ClickHouse vs Graphite

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of ClickHouse and Graphite so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how ClickHouse and Graphite perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

ClickHouse Overview

ClickHouse is an open source columnar database management system designed for high-performance online analytical processing (OLAP) tasks. It was developed by Yandex, a leading Russian technology company. ClickHouse is known for its ability to process large volumes of data in real-time, providing fast query performance and real-time analytics. Its columnar storage architecture enables efficient data compression and faster query execution, making it suitable for large-scale data analytics and business intelligence applications.

Graphite Overview

Graphite is an open-source monitoring and graphing tool created in 2006 by Orbitz and open sourced in 2008. Graphite is designed for storing time series data and is widely used for collecting, storing, and visualizing metrics from various sources, such as application performance, system monitoring, and business analytics.

ClickHouse for Time Series Data

ClickHouse can be used for storing and analyzing time series data effectively, although it is not explicitly optimized for working with time series data. While ClickHouse can query time series data very quickly once ingested, it tends to struggle with very high write scenarios where data needs to be ingested in smaller batches so it can be analyzed in real time.

Graphite for Time Series Data

Graphite is specifically designed and optimized for time series data. It uses the Whisper database format, which efficiently stores and manages time series data by automatically aggregating and expiring data based on user-defined retention policies. Graphite supports a wide range of functions for querying, transforming, and aggregating time series data, enabling users to create custom graphs and dashboards. However, as Graphite focuses exclusively on time series data, it may not be suitable for other types of data or use cases that require more advanced data modeling or querying capabilities.

ClickHouse Key Concepts

• Columnar storage: ClickHouse stores data in a columnar format, which means that data for each column is stored separately. This enables efficient compression and faster query execution, as only the required columns are read during query execution.
• Distributed processing: ClickHouse supports distributed processing, allowing queries to be executed across multiple nodes in a cluster, improving query performance and scalability.
• Data replication: ClickHouse provides data replication, ensuring data availability and fault tolerance in case of hardware failures or node outages.
• Materialized Views: ClickHouse supports materialized views, which are precomputed query results stored as tables. Materialized views can significantly improve query performance, as they allow for faster data retrieval by avoiding the need to recompute the results for each query.

Graphite Key Concepts

• Metric: A metric in Graphite represents a time series data point, consisting of a path (name), timestamp, and value.
• Series: A series is a collection of metrics that are all related to the same thing. For example, you might have a series for CPU usage, a series for memory usage, and a series for disk usage.
• Whisper: Whisper is a fixed-size, file-based time series database format used by Graphite. It automatically manages data retention and aggregation.
• Carbon: Carbon is the daemon responsible for receiving, caching, and storing metrics in Graphite. It listens for incoming metrics and writes them to Whisper files.
• Graphite-web: Graphite-web is the web application that provides a user interface for visualizing and querying the stored time series data.

ClickHouse Architecture

ClickHouse’s architecture is designed to support high-performance analytics on large datasets. ClickHouse stores data in a columnar format. This enables efficient data compression and faster query execution, as only the required columns are read during query execution. ClickHouse also supports distributed processing, which allows for queries to be executed across multiple nodes in a cluster. ClickHouse uses the MergeTree storage engine as its primary table engine. MergeTree is designed for high-performance OLAP tasks and supports data replication, data partitioning, and indexing.

Graphite Architecture

Graphite’s architecture consists of several components, including Carbon, Whisper, and Graphite-web. Carbon is responsible for receiving metrics from various sources, caching them in memory, and storing them in Whisper files. Whisper is a file-based time series database format that efficiently manages data retention and aggregation. Graphite-web is the web application that provides a user interface for querying and visualizing the stored time series data. Graphite can be deployed on a single server or distributed across multiple servers for improved performance and scalability.