170 lines
4.0 KiB
Python
170 lines
4.0 KiB
Python
import sqlparse
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import sqlglot
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from sqlglot.expressions import ColumnDef
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def extract_create_table(sql_script):
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# 解析 SQL 脚本
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parsed = sqlparse.parse(sql_script)
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create_table_statements = []
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for statement in parsed:
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# 关闭格式化选项保持原样
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stripped = sqlparse.format(
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statement.value,
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strip_comments=True,
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reindent=False,
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keyword_case="lower"
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)
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# 跳过空语句
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if not stripped.strip():
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continue
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# 可修改条件来匹配其他语句类型
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if stripped.upper().strip().startswith(("CREATE TABLE")):
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create_table_statements.append(stripped)
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return "\n".join(create_table_statements)
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# 原始 SQL 脚本
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sql_script = """
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BEGIN;
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/*
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DROP TABLE ods.track_log_002;
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*/
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-- Type: TABLE ; Name: track_log_002; Owner: sdk_statis_developer
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CREATE TABLE ods.track_log_002 (
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appid bigint NOT NULL,
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app_ver text,
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sdk_ver text,
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channel text,
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country text,
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province text,
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city text,
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isp text,
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ip text,
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device_width integer,
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device_height integer,
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device_id text NOT NULL,
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device_lang text,
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device_model text,
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device_brand text,
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device_os text,
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device_type text,
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event_name text NOT NULL,
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event_type text,
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event_time bigint NOT NULL,
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net_type text,
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user_id text,
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order_id text,
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amount bigint,
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platform text,
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status integer,
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servid text,
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server_name text,
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role_id text,
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role_name text,
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role_level text,
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job_id text,
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job_name text,
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var1 text,
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var2 text,
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var3 text,
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var4 text,
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var5 text,
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var6 text,
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var7 text,
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var8 text,
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var9 text,
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var10 text,
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var11 text,
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var12 text,
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var13 text,
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var14 text,
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var15 text,
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var16 text,
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var17 text,
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var18 text,
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var19 text,
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var20 text,
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var21 text,
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var22 text,
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var23 text,
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var24 text,
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var25 text,
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var26 text,
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var27 text,
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var28 text,
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var29 text,
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var30 text,
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ds text NOT NULL,
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prodid text,
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prod_name text,
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sub_servid text,
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sub_server_name text
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)
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PARTITION BY LIST (ds)with (
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orientation = 'column',
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storage_format = 'orc',
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auto_partitioning_enable = 'true',
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auto_partitioning_num_hot = '90',
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auto_partitioning_num_precreate = '2',
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auto_partitioning_num_retention = '191',
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auto_partitioning_schd_start_time = '1970-01-01 00:00:00',
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auto_partitioning_time_format = '',
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auto_partitioning_time_unit = 'day',
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auto_partitioning_time_zone = 'PRC',
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bitmap_columns = 'appid,event_name,ds,role_id,device_id,servid,user_id,country,channel,province,status,city,device_width,var4,var3,var2,var1,amount,device_height,var12,var13,var14,var15,var10,var11,var9,var8,var7,var6,var5,event_time',
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clustering_key = 'appid:asc',
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dictionary_encoding_columns = '',
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segment_key = 'event_time',
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table_group = 'sdk_statis_tg_s80',
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table_storage_mode = 'hot',
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time_to_live_in_seconds = '16416000'
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);
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COMMENT ON TABLE ods.track_log_002 IS NULL;
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ALTER TABLE ods.track_log_002 OWNER TO sdk_statis_developer;
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END;
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"""
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# 执行解析
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result = extract_create_table(sql_script)
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re_create_table_sql = sqlglot.transpile(result, read="postgres", write="hive")[0]
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parsed = sqlglot.parse_one(re_create_table_sql, read='hive')
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# 获取表名
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table_name = parsed.this.this
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columns = []
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# 遍历所有可能包含列定义的子表达式
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for expression in parsed.walk():
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if isinstance(expression[0], ColumnDef):
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# 获取列名
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column_name = expression[0].this.this
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# 获取数据类型
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column_type = expression[0].args['kind'].this.name.upper()
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# 如果是TEXT类型，则转换为STRING
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if column_type == 'TEXT':
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column_type = 'STRING'
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columns.append({'name': column_name, 'type': column_type})
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# 输出表名和字段信息
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print(f"表名称: {table_name}")
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# 输出结果
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for column in columns:
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print(f"字段名称: {column['name']}, 字段类型: {column['type']}")
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