InfluxDB vs LinDB

Overview

InfluxDB

Stacks1.0K

Followers1.2K

Votes175

LinDB

Stacks1

Followers11

Votes0

InfluxDB vs LinDB: What are the differences?

Introduction

InfluxDB and LinDB are both time-series databases designed to handle large volumes of time-stamped data efficiently. However, there are key differences between the two that make them suitable for different use cases.

Data Model: InfluxDB uses a tag set approach for data organization, where each data point consists of measurement, tag set, and field set. On the other hand, LinDB adopts a columnar storage model similar to traditional relational databases, which allows for efficient querying and analysis of time-series data.
Architecture: InfluxDB follows a master-slave architecture, where data is distributed across multiple nodes with a single leader node managing the cluster. LinDB, on the other hand, employs a peer-to-peer architecture, where each node in the cluster is equal and independent, enabling better scalability and fault tolerance.
Query Language: InfluxDB uses its own query language called InfluxQL, which is SQL-like and optimized for time-series data. LinDB supports SQL as its query language, making it easier for users familiar with relational databases to work with time-series data.
Storage Engine: InfluxDB uses the Time-structured Merge Tree (TSM) storage engine, which is optimized for time-series data and provides efficient compression and retrieval of data points. LinDB utilizes a Log-Structured Merge Tree (LSM) storage engine, which offers high write throughput and low latency for time-series data ingestion.
High Availability: InfluxDB provides high availability through its clustering capabilities, allowing for automatic failover and data redundancy across nodes. LinDB offers high availability through its peer-to-peer architecture, ensuring that each node can independently handle data queries and ingestions without relying on a centralized leader node.
Community Support: InfluxDB has a larger community with extensive documentation, tutorials, and plugins available for users to enhance their experience with the database. LinDB, being a newer entrant into the time-series database market, is gradually building its community support but may have limited resources and plugins compared to InfluxDB.

In Summary, the key differences between InfluxDB and LinDB lie in their data model, architecture, query language, storage engine, high availability mechanisms, and community support.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on InfluxDB, LinDB

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

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	LinDB
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.	It is a scalable, distributed, high performance, high availability Time Series Database. It takes a lot of best practice of TSDB and implements some optimizations based on the characteristics of time series data. It supports rollup in specific interval automatically after creating the database.
Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer	Horizontal scalability; Multi-Active IDCs native
Statistics
Stacks 1.0K	Stacks 1
Followers 1.2K	Followers 11
Votes 175	Votes 0
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	No community feedback yet
Integrations
No integrations available	Golang

What are some alternatives to InfluxDB, LinDB?

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.

Related Comparisons

InfluxDB vs LinDB: What are the differences?

Introduction

Data Model: InfluxDB uses a tag set approach for data organization, where each data point consists of measurement, tag set, and field set. On the other hand, LinDB adopts a columnar storage model similar to traditional relational databases, which allows for efficient querying and analysis of time-series data.
Architecture: InfluxDB follows a master-slave architecture, where data is distributed across multiple nodes with a single leader node managing the cluster. LinDB, on the other hand, employs a peer-to-peer architecture, where each node in the cluster is equal and independent, enabling better scalability and fault tolerance.
Query Language: InfluxDB uses its own query language called InfluxQL, which is SQL-like and optimized for time-series data. LinDB supports SQL as its query language, making it easier for users familiar with relational databases to work with time-series data.
Storage Engine: InfluxDB uses the Time-structured Merge Tree (TSM) storage engine, which is optimized for time-series data and provides efficient compression and retrieval of data points. LinDB utilizes a Log-Structured Merge Tree (LSM) storage engine, which offers high write throughput and low latency for time-series data ingestion.
High Availability: InfluxDB provides high availability through its clustering capabilities, allowing for automatic failover and data redundancy across nodes. LinDB offers high availability through its peer-to-peer architecture, ensuring that each node can independently handle data queries and ingestions without relying on a centralized leader node.
Community Support: InfluxDB has a larger community with extensive documentation, tutorials, and plugins available for users to enhance their experience with the database. LinDB, being a newer entrant into the time-series database market, is gradually building its community support but may have limited resources and plugins compared to InfluxDB.

In Summary, the key differences between InfluxDB and LinDB lie in their data model, architecture, query language, storage engine, high availability mechanisms, and community support.

InfluxDB vs LinDB

Overview

InfluxDB vs LinDB: What are the differences?