docs: add sql example to the overview of user guide (#1057)

Author: nicecui

docs/nightly/en/user-guide/overview.md

@@ -1,10 +1,70 @@
 # Overview
 
+Welcome to the user guide for GreptimeDB.
+
+GreptimeDB is the unified time series database for metrics, events, and logs,
+providing real-time insights from Edge to Cloud at any scale.
+This guide will help you explore each powerful feature of GreptimeDB.
+
+## SQL query example
+
+Let's start with a SQL query example.
+
+To monitor the performance and reliability of specific metrics, 
+engineers commonly analyze data over time at regular intervals using queries.
+This often involves joining two data sources.
+However, executing a query like the one below was previously impossible,
+which is now possible with GreptimeDB.
+
+```sql
+SELECT
+    host,
+    approx_percentile_cont(latency, 0.95) RANGE '15s' as p95_latency,
+    count(error) RANGE '15s' as num_errors,
+FROM
+    metrics INNER JOIN logs on metrics.host = logs.host
+WHERE
+    time > now() - INTERVAL '1 hour' AND
+    matches(path, '/api/v1/avator')
+ALIGN '5s' BY (host) FILL PREV
+```
+
+This query analyzes the performance and errors of a specific API path (`/api/v1/avator`) over the past hour.
+It calculates the 95th percentile latency and the number of errors in 15-second intervals and aligns the results to 5-second intervals for continuity and readability.
+
+Break down the query step by step:
+
+1. SELECT clause: 
+    - `host`: Selects the host field.
+    - `approx_percentile_cont(latency, 0.95) RANGE '15s' as p95_latency`: Calculates the 95th percentile of latency within a 15-second range and labels it as p95_latency.
+    - `count(error) RANGE '15s' as num_errors`: Counts the number of errors within a 15-second range and labels it as num_errors.
+2. FROM clause: 
+    - `metrics INNER JOIN logs on metrics.host = logs.host`: Joins the metrics and logs tables on the host field.
+3. WHERE clause: 
+    - `time > now() - INTERVAL '1 hour'`: Filters the records to include only those from the past hour.
+    - `matches(path, '/api/v1/avator')`: Filters the records to include only those matching the path `/api/v1/avator`.
+4. ALIGN clause:
+    - `ALIGN '5s' BY (host) FILL PREV`: Aligns the results to every 5 seconds and fills in missing values with the previous non-null value.
+
+Next, let's analyze the key features of GreptimeDB demonstrated by this query example:
+
+- **Unified Storage:** GreptimeDB stores both time-series metrics and [logs](/user-guide/logs/overview.md) in one database. The simplified architecture and data consistency enhances the ability to analyze and troubleshoot issues, and can lead to cost savings and improved system performance.
+- **Unique Data Model:** The unique [data model](/user-guide/concepts/data-model.md) with time index and full-text index greatly improves query performance and has stood the test of large data sets. It not only supports metric [insertion](/user-guide/write-data/overview.md) and [query](/user-guide/query-data/overview.md), but also provides a very friendly way to [write](/user-guide/logs/write-logs.md) and [query](/user-guide/logs/query-logs.md) logs.
+- **Range Queries:** GreptimeDB supports [range queries](/user-guide/query-data/sql#aggregate-data-by-time-window) to evaluate [expressions](/reference/sql/functions/overview.md) over time, providing insights into metric trends. You can also [continuously aggregate](/user-guide/continuous-aggregation/overview) data for further analysis.
+- **SQL and Multiple Protocols:** GreptimeDB uses SQL as the main query language and supports [multiple protocols](/user-guide/clients/overview.md#protocols), which greatly reduces the learning curve and development cost. You can easily migrate from Prometheus or [Influxdb to GreptimeDB](/user-guide/migrate-to-greptimedb/migrate-from-influxdb), or just start with GreptimeDB.
+- **JOIN Operations:** The data model of GreptimeDB's time series tables makes it the first time series database to support [JOIN operations](/reference/sql/join.md).
+
+Having understood these features, you can now go directly to exploring the features that interest you, or continue reading the next step in the sequence.
+
+## Next steps
+
 * [Concepts](./concepts/overview.md)
 * [Clients](./clients/overview.md)
 * [Table Management](./table-management.md)
+* [Migrate to GreptimeDB](./migrate-to-greptimedb/migrate-from-influxdb.md)
 * [Write data](./write-data/overview.md)
 * [Query data](./query-data/overview.md)
+* [Continuous Aggregation](./continuous-aggregation/overview.md)
 * [Python Scripts](./python-scripts/overview.md)
 * [Operations](./operations/overview.md)
 * [Cluster](./cluster.md)

docs/nightly/zh/user-guide/overview.md

@@ -1,10 +1,70 @@
 # 概述
 
-- [概念](../user-guide/concepts/overview.md)
-- [客户端](../user-guide/clients/overview.md)
-- [表管理](../user-guide/table-management.md)
-- [数据写入](../user-guide/write-data/overview.md)
-- [数据查询](../user-guide/query-data/overview.md)
-- [Python 脚本](../user-guide/python-scripts/overview.md)
-- [运维操作](../user-guide/operations/overview.md)
-- [集群](../user-guide/cluster.md)
+欢迎使用 GreptimeDB 用户指南。
+
+GreptimeDB 是用于指标、事件和日志的统一时间序列数据库，
+可提供从边缘到云的任何规模的实时洞察。
+本指南将帮助你探索 GreptimeDB 的每个强大功能。
+
+## SQL 查询示例
+
+让我们从一个 SQL 查询示例开始。
+
+为了监控特定指标的性能和可靠性，
+工程师通常定期查询并分析一段时间内的数据。
+在分析过程中通常涉及到 JOIN 两个数据源，
+但如下方的查询在之前是不可能的，
+而现在使用 GreptimeDB 就可以做到：
+
+```sql
+SELECT
+  host,
+  approx_percentile_cont(latency, 0.95) RANGE '15s' as p95_latency,
+  count(error) RANGE '15s' as num_errors,
+FROM
+  metrics INNER JOIN logs on metrics.host = logs.host
+WHERE
+  time > now() - INTERVAL '1 hour' AND
+  matches(path, '/api/v1/avator')
+ALIGN '5s' BY (host) FILL PREV
+```
+
+该查询分析了过去一小时内特定 API 路径 (`/api/v1/avator`) 的性能和错误。
+它计算了每个 15 秒间隔内的第 95 百分位延迟和错误数量，并将结果对齐到每个 5 秒间隔以保持连续性和可读性。
+
+逐步解析该查询：
+
+1. SELECT 子句：
+  - `host`：选择 host 字段。
+  - `approx_percentile_cont(latency, 0.95) RANGE '15s' as p95_latency`：计算 15 秒范围内的第 95 百分位延迟，并将其标记为 p95_latency。
+  - `count(error) RANGE '15s' as num_errors`：计算 15 秒范围内的错误数量，并将其标记为 num_errors。
+2. FROM 子句：
+  - `metrics INNER JOIN logs on metrics.host = logs.host`：在 host 字段上将 metrics 和 logs 表进行连接。
+3. WHERE 子句：
+  - `time > now() - INTERVAL '1 hour'`：筛选出过去一小时内的记录。
+  - `matches(path, '/api/v1/avator')`：筛选出特定 API 路径 `/api/v1/avator` 的记录。
+4. ALIGN 子句：
+  - `ALIGN '5s' BY (host) FILL PREV`：将结果对齐到每 5 秒，并使用前一个非空值填充缺失值。
+
+接下来解析一下该查询示例展示的 GreptimeDB 关键功能：
+
+- **统一存储：** GreptimeDB 将时间序列指标和 [日志](/user-guide/logs/overview.md) 存储在一个数据库中。简化的架构和数据一致性增强了分析和解决问题的能力，并可节省成本且提高系统性能。
+- **独特的数据模型：** 独特的[数据模型](/user-guide/concepts/data-model.md)搭配时间索引和全文索引，大大提升了查询性能，并在超大数据集上也经受住了考验。它不仅支持[数据指标的插入](/user-guide/write-data/overview.md)和[查询](/user-guide/query-data/overview.md)，也提供了非常友好的方式便于日志的[写入](/user-guide/logs/write-logs.md)和[查询](/user-guide/logs/query-logs.md)。
+- **范围查询：** GreptimeDB 支持[范围查询](/user-guide/query-data/sql#aggregate-data-by-time-window)来计算一段时间内的[表达式](/reference/sql/functions/overview.md)，从而了解指标趋势。你还可以[持续聚合](/user-guide/continuous-aggregation/overview)数据以进行进一步分析。
+- **SQL 和多种协议：** GreptimeDB 使用 SQL 作为主要查询语言，并支持[多种协议](/user-guide/clients/overview.md#protocols)，大大降低了学习曲线和接入成本。你可以轻松从 Prometheus 或 [Influxdb 迁移](/user-guide/migrate-to-greptimedb/migrate-from-influxdb)至 GreptimeDB，或者从 0 接入 GreptimeDB。
+- **JOIN 操作：** GreptimeDB 的时间序列表的数据模型，使其成为第一个支持[JOIN 操作](reference/sql/join.md)的时序数据库。
+
+了解了这些功能后，你现在可以直接探索感兴趣的功能，或按顺序继续阅读下一步骤。
+
+## 下一步
+
+* [概念](./concepts/overview.md)
+* [客户端](./clients/overview.md)
+* [表管理](./table-management.md)
+* [迁移到 GreptimeDB](./migrate-to-greptimedb/migrate-from-influxdb.md)
+* [数据写入](./write-data/overview.md)
+* [数据查询](./query-data/overview.md)
+* [持续聚合](./continuous-aggregation/overview.md)
+* [Python 脚本](./python-scripts/overview.md)
+* [运维操作](./operations/overview.md)
+* [集群](./cluster.md)