Flase positive: (PHYSICAL_DATABASE_DESIGN ANTI-PATTERN) Values In Definition

What Is SQLCheck?

sqlcheck automatically detects common SQL anti-patterns. Such anti-patterns often slow down queries. Addressing them will, therefore, help accelerate queries.

sqlcheck targets all major SQL dialects.

For development updates on sqlcheck and general news on next generation database systems, follow me at @joy_arulraj.

What it can do

Right now SQLCheck can detect the following anti-patterns:

Logical Database Design Anti-Patterns

Physical Database Design Anti-Patterns

Query Anti-Patterns

Application Development Anti-Patterns

Readable Passwords

Installation

###UBUNTU/MINT (DEBIAN)

Download the debian package from the latest release.

wget https://github.com/jarulraj/sqlcheck/releases/download/v1.3/sqlcheck-x86_64.deb

Install it by running the following command.

dpkg -i sqlcheck-x86_64.deb

FEDORA/CENTOS (RPM)

Download the rpm package from the latest release.

wget https://github.com/jarulraj/sqlcheck/releases/download/v1.3/sqlcheck-x86_64.rpm

Install it by running the following command.

yum --nogpgcheck localinstall sqlcheck-x86_64.rpm

MAC (DMG)

Download the dmg package from the latest release.

wget https://github.com/jarulraj/sqlcheck/releases/download/v1.3/sqlcheck-x86_64.dmg

Click the dmg to mount the package. This will mount it in the Volumes directory.
Open the Terminal app. This page contains more details on finding the app.
Copy over the SQLCheck binary file to desired local directory.

cp /Volumes/sqlcheck-x86_64/bin/sqlcheck /usr/local/bin/

WINDOWS (EXE)

Download the exe file from the latest release.

wget https://github.com/jarulraj/sqlcheck/releases/download/v1.3/sqlcheck.exe

Open a terminal (like cmd.exe) and run the executable.

ZIP

Download the zip package from the latest release.

wget https://github.com/jarulraj/sqlcheck/releases/download/v1.3/sqlcheck-x86_64.zip

Unzip it and find the SQLCheck binary (bin/sqlcheck).

unzip sqlcheck-x86_64.zip

SOURCE CODE

SQLCheck has the following software dependencies:

g++ 4.9+
cmake (Cmake installation guide)

First, clone the repository (with --recursive option).

git clone --recursive https://github.com/jarulraj/sqlcheck.git

Next, run the following commands to build and install SQLCheck:

./bootstrap
cd build
cmake -DCMAKE_BUILD_TYPE=RELEASE ..
make
make install

TESTS

To run SQLCheck unit tests during development, run:

./build/test/test_suite

Usage

$ sqlcheck -h

Command line options : sqlcheck <options>
   -f --file_name          :  file name
   -r --risk_level         :  set of anti-patterns to check
                           :  1 (all anti-patterns, default) 
                           :  2 (only medium and high risk anti-patterns) 
                           :  3 (only high risk anti-patterns) 
   -c --color_mode         :  color mode 
   -v --verbose_mode       :  verbose mode

$ sqlcheck -f examples/top_mutexes.sql -v 

-------------------------------------------------
> RISK LEVEL    :: ALL ANTI-PATTERNS
> SQL FILE NAME :: examples/top_mutexes.sql
-------------------------------------------------
==================== Results ===================

-------------------------------------------------
SQL Statement: with top_mutexes as ( select--+ leading(t1 s1 v1 v2 t2 s2) use_hash(s1)
use_nl(v1) use_hash(s2) materialize t1.hsecs ,s1.* ,s2.sleeps as end_sleeps
,s2.wait_time as end_wait_time ,s2.sleeps-s1.sleeps as delta_sleeps ,t2.hsecs -
t1.hsecs as delta_hsecs --,s2.* from v$timer t1 ,v$mutex_sleep s1 ,(select/*+
no_merge */ sum(level) a from dual connect by level<=1e6) v1 ,v$timer t2
,v$mutex_sleep s2 where s1.mutex_type=s2.mutex_type and s1.location=s2.location
) select * from top_mutexes order by delta_sleeps desc;

[examples/top_mutexes.sql]: (HIGH RISK) (QUERY ANTI-PATTERN) SELECT *

● Inefficiency in moving data to the consumer:

When you SELECT *, you're often retrieving more columns from the database than
your application really needs to function. This causes more data to move from
the database server to the client, slowing access and increasing load on your
machines, as well as taking more time to travel across the network. This is
especially true when someone adds new columns to underlying tables that didn't
exist and weren't needed when the original consumers coded their data access.   


● Indexing issues:

Consider a scenario where you want to tune a query to a high level of
performance. If you were to use *, and it returned more columns than you
actually needed, the server would often have to perform more expensive methods
to retrieve your data than it otherwise might. For example, you wouldn't be able
to create an index which simply covered the columns in your SELECT list, and
even if you did (including all columns [shudder]), the next developer who came around
and added a column to the underlying table would cause the optimizer to ignore
your optimized covering index, and you'd likely find that the performance of
your query would drop substantially for no readily apparent reason.    
[Matching Expression: select *]

[examples/top_mutexes.sql]: (LOW RISK) (QUERY ANTI-PATTERN) Spaghetti Query Alert   

● Split up a complex spaghetti query into several simpler queries:

SQL is a very expressive language—you can accomplish a lot in a single query
or statement. But that doesn't mean it's mandatory or even a good idea to
approach every task with the assumption it has to be done in one line of code.
One common unintended consequence of producing all your results in one query is
a Cartesian product. This happens when two of the tables in the query have no
condition restricting their relationship. Without such a restriction, the join
of two tables pairs each row in the first table to every row in the other table.
Each such pairing becomes a row of the result set, and you end up with many more
rows than you expect. 

It's important to consider that these queries are simply
hard to write, hard to modify, and hard to debug. You should expect to get
regular requests for incremental enhancements to your database applications.
Managers want more complex reports and more fields in a user interface. If you
design intricate, monolithic SQL queries, it's more costly and time-consuming to
make enhancements to them. Your time is worth something, both to you and to your
project. Split up a complex spaghetti query into several simpler queries. When
you split up a complex SQL query, the result may be many similar queries,
perhaps varying slightly depending on data values. Writing these queries is a
chore, so it's a good application of SQL code generation. Although SQL makes it
seem possible to solve a complex problem in a single line of code, don't be
tempted to build a house of cards.   

==================== Summary ===================   
All Anti-Patterns  :: 2   
>  High Risk   :: 1   
>  Medium Risk :: 0   
>  Low Risk    :: 1

References

(1) SQL Anti-patterns: Avoiding the Pitfalls of Database Programming, Bill Karwin
(2) Common SQL Anti-patterns, StackOverflow

Contributions

Contributions to SQLCheck are always welcome. You can contribute in different ways:

Open an issue with suggestions for improvements and errors you're facing;
Fork this repository and submit a pull request;
Improve the documentation.

License

Licensed under the Apache License.

	// PHYSICAL DATABASE DESIGN

	void CheckFloat(Configuration& state,
	const std::string& sql_statement,
	bool& print_statement){

	std::regex pattern("(float)\|(real)\|(double precision)\|(0\\.000[0-9]*)");
	std::string title = "Imprecise Data Type";
	PatternType pattern_type = PatternType::PATTERN_TYPE_PHYSICAL_DATABASE_DESIGN;

	auto message =
	"● Use precise data types: "
	"Virtually any use of FLOAT, REAL, or DOUBLE PRECISION data types is suspect. "
	"Most applications that use floating-point numbers don't require the range of "
	"values supported by IEEE 754 formats. The cumulative impact of inexact "
	"floating-point numbers is severe when calculating aggregates. "
	"Instead of FLOAT or its siblings, use the NUMERIC or DECIMAL SQL data types "
	"for fixed-precision fractional numbers. These data types store numeric values "
	"exactly, up to the precision you specify in the column definition. "
	"Do not use FLOAT if you can avoid it.";

	CheckPattern(state,
	sql_statement,
	print_statement,
	pattern,
	RISK_LEVEL_MEDIUM,
	pattern_type,
	title,
	message,
	true);

	}

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sqlcheck's Introduction

What Is SQLCheck?

What it can do

Logical Database Design Anti-Patterns

Physical Database Design Anti-Patterns

Query Anti-Patterns

Application Development Anti-Patterns

Installation

FEDORA/CENTOS (RPM)

MAC (DMG)

WINDOWS (EXE)

ZIP

SOURCE CODE

TESTS

Usage

References

Contributions

License

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