InfluxDB vs TimescaleDB

Overview

InfluxDB

Stacks1.1K

Followers1.2K

Votes175

TimescaleDB

Stacks223

Followers374

Votes44

GitHub Stars20.6K

Forks988

InfluxDB vs TimescaleDB: What are the differences?

Introduction

This markdown code provides a comparison between InfluxDB and TimescaleDB, highlighting their key differences.

Data Model: InfluxDB is a time-series database designed specifically for handling time-based data, whereas TimescaleDB is an extension of PostgreSQL that adds time-series capabilities. InfluxDB uses a specialized data model optimized for time-series data, with measurements, fields, tags, and timestamps, while TimescaleDB uses the same relational model as PostgreSQL, with tables, columns, and rows.
Scalability: InfluxDB provides horizontal scalability out-of-the-box, allowing you to easily add more nodes to the cluster to handle increases in data and query volume. On the other hand, TimescaleDB leverages sharding techniques for scalability, distributing the data across multiple server nodes, but requires more manual configuration for scaling.
Data Partitioning: InfluxDB automatically partitions data across time intervals called shards, which allows for efficient storage and querying of time-series data. In contrast, TimescaleDB requires manual partitioning based on time intervals using hypertables, which involves specifying partitioning policies and managing partitions yourself.
SQL Support: TimescaleDB is built as an extension to PostgreSQL, and therefore inherits all the SQL capabilities of PostgreSQL. This means you can use the full power of SQL to query your time-series data in TimescaleDB. InfluxDB, on the other hand, provides a simplified query language called InfluxQL, which is specifically designed for time-series data and may be more intuitive for time-series analysis.
Data Retention and Compression: InfluxDB offers built-in data retention policies that allow you to automatically expire old data, while also providing efficient compression techniques to reduce storage requirements. TimescaleDB does not provide native data retention policies but offers the flexibility of using PostgreSQL's mechanisms for data retention. TimescaleDB also supports compression, but it is not as tightly integrated as in InfluxDB.
Ecosystem and Integrations: InfluxDB has a mature ecosystem with a wide range of integrations and libraries, making it easier to integrate with other tools and systems for data collection, monitoring, and analysis. TimescaleDB, being an extension of PostgreSQL, benefits from the vast ecosystem and integrations available for PostgreSQL, making it suitable for use alongside other non-time-series data.

In Summary, InfluxDB and TimescaleDB differ in their data model, scalability, data partitioning, SQL support, data retention and compression, and ecosystem/integrations.

Advice on InfluxDB, TimescaleDB

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

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

InfluxDB	TimescaleDB
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.	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.
Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer	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 1.1K	Stacks 223
Followers 1.2K	Followers 374
Votes 175	Votes 44
Pros & Cons
Pros 59 Time-series data analysis 30 Easy setup, no dependencies 24 Fast, scalable & open source 21 Open source 20 Real-time analytics Cons 4 Instability 1 Proprietary query language 1 HA or Clustering is only in paid version	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 InfluxDB, 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.

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 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.

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InfluxDB vs TimescaleDB: What are the differences?

Introduction

This markdown code provides a comparison between InfluxDB and TimescaleDB, highlighting their key differences.

Data Model: InfluxDB is a time-series database designed specifically for handling time-based data, whereas TimescaleDB is an extension of PostgreSQL that adds time-series capabilities. InfluxDB uses a specialized data model optimized for time-series data, with measurements, fields, tags, and timestamps, while TimescaleDB uses the same relational model as PostgreSQL, with tables, columns, and rows.
Scalability: InfluxDB provides horizontal scalability out-of-the-box, allowing you to easily add more nodes to the cluster to handle increases in data and query volume. On the other hand, TimescaleDB leverages sharding techniques for scalability, distributing the data across multiple server nodes, but requires more manual configuration for scaling.
Data Partitioning: InfluxDB automatically partitions data across time intervals called shards, which allows for efficient storage and querying of time-series data. In contrast, TimescaleDB requires manual partitioning based on time intervals using hypertables, which involves specifying partitioning policies and managing partitions yourself.
SQL Support: TimescaleDB is built as an extension to PostgreSQL, and therefore inherits all the SQL capabilities of PostgreSQL. This means you can use the full power of SQL to query your time-series data in TimescaleDB. InfluxDB, on the other hand, provides a simplified query language called InfluxQL, which is specifically designed for time-series data and may be more intuitive for time-series analysis.
Data Retention and Compression: InfluxDB offers built-in data retention policies that allow you to automatically expire old data, while also providing efficient compression techniques to reduce storage requirements. TimescaleDB does not provide native data retention policies but offers the flexibility of using PostgreSQL's mechanisms for data retention. TimescaleDB also supports compression, but it is not as tightly integrated as in InfluxDB.
Ecosystem and Integrations: InfluxDB has a mature ecosystem with a wide range of integrations and libraries, making it easier to integrate with other tools and systems for data collection, monitoring, and analysis. TimescaleDB, being an extension of PostgreSQL, benefits from the vast ecosystem and integrations available for PostgreSQL, making it suitable for use alongside other non-time-series data.

In Summary, InfluxDB and TimescaleDB differ in their data model, scalability, data partitioning, SQL support, data retention and compression, and ecosystem/integrations.