omni-to-databricks-metric-view

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Omni → Databricks Metric View

Converts an Omni topic into a Databricks Metric View by exploring the Omni model via API, translating its field definitions into the Databricks Metric View embedded YAML format, and executing via the Databricks CLI.

See FIELD-MAPPING.md for full before/after translation examples and YAML-REFERENCE.md for the complete YAML structure, aggregate type, and format mapping tables.

通过API探索Omni模型，将其字段定义转换为Databricks Metric View的嵌入式YAML格式，并通过Databricks CLI执行，从而将Omni主题转换为Databricks Metric View。

查看FIELD-MAPPING.md获取完整的转换前后示例，查看YAML-REFERENCE.md获取完整的YAML结构、聚合类型和格式映射表。

Prerequisites

前提条件

bash

undefined

bash

undefined

Verify the Omni CLI is installed — if not, ask the user to install it

See: https://github.com/exploreomni/cli#readme

command -v omni >/dev/null || echo "ERROR: Omni CLI is not installed."


```bash

command -v omni >/dev/null || echo "ERROR: Omni CLI is not installed."


```bash

Show available profiles and select the appropriate one

omni config show

If multiple profiles exist, ask the user which to use, then switch:

omni config use <profile-name>


```bash

omni config use <profile-name>


```bash

Databricks CLI — verify installed and check profiles

databricks --version cat ~/.databrickscfg


---

> **Tip**: Use `-o json` to force structured output for programmatic parsing, or `-o human` for readable tables. The default is `auto` (human in a TTY, JSON when piped).

databricks --version cat ~/.databrickscfg


---

> **提示**：使用`-o json`强制输出结构化内容以便程序化解析，或使用`-o human`输出易读的表格。默认值为`auto`（在终端中输出人类可读格式，管道传输时输出JSON）。

Workflow

工作流程

Step 1 — Gather Requirements

步骤1 — 收集需求

Ask the user:

Which Omni topic do they want to convert? (e.g.,
```
orders
```
)
What is the Unity Catalog destination? (
```
catalog.schema
```
) (e.g.,
```
main.sales
```
)
What is the Databricks SQL Warehouse ID? (run
```
databricks sql warehouses list
```
to find it)
Is this a new metric view or does one already exist at
```
catalog.schema.[topic_name]_mv
```
?
Which Databricks CLI profile to use (optional — only if the user has multiple profiles)?

⚠️ STOP — Confirm all answers before proceeding. The metric view will be named
[topic_name]_mv
by default.

询问用户：

他们想要转换哪个Omni主题？（例如：
```
orders
```
）
Unity Catalog目标位置是什么？（
```
catalog.schema
```
）（例如：
```
main.sales
```
）
Databricks SQL仓库ID是什么？（运行
```
databricks sql warehouses list
```
查找）
这是一个新的metric view，还是在
```
catalog.schema.[topic_name]_mv
```
位置已存在一个？
使用哪个Databricks CLI配置文件（可选 — 仅当用户有多个配置文件时）？

⚠️ 停止 — 继续前确认所有答案。Metric View默认命名为
[topic_name]_mv
。

Step 2 — Explore the Omni Model

步骤2 — 探索Omni模型

2a. Find the model ID

2a. 查找模型ID

bash

omni models list --modelkind SHARED

Identify the Shared Model and note its

id

. Always prefer the Shared Model over Schema or Workbook models.

bash

omni models list --modelkind SHARED

识别共享模型并记录其

id

。优先选择共享模型而非Schema或工作簿模型。

2b. Fetch the topic file

2b. 获取主题文件

bash

omni models yaml-get <modelId> --filename <topic_name>.topic

From the topic file extract:

base_view

joins

fields

always_filter

ai_context

sample_queries

bash

omni models yaml-get <modelId> --filename <topic_name>.topic

从主题文件中提取：

base_view

、

joins

、

fields

、

always_filter

、

ai_context

、

sample_queries

。

2c. Fetch the relationships file

2c. 获取关系文件

bash

omni models yaml-get <modelId> --filename relationships

bash

omni models yaml-get <modelId> --filename relationships

2d. Fetch each view file referenced in the topic

2d. 获取主题中引用的每个视图文件

For every view in

base_view

and

joins

bash

omni models yaml-get <modelId> --filename <view_name>.view

If a view is prefixed with
omni_dbt_
, fetch the file starting with
omni_dbt_
. Skip any view backed by
derived_table.sql
— it has no physical table.

对于

base_view

和

joins

中的每个视图：

bash

omni models yaml-get <modelId> --filename <view_name>.view

如果视图前缀为
omni_dbt_
，则获取以
omni_dbt_
开头的文件。跳过任何由
derived_table.sql
支持的视图 — 它没有物理表。

Step 3 — Identify Tables and Joins

步骤3 — 识别表与关联

Map view names to fully-qualified Databricks table references (

catalog.schema.table

Omni view name	Databricks table
`ecomm__order_items`	`catalog.ecomm.order_items`
`omni_dbt_ecomm__order_items`	`catalog.ecomm.order_items` (strip `omni_dbt_` )

The

__

separator maps to schema (left) and table (right). Confirm the catalog prefix with the user.

The

joins

indentation defines the join chain — a view indented beneath another joins into its parent:

yaml

joins:
  user_order_facts: {}          # skip — derived CTE
  ecomm__users: {}              # joins to base_view
  ecomm__inventory_items:       # joins to base_view
    ecomm__products:            # joins to inventory_items

Find the dimension with

primary_key: true

in each view — list it first among that table's dimensions.

✋ STOP — Confirm the full table list and join hierarchy with the user before continuing.

将视图名称映射到Databricks的全限定表引用（

catalog.schema.table

）：

Omni视图名称	Databricks表
`ecomm__order_items`	`catalog.ecomm.order_items`
`omni_dbt_ecomm__order_items`	`catalog.ecomm.order_items` （移除 `omni_dbt_` ）

__

分隔符映射为模式（左侧）和表（右侧）。与用户确认目录前缀。

joins

的缩进定义了关联链 — 缩进在另一个视图下的视图会关联到其父视图：

yaml

joins:
  user_order_facts: {}          # skip — derived CTE
  ecomm__users: {}              # joins to base_view
  ecomm__inventory_items:       # joins to base_view
    ecomm__products:            # joins to inventory_items

在每个视图中找到带有

primary_key: true

的维度 — 将其列为该表维度的第一个。

✋ 停止 — 继续前与用户确认完整的表列表和关联层级。

Step 4 — Resolve the Field List

步骤4 — 解析字段列表

Syntax	Meaning
(no `fields` parameter)	Include all fields from all views
`all_views.` / `view.`	Include all fields from all views / named view
`tag:<value>`	Include all fields with this tag
`view.field`	Include this specific field
`-view.field`	Exclude this field (always wins over wildcard inclusions)

Process inclusions first, then apply exclusions. Also remove any field with

hidden: true

unless explicitly included by name.

语法	含义
（无 `fields` 参数）	包含所有视图中的全部字段
`all_views.` / `view.`	包含所有视图/指定视图中的全部字段
`tag:<value>`	包含带有此标签的所有字段
`view.field`	包含此特定字段
`-view.field`	排除此字段（始终优先于通配符包含）

先处理包含规则，再应用排除规则。同时移除所有带有

hidden: true

的字段，除非通过名称明确包含。

Step 5 — Build Join Definitions

步骤5 — 构建关联定义

Using the hierarchy from Step 3 and

relationships.yaml

, extract join columns from

on_sql

and build the

on:

clause. Use the view name as the join

name

Star schema (single-level):

yaml

joins:
  - name: ecomm__users
    source: catalog.ecomm.users
    'on': source.user_id = ecomm__users.id

Snowflake schema (multi-hop):

yaml

joins:
  - name: ecomm__inventory_items
    source: catalog.ecomm.inventory_items
    'on': source.inventory_item_id = ecomm__inventory_items.id
    joins:
      - name: ecomm__products
        source: catalog.ecomm.products
        'on': ecomm__inventory_items.product_id = ecomm__products.id

⚠️
on
is a YAML 1.1 reserved word — always single-quote the key as
'on':
. Columns from nested (2+ level) joins cannot be used in
expr
— flatten them through a denormalized direct join instead.

使用步骤3中的层级和

relationships.yaml

，从

on_sql

中提取关联列并构建

on:

子句。使用视图名称作为关联

name

。

星型模式（单层级）：

yaml

joins:
  - name: ecomm__users
    source: catalog.ecomm.users
    'on': source.user_id = ecomm__users.id

雪花型模式（多层级）：

yaml

joins:
  - name: ecomm__inventory_items
    source: catalog.ecomm.inventory_items
    'on': source.inventory_item_id = ecomm__inventory_items.id
    joins:
      - name: ecomm__products
        source: catalog.ecomm.products
        'on': ecomm__inventory_items.product_id = ecomm__products.id

⚠️
on
是YAML 1.1的保留字 — 始终将键用单引号括起来，即
'on':
。嵌套（2级及以上）关联中的列不能用于
expr
— 改为通过非规范化直接关联来展平它们。

Step 6 — Map Dimensions and Measures

步骤6 — 映射维度与度量

For each field that survived Step 4, translate it using the rules below. See FIELD-MAPPING.md for full examples.

Dimension quick reference:

Omni field type	Databricks translation
Standard string/number	`expr: COLUMN`
`type: time` (no timeframes)	Single timestamp dimension
`type: time` + `timeframes`	One `DATE_TRUNC(...)` dimension per timeframe
`groups:`	`CASE WHEN ... END` expression
`bin_boundaries:`	`CASE WHEN` range expression
`duration:`	`DATEDIFF(unit, start, end)` expression
`type: yesno`	BOOLEAN dimension (not a filter; omit `data_type` )

Measure quick reference:

Omni measure type	Databricks translation
`aggregate_type: sum/avg/max/min`	`SUM(col)` / `AVG(col)` / etc.
`aggregate_type: count`	`COUNT(*)`
`aggregate_type: count_distinct`	`COUNT(DISTINCT col)`
Derived (refs other measures)	`MEASURE(measure_a) op MEASURE(measure_b)` — define atomics first
`filters:` on a measure	`AGG(col) FILTER (WHERE condition)`

Strip Omni's

${view.column}

refs to bare column names (or

join_name.column

for joined fields). Use

display_name

for the Omni

label

comment

for

description

, and carry

synonyms

directly. See YAML-REFERENCE.md for format and aggregate type mapping tables.

If the topic has

ai_context

, carry it into the metric view's top-level

comment

✋ STOP — Review all dimensions, measures, and join definitions with the user before generating the final output.

对于步骤4中保留的每个字段，使用以下规则进行转换。查看FIELD-MAPPING.md获取完整示例。

维度速查：

Omni字段类型	Databricks转换结果
标准字符串/数字	`expr: COLUMN`
`type: time` （无时间范围）	单个时间戳维度
`type: time` + `timeframes`	每个时间范围对应一个 `DATE_TRUNC(...)` 维度
`groups:`	`CASE WHEN ... END` 表达式
`bin_boundaries:`	`CASE WHEN` 范围表达式
`duration:`	`DATEDIFF(unit, start, end)` 表达式
`type: yesno`	BOOLEAN维度（不是过滤器；省略 `data_type` ）

度量速查：

Omni度量类型	Databricks转换结果
`aggregate_type: sum/avg/max/min`	`SUM(col)` / `AVG(col)` / 等
`aggregate_type: count`	`COUNT(*)`
`aggregate_type: count_distinct`	`COUNT(DISTINCT col)`
派生（引用其他度量）	`MEASURE(measure_a) op MEASURE(measure_b)` — 先定义原子度量
度量上的 `filters:`	`AGG(col) FILTER (WHERE condition)`

将Omni的

${view.column}

引用简化为裸列名（或关联字段使用

join_name.column

）。使用

display_name

对应Omni的

label

，

comment

对应

description

，直接保留

synonyms

。查看YAML-REFERENCE.md获取格式和聚合类型映射表。

如果主题包含

ai_context

，将其带入metric view顶级的

comment

中。

✋ 停止 — 生成最终输出前，与用户审核所有维度、度量和关联定义。

Step 7 — Check for Existing Metric View

步骤7 — 检查现有Metric View

bash

databricks api post /api/2.0/sql/statements \
  --json "{\"warehouse_id\": \"<WAREHOUSE_ID>\", \"statement\": \"SHOW VIEWS IN <catalog>.<schema> LIKE '%_mv'\", \"wait_timeout\": \"30s\", \"catalog\": \"<CATALOG>\", \"schema\": \"<SCHEMA>\"}"

View does not exist → use
```
CREATE OR REPLACE VIEW ... WITH METRICS
```
View already exists → use
```
ALTER VIEW ... AS $$ ... $$
```

bash

databricks api post /api/2.0/sql/statements \
  --json "{\"warehouse_id\": \"<WAREHOUSE_ID>\", \"statement\": \"SHOW VIEWS IN <catalog>.<schema> LIKE '%_mv'\", \"wait_timeout\": \"30s\", \"catalog\": \"<CATALOG>\", \"schema\": \"<SCHEMA>\"}"

视图不存在 → 使用
```
CREATE OR REPLACE VIEW ... WITH METRICS
```
视图已存在 → 使用
```
ALTER VIEW ... AS $$ ... $$
```

Step 8 — Generate and Execute the SQL

步骤8 — 生成并执行SQL

Write the SQL to a temp file:

sql

-- CREATE (new view)
CREATE OR REPLACE VIEW catalog.schema.orders_mv
WITH METRICS
LANGUAGE YAML
AS $$
version: 1.1
comment: "..."
source: catalog.ecomm.order_items

joins:
  - name: ecomm__users
    source: catalog.ecomm.users
    'on': source.user_id = ecomm__users.id

dimensions:
  - name: id
    expr: id
    display_name: "Order ID"

  - name: status
    expr: status
    display_name: "Order Status"

measures:
  - name: order_count
    expr: COUNT(*)
    display_name: "Order Count"

  - name: total_sale_price
    expr: SUM(sale_price)
    display_name: "Total Sale Price"
    format:
      type: currency
      currency_code: USD
$$

sql

-- ALTER (existing view)
ALTER VIEW catalog.schema.orders_mv AS $$
version: 1.1
...
$$

Execute via the SQL Statements API (

databricks sql execute

does not exist in CLI v0.295.0+):

bash

databricks api post /api/2.0/sql/statements \
  --json "{
    \"warehouse_id\": \"<WAREHOUSE_ID>\",
    \"statement\": $(cat /tmp/orders_mv.sql | python3 -c 'import json,sys; print(json.dumps(sys.stdin.read()))'),
    \"wait_timeout\": \"50s\",
    \"catalog\": \"<CATALOG>\",
    \"schema\": \"<SCHEMA>\"
  }"

Check the response for

"state": "SUCCEEDED"

. If

"state": "FAILED"

, read

status.error.message

and see the Troubleshooting section below.

✋ STOP — Confirm which group or user should receive access before running the GRANT. This is a permission change visible to others.

Grant access:

bash

databricks api post /api/2.0/sql/statements \
  --json "{\"warehouse_id\": \"<WAREHOUSE_ID>\", \"statement\": \"GRANT SELECT ON VIEW catalog.schema.orders_mv TO \`group_name\`\", \"wait_timeout\": \"30s\", \"catalog\": \"<CATALOG>\", \"schema\": \"<SCHEMA>\"}"

将SQL写入临时文件：

sql

-- CREATE (new view)
CREATE OR REPLACE VIEW catalog.schema.orders_mv
WITH METRICS
LANGUAGE YAML
AS $$
version: 1.1
comment: "..."
source: catalog.ecomm.order_items

joins:
  - name: ecomm__users
    source: catalog.ecomm.users
    'on': source.user_id = ecomm__users.id

dimensions:
  - name: id
    expr: id
    display_name: "Order ID"

  - name: status
    expr: status
    display_name: "Order Status"

measures:
  - name: order_count
    expr: COUNT(*)
    display_name: "Order Count"

  - name: total_sale_price
    expr: SUM(sale_price)
    display_name: "Total Sale Price"
    format:
      type: currency
      currency_code: USD
$$

sql

-- ALTER (existing view)
ALTER VIEW catalog.schema.orders_mv AS $$
version: 1.1
...
$$

通过SQL Statements API执行（CLI v0.295.0+中不存在

databricks sql execute

）：

bash

databricks api post /api/2.0/sql/statements \
  --json "{
    \"warehouse_id\": \"<WAREHOUSE_ID>\",
    \"statement\": $(cat /tmp/orders_mv.sql | python3 -c 'import json,sys; print(json.dumps(sys.stdin.read()))'),
    \"wait_timeout\": \"50s\",
    \"catalog\": \"<CATALOG>\",
    \"schema\": \"<SCHEMA>\"
  }"

检查响应中的

"state": "SUCCEEDED"

。如果

"state": "FAILED"

，查看

status.error.message

并参考下方的故障排除部分。

✋ 停止 — 运行GRANT前，确认哪些组或用户应获得访问权限。这是对他人可见的权限变更。

授予访问权限：

bash

databricks api post /api/2.0/sql/statements \
  --json "{\"warehouse_id\": \"<WAREHOUSE_ID>\", \"statement\": \"GRANT SELECT ON VIEW catalog.schema.orders_mv TO \`group_name\`\", \"wait_timeout\": \"30s\", \"catalog\": \"<CATALOG>\", \"schema\": \"<SCHEMA>\"}"

Troubleshooting

故障排除

When the SQL Statements API returns

"state": "FAILED"

, read

status.error.message

Error message contains	Likely cause	Fix
`METRIC_VIEW_INVALID_VIEW_DEFINITION`	Invalid YAML field or value	Check the field name against the valid keys ( `name` , `expr` , `display_name` , `comment` , `synonyms` , `format` ). Common mistakes: using `description` instead of `comment` , unsupported `decimal_places` .
`warehouse not running` / `RESOURCE_DOES_NOT_EXIST`	Warehouse is stopped or wrong ID	Start the warehouse in the Databricks UI or verify the ID with `databricks api get /api/2.0/sql/warehouses` .
`PERMISSION_DENIED`	The CLI profile lacks privileges	Check the profile's permissions on the catalog/schema with `databricks api get /api/2.0/unity-catalog/permissions/...` .
`TABLE_OR_VIEW_NOT_FOUND`	A source or join table doesn't exist in Unity Catalog	Verify each table reference with `SHOW TABLES IN <catalog>.<schema>` .
`on` parse error / unexpected key	`on:` not quoted	Always write `'on':` (single-quoted) — it is a YAML 1.1 reserved word.
`wait_timeout` value error	Timeout out of range	`wait_timeout` must be between `5s` and `50s` .

If the error message is truncated, run the same statement with

"wait_timeout": "5s"

to get the full synchronous error response.

当SQL Statements API返回

"state": "FAILED"

时，查看

status.error.message

：

错误消息包含	可能原因	修复方案
`METRIC_VIEW_INVALID_VIEW_DEFINITION`	YAML字段或值无效	检查字段名称是否符合有效键（ `name` 、 `expr` 、 `display_name` 、 `comment` 、 `synonyms` 、 `format` ）。常见错误：使用 `description` 而非 `comment` 、不支持的 `decimal_places` 。
`warehouse not running` / `RESOURCE_DOES_NOT_EXIST`	仓库已停止或ID错误	在Databricks UI中启动仓库，或通过 `databricks api get /api/2.0/sql/warehouses` 验证ID。
`PERMISSION_DENIED`	CLI配置文件缺少权限	通过 `databricks api get /api/2.0/unity-catalog/permissions/...` 检查配置文件在目录/模式上的权限。
`TABLE_OR_VIEW_NOT_FOUND`	源表或关联表在Unity Catalog中不存在	通过 `SHOW TABLES IN <catalog>.<schema>` 验证每个表引用。
`on` 解析错误/意外键	`on:` 未加引号	始终写入 `'on':` （单引号括起）— 它是YAML 1.1的保留字。
`wait_timeout` 值错误	超时超出范围	`wait_timeout` 必须在 `5s` 到 `50s` 之间。

如果错误消息被截断，使用

"wait_timeout": "5s"

运行相同语句以获取完整的同步错误响应。

Critical Rules

关键规则

Naming: Name the metric view
```
[topic_name]_mv
```
(snake_case, lowercase)
CREATE vs ALTER: Check for existence first —
```
CREATE OR REPLACE
```
for new,
```
ALTER VIEW
```
for existing
Version: Always use
```
version: 1.1
```
(requires Databricks Runtime 17.2+)
Skip derived CTEs: Views with
```
derived_table.sql
```
have no physical table — skip and warn the user
Confirm before executing: Show the full generated SQL to the user before running
Boolean fields: Map
```
type: yesno
```
as BOOLEAN dimensions — not filters.
```
data_type
```
is not a valid field — omit it
Composed measures: Use
```
MEASURE()
```
syntax; define atomic measures before composed ones
YAML quoting:
```
on
```
is a YAML 1.1 reserved word — always write
```
'on':
```
(single-quoted)
No SELECT *: All fields must be explicitly defined
MAP columns: Skip joins to tables containing
```
MAP
```
type columns — not supported
Nested join refs: Only direct star join columns (1 level) can be used in
```
expr
```
. Flatten snowflake schema joins through a denormalized direct join
Warehouse ID required: Always confirm before execution — cannot be inferred
Exclusions win:
```
-view.field
```
always overrides any wildcard inclusion

Format type values are lowercase:

number

currency

date

date_time

percentage

byte

Date format required:
```
type: date
```
and
```
type: date_time
```
both require
```
date_format
```
Currency format: Use
```
currency_code: USD
```
not
```
iso_code: USD
```
decimal_places
unsupported: Omit it entirely — causes a parse error

CLI execution: Use

databricks api post /api/2.0/sql/statements

;

wait_timeout

must be

5s

–

50s

Omni CLI flag: Use
```
--filename
```
(not
```
--file-name
```
)
Field description key: Use
```
comment:
```
not
```
description:
```
—
```
description
```
is not a recognized field and causes a parse error

命名：Metric View命名为
```
[topic_name]_mv
```
（蛇形命名法，小写）
CREATE vs ALTER：先检查是否存在 — 新视图使用
```
CREATE OR REPLACE
```
，现有视图使用
```
ALTER VIEW
```
版本：始终使用
```
version: 1.1
```
（需要Databricks Runtime 17.2+）
跳过派生CTE：带有
```
derived_table.sql
```
的视图没有物理表 — 跳过并提醒用户
执行前确认：运行前向用户展示完整的生成SQL
布尔字段：将
```
type: yesno
```
映射为BOOLEAN维度 — 不是过滤器。
```
data_type
```
不是有效字段 — 省略它
组合度量：使用
```
MEASURE()
```
语法；先定义原子度量再定义组合度量
YAML引号：
```
on
```
是YAML 1.1的保留字 — 始终写入
```
'on':
```
（单引号括起）
**禁止SELECT ***：所有字段必须显式定义
MAP列：跳过关联到包含
```
MAP
```
类型列的表 — 不支持
嵌套关联引用：只有直接星型关联列（1级）可用于
```
expr
```
。通过非规范化直接关联展平雪花模式关联
仓库ID必填：执行前必须确认 — 无法推断
排除优先：
```
-view.field
```
始终覆盖任何通配符包含

格式类型值小写：

number

、

currency

、

date

、

date_time

、

percentage

、

byte

日期格式必填：
```
type: date
```
和
```
type: date_time
```
都需要
```
date_format
```
货币格式：使用
```
currency_code: USD
```
而非
```
iso_code: USD
```
decimal_places
不支持：完全省略它 — 会导致解析错误

CLI执行：使用

databricks api post /api/2.0/sql/statements

；

wait_timeout

必须为

5s

–

50s

Omni CLI标志：使用
```
--filename
```
（而非
```
--file-name
```
）
字段描述键：使用
```
comment:
```
而非
```
description:
```
—
```
description
```
不是可识别字段，会导致解析错误

omni-to-databricks-metric-view

Original

Translation

Omni → Databricks Metric View

Omni → Databricks Metric View

Prerequisites

前提条件

Verify the Omni CLI is installed — if not, ask the user to install it

Verify the Omni CLI is installed — if not, ask the user to install it

See: https://github.com/exploreomni/cli#readme

See: https://github.com/exploreomni/cli#readme

Show available profiles and select the appropriate one

Show available profiles and select the appropriate one

If multiple profiles exist, ask the user which to use, then switch:

If multiple profiles exist, ask the user which to use, then switch:

Databricks CLI — verify installed and check profiles

Databricks CLI — verify installed and check profiles

Workflow

工作流程

Step 1 — Gather Requirements

步骤1 — 收集需求

Step 2 — Explore the Omni Model

步骤2 — 探索Omni模型

2a. Find the model ID

2a. 查找模型ID

2b. Fetch the topic file

2b. 获取主题文件

2c. Fetch the relationships file

2c. 获取关系文件

2d. Fetch each view file referenced in the topic

2d. 获取主题中引用的每个视图文件

Step 3 — Identify Tables and Joins

步骤3 — 识别表与关联

Step 4 — Resolve the Field List

步骤4 — 解析字段列表

Step 5 — Build Join Definitions

步骤5 — 构建关联定义

Step 6 — Map Dimensions and Measures

步骤6 — 映射维度与度量

Step 7 — Check for Existing Metric View

步骤7 — 检查现有Metric View

Step 8 — Generate and Execute the SQL

步骤8 — 生成并执行SQL

Troubleshooting

故障排除

Critical Rules

关键规则

Reference

参考资料