Merge remote-tracking branch 'origin/master'
# Conflicts: # piflow-bundle/piflow-bundle.iml # piflow-core/piflow-core.iml
This commit is contained in:
judy0131
commit
01bf0250f2
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@ -1,23 +0,0 @@
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server.ip=10.0.86.98
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server.port=8001
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#spark.master=spark://10.0.86.89:7077
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#spark.master=spark://10.0.86.191:7077
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spark.master=yarn
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spark.deploy.mode=cluster
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yarn.resourcemanager.hostname=10.0.86.191
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yarn.resourcemanager.address=10.0.86.191:8032
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yarn.access.namenode=hdfs://10.0.86.191:9000
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yarn.stagingDir=hdfs://10.0.86.191:9000/tmp/
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yarn.jars=hdfs://10.0.86.191:9000/user/spark/share/lib/*.jar
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hive.metastore.uris=thrift://10.0.86.191:9083
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#piflow.bundle=/opt/project/piflow/out/artifacts/piflow_bundle/piflow-bundle.jar
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piflow.bundle=/opt/project/piflow/piflow-server/target/piflow-server-0.9.jar
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yarn.url=http://10.0.86.191:8088/ws/v1/cluster/apps/
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checkpoint.path=hdfs://10.0.86.89:9000/xjzhu/piflow/checkpoints/
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log.path=/opt/project/piflow/logs
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icon.path=/opt/project/piflow/icon
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@ -53,4 +53,12 @@ public class JedisClusterImplSer implements Serializable {
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private void setJedisCluster(JedisCluster jedisCluster) {
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this.jedisCluster = jedisCluster;
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}
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private void close() {
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try {
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this.jedisCluster.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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}
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Before Width: | Height: | Size: 2.9 KiB After Width: | Height: | Size: 36 KiB |
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@ -0,0 +1,5 @@
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package cn.piflow.bundle.nsfc.distinct
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class HivePRDDistinct {
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}
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@ -0,0 +1,167 @@
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package cn.piflow.bundle.nsfc.distinct
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import cn.piflow.bundle.util.JedisClusterImplSer
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import cn.piflow.{JobContext, JobInputStream, JobOutputStream, ProcessContext}
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import cn.piflow.conf.{ConfigurableStop, PortEnum, StopGroup}
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import cn.piflow.conf.bean.PropertyDescriptor
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import cn.piflow.conf.util.{ImageUtil, MapUtil}
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import org.apache.spark.rdd.RDD
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import org.apache.spark.sql.types.{StringType, StructField, StructType}
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import org.apache.spark.sql.{Column, DataFrame, Row, SaveMode, SparkSession, TypedColumn}
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import redis.clients.jedis.HostAndPort
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class HiveDistinct extends ConfigurableStop{
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override val authorEmail: String = "xiaomeng7890@gmail.com"
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override val description: String = ""
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override val inportList: List[String] = List(PortEnum.DefaultPort.toString)
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override val outportList: List[String] = List("Relation", "Entity")
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// val primaryKey:String = _
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// val subPrimaryKey:String = _
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// val idKeyName : String = _
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// val processKey = _
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// val timeFields = _
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// val subTimeFields = _
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var tableName : String = _ //after wash
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var noChangeSource : String = _
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var sourceField : String = _
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var timeField : String = _
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var idKeys : String = _
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var noChange : Boolean = _
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var baseOnTime : Boolean = _
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var baseOnField : Boolean = _
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var distinctRule : String = _
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var distinctFields : String = _
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var primaryKey : String = _
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var distinctTableType : String = _
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var rel_fields : String = _ //"id,prj_code,psn_code"
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var used_fields : String = _ //"id,psn_code,zh_name,id_type,id_no,birthday,gender,degree_code,nation,tel,email,org_code,zero,identity_card,military_id,passport,four,home_return_permit,mainland_travel_permit_for_taiwan_residents"
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var redis_server_ip : String = _
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var redis_server_port : Int = _
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var redis_server_passwd : String = _
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var jedisCluster : JedisClusterImplSer = _
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// val subTableName = _
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override def setProperties(map: Map[String, Any]): Unit = {
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redis_server_ip = MapUtil.get(map,"redis ip").asInstanceOf[String]
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redis_server_port = MapUtil.get(map,"redis port").asInstanceOf[Int]
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redis_server_passwd = MapUtil.get(map,"redis passwd").asInstanceOf[String]
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}
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override def getPropertyDescriptor(): List[PropertyDescriptor] = {
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var descriptor : List[PropertyDescriptor] = List()
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val redis_passwd = new PropertyDescriptor().
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name("redis passwd").
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displayName("redis passwd").
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description("redis server passwd").
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required(true)
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descriptor = redis_passwd :: descriptor
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val redis_port = new PropertyDescriptor().
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name("redis port").
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displayName("redis port").
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description("redis server port").
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required(true)
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descriptor = redis_port :: descriptor
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val redis_server = new PropertyDescriptor().
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name("redis server").
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displayName("redis server").
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description("redis server ip").
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required(true)
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descriptor = redis_server :: descriptor
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descriptor
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}
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override def getIcon(): Array[Byte] = ImageUtil.getImage("icon/hive/SelectHiveQL.png")
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override def getGroup(): List[String] = {
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List(StopGroup.NSFC.toString, "sha0w", "distinct")
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}
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override def initialize(ctx: ProcessContext): Unit = {
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jedisCluster = new JedisClusterImplSer(new HostAndPort(redis_server_ip, redis_server_port), redis_server_passwd)
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}
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override def perform(in: JobInputStream, out: JobOutputStream, pec: JobContext): Unit = {
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if (noChange){
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out.write(in.read())
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return
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}
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val inDF = in.read().select(used_fields.split(",").map(s => new Column(s)) : _*)
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val spark = pec.get[SparkSession]()
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val inSchema = inDF.schema
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val primaryIndex = inDF.schema.fieldIndex(primaryKey)
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var pairRDD = inDF.rdd.map(row => (row.getString(primaryIndex), {
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row
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}))
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var processKeyArray = distinctFields.split(",")
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processKeyArray += idKeys
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processKeyArray.foreach(key => { //对这里的每一组key
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pairRDD = pairRDD.map(row => (cn.piflow.bundle.util.NSFCUtil.mkRowKey(inSchema, row._2, key), row)) //生成key pair， 若key不存在则生成UUID
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.groupByKey
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.map(i => (i._1 , {
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cn.piflow.bundle.util.RedisUtil.recordMKDup(i._2, tableName ,jedisCluster.getJedisCluster) //Mark需要合并的key
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})) //opt:R/W deliver this part record the mk Dup
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.values
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.filter(r => {
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r._2 != null
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}) //reduce can combine two duplicate value)
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})
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var keykeyRDD = pairRDD.map(r => (r._1, cn.piflow.bundle.util.RedisUtil.checkRedis(r, inSchema, tableName,distinctTableType, distinctRule.split(",") , jedisCluster.getJedisCluster),r._2))
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var backToHiveRdd = keykeyRDD.map(r => (r._1, {
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var row = r._3
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var psn = r._2
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var id = r._1
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var rowArray = row.toSeq
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jedisCluster.getJedisCluster.hset(tableName + "@" + id, distinctTableType + "IdPSN" , psn)
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Row.fromSeq(rowArray.updated(primaryIndex,psn))
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})).filter(r => {
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!jedisCluster.getJedisCluster.hexists(tableName + "@" + r._1, distinctTableType + "PSNExist")
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}).values
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println("=====================" + backToHiveRdd.count + "========================") //active
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val df_backToHive = spark.sqlContext.createDataFrame(
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backToHiveRdd, inSchema
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)
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val rel_fields_arr = rel_fields.split(",")
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if (rel_fields_arr.size == 3) {
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var df_relationship : DataFrame = inDF.select(rel_fields_arr.map(x => new Column(x)) : _*)
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//此处应对pj_member进行映射
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var rela_schema = df_relationship.schema
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var relation_rdd = df_relationship.rdd
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val id_1 = rel_fields_arr(0) //id
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val id_2 = rel_fields_arr(1) //prj
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val id_3 = rel_fields_arr(2) //psn
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var backToHiveRelation = relation_rdd.map(row => (row.getString(rela_schema.fieldIndex(id_1))
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,row.getString(rela_schema.fieldIndex(id_2))))
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.map(r => (r._1, r._2, cn.piflow.bundle.util.RedisUtil.getMKFather(r._1, tableName, distinctTableType, jedisCluster.getJedisCluster)
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)).map(i => Row.fromSeq(Array(i._1,i._2,i._3)))
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var df_backToHiveRelation = spark.sqlContext.createDataFrame(backToHiveRelation, rela_schema)
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jedisCluster.close()
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out.write("Relation", df_backToHiveRelation)
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}
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else {
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var df_relationship : DataFrame = inDF.select(rel_fields_arr.map(x => new Column(x)) : _*)
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if (rel_fields_arr.size != 4) throw new Exception("wrong input rel schema size, should be 3 or 4")
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var rela_schema = StructType(rel_fields_arr.map(StructField(_, StringType, nullable = true)))
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var relation_rdd = df_relationship.rdd.map(i => {Row.fromSeq(i.toSeq.+:(cn.piflow.bundle.util.NSFCUtil.generateUUID()))})
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//id,prp_code,psn_code,seq_no
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var backToHiveRelation =
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relation_rdd.
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map(row => (row.getString(rela_schema.fieldIndex(rel_fields_arr(0))),
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row.getString(rela_schema.fieldIndex(rel_fields_arr(1))),
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row.getString(rela_schema.fieldIndex(rel_fields_arr(2))),
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||||
row.getString(rela_schema.fieldIndex(rel_fields_arr(3)))))
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.map(r => (r._1, r._2, cn.piflow.bundle.util.RedisUtil.getMKFather(r._3, tableName,distinctTableType,jedisCluster.getJedisCluster), r._4))
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.map(i => Row.fromSeq(Array(i._1,i._2,i._3,i._4)))
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var df_backToHiveRelation = spark.sqlContext.createDataFrame(backToHiveRelation, rela_schema)
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jedisCluster.close()
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out.write("Relation", df_backToHiveRelation)
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}
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out.write("Entity", df_backToHive)
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||||
}
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}
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@ -0,0 +1,106 @@
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package cn.piflow.bundle.nsfc.distinct
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|
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import cn.piflow.bundle.util.JedisClusterImplSer
|
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import cn.piflow.{JobContext, JobInputStream, JobOutputStream, ProcessContext}
|
||||
import cn.piflow.conf.{ConfigurableStop, PortEnum, StopGroup}
|
||||
import cn.piflow.conf.bean.PropertyDescriptor
|
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import cn.piflow.conf.util.{ImageUtil, MapUtil}
|
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import redis.clients.jedis.HostAndPort
|
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|
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class RedisDistinctCachePersist extends ConfigurableStop {
|
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override val authorEmail: String = "xiaomeng7890@gmail.com"
|
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override val description: String = "persist the df into redis server, which will be used to distinct"
|
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override val inportList: List[String] = List(PortEnum.DefaultPort.toString)
|
||||
override val outportList: List[String] = List(PortEnum.DefaultPort.toString)
|
||||
|
||||
var persist_needed_fields : String = _
|
||||
var persist_primary_field : String = _
|
||||
var distinct_rule : String = _
|
||||
var redis_server_ip : String = _
|
||||
var redis_server_port : Int = _
|
||||
var redis_server_passwd : String = _
|
||||
|
||||
override def setProperties(map: Map[String, Any]): Unit = {
|
||||
persist_needed_fields = MapUtil.get(map,"distinct field").asInstanceOf[String]
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persist_primary_field = MapUtil.get(map,"primary field").asInstanceOf[String]
|
||||
distinct_rule = MapUtil.get(map,"distinct rule").asInstanceOf[String]
|
||||
redis_server_ip = MapUtil.get(map,"redis ip").asInstanceOf[String]
|
||||
redis_server_port = MapUtil.get(map,"redis port").asInstanceOf[Int]
|
||||
redis_server_passwd = MapUtil.get(map,"redis passwd").asInstanceOf[String]
|
||||
}
|
||||
|
||||
override def getPropertyDescriptor(): List[PropertyDescriptor] = {
|
||||
var descriptor : List[PropertyDescriptor] = List()
|
||||
val redis_passwd = new PropertyDescriptor().
|
||||
name("redis passwd").
|
||||
displayName("redis passwd").
|
||||
description("redis server passwd").
|
||||
required(true)
|
||||
descriptor = redis_passwd :: descriptor
|
||||
|
||||
val redis_port = new PropertyDescriptor().
|
||||
name("redis port").
|
||||
displayName("redis port").
|
||||
description("redis server port").
|
||||
required(true)
|
||||
descriptor = redis_port :: descriptor
|
||||
|
||||
val redis_server = new PropertyDescriptor().
|
||||
name("redis server").
|
||||
displayName("redis server").
|
||||
description("redis server ip").
|
||||
required(true)
|
||||
descriptor = redis_server :: descriptor
|
||||
|
||||
val primary_field = new PropertyDescriptor().
|
||||
name("primary field").
|
||||
displayName("primary field").
|
||||
description("the primary key").
|
||||
defaultValue("psn_code").
|
||||
required(true)
|
||||
descriptor = primary_field :: descriptor
|
||||
|
||||
val distinct_field = new PropertyDescriptor().
|
||||
name("distinct field").
|
||||
displayName("distinct field").
|
||||
description("the fields needed in distinct and distinct rule").
|
||||
defaultValue("zh_name,email,tel").
|
||||
required(true)
|
||||
descriptor = distinct_field :: descriptor
|
||||
|
||||
val distinct_rule = new PropertyDescriptor().
|
||||
name("distinct rule").
|
||||
displayName("distinct rule").
|
||||
description("the rule to organize distinct").
|
||||
defaultValue("zh_name&email,zh_name&tel").
|
||||
required(true)
|
||||
descriptor = distinct_rule :: descriptor
|
||||
|
||||
|
||||
descriptor
|
||||
}
|
||||
|
||||
override def getIcon(): Array[Byte] = ImageUtil.getImage("icon/hive/SelectHiveQL.png")
|
||||
|
||||
override def getGroup(): List[String] = List(StopGroup.NSFC.toString, "sha0w", "distinct")
|
||||
|
||||
override def initialize(ctx: ProcessContext): Unit = {}
|
||||
|
||||
override def perform(in: JobInputStream, out: JobOutputStream, pec: JobContext): Unit = {
|
||||
val jedisCluster : JedisClusterImplSer =
|
||||
new JedisClusterImplSer(new HostAndPort(redis_server_ip, redis_server_port), redis_server_passwd)
|
||||
val df = in.read()
|
||||
val mPrimaryKeyIndex = df.schema.fieldIndex(persist_primary_field) //PSNCODE
|
||||
val df_mperson_fields = persist_needed_fields.split(",").+:(persist_primary_field)
|
||||
val s1 = df_mperson_fields.map(i => (i, {
|
||||
df.schema.fieldIndex(i)
|
||||
})).toMap[String, Int] //生产字段名 -》 index的键值对
|
||||
df.rdd.foreach(
|
||||
row => {
|
||||
cn.piflow.bundle.util.RedisUtil.putRedis(
|
||||
row, s1, distinct_rule, mPrimaryKeyIndex, jedisCluster.getJedisCluster) // create the redis dataset
|
||||
}
|
||||
)
|
||||
out.write(df)
|
||||
}
|
||||
}
|
||||
|
|
@ -4,7 +4,7 @@ import java.text.SimpleDateFormat
|
|||
import java.util.{Date, UUID}
|
||||
|
||||
import org.apache.spark.sql.Row
|
||||
import org.apache.spark.sql.types.{StringType, StructField, StructType}
|
||||
import org.apache.spark.sql.types.{LongType, StringType, StructField, StructType}
|
||||
|
||||
object NSFCUtil {
|
||||
val dateFormat: SimpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
|
||||
|
|
@ -35,8 +35,10 @@ object NSFCUtil {
|
|||
for (index <- 0 until beforeSchema.length) {
|
||||
val name = beforeSchema(index).name
|
||||
name match {
|
||||
case `idTypeField` => if (beforeRowSeq(index) == null) card_type = "null" else card_type = String.valueOf(beforeRowSeq(index))
|
||||
case `idField` => if (beforeRowSeq(index) == null) card_code = "null" else card_code = String.valueOf(beforeRowSeq(index))
|
||||
case `idTypeField` => if (beforeRowSeq(index) == null)
|
||||
card_type = "null" else card_type = String.valueOf(beforeRowSeq(index))
|
||||
case `idField` => if (beforeRowSeq(index) == null)
|
||||
card_code = "null" else card_code = String.valueOf(beforeRowSeq(index))
|
||||
case _ => afterMap.put(beforeSchema(index).name, beforeRowSeq(index))
|
||||
}
|
||||
}
|
||||
|
|
@ -59,6 +61,7 @@ object NSFCUtil {
|
|||
afterMap.put("mainland_travel_permit_for_taiwan_residents", ifNUll(six))
|
||||
afterMap.put("source", source) // 加入source字段
|
||||
afterMap.put("uuid", UUID.randomUUID().toString)
|
||||
afterMap.put("id_hash", (card_code + card_type).##.toString)
|
||||
for (index <- 0 until afterSchema.length) {
|
||||
if (!afterMap.keySet.contains(afterSchema(index).name)) {
|
||||
afterMap.put(afterSchema(index).name, null)
|
||||
|
|
@ -81,7 +84,8 @@ object NSFCUtil {
|
|||
afterSchema = afterSchema.add(f.name, f.dataType,nullable = true)
|
||||
})
|
||||
afterSchema = afterSchema.add("source", StringType, nullable = true)
|
||||
afterSchema.add("uuid", StringType, nullable = true)
|
||||
afterSchema = afterSchema.add("uuid", StringType, nullable = true)
|
||||
afterSchema.add("id_hash", StringType, nullable = true)
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ package cn.piflow.bundle.util
|
|||
import java.util
|
||||
|
||||
import org.apache.spark.sql.{Dataset, Row}
|
||||
import redis.clients.jedis.JedisCluster
|
||||
object RedisUtil extends Serializable {
|
||||
def manipulateRow(row:Row,column_name:String,jedisClusterImplSer: JedisClusterImplSer):Unit={
|
||||
var hm:util.HashMap[String,String]=new util.HashMap()
|
||||
|
|
@ -18,5 +19,37 @@ object RedisUtil extends Serializable {
|
|||
jedisClusterImplSer.getJedisCluster.hmset(key,hm)
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
*
|
||||
* @param row 存入redis的行
|
||||
* @param map 字段名称与位置索引
|
||||
* @param build 字段名 <- 用以去重的字段
|
||||
* @param valueIndex PSNCODE的位置
|
||||
* @param jedisCluster
|
||||
*/
|
||||
def putRedis(row: Row, map: Map[String, Int], build :String, valueIndex:Int, jedisCluster: JedisCluster): Unit = {
|
||||
var hasNull = false
|
||||
var value = row.getString(valueIndex) //get the primary key
|
||||
build.split(",").foreach(idKey => { //name&tel,name&email
|
||||
var field = idKey
|
||||
var key = ""
|
||||
if (idKey.contains("&")) { //name&tel
|
||||
val sl = idKey.split("&")
|
||||
sl.foreach(s_ => {
|
||||
if (!row.isNullAt(map(s_))) {
|
||||
key += row.getString(map(s_))
|
||||
} else {hasNull = true}
|
||||
})
|
||||
}
|
||||
else {
|
||||
if (!row.isNullAt(map(idKey))) {
|
||||
key += row.getString(map(idKey))
|
||||
} else {hasNull = true}
|
||||
}
|
||||
if (!hasNull) {
|
||||
jedisCluster.hset(key,field,value) // combined keys - fields - psn_code
|
||||
// println(key + ":" + field + ":" + value) test pass
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,8 @@
|
|||
<configuration>
|
||||
|
||||
<property>
|
||||
<name>hive.metastore.schema.verification</name>
|
||||
<value>true</value>
|
||||
</property>
|
||||
|
||||
</configuration>
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
|
||||
# Set everything to be logged to the console
|
||||
log4j.rootCategory=INFO, console
|
||||
log4j.appender.console=org.apache.log4j.ConsoleAppender
|
||||
log4j.appender.console.target=System.err
|
||||
log4j.appender.console.layout=org.apache.log4j.PatternLayout
|
||||
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
|
||||
|
||||
# Set the default spark-shell log level to WARN. When running the spark-shell, the
|
||||
# log level for this class is used to overwrite the root logger's log level, so that
|
||||
# the user can have different defaults for the shell and regular Spark apps.
|
||||
log4j.logger.org.apache.spark.repl.Main=WARN
|
||||
|
||||
# Settings to quiet third party logs that are too verbose
|
||||
log4j.logger.org.spark_project.jetty=WARN
|
||||
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
|
||||
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
|
||||
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
|
||||
log4j.logger.org.apache.parquet=ERROR
|
||||
log4j.logger.parquet=ERROR
|
||||
|
||||
# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
|
||||
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
|
||||
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR
|
||||
|
|
@ -1,9 +1,9 @@
|
|||
1.maven error
|
||||
apt-get install maven
|
||||
mvn install:install-file -Dfile=/root/Desktop/dblp/piflow-bundle/lib/spark-xml_2.11-0.4.2.jar -DgroupId=com.databricks -DartifactId=spark-xml_2.11 -Dversion=0.4.2 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/root/Desktop/dblp/piflow-bundle/lib/java_memcached-release_2.6.6.jar -DgroupId=com.memcached -DartifactId=java_memcached-release -Dversion=2.6.6 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/root/Desktop/dblp/piflow-bundle/lib/ojdbc6.jar -DgroupId=jdbc_oracle -DartifactId=ojdbc -Dversion=6.0.0 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/root/Desktop/dblp/piflow-bundle/lib/edtftpj.jar -DgroupId=ftpClient -DartifactId=edtftp -Dversion=1.0.0 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/opt/project/piflow/piflow-bundle/lib/spark-xml_2.11-0.4.2.jar -DgroupId=com.databricks -DartifactId=spark-xml_2.11 -Dversion=0.4.2 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/opt/project/piflow/piflow-bundle/lib/java_memcached-release_2.6.6.jar -DgroupId=com.memcached -DartifactId=java_memcached-release -Dversion=2.6.6 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/opt/project/piflow/piflow-bundle/lib/ojdbc6.jar -DgroupId=jdbc_oracle -DartifactId=ojdbc -Dversion=6.0.0 -Dpackaging=jar
|
||||
mvn install:install-file -Dfile=/opt/project/piflow/piflow-bundle/lib/edtftpj.jar -DgroupId=ftpClient -DartifactId=edtftp -Dversion=1.0.0 -Dpackaging=jar
|
||||
|
||||
2.packaging
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue