1634888468

# 4 killer Mistakes You Need To Avoid During Multiple linear Regression

Multiple linear regression (MLR) is a widely used machine learning algorithm. Plenty of business issues can achieve an appreciable solution with the ML model designed with MLR. Although the equations associated with MLR may seem profoundly chaotic and hard to understand at first sight, once you understand the concept, you will surely fall in love with MLR.

MLR is very popular amongst data scientists with plenty of application flexibility, but four killer mistakes can wreck the entire advantages of the same. So let's shed light on those four killer blunders.

1. Snubbing the linear relationship

Such kinds of mistakes indicate that you have deviated from the key concept of MLR.

The key intention of the MLR application is to derive the straight association between the dependent and independent variables. Hence, nothing can be the biggest mistake that takes this point lightly.

So, before designing the ML model, you need to ensure the following things:

• First, both the RHS and LHS equations are linear
• If either side is not linear, you need to run the transformation approach for independent variables.
• There are scopes of generating a non-linear linking function to remodel the dependent variables as an alternative to the second point.
1. Choosing ill-fitting confirmatory techniques

No matter how effectively you have programmed your multiple linear regressions if you choose a wrong confirmatory technique, then remodelling your MLR base on the same will be a complete blunder.

The majority of ML beginners choose stepwise regression as the confirmatory tool of MLR. This may lead to

• Data overfit
• Data chaos
• Interruption in upcoming data replication

The best way to avoid the blunder of the ill-fitting cross-checking technique is to go with model selection regression. This advanced regression-based cross-checking technique offers precise results with an automated modelling feature.

1. Unnecessary expansion of regression

You may think expanding your regression gives your job more credit. But do you know, such unnecessary expansion may lead to imperfect upshots?

While carrying out MLR, your target should be to use the minimum possible length of regression, especially if you are working on a single set of data. Because doing so, may

• Distract yourself from the ultimate series of solutions you are searching for.
• Provide incorrect best-fit results.

However, suppose you have the option of considering multiple datasets. In that case, you can cross-check the regression for best-fit results towards the dataset under consideration by running a predictive value comparison on the rest of the datasets.

The efficacy of your data modelling lies in the applicability of the same on other datasets.

Now, the perspective of good-fit depends on the efficacy of correlation between the considered predictive values and the random distribution model associated with the particular dataset under consideration.

Hence, migration towards another dataset will degrade the predictive power of your MLR. This will lead to the downfall in variable insight generation and business decision making potency.

I hope these articles will help you stay cautious about making such killer mistakes in multiple linear regression, but these are the only few. MLR techniques are associated with lots of other pitfalls. So apart from studying the regression techniques and associated programming, you need to be aware of that pitfall too.

If you are interested in learning MLP for data science, you can join the data science certification courses of Learbay. These courses are designed with a comprehensive statistical learning module that will help you become a data science expert within a few months.

1648900800

## plpgsql_check

I founded this project, because I wanted to publish the code I wrote in the last two years, when I tried to write enhanced checking for PostgreSQL upstream. It was not fully successful - integration into upstream requires some larger plpgsql refactoring - probably it will not be done in next years (now is Dec 2013). But written code is fully functional and can be used in production (and it is used in production). So, I created this extension to be available for all plpgsql developers.

If you like it and if you would to join to development of this extension, register yourself to postgresql extension hacking google group.

Features

• check fields of referenced database objects and types inside embedded SQL
• using correct types of function parameters
• unused variables and function argumens, unmodified OUT argumens
• partially detection of dead code (due RETURN command)
• detection of missing RETURN command in function
• try to identify unwanted hidden casts, that can be performance issue like unused indexes
• possibility to collect relations and functions used by function
• possibility to check EXECUTE stmt agaist SQL injection vulnerability

I invite any ideas, patches, bugreports.

plpgsql_check is next generation of plpgsql_lint. It allows to check source code by explicit call plpgsql_check_function.

PostgreSQL PostgreSQL 10, 11, 12, 13 and 14 are supported.

The SQL statements inside PL/pgSQL functions are checked by validator for semantic errors. These errors can be found by plpgsql_check_function:

Active mode

``````postgres=# CREATE EXTENSION plpgsql_check;
postgres=# CREATE TABLE t1(a int, b int);
CREATE TABLE

postgres=#
CREATE OR REPLACE FUNCTION public.f1()
RETURNS void
LANGUAGE plpgsql
AS \$function\$
DECLARE r record;
BEGIN
FOR r IN SELECT * FROM t1
LOOP
RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
END LOOP;
END;
\$function\$;

CREATE FUNCTION

postgres=# select f1(); -- execution doesn't find a bug due to empty table t1
f1
────

(1 row)

postgres=# \x
Expanded display is on.
postgres=# select * from plpgsql_check_function_tb('f1()');
─[ RECORD 1 ]───────────────────────────
functionid │ f1
lineno     │ 6
statement  │ RAISE
sqlstate   │ 42703
message    │ record "r" has no field "c"
detail     │ [null]
hint       │ [null]
level      │ error
position   │ 0
query      │ [null]

postgres=# \sf+ f1
CREATE OR REPLACE FUNCTION public.f1()
RETURNS void
LANGUAGE plpgsql
1       AS \$function\$
2       DECLARE r record;
3       BEGIN
4         FOR r IN SELECT * FROM t1
5         LOOP
6           RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
7         END LOOP;
8       END;
9       \$function\$
``````

Function plpgsql_check_function() has three possible formats: text, json or xml

``````select * from plpgsql_check_function('f1()', fatal_errors := false);
plpgsql_check_function
------------------------------------------------------------------------
error:42703:4:SQL statement:column "c" of relation "t1" does not exist
Query: update t1 set c = 30
--                   ^
error:42P01:7:RAISE:missing FROM-clause entry for table "r"
Query: SELECT r.c
--            ^
error:42601:7:RAISE:too few parameters specified for RAISE
(7 rows)

postgres=# select * from plpgsql_check_function('fx()', format:='xml');
plpgsql_check_function
────────────────────────────────────────────────────────────────
<Function oid="16400">                                        ↵
<Issue>                                                     ↵
<Level>error</level>                                      ↵
<Sqlstate>42P01</Sqlstate>                                ↵
<Message>relation "foo111" does not exist</Message>       ↵
<Stmt lineno="3">RETURN</Stmt>                            ↵
<Query position="23">SELECT (select a from foo111)</Query>↵
</Issue>                                                    ↵
</Function>
(1 row)
``````

## Arguments

You can set level of warnings via function's parameters:

### Mandatory arguments

• function name or function signature - these functions requires function specification. Any function in PostgreSQL can be specified by Oid or by name or by signature. When you know oid or complete function's signature, you can use a regprocedure type parameter like `'fx()'::regprocedure` or `16799::regprocedure`. Possible alternative is using a name only, when function's name is unique - like `'fx'`. When the name is not unique or the function doesn't exists it raises a error.

### Optional arguments

`relid DEFAULT 0` - oid of relation assigned with trigger function. It is necessary for check of any trigger function.

`fatal_errors boolean DEFAULT true` - stop on first error

`other_warnings boolean DEFAULT true` - show warnings like different attributes number in assignmenet on left and right side, variable overlaps function's parameter, unused variables, unwanted casting, ..

`extra_warnings boolean DEFAULT true` - show warnings like missing `RETURN`, shadowed variables, dead code, never read (unused) function's parameter, unmodified variables, modified auto variables, ..

`performance_warnings boolean DEFAULT false` - performance related warnings like declared type with type modificator, casting, implicit casts in where clause (can be reason why index is not used), ..

`security_warnings boolean DEFAULT false` - security related checks like SQL injection vulnerability detection

`anyelementtype regtype DEFAULT 'int'` - a real type used instead anyelement type

`anyenumtype regtype DEFAULT '-'` - a real type used instead anyenum type

`anyrangetype regtype DEFAULT 'int4range'` - a real type used instead anyrange type

`anycompatibletype DEFAULT 'int'` - a real type used instead anycompatible type

`anycompatiblerangetype DEFAULT 'int4range'` - a real type used instead anycompatible range type

`without_warnings DEFAULT false` - disable all warnings

`all_warnings DEFAULT false` - enable all warnings

`newtable DEFAULT NULL`, `oldtable DEFAULT NULL` - the names of NEW or OLD transitive tables. These parameters are required when transitive tables are used.

## Triggers

When you want to check any trigger, you have to enter a relation that will be used together with trigger function

``````CREATE TABLE bar(a int, b int);

postgres=# \sf+ foo_trg
CREATE OR REPLACE FUNCTION public.foo_trg()
RETURNS trigger
LANGUAGE plpgsql
1       AS \$function\$
2       BEGIN
3         NEW.c := NEW.a + NEW.b;
4         RETURN NEW;
5       END;
6       \$function\$
``````

Missing relation specification

``````postgres=# select * from plpgsql_check_function('foo_trg()');
ERROR:  missing trigger relation
HINT:  Trigger relation oid must be valid
``````

Correct trigger checking (with specified relation)

``````postgres=# select * from plpgsql_check_function('foo_trg()', 'bar');
plpgsql_check_function
--------------------------------------------------------
error:42703:3:assignment:record "new" has no field "c"
(1 row)
``````

For triggers with transitive tables you can set a `oldtable` or `newtable` parameters:

``````create or replace function footab_trig_func()
returns trigger as \$\$
declare x int;
begin
if false then
-- should be ok;
select count(*) from newtab into x;

-- should fail;
select count(*) from newtab where d = 10 into x;
end if;
return null;
end;
\$\$ language plpgsql;

select * from plpgsql_check_function('footab_trig_func','footab', newtable := 'newtab');
``````

## Mass check

You can use the plpgsql_check_function for mass check functions and mass check triggers. Please, test following queries:

``````-- check all nontrigger plpgsql functions
SELECT p.oid, p.proname, plpgsql_check_function(p.oid)
FROM pg_catalog.pg_namespace n
JOIN pg_catalog.pg_proc p ON pronamespace = n.oid
JOIN pg_catalog.pg_language l ON p.prolang = l.oid
WHERE l.lanname = 'plpgsql' AND p.prorettype <> 2279;
``````

or

``````SELECT p.proname, tgrelid::regclass, cf.*
FROM pg_proc p
JOIN pg_trigger t ON t.tgfoid = p.oid
JOIN pg_language l ON p.prolang = l.oid
JOIN pg_namespace n ON p.pronamespace = n.oid,
LATERAL plpgsql_check_function(p.oid, t.tgrelid) cf
WHERE n.nspname = 'public' and l.lanname = 'plpgsql'
``````

or

``````-- check all plpgsql functions (functions or trigger functions with defined triggers)
SELECT
(pcf).functionid::regprocedure, (pcf).lineno, (pcf).statement,
(pcf).sqlstate, (pcf).message, (pcf).detail, (pcf).hint, (pcf).level,
(pcf)."position", (pcf).query, (pcf).context
FROM
(
SELECT
plpgsql_check_function_tb(pg_proc.oid, COALESCE(pg_trigger.tgrelid, 0)) AS pcf
FROM pg_proc
LEFT JOIN pg_trigger
ON (pg_trigger.tgfoid = pg_proc.oid)
WHERE
prolang = (SELECT lang.oid FROM pg_language lang WHERE lang.lanname = 'plpgsql') AND
pronamespace <> (SELECT nsp.oid FROM pg_namespace nsp WHERE nsp.nspname = 'pg_catalog') AND
-- ignore unused triggers
(pg_proc.prorettype <> (SELECT typ.oid FROM pg_type typ WHERE typ.typname = 'trigger') OR
pg_trigger.tgfoid IS NOT NULL)
OFFSET 0
) ss
ORDER BY (pcf).functionid::regprocedure::text, (pcf).lineno
``````

Passive mode

Functions should be checked on start - plpgsql_check module must be loaded.

## Configuration

``````plpgsql_check.mode = [ disabled | by_function | fresh_start | every_start ]
plpgsql_check.fatal_errors = [ yes | no ]

plpgsql_check.show_nonperformance_warnings = false
plpgsql_check.show_performance_warnings = false
``````

Default mode is by_function, that means that the enhanced check is done only in active mode - by plpgsql_check_function. `fresh_start` means cold start.

You can enable passive mode by

``````load 'plpgsql'; -- 1.1 and higher doesn't need it
set plpgsql_check.mode = 'every_start';

SELECT fx(10); -- run functions - function is checked before runtime starts it
``````

Limits

plpgsql_check should find almost all errors on really static code. When developer use some PLpgSQL's dynamic features like dynamic SQL or record data type, then false positives are possible. These should be rare - in well written code - and then the affected function should be redesigned or plpgsql_check should be disabled for this function.

``````CREATE OR REPLACE FUNCTION f1()
RETURNS void AS \$\$
DECLARE r record;
BEGIN
FOR r IN EXECUTE 'SELECT * FROM t1'
LOOP
RAISE NOTICE '%', r.c;
END LOOP;
END;
\$\$ LANGUAGE plpgsql SET plpgsql.enable_check TO false;
``````

A usage of plpgsql_check adds a small overhead (in enabled passive mode) and you should use it only in develop or preprod environments.

## Dynamic SQL

This module doesn't check queries that are assembled in runtime. It is not possible to identify results of dynamic queries - so plpgsql_check cannot to set correct type to record variables and cannot to check a dependent SQLs and expressions.

When type of record's variable is not know, you can assign it explicitly with pragma `type`:

``````DECLARE r record;
BEGIN
EXECUTE format('SELECT * FROM %I', _tablename) INTO r;
PERFORM plpgsql_check_pragma('type: r (id int, processed bool)');
IF NOT r.processed THEN
...
``````

Attention: The SQL injection check can detect only some SQL injection vulnerabilities. This tool cannot be used for security audit! Some issues should not be detected. This check can raise false alarms too - probably when variable is sanitized by other command or when value is of some compose type.

## Refcursors

plpgsql_check should not to detect structure of referenced cursors. A reference on cursor in PLpgSQL is implemented as name of global cursor. In check time, the name is not known (not in all possibilities), and global cursor doesn't exist. It is significant break for any static analyse. PLpgSQL cannot to set correct type for record variables and cannot to check a dependent SQLs and expressions. A solution is same like dynamic SQL. Don't use record variable as target when you use refcursor type or disable plpgsql_check for these functions.

``````CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS \$\$
DECLARE
rec_var record;
BEGIN
FETCH refcur_var INTO rec_var; -- this is STOP for plpgsql_check
RAISE NOTICE '%', rec_var;     -- record rec_var is not assigned yet error
``````

In this case a record type should not be used (use known rowtype instead):

``````CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS \$\$
DECLARE
rec_var some_rowtype;
BEGIN
FETCH refcur_var INTO rec_var;
RAISE NOTICE '%', rec_var;
``````

## Temporary tables

plpgsql_check cannot verify queries over temporary tables that are created in plpgsql's function runtime. For this use case it is necessary to create a fake temp table or disable plpgsql_check for this function.

In reality temp tables are stored in own (per user) schema with higher priority than persistent tables. So you can do (with following trick safetly):

``````CREATE OR REPLACE FUNCTION public.disable_dml()
RETURNS trigger
LANGUAGE plpgsql AS \$function\$
BEGIN
RAISE EXCEPTION SQLSTATE '42P01'
USING message = format('this instance of %I table doesn''t allow any DML operation', TG_TABLE_NAME),
hint = format('you should to run "CREATE TEMP TABLE %1\$I(LIKE %1\$I INCLUDING ALL);" statement',
TG_TABLE_NAME);
RETURN NULL;
END;
\$function\$;

CREATE TABLE foo(a int, b int); -- doesn't hold data ever
CREATE TRIGGER foo_disable_dml
BEFORE INSERT OR UPDATE OR DELETE ON foo
EXECUTE PROCEDURE disable_dml();

postgres=# INSERT INTO  foo VALUES(10,20);
ERROR:  this instance of foo table doesn't allow any DML operation
HINT:  you should to run "CREATE TEMP TABLE foo(LIKE foo INCLUDING ALL);" statement
postgres=#

CREATE TABLE
postgres=# INSERT INTO  foo VALUES(10,20);
INSERT 0 1
``````

This trick emulates GLOBAL TEMP tables partially and it allows a statical validation. Other possibility is using a [template foreign data wrapper] (https://github.com/okbob/template_fdw)

You can use pragma `table` and create ephemeral table:

``````BEGIN
CREATE TEMP TABLE xxx(a int);
PERFORM plpgsql_check_pragma('table: xxx(a int)');
INSERT INTO xxx VALUES(10);
``````

Dependency list

A function plpgsql_show_dependency_tb can show all functions, operators and relations used inside processed function:

``````postgres=# select * from plpgsql_show_dependency_tb('testfunc(int,float)');
┌──────────┬───────┬────────┬─────────┬────────────────────────────┐
│   type   │  oid  │ schema │  name   │           params           │
╞══════════╪═══════╪════════╪═════════╪════════════════════════════╡
│ FUNCTION │ 36008 │ public │ myfunc1 │ (integer,double precision) │
│ FUNCTION │ 35999 │ public │ myfunc2 │ (integer,double precision) │
│ OPERATOR │ 36007 │ public │ **      │ (integer,integer)          │
│ RELATION │ 36005 │ public │ myview  │                            │
│ RELATION │ 36002 │ public │ mytable │                            │
└──────────┴───────┴────────┴─────────┴────────────────────────────┘
(4 rows)
``````

Profiler

The plpgsql_check contains simple profiler of plpgsql functions and procedures. It can work with/without a access to shared memory. It depends on `shared_preload_libraries` config. When plpgsql_check was initialized by `shared_preload_libraries`, then it can allocate shared memory, and function's profiles are stored there. When plpgsql_check cannot to allocate shared momory, the profile is stored in session memory.

Due dependencies, `shared_preload_libraries` should to contains `plpgsql` first

``````postgres=# show shared_preload_libraries ;
┌──────────────────────────┐
╞══════════════════════════╡
│ plpgsql,plpgsql_check    │
└──────────────────────────┘
(1 row)
``````

The profiler is active when GUC `plpgsql_check.profiler` is on. The profiler doesn't require shared memory, but if there are not shared memory, then the profile is limmitted just to active session.

When plpgsql_check is initialized by `shared_preload_libraries`, another GUC is available to configure the amount of shared memory used by the profiler: `plpgsql_check.profiler_max_shared_chunks`. This defines the maximum number of statements chunk that can be stored in shared memory. For each plpgsql function (or procedure), the whole content is split into chunks of 30 statements. If needed, multiple chunks can be used to store the whole content of a single function. A single chunk is 1704 bytes. The default value for this GUC is 15000, which should be enough for big projects containing hundred of thousands of statements in plpgsql, and will consume about 24MB of memory. If your project doesn't require that much number of chunks, you can set this parameter to a smaller number in order to decrease the memory usage. The minimum value is 50 (which should consume about 83kB of memory), and the maximum value is 100000 (which should consume about 163MB of memory). Changing this parameter requires a PostgreSQL restart.

The profiler will also retrieve the query identifier for each instruction that contains an expression or optimizable statement. Note that this requires pg_stat_statements, or another similar third-party extension), to be installed. There are some limitations to the query identifier retrieval:

• if a plpgsql expression contains underlying statements, only the top level query identifier will be retrieved
• the profiler doesn't compute query identifier by itself but relies on external extension, such as pg_stat_statements, for that. It means that depending on the external extension behavior, you may not be able to see a query identifier for some statements. That's for instance the case with DDL statements, as pg_stat_statements doesn't expose the query identifier for such queries.
• a query identifier is retrieved only for instructions containing expressions. This means that plpgsql_profiler_function_tb() function can report less query identifier than instructions on a single line.

Attention: A update of shared profiles can decrease performance on servers under higher load.

The profile can be displayed by function `plpgsql_profiler_function_tb`:

``````postgres=# select lineno, avg_time, source from plpgsql_profiler_function_tb('fx(int)');
┌────────┬──────────┬───────────────────────────────────────────────────────────────────┐
│ lineno │ avg_time │                              source                               │
╞════════╪══════════╪═══════════════════════════════════════════════════════════════════╡
│      1 │          │                                                                   │
│      2 │          │ declare result int = 0;                                           │
│      3 │    0.075 │ begin                                                             │
│      4 │    0.202 │   for i in 1..\$1 loop                                             │
│      5 │    0.005 │     select result + i into result; select result + i into result; │
│      6 │          │   end loop;                                                       │
│      7 │        0 │   return result;                                                  │
│      8 │          │ end;                                                              │
└────────┴──────────┴───────────────────────────────────────────────────────────────────┘
(9 rows)
``````

The profile per statements (not per line) can be displayed by function plpgsql_profiler_function_statements_tb:

``````        CREATE OR REPLACE FUNCTION public.fx1(a integer)
RETURNS integer
LANGUAGE plpgsql
1       AS \$function\$
2       begin
3         if a > 10 then
4           raise notice 'ahoj';
5           return -1;
6         else
7           raise notice 'nazdar';
8           return 1;
9         end if;
10      end;
11      \$function\$

postgres=# select stmtid, parent_stmtid, parent_note, lineno, exec_stmts, stmtname
from plpgsql_profiler_function_statements_tb('fx1');
┌────────┬───────────────┬─────────────┬────────┬────────────┬─────────────────┐
│ stmtid │ parent_stmtid │ parent_note │ lineno │ exec_stmts │    stmtname     │
╞════════╪═══════════════╪═════════════╪════════╪════════════╪═════════════════╡
│      0 │             ∅ │ ∅           │      2 │          0 │ statement block │
│      1 │             0 │ body        │      3 │          0 │ IF              │
│      2 │             1 │ then body   │      4 │          0 │ RAISE           │
│      3 │             1 │ then body   │      5 │          0 │ RETURN          │
│      4 │             1 │ else body   │      7 │          0 │ RAISE           │
│      5 │             1 │ else body   │      8 │          0 │ RETURN          │
└────────┴───────────────┴─────────────┴────────┴────────────┴─────────────────┘
(6 rows)
``````

All stored profiles can be displayed by calling function `plpgsql_profiler_functions_all`:

``````postgres=# select * from plpgsql_profiler_functions_all();
┌───────────────────────┬────────────┬────────────┬──────────┬─────────────┬──────────┬──────────┐
│        funcoid        │ exec_count │ total_time │ avg_time │ stddev_time │ min_time │ max_time │
╞═══════════════════════╪════════════╪════════════╪══════════╪═════════════╪══════════╪══════════╡
│ fxx(double precision) │          1 │       0.01 │     0.01 │        0.00 │     0.01 │     0.01 │
└───────────────────────┴────────────┴────────────┴──────────┴─────────────┴──────────┴──────────┘
(1 row)
``````

There are two functions for cleaning stored profiles: `plpgsql_profiler_reset_all()` and `plpgsql_profiler_reset(regprocedure)`.

## Coverage metrics

plpgsql_check provides two functions:

• `plpgsql_coverage_statements(name)`
• `plpgsql_coverage_branches(name)`

## Note

There is another very good PLpgSQL profiler - https://bitbucket.org/openscg/plprofiler

My extension is designed to be simple for use and practical. Nothing more or less.

plprofiler is more complex. It build call graphs and from this graph it can creates flame graph of execution times.

Both extensions can be used together with buildin PostgreSQL's feature - tracking functions.

``````set track_functions to 'pl';
...
select * from pg_stat_user_functions;
``````

Tracer

plpgsql_check provides a tracing possibility - in this mode you can see notices on start or end functions (terse and default verbosity) and start or end statements (verbose verbosity). For default and verbose verbosity the content of function arguments is displayed. The content of related variables are displayed when verbosity is verbose.

``````postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #0 ->> start of inline_code_block (Oid=0)
NOTICE:  #2   ->> start of function fx(integer,integer,date,text) (Oid=16405)
NOTICE:  #2        call by inline_code_block line 1 at PERFORM
NOTICE:  #2       "a" => '10', "b" => null, "c" => '2020-08-03', "d" => 'stěhule'
NOTICE:  #4     ->> start of function fx(integer) (Oid=16404)
NOTICE:  #4          call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4         "a" => '10'
NOTICE:  #4     <<- end of function fx (elapsed time=0.098 ms)
NOTICE:  #2   <<- end of function fx (elapsed time=0.399 ms)
NOTICE:  #0 <<- end of block (elapsed time=0.754 ms)
``````

The number after `#` is a execution frame counter (this number is related to deep of error context stack). It allows to pair start end and of function.

Tracing is enabled by setting `plpgsql_check.tracer` to `on`. Attention - enabling this behaviour has significant negative impact on performance (unlike the profiler). You can set a level for output used by tracer `plpgsql_check.tracer_errlevel` (default is `notice`). The output content is limited by length specified by `plpgsql_check.tracer_variable_max_length` configuration variable.

In terse verbose mode the output is reduced:

``````postgres=# set plpgsql_check.tracer_verbosity TO terse;
SET
postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #0 start of inline code block (oid=0)
NOTICE:  #2 start of fx (oid=16405)
NOTICE:  #4 start of fx (oid=16404)
NOTICE:  #4 end of fx
NOTICE:  #2 end of fx
NOTICE:  #0 end of inline code block
``````

In verbose mode the output is extended about statement details:

``````postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #0            ->> start of block inline_code_block (oid=0)
NOTICE:  #0.1       1  --> start of PERFORM
NOTICE:  #2              ->> start of function fx(integer,integer,date,text) (oid=16405)
NOTICE:  #2                   call by inline_code_block line 1 at PERFORM
NOTICE:  #2                  "a" => '10', "b" => null, "c" => '2020-08-04', "d" => 'stěhule'
NOTICE:  #2.1       1    --> start of PERFORM
NOTICE:  #2.1                "a" => '10'
NOTICE:  #4                ->> start of function fx(integer) (oid=16404)
NOTICE:  #4                     call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4                    "a" => '10'
NOTICE:  #4.1       6      --> start of assignment
NOTICE:  #4.1                  "a" => '10', "b" => '20'
NOTICE:  #4.1              <-- end of assignment (elapsed time=0.076 ms)
NOTICE:  #4.1                  "res" => '130'
NOTICE:  #4.2       7      --> start of RETURN
NOTICE:  #4.2                  "res" => '130'
NOTICE:  #4.2              <-- end of RETURN (elapsed time=0.054 ms)
NOTICE:  #4                <<- end of function fx (elapsed time=0.373 ms)
NOTICE:  #2.1            <-- end of PERFORM (elapsed time=0.589 ms)
NOTICE:  #2              <<- end of function fx (elapsed time=0.727 ms)
NOTICE:  #0.1          <-- end of PERFORM (elapsed time=1.147 ms)
NOTICE:  #0            <<- end of block (elapsed time=1.286 ms)
``````

Special feature of tracer is tracing of `ASSERT` statement when `plpgsql_check.trace_assert` is `on`. When `plpgsql_check.trace_assert_verbosity` is `DEFAULT`, then all function's or procedure's variables are displayed when assert expression is false. When this configuration is `VERBOSE` then all variables from all plpgsql frames are displayed. This behaviour is independent on `plpgsql.check_asserts` value. It can be used, although the assertions are disabled in plpgsql runtime.

``````postgres=# set plpgsql_check.tracer to off;
postgres=# set plpgsql_check.trace_assert_verbosity TO verbose;

postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
ERROR:  assertion failed
CONTEXT:  PL/pgSQL function fx(integer) line 12 at ASSERT
SQL statement "SELECT fx(a)"
PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
SQL statement "SELECT fx(10,null, 'now', e'stěhule')"
PL/pgSQL function inline_code_block line 1 at PERFORM

postgres=# set plpgsql.check_asserts to off;
SET
postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
DO
``````

## Attention - SECURITY

Tracer prints content of variables or function arguments. For security definer function, this content can hold security sensitive data. This is reason why tracer is disabled by default and should be enabled only with super user rights `plpgsql_check.enable_tracer`.

Pragma

You can configure plpgsql_check behave inside checked function with "pragma" function. This is a analogy of PL/SQL or ADA language of PRAGMA feature. PLpgSQL doesn't support PRAGMA, but plpgsql_check detects function named `plpgsql_check_pragma` and get options from parameters of this function. These plpgsql_check options are valid to end of group of statements.

``````CREATE OR REPLACE FUNCTION test()
RETURNS void AS \$\$
BEGIN
...
-- for following statements disable check
PERFORM plpgsql_check_pragma('disable:check');
...
-- enable check again
PERFORM plpgsql_check_pragma('enable:check');
...
END;
\$\$ LANGUAGE plpgsql;
``````

The function `plpgsql_check_pragma` is immutable function that returns one. It is defined by `plpgsql_check` extension. You can declare alternative `plpgsql_check_pragma` function like:

``````CREATE OR REPLACE FUNCTION plpgsql_check_pragma(VARIADIC args[])
RETURNS int AS \$\$
SELECT 1
\$\$ LANGUAGE sql IMMUTABLE;
``````

Using pragma function in declaration part of top block sets options on function level too.

``````CREATE OR REPLACE FUNCTION test()
RETURNS void AS \$\$
DECLARE
aux int := plpgsql_check_pragma('disable:extra_warnings');
...
``````

Shorter syntax for pragma is supported too:

``````CREATE OR REPLACE FUNCTION test()
RETURNS void AS \$\$
DECLARE r record;
BEGIN
PERFORM 'PRAGMA:TYPE:r (a int, b int)';
PERFORM 'PRAGMA:TABLE: x (like pg_class)';
...
``````

## Supported pragmas

`echo:str` - print string (for testing)

`status:check`,`status:tracer`, `status:other_warnings`, `status:performance_warnings`, `status:extra_warnings`,`status:security_warnings`

`enable:check`,`enable:tracer`, `enable:other_warnings`, `enable:performance_warnings`, `enable:extra_warnings`,`enable:security_warnings`

`disable:check`,`disable:tracer`, `disable:other_warnings`, `disable:performance_warnings`, `disable:extra_warnings`,`disable:security_warnings`

`type:varname typename` or `type:varname (fieldname type, ...)` - set type to variable of record type

`table: name (column_name type, ...)` or `table: name (like tablename)` - create ephereal table

Pragmas `enable:tracer` and `disable:tracer`are active for Postgres 12 and higher

Compilation

You need a development environment for PostgreSQL extensions:

``````make clean
make install
``````

result:

``````[pavel@localhost plpgsql_check]\$ make USE_PGXS=1 clean
rm -f plpgsql_check.so   libplpgsql_check.a  libplpgsql_check.pc
rm -f plpgsql_check.o
rm -rf results/ regression.diffs regression.out tmp_check/ log/
[pavel@localhost plpgsql_check]\$ make USE_PGXS=1 all
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -I. -I./ -I/usr/local/pgsql/include/server -I/usr/local/pgsql/include/internal -D_GNU_SOURCE   -c -o plpgsql_check.o plpgsql_check.c
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -shared -o plpgsql_check.so plpgsql_check.o -L/usr/local/pgsql/lib -Wl,--as-needed -Wl,-rpath,'/usr/local/pgsql/lib',--enable-new-dtags
[pavel@localhost plpgsql_check]\$ su root
[root@localhost plpgsql_check]# make USE_PGXS=1 install
/usr/bin/mkdir -p '/usr/local/pgsql/lib'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/install -c -m 755  plpgsql_check.so '/usr/local/pgsql/lib/plpgsql_check.so'
/usr/bin/install -c -m 644 plpgsql_check.control '/usr/local/pgsql/share/extension/'
/usr/bin/install -c -m 644 plpgsql_check--0.9.sql '/usr/local/pgsql/share/extension/'
[root@localhost plpgsql_check]# exit
[pavel@localhost plpgsql_check]\$ make USE_PGXS=1 installcheck
/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/test/regress/pg_regress --inputdir=./ --psqldir='/usr/local/pgsql/bin'    --dbname=pl_regression --load-language=plpgsql --dbname=contrib_regression plpgsql_check_passive plpgsql_check_active plpgsql_check_active-9.5
(using postmaster on Unix socket, default port)
============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== installing plpgsql                     ==============
CREATE LANGUAGE
============== running regression test queries        ==============
test plpgsql_check_passive    ... ok
test plpgsql_check_active     ... ok
test plpgsql_check_active-9.5 ... ok

=====================
All 3 tests passed.
=====================
``````

## Compilation on Ubuntu

Sometimes successful compilation can require libicu-dev package (PostgreSQL 10 and higher - when pg was compiled with ICU support)

``````sudo apt install libicu-dev
``````

## Compilation plpgsql_check on Windows

You can check precompiled dll libraries http://okbob.blogspot.cz/2015/02/plpgsqlcheck-is-available-for-microsoft.html

or compile by self:

4. Build plpgsql_check.dll
5. Install plugin
6. copy `plpgsql_check.dll` to `PostgreSQL\14\lib`
7. copy `plpgsql_check.control` and `plpgsql_check--2.1.sql` to `PostgreSQL\14\share\extension`

## Checked on

• gcc on Linux (against all supported PostgreSQL)
• clang 3.4 on Linux (against PostgreSQL 10)
• for success regress tests the PostgreSQL 10 or higher is required

Compilation against PostgreSQL 10 requires libICU!

Licence

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

If you like it, send a postcard to address

``````Pavel Stehule
Skalice 12
256 01 Benesov u Prahy
Czech Republic
``````

I invite any questions, comments, bug reports, patches on mail address pavel.stehule@gmail.com

Author: okbob
Source Code: https://github.com/okbob/plpgsql_check

1648803600

## plpgsql_check

I founded this project, because I wanted to publish the code I wrote in the last two years, when I tried to write enhanced checking for PostgreSQL upstream. It was not fully successful - integration into upstream requires some larger plpgsql refactoring - probably it will not be done in next years (now is Dec 2013). But written code is fully functional and can be used in production (and it is used in production). So, I created this extension to be available for all plpgsql developers.

If you like it and if you would to join to development of this extension, register yourself to postgresql extension hacking google group.

Features

• check fields of referenced database objects and types inside embedded SQL
• using correct types of function parameters
• unused variables and function argumens, unmodified OUT argumens
• partially detection of dead code (due RETURN command)
• detection of missing RETURN command in function
• try to identify unwanted hidden casts, that can be performance issue like unused indexes
• possibility to collect relations and functions used by function
• possibility to check EXECUTE stmt agaist SQL injection vulnerability

I invite any ideas, patches, bugreports.

plpgsql_check is next generation of plpgsql_lint. It allows to check source code by explicit call plpgsql_check_function.

PostgreSQL PostgreSQL 10, 11, 12, 13 and 14 are supported.

The SQL statements inside PL/pgSQL functions are checked by validator for semantic errors. These errors can be found by plpgsql_check_function:

Active mode

``````postgres=# CREATE EXTENSION plpgsql_check;
postgres=# CREATE TABLE t1(a int, b int);
CREATE TABLE

postgres=#
CREATE OR REPLACE FUNCTION public.f1()
RETURNS void
LANGUAGE plpgsql
AS \$function\$
DECLARE r record;
BEGIN
FOR r IN SELECT * FROM t1
LOOP
RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
END LOOP;
END;
\$function\$;

CREATE FUNCTION

postgres=# select f1(); -- execution doesn't find a bug due to empty table t1
f1
────

(1 row)

postgres=# \x
Expanded display is on.
postgres=# select * from plpgsql_check_function_tb('f1()');
─[ RECORD 1 ]───────────────────────────
functionid │ f1
lineno     │ 6
statement  │ RAISE
sqlstate   │ 42703
message    │ record "r" has no field "c"
detail     │ [null]
hint       │ [null]
level      │ error
position   │ 0
query      │ [null]

postgres=# \sf+ f1
CREATE OR REPLACE FUNCTION public.f1()
RETURNS void
LANGUAGE plpgsql
1       AS \$function\$
2       DECLARE r record;
3       BEGIN
4         FOR r IN SELECT * FROM t1
5         LOOP
6           RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
7         END LOOP;
8       END;
9       \$function\$
``````

Function plpgsql_check_function() has three possible formats: text, json or xml

``````select * from plpgsql_check_function('f1()', fatal_errors := false);
plpgsql_check_function
------------------------------------------------------------------------
error:42703:4:SQL statement:column "c" of relation "t1" does not exist
Query: update t1 set c = 30
--                   ^
error:42P01:7:RAISE:missing FROM-clause entry for table "r"
Query: SELECT r.c
--            ^
error:42601:7:RAISE:too few parameters specified for RAISE
(7 rows)

postgres=# select * from plpgsql_check_function('fx()', format:='xml');
plpgsql_check_function
────────────────────────────────────────────────────────────────
<Function oid="16400">                                        ↵
<Issue>                                                     ↵
<Level>error</level>                                      ↵
<Sqlstate>42P01</Sqlstate>                                ↵
<Message>relation "foo111" does not exist</Message>       ↵
<Stmt lineno="3">RETURN</Stmt>                            ↵
<Query position="23">SELECT (select a from foo111)</Query>↵
</Issue>                                                    ↵
</Function>
(1 row)
``````

## Arguments

You can set level of warnings via function's parameters:

### Mandatory arguments

• function name or function signature - these functions requires function specification. Any function in PostgreSQL can be specified by Oid or by name or by signature. When you know oid or complete function's signature, you can use a regprocedure type parameter like `'fx()'::regprocedure` or `16799::regprocedure`. Possible alternative is using a name only, when function's name is unique - like `'fx'`. When the name is not unique or the function doesn't exists it raises a error.

### Optional arguments

`relid DEFAULT 0` - oid of relation assigned with trigger function. It is necessary for check of any trigger function.

`fatal_errors boolean DEFAULT true` - stop on first error

`other_warnings boolean DEFAULT true` - show warnings like different attributes number in assignmenet on left and right side, variable overlaps function's parameter, unused variables, unwanted casting, ..

`extra_warnings boolean DEFAULT true` - show warnings like missing `RETURN`, shadowed variables, dead code, never read (unused) function's parameter, unmodified variables, modified auto variables, ..

`performance_warnings boolean DEFAULT false` - performance related warnings like declared type with type modificator, casting, implicit casts in where clause (can be reason why index is not used), ..

`security_warnings boolean DEFAULT false` - security related checks like SQL injection vulnerability detection

`anyelementtype regtype DEFAULT 'int'` - a real type used instead anyelement type

`anyenumtype regtype DEFAULT '-'` - a real type used instead anyenum type

`anyrangetype regtype DEFAULT 'int4range'` - a real type used instead anyrange type

`anycompatibletype DEFAULT 'int'` - a real type used instead anycompatible type

`anycompatiblerangetype DEFAULT 'int4range'` - a real type used instead anycompatible range type

`without_warnings DEFAULT false` - disable all warnings

`all_warnings DEFAULT false` - enable all warnings

`newtable DEFAULT NULL`, `oldtable DEFAULT NULL` - the names of NEW or OLD transitive tables. These parameters are required when transitive tables are used.

## Triggers

When you want to check any trigger, you have to enter a relation that will be used together with trigger function

``````CREATE TABLE bar(a int, b int);

postgres=# \sf+ foo_trg
CREATE OR REPLACE FUNCTION public.foo_trg()
RETURNS trigger
LANGUAGE plpgsql
1       AS \$function\$
2       BEGIN
3         NEW.c := NEW.a + NEW.b;
4         RETURN NEW;
5       END;
6       \$function\$
``````

Missing relation specification

``````postgres=# select * from plpgsql_check_function('foo_trg()');
ERROR:  missing trigger relation
HINT:  Trigger relation oid must be valid
``````

Correct trigger checking (with specified relation)

``````postgres=# select * from plpgsql_check_function('foo_trg()', 'bar');
plpgsql_check_function
--------------------------------------------------------
error:42703:3:assignment:record "new" has no field "c"
(1 row)
``````

For triggers with transitive tables you can set a `oldtable` or `newtable` parameters:

``````create or replace function footab_trig_func()
returns trigger as \$\$
declare x int;
begin
if false then
-- should be ok;
select count(*) from newtab into x;

-- should fail;
select count(*) from newtab where d = 10 into x;
end if;
return null;
end;
\$\$ language plpgsql;

select * from plpgsql_check_function('footab_trig_func','footab', newtable := 'newtab');
``````

## Mass check

You can use the plpgsql_check_function for mass check functions and mass check triggers. Please, test following queries:

``````-- check all nontrigger plpgsql functions
SELECT p.oid, p.proname, plpgsql_check_function(p.oid)
FROM pg_catalog.pg_namespace n
JOIN pg_catalog.pg_proc p ON pronamespace = n.oid
JOIN pg_catalog.pg_language l ON p.prolang = l.oid
WHERE l.lanname = 'plpgsql' AND p.prorettype <> 2279;
``````

or

``````SELECT p.proname, tgrelid::regclass, cf.*
FROM pg_proc p
JOIN pg_trigger t ON t.tgfoid = p.oid
JOIN pg_language l ON p.prolang = l.oid
JOIN pg_namespace n ON p.pronamespace = n.oid,
LATERAL plpgsql_check_function(p.oid, t.tgrelid) cf
WHERE n.nspname = 'public' and l.lanname = 'plpgsql'
``````

or

``````-- check all plpgsql functions (functions or trigger functions with defined triggers)
SELECT
(pcf).functionid::regprocedure, (pcf).lineno, (pcf).statement,
(pcf).sqlstate, (pcf).message, (pcf).detail, (pcf).hint, (pcf).level,
(pcf)."position", (pcf).query, (pcf).context
FROM
(
SELECT
plpgsql_check_function_tb(pg_proc.oid, COALESCE(pg_trigger.tgrelid, 0)) AS pcf
FROM pg_proc
LEFT JOIN pg_trigger
ON (pg_trigger.tgfoid = pg_proc.oid)
WHERE
prolang = (SELECT lang.oid FROM pg_language lang WHERE lang.lanname = 'plpgsql') AND
pronamespace <> (SELECT nsp.oid FROM pg_namespace nsp WHERE nsp.nspname = 'pg_catalog') AND
-- ignore unused triggers
(pg_proc.prorettype <> (SELECT typ.oid FROM pg_type typ WHERE typ.typname = 'trigger') OR
pg_trigger.tgfoid IS NOT NULL)
OFFSET 0
) ss
ORDER BY (pcf).functionid::regprocedure::text, (pcf).lineno
``````

Passive mode

Functions should be checked on start - plpgsql_check module must be loaded.

## Configuration

``````plpgsql_check.mode = [ disabled | by_function | fresh_start | every_start ]
plpgsql_check.fatal_errors = [ yes | no ]

plpgsql_check.show_nonperformance_warnings = false
plpgsql_check.show_performance_warnings = false
``````

Default mode is by_function, that means that the enhanced check is done only in active mode - by plpgsql_check_function. `fresh_start` means cold start.

You can enable passive mode by

``````load 'plpgsql'; -- 1.1 and higher doesn't need it
set plpgsql_check.mode = 'every_start';

SELECT fx(10); -- run functions - function is checked before runtime starts it
``````

Limits

plpgsql_check should find almost all errors on really static code. When developer use some PLpgSQL's dynamic features like dynamic SQL or record data type, then false positives are possible. These should be rare - in well written code - and then the affected function should be redesigned or plpgsql_check should be disabled for this function.

``````CREATE OR REPLACE FUNCTION f1()
RETURNS void AS \$\$
DECLARE r record;
BEGIN
FOR r IN EXECUTE 'SELECT * FROM t1'
LOOP
RAISE NOTICE '%', r.c;
END LOOP;
END;
\$\$ LANGUAGE plpgsql SET plpgsql.enable_check TO false;
``````

A usage of plpgsql_check adds a small overhead (in enabled passive mode) and you should use it only in develop or preprod environments.

## Dynamic SQL

This module doesn't check queries that are assembled in runtime. It is not possible to identify results of dynamic queries - so plpgsql_check cannot to set correct type to record variables and cannot to check a dependent SQLs and expressions.

When type of record's variable is not know, you can assign it explicitly with pragma `type`:

``````DECLARE r record;
BEGIN
EXECUTE format('SELECT * FROM %I', _tablename) INTO r;
PERFORM plpgsql_check_pragma('type: r (id int, processed bool)');
IF NOT r.processed THEN
...
``````

Attention: The SQL injection check can detect only some SQL injection vulnerabilities. This tool cannot be used for security audit! Some issues should not be detected. This check can raise false alarms too - probably when variable is sanitized by other command or when value is of some compose type.

## Refcursors

plpgsql_check should not to detect structure of referenced cursors. A reference on cursor in PLpgSQL is implemented as name of global cursor. In check time, the name is not known (not in all possibilities), and global cursor doesn't exist. It is significant break for any static analyse. PLpgSQL cannot to set correct type for record variables and cannot to check a dependent SQLs and expressions. A solution is same like dynamic SQL. Don't use record variable as target when you use refcursor type or disable plpgsql_check for these functions.

``````CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS \$\$
DECLARE
rec_var record;
BEGIN
FETCH refcur_var INTO rec_var; -- this is STOP for plpgsql_check
RAISE NOTICE '%', rec_var;     -- record rec_var is not assigned yet error
``````

In this case a record type should not be used (use known rowtype instead):

``````CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS \$\$
DECLARE
rec_var some_rowtype;
BEGIN
FETCH refcur_var INTO rec_var;
RAISE NOTICE '%', rec_var;
``````

## Temporary tables

plpgsql_check cannot verify queries over temporary tables that are created in plpgsql's function runtime. For this use case it is necessary to create a fake temp table or disable plpgsql_check for this function.

In reality temp tables are stored in own (per user) schema with higher priority than persistent tables. So you can do (with following trick safetly):

``````CREATE OR REPLACE FUNCTION public.disable_dml()
RETURNS trigger
LANGUAGE plpgsql AS \$function\$
BEGIN
RAISE EXCEPTION SQLSTATE '42P01'
USING message = format('this instance of %I table doesn''t allow any DML operation', TG_TABLE_NAME),
hint = format('you should to run "CREATE TEMP TABLE %1\$I(LIKE %1\$I INCLUDING ALL);" statement',
TG_TABLE_NAME);
RETURN NULL;
END;
\$function\$;

CREATE TABLE foo(a int, b int); -- doesn't hold data ever
CREATE TRIGGER foo_disable_dml
BEFORE INSERT OR UPDATE OR DELETE ON foo
EXECUTE PROCEDURE disable_dml();

postgres=# INSERT INTO  foo VALUES(10,20);
ERROR:  this instance of foo table doesn't allow any DML operation
HINT:  you should to run "CREATE TEMP TABLE foo(LIKE foo INCLUDING ALL);" statement
postgres=#

CREATE TABLE
postgres=# INSERT INTO  foo VALUES(10,20);
INSERT 0 1
``````

This trick emulates GLOBAL TEMP tables partially and it allows a statical validation. Other possibility is using a [template foreign data wrapper] (https://github.com/okbob/template_fdw)

You can use pragma `table` and create ephemeral table:

``````BEGIN
CREATE TEMP TABLE xxx(a int);
PERFORM plpgsql_check_pragma('table: xxx(a int)');
INSERT INTO xxx VALUES(10);
``````

Dependency list

A function plpgsql_show_dependency_tb can show all functions, operators and relations used inside processed function:

``````postgres=# select * from plpgsql_show_dependency_tb('testfunc(int,float)');
┌──────────┬───────┬────────┬─────────┬────────────────────────────┐
│   type   │  oid  │ schema │  name   │           params           │
╞══════════╪═══════╪════════╪═════════╪════════════════════════════╡
│ FUNCTION │ 36008 │ public │ myfunc1 │ (integer,double precision) │
│ FUNCTION │ 35999 │ public │ myfunc2 │ (integer,double precision) │
│ OPERATOR │ 36007 │ public │ **      │ (integer,integer)          │
│ RELATION │ 36005 │ public │ myview  │                            │
│ RELATION │ 36002 │ public │ mytable │                            │
└──────────┴───────┴────────┴─────────┴────────────────────────────┘
(4 rows)
``````

Profiler

The plpgsql_check contains simple profiler of plpgsql functions and procedures. It can work with/without a access to shared memory. It depends on `shared_preload_libraries` config. When plpgsql_check was initialized by `shared_preload_libraries`, then it can allocate shared memory, and function's profiles are stored there. When plpgsql_check cannot to allocate shared momory, the profile is stored in session memory.

Due dependencies, `shared_preload_libraries` should to contains `plpgsql` first

``````postgres=# show shared_preload_libraries ;
┌──────────────────────────┐
╞══════════════════════════╡
│ plpgsql,plpgsql_check    │
└──────────────────────────┘
(1 row)
``````

The profiler is active when GUC `plpgsql_check.profiler` is on. The profiler doesn't require shared memory, but if there are not shared memory, then the profile is limmitted just to active session.

When plpgsql_check is initialized by `shared_preload_libraries`, another GUC is available to configure the amount of shared memory used by the profiler: `plpgsql_check.profiler_max_shared_chunks`. This defines the maximum number of statements chunk that can be stored in shared memory. For each plpgsql function (or procedure), the whole content is split into chunks of 30 statements. If needed, multiple chunks can be used to store the whole content of a single function. A single chunk is 1704 bytes. The default value for this GUC is 15000, which should be enough for big projects containing hundred of thousands of statements in plpgsql, and will consume about 24MB of memory. If your project doesn't require that much number of chunks, you can set this parameter to a smaller number in order to decrease the memory usage. The minimum value is 50 (which should consume about 83kB of memory), and the maximum value is 100000 (which should consume about 163MB of memory). Changing this parameter requires a PostgreSQL restart.

The profiler will also retrieve the query identifier for each instruction that contains an expression or optimizable statement. Note that this requires pg_stat_statements, or another similar third-party extension), to be installed. There are some limitations to the query identifier retrieval:

• if a plpgsql expression contains underlying statements, only the top level query identifier will be retrieved
• the profiler doesn't compute query identifier by itself but relies on external extension, such as pg_stat_statements, for that. It means that depending on the external extension behavior, you may not be able to see a query identifier for some statements. That's for instance the case with DDL statements, as pg_stat_statements doesn't expose the query identifier for such queries.
• a query identifier is retrieved only for instructions containing expressions. This means that plpgsql_profiler_function_tb() function can report less query identifier than instructions on a single line.

Attention: A update of shared profiles can decrease performance on servers under higher load.

The profile can be displayed by function `plpgsql_profiler_function_tb`:

``````postgres=# select lineno, avg_time, source from plpgsql_profiler_function_tb('fx(int)');
┌────────┬──────────┬───────────────────────────────────────────────────────────────────┐
│ lineno │ avg_time │                              source                               │
╞════════╪══════════╪═══════════════════════════════════════════════════════════════════╡
│      1 │          │                                                                   │
│      2 │          │ declare result int = 0;                                           │
│      3 │    0.075 │ begin                                                             │
│      4 │    0.202 │   for i in 1..\$1 loop                                             │
│      5 │    0.005 │     select result + i into result; select result + i into result; │
│      6 │          │   end loop;                                                       │
│      7 │        0 │   return result;                                                  │
│      8 │          │ end;                                                              │
└────────┴──────────┴───────────────────────────────────────────────────────────────────┘
(9 rows)
``````

The profile per statements (not per line) can be displayed by function plpgsql_profiler_function_statements_tb:

``````        CREATE OR REPLACE FUNCTION public.fx1(a integer)
RETURNS integer
LANGUAGE plpgsql
1       AS \$function\$
2       begin
3         if a > 10 then
4           raise notice 'ahoj';
5           return -1;
6         else
7           raise notice 'nazdar';
8           return 1;
9         end if;
10      end;
11      \$function\$

postgres=# select stmtid, parent_stmtid, parent_note, lineno, exec_stmts, stmtname
from plpgsql_profiler_function_statements_tb('fx1');
┌────────┬───────────────┬─────────────┬────────┬────────────┬─────────────────┐
│ stmtid │ parent_stmtid │ parent_note │ lineno │ exec_stmts │    stmtname     │
╞════════╪═══════════════╪═════════════╪════════╪════════════╪═════════════════╡
│      0 │             ∅ │ ∅           │      2 │          0 │ statement block │
│      1 │             0 │ body        │      3 │          0 │ IF              │
│      2 │             1 │ then body   │      4 │          0 │ RAISE           │
│      3 │             1 │ then body   │      5 │          0 │ RETURN          │
│      4 │             1 │ else body   │      7 │          0 │ RAISE           │
│      5 │             1 │ else body   │      8 │          0 │ RETURN          │
└────────┴───────────────┴─────────────┴────────┴────────────┴─────────────────┘
(6 rows)
``````

All stored profiles can be displayed by calling function `plpgsql_profiler_functions_all`:

``````postgres=# select * from plpgsql_profiler_functions_all();
┌───────────────────────┬────────────┬────────────┬──────────┬─────────────┬──────────┬──────────┐
│        funcoid        │ exec_count │ total_time │ avg_time │ stddev_time │ min_time │ max_time │
╞═══════════════════════╪════════════╪════════════╪══════════╪═════════════╪══════════╪══════════╡
│ fxx(double precision) │          1 │       0.01 │     0.01 │        0.00 │     0.01 │     0.01 │
└───────────────────────┴────────────┴────────────┴──────────┴─────────────┴──────────┴──────────┘
(1 row)
``````

There are two functions for cleaning stored profiles: `plpgsql_profiler_reset_all()` and `plpgsql_profiler_reset(regprocedure)`.

## Coverage metrics

plpgsql_check provides two functions:

• `plpgsql_coverage_statements(name)`
• `plpgsql_coverage_branches(name)`

## Note

There is another very good PLpgSQL profiler - https://bitbucket.org/openscg/plprofiler

My extension is designed to be simple for use and practical. Nothing more or less.

plprofiler is more complex. It build call graphs and from this graph it can creates flame graph of execution times.

Both extensions can be used together with buildin PostgreSQL's feature - tracking functions.

``````set track_functions to 'pl';
...
select * from pg_stat_user_functions;
``````

Tracer

plpgsql_check provides a tracing possibility - in this mode you can see notices on start or end functions (terse and default verbosity) and start or end statements (verbose verbosity). For default and verbose verbosity the content of function arguments is displayed. The content of related variables are displayed when verbosity is verbose.

``````postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #0 ->> start of inline_code_block (Oid=0)
NOTICE:  #2   ->> start of function fx(integer,integer,date,text) (Oid=16405)
NOTICE:  #2        call by inline_code_block line 1 at PERFORM
NOTICE:  #2       "a" => '10', "b" => null, "c" => '2020-08-03', "d" => 'stěhule'
NOTICE:  #4     ->> start of function fx(integer) (Oid=16404)
NOTICE:  #4          call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4         "a" => '10'
NOTICE:  #4     <<- end of function fx (elapsed time=0.098 ms)
NOTICE:  #2   <<- end of function fx (elapsed time=0.399 ms)
NOTICE:  #0 <<- end of block (elapsed time=0.754 ms)
``````

The number after `#` is a execution frame counter (this number is related to deep of error context stack). It allows to pair start end and of function.

Tracing is enabled by setting `plpgsql_check.tracer` to `on`. Attention - enabling this behaviour has significant negative impact on performance (unlike the profiler). You can set a level for output used by tracer `plpgsql_check.tracer_errlevel` (default is `notice`). The output content is limited by length specified by `plpgsql_check.tracer_variable_max_length` configuration variable.

In terse verbose mode the output is reduced:

``````postgres=# set plpgsql_check.tracer_verbosity TO terse;
SET
postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #0 start of inline code block (oid=0)
NOTICE:  #2 start of fx (oid=16405)
NOTICE:  #4 start of fx (oid=16404)
NOTICE:  #4 end of fx
NOTICE:  #2 end of fx
NOTICE:  #0 end of inline code block
``````

In verbose mode the output is extended about statement details:

``````postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #0            ->> start of block inline_code_block (oid=0)
NOTICE:  #0.1       1  --> start of PERFORM
NOTICE:  #2              ->> start of function fx(integer,integer,date,text) (oid=16405)
NOTICE:  #2                   call by inline_code_block line 1 at PERFORM
NOTICE:  #2                  "a" => '10', "b" => null, "c" => '2020-08-04', "d" => 'stěhule'
NOTICE:  #2.1       1    --> start of PERFORM
NOTICE:  #2.1                "a" => '10'
NOTICE:  #4                ->> start of function fx(integer) (oid=16404)
NOTICE:  #4                     call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4                    "a" => '10'
NOTICE:  #4.1       6      --> start of assignment
NOTICE:  #4.1                  "a" => '10', "b" => '20'
NOTICE:  #4.1              <-- end of assignment (elapsed time=0.076 ms)
NOTICE:  #4.1                  "res" => '130'
NOTICE:  #4.2       7      --> start of RETURN
NOTICE:  #4.2                  "res" => '130'
NOTICE:  #4.2              <-- end of RETURN (elapsed time=0.054 ms)
NOTICE:  #4                <<- end of function fx (elapsed time=0.373 ms)
NOTICE:  #2.1            <-- end of PERFORM (elapsed time=0.589 ms)
NOTICE:  #2              <<- end of function fx (elapsed time=0.727 ms)
NOTICE:  #0.1          <-- end of PERFORM (elapsed time=1.147 ms)
NOTICE:  #0            <<- end of block (elapsed time=1.286 ms)
``````

Special feature of tracer is tracing of `ASSERT` statement when `plpgsql_check.trace_assert` is `on`. When `plpgsql_check.trace_assert_verbosity` is `DEFAULT`, then all function's or procedure's variables are displayed when assert expression is false. When this configuration is `VERBOSE` then all variables from all plpgsql frames are displayed. This behaviour is independent on `plpgsql.check_asserts` value. It can be used, although the assertions are disabled in plpgsql runtime.

``````postgres=# set plpgsql_check.tracer to off;
postgres=# set plpgsql_check.trace_assert_verbosity TO verbose;

postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
ERROR:  assertion failed
CONTEXT:  PL/pgSQL function fx(integer) line 12 at ASSERT
SQL statement "SELECT fx(a)"
PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
SQL statement "SELECT fx(10,null, 'now', e'stěhule')"
PL/pgSQL function inline_code_block line 1 at PERFORM

postgres=# set plpgsql.check_asserts to off;
SET
postgres=# do \$\$ begin perform fx(10,null, 'now', e'stěhule'); end; \$\$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
DO
``````

## Attention - SECURITY

Tracer prints content of variables or function arguments. For security definer function, this content can hold security sensitive data. This is reason why tracer is disabled by default and should be enabled only with super user rights `plpgsql_check.enable_tracer`.

Pragma

You can configure plpgsql_check behave inside checked function with "pragma" function. This is a analogy of PL/SQL or ADA language of PRAGMA feature. PLpgSQL doesn't support PRAGMA, but plpgsql_check detects function named `plpgsql_check_pragma` and get options from parameters of this function. These plpgsql_check options are valid to end of group of statements.

``````CREATE OR REPLACE FUNCTION test()
RETURNS void AS \$\$
BEGIN
...
-- for following statements disable check
PERFORM plpgsql_check_pragma('disable:check');
...
-- enable check again
PERFORM plpgsql_check_pragma('enable:check');
...
END;
\$\$ LANGUAGE plpgsql;
``````

The function `plpgsql_check_pragma` is immutable function that returns one. It is defined by `plpgsql_check` extension. You can declare alternative `plpgsql_check_pragma` function like:

``````CREATE OR REPLACE FUNCTION plpgsql_check_pragma(VARIADIC args[])
RETURNS int AS \$\$
SELECT 1
\$\$ LANGUAGE sql IMMUTABLE;
``````

Using pragma function in declaration part of top block sets options on function level too.

``````CREATE OR REPLACE FUNCTION test()
RETURNS void AS \$\$
DECLARE
aux int := plpgsql_check_pragma('disable:extra_warnings');
...
``````

Shorter syntax for pragma is supported too:

``````CREATE OR REPLACE FUNCTION test()
RETURNS void AS \$\$
DECLARE r record;
BEGIN
PERFORM 'PRAGMA:TYPE:r (a int, b int)';
PERFORM 'PRAGMA:TABLE: x (like pg_class)';
...
``````

## Supported pragmas

`echo:str` - print string (for testing)

`status:check`,`status:tracer`, `status:other_warnings`, `status:performance_warnings`, `status:extra_warnings`,`status:security_warnings`

`enable:check`,`enable:tracer`, `enable:other_warnings`, `enable:performance_warnings`, `enable:extra_warnings`,`enable:security_warnings`

`disable:check`,`disable:tracer`, `disable:other_warnings`, `disable:performance_warnings`, `disable:extra_warnings`,`disable:security_warnings`

`type:varname typename` or `type:varname (fieldname type, ...)` - set type to variable of record type

`table: name (column_name type, ...)` or `table: name (like tablename)` - create ephereal table

Pragmas `enable:tracer` and `disable:tracer`are active for Postgres 12 and higher

Compilation

You need a development environment for PostgreSQL extensions:

``````make clean
make install
``````

result:

``````[pavel@localhost plpgsql_check]\$ make USE_PGXS=1 clean
rm -f plpgsql_check.so   libplpgsql_check.a  libplpgsql_check.pc
rm -f plpgsql_check.o
rm -rf results/ regression.diffs regression.out tmp_check/ log/
[pavel@localhost plpgsql_check]\$ make USE_PGXS=1 all
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -I. -I./ -I/usr/local/pgsql/include/server -I/usr/local/pgsql/include/internal -D_GNU_SOURCE   -c -o plpgsql_check.o plpgsql_check.c
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -shared -o plpgsql_check.so plpgsql_check.o -L/usr/local/pgsql/lib -Wl,--as-needed -Wl,-rpath,'/usr/local/pgsql/lib',--enable-new-dtags
[pavel@localhost plpgsql_check]\$ su root
[root@localhost plpgsql_check]# make USE_PGXS=1 install
/usr/bin/mkdir -p '/usr/local/pgsql/lib'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/install -c -m 755  plpgsql_check.so '/usr/local/pgsql/lib/plpgsql_check.so'
/usr/bin/install -c -m 644 plpgsql_check.control '/usr/local/pgsql/share/extension/'
/usr/bin/install -c -m 644 plpgsql_check--0.9.sql '/usr/local/pgsql/share/extension/'
[root@localhost plpgsql_check]# exit
[pavel@localhost plpgsql_check]\$ make USE_PGXS=1 installcheck
/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/test/regress/pg_regress --inputdir=./ --psqldir='/usr/local/pgsql/bin'    --dbname=pl_regression --load-language=plpgsql --dbname=contrib_regression plpgsql_check_passive plpgsql_check_active plpgsql_check_active-9.5
(using postmaster on Unix socket, default port)
============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== installing plpgsql                     ==============
CREATE LANGUAGE
============== running regression test queries        ==============
test plpgsql_check_passive    ... ok
test plpgsql_check_active     ... ok
test plpgsql_check_active-9.5 ... ok

=====================
All 3 tests passed.
=====================
``````

## Compilation on Ubuntu

Sometimes successful compilation can require libicu-dev package (PostgreSQL 10 and higher - when pg was compiled with ICU support)

``````sudo apt install libicu-dev
``````

## Compilation plpgsql_check on Windows

You can check precompiled dll libraries http://okbob.blogspot.cz/2015/02/plpgsqlcheck-is-available-for-microsoft.html

or compile by self:

4. Build plpgsql_check.dll
5. Install plugin
6. copy `plpgsql_check.dll` to `PostgreSQL\14\lib`
7. copy `plpgsql_check.control` and `plpgsql_check--2.1.sql` to `PostgreSQL\14\share\extension`

## Checked on

• gcc on Linux (against all supported PostgreSQL)
• clang 3.4 on Linux (against PostgreSQL 10)
• for success regress tests the PostgreSQL 10 or higher is required

Compilation against PostgreSQL 10 requires libICU!

Licence

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

If you like it, send a postcard to address

``````Pavel Stehule
Skalice 12
256 01 Benesov u Prahy
Czech Republic
``````

I invite any questions, comments, bug reports, patches on mail address pavel.stehule@gmail.com

Author: okbob
Source Code: https://github.com/okbob/plpgsql_check

1653475560

## A Pure PHP Implementation Of The MessagePack Serialization Format

msgpack.php

A pure PHP implementation of the MessagePack serialization format.

## Installation

The recommended way to install the library is through Composer:

``````composer require rybakit/msgpack
``````

## Usage

### Packing

To pack values you can either use an instance of a `Packer`:

``````\$packer = new Packer();
\$packed = \$packer->pack(\$value);
``````

or call a static method on the `MessagePack` class:

``````\$packed = MessagePack::pack(\$value);
``````

In the examples above, the method `pack` automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines `map` and `array` types, which are represented by a single `array` type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from `0` and as a MessagePack map otherwise:

``````\$mpArr1 = \$packer->pack([1, 2]);               // MP array [1, 2]
\$mpArr2 = \$packer->pack([0 => 1, 1 => 2]);     // MP array [1, 2]
\$mpMap1 = \$packer->pack([0 => 1, 2 => 3]);     // MP map {0: 1, 2: 3}
\$mpMap2 = \$packer->pack([1 => 2, 2 => 3]);     // MP map {1: 2, 2: 3}
\$mpMap3 = \$packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}
``````

However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the `packMap` method:

``````\$mpMap = \$packer->packMap([1, 2]); // {0: 1, 1: 2}
``````

Here is a list of type-specific packing methods:

``````\$packer->packNil();           // MP nil
\$packer->packBool(true);      // MP bool
\$packer->packInt(42);         // MP int
\$packer->packFloat(M_PI);     // MP float (32 or 64)
\$packer->packFloat32(M_PI);   // MP float 32
\$packer->packFloat64(M_PI);   // MP float 64
\$packer->packStr('foo');      // MP str
\$packer->packBin("\x80");     // MP bin
\$packer->packArray([1, 2]);   // MP array
\$packer->packMap(['a' => 1]); // MP map
\$packer->packExt(1, "\xaa");  // MP ext
``````

Check the "Custom types" section below on how to pack custom types.

#### Packing options

The `Packer` object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):

The type detection mode (`DETECT_STR_BIN`/`DETECT_ARR_MAP`) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this, `Map` and `Bin`. Check the "Custom types" section below for details.

Examples:

``````// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
\$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);

// these will throw MessagePack\Exception\InvalidOptionException
\$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
\$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);
``````

### Unpacking

To unpack data you can either use an instance of a `BufferUnpacker`:

``````\$unpacker = new BufferUnpacker();

\$unpacker->reset(\$packed);
\$value = \$unpacker->unpack();
``````

or call a static method on the `MessagePack` class:

``````\$value = MessagePack::unpack(\$packed);
``````

If the packed data is received in chunks (e.g. when reading from a stream), use the `tryUnpack` method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an `InsufficientDataException`:

``````while (\$chunk = ...) {
\$unpacker->append(\$chunk);
if (\$messages = \$unpacker->tryUnpack()) {
return \$messages;
}
}
``````

If you want to unpack from a specific position in a buffer, use `seek`:

``````\$unpacker->seek(42); // set position equal to 42 bytes
\$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer
``````

To skip bytes from the current position, use `skip`:

``````\$unpacker->skip(10); // set position to 10 bytes ahead of the current position
``````

To get the number of remaining (unread) bytes in the buffer:

``````\$unreadBytesCount = \$unpacker->getRemainingCount();
``````

To check whether the buffer has unread data:

``````\$hasUnreadBytes = \$unpacker->hasRemaining();
``````

If needed, you can remove already read data from the buffer by calling:

``````\$releasedBytesCount = \$unpacker->release();
``````

With the `read` method you can read raw (packed) data:

``````\$packedData = \$unpacker->read(2); // read 2 bytes
``````

Besides the above methods `BufferUnpacker` provides type-specific unpacking methods, namely:

``````\$unpacker->unpackNil();   // PHP null
\$unpacker->unpackBool();  // PHP bool
\$unpacker->unpackInt();   // PHP int
\$unpacker->unpackFloat(); // PHP float
\$unpacker->unpackStr();   // PHP UTF-8 string
\$unpacker->unpackBin();   // PHP binary string
\$unpacker->unpackArray(); // PHP sequential array
\$unpacker->unpackMap();   // PHP associative array
\$unpacker->unpackExt();   // PHP MessagePack\Type\Ext object
``````

#### Unpacking options

The `BufferUnpacker` object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):

1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.

2. Make sure the GMP extension is enabled.

3. Make sure the Decimal extension is enabled.

Examples:

``````\$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";

\$unpacker = new BufferUnpacker(\$packedUint64);
var_dump(\$unpacker->unpack()); // string(20) "18446744073709551615"

\$unpacker = new BufferUnpacker(\$packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump(\$unpacker->unpack()); // object(GMP) {...}

\$unpacker = new BufferUnpacker(\$packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump(\$unpacker->unpack()); // object(Decimal\Decimal) {...}
``````

### Custom types

In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.

#### Type objects

If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the `Map` type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:

``````\$packer = new Packer();

\$packedMap = \$packer->pack(new Map([1, 2, 3]));
\$packedArray = \$packer->pack([1, 2, 3]);
``````

More type examples can be found in the src/Type directory.

#### Type transformers

As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.

A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack `bin` format type using one of the supplied transformers, `StreamTransformer`:

``````\$packer = new Packer(null, [new StreamTransformer()]);

\$packedBin = \$packer->pack(fopen('/path/to/file', 'r+'));
``````

More type transformer examples can be found in the src/TypeTransformer directory.

#### Extensions

In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).

An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.

The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.

Timestamp

The Timestamp extension type is a predefined type. Support for this type in the library is done through the `TimestampExtension` class. This class is responsible for handling `Timestamp` objects, which represent the number of seconds and optional adjustment in nanoseconds:

``````\$timestampExtension = new TimestampExtension();

\$packer = new Packer();
\$packer = \$packer->extendWith(\$timestampExtension);

\$unpacker = new BufferUnpacker();
\$unpacker = \$unpacker->extendWith(\$timestampExtension);

\$packedTimestamp = \$packer->pack(Timestamp::now());
\$timestamp = \$unpacker->reset(\$packedTimestamp)->unpack();

\$seconds = \$timestamp->getSeconds();
\$nanoseconds = \$timestamp->getNanoseconds();
``````

When using the `MessagePack` class, the Timestamp extension is already registered:

``````\$packedTimestamp = MessagePack::pack(Timestamp::now());
\$timestamp = MessagePack::unpack(\$packedTimestamp);
``````

Application-specific extensions

In addition, the format can be extended with your own types. For example, to make the built-in PHP `DateTime` objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from `0` to `127`).

More extension examples can be found in the examples/MessagePack directory.

## Exceptions

If an error occurs during packing/unpacking, a `PackingFailedException` or an `UnpackingFailedException` will be thrown, respectively. In addition, an `InsufficientDataException` can be thrown during unpacking.

An `InvalidOptionException` will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.

## Tests

Run tests as follows:

``````vendor/bin/phpunit
``````

Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:

``````./dockerfile.sh | docker build -t msgpack -
``````

The command above will create a container named `msgpack` with PHP 8.1 runtime. You may change the default runtime by defining the `PHP_IMAGE` environment variable:

``````PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -
``````

See a list of various images here.

Then run the unit tests:

``````docker run --rm -v \$PWD:/msgpack -w /msgpack msgpack
``````

#### Fuzzing

To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:

``````php-fuzzer fuzz tests/fuzz_buffer_unpacker.php
``````

#### Performance

To check performance, run:

``````php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
``````

Example output

``````Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total                  2.7618          4.0820
Skipped                     4               4
Failed                      0               0
Ignored                     0               0
``````

With JIT:

``````php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
``````

Example output

``````Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total                  1.6432          1.9674
Skipped                     4               4
Failed                      0               0
Ignored                     0               0
``````

You may change default benchmark settings by defining the following environment variables:

For example:

``````export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'
``````

Another example, benchmarking both the library and the PECL extension:

``````MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
``````

Example output

``````Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  1.5625          2.3866        0.7735          0.7243
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7
``````

With JIT:

``````MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
``````

Example output

``````Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  0.9642          1.0909        0.8224          0.7213
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7
``````

Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.

The library is released under the MIT License. See the bundled LICENSE file for details.

Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php

1669952228

## Dijkstra's Algorithm Explained with Examples

### In this tutorial, you'll learn: What is Dijkstra's Algorithm and how Dijkstra's algorithm works with the help of visual guides.

You can use algorithms in programming to solve specific problems through a set of precise instructions or procedures.

Dijkstra's algorithm is one of many graph algorithms you'll come across. It is used to find the shortest path from a fixed node to all other nodes in a graph.

There are different representations of Dijkstra's algorithm. You can either find the shortest path between two nodes, or the shortest path from a fixed node to the rest of the nodes in a graph.

In this article, you'll learn how Dijkstra's algorithm works with the help of visual guides.

## How Does Dijkstra’s Algorithm Work?

Before we dive into more detailed visual examples, you need to understand how Dijkstra's algorithm works.

Although the theoretical explanation may seem a bit abstract, it'll help you understand the practical aspect better.

In a given graph containing different nodes, we are required to get the shortest path from a given node to the rest of the nodes.

These nodes can represent any object like the names of cities, letters, and so on.

Between each node is a number denoting the distance between two nodes, as you can see in the image below:

We usually work with two arrays – one for visited nodes, and another for unvisited nodes. You'll learn more about the arrays in the next section.

When a node is visited, the algorithm calculates how long it took to get to the node and stores the distance. If a shorter path to a node is found, the initial value assigned for the distance is updated.

Note that a node cannot be visited twice.

The algorithm runs recursively until all the nodes have been visited.

## Dijkstra's Algorithm Example

In this section, we'll take a look at a practical example that shows how Dijkstra's algorithm works.

Here's the graph we'll be working with:

We'll use the table below to put down the visited nodes and their distance from the fixed node:

Visited nodes = []
Unvisited nodes = [A,B,C,D,E]

Above, we have a table showing each node and the shortest distance from the that node to the fixed node. We are yet to choose the fixed node.

Note that the distance for each node in the table is currently denoted as infinity (∞). This is because we don't know the shortest distance yet.

We also have two arrays – visited and unvisited. Whenever a node is visited, it is added to the visited nodes array.

Let's get started!

To simplify things, I'll break the process down into iterations. You'll see what happens in each step with the aid of diagrams.

### Iteration #1

The first iteration might seem confusing, but that's totally fine. Once we start repeating the process in each iteration, you'll have a clearer picture of how the algorithm works.

Step #1 - Pick an unvisited node

We'll choose A as the fixed node. So we'll find the shortest distance from A to every other node in the graph.

We're going to give A a distance of 0 because it is the initial node. So the table would look like this:

Step #2 - Find the distance from current node

The next thing to do after choosing a node is to find the distance from it to the unvisited nodes around it.

The two unvisited nodes directly linked to A are B and C.

To get the distance from A to B:

0 + 4 = 4

0 being the value of the current node (A), and 4 being the distance between A and B in the graph.

To get the distance from A to C:

0 + 2 = 2

Step #3 - Update table with known distances

In the last step, we got 4 and 2 as the values of B and C respectively. So we'll update the table with those values:

Step #4 - Update arrays

At this point, the first iteration is complete. We'll move node A to the visited nodes array:

Visited nodes = [A]
Unvisited nodes = [B,C,D,E]

Before we proceed to the next iteration, you should know the following:

• Once a node has been visited, it cannot be linked to the current node. Refer to step #2 in the iteration above and step #2 in the next iteration.
• A node cannot be visited twice.
• You can only update the shortest known distance if you get a value smaller than the recorded distance.

### Iteration #2

Step #1 - Pick an unvisited node

We have four unvisited nodes — [B,C,D,E]. So how do you know which node to pick for the next iteration?

Well, we pick the node with the smallest known distance recorded in the table. Here's the table:

So we're going with node C.

Step #2 - Find the distance from current node

To find the distance from the current node to the fixed node, we have to consider the nodes linked to the current node.

The nodes linked to the current node are A and B.

But A has been visited in the previous iteration so it will not be linked to the current node. That is:

From the diagram above,

• The green color denotes the current node.
• The blue color denotes the visited nodes. We cannot link to them or visit them again.
• The red color shows the link from the unvisited nodes to the current node.

To find the distance from C to B:

2 + 1 = 3

2 above is recorded distance for node C while 1 is the distance between C and B in the graph.

Step #3 - Update table with known distances

In the last step, we got the value of B to be 3. In the first iteration, it was 4.

We're going to update the distance in the table to 3.

So, A --> B = 4 (First iteration).

A --> C --> B = 3 (Second iteration).

The algorithm has helped us find the shortest path to B from A.

Step #4 - Update arrays

We're done with the last visited node. Let's add it to the visited nodes array:

Visited nodes = [A,C]
Unvisited nodes = [B,D,E]

### Iteration #3

Step #1 - Pick an unvisited node

We're down to three unvisited nodes — [B,D,E]. From the array, B has the shortest known distance.

To restate what is going on in the diagram above:

• The green color denotes the current node.
• The blue color denotes the visited nodes. We cannot link to them or visit them again.
• The red color shows the link from the unvisited nodes to the current node.

Step #2 - Find the distance from current node

The nodes linked to the current node are D and E.

B (the current node) has a value of 3. Therefore,

For node D, 3 + 3 = 6.

For node E, 3 + 2 = 5.

Step #3 - Update table with known distances

Step #4 - Update arrays

Visited nodes = [A,C,B]
Unvisited nodes = [D,E]

### Iteration #4

Step #1 - Pick an unvisited node

Like other iterations, we'll go with the unvisited node with the shortest known distance. That is E.

Step #2 - Find the distance from current node

According to our table, E has a value of 5.

For D in the current iteration,

5 + 5 = 10.

The value gotten for D here is 10, which is greater than the recorded value of 6 in the previous iteration. For this reason, we'll not update the table.

Step #3 - Update table with known distances

Our table remains the same:

Step #4 - Update arrays

Visited nodes = [A,C,B,E]
Unvisited nodes = [D]

### Iteration #5

Step #1 - Pick an unvisited node

We're currently left with one node in the unvisited array — D.

Step #2 - Find the distance from current node

The algorithm has gotten to the last iteration. This is because all nodes linked to the current node have been visited already so we can't link to them.

Step #3 - Update table with known distances

Our table remains the same:

At this point, we have updated the table with the shortest distance from the fixed node to every other node in the graph.

Step #4 - Update arrays

Visited nodes = [A,C,B,E,D]
Unvisited nodes = []

As can be seen above, we have no nodes left to visit. Using Dijkstra's algorithm, we've found the shortest distance from the fixed node to others nodes in the graph.

## Dijkstra's Algorithm Pseudocode Example

The pseudocode example in this section was gotten from Wikipedia. Here it is:

`````` 1  function Dijkstra(Graph, source):
2
3      for each vertex v in Graph.Vertices:
4          dist[v] ← INFINITY
5          prev[v] ← UNDEFINED
7      dist[source] ← 0
8
9      while Q is not empty:
10          u ← vertex in Q with min dist[u]
11          remove u from Q
12
13          for each neighbor v of u still in Q:
14              alt ← dist[u] + Graph.Edges(u, v)
15              if alt < dist[v]:
16                  dist[v] ← alt
17                  prev[v] ← u
18
19      return dist[], prev[]``````

## Applications of Dijkstra's Algorithm

Here are some of the common applications of Dijkstra's algorithm:

• In maps to get the shortest distance between locations. An example is Google Maps.
• In telecommunications to determine transmission rate.
• In robotic design to determine shortest path for automated robots.

## Summary

In this article, we talked about Dijkstra's algorithm. It is used to find the shortest distance from a fixed node to all other nodes in a graph.

We started by giving a brief summary of how the algorithm works.

We then had a look at an example that further explained Dijkstra's algorithm in steps using visual guides.

We concluded with a pseudocode example and some of the applications of Dijkstra's algorithm.

Happy coding!

Original article source at https://www.freecodecamp.org

#algorithm #datastructures

1594271340

## A Deep Dive into Linear Regression

Let’s begin our journey with the truth — machines never learn. What a typical machine learning algorithm does is find a mathematical equation that, when applied to a given set of training data, produces a prediction that is very close to the actual output.

Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.

However, most of the algorithms are so complex and intimidating that it gives our mere human intelligence the feel of actual learning, effectively hiding the underlying math within. There goes a dictum that if you can implement the algorithm, you know the algorithm. This saying is lost in the dense jungle of libraries and inbuilt modules which programming languages provide, reducing us to regular programmers calling an API and strengthening further this notion of a black box. Our quest will be to unravel the mysteries of this so-called ‘black box’ which magically produces accurate predictions, detects objects, diagnoses diseases and claims to surpass human intelligence one day.

We will start with one of the not-so-complex and easy to visualize algorithm in the ML paradigm — Linear Regression. The article is divided into the following sections:

1. Need for Linear Regression

2. Visualizing Linear Regression

3. Deriving the formula for weight matrix W

4. Using the formula and performing linear regression on a real world data set

Note: Knowledge on Linear Algebra, a little bit of Calculus and Matrices are a prerequisite to understanding this article

Also, a basic understanding of python, NumPy, and Matplotlib are a must.

## 1) Need for Linear regression

Regression means predicting a real valued number from a given set of input variables. Eg. Predicting temperature based on month of the year, humidity, altitude above sea level, etc. Linear Regression would therefore mean predicting a real valued number that follows a linear trend. Linear regression is the first line of attack to discover correlations in our data.

Now, the first thing that comes to our mind when we hear the word linear is, a line.

Yes! In linear regression, we try to fit a line that best generalizes all the data points in the data set. By generalizing, we mean we try to fit a line that passes very close to all the data points.

But how do we ensure that this happens? To understand this, let’s visualize a 1-D Linear Regression. This is also called as Simple Linear Regression

#calculus #machine-learning #linear-regression-math #linear-regression #linear-regression-python #python