在SAP HANA Express Edition里进行文本分析
这个练习会使用SAP HANA Express Edition的文本语义分析引擎对JSON格式的documents进行语义分析。
首先创建一个column table,对其index开启fuzzy text search(模糊搜索)功能。
上述描述的操作可以用下面的SQL语句来完成:
create column table food_analysis
(
name nvarchar(64),
description text FAST PREPROCESS ON FUZZY SEARCH INDEX ON
);
其中description字段开启了模糊搜索功能。
将存储于名为doc_store的document store collection里的json key-value键值对拷贝到刚刚创建的数据库表里:
insert into food_analysis
with doc_store as (select "name", "description" from food_collection)
select doc_store."name" as name, doc_store."description" as description
from doc_store;
执行上述的sql语句,确保数据全部拷贝到数据库表food_analysis中:
使用下列的sql语句对description字段进行模糊搜索:
select name, score() as similarity, TO_VARCHAR(description)
from food_analysis
where contains(description, 'nuts', fuzzy(0.5,'textsearch=compare'))
order by similarity desc
执行结果:
HANA Express Edition里的linguistic 文本分析步骤也比较简单。
首先还是创建一个数据库表:
create column table food_sentiment
(
name nvarchar(64) primary key,
description nvarchar(2048)
);
将document store里的json数据拷贝到数据库表里:
insert into food_sentiment
with doc_store as (select "name", "description" from food_collection)
select doc_store."name" as name, doc_store."description" as description
from doc_store;
针对description字段创建一个新的index:
CREATE FULLTEXT INDEX FOOD_SENTIMENT_INDEX ON "FOOD_SENTIMENT" ("DESCRIPTION")
CONFIGURATION 'GRAMMATICAL_ROLE_ANALYSIS'
LANGUAGE DETECTION ('EN')
SEARCH ONLY OFF
FAST PREPROCESS OFF
TEXT MINING OFF
TOKEN SEPARATORS ''
TEXT ANALYSIS ON;
上述SQL语句会自动创建一个名为$TA_FOOD_SENTIMENT_INDEX的文本分析表:
该表里的内容:
由此可以发现,之前我们导入到数据库表里的英文句子,被HANA text engine拆解成单词,并且每个单词的词性也自动被HANA解析出来了。
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