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RxDB

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160
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TimescaleDB

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+ 1
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RxDB vs TimescaleDB: What are the differences?

Developers describe RxDB as "A fast, reactive, client-side database". 💻 📱 Reactive, serverless, client-side, offline-first database in javascript. Client-Side Database for Browsers, NodeJS, electron, cordova, react-native and every other javascript-runtime. On the other hand, TimescaleDB is detailed as "Scalable time-series database optimized for fast ingest and complex queries. Purpose-built as a PostgreSQL extension". TimescaleDB is the only open-source time-series database that natively supports full-SQL at scale, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL databases.

RxDB and TimescaleDB can be primarily classified as "Databases" tools.

RxDB and TimescaleDB are both open source tools. It seems that RxDB with 9.39K GitHub stars and 435 forks on GitHub has more adoption than TimescaleDB with 7.28K GitHub stars and 385 GitHub forks.

Advice on RxDB and TimescaleDB
Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 251.4K 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
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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Needs advice
on
InfluxDBInfluxDBMongoDBMongoDB
and
TimescaleDBTimescaleDB

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

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Replies (3)
Yaron Lavi
Recommends
PostgreSQLPostgreSQL

We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.

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Recommends
DruidDruid

Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.

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Ankit Malik
Software Developer at CloudCover · | 3 upvotes · 177.5K views
Recommends
Google BigQueryGoogle BigQuery

if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.

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Decisions about RxDB and TimescaleDB
Benoit Larroque
Principal Engineer at Sqreen · | 2 upvotes · 86.7K views

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

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Pros of RxDB
Pros of TimescaleDB
  • 14
    Good documentation
  • 12
    Subscription to queries
  • 10
    Works
  • 9
    Example projects
  • 8
    Typescript support
  • 1
    Offline first
  • 8
    Open source
  • 7
    Easy Query Language
  • 6
    Time-series data analysis
  • 5
    Established postgresql API and support
  • 4
    Reliable
  • 2
    Chunk-based compression
  • 2
    Postgres integration
  • 2
    Paid support for automatic Retention Policy
  • 2
    Fast and scalable
  • 2
    High-performance
  • 1
    Case studies

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Cons of RxDB
Cons of TimescaleDB
  • 3
    Bulk operation for updates and other operation
  • 5
    Licensing issues when running on managed databases

Sign up to add or upvote consMake informed product decisions

What is RxDB?

💻 📱 Reactive, serverless, client-side, offline-first database in javascript. Client-Side Database for Browsers, NodeJS, electron, cordova, react-native and every other javascript-runtime.

What is TimescaleDB?

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.

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Jobs that mention RxDB and TimescaleDB as a desired skillset
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What are some alternatives to RxDB and 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.
Pouchdb
PouchDB enables applications to store data locally while offline, then synchronize it with CouchDB and compatible servers when the application is back online, keeping the user's data in sync no matter where they next login.
WatermelonDB
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.
NeDB
Embedded persistent or in memory database for Node.js, nw.js, Electron and browsers, 100% JavaScript, no binary dependency. API is a subset of MongoDB's and it's plenty fast.
LokiJS
LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.
See all alternatives