---
title: 快速入门
weight: 4
type: docs
aliases:
- /dev/table/materialized-table/quickstart.html
---
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# 快速入门

本入门指南将帮助你快速了解并开始使用物化表。内容包括环境设置，以及创建、修改和删除持续模式和全量模式的物化表。

# 环境搭建

## 目录准备

**请将下面的示例路径替换为你机器上的实际路径。**

- 创建 Catalog Store 和 Catalog 所需的目录

```
# 用于 File Catalog Store 保存 Catalog 属性
mkdir -p {catalog_store_path}

# 用于 test-filesystem Catalog 保存元数据和表数据
mkdir -p {catalog_path}

# 用于 test-filesystem Catalog 的默认数据库
mkdir -p {catalog_path}/mydb
```

- 创建目录分别用于保存 Checkpoints 和 Savepoints：

```
mkdir -p {checkpoints_path}

mkdir -p {savepoints_path}
```

## 资源准备

这里的方法和[本地安装]({{< ref "docs/try-flink/local_installation" >}})中记录的步骤类似。Flink 可以运行在任何类 UNIX 的操作系统下面，例如：Linux, Mac OS X 和 Cygwin (for Windows)。

[下载](https://flink.apache.org/downloads/) Flink 最新的二进制包并进行解压：

```
tar -xzf flink-*.tgz
```

[下载](https://https://repo.maven.apache.org/maven2/org/apache/flink/flink-table-filesystem-test-utils/) test-filesystem 连接器， 并将其放入 lib 目录：

```
cp flink-table-filesystem-test-utils-{VERSION}.jar flink-*/lib/
```

## 配置准备

在 `config.yaml` 文件中添加以下配置：

```yaml
execution:
  checkpoints:
    dir: file://{checkpoints_path}

# 配置 file catalog store
table:
  catalog-store:
    kind: file
    file:
      path: {catalog_store_path}

# 配置 embedded 调度器 
workflow-scheduler:
  type: embedded

# 配置 SQL gateway 的地址和端口
sql-gateway:
  endpoint:
    rest:
      address: 127.0.0.1
      port: 8083
```

## 启动 Flink Cluster

运行以下脚本，即可在本地启动集群：

```
./bin/start-cluster.sh
```

## 启动 SQL Gateway

运行以下脚本，即可在本地启动 SQL Gateway：

```
./sql-gateway.sh start
```

## 启动 SQL Client

运行以下脚本，即可在本地启动 SQL Client 客户端并连接到指定的 SQL Gateway：

```
./sql-client.sh gateway --endpoint http://127.0.0.1:8083
```

## 创建 Catalog 和 Source 表

- 创建 test-filesystem catalog 用于后续创建物化表。

```sql
CREATE CATALOG mt_cat WITH (
  'type' = 'test-filesystem',
  'path' = '{catalog_path}',
  'default-database' = 'mydb'
);

USE CATALOG mt_cat;
```

- 创建 Source 表作为物化表的数据源。

```sql
-- 1. 创建 Source 表，并指定 Source 表的数据格式为 json
CREATE TABLE json_source (
  order_id BIGINT,
  user_id BIGINT,
  user_name STRING,
  order_created_at STRING,
  payment_amount_cents BIGINT
) WITH (
  'format' = 'json',
  'source.monitor-interval' = '10s'
);

-- 2. 插入一些测试数据
INSERT INTO json_source VALUES 
  (1001, 1, 'user1', '2024-06-19', 10),
  (1002, 2, 'user2', '2024-06-19', 20),
  (1003, 3, 'user3', '2024-06-19', 30),
  (1004, 4, 'user4', '2024-06-19', 40),
  (1005, 1, 'user1', '2024-06-20', 10),
  (1006, 2, 'user2', '2024-06-20', 20),
  (1007, 3, 'user3', '2024-06-20', 30),
  (1008, 4, 'user4', '2024-06-20', 40);
```

# 创建持续模式物化表

## 创建物化表

创建一个持续模式的物化表，对应的数据新鲜度为 `30` 秒。通过 `http://localhost:8081` 页面可以查看对应的 Flink 流作业，该作业处于 `RUNNING` 状态，对应的 `checkpoint` 间隔为 `30` 秒。

```sql
CREATE MATERIALIZED TABLE continuous_users_shops
PARTITIONED BY (ds)
WITH (
  'format' = 'debezium-json',
  'sink.rolling-policy.rollover-interval' = '10s',
  'sink.rolling-policy.check-interval' = '10s'
)
FRESHNESS = INTERVAL '30' SECOND
AS SELECT
  user_id,
  ds,
  SUM (payment_amount_cents) AS payed_buy_fee_sum,
  SUM (1) AS PV
FROM (
  SELECT user_id, order_created_at AS ds, payment_amount_cents
    FROM json_source
  ) AS tmp
GROUP BY user_id, ds;
```

## 暂停物化表

暂停物化表的刷新管道。在 `http://localhost:8081` 页面上，你会看到负责持续刷新物化表的 Flink 流作业已处于 `FINISHED` 状态。在执行暂停操作前，请确保设置 `savepoint` 路径。

```sql
-- 暂停前设置 savepoint 路径
SET 'execution.checkpointing.savepoint-dir' = 'file://{savepoint_path}';

ALTER MATERIALIZED TABLE continuous_users_shops SUSPEND;
```

## 查询物化表

查询物化表数据并确认数据已成功写入。

```sql
SELECT * FROM continuous_users_shops;
```

## 恢复物化表

恢复物化表的刷新管道，你会发现一个新的 Flink 流作业从指定的 `savepoint` 路径启动，用于持续刷新物化表，可以在 `http://localhost:8081` 页面上查看。

```sql
ALTER MATERIALIZED TABLE continuous_users_shops RESUME;
```

## 删除物化表

删除物化表后，你会发现用于持续刷新物化表的 Flink 流作业在 `http://localhost:8081` 页面上转变为 `CANCELED` 状态。

```sql
DROP MATERIALIZED TABLE continuous_users_shops;
```

# 创建全量模式物化表

## 创建物化表

创建一个全量模式的物化表，对应的数据新鲜度为 1 分钟。（此处设置为 1 分钟只是为了方便测试）你会发现用于定期刷新物化表的 Flink 批作业每 1 分钟调度一次，可以在 `http://localhost:8081` 页面上查看。

```sql
CREATE MATERIALIZED TABLE full_users_shops
PARTITIONED BY (ds)
WITH (
  'format' = 'json',
  'partition.fields.ds.date-formatter' = 'yyyy-MM-dd'
)
FRESHNESS = INTERVAL '1' MINUTE
REFRESH_MODE = FULL
AS SELECT
  user_id,
  ds,
  SUM (payment_amount_cents) AS payed_buy_fee_sum,
  SUM (1) AS PV
FROM (
  SELECT user_id, order_created_at AS ds, payment_amount_cents
  FROM json_source
) AS tmp
GROUP BY user_id, ds;
```

## 查询物化表

向当天的分区插入一些数据，等待至少 1 分钟，然后查询物化表结果，只能查询到当天分区的数据。

```sql
INSERT INTO json_source VALUES 
  (1001, 1, 'user1', CAST(CURRENT_DATE AS STRING), 10),
  (1002, 2, 'user2', CAST(CURRENT_DATE AS STRING), 20),
  (1003, 3, 'user3', CAST(CURRENT_DATE AS STRING), 30),
  (1004, 4, 'user4', CAST(CURRENT_DATE AS STRING), 40);
```

```sql
SELECT * FROM full_users_shops;
```

## 手动刷新历史分区

手动刷新分区 `ds='2024-06-20'`，并验证物化表中该日期的数据。你可以在 `http://localhost:8081` 页面上找到当前正在运行的 Flink 批作业。

```sql
-- 手动刷新历史分区
ALTER MATERIALIZED TABLE full_users_shops REFRESH PARTITION(ds='2024-06-20');

-- 查询物化表数据
SELECT * FROM full_users_shops;
```

## 暂停和恢复物化表

通过暂停和恢复操作，你可以控制物化表的刷新作业。一旦暂停，负责定期刷新物化表的 Flink 批作业将不再被调度执行。当恢复时，Flink 批作业将重新开始调度以刷新物化表。你可在 `http://localhost:8081` 页面上查看 Flink 作业的调度状态。

```sql
-- 暂停后台刷新任务
ALTER MATERIALIZED TABLE full_users_shops SUSPEND;

-- 恢复后台刷新任务
ALTER MATERIALIZED TABLE full_users_shops RESUME;
```

## 删除物化表

删除物化表后，负责定期刷新该物化表的 Flink 批作业将不再被调度执行。你可以在 `http://localhost:8081` 页面上进行确认。

```sql
DROP MATERIALIZED TABLE full_users_shops;
```
