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refactor: 重构目录结构以支持 i18n
创建 'i18n' 目录以存放多语言内容。将所有现有的中文内容（文档、提示词、技能、README）移动到 'i18n/zh/' 中。添加了新的根 README 作为语言入口，并为英文（'en'）翻译创建了占位符结构。
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# Timescaledb - Performance
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**Pages:** 2
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---
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## Alerting
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**URL:** llms-txt#alerting
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**Contents:**
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- Grafana
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- Other alerting tools
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Early issue detecting and prevention, ensuring high availability, and performance optimization are only a few of the reasons why alerting plays a major role for modern applications, databases, and services.
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There are a variety of different alerting solutions you can use in conjunction
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with Tiger Cloud that are part of the Postgres ecosystem. Regardless of
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whether you are creating custom alerts embedded in your applications, or using
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third-party alerting tools to monitor event data across your organization, there
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are a wide selection of tools available.
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Grafana is a great way to visualize your analytical queries, and it has a
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first-class integration with Tiger Data products. Beyond data visualization, Grafana
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also provides alerting functionality to keep you notified of anomalies.
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Within Grafana, you can [define alert rules][define alert rules] which are
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time-based thresholds for your dashboard data (for example, "Average CPU usage
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greater than 80 percent for 5 minutes"). When those alert rules are triggered,
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Grafana sends a message via the chosen notification channel. Grafana provides
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integration with webhooks, email and more than a dozen external services
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including Slack and PagerDuty.
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To get started, first download and install [Grafana][Grafana-install]. Next, add
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a new [Postgres data source][PostgreSQL datasource] that points to your
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Tiger Cloud service. This data source was built by Tiger Data engineers, and
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it is designed to take advantage of the database's time-series capabilities.
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From there, proceed to your dashboard and set up alert rules as described above.
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Alerting is only available in Grafana v4.0 and later.
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## Other alerting tools
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Tiger Cloud works with a variety of alerting tools within the Postgres
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ecosystem. Users can use these tools to set up notifications about meaningful
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events that signify notable changes to the system.
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Some popular alerting tools that work with Tiger Cloud include:
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* [DataDog][datadog-install]
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* [Nagios][nagios-install]
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* [Zabbix][zabbix-install]
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See the [integration guides][integration-docs] for details.
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===== PAGE: https://docs.tigerdata.com/use-timescale/data-retention/ =====
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---
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## Improve query and upsert performance
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**URL:** llms-txt#improve-query-and-upsert-performance
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**Contents:**
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- Segmenting and ordering data
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- Improve performance in the columnstore by segmenting and ordering data
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Real-time analytics applications require more than fast inserts and analytical queries. They also need high performance
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when retrieving individual records, enforcing constraints, or performing upserts, something that OLAP/columnar databases
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lack. This pages explains how to improve performance by segmenting and ordering data.
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To improve query performance using indexes, see [About indexes][about-index] and [Indexing data][create-index].
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## Segmenting and ordering data
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To optimize query performance, TimescaleDB enables you to explicitly control the way your data is physically organized
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in the columnstore. By structuring data effectively, queries can minimize disk reads and execute more efficiently, using
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vectorized execution for parallel batch processing where possible.
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<center>
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<img
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class="main-content__illustration"
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width="80%"
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src="https://assets.timescale.com/docs/images/columnstore-segmentby.png"
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alt=""
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/>
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</center>
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* **Group related data together to improve scan efficiency**: organizing rows into logical segments ensures that queries
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filtering by a specific value only scan relevant data sections. For example, in the above, querying for a specific ID
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is particularly fast.
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* **Sort data within segments to accelerate range queries**: defining a consistent order reduces the need for post-query
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sorting, making time-based queries and range scans more efficient.
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* **Reduce disk reads and maximize vectorized execution**: a well-structured storage layout enables efficient batch
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processing (Single Instruction, Multiple Data, or SIMD vectorization) and parallel execution, optimizing query performance.
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By combining segmentation and ordering, TimescaleDB ensures that columnar queries are not only fast but also
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resource-efficient, enabling high-performance real-time analytics.
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### Improve performance in the columnstore by segmenting and ordering data
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Ordering data in the columnstore has a large impact on the compression ratio and performance of your queries.
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Rows that change over a dimension should be close to each other. As hypertables contain time-series data,
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they are partitioned by time. This makes the time column a perfect candidate for ordering your data since the
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measurements evolve as time goes on.
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If you use `orderby` as your only columnstore setting, you get a good enough compression ratio to save a lot of
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storage and your queries are faster. However, if you only use `orderby`, you always have to access your data using the
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time dimension, then filter the rows returned on other criteria.
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Accessing the data effectively depends on your use case and your queries. You segment data in the columnstore
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to match the way you want to access it. That is, in a way that makes it easier for your queries to fetch the right data
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at the right time. When you segment your data to access specific columns, your queries are optimized and yield even better performance.
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For example, to access information about a single device with a specific `device_id`, you segment on the `device_id` column.
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This enables you to run analytical queries on compressed data in the columnstore much faster.
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For example for the following hypertable:
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1. **Execute a query on a regular hypertable**
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1. Query your data
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Gives the following result:
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1. **Execute a query on the same data segmented and ordered in the columnstore**
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1. Control the way your data is ordered in the columnstore:
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1. Query your data
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Gives the following result:
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As you see, using `orderby` and `segmentby` not only reduces the amount of space taken by your data, but also
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vastly improves query speed.
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The number of rows that are compressed together in a single batch (like the ones we see above) is 1000.
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If your chunk does not contain enough data to create big enough batches, your compression ratio will be reduced.
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This needs to be taken into account when you define your columnstore settings.
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===== PAGE: https://docs.tigerdata.com/use-timescale/hypercore/modify-data-in-hypercore/ =====
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**Examples:**
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Example 1 (sql):
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```sql
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CREATE TABLE metrics (
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time TIMESTAMPTZ,
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user_id INT,
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device_id INT,
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data JSONB
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) WITH (
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tsdb.hypertable,
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tsdb.partition_column='time'
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);
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```
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Example 2 (sql):
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```sql
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SELECT device_id, AVG(cpu) AS avg_cpu, AVG(disk_io) AS avg_disk_io
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FROM metrics
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WHERE device_id = 5
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GROUP BY device_id;
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```
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Example 3 (sql):
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```sql
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device_id | avg_cpu | avg_disk_io
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-----------+--------------------+---------------------
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5 | 0.4972598866221261 | 0.49820356730280524
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(1 row)
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Time: 177,399 ms
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```
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Example 4 (sql):
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```sql
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ALTER TABLE metrics SET (
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timescaledb.enable_columnstore = true,
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timescaledb.orderby = 'time',
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timescaledb.segmentby = 'device_id'
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);
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```
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---
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