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-o3-pipe-i./include-c PRINT_DATA.C CC-d_file_offset_bits= --wall-g-o3-pipe-i./include-c CHECK_DATA.C CC-d_file_offset_bits= --wall-g-o3-pipe-i./include sql_parser.o c_parser.o tables_dict.o print_data.o check_data.o-o c_parser-pthread-LM cc-d_file_offset_bits= --wall-g-o3-pipe-i./include-o Innochecksum_changer INNOCHECKSUM.C View Code To create a recovery database Create Database recovery; Extracting a data dictionary Create database recovery; . /stream_parser-f/data/mysql//ibdata1 mkdir-p

declared, then use the user name that originated the connection.-A--data-onlyOnly output data, no output mode (data definition).This option is only meaningful for plain text formatting. For the archive format, you can declare options when calling Pg_restore.-B--blobsContains large objects in the dump. You must select a non-text output format.-C--cleanOutputs the command to clean (delete) The database object before creating the database Creation command.This option is only meaningful for plain t

This article mainly describes the use of JSON in Python, sample code based on the python2.x version, the need for friends can refer to the JSON advanced Python's Dict objects can be serialized directly into JSON {}, but many times we prefer to use class to represent objects, such as defining student classes, and then serializing: ? 1 2 3 4 5 6 7 8 9 10 Import JSON class Student (object): Def __init__ (self, name, age, score): Self.name = name Self.age = Age Self.score = SC Or

A---dumps----(JSON string)----loads---B;OBJ---stringify---(JSON string)----Parse---str; (js at the front) var obj1={name: "WC", age:18};var str=json.stringify (OBJ1);Console.log (str);var str1= ' {' name ': ' WC ', ' age ': 18} ';var obj=json.parse (STR1);Console.log (obj);Serialization ModuleThe two modules used in Python for serialization: JSON cross-platform cross-language data transfer format for conversion between "string" and "Python Ba

In a project, you need to get a JSON-formatted data from the cloud, save it to a local file, and then read it, using the JSON dumps and loads methods to serialize and deserialize the data. Specifically, the Dumps method, which can sequence JSON-formatted data into Python-related data types, is typically used for printing, and the loads method instead converts the Python data type to the JSON corresponding d

‘:‘A‘,‘c‘:3.0,‘b‘:(2,4)}] #python的dict类型的数据是没有顺序存储的JSON: [{"a":"A","c":3.0,"b":[2,4]}] The output of JSON is similar to data, except for some subtle changes such as Python's tuple type becoming an array of JSON, the code conversion rules for Python to JSON are:The Json.loads method handles decoding (decoding) conversions of simple data typesimport jsondata = [{‘a‘:"A",‘b‘:(2,4),‘c‘:3.0}] #list对象data_string = json.dumps(data)print "ENCODED:",data_stringdecoded = json.loads(data_string)print "DE

I. Use of file JSONJSON introduction: JSON Full name JavaScripts object Notation, is a lightweight data interchange format, the most extensive application of JSON as a Web server and client communication in AIAX data format, is now also used in httpd requests, So the various learning of JSON is a natural thing. The following are the two most common ways of working in peacetime. #/usr/bin/python#coding =utf-8# @Time: 2017/11/12 22:27# @Auther: Liuzhenchuan# @File: json-on. PY#1. Four ways to JSO

oracle| Reference Oracle Internal Events: Introduction: (Introduction) There are four kinds of events: Immediate dumps Conditional dumps Trace dumps Events that change database behaviour Each event has a number that is the same as the Oracle error message. Such as 10046 and ORA-10046 Each event has a level, which can be the following: Range 1 to 10 Bit

I. Memory dumps 1). Global Area ALTER session SET EVENTS ' immediate trace name Global_area level n '; 1 contains PGA 2 contains SGA 4 contains UGA 8 contains indrect memory 2). Library CacheALTER session SET EVENTS ' immediate trace name Library_cache level n '; 1 Library Cache statistic Information 2 contains hash table histogram 3 contains object handle 4 contains object structure (Heap 0) 3). Row Cache ALTER session SET EVENTS ' immediate tra

illustration: The "Transshipment (factory)/in Transfer (plant)" Inventory for Materials Z303305 is 100 (in-transit inventory formed by MB1B Mobile Type 303 dumps)  the "interim plant/in Transfer (plant)" Inventory of material Z313315 (transfer between different location of the same factory) is 0  Inventory of "in-transit inventory/stock in Transit" for material ZSTO1 100 From this, we can conclude that the stock in transit (factory) in the factory

performance analysis and tuning. Background knowledge Main methods of performance analysis Monitoring: Monitoring is a common way to view the run-time behavior of an application. There are often multiple views (view) that display CPU usage, memory usage, thread state, and other useful information in real time, so that users can quickly discover the key to the problem. Dump: The profiling tool obtains current state data from memory and stores it in a file for static profiling. A Java program

{} Dict [] List "String" Str 1234.56 int or float True/false True/false Null None Python's built-in json modules provide a very sophisticated translation of Python objects into JSON format. Let's look at how to turn the Python object into a JSON:Import JSON>>> d = dict (name='Bob', age=20, score=88)>>> Json.dumps (d) ' {"Age": +, "score": "," "Name": "Bob"} '

Python json serialization deserialization and Chinese Encoding In a project, you need to obtain a json-format data from the cloud, save it to a local file, and then read it out. In this case, you need to use the json dumps method and loads method, to achieve data serialization and deserialization. Specifically, the dumps method can set the data sequence in json format to the related data type of python, whi

read out to form an object. How do---serialize?Two modules are available in Python for serialization. Are pickle and JSON, respectively.PicklePickle is a unique serialization module in Python, so-called exclusive, which means that it cannot interact with the serialization of other programming languages because pickle converts the data object to bytesImport Pickle>>> d=[1,2,3,4]>>> pickle.dumps (d) b'\x80\x03]q\ X00 (k\x01k\x02k\x03k\x04e. '>>> type (pickle.dumps (d))class'bytes' > #类型为byte

testing the sticks and subcontracting is attached below # Python Version: 3.5.1import socketimport timeimport structimport jsonhost = "localhost" port = 1234 ADDR = (host, port) if _ name _ = '_ main __': client = socket. socket () client. connect (ADDR) # normal data packet definition ver = 1 body = json. dumps (dict (hello = "world") print (body) cmd = 101 header = [ver, body. _ len _ (), cmd] headPack = struct. pack ("! 3I ", * header) sendData1 =

, we use python, python has a modifier for functions. So the implementation scheme is as follows: Define a decorator. If the data is obtained before, the data in the cache is directly obtained. If the data is not obtained before, the data is pulled from the website and saved to the cache. The Code is as follows:Copy codeThe Code is as follows:Def get_dump_data (dir_name, url ):M = hashlib. md5 (url)Filename = m. hexdigest ()Full_file_name = \ 'dumps/%

JSON Advanced Python's Dict object can be serialized directly into the JSON {}, but, many times, we prefer to use class to represent objects, such as defining the student class, and then serializing: Import Jsonclass Student (object): def __init__ (self, name, age, score): self.name = name self.age = Age Self.score = scores = Student (' Bob ', ', ') print (Json.dumps (s)) Run the code and relentlessly get a typeerror: Traceback (most recent): ... TypeError: The reason for the error

obtained data and retrieve it directly from the cache when necessary. OK. the target already exists. how can this problem be achieved? If it is in C ++, this is a very troublesome thing, and the code written must be ugly, but fortunately, we use python, python has a modifier for functions. So the implementation scheme is as follows: Define a decorator. if the data is obtained before, the data in the cache is directly obtained. if the data is not obtained before, the data is pulled from the webs

expiration, and more.Here's the official Bench-mark data:The test completed 50 concurrent executions of 100,000 requests.The set and get value is a 256-byte string.Linux box is running Linux 2.6, which is X3320 Xeon 2.5 GHz.Text execution uses the loopback interface (127.0.0.1).Result: The write speed is 110,000 times/s, the reading speed is 81,000 times/s.Redis Common commands:In the case of DB, Redis scores have been amazing, and not to mention the Memcachedb and Tokyocabinet, the original me

static file import json return HttpResponse (json. dumps (ret) # return render(request,'upload.html ',) The background and front-end js have a pitfall on file path reproduction: Note: Save the image in img under static file directory (the prefix of the static file path is also static) The path we want to introduce the image to the django front-end is:/static/img/xx.png. Note that '/' must be added before static, this magic '/' represents the main dir