Overview

Microsoft SQL Server

Stacks20.2K

Followers15.5K

Votes540

TimescaleDB

Stacks223

Followers374

Votes44

GitHub Stars20.6K

Forks988

Microsoft SQL Server vs TimescaleDB: What are the differences?

Introduction

In this article, we will explore the key differences between Microsoft SQL Server and TimescaleDB. Both databases are widely used in the industry but have distinct features and purposes. Let's dive into the differences between these two databases.

Data Model: Microsoft SQL Server follows a relational data model, storing data in tables with predefined schemas. It relies on structured query language (SQL) for data retrieval and manipulation. On the other hand, TimescaleDB is built on top of PostgreSQL and extends it to provide native support for time-series data. It introduces the concept of hypertables, which allow automatic partitioning and scaling of time-series data, making it more efficient for storing and querying time-series data.
Scalability: While Microsoft SQL Server can scale vertically by adding more resources to a single server, TimescaleDB focuses on horizontal scalability. It allows data to be distributed across multiple servers, enabling better performance for large-scale deployments. TimescaleDB achieves this through automatic data partitioning and parallel query execution, making it suitable for handling massive volumes of time-series data.
Performance: Microsoft SQL Server is optimized for general-purpose workload management, providing excellent performance for complex queries across different types of data. TimescaleDB, on the other hand, is designed specifically for time-series data and offers high-performance features tailored for time-based analytical queries. Its automatic data partitioning and indexing strategies ensure faster query execution on time-series data.
Data Storage: In terms of data storage, Microsoft SQL Server typically uses a single-node architecture. It allows a single server to store and manage all the data, providing transactional consistency. In contrast, TimescaleDB utilizes a distributed architecture, spreading the data across multiple nodes. This distributed approach enables better data resilience, fault tolerance, and the ability to handle large volumes of data.
Community and Ecosystem: Microsoft SQL Server has a long-standing presence in the industry and a large user community. It offers extensive documentation, community support, and a wide range of tools and integrations. TimescaleDB, being built on PostgreSQL, benefits from the existing PostgreSQL ecosystem and community. It inherits many features and plugins from PostgreSQL, including support for various programming languages, query optimizers, and extensions.
Cost: Another significant difference is the cost aspect. Microsoft SQL Server is a commercial database, and licensing costs may apply based on server capacity and features. In contrast, TimescaleDB is an open-source extension built on PostgreSQL, making it a cost-effective choice for organizations seeking efficient time-series data handling without additional licensing costs.

In summary, Microsoft SQL Server follows a relational data model with a focus on general-purpose workload management, while TimescaleDB is specifically designed for time-series data with features like automatic partitioning and support for hypertables. TimescaleDB emphasizes horizontal scalability, high-performance time-series data handling, and is an open-source alternative to commercial databases.

Advice on Microsoft SQL Server, TimescaleDB

Erin

IT Specialist

Mar 10, 2020

Needs adviceon

Microsoft SQL Server

MySQL

PostgreSQL

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:

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

668k views668k

Comments

Anonymous

Apr 21, 2020

Needs advice

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

381k views381k

Comments

Umair

Technical Architect at ERP Studio

Feb 12, 2021

Needs adviceon

PostgreSQL

TimescaleDB

Druid

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.

462k views462k

Comments

Detailed Comparison

Microsoft SQL Server	TimescaleDB
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
-	Packaged as a PostgreSQL extension;Full ANSI SQL;JOINs (e.g., across PostgreSQL tables);Complex queries;Secondary indexes;Composite indexes;Support for very high cardinality data;Triggers;Constraints;UPSERTS;JSON/JSONB;Ability to ingest out of order data;Ability to perform accurate rollups;Data retention policies;Fast deletes;Integration with PostGIS and the rest of the PostgreSQL ecosystem;
Statistics
GitHub Stars -	GitHub Stars 20.6K
GitHub Forks -	GitHub Forks 988
Stacks 20.2K	Stacks 223
Followers 15.5K	Followers 374
Votes 540	Votes 44
Pros & Cons
Pros 139 Reliable and easy to use 101 High performance 95 Great with .net 65 Works well with .net 56 Easy to maintain Cons 4 Expensive Licensing 2 Microsoft 1 Allwayon can loose data in asycronious mode 1 The maximum number of connections is only 14000 connect 1 Data pages is only 8k	Pros 9 Open source 8 Easy Query Language 7 Time-series data analysis 5 Established postgresql API and support 4 Reliable Cons 5 Licensing issues when running on managed databases
Integrations
No integrations available	Prometheus Equinix Metal Ruby PostgreSQL Django Kubernetes pgAdmin Python Kafka Datadog

What are some alternatives to Microsoft SQL Server, TimescaleDB?

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.

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.

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.

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.