hive 在kettle 作业return code 2 的问题 Unexpected exception: Unexpected exception: Unexpected exception:

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今天突然进去跑不了数据老是报错return code 2 .之前可以跑通的。

日志如下

矢量查询(Vectorized query) 每次处理数据时会将1024行数据组成一个batch进行处理，而不是一行一行进行处理，这样能够显著提高执行速度。
可以通过设置
set hive.vectorized.execution.enabled = true; 但是如果数据量很大，她的batch就会很多，有可能导致return code -2

解决方法：
set hive.vectorized.execution.enabled=false; 控制是否启用查询执行的向量模式。

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    at org.apache.hadoop.hive.ql.exec.mr.ExecMapper.map(ExecMapper.java:179)
    at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54)
    at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:453)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:343)
    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1796)
    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Hive Runtime Error while processing row 、
    at org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:507)
    at org.apache.hadoop.hive.ql.exec.mr.ExecMapper.map(ExecMapper.java:170)
    ... 8 more
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unexpected exception: Unexpected exception: Unexpected exception: Unexpected exception: Unexpected exception: Unexpected exception: parquet.column.values.dictionary.DictionaryValuesWriter$PlainBinaryDictionaryValuesWriter
    at org.apache.hadoop.hive.ql.exec.MapJoinOperator.processOp(MapJoinOperator.java:318)
    at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:815)
    at org.apache.hadoop.hive.ql.exec.TableScanOperator.processOp(TableScanOperator.java:97)
    at org.apache.hadoop.hive.ql.exec.MapOperator$MapOpCtx.forward(MapOperator.java:157)
    at org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:497)
    ... 9 more
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unexpected exception: Unexpected exception: Unexpected exception: Unexpected exception: Unexpected exception: parquet.column.values.dictionary.DictionaryValuesWriter$PlainBinaryDictionaryValuesWriter
    at org.apache.hadoop.hive.ql.exec.MapJoinOperator.processOp(MapJoinOperator.java:318)
    at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:815)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.internalForward(CommonJoinOperator.java:638)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.genAllOneUniqueJoinObject(CommonJoinOperator.java:670)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.checkAndGenObject(CommonJoinOperator.java:748)
    at org.apache.hadoop.hive.ql.exec.MapJoinOperator.processOp(MapJoinOperator.java:306)
    ... 13 more
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unexpected exception: Unexpected exception: Unexpected exception: Unexpected exception: parquet.column.values.dictionary.DictionaryValuesWriter$PlainBinaryDictionaryValuesWriter
    at org.apache.hadoop.hive.ql.exec.MapJoinOperator.processOp(MapJoinOperator.java:318)
    at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:815)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.internalForward(CommonJoinOperator.java:638)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.createForwardJoinObject(CommonJoinOperator.java:433)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.genObject(CommonJoinOperator.java:525)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.genObject(CommonJoinOperator.java:522)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.genJoinObject(CommonJoinOperator.java:451)
    at org.apache.hadoop.hive.ql.exec.CommonJoinOperator.checkAndGenObject(CommonJoinOperator.java:752)
    at org.apache.hadoo

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2、机器人控制技术：阻抗/导纳控制与强化学习的融合 本文探讨了阻抗/导纳控制与强化学习在机器人控制中的融合应用。阻抗/导纳控制广泛应用于外骨骼和跟踪控制，通过改进方案提升建模误差下的鲁棒性；强化学习则为解决维度灾难、实现最优交互提供了有效途径，尤其在大输入空间、连续时间系统、冗余机器人及鲁棒控制中表现突出。结合近似器与合理奖励设计，强化学习可在无模型情况下实现高效控制。未来，两者融合有望推动更智能、更安全的机器人系统发展。 阅读详情

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深度学习工具包 DeepLearningToolbox

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]); nn.output = 'softmax'; % 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, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition
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