1、环境准备
1.1 flume组件开发环境
flume组件依赖的jar如下：
<!-- provided -->
<dependency>
<groupId>commons-lang</groupId>
<artifactId>commons-lang</artifactId>
<version>2.5</version>
</dependency>
<!-- hbase相关组件 -->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<exclusions>
<exclusion>
<groupId>io.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
<!-- flume相关插件 -->
<dependency>
<groupId>org.apache.flume.flume-ng-sinks</groupId>
<artifactId>flume-ng-hbase-sink</artifactId>
<version>${version.flume}</version>
</dependency>
<!-- JDK依赖 -->
<dependency>
<groupId>jdk.tools</groupId>
<artifactId>jdk.tools</artifactId>
<version>${version.java}</version>
<scope>system</scope>
<systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
</dependency>
<!-- log -->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
</dependency>
1.2 自定义hbase序列化
下面定义了接口常量，虽然有些反模式，但较为容易理解。注意：java通过thrift框架生成文件，需要去除__isset_bitfield字段
import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
public interface DcmFlumeBaseConstant {
String SEPARATE = "$";
Gson GSON = new GsonBuilder().create();
Charset UTF_8 = Charset.forName("UTF-8");
Charset ISO_8859_1 = Charset.forName("ISO-8859-1");
String __isset_bitfield = "__isset_bitfield";
interface Fields {
String ID = "id";
}
}
《flume构建高可用、可扩展的海量日志采集系统》中描述Flume和HBase交互环境中，只关心Put和Increment，也就是下面的PutRequest和AtomicIncrementRequest
Flume有两类HBase Sink，一类是Hbase sink和Async Hbase sink，Hbase sink采取的是逐个向hbase集群发送事件，而Async Hbase sink则采用非阻塞的且使用多线程写数据到Hbase。显然，两种方式Async Hbase sink的安全性较Hbase sink低，而性能要较Hbase sink高。
import org.apache.commons.lang.StringUtils;
import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.conf.ComponentConfiguration;
import org.apache.flume.sink.hbase.AsyncHbaseEventSerializer;
import org.apache.hadoop.hbase.util.Bytes;
import org.hbase.async.AtomicIncrementRequest;
import org.hbase.async.PutRequest;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
* json数据hbase序列化，row_key默认使用ID
*
* @author dzm
*/
public class HbaseSerializer implements AsyncHbaseEventSerializer {
private static final Logger logger = LoggerFactory.getLogger(HbaseSerializer.class);
public static final ArrayList<PutRequest> EMPTY_ARRAYLIST = new ArrayList<>();
// hbase中的表
private byte[] table;
// 列簇
private byte[] cf;
private Event currentEvent;
@Override
public void initialize(byte[] table, byte[] cf) {
this.table = table;
this.cf = cf;
}
@Override
public void configure(Context context) {
}
@Override
public void configure(ComponentConfiguration conf) {
}
@SuppressWarnings("unchecked")
@Override
public List<PutRequest> getActions() {
String eventStr = new String(currentEvent.getBody(), DcmFlumeBaseConstant.UTF_8);
if (logger.isDebugEnabled()) {
logger.debug("event body: " + eventStr);
}
if (StringUtils.isNotEmpty(eventStr)) {
// 这里看到数据取自eventStr
Map<String, Object> dataMap = DcmFlumeBaseConstant.GSON.fromJson(eventStr, HashMap.class);
try {
filter(dataMap);
} catch (Exception e) {
return EMPTY_ARRAYLIST;
}
if (dataMap == null || dataMap.size() == 0) {
return EMPTY_ARRAYLIST;
}
List<PutRequest> rows = new ArrayList<PutRequest>();
// hbase中写入行记录
List<byte[]> qualifiers = new ArrayList<byte[]>();
List<byte[]> values = new ArrayList<byte[]>();
for (Map.Entry<String, Object> entry : dataMap.entrySet()) {
// __isset_bitfield为thrift字段，不需要存储
if (DcmFlumeBaseConstant.__isset_bitfield.equals(entry.getKey())) {
continue;
}
if (entry.getValue() != null) {
// 为空的数据，不用写入到hbase中
if (entry.getValue() instanceof java.util.List) {
// list类型在detail的sink中处理
} else if (StringUtils.isNotEmpty(entry.getValue().toString())) {
qualifiers.add(Bytes.toBytes(entry.getKey()));
values.add(Bytes.toBytes(entry.getValue().toString()));
}
}
}
byte[][] qualifiersB = new byte[qualifiers.size()][];
qualifiers.toArray(qualifiersB);
byte[][] valuesB = new byte[values.size()][];
values.toArray(valuesB);
// getRowKey(dataMap)获取rowkey
PutRequest put = new PutRequest(table, getRowKey(dataMap), cf, qualifiersB, valuesB);
rows.add(put);
return rows;
}
return EMPTY_ARRAYLIST;
}
/**
* Hbase的 row key=id
*
* @param dataMap
* @return
*/
protected byte[] getRowKey(Map<String, Object> dataMap) {
String rowKey = dataMap.get(DcmFlumeBaseConstant.Fields.ID).toString();
dataMap.remove("DcmFlumeBaseConstant.Fields.ID");
return Bytes.toBytes(rowKey);
}
@Override
public void setEvent(Event event) {
this.currentEvent = event;
}
@Override
public void cleanUp() {
table = null;
cf = null;
currentEvent = null;
}
/**
* hbase计数器，不需要
*/
@Override
public List<AtomicIncrementRequest> getIncrements() {
return new ArrayList<AtomicIncrementRequest>();
}
/**
* 是否需要保存
*
* @param dataMap
*/
public void filter(Map<String, Object> dataMap) {
}
如果想更改一下rowkey，设为自己的
private static String DATE_FORMAT = "yyyyMMddHHmmss";
/**
* 对传入的数据进行过滤
*
* @param dataMap
*/
@Override
public void filter(Map<String, Object> dataMap) {
if (!dataMap.containsKey(("id"))) {
dataMap = null;
}
String userId = dataMap.get("id").toString();
String eventType = dataMap.get("eventType").toString();
String time = DateUtil.formatDate(DateUtil.parseDate(dataMap.get("time")), DATE_FORMAT);
dataMap.put("userId", userId);
dataMap.put("id", userId + DcmFlumeBaseConstant.SEPARATE + eventType + DcmFlumeBaseConstant.SEPARATE + time);
}
1.3 hadoop、hbase环境变量
flume写入hadoop、hbase需要一些jar，因为jar太多，最简单的办法是通过环境变量来设置，
hadoop、hbase自身是不用启动的
# flume
export FLUME_HOME=/application/flume
export PATH=$PATH:$FLUME_HOME/bin
# hadoop hbase
export HADOOP_HOME=/application/hadoop
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
export HBASE_HOME=/application/hbase
export PATH=$PATH:$HBASE_HOME/bin
export HBASE_LIBRARY_PATH=$HBASE_HOME/lib/native/Linux-amd64-64
2 接口实现
2.1 flume配置文件
参考Flume1.7.0入门：安装、部署、及flume的案例，Source是采集数据,sink是从Channel收集数据，运行在一个独立线程,sink组件是用于把数据发送到目的地的组件，Channel:连接 sources 和 sinks ，这个有点像一个队列,source组件把数据收集来以后，临时存放在channel中，即channel组件在agent中是专门用来存放临时数据的——对采集到的数据进行简单的缓存，可以存放在memory、jdbc、file等等。
下面的配置信息，可以解读为：35001端口采集avro对象，采集后的数据先保存在kafka中，接着由sink来处理，序列化采用我自定义的HbaseSerializer,将数据写入到hbase中。
那么assemble是做什么的呢？它用于定义source和sink的绑定关系，两者之间通过channel进行关联
# 上传flume-xx-conf.properties到$FLUME_HOME/conf目录下
[root@bwsc73 conf]# cat dcm-order-flume-conf.properties
# read from kafka and write to hbase
dcm-order-agent.sources = dcm-order-source
dcm-order-agent.channels = dcm-order-channel
dcm-order-agent.sinks = dcm-order-sink
# source
dcm-order-agent.sources.dcm-order-source.type=avro
dcm-order-agent.sources.dcm-order-source.bind=0.0.0.0
dcm-order-agent.sources.dcm-order-source.port=35001
# channel
dcm-order-agent.channels.dcm-order-channel.flumeBatchSize = 100
dcm-order-agent.channels.dcm-order-channel.type = org.apache.flume.channel.kafka.KafkaChannel
dcm-order-agent.channels.dcm-order-channel.kafka.bootstrap.servers = bwsc68:9092,bwsc68:9092,bwsc70:9092
dcm-order-agent.channels.dcm-order-channel.kafka.topic = dcm_order
dcm-order-agent.channels.dcm-order-channel.kafka.consumer.group.id = dcm_order_flume_channel
#dcm-order-agent.channels.dcm-order-channel.kafka.consumer.auto.offset.reset = latest
# sink
dcm-order-agent.sinks.dcm-order-sink.type = asynchbase
dcm-order-agent.sinks.dcm-order-sink.table = dcm_order
dcm-order-agent.sinks.dcm-order-sink.columnFamily = i
dcm-order-agent.sinks.dcm-order-sink.zookeeperQuorum = bwsc65:2181,bwsc66:2181,bwsc67:2181
dcm-order-agent.sinks.dcm-order-sink.serializer = com.dzm.dcm.flume.sink.HbaseSerializer
# assemble
dcm-order-agent.sources.dcm-order-source.channels = dcm-order-channel
dcm-order-agent.sinks.dcm-order-sink.channel = dcm-order-channel
3 flume组件部署
3.1 文件上传在哪里
mkdir -p $FLUME_HOME/plugins.d/your_project/lib
上传到jar文件至$FLUME_HOME/plugins.d/your_project/lib目录
3.2 启动
# 在$FLUME_HOME目录输入以下命令启动(注：必须在flume根目录启动)
nohup bin/flume-ng agent -n your-order-agent -c conf -f conf/your-order-flume-conf.properties > your-order-flume.out 2>&1 &
从下面可以看到那些依赖的jar，停止flume的进程，通过kill命令即可
[root@bwsc73 conf]# ps -ef|grep flume-multi-hbase-sink
root 6122 4792 0 11:53 pts/1 00:00:00 grep --color=auto flume-multi-hbase-sink
root 20523 1 0 Jun12 ? 01:20:57 /usr/java/jdk1.8.0_151/bin/java -Xmx20m -cp /application/flume/lib/*:/application/flume/plugins.d/bw-msg-flume/lib/*:/application/flume/plugins.d/dcm-flume/lib/*:/application/flume/plugins.d/flume-invoice-bft/lib/*:/application/flume/plugins.d/flume-invoice-wp/lib/*:/application/flume/plugins.d/flume-invoice-wp/libext/*:/application/hadoop-2.6.4/etc/hadoop:/application/hadoop-2.6.4/share/hadoop/common/lib/*:/application/hadoop-2.6.4/share/hadoop/common/*:/application/hadoop-2.6.4/share/hadoop/hdfs:/application/hadoop-2.6.4/share/hadoop/hdfs/lib/*:/application/hadoop-2.6.4/share/hadoop/hdfs/*:/application/hadoop-2.6.4/share/hadoop/yarn/lib/*:/application/hadoop-2.6.4/share/hadoop/yarn/*:/application/hadoop-2.6.4/share/hadoop/mapreduce/lib/*:/application/hadoop-2.6.4/share/hadoop/mapreduce/*:/application/hadoop/contrib/capacity-scheduler/*.jar:/application/hbase/conf:/usr/java/jdk/lib/tools.jar:/application/hbase:/application/hbase/lib/activation-1.1.jar:/application/hbase/lib/antisamy-1.4.3.jar:/application/hbase/lib/aopalliance-1.0.jar:/application/hbase/lib/apacheds-i18n-2.0.0-M15.jar:/application/hbase/lib/apacheds-kerberos-codec-2.0.0-M15.jar:/application/hbase/lib/api-asn1-api-1.0.0-M20.jar:/application/hbase/lib/api-util-1.0.0-M20.jar:/application/hbase/lib/asm-3.1.jar:/application/hbase/lib/avro-1.7.4.jar:/application/hbase/lib/batik-css-1.7.jar:/application/hbase/lib/batik-ext-1.7.jar:/application/hbase/lib/batik-util-1.7.jar:/application/hbase/lib/bsh-core-2.0b4.jar:/application/hbase/lib/commons-beanutils-1.7.0.jar:/application/hbase/lib/commons-beanutils-core-1.7.0.jar:/application/hbase/lib/commons-cli-1.2.jar:/application/hbase/lib/commons-codec-1.9.jar:/application/hbase/lib/commons-collections-3.2.2.jar:/application/hbase/lib/commons-compress-1.4.1.jar:/application/hbase/lib/commons-configuration-1.6.jar:/application/hbase/lib/commons-daemon-1.0.13.jar:/application/hbase/lib/commons-digester-1.8.jar:/application/hbase/lib/commons-el-1.0.jar:/application/hbase/lib/commons-fileupload-1.2.jar:/application/hbase/lib/commons-httpclient-3.1.jar:/application/hbase/lib/commons-io-2.4.jar:/application/hbase/lib/commons-lang-2.6.jar:/application/hbase/lib/commons-logging-1.2.jar:/application/hbase/lib/commons-math-2.2.jar:/application/hbase/lib/commons-math3-3.1.1.jar:/application/hbase/lib/commons-net-3.1.jar:/application/hbase/lib/disruptor-3.3.0.jar:/application/hbase/lib/esapi-2.1.0.jar:/application/hbase/lib/findbugs-annotations-1.3.9-1.jar:/application/hbase/lib/guava-12.0.1.jar:/application/hbase/lib/guice-3.0.jar:/application/hbase/lib/guice-servlet-3.0.jar:/application/hbase/lib/hadoop-annotations-2.5.1.jar:/application/hbase/lib/hadoop-auth-2.5.1.jar:/application/hbase/lib/hadoop-client-2.5.1.jar:/application/hbase/lib/hadoop-common-2.5.1.jar:/application/hbase/lib/hadoop-hdfs-2.5.1.jar:/application/hbase/lib/hadoop-mapreduce-client-app-2.5.1.jar:/application/hbase/lib/hadoop-mapreduce-client-common-2.5.1.jar:/application/hbase/lib/hadoop-mapreduce-client-core-2.5.1.jar:/application/hbase/lib/hadoop-mapreduce-client-jobclient-2.5.1.jar:/application/hbase/lib/hadoop-mapreduce-client-shuffle-2.5.1.jar:/application/hbase/lib/hadoop-yarn-api-2.5.1.jar:/application/hbase/lib/hadoop-yarn-client-2.5.1.jar:/application/hbase/lib/hadoop-yarn-common-2.5.1.jar:/application/hbase/lib/hadoop-yarn-server-common-2.5.1.jar:/application/hbase/lib/hbase-annotations-1.1.4.jar:/application/hbase/lib/hbase-annotations-1.1.4-tests.jar:/application/hbase/lib/hbase-client-1.1.4.jar:/application/hbase/lib/hbase-common-1.1.4.jar:/application/hbase/lib/hbase-common-1.1.4-tests.jar:/application/hbase/lib/hbase-examples-1.1.4.jar:/application/hbase/lib/hbase-hadoop2-compat-1.1.4.jar:/application/hbase/lib/hbase-hadoop-compat-1.1.4.jar:/application/hbase/lib/hbase-it-1.1.4.jar:/application/hbase/lib/hbase-it-1.1.4-tests.jar:/application/hbase/lib/hbase-prefix-tree-1.1.4.jar:/application/hbase/lib/hbase-procedure-1.1.4.jar:/application/hbase/lib/hbase-protocol-1.1.4.jar:/application/hbase/lib/hbase-resource-bundle-1.1.4.jar:/application/hbase/lib/hbase-rest-1.1.4.jar:/application/hbase/lib/hbase-server-1.1.4.jar:/application/hbase/lib/hbase-server-1.1.4-tests.jar:/application/hbase/lib/hbase-shell-1.1.4.jar:/application/hbase/lib/hbase-thrift-1.1.4.jar:/application/hbase/lib/htrace-core-3.1.0-incubating.jar:/application/hbase/lib/httpclient-4.2.5.jar:/application/hbase/lib/httpcore-4.1.3.jar:/application/hbase/lib/jackson-core-asl-1.9.13.jar:/application/hbase/lib/jackson-jaxrs-1.9.13.jar:/application/hbase/lib/jackson-mapper-asl-1.9.13.jar:/application/hbase/lib/jackson-xc-1.9.13.jar:/application/hbase/lib/jamon-runtime-2.3.1.jar:/application/hbase/lib/jasper-compiler-5.5.23.jar:/application/hbase/lib/jasper-runtime-5.5.23.jar:/application/hbase/lib/javax.inject-1.jar:/application/hbase/lib/java-xmlbuilder-0.4.jar:/application/hbase/lib/jaxb-api-2.2.2.jar:/application/hbase/lib/jaxb-impl-2.2.3-1.jar:/application/hbase/lib/jcodings-1.0.8.jar:/application/hbase/lib/jersey-client-1.9.jar:/application/hbase/lib/jersey-core-1.9.jar:/application/hbase/lib/jersey-guice-1.9.jar:/application/hbase/lib/jersey-json-1.9.jar:/application/hbase/lib/jersey-server-1.9.jar:/application/hbase/lib/jets3t-0.9.0.jar:/application/hbase/lib/jettison-1.3.3.jar:/application/hbase/lib/jetty-6.1.26.jar:/application/hbase/lib/jetty-sslengine-6.1.26.jar:/application/hbase/lib/jetty-util-6.1.26.jar:/application/hbase/lib/joni-2.1.2.jar:/application/hbase/lib/jruby-complete-1.6.8.jar:/application/hbase/lib/jsch-0.1.42.jar:/application/hbase/lib/jsp-2.1-6.1.14.jar:/application/hbase/lib/jsp-api-2.1-6.1.14.jar:/application/hbase/lib/jsr305-1.3.9.jar:/application/hbase/lib/junit-4.12.jar:/application/hbase/lib/leveldbjni-all-1.8.jar:/application/hbase/lib/libthrift-0.9.0.jar:/application/hbase/lib/log4j-1.2.17.jar:/application/hbase/lib/metrics-core-2.2.0.jar:/application/hbase/lib/nekohtml-1.9.12.jar:/application/hbase/lib/netty-3.2.4.Final.jar:/application/hbase/lib/netty-all-4.0.23.Final.jar:/application/hbase/lib/paranamer-2.3.jar:/application/hbase/lib/protobuf-java-2.5.0.jar:/application/hbase/lib/servlet-api-2.5-6.1.14.jar:/application/hbase/lib/servlet-api-2.5.jar:/application/hbase/lib/slf4j-api-1.7.7.jar:/application/hbase/lib/slf4j-log4j12-1.7.5.jar:/application/hbase/lib/snappy-java-1.0.4.1.jar:/application/hbase/lib/spymemcached-2.11.6.jar:/application/hbase/lib/xalan-2.7.0.jar:/application/hbase/lib/xml-apis-1.3.03.jar:/application/hbase/lib/xml-apis-ext-1.3.04.jar:/application/hbase/lib/xmlenc-0.52.jar:/application/hbase/lib/xom-1.2.5.jar:/application/hbase/lib/xz-1.0.jar:/application/hbase/lib/zookeeper-3.4.6.jar:/application/hadoop-2.6.4/etc/hadoop:/application/hadoop-2.6.4/share/hadoop/common/lib/*:/application/hadoop-2.6.4/share/hadoop/common/*:/application/hadoop-2.6.4/share/hadoop/hdfs:/application/hadoop-2.6.4/share/hadoop/hdfs/lib/*:/application/hadoop-2.6.4/share/hadoop/hdfs/*:/application/hadoop-2.6.4/share/hadoop/yarn/lib/*:/application/hadoop-2.6.4/share/hadoop/yarn/*:/application/hadoop-2.6.4/share/hadoop/mapreduce/lib/*:/application/hadoop-2.6.4/share/hadoop/mapreduce/*:/application/hadoop/contrib/capacity-scheduler/*.jar:/application/hbase/conf:/lib/* -Dja
杀掉全部flume进程
ps -ef | grep flume | grep -v grep | awk '{print $2}' | xargs kill -9
3.3 调试
#查看日志
tail -fn 100 logs/flume.log
# 如果需要检查报文是否接收成功，修改flume/conf/log4j.properties，加入配置，配置需要debug模式的包名全路径后重新启动
log4j.lodcgger.com.your_package.your_path= DEBUG
3.4 haproxy配置
35001端口在properties文件中
frontend dcm_order_flume_front
bind *:35101
mode tcp
log global
option tcplog
timeout client 3600s
backlog 4096
maxconn 1000000
default_backend dcm_order_flume_back
backend dcm_order_flume_back
mode tcp
option log-health-checks
option redispatch
option tcplog
balance roundrobin
timeout connect 1s
timeout queue 5s
timeout server 3600s
balance roundrobin
server f1 bwhs180:35001 check inter 2000 rise 3 fall 3 weight 1
server f2 bwhs181:35001 check inter 2000 rise 3 fall 3 weight 1
server f3 bwhs183:35001 check inter 2000 rise 3 fall 3 weight 1
3.5 flume进程是否活着在
根据端口来确定进程是否还活着，如果挂掉了，那么就重启一下
hdfsAgent=`lsof -i:36001 | awk 'NR==2{print $2}'`
echo $hdfsAgent
if [ "$hdfsAgent" = "" ]; then
echo "hdfs-agent is restart!"
nohup bin/flume-ng agent -n hdfs-agent -c conf -f conf/hdfs-flume-conf.properties &
else
echo "hdfs-agent is alive!"
fi
另外重启flume，可以参考
hdfsAgent=`ps -ef | grep flume | grep hdfs-agent | awk '{print $2}'`
echo $hdfsAgent
if [ "$hdfsAgent" = "" ]; then
echo "hdfs-agent is restart!"
nohup bin/flume-ng agent -n hdfs-agent -c conf -f conf/hdfs-flume-conf.properties &
else
echo "hdfs-agent is alive!"
fi
4 问题集
4.1 java.lang.OutOfMemoryError: Java heap space
参考flume系列之Java heap space大小设置，在conf/flume-env.sh中增加配置
export JAVA_OPTS="-Xms512m -Xmx2000m -Dcom.sun.management.jmxremote"
即使增加了这个配置，还是会发生kafka相关操作及问题汇总，总是会出现org.apache.kafka.common.errors.NotLeaderForPartitionException的问题，前期研究第4.1.2章 flume的拓扑结构，就想到了，flume中source节点与sink节点分开，这样就可以降低flume资源消耗的风险.
23 Jul 2019 13:58:41,136 ERROR [kafka-producer-network-thread | producer-1] (org.apache.kafka.common.utils.KafkaThread$1.uncaughtException:30) - Uncaught exception in kafka-producer-network-thread | producer-1:
java.lang.OutOfMemoryError: Java heap space
at java.util.HashMap.newNode(HashMap.java:1747)
at java.util.HashMap.putVal(HashMap.java:631)
at java.util.HashMap.put(HashMap.java:612)
at org.apache.kafka.common.Cluster.<init>(Cluster.java:48)
at org.apache.kafka.common.requests.MetadataResponse.<init>(MetadataResponse.java:176)
at org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.handleResponse(NetworkClient.java:578)
at org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.maybeHandleCompletedReceive(NetworkClient.java:565)
at org.apache.kafka.clients.NetworkClient.handleCompletedReceives(NetworkClient.java:441)
at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:265)
at org.apache.kafka.clients.producer.internals.Sender.run(Sender.java:216)
at org.apache.kafka.clients.producer.internals.Sender.run(Sender.java:128)
at java.lang.Thread.run(Thread.java:748)
23 Jul 2019 13:58:41,136 ERROR [Thread-1] (org.apache.thrift.server.TThreadedSelectorServer$SelectorThread.run:544) - run() exiting due to uncaught error
java.lang.OutOfMemoryError: Java heap space
at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57)
at java.nio.ByteBuffer.allocate(ByteBuffer.java:335)
at org.apache.thrift.server.AbstractNonblockingServer$FrameBuffer.read(AbstractNonblockingServer.java:338)
at org.apache.thrift.server.AbstractNonblockingServer$AbstractSelectThread.handleRead(AbstractNonblockingServer.java:202)
at org.apache.thrift.server.TThreadedSelectorServer$SelectorThread.select(TThreadedSelectorServer.java:576)
at org.apache.thrift.server.TThreadedSelectorServer$SelectorThread.run(TThreadedSelectorServer.java:536)
4.2 SinkRunner-PollingRunner-DefaultSinkProcessor
26 Jul 2019 08:49:38,274 ERROR [SinkRunner-PollingRunner-DefaultSinkProcessor] (org.apache.kafka.clients.consumer.internals.ConsumerCoordinator$OffsetCommitResponseHandler.handle:550) - Error UNKNOWN_MEMBER_ID occurred while committing offsets for group flume_scrapy_snapshot_channel
26 Jul 2019 08:49:38,274 ERROR [SinkRunner-PollingRunner-DefaultSinkProcessor] (org.apache.flume.SinkRunner$PollingRunner.run:158) - Unable to deliver event. Exception follows.
org.apache.kafka.clients.consumer.CommitFailedException: Commit cannot be completed due to group rebalance
at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator$OffsetCommitResponseHandler.handle(ConsumerCoordinator.java:552)
at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator$OffsetCommitResponseHandler.handle(ConsumerCoordinator.java:493)
at org.apache.kafka.clients.consumer.internals.AbstractCoordinator$CoordinatorResponseHandler.onSuccess(AbstractCoordinator.java:665)
at org.apache.kafka.clients.consumer.internals.AbstractCoordinator$CoordinatorResponseHandler.onSuccess(AbstractCoordinator.java:644)
at org.apache.kafka.clients.consumer.internals.RequestFuture$1.onSuccess(RequestFuture.java:167)
at org.apache.kafka.clients.consumer.internals.RequestFuture.fireSuccess(RequestFuture.java:133)
at org.apache.kafka.clients.consumer.internals.RequestFuture.complete(RequestFuture.java:107)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient$RequestFutureCompletionHandler.onComplete(ConsumerNetworkClient.java:380)
at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:274)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.clientPoll(ConsumerNetworkClient.java:320)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:213)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:193)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:163)
at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.commitOffsetsSync(ConsumerCoordinator.java:358)
at org.apache.kafka.clients.consumer.KafkaConsumer.commitSync(KafkaConsumer.java:968)
at org.apache.flume.channel.kafka.KafkaChannel$ConsumerAndRecords.commitOffsets(KafkaChannel.java:684)
at org.apache.flume.channel.kafka.KafkaChannel$KafkaTransaction.doCommit(KafkaChannel.java:567)
at org.apache.flume.channel.BasicTransactionSemantics.commit(BasicTransactionSemantics.java:151)
at com.bwjf.flume.hbase.flume.sink.MultiAsyncHBaseSink.process(MultiAsyncHBaseSink.java:288)
at org.apache.flume.sink.DefaultSinkProcessor.process(DefaultSinkProcessor.java:67)
at org.apache.flume.SinkRunner$PollingRunn
