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EAN-139780811703147   EAN-13 barcode 9780811703147
Product NameRolling Thunder against the Rising Sun: The Combat History of U.S. Army Tank Battalions in the Pacific in WWII
LanguageEnglish
CategoryBook / Magazine / Publication
Short DescriptionHardcover
Amazon.comA Buy on Amazon ~ 0811703142
Price New15.00 US Dollars    (curriencies)
Price Used6.50 US Dollars    (curriencies)
Width1.37 inches    (convert)
Height8.91 inches    (convert)
Length6.39 inches    (convert)
Weight25.6 ounces    (convert)
AuthorGene Eric Salecker
Page Count464
BindingHardcover
Published04/16/2008
FeaturesUsed Book in Good Condition
Long DescriptionAlthough the history of armor in World War II has captured the attention of countless authors, no one has yet chronicled the extensive use of tanks in the Pacific--until now. In comprehensive detail Gene Eric Salecker describes the exploits of American tanks on the jungle islands where troops engaged in savage combat and encountered unforgiving weather and terrain. Stationed in the Philippines when the Japanese attacked the islands in 1941, the U.S. Army's independent tank battalions fought from the very start of the war. From New Guinea and the Solomons to the Ryukyus, American armor proved instrumental in winning World War II in the Pacific.First work dedicated solely to the use of Army tanks in the Pacific Theater Covers armor battles in the Philippines, Makin, the Solomons, Rabaul, New Guinea, Saipan, Guam, and Okinawa
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Modified04-28-2017 6:09:31pm
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SHA256e2dd4860c6017cb944b45d9fe5f7e4a09c52e92c5f822b3990fc3b281d29d217
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An article of interest

Making use of the tools we offer

Importing our data into your MySQL database

Here we will demonstrate the most basic example of importing the CSV data files that we produce on this site into your MySQL database.

For information about various databases you can use and how to import CSV files into them, please view the overview article "Importing CSV data into your database".

For this example, we are going to import the product data CSV file out of the sample_ean_data.zip but this same process will work on the full data download file. We will also be executing the commands in the MySQL Workbench but you can also use the command line tool with the same commands if you like.

First, start by creating a blank table. Use the table layout described in the read_me file for the most up-to-date table layout. It is suggested that you not use any indexing at this point. You can add indexes later. It is most likely that you will have your own tables where you want to store your data so importing the CSV files can be done into temporary tables and then later copied over to your tables. Leaving off the indexes and constraints on these import tables reduces the risk of import errors. Here is an example:

create table ean_product
(
    EAN13             varchar(13),
    UPCA              varchar(12),
    UPCE              varchar(8),
    SKU               varchar(200),
    PriceNew          numeric(15,2),
    PriceUsed         numeric(15,2),
    PriceDate         date,
    company           varchar(13),
    product           varchar(100),
    description       varchar(100),
    category          int,
    url               varchar(500),
    created           datetime,
    modified          datetime
);

Next we perform the import using the LOAD DATA INFILE command. The path to the file depends on where you saved the data and which operating system you are on. For Windows users you might find your file on the C: drive and Linux users may find your date in your home (~) folder. This example shows a Linux import. Only the path would be different between the operating systems.

LOAD DATA LOCAL
    INFILE '~/sample_ean_data/sample_ean_product.csv' 
    INTO TABLE ean_product
    FIELDS TERMINATED BY ',' ENCLOSED BY '"' ESCAPED BY '\\'
    LINES TERMINATED BY '\r\n'
    IGNORE 1 LINES;

Finally, lets look at the data that we just imported.

SELECT * FROM EAN_PRODUCT;

You may have seen some warnings after the import command. If you are concerned about these warnings, examine the data. It could be that some data has grown beyond the size specified in the read_me file. If you are worried, make the fields larger and try the process again after deleting all of the data out of the table.