1.??Log4j Appender
1.1.? 使用說明
1.1.2.??Client端Log4j配置文件
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（黃色文字為需要配置的內(nèi)容） log4j.rootLogger= INFO,A1,R ? ? # ConsoleAppender out log4j.appender.A1= org . apache .log4j.ConsoleAppender log4j.appender.A1.layout= org . apache .log4j.PatternLayout log4j.appender.A1.layout.ConversionPattern= %d{ yyyy /MM/ dd HH : mm:ss} %-5p %-10C {1} ? %m%n # File out // 日志 Appender 修改為 flume 提供的 Log4jAppender log4j.appender.R= org . apache . flume .clients.log4jappender.Log4jAppender log4j.appender.R.File= ${ catalina .home}/logs/ ultraIDCPServer .log // 日志需要發(fā)送到的端口號，該端口要有 ARVO 類型的 source 在監(jiān)聽 log4j.appender.R.Port = 44444 // 日志需要發(fā)送到的主機 ip ，該主機運行著 ARVO 類型的 source log4j.appender.R.Hostname = localhost log4j.appender.R.MaxFileSize= 102400KB # log4j.appender.R.MaxBackupIndex=5 log4j.appender.R.layout= org . apache .log4j.PatternLayout log4j.appender.R.layout.ConversionPattern= %d{ yyyy /MM/ dd HH\ : mm \: ss } %-5p %-10C {1} ? %m%n log4j.appender.R.encoding= UTF-8 ? log4j.logger.com.ultrapower.ultracollector.webservice.MessageIntercommunionInterfaceImpl= INFO, webservice log4j.appender.webservice= org . apache .log4j.FileAppender log4j.appender.webservice.File= ${ catalina .home}/logs/logsMsgIntercommunionInterface.log log4j.appender.webservice.layout= org . apache .log4j.PatternLayout log4j.appender.webservice.layout.ConversionPattern= %d{ yyyy /MM/ dd HH\ : mm \: ss } %-5p [%t] %l %X - %m%n log4j.appender.webservice.encoding= UTF-8 注： Log4jAppender 繼承自 AppenderSkeleton ，沒有日志文件達到特定大小，轉(zhuǎn)換到新的文件的功能 |
1.1.3.??flume agent配置
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agent1.sources = source1 agent1.sinks = sink1 agent1.channels = channel1 # Describe/configure source1 agent1.sources.source1.type = avro agent1.sources.source1.bind = 192.168.0.141 agent1.sources.source1.port = 44444 ? # Describe sink1 agent1.sinks.sink1.type = FILE_ROLL agent1.sinks.sink1.sink.directory = /home/yubojie/flume/apache-flume-1.2.0/flume-out ? # Use a channel which buffers events in memory agent1.channels.channel1.type = memory agent1.channels.channel1.capacity = 1000 agent1.channels.channel1.transactionCapactiy = 100 ? # Bind the source and sink to the channel agent1.sources.source1.channels = channel1 agent1.sinks.sink1.channel = channel1 注：生成的文件的規(guī)則為每隔固定時間間隔生成一個新的文件，文件里面保存該時間段 agent 接收到的信息 |
1.2.? 分析
1.???????使用簡便，工作量小。
2.???????用戶應(yīng)用程序使用log4j作為日志記錄jar包，而且項目中使用的jar包要在log4j-1.2.15版本以上，
3.???????應(yīng)用系統(tǒng)必須將flume所需jar包引入到項目中。如下所示為所有必須jar包：可能會存在jar沖突，影響應(yīng)用運行
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4.???????能夠提供可靠的數(shù)據(jù)傳輸，使用flume log4jAppender采集日志可以不在客戶機上啟動進程，而只通過修改logapppender直接把日志信息發(fā)送到采集機（參見圖一），此種情況可以保證采集機接受到數(shù)據(jù)之后的數(shù)據(jù)可靠性，但是客戶機與采集機連接失敗時候數(shù)據(jù)會丟失。改進方案是在客戶機上啟動一個agent，這樣可以保證客戶機和采集機不能連通時，當(dāng)能連通是日志也被采集上來，不會發(fā)送數(shù)據(jù)的丟失（參見圖二），為了可靠性，需在客戶機上啟動進程
???????????????
1.3.? 日志代碼
Log.info(“this message has DEBUG in it”); |
1.4.? 采集到的數(shù)據(jù)樣例
this message has DEBUG in itthis message has DEBUG in it |
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2.??Exec source（放棄）
The problem with ExecSource and other asynchronous sources is that thesource can not guarantee that if there is a failure to put the event into theChannel the client knows about it. In such cases, the data will be lost. As afor instance, one of the most commonly requested features is the tail -F [file] -like use casewhere an application writes to a log file on disk and Flume tails the file,sending each line as an event. While this is possible, there’s an obviousproblem; what happens if the channel fills up and Flume can’t send an event?Flume has no way of indicating to the application writing the log file that itneeds to retain the log or that the event hasn’t been sent, for some reason. Ifthis doesn’t make sense, you need only know this: Your application can neverguarantee data has been received when using a unidirectional asynchronousinterface such as ExecSource! As an extension of this warning - and to becompletely clear - there is absolutely zero guarantee of event delivery whenusing this source. You have been warned.
注：即使是 agent 內(nèi)部的可靠性都不能保證
2.1.? 使用說明
2.1.1.??flume agent配置
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2.2.? 分析
1.?????????tail方式采集日志需要宿主主機能夠執(zhí)行tail命令，應(yīng)該是只有l(wèi)inux系統(tǒng)可以執(zhí)行，不支持window系統(tǒng)日志采集
2.?????????EXEC采用異步方式采集，會發(fā)生日志丟失，即使在節(jié)點內(nèi)的數(shù)據(jù)也不能保證數(shù)據(jù)的完整
3.?????????tail方式采集需要宿主操作系統(tǒng)支持tail命令，即原始的windows操作系統(tǒng)不支持tail命令采集
2.3.? 采集到的數(shù)據(jù)樣例
2012/10/26 02:36:34 INFO? LogTest???? this message has DEBUG 中文 in it2012/10/26 02:40:12 INFO? LogTest???? this message has DEBUG 中文 in it |
2.4.? 日志代碼
Log.info(“this message has DEBUG 中文 in it”); |
3.?? Syslog
Passing messages using syslogprotocol doesn't work well for longer messages. ?The syslog appender forLog4j is hardcoded to linewrap around 1024 characters in order to comply withthe RFC. ?I got a sample program logging to syslog, picking it up with asyslogUdp source, with a JSON layout (to avoid new-lines in stack traces) onlyto find that anything but the smallest stack trace line-wrapped anyway. ?Ican't see a way to reliably reconstruct the stack trace once it is wrapped andsent through the flume chain.（ 注：內(nèi)容不確定是否 1.2 版本 ）
?? Syslog TCP需要指定eventsize，默認為2500
?? Syslog UDP為不可靠傳輸，數(shù)據(jù)傳輸過程中可能出現(xiàn)丟失數(shù)據(jù)的情況。
3.1.? 使用說明
3.1.1.??Client端示例代碼
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import ? java.io.IOException; import java.io.OutputStream ; import ? java.net.Socket; import ? java.net.UnknownHostException; ? ? public class ? SyslogTcp { ? public static void ? main(String args[]){ ??? ? ?Socket client = ? null ; ??? ? ? OutputStream ? out = null ; ??? ? try ? { ?????? ? client = ? new ? Socket( "127.0.0.1" , 5140); ?????? ? out= client.getOutputStream();? ?????? ? ?String event = ? "<4>hello\n" ;? ?????? ? ?out.write(event.getBytes());? ?????? ? ?out.flush();? ?????? ? ?System. out .println( " 發(fā)送成功 ? " ); ??? ? } ? catch ? (UnknownHostException e) { ?????? ? // TODO ? Auto-generated catch block ?????? ? e.printStackTrace(); ??? ? } ? catch ? (IOException e) { ?????? ? // TODO ? Auto-generated catch block ?????? ? e.printStackTrace(); ??? ? } ? finally { ?????? ? try ? { ?????????? ? out.close(); ?????? ? } ? catch ? (IOException e) { ?????????? ? // TODO ? Auto-generated catch block ?????????? ? e.printStackTrace(); ?????? ? }? ?????? ? ? try ? { ?????????? ? client.close(); ?????? ? } ? catch ? (IOException e) { ?????????? ? // TODO ? Auto-generated catch block ?????????? ? e.printStackTrace(); ?????? ? } ??? ? } ??? ? ?? ? } } ? |
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3.1.2.??日志接收的flume agent配置
3.2.????? 分析
需要編寫Client采集代碼，增量采集日志信息通過socket發(fā)送到flume agent；對于長數(shù)據(jù)處理不是很理想。可靠性可以參考log4j appender的方式來保證。
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4.??日志過濾 Interceptor （ FLUME-1358）
Flume支持依據(jù)正則表達式過濾event，但是在1.2.0的源代碼中沒有發(fā)現(xiàn)具體實現(xiàn)的代碼，根據(jù)FLUME-1358的說明信息，可以將RegexFilteringInterceptor類加入到代碼中使用。
需要的操作為：
添加類RegexFilteringInterceptor
修改InterceptorType，添加type與類的映射關(guān)系：
REGEX_FILTER(org.apache.flume.interceptor.RegexFilteringInterceptor.Builder.class)
4.1.? Regex FilteringInterceptor說明
This interceptor filters events selectively by interpreting the eventbody as text and matching the text against a configured regular expression. Thesupplied regular expression can be used to include events or exclude events.
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Property Name |
Default |
Description |
|
type |
– |
The component type name has to be ? REGEX_FILTER |
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regex |
”.*” |
Regular expression for matching against events |
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excludeRegex |
false |
If true, regex determines events to exclude, otherwise regex determines events to include. |
4.2.? 使用說明（測試配置）
4.2.1.??日志接收的Flume agent配置
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agent1.sources = source1 agent1.sinks = sink1 agent1.channels = channel1 ? # Describe/configure source1 agent1.sources.source1.type = ? avro agent1.sources.source1.bind = ? localhost agent1.sources.source1.port = 5140 ? ? agent1.sources.source1. interceptors ? = inter1 agent1.sources.source1. interceptors .inter1.type = REGEX_FILTER agent1.sources.source1. interceptors .inter1. regex ? = .*DEBUG.* agent1.sources.source1. interceptors .inter1.excludeRegex = false ? ? ? ? # Describe sink1 #agent1.sinks.sink1.type = ? avro #agent1.sinks.sink1.channels = channel1 #agent1.sinks.sink1. hostname ? = 192.168.0.144 #agent1.sinks.sink1.port = 44444 ? agent1.sinks.sink1.type = FILE_ROLL agent1.sinks.sink1.sink.directory = E:\\file-out ? ? # Use a channel which buffers events in memory agent1.channels.channel1.type = memory agent1.channels.channel1.capacity = 1000 agent1.channels.channel1.transactionCapactiy = 100 ? # Bind the source and sink to the channel agent1.sources.source1.channels = channel1 agent1.sinks.sink1.channel = channel1 |
5.??HDFS SINK
5.1.? 使用說明
輸出到hdfs的數(shù)據(jù)，首先在hdfs上創(chuàng)建文件.tmp,然后文件關(guān)閉時，將tmp后綴去掉，存儲方案與file輸出類似，可以設(shè)定時間間隔、文件大小、接受事件條數(shù)作為滾動生成新文件的依據(jù)，默認30s
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5.2.? 可配置項
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Name |
Default |
Description |
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channel |
– |
? |
|
type |
– |
The component type name, needs to be ? hdfs |
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hdfs.path |
– |
HDFS directory path (eg hdfs://namenode/flume/webdata/) |
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hdfs.filePrefix |
FlumeData |
Name prefixed to files created by Flume in hdfs directory |
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hdfs.rollInterval |
30 |
Number of seconds to wait before rolling current file (0 = never roll based on time interval) |
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hdfs.rollSize |
1024 |
File size to trigger roll, in bytes (0: never roll based on file size) |
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hdfs.rollCount |
10 |
Number of events written to file before it rolled (0 = never roll based on number of events) |
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hdfs.batchSize |
1 |
number of events written to file before it flushed to HDFS |
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hdfs.txnEventMax |
100 |
? |
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hdfs.codeC |
– |
Compression codec. one of following : gzip, bzip2, lzo, snappy |
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hdfs.fileType |
SequenceFile |
File format: currently ? SequenceFile , DataStream ? or CompressedStream (1)DataStream will not compress output file and please don’t set codeC (2)CompressedStream requires set hdfs.codeC with an available codeC |
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hdfs.maxOpenFiles |
5000 |
? |
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hdfs.writeFormat |
– |
“Text” or “Writable” |
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hdfs.appendTimeout |
1000 |
? |
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hdfs.callTimeout |
10000 |
? |
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hdfs.threadsPoolSize |
10 |
Number of threads per HDFS sink for HDFS IO ops (open, write, etc.) |
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hdfs.rollTimerPoolSize |
1 |
Number of threads per HDFS sink for scheduling timed file rolling |
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hdfs.kerberosPrincipal |
– |
Kerberos user principal for accessing secure HDFS |
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hdfs.kerberosKeytab |
– |
Kerberos keytab for accessing secure HDFS |
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hdfs.round |
false |
Should the timestamp be rounded down (if true, affects all time based escape sequences except %t) |
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hdfs.roundValue |
1 |
Rounded down to the highest multiple of this (in the unit configured using hdfs.roundUnit ), less than current time. |
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hdfs.roundUnit |
second |
The unit of the round down value - ? second , minute ? or hour . |
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serializer |
TEXT |
Other possible options include ? AVRO_EVENT ? or the fully-qualified class name of an implementation of the EventSerializer.Builder ? interface. |
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serializer.* |
? |
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5.3.? Agent配置樣例
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6.??多agent采集文件到hdfs
6.1.? 準備工作
1.?????????文件采集類打包成jar放到flume/apache-flume-1.2.0/lib目錄下
2.?????????創(chuàng)建fileSourceRecorder.properties空文件放到flume/apache-flume-1.2.0/conf下（將要修改為如果文件不存在則創(chuàng)建該文件，后續(xù)將不用再創(chuàng)建這個文件）
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6.2.? agent配置文件
6.2.1.??agent1
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# example.conf: A single-node Flume configuration????? ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? # Name the components on this agent???????????????? ??????????????????????????????????????????????? ???? agent1.sources = source1?????????????????????????????????????????????????????????????????????????????? agent1.sinks = sink1?????????????????????????????????????????????????????????????????????????? agent1.channels = channel1???????????????????????? ??????????????????????????????????????????????????? # Describe/configure source1??????????????????????? agent1.sources.source1.type = com.ultrapower.ultracollector.flume.source.file.FileSource???????????????????????????????????????????????? ? agent1.sources.source1.path = /home/yubojie/logs/ultraIDCPServer.log #gbk,utf-8? agent1.sources.source1.encoding = utf-8 agent1.sources.source1.onceMaxReadByte = 999 agent1.sources.source1.cacheQueueSize = 10 agent1.sources.source1.noChangeSleepTime = 1000 agent1.sources.source1.batchCommitSize = 5 agent1.sources.source1.batchWaitTime = 500???????? ??????? #agent1.sources.source1.type = avro #agent1.sources.source1.bind = localhost #agent1.sources.source1.port = 44444 ????????????????????????????????????? ???????????????????????????????????????????????? # Describe sink1??????????????????????????????????????????????????????????????????????????????????????? #agent1.sinks.sink1.type = logger?????????? #agent1.sinks.sink1.type = FILE_ROLL #agent1.sinks.sink1.sink.directory = E:/file-out #agent1.sinks.sink1.sink.fileName = a.log agent1.sinks.sink1.type = hdfs #agent1.sinks.sink1.hdfs.path = hdfs://192.168.98.20:9000/user/hadoop/yubojietest agent1.sinks.sink1.hdfs.path = hdfs://192.168.0.153:9000/user/file agent1.sinks.sink1.hdfs.callTimeout = 20000 agent1.sinks.sink1.hdfs.fileType = DataStream #agent1.sinks.sink1.sink.rollInterval = 30???????? ??????????????? ?????????????????????????????????????????? # Use a channel which buffers events in memory???????? agent1.channels.channel1.type = memory????????????? agent1.channels.channel1.capacity = 1000??????????? agent1.channels.channel1.transactionCapactiy = 100? ?????????????????????????????????????????? ?????????????????????????????????????????????????? # Bind the source and sink to the channel?????????????????????????????????????????????????????????????? agent1.sources.source1.channels = channel1 agent1.sinks.sink1.channel = channel1??????? ???? ########################## test method ######################################## ? #########start flume agent #########? #agent -n agent1 -f .\conf\flume-conf.properties.template.file.signle ? ######### client send message ######### ? # $ bin/flume-ng avro-client -H localhost -p 44444 -F 'F:/1/log.log' |
6.2.2.??agent2
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# example.conf: A single-node Flume configuration????? ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? # Name the components on this agent?????????? ?????? ??????????????????????????????????????????????????? agent2.sources = source1?????????????????????????????????????????????????????????????????????????????? agent2.sinks = sink1?????????????????????????????????????????????????????????????????????????? agent2.channels = channel1???????????????????????? ??????????????????????????????????????????????????? # Describe/configure source1??????????????????????? agent2.sources.source1.type = com.ultrapower.ultracollector.flume.source.file.FileSource???????????????????????????????????????????????? ? agent2.sources.source1.path = /home/yubojie/logtest/logs/ultraIDCPServer.log #gbk,utf-8? agent2.sources.source1.encoding = utf-8 agent2.sources.source1.onceMaxReadByte = 999 agent2.sources.source1.cacheQueueSize = 10 agent2.sources.source1.noChangeSleepTime = 1000 agent2.sources.source1.batchCommitSize = 5 agent2.sources.source1.batchWaitTime = 500???????? ??????? #agent1.sources.source1.type = avro #agent1.sources.source1.bind = localhost #agent1.sources.source1.port = 44444 ????????????????????????????????????????????????????????????????????????????????????? # Describe sink1??????????????????????????????????????????? ???????????????????????????????????????????? #agent1.sinks.sink1.type = logger?????????? #agent1.sinks.sink1.type = FILE_ROLL #agent1.sinks.sink1.sink.directory = E:/file-out #agent1.sinks.sink1.sink.fileName = a.log agent2.sinks.sink1.type = hdfs #agent1.sinks.sink1.hdfs.path = hdfs://192.168.98.20:9000/user/hadoop/yubojietest agent2.sinks.sink1.hdfs.path = hdfs://192.168.0.153:9000/user/file agent2.sinks.sink1.hdfs.callTimeout = 20000 agent2.sinks.sink1.hdfs.fileType = DataStream #agent1.sinks.sink1.sink.rollInterval = 30???????? ??????????????? ?????????????????????????????????????????? # Use a channel which buffers events in memory???????? agent2.channels.channel1.type = memory????????????? agent2.channels.channel1.capacity = 1000??????????? agent2.channels.channel1.transactionCapactiy = 100? ?????????????????????????????????????????? ?????????????????????????????????????????????????? # Bind the source and sink to the channel?????????????????????????????????????????????? ???????????????? agent2.sources.source1.channels = channel1 agent2.sinks.sink1.channel = channel1??????? ???? ########################## test method ######################################## ? #########start flume agent #########? #agent -n agent1 -f .\conf\flume-conf.properties.template.file.signle ? ######### client send message ######### ? # $ bin/flume-ng avro-client -H localhost -p 44444 -F 'F:/1/log.log' |
6.3.? 啟動命令
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flume-ng agent -name agent1 -c conf -f ../conf/flume-conf.properties?? //agent1監(jiān)控/home/yubojie/logs/ultraIDCPServer.log flume-ng agent -name agent2 -c conf -f ../conf/flume-conf2.properties //agent2監(jiān)控/home/yubojie/logtest/logs/ultraIDCPServer.log |
6.4.? 測試結(jié)果
1.?????????agent1和agent2各自監(jiān)控相應(yīng)文件，互不干涉
2.?????????文件各自輸出到hdfs生成各自的文件
6.??參考資料：
資料
RegexFilteringInterceptor源代碼
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? ? package org.apache.flume.interceptor; ? import static org.apache.flume.interceptor.RegexFilteringInterceptor.Constants. DEFAULT_EXCLUDE_EVENTS ; import static ? org.apache.flume.interceptor.RegexFilteringInterceptor.Constants. DEFAULT_REGEX ; import static ? org.apache.flume.interceptor.RegexFilteringInterceptor.Constants. EXCLUDE_EVENTS ; import static ? org.apache.flume.interceptor.RegexFilteringInterceptor.Constants. REGEX ; ? import ? java.util.List; import ? java.util.regex.Pattern; ? import ? org.apache.flume.Context; import ? org.apache.flume.Event; import ? org.slf4j.Logger; import org.slf4j.LoggerFactory ; ? import ? com.google.common.collect.Lists; ? public class ? RegexFilteringInterceptor implements ? Interceptor { ? ? ? private static final ? Logger logger ? = LoggerFactory ????? ? . getLogger (RegexFilteringInterceptor. class ); ? ? ? private final ? Pattern regex ; ? ? private final boolean excludeEvents ; ? ? ? /** ?? ? * Only {@link RegexFilteringInterceptor.Builder} can build me ?? ? */ ? ? private ? RegexFilteringInterceptor(Pattern regex, boolean ? excludeEvents) { ??? ? this . regex ? = regex; ??? ? this . excludeEvents ? = excludeEvents; ? ? } ? ? ? @Override ? ? public void ? initialize() { ??? ? // no- op ? ? } ? ? ? ? @Override ? ? /** ?? ? * Returns the event if it passes the regular expression filter and null ?? ? * otherwise. ?? ? */ ? ? public ? Event intercept(Event event) { ??? ? // We've already ensured here that at most one of includeRegex and ??? ? // excludeRegex are defined. ? ??? ? if ? (! excludeEvents ) { ????? ? if ? ( regex .matcher( new ? String(event.getBody())).find()) { ??????? ? return ? event; ????? ? } ????? ? else ? { ??????? ? return null ; ????? ? } ??? ? } ??? ? else ? { ????? ? if ? ( regex .matcher( new ? String(event.getBody())).find()) { ??????? ? return null ; ????? ? } ????? ? else ? { ??????? ? return ? event; ????? ? } ??? ? } ? ? } ? ? ? /** ?? ? * Returns the set of events which pass filters, according to ?? ? * {@link #intercept(Event)} . ?? ? * @param events ?? ? * @return ?? ? */ ? ? @Override ? ? public ? List<Event> intercept(List<Event> events) { ??? ? List<Event> out = Lists. newArrayList (); ??? ? for ? (Event event : events) { ????? ? Event outEvent = intercept(event); ????? ? if ? (outEvent != null ) { out.add(outEvent); } ??? ? } ??? ? return ? out; ? ? } ? ? @Override ? ? public void ? close() { ??? ? // no- op ? ? } ? ? /** ?? ? * Builder which builds new instance of the StaticInterceptor. ?? ? */ ? ? public static class ? Builder implements ? Interceptor.Builder { ? ??? ? private ? Pattern regex ; ??? ? private boolean excludeEvents ; ??? ? @Override ??? ? public void ? configure(Context context) { ????? ? String regexString = context.getString( REGEX , DEFAULT_REGEX ); ????? ? regex ? = Pattern. compile (regexString); ????? ? excludeEvents ? = context.getBoolean( EXCLUDE_EVENTS , ????????? ? DEFAULT_EXCLUDE_EVENTS ); ??? ? } ??? ? @Override ??? ? public ? Interceptor build() { ????? ? logger .info(String. format ( ????????? ? "Creating RegexFilteringInterceptor: regex=%s,excludeEvents=%s" , ????????? ? regex , excludeEvents )); ????? ? return new ? RegexFilteringInterceptor( regex , excludeEvents ); ??? ? } ? ? } ? ? public static class ? Constants { ??? ? public static final ? String REGEX ? = "regex" ; ??? ? public static final ? String DEFAULT_REGEX ? = ".*" ; ??? ? public static final ? String EXCLUDE_EVENTS ? = "excludeEvents" ; ??? ? public static final boolean DEFAULT_EXCLUDE_EVENTS ? = ? false ; ? ? } } |
InterceptorType 源代碼
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黃色為添加內(nèi)容 package org.apache.flume.interceptor; ? public enum InterceptorType { ? ? TIMESTAMP(org.apache.flume.interceptor.TimestampInterceptor.Builder.class), ? HOST(org.apache.flume.interceptor.HostInterceptor.Builder.class), ? ? REGEX_FILTER(org.apache.flume.interceptor.RegexFilteringInterceptor.Builder.class) , ? ; ? ? private final Class<? extends Interceptor.Builder> builderClass; ? ? private InterceptorType(Class<? extends Interceptor.Builder> builderClass) { ??? this.builderClass = builderClass; ? } ? ? public Class<? extends Interceptor.Builder> getBuilderClass() { ??? return builderClass; ? } ? } |
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