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docs/zh/faq/use-cases/time-series.md
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---
title: Can I use ClickHouse as a time-series database?
title: 我能把 ClickHouse 当做时序数据库来使用吗?
toc_hidden: true
toc_priority: 101
---
# Can I Use ClickHouse As a Time-Series Database? {#can-i-use-clickhouse-as-a-time-series-database}
# 我能把 ClickHouse 当做时序数据库来使用吗? {#can-i-use-clickhouse-as-a-time-series-database}
ClickHouse is a generic data storage solution for [OLAP](../../faq/general/olap.md) workloads, while there are many specialized time-series database management systems. Nevertheless, ClickHouse’s [focus on query execution speed](../../faq/general/why-clickhouse-is-so-fast.md) allows it to outperform specialized systems in many cases. There are many independent benchmarks on this topic out there, so we’re not going to conduct one here. Instead, let’s focus on ClickHouse features that are important to use if that’s your use case.
ClickHouse是一个通用的数据存储解决方案[OLAP](../../faq/general/olap.md)的工作负载，而有许多专门的时间序列数据库管理系统。然而，ClickHouse的[专注于查询执行速度](../../faq/general/why-clickhouse-is-so-fast.md)使得它在许多情况下的性能优于专门的系统。关于这个话题有很多独立的基准，所以我们不打算在这里进行论述。相反，让我们将重点放在ClickHouse的重要功能(如果这是你的用例)上。
First of all, there are **[specialized codecs](../../sql-reference/statements/create/table.md#create-query-specialized-codecs)** which make typical time-series. Either common algorithms like `DoubleDelta` and `Gorilla` or specific to ClickHouse like `T64`.
Second, time-series queries often hit only recent data, like one day or one week old. It makes sense to use servers that have both fast nVME/SSD drives and high-capacity HDD drives. ClickHouse [TTL](../../engines/table-engines/mergetree-family/mergetree.md#table_engine-mergetree-multiple-volumes) feature allows to configure keeping fresh hot data on fast drives and gradually move it to slower drives as it ages. Rollup or removal of even older data is also possible if your requirements demand it.
Even though it’s against ClickHouse philosophy of storing and processing raw data, you can use [materialized views](../../sql-reference/statements/create/view.md) to fit into even tighter latency or costs requirements.
首先，有 **[specialized codecs](../../sql-reference/statements/create/table.md#create-query-specialized-codecs)**，这是典型的时间序列。无论是常见的算法，如“DoubleDelta”和“Gorilla”，或特定的ClickHouse 数据类型如“T64”。
其次，时间序列查询通常只访问最近的数据，比如一天或一周以前的数据。使用具有快速nVME/SSD驱动器和高容量HDD驱动器的服务器是有意义的。ClickHouse [TTL](../../engines/table-engines/mergetree-family/mergetree.md#table_engine-mergetree-multiple-volumes)特性允许配置在快速硬盘上保持新鲜的热数据，并随着数据的老化逐渐移动到较慢的硬盘上。如果您的需求需要，也可以汇总或删除更旧的数据。
尽管这与ClickHouse存储和处理原始数据的理念相违背，但你可以使用[materialized views](../../sql-reference/statements/create/view.md)来适应更紧迫的延迟或成本需求。