1604654820
Intuitive User Interface and 4 Simple Principles to Create It
What if I told you that you can create intuitive user interface by following four simple principles? Would you want to know what these four principles are? And, what about how to use them to create more intuitive user interfaces? This article will tell you what these four simple principles are. Then, it will give you some ideas on how to apply them. Without further ado, let’s begin.
What makes user interface intuitive
When can we say that some interface is intuitive? One definition of an intuitive user interface I would love to use is that it is an interface users understand and use without the need to remember or memorize almost anything. The problem is with this definition is that it forbids any interface that is new and innovative. It is almost like saying “simply don’t reinvent the wheel”.
How far can we get with this philosophy? Probably not too far beyond the wheel. Therefore, we need a little bit looser definition. It has to be a definition that will help us distinguish between user interface that is intuitive and that is not. However, it also has to be a definition that will still leave some space for innovation and trying new and even very bold ideas.
With this in mind, I think it is safe to say that intuitive user interface is an interface that is almost self-explanatory. There might be some need to learn few new patterns, thinking models or behavior. However, this learning phase should not take too long and the user shouldn’t need to repeat it over and over again. Learn only once and quickly and never forget. Now, how to achieve this?
#design development #ux psychology
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Plpgsql Check: Extension That Allows to Check Plpgsql Source Code.
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;
LOAD
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()'::regprocedureor16799::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
load 'plpgsql_check';
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 ;
┌──────────────────────────┐
│ 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:tracerare 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
Password: *******
[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:
- Download and install PostgreSQL for Win32 from http://www.enterprisedb.com
- Download and install Microsoft Visual C++ Express
- Lern tutorial http://blog.2ndquadrant.com/compiling-postgresql-extensions-visual-studio-windows
- Build plpgsql_check.dll
- Install plugin
- copy
plpgsql_check.dlltoPostgreSQL\14\lib - copy
plpgsql_check.controlandplpgsql_check--2.1.sqltoPostgreSQL\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
Copyright (c) Pavel Stehule (pavel.stehule@gmail.com)
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
License: View license
1648803600
Plpgsql Check: Extension That Allows to Check Plpgsql Source Code.
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;
LOAD
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()'::regprocedureor16799::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
load 'plpgsql_check';
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 ;
┌──────────────────────────┐
│ 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:tracerare 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
Password: *******
[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:
- Download and install PostgreSQL for Win32 from http://www.enterprisedb.com
- Download and install Microsoft Visual C++ Express
- Lern tutorial http://blog.2ndquadrant.com/compiling-postgresql-extensions-visual-studio-windows
- Build plpgsql_check.dll
- Install plugin
- copy
plpgsql_check.dlltoPostgreSQL\14\lib - copy
plpgsql_check.controlandplpgsql_check--2.1.sqltoPostgreSQL\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
Copyright (c) Pavel Stehule (pavel.stehule@gmail.com)
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
License: View license
1653475560
A Pure PHP Implementation Of The MessagePack Serialization Format
msgpack.php
A pure PHP implementation of the MessagePack serialization format.
Features
- Fully compliant with the latest MessagePack specification
- Supports streaming unpacking
- Supports unsigned 64-bit integers handling
- Supports object serialization
- Fully tested
- Relatively fast
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):
| Name | Description |
|---|---|
FORCE_STR | Forces PHP strings to be packed as MessagePack UTF-8 strings |
FORCE_BIN | Forces PHP strings to be packed as MessagePack binary data |
DETECT_STR_BIN | Detects MessagePack str/bin type automatically |
FORCE_ARR | Forces PHP arrays to be packed as MessagePack arrays |
FORCE_MAP | Forces PHP arrays to be packed as MessagePack maps |
DETECT_ARR_MAP | Detects MessagePack array/map type automatically |
FORCE_FLOAT32 | Forces PHP floats to be packed as 32-bits MessagePack floats |
FORCE_FLOAT64 | Forces PHP floats to be packed as 64-bits MessagePack floats |
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,MapandBin. 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):
| Name | Description |
|---|---|
BIGINT_AS_STR | Converts overflowed integers to strings [1] |
BIGINT_AS_GMP | Converts overflowed integers to GMP objects [2] |
BIGINT_AS_DEC | Converts overflowed integers to Decimal\Decimal objects [3] |
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.
To learn more about how extension types can be useful, check out this article.
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:
| Name | Default |
|---|---|
| MP_BENCH_TARGETS | pure_p,pure_u, see a list of available targets |
| MP_BENCH_ITERATIONS | 100_000 |
| MP_BENCH_DURATION | not set |
| MP_BENCH_ROUNDS | 3 |
| MP_BENCH_TESTS | -@slow, see a list of available tests |
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.
License
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
License: MIT License
1655630160
PyTumblr: A Python Tumblr API v2 Client
PyTumblr
Installation
Install via pip:
$ pip install pytumblrInstall from source:
$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py installUsage
Create a client
A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:
client = pytumblr.TumblrRestClient(
'<consumer_key>',
'<consumer_secret>',
'<oauth_token>',
'<oauth_secret>',
)
client.info() # Grabs the current user informationTwo easy ways to get your credentials to are:
- The built-in
interactive_console.pytool (if you already have a consumer key & secret) - The Tumblr API console at https://api.tumblr.com/console
- Get sample login code at https://api.tumblr.com/console/calls/user/info
Supported Methods
User Methods
client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user
client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog
client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a postBlog Methods
client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blogPost Methods
Creating posts
PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.
The default supported types are described below.
- state - a string, the state of the post. Supported types are published, draft, queue, private
- tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
- tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
- date - a string, the customized GMT that you want
- format - a string, the format that your post is in. Support types are html or markdown
- slug - a string, the slug for the url of the post you want
We'll show examples throughout of these default examples while showcasing all the specific post types.
Creating a photo post
Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload
#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")
#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
tweet="Woah this is an incredible sweet post [URL]",
data="/Users/johnb/path/to/my/image.jpg")
#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
caption="## Mega sweet kittens")Creating a text post
Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html
#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")Creating a quote post
Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported
#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")Creating a link post
- title - a string, the title of post that you want. Supports HTML entities.
- url - a string, the url that you want to create a link post for.
- description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
description="Search is pretty cool when a duck does it.")Creating a chat post
Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)
#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])Creating an audio post
Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr
#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")
#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")Creating a video post
Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload
#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
embed="http://www.youtube.com/watch?v=40pUYLacrj4")
#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")Editing a post
Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.
client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")Reblogging a Post
Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.
client.reblog(blogName, id=125356, reblog_key="reblog_key")Deleting a post
Deleting just requires that you own the post and have the post id
client.delete_post(blogName, 123456) # Deletes your post :(A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):
client.create_text(blogName, tags=['hello', 'world'], ...)Getting notes for a post
In order to get the notes for a post, you need to have the post id and the blog that it is on.
data = client.notes(blogName, id='123456')The results include a timestamp you can use to make future calls.
data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])Tagged Methods
# get posts with a given tag
client.tagged(tag, **params)Using the interactive console
This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).
You'll need pyyaml installed to run it, but then it's just:
$ python interactive-console.pyand away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.
Running tests
The tests (and coverage reports) are run with nose, like this:
python setup.py testAuthor: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license
1669003576
Exploring Mutable and Immutable in Python
In this Python article, let's learn about Mutable and Immutable in Python.
Mutable and Immutable in Python
Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.
Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.
Mutable Definition
Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.
Immutable Definition
Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.
List of Mutable and Immutable objects
Objects of built-in type that are mutable are:
- Lists
- Sets
- Dictionaries
- User-Defined Classes (It purely depends upon the user to define the characteristics)
Objects of built-in type that are immutable are:
- Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
- Strings
- Tuples
- Frozen Sets
- User-Defined Classes (It purely depends upon the user to define the characteristics)
Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.
Objects in Python
In Python, everything is treated as an object. Every object has these three attributes:
- Identity – This refers to the address that the object refers to in the computer’s memory.
- Type – This refers to the kind of object that is created. For example- integer, list, string etc.
- Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3
While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.
Check out this free python certificate course to get started with Python.
Mutable Objects in Python
I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:
#Creating a list which contains name of Indian cities
cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]
# Printing the elements from the list cities, separated by a comma & space
for city in cities:
print(city, end=’, ’)
Output [1]: Delhi, Mumbai, Kolkata
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(cities)))
Output [2]: 0x1691d7de8c8
#Adding a new city to the list cities
cities.append(‘Chennai’)
#Printing the elements from the list cities, separated by a comma & space
for city in cities:
print(city, end=’, ’)
Output [3]: Delhi, Mumbai, Kolkata, Chennai
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(cities)))
Output [4]: 0x1691d7de8c8
The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.
Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.
Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0.
#Creating a Tuple with variable name ‘foo’
foo = (1, 2)
#Changing the index[0] value from 1 to 3
foo[0] = 3
TypeError: 'tuple' object does not support item assignment
Immutable Objects in Python
Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:
#Creating a Tuple which contains English name of weekdays
weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’
# Printing the elements of tuple weekdays
print(weekdays)
Output [1]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(weekdays)))
Output [2]: 0x1691cc35090
#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’
weekdays += ‘Pythonday’,
#Printing the elements of tuple weekdays
print(weekdays)
Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(weekdays)))
Output [4]: 0x1691cc8ad68
This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it. Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.
Also Read: Understanding the Exploratory Data Analysis (EDA) in Python
Where can you use mutable and immutable objects:
Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.
Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.
Watch outs: Non transitive nature of Immutability:
OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–
#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements
#The elements (lists) contains the name, age & gender
person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the tuple
print(person)
Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(person)))
Output [2]: 0x1691ef47f88
#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4
person[0][1] = 4
#printing the updated tuple
print(person)
Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(person)))
Output [4]: 0x1691ef47f88
In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.
Also Read: Real-Time Object Detection Using TensorFlow
Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–
#creating a list (mutable object) which contains tuples(immutable) as it’s elements
list1 = [(1, 2, 3), (4, 5, 6)]
#printing the list
print(list1)
Output [1]: [(1, 2, 3), (4, 5, 6)]
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(list1)))
Output [2]: 0x1691d5b13c8 #changing object reference at index 0
list1[0] = (7, 8, 9)
#printing the list
Output [3]: [(7, 8, 9), (4, 5, 6)]#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(list1)))
Output [4]: 0x1691d5b13c8
As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.
Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’
x = 10
#printing the value of ‘x’
print(x)
Output [1]: 10
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(x)))
Output [2]: 0x538fb560
#creating an object of integer type with value 10 and reference variable name ‘y’
y = 10
#printing the value of ‘y’
print(y)
Output [3]: 10
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(y)))
Output [4]: 0x538fb560
As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.
Quick check – Python Data Structures
Immutability of Tuple
Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.
Exceptions in immutability
Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.
Consider a tuple ‘tup’.
Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;
We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.
FAQs
1. Difference between mutable vs immutable in Python?
| Mutable Object | Immutable Object |
| State of the object can be modified after it is created. | State of the object can’t be modified once it is created. |
| They are not thread safe. | They are thread safe |
| Mutable classes are not final. | It is important to make the class final before creating an immutable object. |
2. What are the mutable and immutable data types in Python?
- Some mutable data types in Python are:
list, dictionary, set, user-defined classes.
- Some immutable data types are:
int, float, decimal, bool, string, tuple, range.
3. Are lists mutable in Python?
Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)
4. Why are tuples called immutable types?
Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.
A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.
5. Are sets mutable in Python?
A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.
6. Are strings mutable in Python?
Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.
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Original article source at: https://www.mygreatlearning.com