Clickhouse vs InfluxDB

Overview

InfluxDB

Stacks1.1K

Followers1.2K

Votes175

Clickhouse

Stacks440

Followers543

Votes85

Clickhouse vs InfluxDB: What are the differences?

Clickhouse and InfluxDB are both popular time-series databases used for storing and analyzing time-series data. Let's explore the key differences between them.

Data Model: Clickhouse uses a columnar data model, which stores data in columns rather than rows. This allows for efficient compression and faster query performance, especially for analytical workloads. InfluxDB, on the other hand, uses a tag-value data model, which stores data in the form of key-value pairs. This model is optimized for storing and retrieving time-series data, making InfluxDB a preferred choice for real-time monitoring and IoT applications.
Query Language: Clickhouse uses an SQL-like query language called ClickHouse SQL, which provides a wide range of expressive analytical functions and supports complex queries for data analysis. InfluxDB, on the other hand, uses its own query language called InfluxQL, which is specifically designed for time-series data. InfluxQL offers a simplified syntax and focuses on retrieving and manipulating time-series data efficiently.
Architecture: Clickhouse is a distributed columnar database with a shared-nothing architecture, allowing for horizontal scalability and high availability. It can handle massive amounts of data and perform distributed queries across multiple nodes. InfluxDB, on the other hand, is a single-node database by default, but it also offers clustering capabilities for high availability and scalability. The architecture of InfluxDB is optimized for real-time data ingestion and query performance on single nodes or small clusters.
Data Ingestion: Clickhouse provides a variety of ingestion methods, including batch ingestion, streaming ingestion, and data replication. It supports various formats like CSV, JSON, and Apache Kafka for data ingestion. InfluxDB, on the other hand, excels in real-time data ingestion and provides a built-in HTTP API and line protocol for data ingestion. It also has integrations with popular monitoring and IoT platforms, making it easier to ingest data from various sources.
Data Processing: Clickhouse is primarily designed for offline data analytics and supports complex analytical queries like window functions, subqueries, and joins. It also provides support for materialized views and data aggregation. InfluxDB, on the other hand, is focused on real-time data processing and supports continuous queries, downsampling, and data retention policies. It also offers built-in functions for anomaly detection and data downsampling.
Ecosystem and Integrations: Clickhouse has a growing ecosystem of integrations with popular data processing frameworks like Apache Spark and Apache Hadoop. It also provides drivers for different programming languages like Python, Java, and Go. InfluxDB, on the other hand, has a strong ecosystem of integrations with monitoring and visualization tools like Grafana and Prometheus. It also provides libraries and clients for various programming languages, making it easy to integrate with existing workflows.

In summary, Clickhouse excels in offline analytical workloads, while InfluxDB is optimized for real-time monitoring and IoT applications.

Advice on InfluxDB, Clickhouse

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	Clickhouse
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 allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer	-
Statistics
Stacks 1.1K	Stacks 440
Followers 1.2K	Followers 543
Votes 175	Votes 85
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 HA or Clustering is only in paid version 1 Proprietary query language	Pros 21 Fast, very very fast 11 Good compression ratio 7 Horizontally scalable 6 Utilizes all CPU resources 5 Great CLI Cons 5 Slow insert operations

What are some alternatives to InfluxDB, Clickhouse?

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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Clickhouse vs InfluxDB: What are the differences?

Clickhouse and InfluxDB are both popular time-series databases used for storing and analyzing time-series data. Let's explore the key differences between them.

Data Model: Clickhouse uses a columnar data model, which stores data in columns rather than rows. This allows for efficient compression and faster query performance, especially for analytical workloads. InfluxDB, on the other hand, uses a tag-value data model, which stores data in the form of key-value pairs. This model is optimized for storing and retrieving time-series data, making InfluxDB a preferred choice for real-time monitoring and IoT applications.
Query Language: Clickhouse uses an SQL-like query language called ClickHouse SQL, which provides a wide range of expressive analytical functions and supports complex queries for data analysis. InfluxDB, on the other hand, uses its own query language called InfluxQL, which is specifically designed for time-series data. InfluxQL offers a simplified syntax and focuses on retrieving and manipulating time-series data efficiently.
Architecture: Clickhouse is a distributed columnar database with a shared-nothing architecture, allowing for horizontal scalability and high availability. It can handle massive amounts of data and perform distributed queries across multiple nodes. InfluxDB, on the other hand, is a single-node database by default, but it also offers clustering capabilities for high availability and scalability. The architecture of InfluxDB is optimized for real-time data ingestion and query performance on single nodes or small clusters.
Data Ingestion: Clickhouse provides a variety of ingestion methods, including batch ingestion, streaming ingestion, and data replication. It supports various formats like CSV, JSON, and Apache Kafka for data ingestion. InfluxDB, on the other hand, excels in real-time data ingestion and provides a built-in HTTP API and line protocol for data ingestion. It also has integrations with popular monitoring and IoT platforms, making it easier to ingest data from various sources.
Data Processing: Clickhouse is primarily designed for offline data analytics and supports complex analytical queries like window functions, subqueries, and joins. It also provides support for materialized views and data aggregation. InfluxDB, on the other hand, is focused on real-time data processing and supports continuous queries, downsampling, and data retention policies. It also offers built-in functions for anomaly detection and data downsampling.
Ecosystem and Integrations: Clickhouse has a growing ecosystem of integrations with popular data processing frameworks like Apache Spark and Apache Hadoop. It also provides drivers for different programming languages like Python, Java, and Go. InfluxDB, on the other hand, has a strong ecosystem of integrations with monitoring and visualization tools like Grafana and Prometheus. It also provides libraries and clients for various programming languages, making it easy to integrate with existing workflows.

In summary, Clickhouse excels in offline analytical workloads, while InfluxDB is optimized for real-time monitoring and IoT applications.