kafka-connect-hive sink插件实现了以ORC和Parquet两种方式向Hive表中写入数据。Connector定期从Kafka轮询数据并将其写入HDFS，来自每个Kafka主题的数据由提供的分区字段进行分区并划分为块，每个数据块都表示为一个HDFS文件，文件名由topic名称+分区编号+offset构成。如果配置中没有指定分区，则使用默认分区方式，每个数据块的大小由已写入HDFS的文件长度、写入HDFS的时间和未写入HDFS的记录数决定。
在阅读该插件的源码过程中，觉得有很多值得学习的地方，特总结如下以备后忘。
一、分区策略
该插件可以配置两种分区策略：
STRICT：要求必须已经创建了所有分区
DYNAMIC：根据PARTITIONBY指定的分区字段创建分区
STRICT方式
实现代码及注释如下：
package com.landoop.streamreactor.connect.hive.sink.partitioning
import com.landoop.streamreactor.connect.hive.{DatabaseName, Partition, TableName}
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.hive.metastore.IMetaStoreClient
import scala.collection.JavaConverters._
import scala.util.control.NonFatal
import scala.util.{Failure, Success, Try}
/**
* A [[PartitionHandler]] that requires any partition
* to already exist in the metastore.
*
* 要求分区已经在metastore中存在
*/
object StrictPartitionHandler extends PartitionHandler {
override def path(partition: Partition,
db: DatabaseName,
tableName: TableName)
(client: IMetaStoreClient,
fs: FileSystem): Try[Path] = {
try {
// 获取Hive metastore中表的存储位置，成功则返回
val part = client.getPartition(db.value, tableName.value, partition.entries.map(_._2).toList.asJava)
Success(new Path(part.getSd.getLocation))
} catch { // 未找到表的存储位置，返回异常
case NonFatal(e) =>
Failure(new RuntimeException(s"Partition '${partition.entries.map(_._2).toList.mkString(",")}' does not exist and strict policy requires upfront creation", e))
}
}
}
DYNAMIC方式
实现代码及注释如下：
package com.landoop.streamreactor.connect.hive.sink.partitioning
import com.landoop.streamreactor.connect.hive.{DatabaseName, Partition, TableName}
import com.typesafe.scalalogging.slf4j.StrictLogging
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.hive.metastore.IMetaStoreClient
import org.apache.hadoop.hive.metastore.api.{StorageDescriptor, Table}
import scala.collection.JavaConverters._
import scala.util.{Failure, Success, Try}
/**
* A [[PartitionHandler]] that creates partitions
* on the fly as required.
*
* The path of the partition is determined by the given
* [[PartitionPathPolicy]] parameter. By default this will
* be an implementation that uses the standard hive
* paths of key1=value1/key2=value2.
*/
class DynamicPartitionHandler(pathPolicy: PartitionPathPolicy = DefaultMetastorePartitionPathPolicy)
extends PartitionHandler with StrictLogging {
override def path(partition: Partition,
db: DatabaseName,
tableName: TableName)
(client: IMetaStoreClient,
fs: FileSystem): Try[Path] = {
def table: Table = client.getTable(db.value, tableName.value)
def create(path: Path, table: Table): Unit = {
logger.debug(s"New partition will be created at $path")
// 设置的表的存储位置信息
val sd = new StorageDescriptor(table.getSd)
sd.setLocation(path.toString)
val params = new java.util.HashMap[String, String]
// 获取分区key的值、分区创建时间
val values = partition.entries.map(_._2).toList.asJava
val ts = (System.currentTimeMillis / 1000).toInt
// 给表设置并创建新分区
val p = new org.apache.hadoop.hive.metastore.api.Partition(values, db.value, tableName.value, ts, 0, sd, params)
logger.debug(s"Updating hive metastore with partition $p")
client.add_partition(p)
logger.info(s"Partition has been created in metastore [$partition]")
}
// 获取分区信息
Try(client.getPartition(db.value, tableName.value, partition.entries.toList.map(_._2).asJava)) match {
case Success(p) => Try { // 成功则返回
new Path(p.getSd.getLocation)
}
case Failure(_) => Try { // 失败则根据分区路径创建策略生成分区路径并返回
val t = table
val tableLocation = new Path(t.getSd.getLocation)
val path = pathPolicy.path(tableLocation, partition)
create(path, t)
path
}
}
}
}
该方式会以标准的Hive分区路径来创建分区，也就是分区字段=分区字段值的方式。
二、文件命名和大小控制
Kafka轮询数据并将其写入HDFS，来自每个Kafka主题的数据由提供的分区字段进行分区并划分为块，每个数据块都表示为一个HDFS文件，这里涉及到两个细节：
如何给文件命名
如何文件分块，文件大小及数量如何控制
接下来逐一看一下相关代码实现，文件命名部分实现代码如下：
package com.landoop.streamreactor.connect.hive.sink.staging
import com.landoop.streamreactor.connect.hive.{Offset, Topic}
import scala.util.Try
trait FilenamePolicy {
val prefix: String
}
object DefaultFilenamePolicy extends FilenamePolicy {
val prefix = "streamreactor"
}
object CommittedFileName {
private val Regex = s"(.+)_(.+)_(\\d+)_(\\d+)_(\\d+)".r
def unapply(filename: String): Option[(String, Topic, Int, Offset, Offset)] = {
filename match {
case Regex(prefix, topic, partition, start, end) =>
// 返回主题名称、分区编号、起始offset和结束offset
Try((prefix, Topic(topic), partition.toInt, Offset(start.toLong), Offset(end.toLong))).toOption
case _ => None
}
}
}
从上面代码可以看出，文件名由topic名称+分区编号+offset构成。假设文件前缀是streamreactor，topic名称是hive_sink_orc，分布编号是0，当前最大的offset是1168，那么最终生成的文件名称就是streamreactor_hive_sink_orc_0_1168。
接下来看看文件的大小是如何控制的。文件的大小主要由sink插件的三个配置项决定，这些配置项信息如下：
WITH_FLUSH_INTERVAL：long类型，表示文件提交的时间间隔，单位是毫秒
WITH_FLUSH_SIZE：long类型，表示执行提交操作之前，已提交到HDFS的文件长度，单位是字节
WITH_FLUSH_COUNT：long类型，表示执行提交操作之前，未提交到HDFS的记录数，一条数据算一个记录
这些参数在CommitPolicy特质中被使用，该特质的信息及实现类如下：
package com.landoop.streamreactor.connect.hive.sink.staging
import com.landoop.streamreactor.connect.hive.TopicPartitionOffset
import com.typesafe.scalalogging.slf4j.StrictLogging
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.kafka.connect.data.Struct
import scala.concurrent.duration.FiniteDuration
/**
* The [[CommitPolicy]] is responsible for determining when
* a file should be flushed (closed on disk, and moved to be visible).
*
* Typical implementations will flush based on number of records,
* file size, or time since the file was opened.
*
* 负责决定文件何时被刷新(在磁盘上关闭，以及移动到可见)，一般情况下基于记录数量、文件大小和文件被打开的时间来刷新
*/
trait CommitPolicy {
/**
* This method is invoked after a file has been written.
*
* If the output file should be committed at this time, then this
* method should return true, otherwise false.
*
* Once a commit has taken place, a new file will be opened
* for the next record.
*
* 该方法在文件被写入之后调用，在这时如果文件应该被提交，该方法返回true，否则返回false。一旦发生了提交，新文件将为下一个记录打开
*
* @param tpo the [[TopicPartitionOffset]] of the last record written 最后一次记录的TopicPartitionOffset
* @param path the path of the file that the struct was written to 文件写入的路径
* @param count the number www.jiahuayulpt.com of records written thus far to the file 到目前为止写入文件的记录数
*
*/
def shouldFlush(struct: Struct, tpo: TopicPartitionOffset, path: Path, count: Long)
(implicit fs: FileSystem): Boolean
}
/**
* Default implementation of [[CommitPolicy]] that will flush the
* output file under the following www.078886.cn/ circumstances:
* - file size reaches limit
* - time since file was created
* - number of files is reached
*
* CommitPolicy 的默认实现，将根据以下场景刷新输出文件：
* 文件大小达到限制
* 文件创建以来的时间
* 达到文件数量
*
* @param interval in millis 毫秒间隔
*/
case class DefaultCommitPolicy(fileSize: Option[Long],
interval: Option[FiniteDuration],
fileCount: Option[Long]) extends CommitPolicy with StrictLogging {
require(fileSize.isDefined || interval.isDefined || fileCount.isDefined)
override def shouldFlush(struct: Struct, tpo: TopicPartitionOffset, path: Path, count: Long)
(implicit fs: FileSystem): Boolean = {
// 返回文件状态
val stat = fs.getFileStatus(path)
val open_time = System.currentTimeMillis() - stat.getModificationTime // 计算文件打开时间
/**
* stat.getLen：文件长度，以字节为单位
* stat.getModificationTime：文件修改时间，以毫秒为单位
*/
fileSize.exists(_ <www.yongshiyule178.com= stat.getLen) || interval.exists(_.toMillis <= open_time) || fileCount.exists(_ <= count)
}
}
现在来分析一下DefaultCommitPolicy类的实现逻辑：
首先，返回HDFS上文件的状态，接着计算文件被打开的时间。最后使用exists函数来执行以下逻辑判断：
fileSize.exists(_ <= stat.getLen)：已提交到HDFS的文件长度stat.getLen是否大于设置的文件长度阈值fileSize
interval.exists(_.toMillis <= open_time)：文件打开时间open_time是否大于设置的文件打开时间阈值interval
fileCount.exists(_ <= count)：未提交到HDFS的记录数count是否大于设置未提交到HDFS的记录数阈值fileCount
以上三个逻辑判断只要任何一个成立，就返回true，接着执行flush操作，将文件刷新到HDFS的对应目录中。这样就很好地控制了文件的大小以及数量，避免过多小文件的产生。
三、异常处理策略
异常处理不当的话，会直接影响服务的高可用，产生不可预估的损失。kafka-connect在处理数据读写的过程中产生的异常默认是直接抛出的，这类异常容易使负责读写的task停止服务，示例异常信息如下：
[2019-02-25 11:03:56,170] ERROR WorkerSinkTask{id=hive-sink-example-0} Task threw an uncaught and unrecoverable exception (org.apache.kafka.connect.runtime.WorkerTask:177)
MetaException(message:Could not dasheng178.com connect to meta store using any of the URIs provided. Most recent failure: org.apache.thrift.transport.TTransportException: java.net.ConnectException: Operation timed out (Connection timed out)
at org.apache.thrift.transport.TSocket.open(TSocket.java:226)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.open(HiveMetaStoreClient.java:477)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:285)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:210)
at com.landoop.streamreactor.connect.hive.sink.HiveSinkTask.start(HiveSinkTask.scala:56)
at org.apache.kafka.connect.runtime.WorkerSinkTask.initializeAndStart(WorkerSinkTask.java:302)
at org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:191)
at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:175)
at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:219)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.net.ConnectException: Operation timed out (Connection timed out)
at java.net.PlainSocketImpl.socketConnect(Native Method)
at java.net.AbstractPlainSocketImpl.doConnect(AbstractPlainSocketImpl.java:350)
at java.net.AbstractPlainSocketImpl.connectToAddress(AbstractPlainSocketImpl.java:206)
at java.net.AbstractPlainSocketImpl.connect(AbstractPlainSocketImpl.java:188)
at java.net.SocksSocketImpl.connect(SocksSocketImpl.java:392)
at java.net.Socket.connect(Socket.java:589)
at org.apache.thrift.transport.TSocket.open(TSocket.java:221)
... 13 more
)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.open(HiveMetaStoreClient.java:525)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:285)
at org.apache.hadoop.hive.www.tiaotiaoylzc.com/ metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:210)
at com.landoop.streamreactor.connect.hive.sink.HiveSinkTask.start(HiveSinkTask.scala:56)
at org.apache.kafka.connect.runtime.WorkerSinkTask.initializeAndStart(WorkerSinkTask.java:302)
at org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:191)
at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:175)
at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:219)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
[2019-02-25 11:03:56,172] ERROR WorkerSinkTask{id=hive-sink-example-0} Task is being killed and will not recover until manually restarted (org.apache.kafka.connect.runtime.WorkerTask:178)
在以上异常信息可以看到，由于连接Hive metastore超时，因此相关的Task被杀死，需要我们手动重启。当然这只是kafka-connect在运行中发生的一个异常，对于这类容易使Task停止工作的异常，需要设置相关的异常处理策略，sink插件在实现中定义了三种异常处理策略，分别如下：
NOOP：表示在异常发生后，不处理异常，继续工作
THROW：表示在异常发生后，直接抛出异常，这样会使服务停止
RETRY：表示在异常发生后，进行重试，相应地，需要定义重试次数，来避免无限重试情况的发生
基于以上三种异常处理策略，sink插件相关的实现类如下：
/*
* Copyright 2017 Datamountaineer.
*
* Licensed 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 www.mytxyl1.com 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.
*/
package com.datamountaineer.streamreactor.connect.errors
import java.util.Date
import com.datamountaineer.streamreactor.connect.errors.ErrorPolicyEnum.ErrorPolicyEnum
import com.typesafe.scalalogging.slf4j.StrictLogging
import org.apache.kafka.connect.errors.RetriableException
/**
* Created by [email protected] on 19/05/16.
* kafka-connect-common
*/
object ErrorPolicyEnum extends Enumeration {
type ErrorPolicyEnum = Value
val NOOP, THROW, RETRY =www.feifanyule.cn Value
}
case class ErrorTracker(retries: Int, maxRetries: Int, lastErrorMessage: String, lastErrorTimestamp: Date, policy: ErrorPolicy)
trait ErrorPolicy extends www.fengshen157.com/ StrictLogging {
def handle(error: Throwable, sink: Boolean = true, retryCount: Int = 0)
}
object ErrorPolicy extends StrictLogging {
def apply(policy: ErrorPolicyEnum): ErrorPolicy = {
policy match {
case ErrorPolicyEnum.NOOP => NoopErrorPolicy()
case ErrorPolicyEnum.THROW => ThrowErrorPolicy()
case ErrorPolicyEnum.RETRY => RetryErrorPolicy()
}
}
}
/**
* 不处理异常策略
*/
case class NoopErrorPolicy(www.yongshi123.cn) extends ErrorPolicy {
override def handle(error: Throwable, sink: Boolean = true, retryCount: Int = 0){
logger.warn(s"Error policy NOOP: ${error.getMessage}. Processing continuing.")
}
}
/**
* 异常抛出处理策略
*/
case class ThrowErrorPolicy() extends ErrorPolicy {
override def handle(error: Throwable, sink: Boolean = true, retryCount: Int = 0){
throw new RuntimeException(error)
}
}
/**
* 异常重试处理策略
*/
case class RetryErrorPolicy() extends ErrorPolicy {
override def handle(error: Throwable, sink: Boolean www.yigouyule2.cn/= true, retryCount: Int) = {
if (retryCount == 0) {
throw new RuntimeException(error)
}
else {
logger.warn(s"Error policy set to RETRY. Remaining attempts $retryCount")
throw new RetriableException(error)
}
}
}
四、总结
基于kafka-connect实现相关数据同步插件时，应该尽可能地利用Kafka的topic信息，并对异常进行适当地处理，这样才可以保证插件的可扩展、高可用。
kafka-connect-hive sink实现要点小结
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转载自www.cnblogs.com/qwangxiao/p/10430502.html
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