├── .idea ├── compiler.xml ├── copyright │ └── profiles_settings.xml ├── libraries │ └── SBT__org_scala_lang_scala_library_2_11_8_jar.xml ├── misc.xml ├── modules.xml ├── modules │ ├── spark_recommend-build.iml │ └── spark_recommend.iml ├── sbt.xml ├── scala_compiler.xml ├── uiDesigner.xml ├── vcs.xml └── workspace.xml ├── README.md ├── build.sbt ├── checkpoint └── 1cc9f243-2b19-4f2c-934a-88ec14e83ba6 │ ├── rdd-121 │ ├── ._partitioner.crc │ ├── .part-00000.crc │ ├── .part-00001.crc │ ├── _partitioner │ ├── part-00000 │ └── part-00001 │ ├── rdd-181 │ ├── .part-00000.crc │ ├── .part-00001.crc │ ├── part-00000 │ └── part-00001 │ └── rdd-61 │ ├── ._partitioner.crc │ ├── .part-00000.crc │ ├── .part-00001.crc │ ├── _partitioner │ ├── part-00000 │ └── part-00001 ├── project ├── build.properties ├── plugins.sbt ├── project │ └── target │ │ └── config-classes │ │ ├── $9f0a30d05c6bc0ace695$$anonfun$$sbtdef$1.class │ │ ├── $9f0a30d05c6bc0ace695$.class │ │ ├── $9f0a30d05c6bc0ace695.cache │ │ └── $9f0a30d05c6bc0ace695.class └── target │ ├── config-classes │ ├── $16d5d8d1446d5290a2c6$$anonfun$$sbtdef$1.class │ ├── $16d5d8d1446d5290a2c6$.class │ ├── $16d5d8d1446d5290a2c6.cache │ ├── $16d5d8d1446d5290a2c6.class │ ├── $422f0cca637471d8282b$$anonfun$$sbtdef$1.class │ ├── $422f0cca637471d8282b$.class │ ├── $422f0cca637471d8282b.cache │ ├── $422f0cca637471d8282b.class │ ├── $53fa29f8c78371e1ae05$$anonfun$$sbtdef$1.class │ ├── $53fa29f8c78371e1ae05$.class │ ├── $53fa29f8c78371e1ae05.cache │ ├── $53fa29f8c78371e1ae05.class │ ├── $6ab7769ba7b5d43838ab$$anonfun$$sbtdef$1.class │ ├── $6ab7769ba7b5d43838ab$.class │ ├── $6ab7769ba7b5d43838ab.cache │ ├── $6ab7769ba7b5d43838ab.class │ ├── $73ba6e6e6fcb8ddc8674$$anonfun$$sbtdef$1$$anonfun$apply$1.class │ ├── $73ba6e6e6fcb8ddc8674$$anonfun$$sbtdef$1.class │ ├── $73ba6e6e6fcb8ddc8674$.class │ ├── $73ba6e6e6fcb8ddc8674.cache │ ├── $73ba6e6e6fcb8ddc8674.class │ ├── $9148c6b7792a232e3c20$$anonfun$$sbtdef$1.class │ ├── $9148c6b7792a232e3c20$.class │ ├── $9148c6b7792a232e3c20.cache │ └── $9148c6b7792a232e3c20.class │ ├── resolution-cache │ ├── default │ │ └── spark_recommend-build │ │ │ └── scala_2.10 │ │ │ └── sbt_0.13 │ │ │ └── 0.1-SNAPSHOT │ │ │ ├── resolved.xml.properties │ │ │ └── resolved.xml.xml │ └── reports │ │ ├── default-spark_recommend-build-compile-internal.xml │ │ ├── default-spark_recommend-build-compile.xml │ │ ├── default-spark_recommend-build-docs.xml │ │ ├── default-spark_recommend-build-optional.xml │ │ ├── default-spark_recommend-build-plugin.xml │ │ ├── default-spark_recommend-build-pom.xml │ │ ├── default-spark_recommend-build-provided.xml │ │ ├── default-spark_recommend-build-runtime-internal.xml │ │ ├── default-spark_recommend-build-runtime.xml │ │ ├── default-spark_recommend-build-scala-tool.xml │ │ ├── default-spark_recommend-build-sources.xml │ │ ├── default-spark_recommend-build-test-internal.xml │ │ ├── default-spark_recommend-build-test.xml │ │ ├── ivy-report.css │ │ └── ivy-report.xsl │ └── streams │ ├── $global │ ├── $global │ │ └── $global │ │ │ └── streams │ │ │ └── out │ ├── dependencyPositions │ │ └── $global │ │ │ └── streams │ │ │ └── update_cache_2.10 │ │ │ ├── input_dsp │ │ │ └── output_dsp │ ├── ivyConfiguration │ │ └── $global │ │ │ └── streams │ │ │ └── out │ ├── ivySbt │ │ └── $global │ │ │ └── streams │ │ │ └── out │ ├── projectDescriptors │ │ └── $global │ │ │ └── streams │ │ │ └── out │ └── update │ │ └── $global │ │ └── streams │ │ ├── out │ │ └── update_cache_2.10 │ │ ├── inputs │ │ └── output │ ├── compile │ ├── $global │ │ └── $global │ │ │ └── discoveredMainClasses │ │ │ └── data │ ├── compile │ │ └── $global │ │ │ └── streams │ │ │ └── out │ ├── compileIncremental │ │ └── $global │ │ │ └── streams │ │ │ ├── export │ │ │ └── out │ ├── copyResources │ │ └── $global │ │ │ └── streams │ │ │ ├── copy-resources │ │ │ └── out │ ├── dependencyClasspath │ │ └── $global │ │ │ └── streams │ │ │ └── export │ ├── exportedProducts │ │ └── $global │ │ │ └── streams │ │ │ └── export │ ├── externalDependencyClasspath │ │ └── $global │ │ │ └── streams │ │ │ └── export │ ├── internalDependencyClasspath │ │ └── $global │ │ │ └── streams │ │ │ └── export │ ├── managedClasspath │ │ └── $global │ │ │ └── streams │ │ │ └── export │ ├── unmanagedClasspath │ │ └── $global │ │ │ └── streams │ │ │ └── export │ └── unmanagedJars │ │ └── $global │ │ └── streams │ │ └── export │ └── runtime │ ├── dependencyClasspath │ └── $global │ │ └── streams │ │ └── export │ ├── exportedProducts │ └── $global │ │ └── streams │ │ └── export │ ├── externalDependencyClasspath │ └── $global │ │ └── streams │ │ └── export │ ├── fullClasspath │ └── $global │ │ └── streams │ │ └── export │ ├── internalDependencyClasspath │ └── $global │ │ └── streams │ │ └── export │ ├── managedClasspath │ └── $global │ │ └── streams │ │ └── export │ ├── unmanagedClasspath │ └── $global │ │ └── streams │ │ └── export │ └── unmanagedJars │ └── $global │ └── streams │ └── export ├── src └── main │ ├── java │ ├── hbase │ │ ├── Hbase_CURD.java │ │ └── readme.txt │ └── kafka │ │ ├── KafkaConsumer.java │ │ └── KafkaProducer.java │ ├── resources │ └── hbase-site.xml │ └── scala │ ├── hbase │ ├── Hbase_CRUD.scala │ └── Spark_RDD.scala │ ├── matrix │ └── TRowMatrix.scala │ ├── spark_streaming │ ├── Demo.scala │ ├── Kafka_similar.scala │ └── Kafka_wc.scala │ └── tags │ ├── HbaseMatrix.scala │ └── localMatrix.scala └── target ├── .history ├── resolution-cache ├── default │ ├── spark_recommend$sbt_2.11 │ │ └── 1.0 │ │ │ ├── resolved.xml.properties │ │ │ └── resolved.xml.xml │ ├── spark_recommend$sources_2.11 │ │ └── 1.0 │ │ │ ├── resolved.xml.properties │ │ │ └── resolved.xml.xml │ └── spark_recommend_2.11 │ │ └── 1.0 │ │ ├── resolved.xml.properties │ │ └── resolved.xml.xml └── reports │ ├── default-spark_recommend$sbt_2.11-default.xml │ ├── default-spark_recommend$sources_2.11-compile-internal.xml │ ├── default-spark_recommend$sources_2.11-compile.xml │ ├── default-spark_recommend$sources_2.11-docs.xml │ ├── default-spark_recommend$sources_2.11-optional.xml │ ├── default-spark_recommend$sources_2.11-plugin.xml │ ├── default-spark_recommend$sources_2.11-pom.xml │ ├── default-spark_recommend$sources_2.11-provided.xml │ ├── default-spark_recommend$sources_2.11-runtime-internal.xml │ ├── default-spark_recommend$sources_2.11-runtime.xml │ ├── default-spark_recommend$sources_2.11-scala-tool.xml │ ├── default-spark_recommend$sources_2.11-sources.xml │ ├── default-spark_recommend$sources_2.11-test-internal.xml │ ├── default-spark_recommend$sources_2.11-test.xml │ ├── default-spark_recommend_2.11-compile-internal.xml │ ├── default-spark_recommend_2.11-compile.xml │ ├── default-spark_recommend_2.11-docs.xml │ ├── default-spark_recommend_2.11-optional.xml │ ├── default-spark_recommend_2.11-plugin.xml │ ├── default-spark_recommend_2.11-pom.xml │ ├── default-spark_recommend_2.11-provided.xml │ ├── default-spark_recommend_2.11-runtime-internal.xml │ ├── default-spark_recommend_2.11-runtime.xml │ ├── default-spark_recommend_2.11-scala-tool.xml │ ├── default-spark_recommend_2.11-sources.xml │ ├── default-spark_recommend_2.11-test-internal.xml │ ├── default-spark_recommend_2.11-test.xml │ ├── ivy-report.css │ └── ivy-report.xsl ├── scala-2.11 └── classes │ ├── hbase-site.xml │ ├── hbase │ ├── Hbase_CRUD$$anonfun$1.class │ ├── Hbase_CRUD$.class │ ├── Hbase_CRUD$delayedInit$body.class │ ├── Hbase_CRUD.class │ ├── Hbase_CURD.class │ ├── Spark_RDD$$anonfun$1.class │ ├── Spark_RDD$$anonfun$2.class │ ├── Spark_RDD$$anonfun$3.class │ ├── Spark_RDD$$anonfun$4.class │ ├── Spark_RDD$.class │ ├── Spark_RDD$delayedInit$body.class │ ├── Spark_RDD.class │ └── readme.txt │ ├── kafka │ ├── KafkaConsumer.class │ └── KafkaProducer.class │ ├── matrix │ ├── TRowMatrix$$anonfun$1.class │ ├── TRowMatrix$$anonfun$2.class │ ├── TRowMatrix$$anonfun$3.class │ ├── TRowMatrix$$anonfun$4.class │ ├── TRowMatrix$$anonfun$matrix$TRowMatrix$$buildRow$1.class │ ├── TRowMatrix$$anonfun$matrix$TRowMatrix$$rowToTransposedTriplet$1.class │ ├── TRowMatrix$.class │ └── TRowMatrix.class │ ├── spark_streaming │ ├── Demo$$anonfun$1.class │ ├── Demo$$anonfun$2.class │ ├── Demo$$anonfun$3.class │ ├── Demo$.class │ ├── Demo$delayedInit$body.class │ ├── Demo.class │ ├── Kafka_similar$.class │ ├── Kafka_similar$delayedInit$body.class │ ├── Kafka_similar.class │ ├── Kafka_wc$$anonfun$1.class │ ├── Kafka_wc$$anonfun$2.class │ ├── Kafka_wc$$anonfun$3.class │ ├── Kafka_wc$$anonfun$4.class │ ├── Kafka_wc$$anonfun$5.class │ ├── Kafka_wc$$anonfun$6.class │ ├── Kafka_wc$.class │ ├── Kafka_wc$delayedInit$body.class │ └── Kafka_wc.class │ └── tags │ ├── HbaseMatrix$$anonfun$1.class │ ├── HbaseMatrix$$anonfun$10.class │ ├── HbaseMatrix$$anonfun$11.class │ ├── HbaseMatrix$$anonfun$2.class │ ├── HbaseMatrix$$anonfun$3.class │ ├── HbaseMatrix$$anonfun$4.class │ ├── HbaseMatrix$$anonfun$5.class │ ├── HbaseMatrix$$anonfun$6.class │ ├── HbaseMatrix$$anonfun$7.class │ ├── HbaseMatrix$$anonfun$8.class │ ├── HbaseMatrix$$anonfun$9.class │ ├── HbaseMatrix$$anonfun$tag_vector$1.class │ ├── HbaseMatrix$.class │ ├── HbaseMatrix$delayedInit$body.class │ ├── HbaseMatrix.class │ ├── localMatrix$$anonfun$1.class │ ├── localMatrix$$anonfun$2$$anonfun$apply$mcVI$sp$1.class │ ├── localMatrix$$anonfun$2.class │ ├── localMatrix$$anonfun$3.class │ ├── localMatrix$$anonfun$4.class │ ├── localMatrix$$anonfun$5.class │ ├── localMatrix$$anonfun$6.class │ ├── localMatrix$$anonfun$7.class │ ├── localMatrix$.class │ ├── localMatrix$delayedInit$body.class │ └── localMatrix.class └── streams ├── $global ├── $global │ └── dumpStructure │ │ └── $global │ │ └── streams │ │ └── out ├── dependencyPositions │ └── $global │ │ └── streams │ │ └── update_cache_2.11 │ │ ├── input_dsp │ │ └── output_dsp ├── ivyConfiguration │ └── $global │ │ └── streams │ │ └── out ├── ivySbt │ └── $global │ │ └── streams │ │ └── out ├── projectDescriptors │ └── $global │ │ └── streams │ │ └── out ├── update │ └── $global │ │ └── streams │ │ ├── out │ │ └── update_cache_2.11 │ │ ├── inputs │ │ └── output ├── updateClassifiers │ └── $global │ │ └── streams │ │ └── out └── updateSbtClassifiers │ └── $global │ └── streams │ └── out ├── compile ├── externalDependencyClasspath │ └── $global │ │ └── streams │ │ └── export ├── managedClasspath │ └── $global │ │ └── streams │ │ └── export ├── unmanagedClasspath │ └── $global │ │ └── streams │ │ └── export └── unmanagedJars │ └── $global │ └── streams │ └── export ├── runtime ├── externalDependencyClasspath │ └── $global │ │ └── streams │ │ └── export ├── managedClasspath │ └── $global │ │ └── streams │ │ └── export ├── unmanagedClasspath │ └── $global │ │ └── streams │ │ └── export └── unmanagedJars │ └── $global │ └── streams │ └── export └── test ├── externalDependencyClasspath └── $global │ └── streams │ └── export ├── managedClasspath └── $global │ └── streams │ └── export ├── unmanagedClasspath └── $global │ └── streams │ └── export └── unmanagedJars └── $global └── streams └── export /.idea/compiler.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | -------------------------------------------------------------------------------- /.idea/copyright/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /.idea/sbt.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 18 | 19 | -------------------------------------------------------------------------------- /.idea/scala_compiler.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/uiDesigner.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # spark_recommend 2 | * java 部分 3 | ..src/main/java/hbase //hbase 增删改查例子，以后用于标签提取和保存 4 | 5 | ..src/main/java/kafka //kafka produce 和 consumer 用于对接spark_streaming 6 | 7 | * scala 部分 8 | 9 | ..src/main/scala/hbase //hbase crud操作 和 rdd操作 10 | 11 | ..src/main/scala/matrix //矩阵处理目录 12 | 13 | ..src/main/scala/spark_streaming //spark streaming 实时处理部分 14 | 15 | ..src/main/scala/tags //标签投放模块 16 | 17 | ..src/main/scala/tags/localMatrix.scala //本地向量和矩阵的使用 18 | 19 | ..src/main/scala/tags/HbaseMatrix.scla //模拟广告投放模块（分布式矩阵的应用） 20 | -------------------------------------------------------------------------------- /build.sbt: -------------------------------------------------------------------------------- 1 | name := "spark_recommend" 2 | 3 | version := "1.0" 4 | 5 | scalaVersion := "2.11.8" 6 | -------------------------------------------------------------------------------- /checkpoint/1cc9f243-2b19-4f2c-934a-88ec14e83ba6/rdd-121/._partitioner.crc: -------------------------------------------------------------------------------- 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See the NOTICE file distributed with 4 | * this work for additional information regarding copyright ownership. 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 6 | * (the "License"); you may not use this file except in compliance with 7 | * the License. You may obtain a copy of the License at 8 | * 9 | * http://www.apache.org/licenses/LICENSE-2.0 10 | * 11 | * Unless required by applicable law or agreed to in writing, software 12 | * distributed under the License is distributed on an "AS IS" BASIS, 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 | * See the License for the specific language governing permissions and 15 | * limitations under the License. 16 | * 17 | */ 18 | 19 | body { 20 | font-family:"Trebuchet MS",Verdana,Geneva,Arial,Helvetica,sans-serif; 21 | font-size:small; 22 | } 23 | 24 | div#logo { 25 | float: right; 26 | padding-left: 10px; 27 | padding-bottom: 10px; 28 | background: white; 29 | text-align: center; 30 | } 31 | 32 | #logo img { 33 | border: 0; 34 | } 35 | 36 | div#date { 37 | font-style: italic; 38 | padding-left: 60px; 39 | padding-bottom: 40px; 40 | } 41 | 42 | 43 | h1 { 44 | margin-bottom:2px; 45 | 46 | border-color:#7A9437; 47 | border-style:solid; 48 | border-width:0 0 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/src/main/java/hbase/Hbase_CURD.java: -------------------------------------------------------------------------------- 1 | package hbase; 2 | 3 | import java.io.IOException; 4 | 5 | import org.apache.hadoop.conf.Configuration; 6 | import org.apache.hadoop.hbase.HBaseConfiguration; 7 | import org.apache.hadoop.hbase.TableName; 8 | import org.apache.hadoop.hbase.client.Connection; 9 | import org.apache.hadoop.hbase.client.ConnectionFactory; 10 | import org.apache.hadoop.hbase.client.Get; 11 | import org.apache.hadoop.hbase.client.Table; 12 | import org.apache.hadoop.hbase.client.Put; 13 | import org.apache.hadoop.hbase.client.Result; 14 | import org.apache.hadoop.hbase.client.ResultScanner; 15 | import org.apache.hadoop.hbase.client.Scan; 16 | import org.apache.hadoop.hbase.util.Bytes; 17 | 18 | /** 19 | * Created by lzz on 6/28/16. 20 | * 相关例子 http://hbase.apache.org/apidocs/index.html 21 | */ 22 | public class Hbase_CURD { 23 | public static void main(String[] args) throws IOException { 24 | // You need a configuration object to tell the client where to connect. 25 | // When you create a HBaseConfiguration, it reads in whatever you've set 26 | // into your hbase-site.xml and in hbase-default.xml, as long as these can 27 | // be found on the CLASSPATH 28 | Configuration config = HBaseConfiguration.create(); 29 | config.set("hbase.master", "hadoop006:16010" ); 30 | config.addResource( "main/resources/hbase-site.xml" ); 31 | // Next you need a Connection to the cluster. Create one. When done with it, 32 | // close it. A try/finally is a good way to ensure it gets closed or use 33 | // the jdk7 idiom, try-with-resources: see 34 | // https://docs.oracle.com/javase/tutorial/essential/exceptions/tryResourceClose.html 35 | // 36 | // Connections are heavyweight. Create one once and keep it around. From a Connection 37 | // you get a Table instance to access Tables, an Admin instance to administer the cluster, 38 | // and RegionLocator to find where regions are out on the cluster. As opposed to Connections, 39 | // Table, Admin and RegionLocator instances are lightweight; create as you need them and then 40 | // close when done. 41 | // 42 | Connection connection = ConnectionFactory.createConnection(config); 43 | try { 44 | 45 | // The below instantiates a Table object that connects you to the "test" table 46 | // (TableName.valueOf turns String into a TableName instance). 47 | // When done with it, close it (Should start a try/finally after this creation so it gets 48 | // closed for sure the jdk7 idiom, try-with-resources: see 49 | // https://docs.oracle.com/javase/tutorial/essential/exceptions/tryResourceClose.html) 50 | Table table = connection.getTable(TableName.valueOf("test")); 51 | try { 52 | 53 | // To add to a row, use Put. A Put constructor takes the name of the row 54 | // you want to insert into as a byte array. In HBase, the Bytes class has 55 | // utility for converting all kinds of java types to byte arrays. In the 56 | // below, we are converting the String "myLittleRow" into a byte array to 57 | // use as a row key for our update. Once you have a Put instance, you can 58 | // adorn it by setting the names of columns you want to update on the row, 59 | // the timestamp to use in your update, etc. If no timestamp, the server 60 | // applies current time to the edits. 61 | Put p = new Put(Bytes.toBytes("myLittleRow")); 62 | 63 | // To set the value you'd like to update in the row 'myLittleRow', specify 64 | // the column family, column qualifier, and value of the table cell you'd 65 | // like to update. The column family must already exist in your table 66 | // schema. The qualifier can be anything. All must be specified as byte 67 | // arrays as hbase is all about byte arrays. Lets pretend the table 68 | // 'test' was created with a family 'cf'. 69 | p.add(Bytes.toBytes("cf"), Bytes.toBytes("someQualifier"), 70 | Bytes.toBytes("Some Value")); 71 | 72 | // Once you've adorned your Put instance with all the updates you want to 73 | // make, to commit it do the following (The HTable#put method takes the 74 | // Put instance you've been building and pushes the changes you made into 75 | // hbase) 76 | table.put(p); 77 | 78 | // Now, to retrieve the data we just wrote. The values that come back are 79 | // Result instances. Generally, a Result is an object that will package up 80 | // the hbase return into the form you find most palatable. 81 | Get g = new Get(Bytes.toBytes("myLittleRow")); 82 | Result r = table.get(g); 83 | byte [] value = r.getValue(Bytes.toBytes("cf"), 84 | Bytes.toBytes("someQualifier")); 85 | 86 | // If we convert the value bytes, we should get back 'Some Value', the 87 | // value we inserted at this location. 88 | String valueStr = Bytes.toString(value); 89 | System.out.println("GET: " + valueStr); 90 | 91 | // Sometimes, you won't know the row you're looking for. In this case, you 92 | // use a Scanner. This will give you cursor-like interface to the contents 93 | // of the table. To set up a Scanner, do like you did above making a Put 94 | // and a Get, create a Scan. Adorn it with column names, etc. 95 | Scan s = new Scan(); 96 | s.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("someQualifier")); 97 | ResultScanner scanner = table.getScanner(s); 98 | try { 99 | // Scanners return Result instances. 100 | // Now, for the actual iteration. One way is to use a while loop like so: 101 | for (Result rr = scanner.next(); rr != null; rr = scanner.next()) { 102 | // print out the row we found and the columns we were looking for 103 | System.out.println("Found row: " + rr); 104 | } 105 | 106 | // The other approach is to use a foreach loop. Scanners are iterable! 107 | // for (Result rr : scanner) { 108 | // System.out.println("Found row: " + rr); 109 | // } 110 | } finally { 111 | // Make sure you close your scanners when you are done! 112 | // Thats why we have it inside a try/finally clause 113 | scanner.close(); 114 | } 115 | 116 | // Close your table and cluster connection. 117 | } finally { 118 | if (table != null) table.close(); 119 | } 120 | } finally { 121 | connection.close(); 122 | } 123 | } 124 | } 125 | -------------------------------------------------------------------------------- /src/main/java/hbase/readme.txt: -------------------------------------------------------------------------------- 1 | # java版本hbase curd 直接中官网上查找就有了 2 | hbase 操作提供了scala版本和java版本，目的是为了适用不同的场景 3 | 1 中实时计算中为了调整保存中hbase中的数据就要用spark streaming操作 4 | 2 标签提取过程把标签写入到hbase调用的是java接口更合适 -------------------------------------------------------------------------------- /src/main/java/kafka/KafkaConsumer.java: -------------------------------------------------------------------------------- 1 | package kafka; 2 | import kafka.consumer.Consumer; 3 | import kafka.consumer.ConsumerConfig; 4 | import kafka.consumer.ConsumerIterator; 5 | import kafka.consumer.KafkaStream; 6 | import kafka.javaapi.consumer.ConsumerConnector; 7 | 8 | import java.util.HashMap; 9 | import java.util.List; 10 | import java.util.Map; 11 | import java.util.Properties; 12 | 13 | 14 | /** 15 | * Created by lzz on 6/14/16. 16 | */ 17 | public class KafkaConsumer extends Thread { 18 | private String topic; 19 | 20 | public KafkaConsumer(String topic){ 21 | super(); 22 | this.topic = topic; 23 | } 24 | 25 | 26 | @Override 27 | public void run() { 28 | ConsumerConnector consumer = createConsumer(); 29 | Map topicCountMap = new HashMap(); 30 | topicCountMap.put(topic, 1); // 一次从主题中获取一个数据 31 | Map>> messageStreams = consumer.createMessageStreams(topicCountMap); 32 | KafkaStream stream = messageStreams.get(topic).get(0);// 获取每次接收到的这个数据 33 | ConsumerIterator iterator = stream.iterator(); 34 | while(iterator.hasNext()){ 35 | String message = new String(iterator.next().message()); 36 | System.out.println("接收到: " + message); 37 | } 38 | } 39 | 40 | private ConsumerConnector createConsumer() { 41 | Properties properties = new Properties(); 42 | properties.put("zookeeper.connect", "192.168.1.223:2181,192.168.1.221:2181,192.168.1.222:2181");//声明zk 43 | properties.put("group.id", "group001");// 必须要使用别的组名称， 如果生产者和消费者都在同一组，则不能访问同一组内的topic数据 44 | return Consumer.createJavaConsumerConnector(new ConsumerConfig(properties)); 45 | } 46 | 47 | 48 | public static void main(String[] args) { 49 | new KafkaConsumer("my-replicated-topic").start();// 使用kafka集群中创建好的主题 test 50 | 51 | } 52 | } 53 | -------------------------------------------------------------------------------- /src/main/java/kafka/KafkaProducer.java: -------------------------------------------------------------------------------- 1 | package kafka; 2 | import kafka.javaapi.producer.Producer; 3 | import kafka.producer.KeyedMessage; 4 | import kafka.producer.ProducerConfig; 5 | import kafka.serializer.StringEncoder; 6 | 7 | import java.io.IOException; 8 | import java.util.Properties; 9 | import java.util.concurrent.TimeUnit; 10 | 11 | /** 12 | * Created by lzz on 6/14/16. 13 | */ 14 | public class KafkaProducer extends Thread { 15 | private String topic; 16 | 17 | public KafkaProducer(String topic){ 18 | super(); 19 | this.topic = topic; 20 | } 21 | 22 | 23 | @Override 24 | public void run() { 25 | Producer producer = createProducer(); 26 | while(true){ 27 | byte[] buffer=new byte[512]; 28 | try { 29 | //用户输入 30 | System.in.read(buffer); 31 | String str=new String(buffer); 32 | String str_format = str.replaceAll("[\\t\\n\\r]", ""); 33 | producer.send(new KeyedMessage(topic, str_format )); 34 | } catch (IOException e) { 35 | e.printStackTrace(); 36 | } 37 | 38 | try { 39 | TimeUnit.SECONDS.sleep(1); 40 | } catch (InterruptedException e) { 41 | e.printStackTrace(); 42 | } 43 | } 44 | } 45 | 46 | private Producer createProducer() { 47 | Properties properties = new Properties(); 48 | properties.put("zookeeper.connect", "192.168.1.221:2181,192.168.1.222:2181,192.168.1.223:2181");//声明zk 49 | properties.put("serializer.class", StringEncoder.class.getName()); 50 | properties.put("metadata.broker.list", "192.168.1.223:9093,192.168.1.223:9094");// 声明kafka broker 51 | return new Producer(new ProducerConfig(properties)); 52 | } 53 | 54 | 55 | public static void main(String[] args) { 56 | new KafkaProducer("my-replicated-topic").start();// 使用kafka集群中创建好的主题 test 57 | 58 | } 59 | } 60 | 61 | 62 | // user1|3|5 63 | -------------------------------------------------------------------------------- /src/main/resources/hbase-site.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 23 | 24 | 25 | 26 | hbase.rootdir 27 | hdfs://192.168.1.221:9000/hbase 28 | 29 | 30 | 31 | hbase.cluster.distributed 32 | true 33 | 34 | 35 | 36 | hbase.zookeeper.quorum 37 | 192.168.1.221:2181,192.168.1.222:2181,192.168.1.223:2181 38 | 39 | 40 | -------------------------------------------------------------------------------- /src/main/scala/hbase/Hbase_CRUD.scala: -------------------------------------------------------------------------------- 1 | package hbase 2 | 3 | import org.apache.hadoop.hbase.util.Bytes 4 | import org.apache.hadoop.hbase.{HBaseConfiguration, HColumnDescriptor, HTableDescriptor, TableName} 5 | import org.apache.hadoop.hbase.client._ 6 | import org.apache.spark.{SparkConf, SparkContext} 7 | 8 | import scala.collection.JavaConversions._ 9 | 10 | /** 11 | * Created by lzz on 6/28/16. 12 | * 相关例子 https://wiki.apache.org/hadoop/Hbase/Scala 13 | */ 14 | object Hbase_CRUD extends App{ 15 | val sparkConf = new SparkConf().setMaster("local") 16 | .setAppName("My App") 17 | .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") 18 | val sc = new SparkContext(sparkConf) 19 | 20 | var conf = HBaseConfiguration.create() 21 | conf.set("hbase.master", "hadoop006:16010" ) 22 | conf.addResource( "/home/lzz/work/idea_work/spark_work/spark_recommend/src/main/resources/hbase-site.xml" ) 23 | //Connection 的创建链接 24 | val conn = ConnectionFactory.createConnection(conf) 25 | //从Connection获得 Admin 26 | val admin = conn.getAdmin 27 | 28 | //创建 test 表 29 | val testTable = TableName.valueOf("test") 30 | val tableDescr = new HTableDescriptor(testTable) 31 | tableDescr.addFamily(new HColumnDescriptor("cf".getBytes)) 32 | println("Creating table `test`. ") 33 | if (admin.tableExists(testTable)) { 34 | admin.disableTable(testTable) 35 | admin.deleteTable(testTable) 36 | } 37 | admin.createTable(tableDescr) 38 | println("Done!") 39 | 40 | try{ 41 | //获取 test 表 42 | val table = conn.getTable(testTable) 43 | try{ 44 | //准备插入一条 key 为 rk001 的数据 45 | val p = new Put("rk001".getBytes) 46 | //为put操作指定 column 和 value 47 | p.addColumn("cf".getBytes,"name".getBytes, "linzhouzhi".getBytes) 48 | p.addColumn("cf".getBytes,"password".getBytes, "22222".getBytes) 49 | //提交 50 | table.put(p) 51 | 52 | //查询某条数据 53 | val g = new Get("rk001".getBytes) 54 | val result = table.get(g) 55 | val value = Bytes.toString(result.getValue("cf".getBytes,"name".getBytes)) 56 | println("get rk001: "+value) 57 | 58 | //扫描数据 59 | val s = new Scan() 60 | s.addColumn("cf".getBytes,"name".getBytes) 61 | val scanner = table.getScanner(s) 62 | 63 | try{ 64 | for(r <- scanner){ 65 | println("found row: "+r) 66 | println("found value: "+Bytes.toString(r.getValue("cf".getBytes,"name".getBytes))) 67 | } 68 | }finally { 69 | //关闭scanner 70 | scanner.close() 71 | } 72 | 73 | //删除 74 | val d = new Delete("rk001".getBytes) 75 | d.addColumn("cf".getBytes,"name".getBytes) 76 | table.delete(d) 77 | 78 | }finally { 79 | if(table != null) table.close() 80 | } 81 | 82 | }finally { 83 | //关闭链接 84 | conn.close() 85 | } 86 | 87 | } 88 | -------------------------------------------------------------------------------- /src/main/scala/hbase/Spark_RDD.scala: -------------------------------------------------------------------------------- 1 | package hbase 2 | 3 | import org.apache.hadoop.hbase.HBaseConfiguration 4 | import org.apache.hadoop.hbase.client.Scan 5 | import org.apache.hadoop.hbase.mapreduce.TableInputFormat 6 | import org.apache.hadoop.hbase.protobuf.ProtobufUtil 7 | import org.apache.hadoop.hbase.util.{Base64, Bytes} 8 | import org.apache.spark.{SparkConf, SparkContext} 9 | 10 | /** 11 | * Created by lzz on 6/28/16. 12 | */ 13 | object Spark_RDD extends App{ 14 | val sparkConf = new SparkConf().setMaster("local") 15 | .setAppName("My App") 16 | .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") 17 | val sc = new SparkContext(sparkConf) 18 | 19 | var hConf = HBaseConfiguration.create() 20 | hConf.set("hbase.master", "hadoop006:16010" ) 21 | hConf.addResource( "main/resources/hbase-site.xml" ) 22 | hConf.set(TableInputFormat.INPUT_TABLE, "user_tags") 23 | 24 | 25 | var scan = new Scan(); 26 | scan.addFamily(Bytes.toBytes("cf")) 27 | var proto = ProtobufUtil.toScan(scan); 28 | var ScanToString = Base64.encodeBytes(proto.toByteArray()); 29 | hConf.set(TableInputFormat.SCAN, ScanToString); 30 | 31 | val usersRDD = sc.newAPIHadoopRDD( hConf, classOf[TableInputFormat], 32 | classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], 33 | classOf[org.apache.hadoop.hbase.client.Result]) 34 | 35 | usersRDD.map( x => x._2 ) 36 | .map( result => ( result.getRow, List( result.getValue( Bytes.toBytes("cf"),Bytes.toBytes("h1")),result.getValue( Bytes.toBytes("cf"),Bytes.toBytes("name") ) ) )) 37 | .map( row => ( new String(row._1), row._2 ) ) 38 | .foreach( 39 | r => ( println( r._1 + "----" + r._2 ) ) 40 | ) 41 | } 42 | -------------------------------------------------------------------------------- /src/main/scala/matrix/TRowMatrix.scala: -------------------------------------------------------------------------------- 1 | package matrix 2 | 3 | import org.apache.spark.mllib.linalg.{Vector, Vectors} 4 | import org.apache.spark.mllib.linalg.distributed.RowMatrix 5 | 6 | /** 7 | * Created by lzz on 6/28/16. 8 | */ 9 | object TRowMatrix { 10 | def transposeRowMatrix(m: RowMatrix): RowMatrix = { 11 | val transposedRowsRDD = m.rows.zipWithIndex.map{case (row, rowIndex) => rowToTransposedTriplet(row, rowIndex)} 12 | .flatMap(x => x) 13 | .groupByKey 14 | .sortByKey().map(_._2) 15 | .map(buildRow) 16 | new RowMatrix(transposedRowsRDD) 17 | } 18 | 19 | private def rowToTransposedTriplet(row: Vector, rowIndex: Long): Array[(Long, (Long, Double))] = { 20 | val indexedRow = row.toArray.zipWithIndex 21 | indexedRow.map{case (value, colIndex) => (colIndex.toLong, (rowIndex, value))} 22 | } 23 | 24 | private def buildRow(rowWithIndexes: Iterable[(Long, Double)]): Vector = { 25 | val resArr = new Array[Double](rowWithIndexes.size) 26 | rowWithIndexes.foreach{case (index, value) => 27 | resArr(index.toInt) = value 28 | } 29 | Vectors.dense(resArr) 30 | } 31 | } 32 | -------------------------------------------------------------------------------- /src/main/scala/spark_streaming/Demo.scala: -------------------------------------------------------------------------------- 1 | package spark_streaming 2 | 3 | import org.apache.spark.streaming.{Seconds, StreamingContext} 4 | import org.apache.spark.SparkConf 5 | 6 | /** 7 | * Created by lzz on 6/28/16. 8 | */ 9 | object Demo extends App{ 10 | val sparkConf = new SparkConf().setMaster("local") 11 | .setAppName("My App") 12 | val ssc = new StreamingContext(sparkConf, Seconds(1)) 13 | val lines = ssc.socketTextStream("localhost", 9999) 14 | val words = lines.flatMap(_.split(" ")) 15 | val pairs = words.map(word => (word, 1)) 16 | val wordCounts = pairs.reduceByKey(_ + _) 17 | wordCounts.print() 18 | ssc.start() 19 | ssc.awaitTermination() 20 | } 21 | -------------------------------------------------------------------------------- /src/main/scala/spark_streaming/Kafka_similar.scala: -------------------------------------------------------------------------------- 1 | package spark_streaming 2 | 3 | import org.apache.hadoop.hbase.{HBaseConfiguration, TableName} 4 | import org.apache.hadoop.hbase.client.{ConnectionFactory, Put} 5 | import org.apache.spark.SparkConf 6 | import org.apache.spark.mllib.linalg._ 7 | import org.apache.spark.streaming.kafka.KafkaUtils 8 | import org.apache.spark.streaming.{Minutes, Seconds, StreamingContext} 9 | import redis.clients.jedis.Jedis 10 | 11 | import scala.collection.immutable.IndexedSeq 12 | 13 | /** 14 | * Created by lzz on 6/28/16. 15 | */ 16 | object Kafka_similar extends App{ 17 | 18 | var conf = HBaseConfiguration.create() 19 | conf.set("hbase.master", "hadoop006:16010" ) 20 | conf.addResource( "/home/lzz/work/idea_work/spark_work/spark_recommend/src/main/resources/hbase-site.xml" ) 21 | //Connection 的创建链接 22 | val conn = ConnectionFactory.createConnection(conf) 23 | val testTable = TableName.valueOf("test") 24 | val table = conn.getTable(testTable) 25 | 26 | val jedis = new Jedis("192.168.1.220",6379) 27 | jedis.incr( "web_80_07_pv" ) 28 | jedis.close() 29 | 30 | val ad_tags: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0)) 31 | //val dm_sparse: Matrix = Matrices.sparse( 3,2, Array(0, 1,6), Array(0, 0, 1,1,2,2), Array(3, 4, 1,2,4,5)) 32 | val user_arr = Array( 1.0, 3.0 ) 33 | val userTags: Vector = Vectors.dense( user_arr ) 34 | ad_tags.multiply( userTags ).toArray.foreach( println ) 35 | 36 | val sparkConf = new SparkConf().setMaster("local") 37 | .setAppName("My App") 38 | val ssc = new StreamingContext(sparkConf, Seconds(5)) 39 | 40 | //val Array(zkQuorum, group, topics, numThreads) = args 41 | val zkQuorum = "192.168.1.221:2181,192.168.1.222:2181,192.168.1.223:2181" 42 | val group = "group001" 43 | val topics = "my-replicated-topic" 44 | val numThreads = 2 45 | 46 | ssc.checkpoint("checkpoint") 47 | val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap 48 | val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2) 49 | val user_tags = lines.map(_.split('|')) 50 | val ad_user = user_tags.map(x => (x(0), format_tag( x ))) 51 | .map( x => (sim_write( x._1, x._2) ) ) 52 | .print() 53 | 54 | // .reduceByKeyAndWindow(_ + _, _ - _, Minutes(10), Seconds(2), 2) 55 | // wordCounts.print() 56 | 57 | ssc.start() 58 | ssc.awaitTermination() 59 | 60 | def format_tag( x: Array[String] ): DenseVector ={ 61 | val tag_arr = Array( x(1).toDouble, x(2).toDouble ) 62 | val tag_vector = Vectors.dense( tag_arr ) 63 | 64 | //ad_tags.multiply( tag_vector ).toJson.zip( Array("ad1","ad2","ad3") ) 65 | ad_tags.multiply( tag_vector ) 66 | } 67 | 68 | def sim_write( key: String, value: Vector ): Unit ={ 69 | val ads = Array( "ad1","ad2","ad3" ) 70 | val ads_size = ads.size 71 | 72 | for( i <- 0 to ads_size ){ 73 | //准备插入一条 key 为 rk001 的数据 74 | val rowkey = key + ads(i) 75 | val p = new Put( rowkey.getBytes ) 76 | //为put操作指定 column 和 value 77 | p.addColumn("cf".getBytes, "value".getBytes(), value(i).toString.getBytes ) 78 | table.put(p) 79 | val tmp = key + "-" + ads(i) + ":" + value(i) 80 | println( tmp ) 81 | } 82 | } 83 | } 84 | -------------------------------------------------------------------------------- /src/main/scala/spark_streaming/Kafka_wc.scala: -------------------------------------------------------------------------------- 1 | package spark_streaming 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.streaming.kafka.KafkaUtils 5 | import org.apache.spark.streaming.{Minutes, Seconds, StreamingContext} 6 | 7 | /** 8 | * Created by lzz on 6/28/16. 9 | */ 10 | object Kafka_wc extends App{ 11 | val sparkConf = new SparkConf().setMaster("local") 12 | .setAppName("My App") 13 | val ssc = new StreamingContext(sparkConf, Seconds(1)) 14 | 15 | //val Array(zkQuorum, group, topics, numThreads) = args 16 | val zkQuorum = "192.168.1.221:2181,192.168.1.222:2181,192.168.1.223:2181" 17 | val group = "group001" 18 | val topics = "my-replicated-topic" 19 | val numThreads = 2 20 | 21 | ssc.checkpoint("checkpoint") 22 | val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap 23 | val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2) 24 | val words = lines.flatMap(_.split(" ")) 25 | val wordCounts = words.map(x => (x, 1L)) 26 | .reduceByKeyAndWindow(_ + _, _ - _, Minutes(10), Seconds(2), 2) 27 | wordCounts.print() 28 | 29 | ssc.start() 30 | ssc.awaitTermination() 31 | 32 | } 33 | -------------------------------------------------------------------------------- /src/main/scala/tags/HbaseMatrix.scala: -------------------------------------------------------------------------------- 1 | package tags 2 | 3 | import org.apache.hadoop.hbase.HBaseConfiguration 4 | import org.apache.hadoop.hbase.client.{Result, Scan} 5 | import org.apache.hadoop.hbase.mapreduce.TableInputFormat 6 | import org.apache.hadoop.hbase.protobuf.ProtobufUtil 7 | import org.apache.hadoop.hbase.util.{Base64, Bytes} 8 | import org.apache.spark.mllib.linalg.distributed.RowMatrix 9 | import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors} 10 | import org.apache.spark.rdd.RDD 11 | import org.apache.spark.{SparkConf, SparkContext} 12 | 13 | /** 14 | * Created by lzz on 6/29/16. 15 | */ 16 | object HbaseMatrix extends App{ 17 | val sparkConf = new SparkConf().setMaster("local") 18 | .setAppName("My App") 19 | .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") 20 | val sc = new SparkContext(sparkConf) 21 | 22 | var conf = HBaseConfiguration.create() 23 | conf.set("hbase.master", "hadoop006:16010" ) 24 | conf.addResource( "main/resources/hbase-site.xml" ) 25 | conf.set(TableInputFormat.INPUT_TABLE, "user_tags") 26 | 27 | 28 | //标签库，可以改成数据库中直接读取 29 | val tags = Array( "tag16", "tag18", "tag9", "tag15", "tag19", "tag6", "tag13", "tag7", "tag23", "tag11", "tag24", "tag27", "tag4", "tag8", "tag12", "tag3" ) 30 | var scan = new Scan(); 31 | scan.addFamily(Bytes.toBytes("cf")) 32 | var proto = ProtobufUtil.toScan(scan); 33 | var ScanToString = Base64.encodeBytes(proto.toByteArray()); 34 | conf.set(TableInputFormat.SCAN, ScanToString); 35 | //从数据库中读取（用户-标签）表的数据 36 | val usersRDD = sc.newAPIHadoopRDD( conf, classOf[TableInputFormat], 37 | classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], 38 | classOf[org.apache.hadoop.hbase.client.Result]) 39 | 40 | //用户-标签 41 | val user_tags = usersRDD.map( x => x._2 ) 42 | .map( result => ( result.getRow, result.getValue( Bytes.toBytes("cf"),Bytes.toBytes("total")) )) 43 | .map( row => ( new String(row._1), new String ( row._2 ) ) ) 44 | .map( row =>( row._1.toString.split( '+' )(0),row._1.toString.split( '+' )(1) + ":" + row._2 ) ) 45 | .groupByKey 46 | .map( row => ( row._1, row._2.toList )) 47 | .sortBy( row => row._1) 48 | 49 | 50 | //每一行转化为 Local dense vector 用于生成矩阵 51 | val rows: RDD[Vector] = user_tags 52 | .map( row => tag_vector( row._2 ) ) 53 | //生成分布式 rowMatrix 矩阵 54 | val mat: RowMatrix = new RowMatrix( rows ) 55 | //某个广告的标签，其中顺序跟 tags的顺序是一样的 56 | val ad_tags = Array(0.0,0.0,0.0,0.0,0.0,0.0,8.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0) 57 | //转化为矩阵用于和用户标签矩阵进行相乘 58 | val dm: Matrix = Matrices.dense( tags.size, 1, ad_tags ) 59 | //相乘得到广告和用户的相识度矩阵（这边不应该这样直接相乘应该要优化） 60 | val ad_recommoned = mat.multiply( dm ).rows.map( x => ( x(0) )) 61 | //获取用户rdd 62 | val user = user_tags.map( x => x._1 ) 63 | //获取topN用户，这些用户就是该广告要投放的 64 | ad_recommoned.zip( user ).sortBy( x => x._1 ).top( 3 ).foreach( println ) 65 | 66 | def tag_vector( list: List[String] ): Vector ={ 67 | var row_tag = new Array[ Double ]( tags.size ) 68 | for( l <- list ){ 69 | val tag_map = l.split( ":" ) 70 | val tag_i = tags.indexOf( tag_map( 0 ) ) 71 | row_tag(tag_i) = tag_map( 1 ).toDouble 72 | } 73 | Vectors.dense( row_tag ) 74 | } 75 | 76 | } 77 | 78 | -------------------------------------------------------------------------------- /src/main/scala/tags/localMatrix.scala: -------------------------------------------------------------------------------- 1 | package tags 2 | import matrix.TRowMatrix 3 | import org.apache.spark.mllib.linalg.{Vectors, _} 4 | import org.apache.spark.mllib.linalg.distributed.RowMatrix 5 | import org.apache.spark.{SparkConf, SparkContext} 6 | 7 | /** 8 | * Created by lzz on 6/28/16. 9 | */ 10 | object localMatrix extends App{ 11 | val sparkConf = new SparkConf().setMaster("local") 12 | .setAppName("My App") 13 | .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") 14 | val sc = new SparkContext(sparkConf) 15 | 16 | //标签数据每个元素对应的是标签中的id 17 | val tags = Array(1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 14, 17) 18 | //用户id 数据 19 | val users = Array(2, 3, 4, 5, 6, 8, 9, 12, 13, 14, 15, 16, 18, 19, 22, 24, 25, 26, 29) 20 | //广告id数据 21 | val ads = Array( 3, 5, 6, 7, 8 ) 22 | // （广告id,标签id,权重） 23 | val ad_tags = Seq( 24 | Vector( 3, 1,1), 25 | Vector( 5, 2,1), 26 | Vector( 6, 3,1), 27 | Vector( 7, 5,1), 28 | Vector( 8, 6,1), 29 | Vector( 6, 8,1), 30 | Vector( 8, 9,1), 31 | Vector( 3, 5,1), 32 | Vector( 3, 5, 1), 33 | Vector( 5, 6, 1), 34 | Vector( 6, 8, 1), 35 | Vector( 7, 9, 1), 36 | Vector( 8, 11,1), 37 | Vector( 6, 12,1), 38 | Vector( 8, 13,1) 39 | ) 40 | // （用户id, 标签id, 投放次数 ） 41 | val user_tags = Seq( 42 | Vector( 2,1,1), 43 | Vector( 2,2,1), 44 | Vector( 3,3,1), 45 | Vector( 4,5,1), 46 | Vector( 5,6,1), 47 | Vector( 6,8,1), 48 | Vector( 8,9,1), 49 | Vector( 9,5,1), 50 | Vector( 12,5, 1), 51 | Vector( 12,6, 1), 52 | Vector( 13,8, 1), 53 | Vector( 14,9, 1), 54 | Vector( 15,11,1), 55 | Vector( 16,12,1), 56 | Vector( 18,13,1), 57 | Vector( 19,14,1), 58 | Vector( 22,17,1), 59 | Vector( 24,1, 1), 60 | Vector( 25,2, 1), 61 | Vector( 26,3, 1), 62 | Vector( 22,5, 1), 63 | Vector( 24,6, 1), 64 | Vector( 6, 8, 1), 65 | Vector( 8, 9, 1), 66 | Vector( 9, 11,1), 67 | Vector( 12,12,1), 68 | Vector( 13,13,1), 69 | Vector( 14,14,1), 70 | Vector( 15,17,1), 71 | Vector( 16,1,1), 72 | Vector( 18,1,1), 73 | Vector( 19,1,1), 74 | Vector( 3,1,1), 75 | Vector( 4,3,1), 76 | Vector( 5,6,1), 77 | Vector( 6,5,1), 78 | Vector( 12,1, 1), 79 | Vector( 13,2, 1), 80 | Vector( 14,3, 1), 81 | Vector( 15,5, 1), 82 | Vector( 16,6, 1), 83 | Vector( 18,8, 1), 84 | Vector( 19,9, 1), 85 | Vector( 22,11,1), 86 | Vector( 24,12,1), 87 | Vector( 25,13,3) 88 | ) 89 | 90 | //创建一个用户和标签的空矩阵 91 | var ut_matrix = Array.ofDim[Double](users.size, tags.size) 92 | // 将user_tags 中的数据添加到矩阵中 93 | for( user_tag <- user_tags ){ 94 | val user_i = users.indexOf( user_tag(0) ) 95 | val tag_i = tags.indexOf( user_tag(1) ) 96 | ut_matrix(user_i)(tag_i) = user_tag(2) 97 | } 98 | 99 | //把二维数组转化为，类型为vector的一维数组 100 | var userSeq = new Array[ Vector ]( users.size ) 101 | for( i <- 0 to (users.size - 1) ){ 102 | val ut_vector: Vector = Vectors.dense( ut_matrix( i ) ) 103 | userSeq( i ) = ut_vector 104 | } 105 | //转化为行矩阵 106 | val userM = new RowMatrix( sc.parallelize( userSeq.toSeq ) ) 107 | println( "用户-标签矩阵××××××××××××" ) 108 | userM.rows.foreach( println ) 109 | 110 | //创建标签-广告矩阵 111 | var at_matrix = Array.ofDim[Double]( tags.size, ads.size ) 112 | for( ad_tag <- ad_tags ){ 113 | val ad_i = ads.indexOf( ad_tag(0) ) 114 | val tag_i = tags.indexOf( ad_tag(1) ) 115 | at_matrix(tag_i)(ad_i) = ad_tag(2) 116 | } 117 | 118 | //按顺序将 标签-广告 矩阵中的数据写入到 adSeq 中用于建立稠密矩阵 119 | var adSeq = new Array[ Double ]( tags.size * ads.size ) 120 | for( i <- 0 to (tags.size - 1) ){ 121 | for( item <- 0 to ( ads.size - 1) ){ 122 | adSeq( i*ads.size + item ) = at_matrix(i)(item) 123 | } 124 | } 125 | //建立稠密矩阵 126 | val adM: DenseMatrix = new DenseMatrix(tags.size, ads.size, adSeq ) 127 | println( "标签-广告矩阵××××××××××××××××××" ) 128 | adM.colIter.foreach( println ) 129 | 130 | //矩阵相乘得到相识矩阵 131 | val rM = TRowMatrix.transposeRowMatrix( userM multiply( adM ) ) 132 | println( "相识矩阵×××××××××××××××" ) 133 | rM.rows.foreach( println ) 134 | 135 | } 136 | -------------------------------------------------------------------------------- /target/.history: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linzhouzhi/spark_recommend/ac3aa70de230441f05ad874dc522a3d596b72bfd/target/.history -------------------------------------------------------------------------------- /target/resolution-cache/default/spark_recommend$sbt_2.11/1.0/resolved.xml.properties: -------------------------------------------------------------------------------- 1 | #default#spark_recommend$sbt_2.11;1.0 resolved revisions 2 | #Tue Jun 28 01:36:47 PDT 2016 3 | +revision\:\#@\#\:+0.13.8\:\#@\#\:+module\:\#@\#\:+sbt\:\#@\#\:+organisation\:\#@\#\:+org.scala-sbt\:\#@\#\:+branch\:\#@\#\:+@\#\:NULL\:\#@\:\#@\#\:=0.13.8 release 0.13.8 null 4 | -------------------------------------------------------------------------------- /target/resolution-cache/default/spark_recommend$sbt_2.11/1.0/resolved.xml.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /target/resolution-cache/default/spark_recommend$sources_2.11/1.0/resolved.xml.properties: -------------------------------------------------------------------------------- 1 | #default#spark_recommend$sources_2.11;1.0 resolved revisions 2 | #Tue Jun 28 01:36:47 PDT 2016 3 | 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/target/resolution-cache/default/spark_recommend_2.11/1.0/resolved.xml.properties: -------------------------------------------------------------------------------- 1 | #default#spark_recommend_2.11;1.0 resolved revisions 2 | #Tue Jun 28 01:36:46 PDT 2016 3 | +revision\:\#@\#\:+2.11.8\:\#@\#\:+module\:\#@\#\:+scala-compiler\:\#@\#\:+organisation\:\#@\#\:+org.scala-lang\:\#@\#\:+branch\:\#@\#\:+@\#\:NULL\:\#@\:\#@\#\:=2.11.8 release 2.11.8 null 4 | +revision\:\#@\#\:+2.11.8\:\#@\#\:+module\:\#@\#\:+scala-library\:\#@\#\:+organisation\:\#@\#\:+org.scala-lang\:\#@\#\:+branch\:\#@\#\:+@\#\:NULL\:\#@\:\#@\#\:=2.11.8 ? 2.11.8 null 5 | -------------------------------------------------------------------------------- /target/resolution-cache/default/spark_recommend_2.11/1.0/resolved.xml.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 9 | 10 | spark_recommend 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /target/resolution-cache/reports/ivy-report.css: -------------------------------------------------------------------------------- 1 | /* 2 | * Licensed to the Apache Software Foundation (ASF) under one or more 3 | * contributor license agreements. 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3px 0; 49 | } 50 | 51 | span#module { 52 | color:#7A9437; 53 | text-decoration:none; 54 | } 55 | 56 | span#organisation { 57 | color:black; 58 | text-decoration:none; 59 | } 60 | 61 | #confmenu { 62 | color: #000; 63 | border-bottom: 2px solid black; 64 | margin: 12px 0px 0px 0px; 65 | padding: 0px; 66 | z-index: 1; 67 | padding-left: 10px 68 | } 69 | 70 | #confmenu li { 71 | display: inline; 72 | overflow: hidden; 73 | list-style-type: none; 74 | } 75 | 76 | #confmenu a, a.active { 77 | color: #DEDECF; 78 | background: #898B5E; 79 | font: bold 1em "Trebuchet MS", Arial, sans-serif; 80 | border: 2px solid black; 81 | padding: 2px 5px 0px 5px; 82 | text-decoration: none; 83 | } 84 | 85 | /* 86 | background: #ABAD85 #CED4BD 87 | background: #DEE4CD 88 | */ 89 | 90 | #confmenu a.active { 91 | color: #7A9437; 92 | background: #DEE4CD; 93 | border-bottom: 3px solid #DEE4CD; 94 | } 95 | 96 | #confmenu a:hover { 97 | color: #fff; 98 | background: #ADC09F; 99 | } 100 | 101 | #confmenu 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