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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. TimescaleDB vs WatermelonDB

TimescaleDB vs WatermelonDB

OverviewDecisionsComparisonAlternatives

Overview

TimescaleDB
TimescaleDB
Stacks227
Followers374
Votes44
GitHub Stars20.6K
Forks988
WatermelonDB
WatermelonDB
Stacks12
Followers123
Votes1
GitHub Stars11.3K
Forks626

TimescaleDB vs WatermelonDB: What are the differences?

## Introduction
In this comparison, we will highlight the key differences between TimescaleDB and WatermelonDB, two popular databases used for different purposes.

1. **Data Model**: TimescaleDB is a time-series database optimized for handling time-series data, making it efficient for storing and querying timestamped data. On the other hand, WatermelonDB is a mobile database that focuses on providing real-time synchronization and offline-first capabilities for mobile applications, making it suitable for offline use.

2. **Scalability**: TimescaleDB is designed for high-performance, scalable time-series data storage and retrieval, making it a preferred choice for applications dealing with large volumes of time-series data. In contrast, WatermelonDB is optimized for mobile use cases and may not be as scalable as TimescaleDB when it comes to handling massive datasets.

3. **Query Language**: TimescaleDB leverages SQL as its query language, providing a familiar interface for developers who are well-versed in SQL and relational databases. WatermelonDB, on the other hand, uses specialized query syntax tailored for working with data in a mobile context, which may require a learning curve for developers used to traditional SQL.

4. **Storage Mechanism**: TimescaleDB uses a traditional disk-based storage mechanism for data persistence, offering durability and reliability for long-term data retention. WatermelonDB, being designed for mobile devices, utilizes SQLite as its underlying storage engine, optimized for lightweight storage and performance on mobile devices with limited resources.

5. **Community Support**: TimescaleDB has a robust and active community of users and contributors, providing extensive documentation, support, and resources for developers using the database. WatermelonDB, being a relatively newer database technology, may have a smaller community and fewer resources available for developers seeking help or guidance.

6. **Use Cases**: TimescaleDB is ideally suited for applications that require efficient storage and analysis of time-series data, such as IoT devices, monitoring systems, and financial data analysis. WatermelonDB, on the other hand, is tailored for mobile applications that need synchronized and offline access to data, catering to use cases like mobile CRM systems, note-taking apps, and messaging platforms.

## Summary
In summary, TimescaleDB excels in handling time-series data with scalability and SQL support, while WatermelonDB is optimized for real-time synchronization and offline usage in mobile applications.

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Advice on TimescaleDB, WatermelonDB

Anonymous
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
Umair

Technical Architect at ERP Studio

Feb 12, 2021

Needs adviceonPostgreSQLPostgreSQLTimescaleDBTimescaleDBDruidDruid

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
Benoit
Benoit

Principal Engineer at Sqreen

Sep 21, 2019

Decided

I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.

The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)

We are combining this with Grafana for display and Telegraf for data collection

155k views155k
Comments

Detailed Comparison

TimescaleDB
TimescaleDB
WatermelonDB
WatermelonDB

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.

WatermelonDB is a new way of dealing with user data in React Native and React web apps. It's optimized for building complex applications in React Native, and the number one goal is real-world performance. In simple words, your app must launch fast.

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
20.6K
GitHub Stars
11.3K
GitHub Forks
988
GitHub Forks
626
Stacks
227
Stacks
12
Followers
374
Followers
123
Votes
44
Votes
1
Pros & Cons
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
Pros
  • 1
    Undefined is not an object (evaluating 'columnSchema.ty
Integrations
Prometheus
Prometheus
Equinix Metal
Equinix Metal
Ruby
Ruby
PostgreSQL
PostgreSQL
Django
Django
Kubernetes
Kubernetes
pgAdmin
pgAdmin
Python
Python
Kafka
Kafka
Datadog
Datadog
RxJS
RxJS
React
React
SQLite
SQLite
React Native
React Native

What are some alternatives to TimescaleDB, WatermelonDB?

MongoDB

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

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

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

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

SQLite

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

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

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

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

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

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.

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