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course-definition.yml

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```
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database page size: 1024
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```
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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In this stage, you'll implement one of SQLite's
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[dot-commands](https://www.sqlite.org/cli.html#special_commands_to_sqlite3_dot_commands_): `.dbinfo`. This command
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database page size: 4096
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number of tables: 2
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```
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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In this stage, you'll extend support for the .dbinfo command added in the previous stage. Specifically, you'll
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implement functionality to print the number of tables. You'll do this by parsing a file that uses the
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formats its output so that every value has a fixed-width. Your program doesn't need to mimic this behaviour. Using
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just one space as a separator should work. Both `apples oranges` and <code>apples &nbsp; oranges</code> will pass
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our tests.
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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In this stage, you'll implement another dot-command:
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[`.tables`](https://www.sqlite.org/cli.html#special_commands_to_sqlite3_dot_commands_). Instead of just printing
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Remember: You don't need to implement a full-blown SQL parser just yet. We'll get to that in the
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next stages. For now you can just split the input by " " and pick the last item to get the table name.
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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Now that you've gotten your feet wet with the [SQLite database file format](https://www.sqlite.org/fileformat.html),
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it's time to move on to actual SQL!
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[`peg`](https://crates.io/crates/peg) and [`regex`](https://crates.io/crates/regex) crates are available in
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`Cargo.toml` if you'd like to use them.
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{{/lang_is_rust}}
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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In this stage, your sqlite3 implementation will need to execute a SQL statement of this form:
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`SELECT <column> FROM <table>`.
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```
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Just like in the previous stage, the order of rows doesn't matter.
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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This stage is similar to the previous one, just that you'll read data from multiple columns instead of just one.
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In this stage, your sqlite3 implementation will need to execute a SQL statement of this form: `SELECT <column1>,<column2> FROM <table>`.
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For now you can assume that the contents of the table are small enough to fit inside the root page. We'll deal
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with tables that span multiple pages in the next stage.
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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In this stage, you'll filter records based on a `WHERE` clause. You'll assume that the query can't be served by
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an index, so you'll visit all records in a table and then filter out the matching ones.
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[Busying Oneself With B-Trees](https://medium.com/basecs/busying-oneself-with-b-trees-78bbf10522e7) is a good place to
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start. For specifics on how SQLite stores B-trees on disk, read the
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[B-tree Pages](https://www.sqlite.org/fileformat.html#b_tree_pages) documentation section.
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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In this stage, you'll filter records based on a `WHERE` clause. You'll assume that the query can't be served by
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an index, so you'll visit all records in a table and then filter out the matching ones.
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You can assume that all queries run by the tester will include `country` in the `WHERE` clause,
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so they can be served by the index. The tester will run multiple randomized queries and expect all of them
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to return results in under 3 seconds.
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{{#reader_is_bot}}
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Do not use a SQLite client library, implement the SQLite logic yourself.
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{{/reader_is_bot}}
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marketing_md: |-
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This stage is similar to the previous one, but focuses on enhancing query performance using an index. In this
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stage, your program will need to read through millions of rows in under 5 seconds.

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