture where a central server handles all the database operations.

  3. Query Language: Another significant difference lies in the query language used by the two databases. Cassandra uses Cassandra Query Language (CQL), a SQL-like language that is specifically designed for Cassandra. CQL supports a subset of SQL features along with additional features that are specific to Cassandra. SQL Server, on the other hand, uses Transact-SQL (T-SQL), a proprietary SQL dialect developed by Microsoft. T-SQL is a comprehensive SQL language that provides extensive support for storing, retrieving, and manipulating data.

  4. Data Model: Cassandra follows a wide-column data model, also known as a columnar or column-family data model. It allows for flexible schema design and supports large, denormalized datasets. SQL Server, on the other hand, follows a relational data model where data is organized into tables with predefined schemas and relationships.

  5. Consistency: Consistency is another key difference between Cassandra and SQL Server. Cassandra offers tunable consistency, allowing users to select the consistency level based on their application's requirements. It provides options like eventual consistency, strong consistency, and various levels in between. SQL Server, on the other hand, offers strong consistency by default, ensuring that data consistency is maintained at all times.

  6. Support and Community: SQL Server is developed and maintained by Microsoft and has a large user base. It has extensive support and a well-established community, which means users can easily find resources and assistance. Cassandra, on the other hand, is an open-source project maintained by the Apache Software Foundation. It also has a strong community, but the support may not be as extensive as SQL Server.

In summary, Cassandra and SQL Server differ in terms of scalability, architecture, query language, data model, consistency, and support. While Cassandra excels in scalability and flexible data models, SQL Server offers strong consistency and extensive support options.

Advice on Cassandra and Microsoft SQL Server

I have a project (in production) that a part of it is generating HTML from JSON object normally we use Microsoft SQL Server only as our main database. but when it comes to this part some team members suggest working with a NoSQL database as we are going to handle JSON data for both retrieval and querying. others replied that will add complexity and we will lose SQL Servers' Unit Of Work which will break the Atomic behavior, and they suggest to continue working with SQL Server since it supports working with JSON. If you have practical experience using JSON with SQL Server, kindly share your feedback.

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Replies (2)
TwoBySea

I agree with the advice you have been given to stick with SQL Server. If you are on the latest SQL Server version you can query inside the JSON field. You should set up a test database with a JSON field and try some queries. Once you understand it and can demonstrate it, show it to the other developers that are suggesting MongoDB. Once they see it working with their own eyes they may drop their position of Mongo over SQL. I would only seriously consider MongoDB if there was no other SQL requirements. I wouldn't do both. I'd be all SQL or all Mongo.

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Kevin Deyne
Principal Software Engineer at Accurate Background · | 2 upvotes · 50.5K views
Recommends

I think the key thing to look for is what kind of queries you're expecting to do on that JSON and how stable that data is going to be. (And if you actually need to store the data as JSON; it's generally pretty inexpensive to generate a JSON object)

MongoDB gets rid of the relational aspect of data in favor of data being very fluid in structure.

So if your JSON is going to vary a lot/is unpredictable/will change over time and you need to run queries efficiently like 'records where the field x exists and its value is higher than 3', that's a great use case for MongoDB.

It's hard to solve this in a standard relational model: Indexing on a single column that has wildly different values is pretty much impossible to do efficiently; and pulling out the data in its own columns is hard because it's hard to predict how many columns you'd have or what their datatypes would be. If this sounds like your predicament, 100% go for MongoDB.

If this is always going to be more or less the same JSON and the fields are going to be predictably the same, then the fact that it's JSON doesn't particularly matter much. Your indexes are going to approach it similar to a long string.

If the queried fields are very predictable, you should probably consider storing the fields as separate columns to have better querying capabilities. Ie if you have {"x":1, "y":2}, {"x":5, "y":6}, {"x":9, "y":0} - just make a table with an x and y column and generate the JSON. The CPU hit is worth it compared to the querying capabilities.

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Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 449.9K 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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I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

  1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
  2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
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Replies (6)

Hi Erin,

Honestly both databases will do the job just fine. I personally prefer Postgres.

Much more important is how you store the audio. While you could technically use a blob type column, it's really not ideal to be storing audio files which are "several hours long" in a database row. Instead consider storing the audio files in an object store (hosted options include backblaze b2 or aws s3) and persisting the key (which references that object) in your database column.

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Aaron Westley
Recommends
on
PostgreSQLPostgreSQL

Hi Erin, Chances are you would want to store the files in a blob type. Both MySQL and Postgres support this. Can you explain a little more about your need to store the files in the database? I may be more effective to store the files on a file system or something like S3. To answer your qustion based on what you are descibing I would slighly lean towards PostgreSQL since it tends to be a little better on the data warehousing side.

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Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 479.9K views
Recommends
on
Amazon AuroraAmazon Aurora

Hi Erin! First of all, you'd probably want to go with a managed service. Don't spin up your own MySQL installation on your own Linux box. If you are on AWS, thet have different offerings for database services. Standard RDS vs. Aurora. Aurora would be my preferred choice given the benefits it offers, storage optimizations it comes with... etc. Such managed services easily allow you to apply new security patches and upgrades, set up backups, replication... etc. Doing this on your own would either be risky, inefficient, or you might just give up. As far as which database to chose, you'll have the choice between Postgresql, MySQL, Maria DB, SQL Server... etc. I personally would recommend MySQL (latest version available), as the official tooling for it (MySQL Workbench) is great, stable, and moreover free. Other database services exist, I'd recommend you also explore Dynamo DB.

Regardless, you'd certainly only keep high-level records, meta data in Database, and the actual files, most-likely in S3, so that you can keep all options open in terms of what you'll do with them.

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Christopher Wray
Web Developer at Soltech LLC · | 3 upvotes · 480.3K views
Recommends
on
DirectusDirectus
at

Hey Erin! I would recommend checking out Directus before you start work on building your own app for them. I just stumbled upon it, and so far extremely happy with the functionalities. If your client is just looking for a simple web app for their own data, then Directus may be a great option. It offers "database mirroring", so that you can connect it to any database and set up functionality around it!

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Recommends
on
PostgreSQLPostgreSQL

Hi Erin,

  • Coming from "Big" DB engines, such as Oracle or MSSQL, go for PostgreSQL. You'll get all the features you need with PostgreSQL.
  • Your case seems to point to a "NoSQL" or Document Database use case. Since you get covered on this with PostgreSQL which achieves excellent performances on JSON based objects, this is a second reason to choose PostgreSQL. MongoDB might be an excellent option as well if you need "sharding" and excellent map-reduce mechanisms for very massive data sets. You really should investigate the NoSQL option for your use case.
  • Starting with AWS Aurora is an excellent advise. since "vendor lock-in" is limited, but I did not check for JSON based object / NoSQL features.
  • If you stick to Linux server, the PostgreSQL or MySQL provided with your distribution are straightforward to install (i.e. apt install postgresql). For PostgreSQL, make sure you're comfortable with the pg_hba.conf, especially for IP restrictions & accesses.

Regards,

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Klaus Nji
Staff Software Engineer at SailPoint Technologies · | 1 upvotes · 480K views
Recommends
on
PostgreSQLPostgreSQL

I recommend Postgres as well. Superior performance overall and a more robust architecture.

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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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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 161.9K 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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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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Decisions about Cassandra and Microsoft SQL Server
Micha Mailänder
CEO & Co-Founder at Dechea · | 14 upvotes · 86.1K views

Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.

Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.

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Asif Khan
Software Development Engineer at Stier Solution Private Limited · | 10 upvotes · 71.3K views

Easy to start, lightweight and open source.

When I started with PHP, MySQL was everywhere so this is how I started with it. I am no expert in databases but I started learning joins, stored procedures, triggers, etc. with MySQL.

Recently used it in one of my projects - Picfam.com with Node.js + Express backend

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Josip Užarević
Senior frontend developer · | 6 upvotes · 71.7K views

Needed to transform intranet desktop application to the web-based one, as mid-term project. My choice was to use Django/Angular stack - Django since it, in conjunction with Python, enabled rapid development, an Angular since it was stable and enterprise-level framework. Deadlines were somewhat tight since the project to migrate was being developed for several years and had a lot of domain knowledge integrated into it. Definitely was good decision, since deadlines was manageable, juniors were able to enter the project very quickly and we were able to continuously deploy very well.

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Pros of Cassandra
Pros of Microsoft SQL Server
  • 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
  • 139
    Reliable and easy to use
  • 101
    High performance
  • 95
    Great with .net
  • 65
    Works well with .net
  • 56
    Easy to maintain
  • 21
    Azure support
  • 17
    Always on
  • 17
    Full Index Support
  • 10
    Enterprise manager is fantastic
  • 9
    In-Memory OLTP Engine
  • 2
    Easy to setup and configure
  • 2
    Security is forefront
  • 1
    Great documentation
  • 1
    Faster Than Oracle
  • 1
    Columnstore indexes
  • 1
    Decent management tools
  • 1
    Docker Delivery
  • 1
    Max numar of connection is 14000

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Cons of Cassandra
Cons of Microsoft SQL Server
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
  • 4
    Expensive Licensing
  • 2
    Microsoft
  • 1
    Data pages is only 8k
  • 1
    Allwayon can loose data in asycronious mode
  • 1
    Replication can loose the data
  • 1
    The maximum number of connections is only 14000 connect

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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 Microsoft SQL Server?

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

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What are some alternatives to Cassandra and Microsoft SQL Server?
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