Hive 数据类型 文件格式

Primitive Data Types --Hive 支持数据类型

Hive supports several sizes of integer and floating-point types, a Boolean type, and
character strings of arbitrary length. Hive v0.8.0 added types for timestamps and binary
fields.

What if you run a query that wants to interpret a string column as a number? You can
explicitly cast one type to another as in the following example, where  s  is  a  string
column that holds a value representing an integer:
... cast(s AS INT) ...;
(To be clear, the AS INT are keywords, so lowercase would be fine.)

Collection Data Types -- 特殊数据类型

Hive supports columns that are structs, maps, and arrays.

Here is a table declaration that demonstrates how to use these types, an employees table
in a fictitious Human Resources application:

CREATE TABLE employees (  name         STRING,  salary       FLOAT,  subordinates ARRAY<STRING>,  deductions   MAP<STRING, FLOAT>,  address      STRUCT<street:STRING, city:STRING, state:STRING, zip:INT>);

Text File Encoding of Data Values -- 文本文件编码

Let’s begin our exploration of file formats by looking at the simplest example, text files.
You are no doubt familiar with text files delimited with commas or tabs, the so-called
comma-separated values (CSVs) or tab-separated values (TSVs), respectively. Hive can
use those  formats  if you want and we’ll show you how shortly. However, there  is a
drawback to both formats; you have to be careful about commas or tabs embedded in
text and not intended as field or column delimiters. For this reason, Hive uses various
control characters by default, which are less likely to appear in value strings. Hive uses
the term field when overriding the default delimiter, as we’ll see shortly.

John Doe^A100000.0^AMary Smith^BTodd Jones^AFederal Taxes^C.2^BState
Taxes^C.05^BInsurance^C.1^A1 Michigan Ave.^BChicago^BIL^B60600
Mary Smith^A80000.0^ABill King^AFederal Taxes^C.2^BState Taxes^C.
05^BInsurance^C.1^A100 Ontario St.^BChicago^BIL^B60601
Todd Jones^A70000.0^AFederal Taxes^C.15^BState Taxes^C.03^BInsurance^C.
1^A200 Chicago Ave.^BOak Park^BIL^B60700
Bill King^A60000.0^AFederal Taxes^C.15^BState Taxes^C.03^BInsurance^C.
1^A300 Obscure Dr.^BObscuria^BIL^B60100
This is a little hard to read, but you would normally let Hive do that for you, of course.

Let’s walk through the first line to understand the structure. First, here is what it would
look like in JavaScript Object Notation (JSON), where we have also inserted the names
from the table schema:
{
  "name":  "John Doe",
  "salary": 100000.0,
  "subordinates": ["Mary Smith", "Todd Jones"],
  "deductions": {
    "Federal Taxes": .2,
    "State Taxes":   .05,
    "Insurance":     .1
  },
  "address": {
    "street": "1 Michigan Ave.",
    "city":   "Chicago",
    "state":  "IL",
    "zip":    60600
  }
}
You’ll note that maps and structs are effectively the same thing in JSON.
Now, here’s how the first line of the text file breaks down:
• John Doe is the name.
• 100000.0 is the salary.
• Mary Smith^BTodd Jones are the subordinates “Mary Smith” and “Todd Jones.”
• Federal Taxes^C.2^BState Taxes^C.05^BInsurance^C.1 are the deductions, where
20% is deducted for “Federal Taxes,” 5% is deducted for “State Taxes,” and 10%
is deducted for “Insurance.”
• 1 Michigan Ave.^BChicago^BIL^B60600 is the address, “1 Michigan Ave., Chicago,
60600.”
You can override these default delimiters. This might be necessary if another applica-
tion writes the data using a different convention. Here  is the same table declaration
again, this time with all the format defaults explicitly specified:

CREATE TABLE employees (  name         STRING,  salary       FLOAT,  subordinates ARRAY<STRING>,  deductions   MAP<STRING, FLOAT>,  address      STRUCT<street:STRING, city:STRING, state:STRING, zip:INT>)ROW FORMAT DELIMITEDFIELDS TERMINATED BY '\001'COLLECTION ITEMS TERMINATED BY '\002'MAP KEYS TERMINATED BY '\003'LINES TERMINATED BY '\n'STORED AS TEXTFILE;