Mysqldump的应用

--print-defaults        Print the program argument list and exit.

--no-defaults           Don't read default options from any option file.

--defaults-file=#       Only read default options from the given file #.

--defaults-extra-file=# Read this file after the global files are read.

  --all               Deprecated. Use --create-options instead.

  -A, --all-databases Dump all the databases. This will be same as --databases

                      with all databases selected.

  -Y, --all-tablespaces 

                      Dump all the tablespaces.

  -y, --no-tablespaces 

                      Do not dump any tablespace information.

  --add-drop-database Add a DROP DATABASE before each create.

  --add-drop-table    Add a DROP TABLE before each create.

  --add-locks         Add locks around INSERT statements.

  --allow-keywords    Allow creation of column names that are keywords.

  --character-sets-dir=name 

                      Directory for character set files.

  -i, --comments      Write additional information.

  --compatible=name   Change the dump to be compatible with a given mode. By

                      default tables are dumped in a format optimized for

                      MySQL. Legal modes are: ansi, mysql323, mysql40,

                      postgresql, oracle, mssql, db2, maxdb, no_key_options,

                      no_table_options, no_field_options. One can use several

                      modes separated by commas. Note: Requires MySQL server

                      version 4.1.0 or higher. This option is ignored with

                      earlier server versions.

  --compact           Give less verbose output (useful for debugging). Disables

                      structure comments and header/footer constructs.  Enables

                      options --skip-add-drop-table --skip-add-locks

                      --skip-comments --skip-disable-keys --skip-set-charset.

  -c, --complete-insert 

                      Use complete insert statements.

  -C, --compress      Use compression in server/client protocol.

  -a, --create-options 

                      Include all MySQL specific create options.

  -B, --databases     Dump several databases. Note the difference in usage; in

                      this case no tables are given. All name arguments are

                      regarded as database names. 'USE db_name;' will be

                      included in the output.

  -#, --debug[=#]     This is a non-debug version. Catch this and exit.

  --debug-check       Check memory and open file usage at exit.

  --debug-info        Print some debug info at exit.

  --default-character-set=name 

                      Set the default character set.

  --delayed-insert    Insert rows with INSERT DELAYED.

  --delete-master-logs 

                      Delete logs on master after backup. This automatically

                      enables --master-data.

  -K, --disable-keys  '/*!40000 ALTER TABLE tb_name DISABLE KEYS */; and

                      '/*!40000 ALTER TABLE tb_name ENABLE KEYS */; will be put

                      in the output.

  -E, --events        Dump events.

  -e, --extended-insert 

                      Use multiple-row INSERT syntax that include several

                      VALUES lists.

  --fields-terminated-by=name 

                      Fields in the output file are terminated by the given

                      string.

  --fields-enclosed-by=name 

                      Fields in the output file are enclosed by the given

                      character.

  --fields-optionally-enclosed-by=name 

                      Fields in the output file are optionally enclosed by the

                      given character.

  --fields-escaped-by=name 

                      Fields in the output file are escaped by the given

                      character.

  --first-slave       Deprecated, renamed to --lock-all-tables.

  -F, --flush-logs    Flush logs file in server before starting dump. Note that

                      if you dump many databases at once (using the option

                      --databases= or --all-databases), the logs will be

                      flushed for each database dumped. The exception is when

                      using --lock-all-tables or --master-data: in this case

                      the logs will be flushed only once, corresponding to the

                      moment all tables are locked. So if you want your dump

                      and the log flush to happen at the same exact moment you

                      should use --lock-all-tables or --master-data with

                      --flush-logs.

  --flush-privileges  Emit a FLUSH PRIVILEGES statement after dumping the mysql

                      database.  This option should be used any time the dump

                      contains the mysql database and any other database that

                      depends on the data in the mysql database for proper

                      restore. 

  -f, --force         Continue even if we get an SQL error.

  -?, --help          Display this help message and exit.

  --hex-blob          Dump binary strings (BINARY, VARBINARY, BLOB) in

                      hexadecimal format.

  -h, --host=name     Connect to host.

  --ignore-table=name Do not dump the specified table. To specify more than one

                      table to ignore, use the directive multiple times, once

                      for each table.  Each table must be specified with both

                      database and table names, e.g.,

                      --ignore-table=database.table.

  --insert-ignore     Insert rows with INSERT IGNORE.

  --lines-terminated-by=name 

                      Lines in the output file are terminated by the given

                      string.

  -x, --lock-all-tables 

                      Locks all tables across all databases. This is achieved

                      by taking a global read lock for the duration of the

                      whole dump. Automatically turns --single-transaction and

                      --lock-tables off.

  -l, --lock-tables   Lock all tables for read.

  --log-error=name    Append warnings and errors to given file.

  --master-data[=#]   This causes the binary log position and filename to be

                      appended to the output. If equal to 1, will print it as a

                      CHANGE MASTER command; if equal to 2, that command will

                      be prefixed with a comment symbol. This option will turn

                      --lock-all-tables on, unless --single-transaction is

                      specified too (in which case a global read lock is only

                      taken a short time at the beginning of the dump; don't

                      forget to read about --single-transaction below). In all

                      cases, any action on logs will happen at the exact moment

                      of the dump. Option automatically turns --lock-tables

                      off.

  --max_allowed_packet=# 

                      The maximum packet length to send to or receive from

                      server.

  --net_buffer_length=# 

                      The buffer size for TCP/IP and socket communication.

  --no-autocommit     Wrap tables with autocommit/commit statements.

  -n, --no-create-db  Suppress the CREATE DATABASE ... IF EXISTS statement that

                      normally is output for each dumped database if

                      --all-databases or --databases is given.

  -t, --no-create-info 

                      Don't write table creation info.

  -d, --no-data       No row information.

  -N, --no-set-names  Suppress the SET NAMES statement

  --opt               Same as --add-drop-table, --add-locks, --create-options,

                      --quick, --extended-insert, --lock-tables, --set-charset,

                      and --disable-keys. Enabled by default, disable with

                      --skip-opt.

  --order-by-primary  Sorts each table's rows by primary key, or first unique

                      key, if such a key exists.  Useful when dumping a MyISAM

                      table to be loaded into an InnoDB table, but will make

                      the dump itself take considerably longer.

  -p, --password[=name] 

                      Password to use when connecting to server. If password is

                      not given it's solicited on the tty.

  -P, --port=#        Port number to use for connection.

  --protocol=name     The protocol to use for connection (tcp, socket, pipe,

                      memory).

  -q, --quick         Don't buffer query, dump directly to stdout.

  -Q, --quote-names   Quote table and column names with backticks (`).

  --replace           Use REPLACE INTO instead of INSERT INTO.

  -r, --result-file=name 

                      Direct output to a given file. This option should be used

                      in MSDOS, because it prevents new line '\n' from being

                      converted to '\r\n' (carriage return + line feed).

  -R, --routines      Dump stored routines (functions and procedures).

  --set-charset       Add 'SET NAMES default_character_set' to the output.

                      Enabled by default; suppress with --skip-set-charset.

  -O, --set-variable=name 

                      Change the value of a variable. Please note that this

                      option is deprecated; you can set variables directly with

                      --variable-name=value.

  --single-transaction 

                      Creates a consistent snapshot by dumping all tables in a

                      single transaction. Works ONLY for tables stored in

                      storage engines which support multiversioning (currently

                      only InnoDB does); the dump is NOT guaranteed to be

                      consistent for other storage engines. While a

                      --single-transaction dump is in process, to ensure a

                      valid dump file (correct table contents and binary log

                      position), no other connection should use the following

                      statements: ALTER TABLE, DROP TABLE, RENAME TABLE,

                      TRUNCATE TABLE, as consistent snapshot is not isolated

                      from them. Option automatically turns off --lock-tables.

  --dump-date         Put a dump date to the end of the output.

  --skip-opt          Disable --opt. Disables --add-drop-table, --add-locks,

                      --create-options, --quick, --extended-insert,

                      --lock-tables, --set-charset, and --disable-keys.

  -S, --socket=name   The socket file to use for connection.

  -T, --tab=name      Create tab-separated textfile for each table to given

                      path. (Create .sql and .txt files.) NOTE: This only works

                      if mysqldump is run on the same machine as the mysqld

                      server.

  --tables            Overrides option --databases (-B).

  --triggers          Dump triggers for each dumped table.

  --tz-utc            SET TIME_ZONE='+00:00' at top of dump to allow dumping of

                      TIMESTAMP data when a server has data in different time

                      zones or data is being moved between servers with

                      different time zones.

  -u, --user=name     User for login if not current user.

  -v, --verbose       Print info about the various stages.

  -V, --version       Output version information and exit.

  -w, --where=name    Dump only selected records. Quotes are mandatory.

  -X, --xml           Dump a database as well formed XML.

 

 

1. mysqldump -uroot -R -p dbname > out.sql 导出数据和过程

 2. mysqldump -uroot -R -d -t -p dbname > out.sql 只导出过程

 

mysqldump 详解:基础与高级应用实战 mysqldump 是 MySQL 自带的命令行工具,用于创建数据库的逻辑备份。它生成一系列 SQL 语句,这些语句可以用来重建数据库的结构和内容。 阅读详情

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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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