A lot of the charts and tables made inPeriscopeare time series, and the queries behind them are often easier when you can join and aggregate against a list of dates. Not having a complete list of dates causes gaps in the results, changing them in a misleading way:
Postgres has a great function for generating a list of dates (seeUse generate_series to get continuous results), and making a list of the last 60 days withgenerate_series
is easy:
<code>select now()::date - generate_series(0, 59)</code>
Accomplishing the same thing in Redshift and MySQL requires a little more work.
Date Series from a Numbers Table
The simplest alternative togenerate_series
is to create a table containing a continuous list of numbers, starting at 0, and select from that table. (If you have a table with a sequentialid
column and never delete rows from it, you can just select theid
column from that table instead of creating a new numbers table).
<code>select n from numbers;</code>
Returns this list of rows: 0, 1, 2, 3...
Now that you have a numbers table, convert each number into a date:
Redshift:
<code>select (getdate()::date - n)::date from numbers</code>
MySQL:
<code>select date_sub(date(now()), interval n day) from numbers</code>
A numbers table is more convenient than a dates table since it never needs to be refreshed with new dates.
Redshift: Date Series using Window Functions
If you don't have the option to create a numbers table, you can build one on the fly using a window function. All you need is a table that has at least as many rows as the number of dates desired. Using a window function, number the rows in any table to get a list of numbers, and then convert that to a list of dates:
<code>select row_number() over (order by true) as nfrom users limit 60</code>
And now creating the list of dates directly:
<code>select (getdate()::date - row_number() over (order by true))::date as nfrom users limit 60</code>
MySQL: Date Series using Variables
With variables in MySQL, we can generate a numbers table by treating a select statement as a for loop:
<code>set @n:=-1;select (select @n:= @n+1) nfrom users limit 60</code>
And now creating the list of dates directly:
<code>set @n:=date(now() + interval 1 day);select (select @n:= @n - interval 1 day) nfrom users limit 60</code>
Now that we've made a list of dates, aggregating and joining data from other tables for time series charts is a breeze!

InnoDBBufferPool reduces disk I/O by caching data and indexing pages, improving database performance. Its working principle includes: 1. Data reading: Read data from BufferPool; 2. Data writing: After modifying the data, write to BufferPool and refresh it to disk regularly; 3. Cache management: Use the LRU algorithm to manage cache pages; 4. Reading mechanism: Load adjacent data pages in advance. By sizing the BufferPool and using multiple instances, database performance can be optimized.

Compared with other programming languages, MySQL is mainly used to store and manage data, while other languages such as Python, Java, and C are used for logical processing and application development. MySQL is known for its high performance, scalability and cross-platform support, suitable for data management needs, while other languages have advantages in their respective fields such as data analytics, enterprise applications, and system programming.

MySQL is worth learning because it is a powerful open source database management system suitable for data storage, management and analysis. 1) MySQL is a relational database that uses SQL to operate data and is suitable for structured data management. 2) The SQL language is the key to interacting with MySQL and supports CRUD operations. 3) The working principle of MySQL includes client/server architecture, storage engine and query optimizer. 4) Basic usage includes creating databases and tables, and advanced usage involves joining tables using JOIN. 5) Common errors include syntax errors and permission issues, and debugging skills include checking syntax and using EXPLAIN commands. 6) Performance optimization involves the use of indexes, optimization of SQL statements and regular maintenance of databases.

MySQL is suitable for beginners to learn database skills. 1. Install MySQL server and client tools. 2. Understand basic SQL queries, such as SELECT. 3. Master data operations: create tables, insert, update, and delete data. 4. Learn advanced skills: subquery and window functions. 5. Debugging and optimization: Check syntax, use indexes, avoid SELECT*, and use LIMIT.

MySQL efficiently manages structured data through table structure and SQL query, and implements inter-table relationships through foreign keys. 1. Define the data format and type when creating a table. 2. Use foreign keys to establish relationships between tables. 3. Improve performance through indexing and query optimization. 4. Regularly backup and monitor databases to ensure data security and performance optimization.

MySQL is an open source relational database management system that is widely used in Web development. Its key features include: 1. Supports multiple storage engines, such as InnoDB and MyISAM, suitable for different scenarios; 2. Provides master-slave replication functions to facilitate load balancing and data backup; 3. Improve query efficiency through query optimization and index use.

SQL is used to interact with MySQL database to realize data addition, deletion, modification, inspection and database design. 1) SQL performs data operations through SELECT, INSERT, UPDATE, DELETE statements; 2) Use CREATE, ALTER, DROP statements for database design and management; 3) Complex queries and data analysis are implemented through SQL to improve business decision-making efficiency.

The basic operations of MySQL include creating databases, tables, and using SQL to perform CRUD operations on data. 1. Create a database: CREATEDATABASEmy_first_db; 2. Create a table: CREATETABLEbooks(idINTAUTO_INCREMENTPRIMARYKEY, titleVARCHAR(100)NOTNULL, authorVARCHAR(100)NOTNULL, published_yearINT); 3. Insert data: INSERTINTObooks(title, author, published_year)VA


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