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fa-home"></i> Home</a> + </li> + + + + + <li class="divider"></li> + + + + + <li class="header">TABLE OF CONTENTS</li> + + + + <li class="chapter " data-level="1.1" data-path="../../"> + + <a href="../../"> + + + <b>1.1.</b> + + Introduction + + </a> + + + + </li> + + <li class="chapter " data-level="1.2" data-path="../../getting_started/"> + + <a href="../../getting_started/"> + + + <b>1.2.</b> + + Getting Started + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.2.1" data-path="../../getting_started/installation.html"> + + <a href="../../getting_started/installation.html"> + + + <b>1.2.1.</b> + + Installation + + </a> + + + + </li> + + <li class="chapter " data-level="1.2.2" data-path="../../getting_started/permanent-functions.html"> + + <a href="../../getting_started/permanent-functions.html"> + + + <b>1.2.2.</b> + + Install as permanent functions + + </a> + + + + </li> + + <li class="chapter " data-level="1.2.3" data-path="../../getting_started/input-format.html"> + + <a href="../../getting_started/input-format.html"> + + + <b>1.2.3.</b> + + Input Format + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.3" data-path="../../tips/"> + + <a href="../../tips/"> + + + <b>1.3.</b> + + Tips for Effective Hivemall + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.3.1" data-path="../../tips/addbias.html"> + + <a href="../../tips/addbias.html"> + + + <b>1.3.1.</b> + + Explicit addBias() for better prediction + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.2" data-path="../../tips/rand_amplify.html"> + + <a href="../../tips/rand_amplify.html"> + + + <b>1.3.2.</b> + + Use rand_amplify() to better prediction results + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.3" data-path="../../tips/rt_prediction.html"> + + <a href="../../tips/rt_prediction.html"> + + + <b>1.3.3.</b> + + Real-time Prediction on RDBMS + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.4" data-path="../../tips/ensemble_learning.html"> + + <a href="../../tips/ensemble_learning.html"> + + + <b>1.3.4.</b> + + Ensemble learning for stable prediction + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.5" data-path="../../tips/mixserver.html"> + + <a href="../../tips/mixserver.html"> + + + <b>1.3.5.</b> + + Mixing models for a better prediction convergence (MIX server) + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.6" data-path="../../tips/emr.html"> + + <a href="../../tips/emr.html"> + + + <b>1.3.6.</b> + + Run Hivemall on Amazon Elastic MapReduce + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.4" data-path="../../tips/general_tips.html"> + + <a href="../../tips/general_tips.html"> + + + <b>1.4.</b> + + General Hive/Hadoop tips + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.4.1" data-path="../../tips/rowid.html"> + + <a href="../../tips/rowid.html"> + + + <b>1.4.1.</b> + + Adding rowid for each row + + </a> + + + + </li> + + <li class="chapter " data-level="1.4.2" data-path="../../tips/hadoop_tuning.html"> + + <a href="../../tips/hadoop_tuning.html"> + + + <b>1.4.2.</b> + + Hadoop tuning for Hivemall + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.5" data-path="../../troubleshooting/"> + + <a href="../../troubleshooting/"> + + + <b>1.5.</b> + + Troubleshooting + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.5.1" data-path="../../troubleshooting/oom.html"> + + <a href="../../troubleshooting/oom.html"> + + + <b>1.5.1.</b> + + OutOfMemoryError in training + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.2" data-path="../../troubleshooting/mapjoin_task_error.html"> + + <a href="../../troubleshooting/mapjoin_task_error.html"> + + + <b>1.5.2.</b> + + SemanticException Generate Map Join Task Error: Cannot serialize object + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.3" data-path="../../troubleshooting/asterisk.html"> + + <a href="../../troubleshooting/asterisk.html"> + + + <b>1.5.3.</b> + + Asterisk argument for UDTF does not work + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.4" data-path="../../troubleshooting/num_mappers.html"> + + <a href="../../troubleshooting/num_mappers.html"> + + + <b>1.5.4.</b> + + The number of mappers is less than input splits in Hadoop 2.x + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.5" data-path="../../troubleshooting/mapjoin_classcastex.html"> + + <a href="../../troubleshooting/mapjoin_classcastex.html"> + + + <b>1.5.5.</b> + + Map-side Join causes ClassCastException on Tez + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part II - Generic Features</li> + + + + <li class="chapter " data-level="2.1" data-path="../../misc/generic_funcs.html"> + + <a href="../../misc/generic_funcs.html"> + + + <b>2.1.</b> + + List of generic Hivemall functions + + </a> + + + + </li> + + <li class="chapter " data-level="2.2" data-path="../../misc/topk.html"> + + <a href="../../misc/topk.html"> + + + <b>2.2.</b> + + Efficient Top-K query processing + + </a> + + + + </li> + + <li class="chapter " data-level="2.3" data-path="../../misc/tokenizer.html"> + + <a href="../../misc/tokenizer.html"> + + + <b>2.3.</b> + + English/Japanese Text Tokenizer + + </a> + + + + </li> + + + + + <li class="header">Part III - Feature Engineering</li> + + + + <li class="chapter " data-level="3.1" data-path="../../ft_engineering/scaling.html"> + + <a href="../../ft_engineering/scaling.html"> + + + <b>3.1.</b> + + Feature Scaling + + </a> + + + + </li> + + <li class="chapter " data-level="3.2" data-path="../../ft_engineering/hashing.html"> + + <a href="../../ft_engineering/hashing.html"> + + + <b>3.2.</b> + + Feature Hashing + + </a> + + + + </li> + + <li class="chapter " data-level="3.3" data-path="../../ft_engineering/tfidf.html"> + + <a href="../../ft_engineering/tfidf.html"> + + + <b>3.3.</b> + + TF-IDF calculation + + </a> + + + + </li> + + <li class="chapter " data-level="3.4" data-path="../../ft_engineering/ft_trans.html"> + + <a href="../../ft_engineering/ft_trans.html"> + + + <b>3.4.</b> + + FEATURE TRANSFORMATION + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="3.4.1" data-path="../../ft_engineering/vectorizer.html"> + + <a href="../../ft_engineering/vectorizer.html"> + + + <b>3.4.1.</b> + + Vectorize Features + + </a> + + + + </li> + + <li class="chapter " data-level="3.4.2" data-path="../../ft_engineering/quantify.html"> + + <a href="../../ft_engineering/quantify.html"> + + + <b>3.4.2.</b> + + Quantify non-number features + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="3.5" data-path="../../ft_engineering/feature_selection.html"> + + <a href="../../ft_engineering/feature_selection.html"> + + + <b>3.5.</b> + + Feature selection + + </a> + + + + </li> + + + + + <li class="header">Part IV - Evaluation</li> + + + + <li class="chapter " data-level="4.1" data-path="../../eval/stat_eval.html"> + + <a href="../../eval/stat_eval.html"> + + + <b>4.1.</b> + + Statistical evaluation of a prediction model + + </a> + + + + </li> + + <li class="chapter " data-level="4.2" data-path="../../eval/datagen.html"> + + <a href="../../eval/datagen.html"> + + + <b>4.2.</b> + + Data Generation + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="4.2.1" data-path="../../eval/lr_datagen.html"> + + <a href="../../eval/lr_datagen.html"> + + + <b>4.2.1.</b> + + Logistic Regression data generation + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part V - Binary classification</li> + + + + <li class="chapter " data-level="5.1" data-path="../../binaryclass/a9a.html"> + + <a href="../../binaryclass/a9a.html"> + + + <b>5.1.</b> + + a9a Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.1.1" data-path="../../binaryclass/a9a_dataset.html"> + + <a href="../../binaryclass/a9a_dataset.html"> + + + <b>5.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.1.2" data-path="../../binaryclass/a9a_lr.html"> + + <a href="../../binaryclass/a9a_lr.html"> + + + <b>5.1.2.</b> + + Logistic Regression + + </a> + + + + </li> + + <li class="chapter " data-level="5.1.3" data-path="../../binaryclass/a9a_minibatch.html"> + + <a href="../../binaryclass/a9a_minibatch.html"> + + + <b>5.1.3.</b> + + Mini-batch Gradient Descent + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.2" data-path="../../binaryclass/news20.html"> + + <a href="../../binaryclass/news20.html"> + + + <b>5.2.</b> + + News20 Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.2.1" data-path="../../binaryclass/news20_dataset.html"> + + <a href="../../binaryclass/news20_dataset.html"> + + + <b>5.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.2" data-path="../../binaryclass/news20_pa.html"> + + <a href="../../binaryclass/news20_pa.html"> + + + <b>5.2.2.</b> + + Perceptron, Passive Aggressive + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.3" data-path="../../binaryclass/news20_scw.html"> + + <a href="../../binaryclass/news20_scw.html"> + + + <b>5.2.3.</b> + + CW, AROW, SCW + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.4" data-path="../../binaryclass/news20_adagrad.html"> + + <a href="../../binaryclass/news20_adagrad.html"> + + + <b>5.2.4.</b> + + AdaGradRDA, AdaGrad, AdaDelta + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.3" data-path="../../binaryclass/kdd2010a.html"> + + <a href="../../binaryclass/kdd2010a.html"> + + + <b>5.3.</b> + + KDD2010a Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.3.1" data-path="../../binaryclass/kdd2010a_dataset.html"> + + <a href="../../binaryclass/kdd2010a_dataset.html"> + + + <b>5.3.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.3.2" data-path="../../binaryclass/kdd2010a_scw.html"> + + <a href="../../binaryclass/kdd2010a_scw.html"> + + + <b>5.3.2.</b> + + PA, CW, AROW, SCW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.4" data-path="../../binaryclass/kdd2010b.html"> + + <a href="../../binaryclass/kdd2010b.html"> + + + <b>5.4.</b> + + KDD2010b Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.4.1" data-path="../../binaryclass/kdd2010b_dataset.html"> + + <a href="../../binaryclass/kdd2010b_dataset.html"> + + + <b>5.4.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.4.2" data-path="../../binaryclass/kdd2010b_arow.html"> + + <a href="../../binaryclass/kdd2010b_arow.html"> + + + <b>5.4.2.</b> + + AROW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.5" data-path="../../binaryclass/webspam.html"> + + <a href="../../binaryclass/webspam.html"> + + + <b>5.5.</b> + + Webspam Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.5.1" data-path="../../binaryclass/webspam_dataset.html"> + + <a href="../../binaryclass/webspam_dataset.html"> + + + <b>5.5.1.</b> + + Data pareparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.5.2" data-path="../../binaryclass/webspam_scw.html"> + + <a href="../../binaryclass/webspam_scw.html"> + + + <b>5.5.2.</b> + + PA1, AROW, SCW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.6" data-path="../../binaryclass/titanic_rf.html"> + + <a href="../../binaryclass/titanic_rf.html"> + + + <b>5.6.</b> + + Kaggle Titanic Tutorial + + </a> + + + + </li> + + + + + <li class="header">Part VI - Multiclass classification</li> + + + + <li class="chapter " data-level="6.1" data-path="../../multiclass/news20.html"> + + <a href="../../multiclass/news20.html"> + + + <b>6.1.</b> + + News20 Multiclass Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="6.1.1" data-path="../../multiclass/news20_dataset.html"> + + <a href="../../multiclass/news20_dataset.html"> + + + <b>6.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.2" data-path="../../multiclass/news20_one-vs-the-rest_dataset.html"> + + <a href="../../multiclass/news20_one-vs-the-rest_dataset.html"> + + + <b>6.1.2.</b> + + Data preparation for one-vs-the-rest classifiers + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.3" data-path="../../multiclass/news20_pa.html"> + + <a href="../../multiclass/news20_pa.html"> + + + <b>6.1.3.</b> + + PA + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.4" data-path="../../multiclass/news20_scw.html"> + + <a href="../../multiclass/news20_scw.html"> + + + <b>6.1.4.</b> + + CW, AROW, SCW + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.5" data-path="../../multiclass/news20_ensemble.html"> + + <a href="../../multiclass/news20_ensemble.html"> + + + <b>6.1.5.</b> + + Ensemble learning + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.6" data-path="../../multiclass/news20_one-vs-the-rest.html"> + + <a href="../../multiclass/news20_one-vs-the-rest.html"> + + + <b>6.1.6.</b> + + one-vs-the-rest classifier + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="6.2" data-path="../../multiclass/iris.html"> + + <a href="../../multiclass/iris.html"> + + + <b>6.2.</b> + + Iris Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="6.2.1" data-path="../../multiclass/iris_dataset.html"> + + <a href="../../multiclass/iris_dataset.html"> + + + <b>6.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.2" data-path="../../multiclass/iris_scw.html"> + + <a href="../../multiclass/iris_scw.html"> + + + <b>6.2.2.</b> + + SCW + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="../../multiclass/iris_randomforest.html"> + + <a href="../../multiclass/iris_randomforest.html"> + + + <b>6.2.3.</b> + + RandomForest + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part VII - Regression</li> + + + + <li class="chapter " data-level="7.1" data-path="../../regression/e2006.html"> + + <a href="../../regression/e2006.html"> + + + <b>7.1.</b> + + E2006-tfidf regression Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="7.1.1" data-path="../../regression/e2006_dataset.html"> + + <a href="../../regression/e2006_dataset.html"> + + + <b>7.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="7.1.2" data-path="../../regression/e2006_arow.html"> + + <a href="../../regression/e2006_arow.html"> + + + <b>7.1.2.</b> + + Passive Aggressive, AROW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="7.2" data-path="../../regression/kddcup12tr2.html"> + + <a href="../../regression/kddcup12tr2.html"> + + + <b>7.2.</b> + + KDDCup 2012 track 2 CTR prediction Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="7.2.1" data-path="../../regression/kddcup12tr2_dataset.html"> + + <a href="../../regression/kddcup12tr2_dataset.html"> + + + <b>7.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.2" data-path="../../regression/kddcup12tr2_lr.html"> + + <a href="../../regression/kddcup12tr2_lr.html"> + + + <b>7.2.2.</b> + + Logistic Regression, Passive Aggressive + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.3" data-path="../../regression/kddcup12tr2_lr_amplify.html"> + + <a href="../../regression/kddcup12tr2_lr_amplify.html"> + + + <b>7.2.3.</b> + + Logistic Regression with Amplifier + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.4" data-path="../../regression/kddcup12tr2_adagrad.html"> + + <a href="../../regression/kddcup12tr2_adagrad.html"> + + + <b>7.2.4.</b> + + AdaGrad, AdaDelta + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part VIII - Recommendation</li> + + + + <li class="chapter " data-level="8.1" data-path="../../recommend/cf.html"> + + <a href="../../recommend/cf.html"> + + + <b>8.1.</b> + + Collaborative Filtering + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.1.1" data-path="../../recommend/item_based_cf.html"> + + <a href="../../recommend/item_based_cf.html"> + + + <b>8.1.1.</b> + + Item-based Collaborative Filtering + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="8.2" data-path="../../recommend/news20.html"> + + <a href="../../recommend/news20.html"> + + + <b>8.2.</b> + + News20 related article recommendation Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.2.1" data-path="../../multiclass/news20_dataset.html"> + + <a href="../../multiclass/news20_dataset.html"> + + + <b>8.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.2" data-path="../../recommend/news20_jaccard.html"> + + <a href="../../recommend/news20_jaccard.html"> + + + <b>8.2.2.</b> + + LSH/Minhash and Jaccard Similarity + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../../recommend/news20_knn.html"> + + <a href="../../recommend/news20_knn.html"> + + + <b>8.2.3.</b> + + LSH/Minhash and Brute-Force Search + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.4" data-path="../../recommend/news20_bbit_minhash.html"> + + <a href="../../recommend/news20_bbit_minhash.html"> + + + <b>8.2.4.</b> + + kNN search using b-Bits Minhash + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="8.3" data-path="../../recommend/movielens.html"> + + <a href="../../recommend/movielens.html"> + + + <b>8.3.</b> + + MovieLens movie recommendation Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.3.1" data-path="../../recommend/movielens_dataset.html"> + + <a href="../../recommend/movielens_dataset.html"> + + + <b>8.3.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.2" data-path="../../recommend/movielens_mf.html"> + + <a href="../../recommend/movielens_mf.html"> + + + <b>8.3.2.</b> + + Matrix Factorization + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.3" data-path="../../recommend/movielens_fm.html"> + + <a href="../../recommend/movielens_fm.html"> + + + <b>8.3.3.</b> + + Factorization Machine + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.4" data-path="../../recommend/movielens_cv.html"> + + <a href="../../recommend/movielens_cv.html"> + + + <b>8.3.4.</b> + + 10-fold Cross Validation (Matrix Factorization) + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part IX - Anomaly Detection</li> + + + + <li class="chapter " data-level="9.1" data-path="../../anomaly/lof.html"> + + <a href="../../anomaly/lof.html"> + + + <b>9.1.</b> + + Outlier Detection using Local Outlier Factor (LOF) + + </a> + + + + </li> + + + + + <li class="header">Part X - Hivemall on Spark</li> + + + + <li class="chapter " data-level="10.1" data-path="misc.html"> + + <a href="misc.html"> + + + <b>10.1.</b> + + Generic features + + </a> + + + + <ul class="articles"> + + + <li class="chapter active" data-level="10.1.1" data-path="topk_join.html"> + + <a href="topk_join.html"> + + + <b>10.1.1.</b> + + Top-k Join processing + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part X - External References</li> + + + + <li class="chapter " data-level="11.1" > + + <a target="_blank" href="https://github.com/maropu/hivemall-spark";> + + + <b>11.1.</b> + + Hivemall on Apache Spark + + </a> + + + + </li> + + <li class="chapter " data-level="11.2" > + + <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome";> + + + <b>11.2.</b> + + Hivemall on Apache Pig + + </a> + + + + </li> + + + + + <li class="divider"></li> + + <li> + <a href="https://www.gitbook.com"; target="blank" class="gitbook-link"> + Published with GitBook + </a> + </li> +</ul> + + + </nav> + + + </div> + + <div class="book-body"> + + <div class="body-inner"> + + + +<div class="book-header" role="navigation"> + + + <!-- Title --> + <h1> + <i class="fa fa-circle-o-notch fa-spin"></i> + <a href="../.." >Top-k Join processing</a> + </h1> +</div> + + + + + <div class="page-wrapper" tabindex="-1" role="main"> + <div class="page-inner"> + +<div id="book-search-results"> + <div class="search-noresults"> + + <section class="normal markdown-section"> + + <!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> +<p><code>leftDf.top_k_join(k: Column, rightDf: DataFrame, joinExprs: Column, score: Column)</code> only joins the top-k records of <code>rightDf</code> for each <code>leftDf</code> record with a join condition <code>joinExprs</code>. An output schema of this operation is the joined schema of <code>leftDf</code> and <code>rightDf</code> plus (rank: Int, score: <code>score</code> type).</p> +<p><code>top_k_join</code> is much IO-efficient as compared to regular joining + ranking operations because <code>top_k_join</code> drops unsatisfied records and writes only top-k records to disks during joins.</p> +<div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><ul> +<li><code>top_k_join</code> is supported in the DataFrame of Spark v2.1.0 or later.</li> +<li>A type of <code>score</code> must be ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, or DecimalType.</li> +<li>If <code>k</code> is less than 0, the order is reverse and <code>top_k_join</code> joins the tail-K records of <code>rightDf</code>.</li> +</ul></div></div> +<h1 id="usage">Usage</h1> +<p>For example, we have two tables below;</p> +<ul> +<li>An input table (<code>leftDf</code>)</li> +</ul> +<table> +<thead> +<tr> +<th style="text-align:center">userId</th> +<th style="text-align:center">group</th> +<th style="text-align:center">x</th> +<th style="text-align:center">y</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">b</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.3</td> +</tr> +<tr> +<td style="text-align:center">2</td> +<td style="text-align:center">a</td> +<td style="text-align:center">0.5</td> +<td style="text-align:center">0.4</td> +</tr> +<tr> +<td style="text-align:center">3</td> +<td style="text-align:center">a</td> +<td style="text-align:center">0.1</td> +<td style="text-align:center">0.8</td> +</tr> +<tr> +<td style="text-align:center">4</td> +<td style="text-align:center">c</td> +<td style="text-align:center">0.2</td> +<td style="text-align:center">0.2</td> +</tr> +<tr> +<td style="text-align:center">5</td> +<td style="text-align:center">a</td> +<td style="text-align:center">0.1</td> +<td style="text-align:center">0.4</td> +</tr> +<tr> +<td style="text-align:center">6</td> +<td style="text-align:center">b</td> +<td style="text-align:center">0.8</td> +<td style="text-align:center">0.3</td> +</tr> +</tbody> +</table> +<ul> +<li>A reference table (<code>rightDf</code>)</li> +</ul> +<table> +<thead> +<tr> +<th style="text-align:center">group</th> +<th style="text-align:center">position</th> +<th style="text-align:center">x</th> +<th style="text-align:center">y</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">a</td> +<td style="text-align:center">pos-1</td> +<td style="text-align:center">0.0</td> +<td style="text-align:center">0.1</td> +</tr> +<tr> +<td style="text-align:center">a</td> +<td style="text-align:center">pos-2</td> +<td style="text-align:center">0.9</td> +<td style="text-align:center">0.3</td> +</tr> +<tr> +<td style="text-align:center">a</td> +<td style="text-align:center">pos-3</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.2</td> +</tr> +<tr> +<td style="text-align:center">b</td> +<td style="text-align:center">pos-4</td> +<td style="text-align:center">0.5</td> +<td style="text-align:center">0.7</td> +</tr> +<tr> +<td style="text-align:center">b</td> +<td style="text-align:center">pos-5</td> +<td style="text-align:center">0.4</td> +<td style="text-align:center">0.2</td> +</tr> +<tr> +<td style="text-align:center">c</td> +<td style="text-align:center">pos-6</td> +<td style="text-align:center">0.8</td> +<td style="text-align:center">0.7</td> +</tr> +<tr> +<td style="text-align:center">c</td> +<td style="text-align:center">pos-7</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.3</td> +</tr> +<tr> +<td style="text-align:center">c</td> +<td style="text-align:center">pos-8</td> +<td style="text-align:center">0.4</td> +<td style="text-align:center">0.2</td> +</tr> +<tr> +<td style="text-align:center">c</td> +<td style="text-align:center">pos-9</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.8</td> +</tr> +</tbody> +</table> +<p>In the two tables, the example computes the nearest <code>position</code> for <code>userId</code> in each <code>group</code>. +The standard way using DataFrame window functions would be as follows:</p> +<pre><code class="lang-scala"><span class="hljs-keyword">val</span> computeDistanceFunc = + sqrt(pow(inputDf(<span class="hljs-string">"x"</span>) - masterDf(<span class="hljs-string">"x"</span>), lit(<span class="hljs-number">2.0</span>)) + pow(inputDf(<span class="hljs-string">"y"</span>) - masterDf(<span class="hljs-string">"y"</span>), lit(<span class="hljs-number">2.0</span>))) + +leftDf.join( + right = rightDf, + joinExpr = leftDf(<span class="hljs-string">"group"</span>) === rightDf(<span class="hljs-string">"group"</span>) + ) + .select(inputDf(<span class="hljs-string">"group"</span>), $<span class="hljs-string">"userId"</span>, $<span class="hljs-string">"posId"</span>, computeDistanceFunc.as(<span class="hljs-string">"score"</span>)) + .withColumn(<span class="hljs-string">"rank"</span>, rank().over(<span class="hljs-type">Window</span>.partitionBy($<span class="hljs-string">"group"</span>, $<span class="hljs-string">"userId"</span>).orderBy($<span class="hljs-string">"score"</span>.desc))) + .where($<span class="hljs-string">"rank"</span> <= <span class="hljs-number">1</span>) +</code></pre> +<p>You can use <code>top_k_join</code> as follows:</p> +<pre><code class="lang-scala">leftDf.top_k_join( + k = lit(<span class="hljs-number">-1</span>), + right = rightDf, + joinExpr = leftDf(<span class="hljs-string">"group"</span>) === rightDf(<span class="hljs-string">"group"</span>), + score = computeDistanceFunc.as(<span class="hljs-string">"score"</span>) + ) +</code></pre> +<p>The result is as follows:</p> +<table> +<thead> +<tr> +<th style="text-align:center">rank</th> +<th style="text-align:center">score</th> +<th style="text-align:center">userId</th> +<th style="text-align:center">group</th> +<th style="text-align:center">x</th> +<th style="text-align:center">y</th> +<th style="text-align:center">group</th> +<th style="text-align:center">position</th> +<th style="text-align:center">x</th> +<th style="text-align:center">y</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">0.100</td> +<td style="text-align:center">4</td> +<td style="text-align:center">c</td> +<td style="text-align:center">0.2</td> +<td style="text-align:center">0.2</td> +<td style="text-align:center">c</td> +<td style="text-align:center">pos9</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.8</td> +</tr> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">0.100</td> +<td style="text-align:center">1</td> +<td style="text-align:center">b</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">b</td> +<td style="text-align:center">pos5</td> +<td style="text-align:center">0.4</td> +<td style="text-align:center">0.2</td> +</tr> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">0.300</td> +<td style="text-align:center">6</td> +<td style="text-align:center">b</td> +<td style="text-align:center">0.8</td> +<td style="text-align:center">0.8</td> +<td style="text-align:center">b</td> +<td style="text-align:center">pos4</td> +<td style="text-align:center">0.5</td> +<td style="text-align:center">0.7</td> +</tr> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">0.200</td> +<td style="text-align:center">2</td> +<td style="text-align:center">a</td> +<td style="text-align:center">0.5</td> +<td style="text-align:center">0.4</td> +<td style="text-align:center">a</td> +<td style="text-align:center">pos3</td> +<td style="text-align:center">0.3</td> +<td style="text-align:center">0.2</td> +</tr> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">0.100</td> +<td style="text-align:center">3</td> +<td style="text-align:center">a</td> +<td style="text-align:center">0.1</td> +<td style="text-align:center">0.8</td> +<td style="text-align:center">a</td> +<td style="text-align:center">pos1</td> +<td style="text-align:center">0.0</td> +<td style="text-align:center">0.1</td> +</tr> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">0.100</td> +<td style="text-align:center">5</td> +<td style="text-align:center">a</td> +<td style="text-align:center">0.1</td> +<td style="text-align:center">0.4</td> +<td style="text-align:center">a</td> +<td style="text-align:center">pos1</td> +<td style="text-align:center">0.0</td> +<td style="text-align:center">0.1</td> +</tr> +</tbody> +</table> +<p><div id="page-footer"><hr><!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> +<p><sub><font color="gray"> +Apache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. +</font></sub></p> +</div></p> + + + </section> + + </div> + <div class="search-results"> + <div class="has-results"> + + <h1 class="search-results-title"><span class='search-results-count'></span> results matching "<span class='search-query'></span>"</h1> + <ul class="search-results-list"></ul> + + </div> + <div class="no-results"> + + <h1 class="search-results-title">No results matching "<span class='search-query'></span>"</h1> + + </div> + </div> +</div> + + </div> + </div> + + </div> + + + + + </div> + + <script> + var gitbook = gitbook || []; + gitbook.push(function() { + gitbook.page.hasChanged({"page":{"title":"Top-k Join processing","level":"10.1.1","depth":2,"next":{"title":"Hivemall on Apache Spark","level":"11.1","depth":1,"url":"https://github.com/maropu/hivemall-spark","ref":"https://github.com/maropu/hivemall-spark","articles":[]},"previous":{"title":"Generic features","level":"10.1","depth":1,"path":"spark/misc/misc.md","ref":"spark/misc/misc.md","articles":[{"title":"Top-k Join processing","level":"10.1.1","depth":2,"path":"spark/misc/topk_join.md","ref":"spark/misc/topk_join.md","articles":[]}]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"e mphasize":{},"callouts":{},"etoc":{"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"http://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"s tyles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"http://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"spark/misc/topk_join.md","mtime":"2017-02-02T02:45:26.000Z","type":"markdown"},"gitbook":{"version":"3.2.2","time":"2017-02-02T05:07:49.882Z"},"basePath":"../..","book":{"language":""}}); 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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/990fed4a/userguide/tips/addbias.html ---------------------------------------------------------------------- diff --git a/userguide/tips/addbias.html b/userguide/tips/addbias.html index dbf8f16..82fcd7f 100644 --- a/userguide/tips/addbias.html +++ b/userguide/tips/addbias.html @@ -634,6 +634,21 @@ </li> + <li class="chapter " data-level="3.5" data-path="../ft_engineering/feature_selection.html"> + + <a href="../ft_engineering/feature_selection.html"> + + + <b>3.5.</b> + + Feature selection + + </a> + + + + </li> + @@ -1567,16 +1582,59 @@ + <li class="header">Part X - Hivemall on Spark</li> + + + + <li class="chapter " data-level="10.1" data-path="../spark/misc/misc.html"> + + <a href="../spark/misc/misc.html"> + + + <b>10.1.</b> + + Generic features + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.1.1" data-path="../spark/misc/topk_join.html"> + + <a href="../spark/misc/topk_join.html"> + + + <b>10.1.1.</b> + + Top-k Join processing + + </a> + + + + </li> + + + </ul> + + </li> + + + + <li class="header">Part X - External References</li> - <li class="chapter " data-level="10.1" > + <li class="chapter " data-level="11.1" > <a target="_blank" href="https://github.com/maropu/hivemall-spark";> - <b>10.1.</b> + <b>11.1.</b> Hivemall on Apache Spark @@ -1586,12 +1644,12 @@ </li> - <li class="chapter " data-level="10.2" > + <li class="chapter " data-level="11.2" > <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome";> - <b>10.2.</b> + <b>11.2.</b> Hivemall on Apache Pig @@ -1753,7 +1811,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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}); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/990fed4a/userguide/tips/ensemble_learning.html ---------------------------------------------------------------------- diff --git a/userguide/tips/ensemble_learning.html b/userguide/tips/ensemble_learning.html index a4a27a9..8f2a33c 100644 --- a/userguide/tips/ensemble_learning.html +++ b/userguide/tips/ensemble_learning.html @@ -634,6 +634,21 @@ </li> + <li class="chapter " data-level="3.5" data-path="../ft_engineering/feature_selection.html"> + + <a href="../ft_engineering/feature_selection.html"> + + + <b>3.5.</b> + + Feature selection + + </a> + + + + </li> + @@ -1567,16 +1582,59 @@ + <li class="header">Part X - Hivemall on Spark</li> + + + + <li class="chapter " data-level="10.1" data-path="../spark/misc/misc.html"> + + <a href="../spark/misc/misc.html"> + + + <b>10.1.</b> + + Generic features + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.1.1" data-path="../spark/misc/topk_join.html"> + + <a href="../spark/misc/topk_join.html"> + + + <b>10.1.1.</b> + + Top-k Join processing + + </a> + + + + </li> + + + </ul> + + </li> + + + + <li class="header">Part X - External References</li> - <li class="chapter " data-level="10.1" > + <li class="chapter " data-level="11.1" > <a target="_blank" href="https://github.com/maropu/hivemall-spark";> - <b>10.1.</b> + <b>11.1.</b> Hivemall on Apache Spark @@ -1586,12 +1644,12 @@ </li> - <li class="chapter " data-level="10.2" > + <li class="chapter " data-level="11.2" > <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome";> - <b>10.2.</b> + <b>11.2.</b> Hivemall on Apache Pig @@ -1930,7 +1988,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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