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Database Design most common pitfalls

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It's easy to see antipatterns in production databases. Our schemas should be simple but extensible, and allow fast SQL queries. In this webinar I discuss what most common antipatterns are, and how to correct them.

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Database Design most common pitfalls

  1. 1. Database Design most common pitfalls
  2. 2. € whoami ● Federico Razzoli ● Freelance consultant ● Working with databases since 2000 hello@federico-razzoli.com federico-razzoli.com ● I worked as a consultant for Percona and Ibuildings (mainly MySQL and MariaDB) ● I worked as a DBA for fast-growing companies like Catawiki, HumanState, TransferWise
  3. 3. Agenda We will talk about… ● The most common design bad practices ● Information that is not easy to represent ● Relational model: why? ● Keys and indexes ● Data types ● Abusing NULL ● Hierarchies (trees) ● Lists ● Inheritance & polymorphism ● Heterogeneous rows ● Misc
  4. 4. Criteria
  5. 5. Criteria ● Queries should be fast ● Data structures should be reasonably simple ● Design must be reasonably extendable
  6. 6. Why Relational?
  7. 7. Specific Use Cases ● Some databases are designed for specific use cases ● In those cases, they may work much better than generic technologies ● Using them when not necessary may lead to use many technologies ● A technology should only be introduced if our company has: ○ Skills ○ Knowledge necessary for troubleshooting ○ Backups ○ High Availability ○ ...
  8. 8. Relational is flexible With the relational model we: ● Are sure that data is written correctly (transactions) ● Can make sure that data is valid (schema, integrity constraints) ● Design tables with access patterns in mind ● To run a query we initially didn’t consider, most of the times we can just add an index
  9. 9. Flexibility example CREATE TABLE user ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, name VARCHAR(100) NOT NULL, surname VARCHAR(100) NOT NULL, email VARCHAR(100) NOT NULL UNIQUE ); SELECT * FROM user WHERE id = 24; SELECT name, surname FROM user WHERE email = 'picard@starfleet.earth'; CREATE INDEX idx_surname_name ON user (surname, name); SELECT name, surname FROM user WHERE surname LIKE 'B%' ORDER BY surname, name;
  10. 10. When Relational is not a good fit ● Heterogeneous data (product catalogue) ● Searchable text ● Graphs ● … However, for simple use cases relational databases include non-relational features, like: ● JSON type and functions ● Arrays (PostgreSQL) ● Fulltext indexes ● ...
  11. 11. Keys and Indexes
  12. 12. Primary Key ● Column or set of columns that identifies each row (unique, not null) ● Usually you want to create an artificial column for this: ○ id ○ or uuid
  13. 13. Poor Primary Keys ● No primary key! ○ In MySQL this causes many performance problems ○ CDC applications need a way to identify each row ● Wrong columns ○ email ■ An email can change over time ■ An email address can be assigned to another person ■ The primary key is a PII! ○ name (eg: city name, product name…) ■ Quite long, especially if it must be UTF-8 ■ Certain names can change over time ○ timestamp ■ Multiple rows could be created at the same timestamp! ■ Long ○ ...
  14. 14. UNIQUE ● An index whose values are distinct, or NULL ● Could theoretically be a primary key, but it’s not
  15. 15. Poor UNIQUE keys ● Columns whose values will always be distinct, no matter if there is an index or not ○ Enforcing unicity implies extra reads, possibly on disk ● Columns that could have duplicates, but they’re unlikely ○ timestamp ○ (last_name, first_name)
  16. 16. Foreign Keys ● References to another table (user.city_id -> city.id) ● In most cases they are bad for performance ● They create problems for operations (ALTER TABLE) ● In MySQL they are not compatible with some other features ○ They don’t activate triggers ○ Table partitioning ○ Tables not using InnoDB ○ Many bugs
  17. 17. Indexing Bad Practices ● Indexing all columns: it won’t work ● Multi-columns indexes in random order ● Indexing columns with few distinct values (eg, boolean) ○ Unless you know what you’re doing ● Indexes contained in other indexes: idx1 (email), idx2 (email, last_name) idx (email, id) UNIQUE unq1 (email), INDEX idx1 (email) ● Non-descriptive index names (like the ones above) Looking at an index name (EXPLAIN), I should know which columns it contains
  18. 18. Quick hints ● Learn how indexes work ○ Google: Federico Razzoli indexes bad practices ● Use pt-duplicate-key-checker, from Percona Toolkit
  19. 19. Data Types
  20. 20. Integer Types ● Don’t use bigger types than necessary ● ...but don’t overoptimise when you are not 100% sure. You’ll hardly see a benefit using TINYINT instead of SMALLINT ● MySQL UNSIGNED is good, column’s max is double ● I discourage the use of exotic MySQL syntax like: ○ MEDIUMINT: non-standard, and 3-bytes variables don’t exist in nature ○ INT(length) ○ ZEROFILL
  21. 21. Real Numbers ● FLOAT and DOUBLE are fast when aggregating many values ● But they are subject to approximation. Don’t use them for prices, etc ● Instead you can use: ○ DECIMAL ○ INT - Multiply a number by 100, for example ○ DECIMAL is slower if heavy arithmetics is performed on many values ○ But storing a transformed value (price*100) can lead to misunderstandings and bugs
  22. 22. Text Values ● Be sure that VARCHAR columns have adequate size for your data ● In PostgreSQL there is no difference between VARCHAR and TEXT, except that for VARCHAR you specify a max size ● In MySQL TEXT and BLOB columns are stored separately ○ Less data read if you often don’t read those columns ○ More read operations if you always use SELECT * ● CHAR is only good for small fixed-size data. The space saving is tiny.
  23. 23. Temporal Types ● TIMESTAMP and DATETIME are mostly interchangeable ● MySQL YEAR is weird. 2-digit values meaning changes over time. Use SMALLINT inxtead. ● MySQL TIME is apparently weird and useless. But not if you consider it as an interval. (range: -838:59:59 .. 838:59:59) ● PostgreSQL has a proper INTERVAL type, which is surely better ● PostgreSQL allows to specify a timezone for each value (TIMESTAMP WITH TIMEZONE) ○ Timezones depend on policy, economy and religion. They may vary by 15 mins. Timezones are created, dismissed, and changed. In one case a timezone was changed by skipping a whole calendar day. ○ Never deal with timezones yourself, no one ever succeeded in history. Store all dates as UTC, use an external library for conversion.
  24. 24. ENUM, SET ● MySQL weird types that include a list of allowed string values ● With ENUM, any number of values from the list are allowed ● With SET, exactly one value from the list is allowed ● '' is always allowed, because. ● Specifying the value by index is allowed, so 0 could match '1' ● Adding, dropping and changing values requires an ALTER TABLE ○ And possibly a locking table rebuild
  25. 25. Instead of ENUM CREATE TABLE account ( state ENUM('active', 'suspended') NOT NULL, ... )
  26. 26. Instead of ENUM CREATE TABLE account ( state_id INT UNSIGNED NOT NULL, ... ) CREATE TABLE state ( id INT UNSIGNED PRIMARY KEY, state VARCHAR(100) NOT NULL UNIQUE ) INSERT INTO state (state) VALUES ('active'), ('suspended');
  27. 27. Abusing NULL
  28. 28. NULL anomalies mysql> SELECT NULL = 1 AS a, NULL <> 1 AS b, NULL IS NULL AS c, 1 IS NOT NULL AS d; +------+------+---+---+ | a | b | c | d | +------+------+---+---+ | NULL | NULL | 1 | 1 | +------+------+---+---+ -- This returns TRUE in MySQL: NULL <=> NULL AND 1 <=> 1
  29. 29. Problematic queries These queries will not return rows with age = NULL or approved = NULL ● WHERE year != 1994 ● WHERE NOT (year = 1994) ● WHERE year > 2000 ● WHERE NOT (year > 2000) ● WHERE approved != TRUE ● WHERE NOT approved And: SELECT CONCAT(year, ' years old') FROM user ...
  30. 30. Bad Reasons for NULL ● Because columns are NULLable by default ● To indicate that a value doesn’t exist ○ Use a special value instead: '' or -1 or 0 or … ○ But this is not always a bad reason: UNIQUE allows multiple NULLs ● Using your tables as spreadsheets
  31. 31. Spreadsheet Example CREATE TABLE user ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, first_name VARCHAR(100) NOT NULL, last_name VARCHAR(100) NOT NULL, email VARCHAR(100) NOT NULL, -- if a user may have multiple URL’s, let’s move them -- to a separate table: -- url { id, user_id, url } url_1 VARCHAR(100), url_2 VARCHAR(100), url_3 VARCHAR(100), url_4 VARCHAR(100), url_5 VARCHAR(100) );
  32. 32. Spreadsheet Example CREATE TABLE user ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, first_name VARCHAR(100) NOT NULL, last_name VARCHAR(100) NOT NULL, email VARCHAR(100) NOT NULL, -- if we may have users bank data or not, -- let’s move them to another table: -- bank { user_id, account_no, account_holder, ... } bank_account_no VARCHAR(50), bank_account_holder VARCHAR(100), bank_iban VARCHAR(100), bank_swift_code VARCHAR(5) );
  33. 33. Hierarchies
  34. 34. Category Hierarchies Antipattern: column-per-level TABLE product (id, category_name, subcategory_name, name, price, ..) ----- TABLE category (id, name) TABLE product (id, category_id, subcategory_id, name, price, ...) Possible problems: ● To add or delete a level, we need to add or drop a column ● A subcategory can be erroneously linked to multiple categories ● A category can be erroneously used as subcategory, and vice versa
  35. 35. Category Hierarchies A better way: TABLE category (id, parent_id, name) TABLE product (id, category_id, name, price, ...) Possible problems: ● Circular dependencies (must be prevented at application level)
  36. 36. Category Networks What if every category can have multiple parents? Antipattern: TABLE category (id, parent_id1, parent_id2, name)
  37. 37. Category Graphs If every category can have multiple parents, correct pattern: TABLE category (id, name) TABLE category_relationship (parent_id, child_id)
  38. 38. Antipattern: Parent List If every category can have multiple parents, correct pattern: TABLE category (id, name, parent_list) INSERT INTO category (parent_list, name) VALUES ('sports/football/wear', 'football shoes'); ● This antipattern is sometimes used because it simplifies certain aspects ● But it overcomplicates other aspects ● Also, up to recently MySQL and MariaDB did not support recursive queries, but now they do
  39. 39. Storing Lists
  40. 40. Tags Column ● Suppose you want to store user-typed tags for posts ● You may be tempted to: CREATE TABLE post ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, tags VARCHAR(200) ); INSERT INTO post (tags, ... ) VALUES (sunday,venus, ... );
  41. 41. Tags Column ● But what about this query? SELECT id FROM post WHERE tags LIKE '%sun%'; ● Mmm, maybe this is better: INSERT INTO post (tags, ... ) VALUES (',events,diversity,', ... ); SELECT id FROM post WHERE tags LIKE '%,sun,%'; However, this query cannot take advantage of indexes
  42. 42. Tag Table CREATE TABLE post ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, ... ); CREATE TABLE tag ( post_id INT UNSIGNED, tag VARCHAR(50), PRIMARY KEY (post_id, tag), INDEX (tag) ); It works. Queries will be able to use indexes.
  43. 43. Tag Array -- PostgreSQL CREATE TABLE post ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, tags TEXT[] ); CREATE INDEX idx_tags on post USING GIN (tags); -- MySQL CREATE TABLE post ( id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY, tags JSON DEFAULT JSON_ARRAY(), INDEX idx_tags (tags) ); -- MariaDB can store JSON arrays, -- but since it cannot index them this solution is not viable
  44. 44. Inheritance And Polymorphism
  45. 45. Not So Different Entities ● Your DB has users, landlords and tenants ● Separate entities with different info ● But sometimes you treat them as one thing ● What to do?
  46. 46. Inheritance ● In the simplest case, they are just subclasses ● For example, landlords and tenants could be types of users ● Common properties are in the parent class -- relational way to represent it: TABLE user (id, first_name, last_name, email) TABLE landlord (id, user_id, vat_number) TABLE tenant (id, user_id, landlord_id) PostgreSQL allows to do this in a more object oriented way, with Table Inheritance
  47. 47. Different Entities ● But sometimes it’s better to consider them different entities ● Antipattern: Union View CREATE VIEW everyone AS (SELECT id, first_name, last_name FROM landlord) UNION (SELECT id, first_name, last_name FROM tenant) ; This makes some queries less verbose, at the cost of making them potentially very slow
  48. 48. Unicity Across Tables /1 ● But maybe both landlords and tenants have emails, and we want to make sure they are UNIQUE ● Question: is there a practical reason?
  49. 49. Unicity Across Tables /2 ● If it is necessary, you’re thinking about the problem in a wrong way ● If emails need be unique, they are a whole entity, so you’ll guarantee unicity on a single table TABLE landlord (id, first_name, last_name, vat_number) TABLE tenant (id, first_name, last_name, landlord_id) TABLE email (id, email UNIQUE, landlord_id, tenant_id) Bloody hell! The solution initially looks great, but linking emails to landlords or tenants in that way is horrific!
  50. 50. Unicity Across Tables /2bis Why? ● Cannot build foreign keys (I don’t recommend it, but…) ● If in the future we want to link emails to suppliers, employees, etc, we’ll need to add columns to the table
  51. 51. Unicity Across Tables /3 Even if we keep the landlord and tenant tables separated, we can create a superset called person. We decided it’s not a parent class, so it can just have an id column. Every landlord, tenant and email is linked to a person. TABLE landlord (id, person_id, first_name, last_name, vat_number) TABLE tenant (id, person_id, first_name, last_name, landlord_id) TABLE person (id) TABLE email (id, person_id, email UNIQUE)
  52. 52. Heterogeneous Rows
  53. 53. Catalog of Products Imagine we have a catalogue of products where: ● Every product has certain common characteristics ● It’s important to be able to run queries on all products ○ SELECT id FROM p WHERE qty = 0; ○ SELECT MAX(price) FROM p GROUP BY vendor; ● Each product type also has a unique set of characteristics
  54. 54. Antipattern: Stylesheet Table ● Keep all products in the same table ● Add a column for every characteristic that applies to at least one product ● Where a column doesn’t make sense, set to NULL Problems: ● Too many columns and indexes ○ Generally bad for query performance, especially INSERTs ○ Generally bad for operations (repair, backup, restore, ALTER TABLE…) ● Adding/removing a product type means to add/remove a set of columns ○ But in practice columns will hardly be removed and will remain unused ● NULL means both “no value for this product” and “doesn’t apply to this type of products”, leading to endless confusion
  55. 55. Antipattern: Table per Type ● Store products of different types in different tables Problems: ● Metadata become data ○ How to get the list of product types? ● Some queries become overcomplicated ○ Get the id’s of out of stock products ○ Most expensive product for each vendor
  56. 56. Hybrid ● A single table for characteristics common to all product types ● A separate table per product type, for non-common characteristics Problems: ● Many JOINs ● Adding/removing product types means to add/remove tables
  57. 57. Semi-Structured Data ● A single table for all products ● A regular column for each column common to all product types ● A semi-structured column for all type-specific characteristics ○ JSON, HStore… ○ Not arrays ○ Not CSV ● Proper indexes on unstructured data (depending on your technology) Problems: ● Still a big table ● Queries on semi-structured data may be complicated and not supported by ORMs
  58. 58. Antipattern: Entity,Attribute,Value TABLE entity (id, name) TABLE attribute (id, entity_id, name) TABLE value (id, attribute_id, value) ● Each product type is an entity ● Each type characteristics are stored in attribute ● Each product is a set of values Example: Entity { id: 24, name: "Bed" } Attribute [ { id: 123, entity_id: 24, name: "material" }, ... ] Value [ { id: 999, attribute_id: 123, value: "wood" } ]
  59. 59. Antipattern: Entity,Attribute,Value Problems: ● We JOIN 3 tables every time we want to get a single value! ● All values must be treated as texts ○ Unless we create multiple value tables: int_value, text_value... ○ Which means, even more JOINs
  60. 60. Misc Antipatterns
  61. 61. Names Beyond Comprehension ● I saw the following table names in production: ○ marco2015 # Marco was the table’s creation ○ jan2015 # jan was the month ○ tmp_tmp_tmp_fix ○ tmp_fix_fix_fix # Because symmetry is cool I forgot many other examples because... “Ultimate horror often paralyses memory in a merciful way.” ― H.P. Lovecraft
  62. 62. Data in Metadata ● Include data in table names ○ invoice_2020, invoice_2019, invoice_2018… ● User a year column instead ● If the table is too big, there are other ways to contain the problem (partitioning)
  63. 63. Bad Names in General ● A names should tell everyone what a table or column is ○ Even to new hires! ○ Even to you… in 5 years from now! ● Otherwise people have to look at other documentation sources ○ ….which typically don’t exist ● Names should follow a standard across all company databases ○ singular/plural, long/short names, ... ● So people don’t have to check how a table / column is called exactly
  64. 64. Thank you for listening! federico-razzoli.com/services Telegram channel: open_source_databases

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