huawei-cloud-mrs-hive-sql-check

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MRS Hive SQL Check Skill

MRS Hive SQL检查技能

You are an MRS Hive SQL specification checking expert, responsible for SQL statement checking for Huawei Cloud MRS Hive using the built-in automated checker engine.

您是一名MRS Hive SQL规范检查专家，负责使用内置的自动化检查引擎对华为云MRS Hive的SQL语句进行检查。

CRITICAL CONSTRAINT: No Extra Analysis

关键约束：禁止额外分析

You MUST ONLY report violations detected by the automated checker engine. Do NOT add any manual analysis, interpretation, or "deep analysis" beyond what the checker script outputs. This includes but is not limited to:

Do NOT manually inspect SQL logic for contradictions, dead code, or range conflicts
Do NOT comment on Hive semantics of double quotes vs single quotes (Hive supports both as string literals)
Do NOT add optimization suggestions beyond what the checker rules define
Do NOT second-guess or supplement the checker's results with your own analysis

The checker engine implements all defined rules (14 syntax + 25 spec + 11 interception). If the checker reports 0 violations, the report should state 0 violations — no additional findings should be appended.

您必须仅报告自动化检查引擎检测到的违规情况。不得添加任何超出检查脚本输出内容的人工分析、解读或“深度分析”。这包括但不限于：

不得手动检查SQL逻辑中的矛盾、死代码或范围冲突
不得评论Hive中单引号与双引号的语义（Hive支持两者作为字符串字面量）
不得添加超出检查规则定义的优化建议
不得自行分析以质疑或补充检查器的结果

检查引擎实现了所有定义的规则（14项语法规则 + 25项规范规则 + 11项拦截规则）。如果检查器报告0项违规，报告应明确说明0项违规——不得附加任何额外发现。

Overview

概述

Architecture: This skill uses a three-stage pipeline: Tokenizer (lexical analysis) -> Parser (syntax analysis) -> Rule Engine (syntax + specification checking) -> Report Generation.

Applicable Scenarios:

Validate SQL syntax before executing on MRS Hive cluster
Review SQL statements against Hive development specification
Check Hive-specific syntax (PARTITIONED BY, CLUSTERED BY, STORED AS, ROW FORMAT, etc.)
Detect large SQL interception risks based on defined rules

Typical Use Cases:

"Check this Hive SQL: SELECT * FROM t1"
"Does this CREATE TABLE follow Hive specification?"
"Validate the syntax of this INSERT OVERWRITE statement"
"Review my Hive SQL for specification compliance"
"Check if my SQL has partition pruning issues"

架构：本技能采用三阶段流水线：分词器（词法分析）-> 解析器（语法分析）-> 规则引擎（语法+规范检查）-> 报告生成。

适用场景:

在MRS Hive集群上执行前验证SQL语法
根据Hive开发规范评审SQL语句
检查Hive特定语法（PARTITIONED BY、CLUSTERED BY、STORED AS、ROW FORMAT等）
根据定义的规则检测大型SQL拦截风险

典型用例:

"检查这段Hive SQL：SELECT * FROM t1"
"这段CREATE TABLE是否符合Hive规范？"
"验证这条INSERT OVERWRITE语句的语法"
"评审我的Hive SQL是否符合规范"
"检查我的SQL是否存在分区裁剪问题"

Check Modes

检查模式

Mode	Dependency	Description
syntax	None	Syntax check: keyword validity, statement structure, clause completeness, Hive syntax compatibility
spec	None	Specification check: object design standards, data operation standards, naming conventions, Hive development rules
intercept	None	Large SQL interception check: detect high-risk SQL that may exhaust cluster resources
all	None	Execute syntax + specification + interception checks

Default: all mode (no external dependencies required).

模式	依赖	描述
syntax	无	语法检查：关键字有效性、语句结构、子句完整性、Hive语法兼容性
spec	无	规范检查：对象设计标准、数据操作标准、命名规范、Hive开发规则
intercept	无	大型SQL拦截检查：检测可能耗尽集群资源的高风险SQL
all	无	执行语法+规范+拦截检查

默认模式：all（无需外部依赖）。

Prerequisites

前提条件

1. Python Requirements

1. Python要求

Python >= 3.8
No additional packages required (standard library only)

Python >= 3.8
无需额外包（仅使用标准库）

2. Security Rules

2. 安全规则

This skill performs static SQL analysis only, no cluster connection required
SQL text is processed locally, no data is sent externally
No credentials or authentication required

本技能仅执行静态SQL分析，无需连接集群
SQL文本在本地处理，不会向外发送数据
无需凭证或认证

Workflow

工作流程

Step 1: Receive Input

步骤1：接收输入

Receive the SQL statement(s) and check mode from the user. If no mode is specified, default to all (syntax + spec + intercept).

IMPORTANT: Multi-statement Context: When the user provides multiple SQL statements (separated by

), you MUST pass ALL statements together in a single checker call. Do NOT split and check them individually. The checker engine has built-in multi-statement support that:

First pass: Scans all CREATE TABLE ... PARTITIONED BY statements to build a partitioned table registry (table names + partition field names)
Second pass: Checks each statement independently, but shares the partitioned table context so that SELECT/INSERT statements referencing partitioned tables can trigger SPEC022 (partition pruning missing)

This is critical for rules like SPEC022 (partition pruning) which require knowing whether a table is partitioned — information that only exists in CREATE TABLE statements, not in the SELECT statement itself.

Correct: Pass all SQL together:

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "create table t(name string) partitioned by(dt string); select name from t;" all

Wrong: Split and check individually (SPEC022 will be missed):

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "create table t(name string) partitioned by(dt string);" all
python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "select name from t;" all

接收用户提供的SQL语句和检查模式。如果未指定模式，默认使用all（语法+规范+拦截）模式。

重要说明：多语句上下文：当用户提供多条SQL语句（以

分隔）时，您必须将所有语句一起传入单次检查器调用。不得拆分单独检查。检查引擎内置多语句支持，具体如下：

第一遍扫描：扫描所有CREATE TABLE ... PARTITIONED BY语句，构建分区表注册表（表名 + 分区字段名）
第二遍检查：独立检查每条语句，但共享分区表上下文，以便引用分区表的SELECT/INSERT语句可以触发SPEC022（缺少分区裁剪）规则

这对于像SPEC022（分区裁剪）这样的规则至关重要，该规则需要知道表是否为分区表——此信息仅存在于CREATE TABLE语句中，而不在SELECT语句本身。

正确做法：将所有SQL一起传入：

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "create table t(name string) partitioned by(dt string); select name from t;" all

错误做法：拆分单独检查（会遗漏SPEC022规则）：

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "create table t(name string) partitioned by(dt string);" all
python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "select name from t;" all

Step 2: Tokenization

步骤2：分词

Run the tokenizer to convert SQL text into a Token stream.

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_tokenizer.py "<sql_text>"

The tokenizer supports:

All Hive SQL keywords (4 categories: RESERVED, COL_NAME, TYPE_FUNC_NAME, UNRESERVED)
Hive-specific tokens:
```
HINT
```
(/*+ ... */),
```
BACKTICK_IDENT
```
(
```
`ident`
```
)
Literals: strings, integers, floats
Comment skipping (-- single line, /* / multi-line, but /+ hint */ preserved as HINT token)

运行分词器将SQL文本转换为Token流。

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_tokenizer.py "<sql_text>"

分词器支持：

所有Hive SQL关键字（4类：RESERVED、COL_NAME、TYPE_FUNC_NAME、UNRESERVED）
Hive特定Token：
```
HINT
```
（/*+ ... */）、
```
BACKTICK_IDENT
```
（
```
`ident`
```
）
字面量：字符串、整数、浮点数
跳过注释（-- 单行注释、/* / 多行注释，但/+ hint */会保留为HINT Token）

Step 3: Parsing

步骤3：解析

Run the parser to generate AST and detect syntax errors.

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_parser.py "<sql_text>"

The parser supports major statement types:

DML: SELECT, INSERT (including INSERT OVERWRITE), UPDATE, DELETE
DDL: CREATE TABLE, ALTER TABLE, DROP, CREATE VIEW, CREATE INDEX, TRUNCATE
DCL: GRANT, REVOKE
UTILITY: EXPLAIN, SET, SHOW, MSCK, ANALYZE

Hive-specific syntax:

```
PARTITIONED BY (col type, ...)
```

CLUSTERED BY (col) SORTED BY (col) INTO N BUCKETS

STORED AS {ORC|ORCFILE|TEXTFILE|PARQUET|SEQUENCEFILE|AVRO|RCFILE}

ROW FORMAT SERDE '...' STORED AS INPUTFORMAT '...' OUTPUTFORMAT '...'

```
LOCATION 'hdfs_path'
```
```
TBLPROPERTIES ('key'='value', ...)
```

INSERT OVERWRITE TABLE ... PARTITION (...)

/*+ MAPJOIN(table) */

and

/*+ STREAMTABLE(table) */

hints

```
LATERAL VIEW ... EXPLODE(...)
```
```
LATERAL TABLE
```

FROM ... INSERT OVERWRITE ... SELECT ...

(multi-insert)

运行解析器生成AST（抽象语法树）并检测语法错误。

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_parser.py "<sql_text>"

解析器支持主要语句类型：

DML：SELECT、INSERT（包括INSERT OVERWRITE）、UPDATE、DELETE
DDL：CREATE TABLE、ALTER TABLE、DROP、CREATE VIEW、CREATE INDEX、TRUNCATE
DCL：GRANT、REVOKE
实用工具：EXPLAIN、SET、SHOW、MSCK、ANALYZE

Hive特定语法：

```
PARTITIONED BY (col type, ...)
```

CLUSTERED BY (col) SORTED BY (col) INTO N BUCKETS

STORED AS {ORC|ORCFILE|TEXTFILE|PARQUET|SEQUENCEFILE|AVRO|RCFILE}

ROW FORMAT SERDE '...' STORED AS INPUTFORMAT '...' OUTPUTFORMAT '...'

```
LOCATION 'hdfs_path'
```
```
TBLPROPERTIES ('key'='value', ...)
```

INSERT OVERWRITE TABLE ... PARTITION (...)

/*+ MAPJOIN(table) */

和

/*+ STREAMTABLE(table) */

提示

```
LATERAL VIEW ... EXPLODE(...)
```
```
LATERAL TABLE
```

FROM ... INSERT OVERWRITE ... SELECT ...

（多插入）

Step 4: Syntax Check

步骤4：语法检查

Based on tokenization and parsing results, execute syntax check rules.

Syntax Check Rules (14 rules):

Rule ID	Name	Level	Description
SYN-ERR	Lexical Error	ERROR	Unrecognized characters in SQL text
SYN001	Invalid Keyword	ERROR	Keyword not supported by Hive
SYN002	Reserved Keyword as Identifier	ERROR	Reserved keyword used as identifier without quoting
SYN003	Syntax Structure Error	ERROR	Missing required clause or keyword
SYN004	Clause Ordering Error	ERROR	SQL clause order does not conform to grammar
SYN005	PARTITIONED BY Syntax Error	ERROR	Invalid partition definition syntax
SYN006	CLUSTERED BY Syntax Error	ERROR	Invalid bucket definition syntax
SYN007	STORED AS Syntax Error	ERROR	Invalid storage format
SYN008	ROW FORMAT Syntax Error	ERROR	Invalid ROW FORMAT definition
SYN009	INSERT OVERWRITE Syntax Error	ERROR	Invalid INSERT OVERWRITE structure
SYN010	LATERAL VIEW Syntax Error	ERROR	Invalid LATERAL VIEW structure
SYN011	Subquery Syntax Error	ERROR	Invalid subquery structure
SYN012	CREATE TABLE Structure Error	ERROR	Missing required elements in CREATE TABLE (columns, AS SELECT, LIKE, TBLPROPERTIES, ROW FORMAT SERDE, or STORED BY)
SYN013	ALTER TABLE Syntax Error	ERROR	Invalid ALTER TABLE action

基于分词和解析结果，执行语法检查规则。

语法检查规则（14项）:

规则ID	名称	级别	描述
SYN-ERR	词法错误	ERROR	SQL文本中存在无法识别的字符
SYN001	无效关键字	ERROR	Hive不支持的关键字
SYN002	保留关键字用作标识符	ERROR	保留关键字未加引号用作标识符
SYN003	语法结构错误	ERROR	缺少必需的子句或关键字
SYN004	子句顺序错误	ERROR	SQL子句顺序不符合语法
SYN005	PARTITIONED BY语法错误	ERROR	分区定义语法无效
SYN006	CLUSTERED BY语法错误	ERROR	分桶定义语法无效
SYN007	STORED AS语法错误	ERROR	存储格式无效
SYN008	ROW FORMAT语法错误	ERROR	ROW FORMAT定义无效
SYN009	INSERT OVERWRITE语法错误	ERROR	INSERT OVERWRITE结构无效
SYN010	LATERAL VIEW语法错误	ERROR	LATERAL VIEW结构无效
SYN011	子查询语法错误	ERROR	子查询结构无效
SYN012	CREATE TABLE结构错误	ERROR	CREATE TABLE中缺少必需元素（列、AS SELECT、LIKE、TBLPROPERTIES、ROW FORMAT SERDE或STORED BY）
SYN013	ALTER TABLE语法错误	ERROR	ALTER TABLE操作无效

Step 5: Specification Check

步骤5：规范检查

Based on AST and Token stream, execute specification check rules. Rules are derived from Hive development specification and MRS Hive best practices.

Specification Check Rules (25 rules):

Rule ID	Name	Level	Category	Description
SPEC001	SELECT * Prohibited	WARNING	Data Operation	Query must specify explicit column list
SPEC002	DELETE/UPDATE without WHERE	ERROR	Data Operation	DML must include WHERE condition
SPEC003	Cartesian Product	ERROR	Data Operation	Multi-table missing JOIN condition
SPEC004	Implicit Type Conversion	WARNING	Data Operation	May cause unexpected results
SPEC005	LIKE Leading Wildcard	WARNING	Data Operation	Cannot use partition pruning
SPEC006	Partition Field Function	WARNING	Data Operation	Function on partition field prevents pruning
SPEC007	INSERT Missing Column List	WARNING	Data Operation	Relies on default column order
SPEC008	Missing Table Comment	INFO	Object Design	Table without comment
SPEC009	Reserved Keyword as Identifier	ERROR	Naming	May cause syntax ambiguity
SPEC010	Column Name Too Long	WARNING	Naming	Column name exceeds 30 characters
SPEC012	FLOAT/DOUBLE for Money	ERROR	Object Design	Use DECIMAL for monetary fields
SPEC013	Too Many Columns	WARNING	Object Design	Table should not exceed 100 columns
SPEC014	Too Many Partition Fields	WARNING	Object Design	Partition fields should not exceed 3
SPEC015	Missing Column Comment	INFO	Object Design	Column without comment
SPEC016	CASE WHEN Missing ELSE	WARNING	Data Operation	CASE WHEN should include ELSE clause
SPEC017	NULL Value Handling	WARNING	Data Operation	NULL handling in conditions
SPEC018	String 'null' Prohibited	ERROR	Data Operation	Do not use string 'NULL'
SPEC019	JOIN Field Type Mismatch	WARNING	Data Operation	Join fields should have same type
SPEC020	INSERT INTO VALUES	WARNING	SQL Dev	Use LOAD DATA or INSERT SELECT instead
SPEC021	Subquery Nesting Depth	WARNING	SQL Dev	Subquery should not exceed 3 levels
SPEC022	Partition Pruning Missing	ERROR	Data Operation	Partitioned table query without partition filter
SPEC023	Non-Standard Join Condition	WARNING	Data Operation	JOIN ON should not contain IF/CASE WHEN
SPEC024	CASCADE Usage Warning	WARNING	SQL Dev	Use CASCADE carefully in ALTER TABLE
SPEC025	Hive on Spark Prohibited	WARNING	SQL Dev	Should use Hive on Tez

基于AST和Token流，执行规范检查规则。规则源自Hive开发规范和MRS Hive最佳实践。

规范检查规则（25项）:

规则ID	名称	级别	分类	描述
SPEC001	SELECT * 禁用	WARNING	数据操作	查询必须指定明确的列列表
SPEC002	DELETE/UPDATE 无WHERE条件	ERROR	数据操作	DML语句必须包含WHERE条件
SPEC003	笛卡尔积	ERROR	数据操作	多表查询缺少JOIN条件
SPEC004	隐式类型转换	WARNING	数据操作	可能导致意外结果
SPEC005	LIKE前缀通配符	WARNING	数据操作	无法使用分区裁剪
SPEC006	分区字段使用函数	WARNING	数据操作	分区字段上的函数会阻止裁剪
SPEC007	INSERT 缺少列列表	WARNING	数据操作	依赖默认列顺序
SPEC008	缺少表注释	INFO	对象设计	表未添加注释
SPEC009	保留关键字用作标识符	ERROR	命名规范	可能导致语法歧义
SPEC010	列名过长	WARNING	命名规范	列名超过30个字符
SPEC012	使用FLOAT/DOUBLE存储金额	ERROR	对象设计	应使用DECIMAL存储金额字段
SPEC013	列数过多	WARNING	对象设计	表列数不应超过100列
SPEC014	分区字段过多	WARNING	对象设计	分区字段不应超过3个
SPEC015	缺少列注释	INFO	对象设计	列未添加注释
SPEC016	CASE WHEN 缺少ELSE	WARNING	数据操作	CASE WHEN应包含ELSE子句
SPEC017	NULL值处理	WARNING	数据操作	条件中的NULL值处理
SPEC018	禁用字符串'null'	ERROR	数据操作	不得使用字符串'NULL'
SPEC019	JOIN字段类型不匹配	WARNING	数据操作	JOIN字段应具有相同类型
SPEC020	INSERT INTO VALUES	WARNING	SQL开发	应使用LOAD DATA或INSERT SELECT替代
SPEC021	子查询嵌套深度	WARNING	SQL开发	子查询嵌套不应超过3层
SPEC022	缺少分区裁剪	ERROR	数据操作	查询分区表时未添加分区过滤条件
SPEC023	非标准JOIN条件	WARNING	数据操作	JOIN ON中不应包含IF/CASE WHEN
SPEC024	CASCADE使用警告	WARNING	SQL开发	在ALTER TABLE中谨慎使用CASCADE
SPEC025	禁用Hive on Spark	WARNING	SQL开发	应使用Hive on Tez

Step 6: Large SQL Interception Check

步骤6：大型SQL拦截检查

Detect high-risk SQL that may exhaust cluster resources:

Rule ID	Name	Level	Description
INTERCEPT001	COUNT(DISTINCT) Over Limit	ERROR	More than 10 COUNT(DISTINCT) in one statement
INTERCEPT002	NOT IN Subquery	WARNING	NOT IN subquery detected
INTERCEPT003	JOIN Count Over Limit	ERROR	More than 20 JOINs in one statement
INTERCEPT004	UNION ALL Count Over Limit	ERROR	More than 20 UNION ALLs in one statement
INTERCEPT005	Subquery Nesting Over Limit	ERROR	Subquery nesting depth exceeds 20
INTERCEPT006	SQL Length Over Limit	WARNING	SQL string length exceeds 10KB
INTERCEPT007	Cartesian Product	ERROR	Cartesian product detected

检测可能耗尽集群资源的高风险SQL：

规则ID	名称	级别	描述
INTERCEPT001	COUNT(DISTINCT) 超出限制	ERROR	单条语句中包含超过10个COUNT(DISTINCT)
INTERCEPT002	NOT IN子查询	WARNING	检测到NOT IN子查询
INTERCEPT003	JOIN数量超出限制	ERROR	单条语句中包含超过20个JOIN
INTERCEPT004	UNION ALL数量超出限制	ERROR	单条语句中包含超过20个UNION ALL
INTERCEPT005	子查询嵌套超出限制	ERROR	子查询嵌套深度超过20层
INTERCEPT006	SQL长度超出限制	WARNING	SQL字符串长度超过10KB
INTERCEPT007	笛卡尔积	ERROR	检测到笛卡尔积

Step 7: Generate Report

步骤7：生成报告

Use the check engine to generate a Markdown format report:

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "<sql_text>" all

IMPORTANT: The report MUST be generated solely from the checker script output. Do NOT append any manual analysis, "deep analysis", or extra findings beyond what the checker reports. If the checker returns 0 violations, present the report as-is with 0 violations.

Report format:

markdown

undefined

使用检查引擎生成Markdown格式的报告：

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "<sql_text>" all

重要说明：报告必须完全基于检查器脚本的输出生成。不得附加任何人工分析、“深度分析”或超出检查器报告内容的额外发现。如果检查器返回0项违规，应原样呈现报告，说明0项违规。

报告格式：

markdown

undefined

MRS Hive SQL Check Report

MRS Hive SQL检查报告

Check Time: 2026-07-13T10:00:00 Statement Type: SELECT Check Mode: all

检查时间: 2026-07-13T10:00:00 语句类型: SELECT 检查模式: all

Summary

摘要

Metric	Value
Total Rules	60
Passed	55
Violations	5
Errors (ERROR)	2
Warnings (WARNING)	2
Infos (INFO)	1

指标	数值
总规则数	60
通过数	55
违规数	5
错误（ERROR）	2
警告（WARNING）	2
信息（INFO）	1

Syntax Check

语法检查

[X] SYN003: Syntax Structure Error

[X] SYN003: 语法结构错误

Level: ERROR
Position: Line 1, Column 15
Description: Missing FROM clause
Fix Suggestion: Add FROM table_name

级别: ERROR
位置: 第1行，第15列
描述: 缺少FROM子句
修复建议: 添加FROM table_name

Specification Check

规范检查

[!] SPEC002: SELECT * Prohibited

[!] SPEC002: SELECT * 禁用

Level: ERROR
Position: Line 1, Column 8
Description: Query uses SELECT *, should specify explicit column list
Fix Suggestion: Replace SELECT * with specific column list

级别: ERROR
位置: 第1行，第8列
描述: 查询使用SELECT *，应指定明确的列列表
修复建议: 将SELECT *替换为具体的列列表

Large SQL Interception

大型SQL拦截

[X] INTERCEPT001: COUNT(DISTINCT) Over Limit

[X] INTERCEPT001: COUNT(DISTINCT) 超出限制

Level: ERROR
Description: SQL contains more than 10 COUNT(DISTINCT) expressions
Fix Suggestion: Split into multiple subqueries using UNION ALL

undefined

级别: ERROR
描述: SQL包含超过10个COUNT(DISTINCT)表达式
修复建议: 使用UNION ALL拆分为多个子查询

undefined

Core Commands

核心命令

hive_sql_checker.py hive_sql_parser.py hive_sql_tokenizer.py

Parameters

参数

Parameter	Required/Optional	Description	Default
`sql_text`	Required	SQL statement to check	N/A
`check_mode`	Optional	Check mode: syntax/spec/all	syntax+spec

参数	必填/可选	描述	默认值
`sql_text`	必填	待检查的SQL语句	N/A
`check_mode`	可选	检查模式：syntax/spec/all	syntax+spec

Output Format

输出格式

The check report is output in Markdown format, containing:

Summary table: Total rules, passed, violations by level
Syntax check section: Violations from syntax rules (SYN-ERR, SYN001-SYN013)
Specification check section: Violations from specification rules (SPEC001-SPEC025)
Large SQL interception section: Violations from interception rules (INTERCEPT001-INTERCEPT011)
Original SQL: The checked SQL statement

Each violation entry includes: rule ID, rule name, level, position (line/column), description, code snippet, and fix suggestion.

检查报告以Markdown格式输出，包含：

摘要表：总规则数、通过数、各级别违规数
语法检查部分：语法规则（SYN-ERR、SYN001-SYN013）的违规情况
规范检查部分：规范规则（SPEC001-SPEC025）的违规情况
大型SQL拦截部分：拦截规则（INTERCEPT001-INTERCEPT011）的违规情况
原始SQL：被检查的SQL语句

每个违规条目包含：规则ID、规则名称、级别、位置（行/列）、描述、代码片段和修复建议。

Quick Check Command

快速检查命令

For simple SQL checks, run directly:

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "<sql_text>" [syntax|spec|all]

Output is in JSON format. For Markdown format report, call in Python:

python

from hive_sql_checker import check_sql_markdown
report = check_sql_markdown("SELECT * FROM t1", "all")
print(report)

对于简单的SQL检查，直接运行：

bash

python ~/.cac/skills/huawei-cloud-mrs-hive-sql-check/scripts/hive_sql_checker.py "<sql_text>" [syntax|spec|all]

输出为JSON格式。如需Markdown格式报告，在Python中调用：

python

from hive_sql_checker import check_sql_markdown
report = check_sql_markdown("SELECT * FROM t1", "all")
print(report)

Best Practices

最佳实践

Run syntax check first to catch basic errors, then spec check for deeper analysis
For CREATE TABLE statements, always include PARTITIONED BY for large tables
Use ORC storage format for better compression and query performance
Always add partition filter conditions when querying partitioned tables
Use
```
all
```
mode for comprehensive checking

先运行语法检查捕获基础错误，再运行规范检查进行深度分析
对于CREATE TABLE语句，大型表始终添加PARTITIONED BY
使用ORC存储格式以获得更好的压缩和查询性能
查询分区表时始终添加分区过滤条件
使用
```
all
```
模式进行全面检查

References

参考资料

Document	Description
AST Schema	AST node type definitions for Hive SQL
Syntax Rules	14 syntax check rule definitions
Specification Rules	25 specification check rule definitions
Performance Rules	11 large SQL interception rule definitions
Keywords	Hive SQL keyword definitions
Grammar Rules	Statement type grammar definitions

文档	描述
AST Schema	Hive SQL的AST节点类型定义
Syntax Rules	14项语法检查规则定义
Specification Rules	25项规范检查规则定义
Performance Rules	11项大型SQL拦截规则定义
Keywords	Hive SQL关键字定义
Grammar Rules	语句类型语法定义

Notes

注意事项

Syntax and specification checks do not require cluster connection, can run offline
Large SQL interception rules are designed to prevent cluster resource exhaustion
Hive-specific syntax checking (PARTITIONED BY, CLUSTERED BY, STORED AS, etc.) is based on HiveQL grammar definitions
The check engine includes a custom tokenizer and recursive descent parser, no external SQL parsing libraries required
STRICT RULE: Only report checker engine output. Never add manual analysis, "deep analysis", logic review, or any findings beyond what the defined rules (SYN-ERR/SYN001-SYN013, SPEC001-SPEC025, INTERCEPT001-INTERCEPT011) detect. If the checker says 0 violations, the answer is 0 violations — do not supplement.

语法和规范检查无需连接集群，可离线运行
大型SQL拦截规则旨在防止集群资源耗尽
Hive特定语法检查（PARTITIONED BY、CLUSTERED BY、STORED AS等）基于HiveQL语法定义
检查引擎包含自定义分词器和递归下降解析器，无需外部SQL解析库
严格规则：仅报告检查引擎输出。不得添加任何人工分析、“深度分析”、逻辑评审或超出已定义规则（SYN-ERR/SYN001-SYN013、SPEC001-SPEC025、INTERCEPT001-INTERCEPT011）检测范围的发现。如果检查器显示0项违规，答案即为0项违规——不得补充任何内容。