Cassandra 总结3

Apache Cassandra3.10集群安装部署 一、安装环境 操作系统:CentOS Linux release 7.2.1511 (Core) Cassandra版本:Apache Cassandra3.10 Oracle JDK:1.8.0_101 集群节点:5;Intel(R) Xeon(R) CPU E5-2620 v3 @ 2.40GHz双核;内存30G;硬盘总共约3T。 二、Apache Cassandra 阅读详情

转载地址:http://www.cnblogs.com/maheng/p/4993016.html

一、启动cassandra客户端 

 首先启动cassandra,然后运行bin\cassandra-cli.bat启动客户端,默认hostname为localhost,port为9160

F:\apache-cassandra-2.1.11-bin\bin>cassandra-cli
Starting Cassandra Client
org.apache.thrift.transport.TTransportException: java.net.ConnectException: Conn
ection refused: connect
at org.apache.thrift.transport.TSocket.open(TSocket.java:187)
at org.apache.thrift.transport.TFramedTransport.open(TFramedTransport.ja
va:81)
at org.apache.cassandra.thrift.TFramedTransportFactory.openTransport(TFr
amedTransportFactory.java:41)
at org.apache.cassandra.cli.CliMain.connect(CliMain.java:66)
at org.apache.cassandra.cli.CliMain.main(CliMain.java:239)
Caused by: java.net.ConnectException: Connection refused: connect
at java.net.DualStackPlainSocketImpl.connect0(Native Method)
at java.net.DualStackPlainSocketImpl.socketConnect(Unknown Source)
at java.net.AbstractPlainSocketImpl.doConnect(Unknown Source)
at java.net.AbstractPlainSocketImpl.connectToAddress(Unknown Source)
at java.net.AbstractPlainSocketImpl.connect(Unknown Source)
at java.net.PlainSocketImpl.connect(Unknown Source)
at java.net.SocksSocketImpl.connect(Unknown Source)
at java.net.Socket.connect(Unknown Source)
at org.apache.thrift.transport.TSocket.open(TSocket.java:182)
… 4 more
Exception connecting to 127.0.0.1/9160. Reason: Connection refused: connect.
Welcome to Cassandra CLI version 2.1.11

The CLI is deprecated and will be removed in Cassandra 2.2. Consider migrating
to cqlsh.
CQL is fully backwards compatible with Thrift data; see http://www.datastax.com/
dev/blog/thrift-to-cql3

Type ‘help;’ or ‘?’ for help.
Type ‘quit;’ or ‘exit;’ to quit.
可以看到Connection refused,如果遇到这个问题首先要检查配置情况(具体看我之前写的总结二),然后看cassandra是否启动,在window7下显示启动成功后可能还在后台初始化数据所以等待一小会后再运行cassandra-cli.bat启动客户端,在window8下启动cassandra后不要关掉窗口,window7下启动成功后关闭窗口cassandra仍然是在后台运行的,但在window8下关闭窗口就相当于关闭了cassandra,所以在window8下需要重新开个dos窗口来运行cassandra-cli.bat启动客户端。

  也可以连接自己定义的hostname和port,如:cassandra-cli -h localhost -p 9160或者cassandra-cli -hostname localhost -port 9160

  在上面我们可以看到这么一句话

The CLI is deprecated and will be removed in Cassandra 2.2. Consider migrating to cqlsh.
所以更新版本的想要运行cassandra客户端就到cqlsh中吧。

二、cassandra cli简介

  首先我么来看下cli中有哪些命令

[default@unknown] ?
Getting around:
? Display this help.
help; Display this help.
help ; Display command-specific help.
exit; Exit this utility.
quit; Exit this utility.

Commands:
assume Apply client side validation.
connect Connect to a Cassandra node.
consistencylevel Sets consisteny level for the client to use.
count Count columns or super columns.
create column family Add a column family to an existing keyspace.
create keyspace Add a keyspace to the cluster.
del Delete a column, super column or row.
decr Decrements a counter column.
describe cluster Describe the cluster configuration.
describe Describe a keyspace and its column families or column fa
mily in current keyspace.
drop column family Remove a column family and its data.
drop keyspace Remove a keyspace and its data.
drop index Remove an existing index from specific column.
get Get rows and columns.
incr Increments a counter column.
list List rows in a column family.
set Set columns.
show api version Show the server API version.
show cluster name Show the cluster name.
show keyspaces Show all keyspaces and their column families.
show schema Show a cli script to create keyspaces and column familie
s.
truncate Drop the data in a column family.
update column family Update the settings for a column family.
update keyspace Update the settings for a keyspace.
use Switch to a keyspace.
我们挑主要的几个讲一下:

1、connect

  connect hostname/port;

2、consistencylevel

  详细信息参考http://www.doc88.com/p-495509829231.html

  由于用的是单节点,所以我们设该值为one就可以了

[default@unknown] consistencylevel as one;
Consistency level is set to ‘ONE’.
3、create keyspace

[default@unknown] create keyspace keyspace1 with placement_strategy=’SimpleStrat
egy’ and strategy_options={replication_factor:1};
6a9f1cd8-758d-3a9a-9f0a-e799b00bb159
  单节点replication_factor设为1就行了。

4、use

[default@unknown] use keyspace1;
Authenticated to keyspace: keyspace1
5、create column family

  column family分为Standard和Super两种类型,默认情况下是Standard。不知道的可以先了解一下cassandra的数据存储结构。

  创建standard类型:

[default@keyspace1] create column family standardFamily with comparator=UTF8Type
;
b7f2a26b-1e36-31e0-a7c2-7d32daf041fd
  某一个Key内的所有Column都是按照它的Name来排序的,comparator是设置排序类型。(关于排序前面有讲到,请自行查阅)

  创建super类型:

[default@keyspace1] create column family superFamily with comparator = UTF8Type
and subcomparator = UTF8Type and column_type = ‘Super’;
2bf4b959-6199-321b-8e56-c8cb2f653ae1
  在super类型中,comparator按照所有superColumn的key来排序,subcomparator按照superColumn中所有column的name排序。

6、set

  插入standard类型的数据:

[default@keyspace1] set standardFamily[‘key1’][‘cloumnName1’] = ‘columnValue1’;
cannot parse ‘columnValue1’ as hex bytes
我们可以看到不能解析,这时候需要调用ascii()函数,utf8()函数等函数来设置数据类型,cassandra中的数据类型有 bytes, integer, long, int, lexicaluuid, timeuuid, utf8, ascii, double, countercolumn。

[default@keyspace1] set standardFamily[‘key1’][‘cloumnName1’] = ascii(‘columnValue1’);
org.apache.cassandra.serializers.MarshalException: cannot parse ‘key1’ as hex bytes
可以看到key也不能解析,事实上key,column_name,column_value都是需要函数解析的

[default@keyspace1] set standardFamily[utf8(‘key1’)][utf8(‘columnName1’)] = utf8
(‘columnValue1’);
Value inserted.
Elapsed time: 52 msec(s).
成功插入,但是这样显然很麻烦,我们可以用assume来设置数据类型。

7、assume

  格式:assume column family validator/comparator/keys/sub_comparator as type;

[default@keyspace1] assume standardFamily keys as utf8;
Assumption for column family ‘standardFamily’ added successfully.
设置key的数据类型为utf8。

[default@keyspace1] assume standardFamily validator as utf8;
Assumption for column family ‘standardFamily’ added successfully.
设置value的数据类型为utf8。

至于comparator/sub_comparator就是指定排序的类型了。(前面有讲到排序类型)

[default@keyspace1] set standardFamily[‘key2’][‘columnName2’] = ‘columnValue2’;
Value inserted.
Elapsed time: 1.85 msec(s).
这样如果也对name设置了排序类型的话就可以直接插入了(提示:如果column_name没有设置排序类型,column_name是仍旧需要函数解析的)

并且assume设置的验证类型在关闭服务器后就会失效,所以我们永久的方法就是直接修改column family的设置,看下面的update。

8、update column family

[default@keyspace1] update column family standardFamily with key_validation_clas
s = UTF8Type and default_validation_class = UTF8Type;
fe850dbb-c170-3639-846d-2ce3ef975462
这样设置就永久保存了。

9、describe

  我们来看下standradFamily的配置情况。

[default@keyspace1] describe standardFamily;

WARNING: CQL3 tables are intentionally omitted from ‘describe’ output.
See https://issues.apache.org/jira/browse/CASSANDRA-4377 for details.

ColumnFamily: standardFamily
  Key Validation Class: org.apache.cassandra.db.marshal.UTF8Type
  Default column value validator: org.apache.cassandra.db.marshal.UTF8Type
  Cells sorted by: org.apache.cassandra.db.marshal.UTF8Type
  GC grace seconds: 864000
  Compaction min/max thresholds: 4/32
  Read repair chance: 0.0
  DC Local Read repair chance: 0.1
  Caching: KEYS_ONLY
  Default time to live: 0
  Bloom Filter FP chance: default
  Index interval: default
  Speculative Retry: NONE
  Built indexes: []
  Compaction Strategy: org.apache.cassandra.db.compaction.SizeTieredCompacti

onStrategy
Compression Options:
sstable_compression: org.apache.cassandra.io.compress.LZ4Compressor
可以看到我们的设置都生效了。

10、get

  获取key对应的value

[default@keyspace1] get standardFamily[‘key1’];
=> (name=columnName1, value=columnValue1, timestamp=1448367633646000)
Returned 1 results.
Elapsed time: 193 msec(s).
  获取key下column_name对应的value

[default@keyspace1] get standardFamily[‘key1’][‘columnName1’];
=> (name=columnName1, value=columnValue1, timestamp=1448367633646000)
Elapsed time: 193 msec(s).
11、list

获取某个column family中的所有数据,也可以用limit来限制查询的数量,默认100

[default@keyspace1] list standardFamily;
Using default limit of 100

Using default cell limit of 100

RowKey: 3

=> (name=3, value=3, timestamp=1448369924110000)

RowKey: key1

=> (name=columnName1, value=columnValue1, timestamp=1448367633646000)

RowKey: 1

=> (name=1, value=1, timestamp=1448368544230000)

RowKey: key2
=> (name=2, value=columnValue2, timestamp=1448369767734000)
=> (name=columnName2, value=columnValue2, timestamp=1448369748230000)

4 Rows Returned.
Elapsed time: 270 msec(s).
[default@keyspace1] list standardFamily limit 1;

Using default cell limit of 100

RowKey: 3
=> (name=3, value=3, timestamp=1448369924110000)

1 Row Returned.
Elapsed time: 24 msec(s).                                                                           
可以看出key是按照cassandra自己的机制排序的,但column或者superColumn是可以自己定义类型排序的。

25、CQL 3 与 Cassandra 1.2 入门 本文介绍了 CQL 3 与 Cassandra 1.2 的基本使用,包括从 Thrift 到 CQL 3 的转变、宽行与复合列的处理、键空间和表的创建、二级索引的使用以及插入和查询数据的操作细节。同时提供了使用 CQL 3 的建议和常见问题解答,帮助开发者更好地理解和应用 CQL 3。 阅读详情

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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(&#39;DeepLearnToolbox&#39;));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(&#39;state&#39;,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&#39;); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand(&#39;state&#39;,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 = &#39;sigm&#39;;%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, &#39;Too big error&#39;);```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(&#39;state&#39;,0)sae = saesetup([784 100]);sae.ae{1}.activation_function = &#39;sigm&#39;;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)&#39;)% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = &#39;sigm&#39;;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, &#39;Too big error&#39;);```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x&#39;,28,28,60000))/255;test_x = double(reshape(test_x&#39;,28,28,10000))/255;train_y = double(train_y&#39;);test_y = double(test_y&#39;);%% 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&#39;ll get around 1.2% errorrand(&#39;state&#39;,0)cnn.layers = { struct(&#39;type&#39;, &#39;i&#39;) %input layer struct(&#39;type&#39;, &#39;c&#39;, &#39;outputmaps&#39;, 6, &#39;kernelsize&#39;, 5) %convolution layer struct(&#39;type&#39;, &#39;s&#39;, &#39;scale&#39;, 2) %sub sampling layer struct(&#39;type&#39;, &#39;c&#39;, &#39;outputmaps&#39;, 12, &#39;kernelsize&#39;, 5) %convolution layer struct(&#39;type&#39;, &#39;s&#39;, &#39;scale&#39;, 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, &#39;Too big error&#39;);```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(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex2 neural net with L2 weight decayrand(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex3 neural net with dropoutrand(&#39;state&#39;,0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; 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% enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, &#39;Too big error&#39;);%% 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(&#39;state&#39;,0)nn = nnsetup([784 20 10]); nn.output = &#39;softmax&#39;; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, &#39;Too big error&#39;);```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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