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[#]: collector: (lujun9972)
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[#]: translator: ( )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (3 ways to use PostgreSQL commands)
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[#]: via: (https://opensource.com/article/20/2/postgresql-commands)
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[#]: author: (Greg Pittman https://opensource.com/users/greg-p)
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3 ways to use PostgreSQL commands
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======
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Whether you need something simple, like a shopping list, or complex,
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like a color swatch generator, PostgreSQL commands make it easy.
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![Team checklist and to dos][1]
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In _[Getting started with PostgreSQL][2]_, I explained how to install, set up, and begin using the open source database software. But there's a lot more you can do with commands in [PostgreSQL][3].
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For example, I use Postgres to keep track of my grocery shopping list. I do most of the grocery shopping in our home, and the bulk of it happens once a week. I go to several places to buy the things on my list because each store offers a particular selection or quality or maybe a better price. Initially, I made an HTML form page to manage my shopping list, but it couldn't save my entries. So, I had to wait to make my list all at once, and by then I usually forgot some items we need or I want.
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Instead, with PostgreSQL, I can enter bits when I think of them as the week goes on and print out the whole thing right before I go shopping. Here's how you can do that, too.
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### Create a simple shopping list
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First, enter the database with the **psql **command, then create a table for your list with:
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```
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`Create table groc (item varchar(20), comment varchar(10));`
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```
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Type commands like the following to add items to your list:
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```
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insert into groc values ('milk', 'K');
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insert into groc values ('bananas', 'KW');
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```
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There are two pieces of information (separated by a comma) inside the parentheses: the item you want to buy and letters indicating where you want to buy it and whether it's something you usually buy every week (W).
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Since **psql** has a history, you can press the Up arrow and edit the data between the parentheses instead of having to type the whole line for each item.
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After entering a handful of items, check what you've entered with:
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```
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Select * from groc order by comment;
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item | comment
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\----------------+---------
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ground coffee | H
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butter | K
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chips | K
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steak | K
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milk | K
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bananas | KW
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raisin bran | KW
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raclette | L
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goat cheese | L
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onion | P
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oranges | P
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potatoes | P
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spinach | PW
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broccoli | PW
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asparagus | PW
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cucumber | PW
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sugarsnap peas | PW
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salmon | S
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(18 rows)
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```
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This command orders the results by the _comment_ column so that the items are grouped by where you buy them to make it easier to shop.
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By using a W to indicate your weekly purchases, you can keep your weekly items on the list when you clear out the table to prepare for the next week's list. To so that, enter:
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```
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`delete from groc where comment not like '%W';`
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```
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Notice that in PostgreSQL, **%** is the wildcard character (instead of an asterisk). So, to save typing, you might type:
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```
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`delete from groc where item like 'goat%';`
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```
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You can't use **item = 'goat%'**; it won't work.
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When you're ready to shop, output your list to print it or send it to your phone with:
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```
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\o groclist.txt
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select * from groc order by comment;
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\o
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```
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The last command, **\o**, with nothing afterward, resets the output to the command line. Otherwise, all output will continue to go to the groc file you created.
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### Analyze complex tables
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This item-by-item entry may be okay for short tables, but what about really big ones? A couple of years ago, I was helping the team at [FreieFarbe.de][4] to create a swatchbook of the free colors (freieFarbe means "free colors" in German) from its HLC color palette, where virtually any imaginable print color can be specified by its hue, luminosity (brightness), and chroma (saturation). The result was the [HLC Color Atlas][5], and here's how we did it.
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The team sent me files with color specifications so I could write Python scripts that would work with Scribus to generate the swatchbooks of color patches easily. One example started like:
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```
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HLC, C, M, Y, K
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H010_L15_C010, 0.5, 49.1, 0.1, 84.5
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H010_L15_C020, 0.0, 79.7, 15.1, 78.9
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H010_L25_C010, 6.1, 38.3, 0.0, 72.5
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H010_L25_C020, 0.0, 61.8, 10.6, 67.9
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H010_L25_C030, 0.0, 79.5, 18.5, 62.7
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H010_L25_C040, 0.4, 94.2, 17.3, 56.5
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H010_L25_C050, 0.0, 100.0, 15.1, 50.6
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H010_L35_C010, 6.1, 32.1, 0.0, 61.8
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H010_L35_C020, 0.0, 51.7, 8.4, 57.5
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H010_L35_C030, 0.0, 68.5, 17.1, 52.5
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H010_L35_C040, 0.0, 81.2, 22.0, 46.2
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H010_L35_C050, 0.0, 91.9, 20.4, 39.3
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H010_L35_C060, 0.1, 100.0, 17.3, 31.5
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H010_L45_C010, 4.3, 27.4, 0.1, 51.3
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```
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This is slightly modified from the original, which separated the data with tabs. I transformed it into a CSV (comma-separated value) file, which I prefer to use with Python. (CSV files are also very useful because they can be imported easily into a spreadsheet program.)
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In each line, the first item is the color name, and it's followed by its C, M, Y, and K color values. The file consisted of 1,793 colors, and I wanted a way to analyze the information to get a sense of the range of values. This is where PostgreSQL comes into play. I did not want to enter all of this data manually—I don't think I could without errors (and headaches). Fortunately, PostgreSQL has a command for this.
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My first step was to create the database with:
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```
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`Create table hlc_cmyk (color varchar(40), c decimal, m decimal, y decimal, k decimal);`
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```
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Then I brought in the data with:
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```
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`\copy hlc_cmyk from '/home/gregp/HLC_Atlas_CMYK_SampleData.csv' with (header, format CSV);`
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```
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The backslash at the beginning is there because using the plain **copy** command is restricted to root and the Postgres superuser. In the parentheses, **header** means the first line contains headings and should be ignored, and **CSV** means the file format is CSV. Note that parentheses are not required around the color name in this method.
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If the operation is successful, I see a message that says **COPY NNNN**, where the N's refer to the number of rows inserted into the table.
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Finally, I can query the table with:
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```
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select * from hlc_cmyk;
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color | c | m | y | k
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\---------------+-------+-------+-------+------
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H010_L15_C010 | 0.5 | 49.1 | 0.1 | 84.5
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H010_L15_C020 | 0.0 | 79.7 | 15.1 | 78.9
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H010_L25_C010 | 6.1 | 38.3 | 0.0 | 72.5
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H010_L25_C020 | 0.0 | 61.8 | 10.6 | 67.9
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H010_L25_C030 | 0.0 | 79.5 | 18.5 | 62.7
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H010_L25_C040 | 0.4 | 94.2 | 17.3 | 56.5
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H010_L25_C050 | 0.0 | 100.0 | 15.1 | 50.6
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H010_L35_C010 | 6.1 | 32.1 | 0.0 | 61.8
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H010_L35_C020 | 0.0 | 51.7 | 8.4 | 57.5
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H010_L35_C030 | 0.0 | 68.5 | 17.1 | 52.5
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```
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It goes on like this for all 1,793 rows of data. In retrospect, I can't say that this query was absolutely necessary for the HLC and Scribus task, but it allayed some of my anxieties about the project.
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To generate the HLC Color Atlas, I automated creating the color charts with Scribus for the 13,000+ colors in those pages of color swatches.
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I could have used the **copy** command to output my data:
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```
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`\copy hlc_cmyk to '/home/gregp/hlc_cmyk_backup.csv' with (header, format CSV);`
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```
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I also could restrict the output according to certain values with a **where** clause.
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For example, the following command will only send the table values for the hues that begin with H10.
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```
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`\copy hlc_cmyk to '/home/gregp/hlc_cmyk_backup.csv' with (header, format CSV) where color like 'H10%';`
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```
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### Back up or transfer a database or table
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The final command I will mention here is **pg_dump**, which is used to back up a PostgreSQL database and runs outside of the **psql** console. For example:
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```
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pg_dump gregp -t hlc_cmyk > hlc.out
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pg_dump gregp > dball.out
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```
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The first line exports the **hlc_cmyk** table along with its structure. The second line dumps all the tables inside the **gregp** database. This is very useful for backing up or transferring a database or tables.
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To transfer a database or table to another computer, first, create a database on the other computer (see my "[getting started][2]" article for details), then do the reverse process:
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```
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`psql -d gregp -f dball.out`
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```
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This creates all the tables and enters the data in one step.
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### Conclusion
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In this article, we have seen how to use the **WHERE** parameter to restrict operations, along with the use of the PostgreSQL wildcard character **%**. We've also seen how to load a large amount of data into a table, then output some or all of the table data to a file, or even your entire database with all its individual tables.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/20/2/postgresql-commands
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作者：[Greg Pittman][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/greg-p
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/todo_checklist_team_metrics_report.png?itok=oB5uQbzf (Team checklist and to dos)
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[2]: https://opensource.com/article/19/11/getting-started-postgresql
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[3]: https://www.postgresql.org/
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[4]: http://freiefarbe.de
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[5]: https://www.freiefarbe.de/en/thema-farbe/hlc-colour-atlas/
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228
translated/tech/20200206 3 ways to use PostgreSQL commands.md
Normal file
228
translated/tech/20200206 3 ways to use PostgreSQL commands.md
Normal file
@ -0,0 +1,228 @@
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[#]: collector: (lujun9972)
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[#]: translator: ( )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (3 ways to use PostgreSQL commands)
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[#]: via: (https://opensource.com/article/20/2/postgresql-commands)
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[#]: author: (Greg Pittman https://opensource.com/users/greg-p)
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3种使用 PostgreSQL 命令的方式
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======
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无论你需要的东西简单（如一个购物清单）亦或复杂（如色卡生成器）
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PostgreSQL 命令都能使它变得容易起来。
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![Team checklist and to dos][1]
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在 _[PostgreSQL 入门][2]_ 一文中, 我解释了如何安装，设置和开始使用开源数据库软件。然而，使用 [PostgreSQL][3] 中的命令可以做更多事情。
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例如，我使用 Postgres 来跟踪我杂货店的购物清单。我杂货店里的大多数购物是在家里进行的，其中每周进行一次大批量的采购。我去几个不同的地方购买清单上的东西，因为每家商店都提供特定的选择或质量，亦或更好的价格。最初，我制作了一个HTML表单页面来管理我的购物清单，但这样无法保存我的输入内容。因此，在想到要购买的物品时我必须要马上列出清单，因为到采购时我常常会忘记一些我需要或想要的东西。
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相反，使用 PostgreSQL，当我想到需要的物品时，我可以随时输入，并在购物前打印出来。你也可以这样做。
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### 创建一个简单的购物清单
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首先，数据库中输入**psql ** 命令，然后用下面的命令创建一个表：
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```
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`Create table groc (item varchar(20), comment varchar(10));`
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```
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输入如下命令在清单中加入商品：
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```
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insert into groc values ('milk', 'K');
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insert into groc values ('bananas', 'KW');
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```
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括号中有两个信息（逗号隔开）：前面是你需要买的东西，后面字母代表你要购买的地点以及哪些东西是你每周通常都要买的（W）。
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因为 **psql** 有历史记录，你可以按向上键在括号内编辑信息，而无需输入商品的整行信息。
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在输入一小部分商品后，输入下面命令来检查前面的输入内容。
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```
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Select * from groc order by comment;
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item | comment
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\----------------+---------
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ground coffee | H
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butter | K
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chips | K
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steak | K
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milk | K
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bananas | KW
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raisin bran | KW
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raclette | L
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goat cheese | L
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onion | P
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oranges | P
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potatoes | P
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spinach | PW
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broccoli | PW
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asparagus | PW
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cucumber | PW
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sugarsnap peas | PW
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salmon | S
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(18 rows)
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```
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此命令按_comment_ 列对结果进行排序，以便按购买地点对商品进行分组，从而是你的购物更加方便。
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使用W来指明你每周要买的东西，当你要清除表单为下周的列表做准备时，你可以将每周的商品保留在购物清单上。输入：
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```
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`delete from groc where comment not like '%W';`
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```
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注意，在 PostgreSQL 中 **%** 表示通配符（而非星号）。所以，要保存输入内容，需要输入：
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```
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`delete from groc where item like 'goat%';`
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```
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不能使用**item = 'goat%'**，这样没用。
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在购物时，用以下命令输出清单并打印出来发送到你的手机：
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```
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\o groclist.txt
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select * from groc order by comment;
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\o
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```
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最后一个命令**\o*，重置输出到命令行。否则，所有的输出会继续输出到你创建的杂货店购物文件中。
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### 分析复杂的表
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This item-by-item entry may be okay for short tables, but what about really big ones? A couple of years ago, I was helping the team at [FreieFarbe.de][4] to create a swatchbook of the free colors (freieFarbe means "free colors" in German) from its HLC color palette, where virtually any imaginable print color can be specified by its hue, luminosity (brightness), and chroma (saturation). The result was the [HLC Color Atlas][5], and here's how we did it.
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逐个输入对于数据量小的表来说没有问题，但是对于数据量大的表呢？几年前，我帮团队从 HLC 调色板中创建一个自由色的色样册。事实上，任何能想象到的打印色都可按色调、亮度、浓度（饱和度）来规定。最终结果是[HLC Color Atlas][5]，下面是我们如何实现的。
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该团队向我发送了具有颜色规范的文件，因此我可以编写可与 Scribus 配合使用的 Python 脚本，以轻松生成色样册。一个例子像这样开始：
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```
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HLC, C, M, Y, K
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H010_L15_C010, 0.5, 49.1, 0.1, 84.5
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H010_L15_C020, 0.0, 79.7, 15.1, 78.9
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H010_L25_C010, 6.1, 38.3, 0.0, 72.5
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H010_L25_C020, 0.0, 61.8, 10.6, 67.9
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H010_L25_C030, 0.0, 79.5, 18.5, 62.7
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H010_L25_C040, 0.4, 94.2, 17.3, 56.5
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H010_L25_C050, 0.0, 100.0, 15.1, 50.6
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H010_L35_C010, 6.1, 32.1, 0.0, 61.8
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H010_L35_C020, 0.0, 51.7, 8.4, 57.5
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H010_L35_C030, 0.0, 68.5, 17.1, 52.5
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H010_L35_C040, 0.0, 81.2, 22.0, 46.2
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H010_L35_C050, 0.0, 91.9, 20.4, 39.3
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H010_L35_C060, 0.1, 100.0, 17.3, 31.5
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H010_L45_C010, 4.3, 27.4, 0.1, 51.3
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```
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这与原文件相比，稍有修改，将数据用制表符分隔。我将其转换成 CSV 格式（逗号分割值），我更喜欢其与 Python 一起使用（CSV 文也很有用因为它可轻松导入到电子表格程序中）。
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在每一行中，第一项是颜色名称，其后是其 C，M，Y 和 K 颜色值。 该文件包含1,793种颜色，我想要一种分析信息的方法，以了解这些值的范围。 这就是 PostgreSQL 发挥作用的地方。 我不想手动输入所有数据-我认为输入过程中我不可能不出错。 幸运的是，PostgreSQL 为此提供了一个命令。
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首先用以下命令创建数据库：
|
||||
|
||||
```
|
||||
`Create table hlc_cmyk (color varchar(40), c decimal, m decimal, y decimal, k decimal);`
|
||||
```
|
||||
|
||||
然后通过以下命令引入数据：
|
||||
|
||||
|
||||
```
|
||||
`\copy hlc_cmyk from '/home/gregp/HLC_Atlas_CMYK_SampleData.csv' with (header, format CSV);`
|
||||
```
|
||||
|
||||
|
||||
开头有反斜杠，是因为使用纯**copy** 命令仅限于 root 用户和 Postgres 的超级用户。 在括号中，**header** 表示第一行包含标题，应忽略，**CSV** 表示文件格式为 CSV。 请注意，在此方法中，颜色名称周围不需要括号。
|
||||
|
||||
如果操作成功，会看到 **COPY NNNN**，其中 N 表示插入到表中的行号。
|
||||
|
||||
最后，可以用下列命令查询：
|
||||
|
||||
```
|
||||
select * from hlc_cmyk;
|
||||
|
||||
color | c | m | y | k
|
||||
\---------------+-------+-------+-------+------
|
||||
H010_L15_C010 | 0.5 | 49.1 | 0.1 | 84.5
|
||||
H010_L15_C020 | 0.0 | 79.7 | 15.1 | 78.9
|
||||
H010_L25_C010 | 6.1 | 38.3 | 0.0 | 72.5
|
||||
H010_L25_C020 | 0.0 | 61.8 | 10.6 | 67.9
|
||||
H010_L25_C030 | 0.0 | 79.5 | 18.5 | 62.7
|
||||
H010_L25_C040 | 0.4 | 94.2 | 17.3 | 56.5
|
||||
H010_L25_C050 | 0.0 | 100.0 | 15.1 | 50.6
|
||||
H010_L35_C010 | 6.1 | 32.1 | 0.0 | 61.8
|
||||
H010_L35_C020 | 0.0 | 51.7 | 8.4 | 57.5
|
||||
H010_L35_C030 | 0.0 | 68.5 | 17.1 | 52.5
|
||||
```
|
||||
|
||||
|
||||
所有1,793行数据都是这样的。 回想起来，我不能说此查询对于HLC和Scribus任务是绝对必要的，但是它减轻了我对该项目的一些担忧。
|
||||
|
||||
为了生成 HLC 色谱，我使用 Scribus 为色板页面中的13,000多种颜色自动创建了颜色图表。
|
||||
|
||||
我可以使用 **copy** 命令输出数据：
|
||||
|
||||
```
|
||||
`\copy hlc_cmyk to '/home/gregp/hlc_cmyk_backup.csv' with (header, format CSV);`
|
||||
```
|
||||
|
||||
|
||||
我还可以使用 ** where ** 子句根据某些值来限制输出。
|
||||
|
||||
例如，以下命令将仅发送以 H10 开头的色调值。
|
||||
|
||||
|
||||
```
|
||||
`\copy hlc_cmyk to '/home/gregp/hlc_cmyk_backup.csv' with (header, format CSV) where color like 'H10%';`
|
||||
```
|
||||
|
||||
### 备份或传输数据库或表
|
||||
|
||||
我在此要提到的最后一个命令是**pg_dump**，它用于备份 PostgreSQL 数据库，并在 **psql** 控制台之外运行。 例如：
|
||||
|
||||
```
|
||||
pg_dump gregp -t hlc_cmyk > hlc.out
|
||||
pg_dump gregp > dball.out
|
||||
```
|
||||
|
||||
第一行是导出 **hlc_cmyk** 表及其结构。第二行将转储 **gregp** 数据库中的所有表。 这对于备份或传输数据库或表非常有用。
|
||||
|
||||
|
||||
要将数据库或表转到另一台电脑（ 查看"[ PostgreSQL 入门][2]" 那篇文章获取详细信息），首先在要转入的电脑上创建一个数据库，然后执行相反的操作。
|
||||
|
||||
```
|
||||
`psql -d gregp -f dball.out`
|
||||
```
|
||||
|
||||
一步创建所有表并输入数据。
|
||||
|
||||
### 总结
|
||||
|
||||
在本文中，我们了解了如何使用 **WHERE** 参数限制操作，以及如何使用 PostgreSQL 通配符 **％**。 我们还了解了如何将大批量数据加载到表中，然后将部分或全部表数据输出到文件，甚至是将整个数据库及其所有单个表输出。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/20/2/postgresql-commands
|
||||
|
||||
作者：[Greg Pittman][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[Morisun029](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/greg-p
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/todo_checklist_team_metrics_report.png?itok=oB5uQbzf (Team checklist and to dos)
|
||||
[2]: https://opensource.com/article/19/11/getting-started-postgresql
|
||||
[3]: https://www.postgresql.org/
|
||||
[4]: http://freiefarbe.de
|
||||
[5]: https://www.freiefarbe.de/en/thema-farbe/hlc-colour-atlas/
|
||||
Loading…
Reference in New Issue
Block a user