├── LICENSE └── readme.md /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # SQL Anti-Patterns 2 | 3 | ### Logical Database Design Anti-Patterns 4 | 5 | * [Multi-Valued Attribute](#multi-valued-attribute) 6 | * [Recursive Dependency](#recursive-dependency) 7 | * [Primary Key Does Not Exist](#Primary-key-does-not-exist) 8 | * [Generic Primary Key](#generic-primary-key) 9 | * [Foreign Key Does Not Exist](#foreign-key-does-not-exist) 10 | * [Entity-Attribute-Value Pattern](#entity-attribute-value-pattern) 11 | * [Metadata Tribbles](#metadata-tribbles) 12 | 13 | ### Physical Database Design Anti-Patterns 14 | 15 | * [Imprecise Data Type](https://github.com/jarulraj/sqlcheck/blob/master/docs/physical/2001.md) 16 | * [Values In Definition](https://github.com/jarulraj/sqlcheck/blob/master/docs/physical/2002.md) 17 | * [Files Are Not SQL Data Types](https://github.com/jarulraj/sqlcheck/blob/master/docs/physical/2003.md) 18 | * [Too Many Indexes](https://github.com/jarulraj/sqlcheck/blob/master/docs/physical/2004.md) 19 | * [Index Attribute Order](https://github.com/jarulraj/sqlcheck/blob/master/docs/physical/2005.md) 20 | 21 | ### Query Anti-Patterns 22 | 23 | * [SELECT *](#select-*) 24 | * [NULL Usage](#null-usage) 25 | * [NOT NULL Usage](#not-null-usage) 26 | * [String Concatenation](#string-concantenation) 27 | * [GROUP BY Usage](#group-by-usage) 28 | * [ORDER BY RAND Usage](#order-by-rand-usage) 29 | * [Pattern Matching Usage](#pattern-matching-usage) 30 | * [Spaghetti Query Alert](#spaghetti-query-alert) 31 | * [Reduce Number of JOINs](#reduce-number-of-joins) 32 | * [Eliminate Unnecessary DISTINCT Conditions](#eliminate-unnecessary-distinct-conditions) 33 | * [Implicit Column Usage](#implicit-column-usage) 34 | * [HAVING Clause Usage](#having-clause-usage) 35 | * [Nested sub queries](#nested-sub-queries) 36 | * [OR Usage](#or-usage) 37 | * [UNION Usage](#union-usage) 38 | * [DISTINCT & JOIN Usage](distinct-&-join-usage) 39 | 40 | ### Application Development Anti-Patterns 41 | 42 | * [Readable Passwords](#readable-passwords) 43 | 44 | # Logical Database Design Anti-Patterns 45 | 46 | ## Multi-Valued Attribute 47 | ### **Store each value in its own column and row:** 48 | Storing a list of IDs as a VARCHAR/TEXT column can cause performance and data integrity 49 | problems. Querying against such a column would require using pattern-matching 50 | expressions. It is awkward and costly to join a comma-separated list to matching rows. 51 | This will make it harder to validate IDs. Think about what is the greatest number of 52 | entries this list must support? Instead of using a multi-valued attribute, 53 | consider storing it in a separate table, so that each individual value of that attribute 54 | occupies a separate row. Such an intersection table implements a many-to-many relationship 55 | between the two referenced tables. This will greatly simplify querying and validating 56 | the IDs. 57 | 58 | ## Recursive Dependency 59 | ### **Avoid recursive relationships:** 60 | It's common for data to have recursive relationships. Data may be organized in a 61 | treelike or hierarchical way. However, creating a foreign key constraint to enforce 62 | the relationship between two columns in the same table lends to awkward querying. 63 | Each level of the tree corresponds to another join. You will need to issue recursive 64 | queries to get all descendants or all ancestors of a node. 65 | A solution is to construct an additional closure table. It involves storing all paths 66 | through the tree, not just those with a direct parent-child relationship. 67 | You might want to compare different hierarchical data designs -- closure table, 68 | path enumeration, nested sets -- and pick one based on your application's needs. 69 | 70 | ## Primary Key Does Not Exist 71 | 72 | ### **Consider adding a primary key:** 73 | A primary key constraint is important when you need to do the following: 74 | prevent a table from containing duplicate rows, 75 | reference individual rows in queries, and 76 | support foreign key references 77 | If you don't use primary key constraints, you create a chore for yourself: 78 | checking for duplicate rows. More often than not, you will need to define 79 | a primary key for every table. Use compound keys when they are appropriate. 80 | 81 | ## Generic Primary Key 82 | 83 | ### **Skip using a generic primary key (id):** 84 | Adding an id column to every table causes several effects that make its 85 | use seem arbitrary. You might end up creating a redundant key or allow 86 | duplicate rows if you add this column in a compound key. 87 | The name id is so generic that it holds no meaning. This is especially 88 | important when you join two tables and they have the same primary 89 | key column name. 90 | 91 | ## Foreign Key Does Not Exist 92 | 93 | ### **Consider adding a foreign key:** 94 | Are you leaving out the application constraints? Even though it seems at 95 | first that skipping foreign key constraints makes your database design 96 | simpler, more flexible, or speedier, you pay for this in other ways. 97 | It becomes your responsibility to write code to ensure referential integrity 98 | manually. Use foreign key constraints to enforce referential integrity. 99 | Foreign keys have another feature you can't mimic using application code: 100 | cascading updates to multiple tables. This feature allows you to 101 | update or delete the parent row and lets the database takes care of any child 102 | rows that reference it. The way you declare the ON UPDATE or ON DELETE clauses 103 | in the foreign key constraint allow you to control the result of a cascading 104 | operation. Make your database mistake-proof with constraints. 105 | 106 | 107 | ## Entity-Attribute-Value Pattern 108 | 109 | 110 | ### **Dynamic schema with variable attributes:** 111 | Are you trying to create a schema where you can define new attributes 112 | at runtime.? This involves storing attributes as rows in an attribute table. 113 | This is referred to as the Entity-Attribute-Value or schemaless pattern. 114 | When you use this pattern, you sacrifice many advantages that a conventional 115 | database design would have given you. You can't make mandatory attributes. 116 | You can't enforce referential integrity. You might find that attributes are 117 | not being named consistently. A solution is to store all related types in one table, 118 | with distinct columns for every attribute that exists in any type 119 | (Single Table Inheritance). Use one attribute to define the subtype of a given row. 120 | Many attributes are subtype-specific, and these columns must 121 | be given a null value on any row storing an object for which the attribute 122 | does not apply the columns with non-null values become sparse. 123 | Another solution is to create a separate table for each subtype 124 | (Concrete Table Inheritance). A third solution mimics inheritance, 125 | as though tables were object-oriented classes (Class Table Inheritance). 126 | Create a single table for the base type, containing attributes common to 127 | all subtypes. Then for each subtype, create another table, with a primary key 128 | that also serves as a foreign key to the base table. 129 | If you have many subtypes or if you must support new attributes frequently, 130 | you can add a BLOB column to store data in a format such as XML or JSON, 131 | which encodes both the attribute names and their values. 132 | This design is best when you can't limit yourself to a finite set of subtypes 133 | and when you need complete flexibility to define new attributes at any time. 134 | 135 | ## Metadata Tribbles 136 | 137 | ### **Store each value with the same meaning in a single column:** 138 | Creating multiple columns in a table indicates that you are trying to store 139 | a multivalued attribute. This design makes it hard to add or remove values, 140 | to ensure the uniqueness of values, and handling growing sets of values. 141 | The best solution is to create a dependent table with one column for the 142 | multivalue attribute. Store the multiple values in multiple rows instead of 143 | multiple columns. Also, define a foreign key in the dependent table to associate 144 | the values to its parent row. 145 | 146 | ### **Breaking down a table or column by year:** 147 | You might be trying to split a single column into multiple columns, 148 | using column names based on distinct values in another attribute. 149 | Each year, you will need to add one more column or table. 150 | You are mixing metadata with data. You will now need to make sure that 151 | the primary key values are unique across all the split columns or tables. 152 | The solution is to use a feature called sharding or horizontal partitioning. 153 | ```(PARTITION BY HASH ( YEAR(...) )```. With this feature, you can gain the 154 | benefits of splitting a large table without the drawbacks. 155 | Partitioning is not defined in the SQL standard, so each brand of database 156 | implements it in their own nonstandard way. 157 | Another remedy for metadata tribbles is to create a dependent table. 158 | Instead of one row per entity with multiple columns for each year, 159 | use multiple rows. Don't let data spawn metadata. 160 | 161 | # Physical Database Design Anti-Patterns 162 | 163 | ## Imprecise Data Type 164 | 165 | ### Use precise data types: 166 | Virtually any use of FLOAT, REAL, or DOUBLE PRECISION data types is suspect. 167 | Most applications that use floating-point numbers don't require the range of 168 | values supported by IEEE 754 formats. The cumulative impact of inexact 169 | floating-point numbers is severe when calculating aggregates. 170 | Instead of FLOAT or its siblings, use the NUMERIC or DECIMAL SQL data types 171 | for fixed-precision fractional numbers. These data types store numeric values 172 | exactly, up to the precision you specify in the column definition. 173 | Do not use FLOAT if you can avoid it. 174 | 175 | 176 | ## Values In Definition 177 | 178 | ### Don't specify values in column definition: 179 | With enum, you declare the values as strings, 180 | but internally the column is stored as the ordinal number of the string 181 | in the enumerated list. The storage is therefore compact, but when you 182 | sort a query by this column, the result is ordered by the ordinal value, 183 | not alphabetically by the string value. You may not expect this behavior. 184 | There's no syntax to add or remove a value from an ENUM or check constraint 185 | you can only redefine the column with a new set of values. 186 | Moreover, if you make a value obsolete, you could upset historical data. 187 | As a matter of policy, changing metadata — that is, changing the definition 188 | of tables and columns—should be infrequent and with attention to testing and 189 | quality assurance. There's a better solution to restrict values in a column: 190 | create a lookup table with one row for each value you allow. 191 | Then declare a foreign key constraint on the old table referencing 192 | the new table. 193 | Use metadata when validating against a fixed set of values. 194 | Use data when validating against a fluid set of values. 195 | 196 | ## Files Are Not SQL Data Types 197 | 198 | ### Resources outside the database are not managed by the database: 199 | It's common for programmers to be unequivocal that we should always 200 | store files external to the database. 201 | Files don't obey DELETE, transaction isolation, rollback, or work well with 202 | database backup tools. They do not obey SQL access privileges and are not SQL 203 | data types. 204 | Resources outside the database are not managed by the database. 205 | You should consider storing blobs inside the database instead of in 206 | external files. You can save the contents of a BLOB column to a file. 207 | 208 | ## Too Many Indexes 209 | 210 | ### Don't create too many indexes: 211 | You benefit from an index only if you run queries that use that index. 212 | There's no benefit to creating indexes that you don't use. 213 | If you cover a database table with indexes, you incur a lot of overhead 214 | with no assurance of payoff. 215 | Consider dropping unnecessary indexes. 216 | If an index provides all the columns we need, then we don't need to read 217 | rows of data from the table at all. Consider using such covering indexes. 218 | Know your data, know your queries, and maintain the right set of indexes. 219 | 220 | ## Index Attribute Order 221 | 222 | ### Align the index attribute order with queries: 223 | If you create a compound index for the columns, make sure that the query 224 | attributes are in the same order as the index attributes, so that the DBMS 225 | can use the index while processing the query. 226 | If the query and index attribute orders are not aligned, then the DBMS might 227 | be unable to use the index during query processing. 228 | 229 | #### Example 230 | 231 | ``` 232 | CREATE INDEX TelephoneBook ON Accounts(last_name, first_name) 233 | SELECT * FROM Accounts ORDER BY first_name, last_name 234 | ``` 235 | 236 | # Query Anti-Patterns 237 | ## SELECT * 238 | 239 | ### Inefficiency in moving data to the consumer: 240 | When you SELECT *, you're often retrieving more columns from the database than 241 | your application really needs to function. This causes more data to move from 242 | the database server to the client, slowing access and increasing load on your 243 | machines, as well as taking more time to travel across the network. This is 244 | especially true when someone adds new columns to underlying tables that didn't 245 | exist and weren't needed when the original consumers coded their data access. 246 | 247 | ### Indexing issues: 248 | Consider a scenario where you want to tune a query to a high level of performance. 249 | If you were to use *, and it returned more columns than you actually needed, 250 | the server would often have to perform more expensive methods to retrieve your 251 | data than it otherwise might. For example, you wouldn't be able to create an index 252 | which simply covered the columns in your SELECT list, and even if you did 253 | (including all columns [shudder]), the next guy who came around and added a column 254 | to the underlying table would cause the optimizer to ignore your optimized covering 255 | index, and you'd likely find that the performance of your query would drop 256 | substantially for no readily apparent reason. 257 | 258 | ### Binding Problems: 259 | When you SELECT *, it's possible to retrieve two columns of the same name from two 260 | different tables. This can often crash your data consumer. Imagine a query that joins 261 | two tables, both of which contain a column called \ID\. How would a consumer know 262 | which was which? SELECT * can also confuse views (at least in some versions SQL Server) 263 | when underlying table structures change -- the view is not rebuilt, and the data which 264 | comes back can be nonsense. And the worst part of it is that you can take care to name 265 | your columns whatever you want, but the next guy who comes along might have no way of 266 | knowing that he has to worry about adding a column which will collide with your 267 | already-developed names. 268 | 269 | ## NULL Usage 270 | 271 | 272 | ### Use NULL as a Unique Value: 273 | NULL is not the same as zero. A number ten greater than an unknown is still an unknown. 274 | NULL is not the same as a string of zero length. 275 | Combining any string with NULL in standard SQL returns NULL. 276 | NULL is not the same as false. Boolean expressions with AND, OR, and NOT also produce 277 | results that some people find confusing. 278 | When you declare a column as NOT NULL, it should be because it would make no sense 279 | for the row to exist without a value in that column. 280 | Use null to signify a missing value for any data type. 281 | 282 | ## NOT NULL Usage 283 | 284 | ### Use NOT NULL only if the column cannot have a missing value: 285 | When you declare a column as NOT NULL, it should be because it would make no sense 286 | for the row to exist without a value in that column. 287 | Use null to signify a missing value for any data type. 288 | 289 | ## String Concatenation 290 | 291 | ### Use COALESCE for string concatenation of nullable columns: 292 | You may need to force a column or expression to be non-null for the sake of 293 | simplifying the query logic, but you don't want that value to be stored. 294 | Use COALESCE function to construct the concatenated expression so that a 295 | null-valued column doesn't make the whole expression become null. 296 | 297 | #### Example 298 | 299 | ``` 300 | SELECT first_name || COALESCE(' ' || middle_initial || ' ', ' ') || last_name 301 | AS full_name FROM Accounts 302 | ``` 303 | 304 | ## GROUP BY Usage 305 | 306 | ### Do not reference non-grouped columns: 307 | Every column in the select-list of a query must have a single value row 308 | per row group. This is called the Single-Value Rule. 309 | Columns named in the GROUP BY clause are guaranteed to be exactly one value 310 | per group, no matter how many rows the group matches. 311 | Most DBMSs report an error if you try to run any query that tries to return 312 | a column other than those columns named in the GROUP BY clause or as 313 | arguments to aggregate functions. 314 | Every expression in the select list must be contained in either an 315 | aggregate function or the GROUP BY clause. 316 | Follow the single-value rule to avoid ambiguous query results. 317 | 318 | ## ORDER BY RAND Usage 319 | 320 | ### Sorting by a nondeterministic expression (RAND()) means the sorting cannot benefit from an index: 321 | There is no index containing the values returned by the random function. 322 | That's the point of them being ran- dom: they are different and 323 | unpredictable each time they're selected. This is a problem for the performance 324 | of the query, because using an index is one of the best ways of speeding up 325 | sorting. The consequence of not using an index is that the query result set 326 | has to be sorted by the database using a slow table scan. 327 | One technique that avoids sorting the table is to choose a random value 328 | between 1 and the greatest primary key value. 329 | Still another technique that avoids problems found in the preceding alternatives 330 | is to count the rows in the data set and return a random number between 0 and 331 | the count. Then use this number as an offset when querying the data set. 332 | Some queries just cannot be optimized consider taking a different approach. 333 | 334 | ## Pattern Matching Usage 335 | 336 | ### Avoid using vanilla pattern matching: 337 | The most important disadvantage of pattern-matching operators is that 338 | they have poor performance. A second problem of simple pattern-matching using LIKE 339 | or regular expressions is that it can find unintended matches. 340 | It's best to use a specialized search engine technology like Apache Lucene, instead of SQL. 341 | Another alternative is to reduce the recurring cost of search by saving the result. 342 | Consider using vendor extensions like FULLTEXT INDEX in MySQL. 343 | More broadly, you don't have to use SQL to solve every problem. 344 | 345 | ## Spaghetti Query Alert 346 | 347 | ### Split up a complex spaghetti query into several simpler queries: 348 | SQL is a very expressive language—you can accomplish a lot in a single query or statement. 349 | But that doesn't mean it's mandatory or even a good idea to approach every task with the 350 | assumption it has to be done in one line of code. 351 | One common unintended consequence of producing all your results in one query is 352 | a Cartesian product. This happens when two of the tables in the query have no condition 353 | restricting their relationship. Without such a restriction, the join of two tables pairs 354 | each row in the first table to every row in the other table. Each such pairing becomes a 355 | row of the result set, and you end up with many more rows than you expect. 356 | It's important to consider that these queries are simply hard to write, hard to modify, 357 | and hard to debug. You should expect to get regular requests for incremental enhancements 358 | to your database applications. Managers want more complex reports and more fields in a 359 | user interface. If you design intricate, monolithic SQL queries, it's more costly and 360 | time-consuming to make enhancements to them. Your time is worth something, both to you 361 | and to your project. 362 | Split up a complex spaghetti query into several simpler queries. 363 | When you split up a complex SQL query, the result may be many similar queries, 364 | perhaps varying slightly depending on data values. Writing these queries is a chore, 365 | so it's a good application of SQL code generation. 366 | Although SQL makes it seem possible to solve a complex problem in a single line of code, 367 | don't be tempted to build a house of cards. 368 | 369 | ## Reduce Number of JOINs 370 | 371 | ### Reduce Number of JOINs: 372 | Too many JOINs is a symptom of complex spaghetti queries. Consider splitting 373 | up the complex query into many simpler queries, and reduce the number of JOINs 374 | 375 | ## Eliminate Unnecessary DISTINCT Conditions 376 | 377 | ### Eliminate Unnecessary DISTINCT Conditions: 378 | Too many DISTINCT conditions is a symptom of complex spaghetti queries. 379 | Consider splitting up the complex query into many simpler queries, 380 | and reduce the number of DISTINCT conditions 381 | It is possible that the DISTINCT condition has no effect if a primary key 382 | column is part of the result set of columns 383 | 384 | ## Implicit Column Usage 385 | 386 | ### Explicitly name columns: 387 | Although using wildcards and unnamed columns satisfies the goal 388 | of less typing, this habit creates several hazards. 389 | This can break application refactoring and can harm performance. 390 | Always spell out all the columns you need, instead of relying on 391 | wild-cards or implicit column lists. 392 | 393 | ## HAVING Clause Usage 394 | 395 | ### Consider removing the HAVING clause: 396 | Rewriting the query's HAVING clause into a predicate will enable the 397 | use of indexes during query processing. 398 | 399 | #### Example 400 | 401 | ``` 402 | SELECT s.cust_id,count(s.cust_id) FROM SH.sales s GROUP BY s.cust_id 403 | HAVING s.cust_id != '1660' AND s.cust_id != '2' 404 | ``` 405 | can be rewritten as: 406 | ``` 407 | SELECT s.cust_id,count(cust_id) FROM SH.sales s WHERE s.cust_id != '1660' 408 | AND s.cust_id !='2' GROUP BY s.cust_id 409 | ``` 410 | 411 | ## Nested sub queries 412 | 413 | ### Un-nest sub queries: 414 | Rewriting nested queries as joins often leads to more efficient 415 | execution and more effective optimization. In general, sub-query unnesting 416 | is always done for correlated sub-queries with, at most, one table in 417 | the FROM clause, which are used in ANY, ALL, and EXISTS predicates. 418 | A uncorrelated sub-query, or a sub-query with more than one table in 419 | the FROM clause, is flattened if it can be decided, based on the query 420 | semantics, that the sub-query returns at most one row. 421 | 422 | #### Example 423 | 424 | ``` 425 | SELECT * FROM SH.products p WHERE p.prod_id = (SELECT s.prod_id FROM SH.sales 426 | s WHERE s.cust_id = 100996 AND s.quantity_sold = 1 ) 427 | ``` 428 | can be rewritten as: 429 | ``` 430 | SELECT p.* FROM SH.products p, sales s WHERE p.prod_id = s.prod_id AND 431 | s.cust_id = 100996 AND s.quantity_sold = 1 432 | ``` 433 | 434 | ## OR Usage 435 | 436 | ### Consider using an IN predicate when querying an indexed column: 437 | The IN-list predicate can be exploited for indexed retrieval and also, 438 | the optimizer can sort the IN-list to match the sort sequence of the index, 439 | leading to more efficient retrieval. Note that the IN-list must contain only 440 | constants, or values that are constant during one execution of the query block, 441 | such as outer references. 442 | 443 | #### Example 444 | 445 | ``` 446 | SELECT s.* FROM SH.sales s WHERE s.prod_id = 14 OR s.prod_id = 17 447 | ``` 448 | can be rewritten as: 449 | ``` 450 | SELECT s.* FROM SH.sales s WHERE s.prod_id IN (14, 17) 451 | ``` 452 | 453 | ## UNION Usage 454 | 455 | ### Consider using UNION ALL if you do not care about duplicates: 456 | Unlike UNION which removes duplicates, UNION ALL allows duplicate tuples. 457 | If you do not care about duplicate tuples, then using UNION ALL would be 458 | a faster option. 459 | 460 | ## DISTINCT & JOIN Usage 461 | 462 | ### Consider using a sub-query with EXISTS instead of DISTINCT: 463 | The DISTINCT keyword removes duplicates after sorting the tuples. 464 | Instead, consider using a sub query with the EXISTS keyword, you can avoid 465 | having to return an entire table. 466 | 467 | #### Example 468 | 469 | ``` 470 | SELECT DISTINCT c.country_id, c.country_name FROM SH.countries c, 471 | SH.customers e WHERE e.country_id = c.country_id 472 | ``` 473 | can be rewritten to: 474 | ``` 475 | SELECT c.country_id, c.country_name FROM SH.countries c WHERE EXISTS 476 | (SELECT 'X' FROM SH.customers e WHERE e.country_id = c.country_id) 477 | ``` 478 | 479 | # Application Development Anti-Patterns 480 | 481 | ## Readable Passwords 482 | 483 | ### Do not store readable passwords: 484 | It's not secure to store a password in clear text or even to pass it over the 485 | network in the clear. If an attacker can read the SQL statement you use to 486 | insert a password, they can see the password plainly. 487 | Additionally, interpolating the user's input string into the SQL query in plain text 488 | exposes it to discovery by an attacker. 489 | If you can read passwords, so can a hacker. 490 | The solution is to encode the password using a one-way cryptographic hash 491 | function. This function transforms its input string into a new string, 492 | called the hash, that is unrecognizable. 493 | Use a salt to thwart dictionary attacks. Don't put the plain-text password 494 | into the SQL query. Instead, compute the hash in your application code, 495 | and use only the hash in the SQL query. 496 | 497 | 498 | # Source 499 | 500 | https://github.com/jarulraj/sqlcheck 501 | --------------------------------------------------------------------------------