Aisu  Joesph

Aisu Joesph

1626490533

Azure Series #2: Single Server Deployment (Output)

No organization that is on the growth path or intending to have a more customer base and new entry into the market will restrict its infrastructure and design for one Database option. There are two levels of Database selection

  • a.  **The needs assessment **
  • **b. Selecting the kind of database **
  • c. Selection of Queues for communication
  • d. Selecting the technology player

Options to choose from:

  1. Transactional Databases:
    • Azure selection — Data Factory, Redis, CosmosDB, Azure SQL, Postgres SQL, MySQL, MariaDB, SQL Database, Maria DB, Managed Server
  2. Data warehousing:
    • Azure selection — CosmosDB
    • Delta Lake — Data Brick’s Lakehouse Architecture.
  3. Non-Relational Database:
  4. _- _Azure selection — CosmosDB
  5. Data Lake:
    • Azure Data Lake
    • Delta Lake — Data Bricks.
  6. Big Data and Analytics:
    • Data Bricks
    • Azure — HDInsights, Azure Synapse Analytics, Event Hubs, Data Lake Storage gen1, Azure Data Explorer Clusters, Data Factories, Azure Data Bricks, Analytics Services, Stream Analytics, Website UI, Cognitive Search, PowerBI, Queries, Reports.
  7. Machine Learning:
    • Azure — Azure Synapse Analytics, Machine Learning, Genomics accounts, Bot Services, Machine Learning Studio, Cognitive Services, Bonsai.

Key Data platform services would like to highlight

  • 1. Azure Data Factory (ADF)
  • 2. Azure Synapse Analytics
  • 3. Azure Stream Analytics
  • 4. Azure Databricks
  • 5. Azure Cognitive Services
  • 6. Azure Data Lake Storage
  • 7. Azure HDInsight
  • 8. Azure CosmosDB
  • 9. Azure SQL Database

#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series

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Buddha Community

Azure Series #2: Single Server Deployment (Output)
Veronica  Roob

Veronica Roob

1653475560

A Pure PHP Implementation Of The MessagePack Serialization Format

msgpack.php

A pure PHP implementation of the MessagePack serialization format.

Features

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):

NameDescription
FORCE_STRForces PHP strings to be packed as MessagePack UTF-8 strings
FORCE_BINForces PHP strings to be packed as MessagePack binary data
DETECT_STR_BINDetects MessagePack str/bin type automatically
  
FORCE_ARRForces PHP arrays to be packed as MessagePack arrays
FORCE_MAPForces PHP arrays to be packed as MessagePack maps
DETECT_ARR_MAPDetects MessagePack array/map type automatically
  
FORCE_FLOAT32Forces PHP floats to be packed as 32-bits MessagePack floats
FORCE_FLOAT64Forces 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, Map and Bin. Check the "Custom types" section below for details.

Examples:

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

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

Unpacking

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

$unpacker = new BufferUnpacker();

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

or call a static method on the MessagePack class:

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

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

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

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

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

To skip bytes from the current position, use skip:

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

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

$unreadBytesCount = $unpacker->getRemainingCount();

To check whether the buffer has unread data:

$hasUnreadBytes = $unpacker->hasRemaining();

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

$releasedBytesCount = $unpacker->release();

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

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

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

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

Unpacking options

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

NameDescription
BIGINT_AS_STRConverts overflowed integers to strings [1]
BIGINT_AS_GMPConverts overflowed integers to GMP objects [2]
BIGINT_AS_DECConverts 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:

NameDefault
MP_BENCH_TARGETSpure_p,pure_u, see a list of available targets
MP_BENCH_ITERATIONS100_000
MP_BENCH_DURATIONnot set
MP_BENCH_ROUNDS3
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

#php 

Aida  Stamm

Aida Stamm

1646789416

Top 17 Machine Learning Algorithms with Scikit-Learn

Machine Learning with Scikit-Learn

Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib.

The functionality that scikit-learn provides include:

  • Regression, including Linear and Logistic Regression
  • Classification, including K-Nearest Neighbors
  • Clustering, including K-Means and K-Means++
  • Model selection
  • Preprocessing, including Min-Max Normalization

In this Article I will explain all machine learning algorithms with scikit-learn which you need to learn as a Data Scientist.

Lets start by importing the libraries:

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from scipy import stats
import pylab as pl

Estimator

Given a scikit-learn estimator object named model, the following methods are available:

Available in all Estimators

model.fit() : fit training data. For supervised learning applications, this accepts two arguments: the data X and the labels y (e.g. model.fit(X, y)). For unsupervised learning applications, this accepts only a single argument, the data X (e.g. model.fit(X)).

Available in supervised estimators

model.predict() : given a trained model, predict the label of a new set of data. This method accepts one argument, the new data X_new (e.g. model.predict(X_new)), and returns the learned label for each object in the array.

model.predict_proba() : For classification problems, some estimators also provide this method, which returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by model.predict(). model.score() : for classification or regression problems, most (all?) estimators implement a score method. Scores are between 0 and 1, with a larger score indicating a better fit.

Available in unsupervised estimators

model.predict() : predict labels in clustering algorithms. model.transform() : given an unsupervised model, transform new data into the new basis. This also accepts one argument X_new, and returns the new representation of the data based on the unsupervised model. model.fit_transform() : some estimators implement this method, which more efficiently performs a fit and a transform on the same input data.

Download and read the data set

data =  pd.read_csv('Iris.csv')
data.head()
print(data.shape)
#Output
(150, 6)
data.info()
#Output
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             150 non-null    int64  
 1   SepalLengthCm  150 non-null    float64
 2   SepalWidthCm   150 non-null    float64
 3   PetalLengthCm  150 non-null    float64
 4   PetalWidthCm   150 non-null    float64
 5   Species        150 non-null    object 
dtypes: float64(4), int64(1), object(1)
memory usage: 7.2+ KB

Visualization

Some graphical representation of information and data.

sns.FacetGrid(data,hue='Species',size=5)\
.map(plt.scatter,'SepalLengthCm','SepalWidthCm')\
.add_legend()
sns.pairplot(data,hue='Species')

Prepare Train and Test

scikit-learn provides a helpful function for partitioning data, train_test_split, which splits out your data into a training set and a test set.

Training and test usually is 70% for training and 30% for test

  • Training set for fitting the model
  • Test set for evaluation only
X = data.iloc[:, :-1].values    #   X -> Feature Variables
y = data.iloc[:, -1].values #   y ->  Target
# Splitting the data into Train and Test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

Algorithm 1 – Linear Regression

It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation **Y= a *X + b.

#converting object data type into int data type using labelEncoder for Linear reagration in this case

XL = data.iloc[:, :-1].values    #   X -> Feature Variables
yL = data.iloc[:, -1].values #   y ->  Target

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
Y_train= le.fit_transform(yL)

print(Y_train)  # this is Y_train categotical to numerical
#Output
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2]
# This is only for Linear Regretion 
X_trainL, X_testL, y_trainL, y_testL = train_test_split(XL, Y_train, test_size = 0.3, random_state = 0)
from sklearn.linear_model import LinearRegression
modelLR = LinearRegression()
modelLR.fit(X_trainL, y_trainL)
Y_pred = modelLR.predict(X_testL)
from sklearn import metrics
#calculating the residuals
print('y-intercept             :' , modelLR.intercept_)
print('beta coefficients       :' , modelLR.coef_)
print('Mean Abs Error MAE      :' ,metrics.mean_absolute_error(y_testL,Y_pred))
print('Mean Sqrt Error MSE     :' ,metrics.mean_squared_error(y_testL,Y_pred))
print('Root Mean Sqrt Error RMSE:' ,np.sqrt(metrics.mean_squared_error(y_testL,Y_pred)))
print('r2 value                :' ,metrics.r2_score(y_testL,Y_pred))
#Output
y-intercept             : -0.024298523519848292
beta coefficients       : [ 0.00680677 -0.10726764 -0.00624275  0.22428158  0.27196685]
Mean Abs Error MAE      : 0.14966835490524963
Mean Sqrt Error MSE     : 0.03255451737969812
Root Mean Sqrt Error RMSE: 0.18042870442282213
r2 value                : 0.9446026069799255

Algorithm 2- Decision Tree

This is one of my favorite algorithm and I use it quite frequently. It is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.

In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.

# Decision Tree's
from sklearn.tree import DecisionTreeClassifier

Model = DecisionTreeClassifier()

Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score
print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       0.95      1.00      0.97        18
 Iris-virginica       1.00      0.91      0.95        11

       accuracy                           0.98        45
      macro avg       0.98      0.97      0.98        45
   weighted avg       0.98      0.98      0.98        45

[[16  0  0]
 [ 0 18  0]
 [ 0  1 10]]
accuracy is 0.9777777777777777

Algorithm 3- RandomForest

Random Forest is a trademark term for an ensemble of decision trees. In Random Forest, we’ve collection of decision trees (so known as “Forest”). To classify a new object based on attributes, each tree gives a classification and we say the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

from sklearn.ensemble import RandomForestClassifier
Model=RandomForestClassifier(max_depth=2)
Model.fit(X_train,y_train)
y_pred=Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_pred,y_test))
#Accuracy Score
print('accuracy is ',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is  1.0

Algorithm 4- Logistic Regression

Don’t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s).

In simple words, it predicts the probability of occurrence of an event by fitting data to a logic function. Hence, it is also known as logic regression. Since, it predicts the probability, its output values lies between 0 and 1 (as expected).

# LogisticRegression
from sklearn.linear_model import LogisticRegression
Model = LogisticRegression()
Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score
print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is 1.0

Algorithm 5- K Nearest Neighbors

It can be used for both classification and regression problems. However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors. The case being assigned to the class is most common amongst its K nearest neighbors measured by a distance function.

# K-Nearest Neighbours
from sklearn.neighbors import KNeighborsClassifier

Model = KNeighborsClassifier(n_neighbors=8)
Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score

print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is 1.0

Algorithm 6- Naive Bayes

It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier would consider all of these properties to independently contribute to the probability that this fruit is an apple.

# Naive Bayes
from sklearn.naive_bayes import GaussianNB
Model = GaussianNB()
Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score
print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is 1.0

Algorithm 7- Support Vector Machines

It is a classification method. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

For example, if we only had two features like Height and Hair length of an individual, we’d first plot these two variables in two dimensional space where each point has two co-ordinates (these co-ordinates are known as Support Vectors)

# Support Vector Machine
from sklearn.svm import SVC

Model = SVC()
Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score

print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is 1.0

Algorithm 8- Radius Neighbors Classifier

In scikit-learn RadiusNeighborsClassifier is very similar to KNeighborsClassifier with the exception of two parameters. First, in RadiusNeighborsClassifier we need to specify the radius of the fixed area used to determine if an observation is a neighbor using radius.

Unless there is some substantive reason for setting radius to some value, it is best to treat it like any other hyperparameter and tune it during model selection. The second useful parameter is outlier_label, which indicates what label to give an observation that has no observations within the radius – which itself can often be a useful tool for identifying outliers.

#Output
from sklearn.neighbors import  RadiusNeighborsClassifier
Model=RadiusNeighborsClassifier(radius=8.0)
Model.fit(X_train,y_train)
y_pred=Model.predict(X_test)

#summary of the predictions made by the classifier
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_test,y_pred))

#Accouracy score
print('accuracy is ', accuracy_score(y_test,y_pred))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is  1.0

Algorithm 9- Passive Aggressive Classifier

PA algorithm is a margin based online learning algorithm for binary classification. Unlike PA algorithm, which is a hard-margin based method, PA-I algorithm is a soft margin based method and robuster to noise.

from sklearn.linear_model import PassiveAggressiveClassifier
Model = PassiveAggressiveClassifier()
Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score
print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       0.89      1.00      0.94        16
Iris-versicolor       0.00      0.00      0.00        18
 Iris-virginica       0.41      1.00      0.58        11

       accuracy                           0.60        45
      macro avg       0.43      0.67      0.51        45
   weighted avg       0.42      0.60      0.48        45

[[16  0  0]
 [ 2  0 16]
 [ 0  0 11]]
accuracy is 0.6

Algorithm 10- BernoulliNB

Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.

# BernoulliNB
from sklearn.naive_bayes import BernoulliNB
Model = BernoulliNB()
Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score
print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       0.00      0.00      0.00        16
Iris-versicolor       0.00      0.00      0.00        18
 Iris-virginica       0.24      1.00      0.39        11

       accuracy                           0.24        45
      macro avg       0.08      0.33      0.13        45
   weighted avg       0.06      0.24      0.10        45

[[ 0  0 16]
 [ 0  0 18]
 [ 0  0 11]]
accuracy is 0.24444444444444444

Algorithm 11- ExtraTreeClassifier

ExtraTreesClassifier is an ensemble learning method fundamentally based on decision trees. ExtraTreesClassifier, like RandomForest, randomizes certain decisions and subsets of data to minimize over-learning from the data and overfitting. Let’s look at some ensemble methods ordered from high to low variance, ending in ExtraTreesClassifier.

# ExtraTreeClassifier
from sklearn.tree import ExtraTreeClassifier

Model = ExtraTreeClassifier()

Model.fit(X_train, y_train)

y_pred = Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
# Accuracy score
print('accuracy is',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      0.94      0.97        18
 Iris-virginica       0.92      1.00      0.96        11

       accuracy                           0.98        45
      macro avg       0.97      0.98      0.98        45
   weighted avg       0.98      0.98      0.98        45

[[16  0  0]
 [ 0 17  1]
 [ 0  0 11]]
accuracy is 0.9777777777777777

Algorithm 12- Bagging classifier

Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.

#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       0.95      1.00      0.97        18
 Iris-virginica       1.00      0.91      0.95        11

       accuracy                           0.98        45
      macro avg       0.98      0.97      0.98        45
   weighted avg       0.98      0.98      0.98        45

[[16  0  0]
 [ 0 18  1]
 [ 0  0 10]]
accuracy is  0.9777777777777777

Algorithm 13-AdaBoost classifier

An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

from sklearn.ensemble import AdaBoostClassifier
Model=AdaBoostClassifier()
Model.fit(X_train,y_train)
y_pred=Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_pred,y_test))
#Accuracy Score
print('accuracy is ',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       0.95      1.00      0.97        18
 Iris-virginica       1.00      0.91      0.95        11

       accuracy                           0.98        45
      macro avg       0.98      0.97      0.98        45
   weighted avg       0.98      0.98      0.98        45

[[16  0  0]
 [ 0 18  1]
 [ 0  0 10]]
accuracy is  0.9777777777777777

Algorithm 14- Gradient Boosting Classifier

GBM is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. Boosting is actually an ensemble of learning algorithms which combines the prediction of several base estimators in order to improve robustness over a single estimator. It combines multiple weak or average predictors to a build strong predictor.

from sklearn.ensemble import GradientBoostingClassifier
Model=GradientBoostingClassifier()
Model.fit(X_train,y_train)
y_pred=Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_pred,y_test))

#Accuracy Score
print('accuracy is ',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       0.95      1.00      0.97        18
 Iris-virginica       1.00      0.91      0.95        11

       accuracy                           0.98        45
      macro avg       0.98      0.97      0.98        45
   weighted avg       0.98      0.98      0.98        45

[[16  0  0]
 [ 0 18  1]
 [ 0  0 10]]
accuracy is  0.9777777777777777

Algorithm 15- Linear Discriminant Analysis

A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.

The model fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.

The fitted model can also be used to reduce the dimensionality of the input by projecting it to the most discriminative directions.

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
Model=LinearDiscriminantAnalysis()
Model.fit(X_train,y_train)
y_pred=Model.predict(X_test)

# Summary of the predictions made by the classifier
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_pred,y_test))

#Accuracy Score
print('accuracy is ',accuracy_score(y_pred,y_test))
#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is  1.0

Algorithm 16- Quadratic Discriminant Analysis

A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.

The model fits a Gaussian density to each class.

#Output
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        16
Iris-versicolor       1.00      1.00      1.00        18
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        45
      macro avg       1.00      1.00      1.00        45
   weighted avg       1.00      1.00      1.00        45

[[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy is  1.0

Algorithm 17- K- means

It is a type of unsupervised algorithm which solves the clustering problem. Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). Data points inside a cluster are homogeneous and heterogeneous to peer groups.

Remember figuring out shapes from ink blots? k means is somewhat similar this activity. You look at the shape and spread to decipher how many different clusters / population are present.

x = data.iloc[:, [1, 2, 3, 4]].values

#Finding the optimum number of clusters for k-means classification
from sklearn.cluster import KMeans
wcss = []

for i in range(1, 11):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
    kmeans.fit(x)
    wcss.append(kmeans.inertia_)
    
#Plotting the results onto a line graph, allowing us to observe 'The elbow'
plt.plot(range(1, 11), wcss)
plt.title('The elbow method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS') # within cluster sum of squares
plt.show()
#Applying kmeans to the dataset / Creating the kmeans classifier
kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
y_kmeans = kmeans.fit_predict(x)
#Visualising the clusters

plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Iris-Setosa')
plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Iris-Versicolour')
plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, c = 'yellow', label = 'Iris-Virginica')

#Plotting the centroids of the clusters
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 100, c = 'green', label = 'Centroids',marker='*')

plt.legend()

I hope you like this article on All Machine Learning Algorithms.

Original article source at https://thecleverprogrammer.com

#machinelearning #algorithms

 

Variable de Impresión de Python

Python es un lenguaje versátil y flexible; a menudo hay más de una forma de lograr algo.

En este tutorial, verá algunas de las formas en que puede imprimir una cadena y una variable juntas.

¡Empecemos!

Cómo usar la print()función en Python

Para imprimir cualquier cosa en Python, se utiliza la print()función - que es la printpalabra clave seguida de un conjunto de apertura y cierre de paréntesis, ().

#how to print a string
print("Hello world")

#how to print an integer
print(7)

#how to print a variable 
#to just print the variable on its own include only the name of it

fave_language = "Python"
print(fave_language)

#output

#Hello world
#7
#Python

Si omite los paréntesis, obtendrá un error:

print "hello world"

#output after running the code:
#File "/Users/dionysialemonaki/python_articles/demo.py", line 1
#    print "hello world"
#    ^^^^^^^^^^^^^^^^^^^
#SyntaxError: Missing parentheses in call to 'print'. Did you mean print(...)?

Si escribe su código Python en Visual Studio Code, con la extensión Python , también obtendrá un subrayado y una pista que indican que algo no está del todo bien:

Captura de pantalla-2021-12-07-a-3.08.14-PM

Como se mencionó anteriormente, la declaración de impresión se utiliza para generar todo tipo de información. Esto incluye datos textuales y numéricos, variables y otros tipos de datos.

También puede imprimir texto (o cadenas) combinado con variables, todo en una declaración.

Verá algunas de las diferentes formas de hacer esto en las secciones siguientes.

Cómo imprimir una variable y una cadena en Python usando la concatenación

Concatenar, según el diccionario, significa enlazar (cosas) juntas en una cadena o serie.

Esto se hace mediante la adición de varias cosas (en este caso la programación - se añaden datos), junto con otros, utilizando el operador de suma Python, +.

Tenga en cuenta que la concatenación solo se usa para cadenas, por lo que si la variable que desea concatenar con el resto de las cadenas es de un tipo de datos entero, tendrá que convertirla en una cadena con la str()función.

En el siguiente ejemplo, quiero imprimir el valor de una variable junto con algún otro texto.

Agrego las cadenas entre comillas dobles y el nombre de la variable sin rodearlo, usando el operador de suma para encadenarlos todos juntos:

fave_language = "Python"

print("I like coding in " + fave_language + " the most")

#output
#I like coding in Python the most

Con la concatenación de cadenas, debe agregar espacios usted mismo, por lo que si en el ejemplo anterior no hubiera incluido ningún espacio entre las comillas, la salida se vería así:

fave_language = "Python"

print("I like coding in" + fave_language + "the most")

#output
#I like coding inPythonthe most

Incluso puede agregar los espacios por separado:

fave_language = "Python"

print("I like coding in" + " " + fave_language + " "  + "the most")

#output
#I like coding in Python the most

Esta no es la forma más preferida de imprimir cadenas y variables, ya que puede ser propensa a errores y consumir mucho tiempo.

Cómo imprimir una variable y una cadena en Python separando cada una con una coma

Puede imprimir texto junto a una variable, separados por comas, en una declaración de impresión.

first_name = "John"

print("Hello",first_name)

#output
#Hello John

En el ejemplo anterior, primero incluí un texto que quería imprimir entre comillas dobles; en este caso, el texto era la cadena Hello.

Después de las comillas de cierre, agregué una coma que separa ese fragmento de texto del valor contenido en el nombre de la variable ( first_nameen este caso) que luego incluí.

Podría haber agregado más texto siguiendo la variable, así:

first_name = "John"

print("Hello",first_name,"good to see you")

#output
#Hello John good to see you

Este método también funciona con más de una variable:

first_name = "John"
last_name = "Doe"

print("Hello",first_name,last_name,"good to see you")

#output
Hello John Doe good to see you

Asegúrate de separar todo con una coma.

Entonces, separa el texto de las variables con una coma, pero también las variables de otras variables, como se muestra arriba.

Si no se hubiera agregado la coma entre first_namey last_name, el código habría arrojado un error:

first_name = "John"
last_name = "Doe"

print("Hello",first_name last_name,"good to see you")

#output
#File "/Users/dionysialemonaki/python_articles/demo.py", line 4
#    print("Hello",first_name last_name,"good to see you")
#                 ^^^^^^^^^^^^^^^^^^^^
#SyntaxError: invalid syntax. Perhaps you forgot a comma?

Como puede ver, los mensajes de error de Python son extremadamente útiles y facilitan un poco el proceso de depuración :)

Cómo imprimir una variable y una cadena en Python usando formato de cadena

Utiliza el formato de cadena al incluir un conjunto de llaves de apertura y cierre {}, en el lugar donde desea agregar el valor de una variable.

first_name = "John"

print("Hello {}, hope you're well!")

En este ejemplo hay una variable, first_name.

Dentro de la declaración impresa hay un conjunto de comillas dobles de apertura y cierre con el texto que debe imprimirse.

Dentro de eso, agregué un conjunto de llaves en el lugar donde quiero agregar el valor de la variable first_name.

Si intento ejecutar este código, tendrá el siguiente resultado:

#output
#Hello {}, hope you're well!

¡En realidad, no imprime el valor de first_name!

Para imprimirlo, necesito agregar el .format()método de cadena al final de la cadena, que es inmediatamente después de las comillas de cierre:

first_name = "John"

print("Hello {}, hope you're well!".format(first_name))

#output
#Hello John, hope you're well!

Cuando hay más de una variable, usa tantas llaves como la cantidad de variables que desee imprimir:

first_name = "John"
last_name = "Doe"

print("Hello {} {}, hope you're well!")

En este ejemplo, he creado dos variables y quiero imprimir ambas, una después de la otra, así que agregué dos juegos de llaves en el lugar donde quiero que se sustituyan las variables.

Ahora, cuando se trata del .format()método, importa el orden en el que coloque los nombres de las variables.

Entonces, el valor del nombre de la variable que se agregará primero en el método estará en el lugar de la primera llave, el valor del nombre de la variable que se agregará en segundo lugar estará en el lugar de la segunda llave, y pronto.

Asegúrese de separar los nombres de las variables con comas dentro del método:

first_name = "John"
last_name = "Doe"

print("Hello {} {}, hope you're well!".format(first_name,last_name))

#output
#Hello John Doe, hope you're well!

Si hubiera invertido el orden de los nombres dentro del método, la salida se vería diferente:

first_name = "John"
last_name = "Doe"

print("Hello {} {}, hope you're well!".format(last_name,first_name))

#output
#Hello Doe John, hope you're well!

Cómo imprimir una variable y una cadena en Python usando f-strings

f-strings son una forma mejor, más legible y concisa de lograr el formato de cadena en comparación con el método que vimos en la sección anterior.

La sintaxis es más sencilla y requiere menos trabajo manual.

La sintaxis general para crear un se f-stringve así:

print(f"I want this text printed to the console!")

#output
#I want this text printed to the console!

Primero incluye el carácter fantes de las comillas de apertura y cierre, dentro de la print()función.

Para imprimir una variable con una cadena en una línea, vuelva a incluir el carácter fen el mismo lugar, justo antes de las comillas.

Luego agrega el texto que desea dentro de las comillas, y en el lugar donde desea agregar el valor de una variable, agrega un conjunto de llaves con el nombre de la variable dentro de ellas:

first_name = "John"

print(f"Hello, {first_name}!")

#output
#Hello, John!

Para imprimir más de una variable, agrega otro conjunto de llaves con el nombre de la segunda variable:

first_name = "John"
last_name = "Doe"

print(f"Hello, {first_name} {last_name}!")

#output
#Hello, John Doe!

El orden en que coloque los nombres de las variables es importante, así que asegúrese de agregarlos de acuerdo con la salida que desee.

Si hubiera invertido el orden de los nombres, obtendría el siguiente resultado:

first_name = "John"
last_name = "Doe"

print(f"Hello, {last_name} {first_name}!")

#output
#Hello, Doe John!

Conclusión

¡Gracias por leer y llegar hasta el final! Ahora conoce algunas formas diferentes de imprimir cadenas y variables juntas en una línea en Python.

Si desea obtener más información sobre Python, consulte la Certificación Python de freeCodeCamp .

Es adecuado para principiantes, ya que comienza desde los fundamentos y se construye gradualmente hacia conceptos más avanzados. También podrás construir cinco proyectos y poner en práctica todos los nuevos conocimientos que adquieras.

¡Feliz codificación!

https://www.freecodecamp.org/news/python-print-variable-how-to-print-a-string-and-variable/

#python 

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()'::regprocedure or 16799::regprocedure. Possible alternative is using a name only, when function's name is unique - like 'fx'. When the name is not unique or the function doesn't exists it raises a error.

Optional arguments

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

fatal_errors boolean DEFAULT true - stop on first error

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

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

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

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

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

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

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

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

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

without_warnings DEFAULT false - disable all warnings

all_warnings DEFAULT false - enable all warnings

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

Triggers

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

CREATE TABLE bar(a int, b int);

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

Missing relation specification

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

Correct trigger checking (with specified relation)

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

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

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

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

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

Mass check

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

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

or

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

or

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

Passive mode

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

Configuration

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

plpgsql_check.show_nonperformance_warnings = false
plpgsql_check.show_performance_warnings = false

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

You can enable passive mode by

load 'plpgsql'; -- 1.1 and higher doesn't need it
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:

  1. Download and install PostgreSQL for Win32 from http://www.enterprisedb.com
  2. Download and install Microsoft Visual C++ Express
  3. Lern tutorial http://blog.2ndquadrant.com/compiling-postgresql-extensions-visual-studio-windows
  4. Build plpgsql_check.dll
  5. Install plugin
  6. copy plpgsql_check.dll to PostgreSQL\14\lib
  7. copy plpgsql_check.control and plpgsql_check--2.1.sql to PostgreSQL\14\share\extension

Checked on

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

Compilation against PostgreSQL 10 requires libICU!

Licence

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

#postgresql 

Franz  Becker

Franz Becker

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()'::regprocedure or 16799::regprocedure. Possible alternative is using a name only, when function's name is unique - like 'fx'. When the name is not unique or the function doesn't exists it raises a error.

Optional arguments

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

fatal_errors boolean DEFAULT true - stop on first error

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

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

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

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

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

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

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

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

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

without_warnings DEFAULT false - disable all warnings

all_warnings DEFAULT false - enable all warnings

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

Triggers

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

CREATE TABLE bar(a int, b int);

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

Missing relation specification

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

Correct trigger checking (with specified relation)

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

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

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

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

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

Mass check

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

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

or

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

or

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

Passive mode

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

Configuration

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

plpgsql_check.show_nonperformance_warnings = false
plpgsql_check.show_performance_warnings = false

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

You can enable passive mode by

load 'plpgsql'; -- 1.1 and higher doesn't need it
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:

  1. Download and install PostgreSQL for Win32 from http://www.enterprisedb.com
  2. Download and install Microsoft Visual C++ Express
  3. Lern tutorial http://blog.2ndquadrant.com/compiling-postgresql-extensions-visual-studio-windows
  4. Build plpgsql_check.dll
  5. Install plugin
  6. copy plpgsql_check.dll to PostgreSQL\14\lib
  7. copy plpgsql_check.control and plpgsql_check--2.1.sql to PostgreSQL\14\share\extension

Checked on

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

Compilation against PostgreSQL 10 requires libICU!

Licence

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

#postgresql