How to properly import tsv to athena - amazon-web-services

Question

I am following this example:
LazySimpleSerDe for CSV, TSV, and Custom-Delimited Files - TSV example
Summary of the code:
CREATE EXTERNAL TABLE flight_delays_tsv (
yr INT,
quarter INT,
month INT,
...
div5longestgtime INT,
div5wheelsoff STRING,
div5tailnum STRING
)
PARTITIONED BY (year STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
ESCAPED BY '\\'
LINES TERMINATED BY '\n'
LOCATION 's3://athena-examples-myregion/flight/tsv/';
My questions are:
My tsv does not have column names
(my tsv)
Is it ok if I just list the columns as c1,c2… and all of them as string ?
I do not understand this:
PARTITIONED BY (year STRING)
in the example, the column ‘year’ is not listed in any of the columns…

Answer 1

Column names
The column names are defined by the CREATE EXTERNAL TABLE command. I recommend you name them something useful so that it is easier to write queries. The column names do not need to match any names in the actual file. (It does not interpret header rows.)
Partitioning
From Partitioning Data - Amazon Athena:
To create a table with partitions, you must define it during the CREATE TABLE statement. Use PARTITIONED BY to define the keys by which to partition data.
The field used to partition the data is NOT stored in the files themselves, which is why they are not in the table definition. Rather, the column value is stored in the name of the directory.
This might seem strange (storing values in a directory name!) but actually makes sense because it avoids situations where an incorrect value is stored in a folder. For example, if there is a year=2018 folder, what happens if a file contains a column where the year is 2017? This is avoided by storing the year in the directory name, such that any files within that directory are assigned the value denoted in the directory name.
Queries can still use WHERE year = 2018 even though it isn't listed as an actual column.
See also: LanguageManual DDL - Apache Hive - Apache Software Foundation
The other neat thing is that data can be updated by simply moving a file to a different directory. In this example, it would change the year value as a result of being in a different directory.
Yes, it's strange, but the trick is to stop thinking of it like a normal database and appreciate the freedom that it offers. For example, appending new data is as simple as dropping a file into a directory. No loading required!