溫馨提示×
您好，登錄后才能下訂單哦！
點擊 登錄注冊 即表示同意《億速云用戶服務(wù)條款》
您好，登錄后才能下訂單哦！
這篇文章主要為大家展示了“Flume如何整合kafka”，內(nèi)容簡而易懂，條理清晰，希望能夠幫助大家解決疑惑，下面讓小編帶領(lǐng)大家一起研究并學(xué)習(xí)一下“Flume如何整合kafka”這篇文章吧。
在CDH 5.2.0 及更高的版本中, Flume 包含一個Kafka source and sink。使用它們可以讓數(shù)據(jù)從Kafka流入Hadoop或者從任何Flume source 流入Kafka。
重要提示:不能配置一個Kafka source發(fā)送數(shù)據(jù)到 a Kafka sink.如果這么做, the Kafka source sets the topic in the event header, overriding the sink configuration and creating an infinite loop, sending messages back and forth between the source and sink. If you need to use both a source and a sink, use an interceptor to modify the event header and set a different topic.
使用Kafka source 讓數(shù)據(jù)從Kafka topics 流入 Hadoop. The Kafka source 可以與任何Flume sink合并, 這樣很容易把數(shù)據(jù)從 Kafka 寫到 HDFS, HBase, 以及Solr.
下面的 Flume 配置示例，是使用 Kafka source 發(fā)送數(shù)據(jù)到 HDFS sink:
tier1.sources = source1
tier1.channels = channel1
tier1.sinks = sink1
tier1.sources.source1.type = org.apache.flume.source.kafka.KafkaSource
tier1.sources.source1.zookeeperConnect = zk01.example.com:2181
tier1.sources.source1.topic = weblogs
tier1.sources.source1.groupId = flume
tier1.sources.source1.channels = channel1
tier1.sources.source1.interceptors = i1
tier1.sources.source1.interceptors.i1.type = timestamp
tier1.sources.source1.kafka.consumer.timeout.ms = 100
tier1.channels.channel1.type = memory
tier1.channels.channel1.capacity = 10000
tier1.channels.channel1.transactionCapacity = 1000
tier1.sinks.sink1.type = hdfs
tier1.sinks.sink1.hdfs.path = /tmp/kafka/%{topic}/%y-%m-%d
tier1.sinks.sink1.hdfs.rollInterval = 5
tier1.sinks.sink1.hdfs.rollSize = 0
tier1.sinks.sink1.hdfs.rollCount = 0
tier1.sinks.sink1.hdfs.fileType = DataStream
tier1.sinks.sink1.channel = channel1為了更高的吞吐量, 可以配置多個Kafka sources讀取一個 topic.如果所有sources配置一個相同的groupID, 并且topic 有多個分區(qū), 設(shè)置每一個source 從不同的分區(qū)讀取數(shù)據(jù),就可以改善效率.
下面的列表描述Kafka source 支持的參數(shù); 必須的參數(shù)使用粗體列出.
Table 1. Kafka Source Properties
| Property Name | Default Value | Description |
|---|---|---|
| type | 必須設(shè)置為org.apache.flume.source.kafka.KafkaSource. | |
| zookeeperConnect | The URI of the ZooKeeper server or quorum used by Kafka. This can be a single node (for example, zk01.example.com:2181) or a comma-separated list of nodes in a ZooKeeper quorum (for example, zk01.example.com:2181,zk02.example.com:2181, zk03.example.com:2181). | |
| topic | source 讀取消息的Kafka topic。 Flume 每個source只支持一個 topic.。 | |
| groupID | flume | The unique identifier of the Kafka consumer group. Set the same groupID in all sources to indicate that they belong to the same consumer group. |
| batchSize | 1000 | 向channel寫入消息的最多條數(shù) |
| batchDurationMillis | 1000 | 向channel書寫的最大時間 (毫秒) 。 |
| 其他Kafka consumer 支持的屬性 | 通過Kafka source配置Kafka consumer?？梢允褂萌魏蝐onsumer 支持的屬性。 Prepend the consumer property name with the prefix kafka. (for example, kafka.fetch.min.bytes). See the Kafka documentation for the full list of Kafka consumer properties. |
調(diào)優(yōu)
Kafka source 重寫了兩個Kafka consumer 的屬性:
auto.commit.enable 設(shè)置為 false by the source, and every batch is committed. 為了改善性能, 設(shè)置為 true 改為使用 kafka.auto.commit.enable。 這個可能會丟失數(shù)據(jù) if the source goes down before committing.
consumer.timeout.ms設(shè)置為 10, so when Flume polls Kafka for new data, it waits no more than 10 ms for the data to be available. Setting this to a higher value can reduce CPU utilization due to less frequent polling, but introduces latency in writing batches to the channel.
使用Kafka sink 從一個 Flume source發(fā)送數(shù)據(jù)到 Kafka . You can use the Kafka sink in addition to Flume sinks such as HBase or HDFS.
The following Flume configuration example uses a Kafka sink with an exec source:
tier1.sources = source1 tier1.channels = channel1 tier1.sinks = sink1 tier1.sources.source1.type = exec tier1.sources.source1.command = /usr/bin/vmstat 1 tier1.sources.source1.channels = channel1 tier1.channels.channel1.type = memory tier1.channels.channel1.capacity = 10000 tier1.channels.channel1.transactionCapacity = 1000 tier1.sinks.sink1.type = org.apache.flume.sink.kafka.KafkaSink tier1.sinks.sink1.topic = sink1 tier1.sinks.sink1.brokerList = kafka01.example.com:9092,kafka02.example.com:9092 tier1.sinks.sink1.channel = channel1 tier1.sinks.sink1.batchSize = 20
The following table describes parameters the Kafka sink supports; required properties are listed in bold.
Table 2. Kafka Sink Properties
| Property Name | Default Value | Description |
|---|---|---|
| type | 必須設(shè)置為： org.apache.flume.sink.kafka.KafkaSink. | |
| brokerList | The brokers the Kafka sink uses to discover topic partitions, formatted as a comma-separated list of hostname:port entries. You do not need to specify the entire list of brokers, but Cloudera recommends that you specify at least two for high availability. | |
| topic | default-flume-topic | The Kafka topic to which messages are published by default. If the event header contains a topic field, the event is published to the designated topic, overriding the configured topic. |
| batchSize | 100 | The number of messages to process in a single batch. Specifying a larger batchSize can improve throughput and increase latency. |
| requiredAcks | 1 | The number of replicas that must acknowledge a message before it is written successfully. Possible values are 0 (do not wait for an acknowledgement), 1 (wait for the leader to acknowledge only), and -1 (wait for all replicas to acknowledge). To avoid potential loss of data in case of a leader failure, set this to -1. |
| 其他Kafka producer所支持的屬性 | Used to configure the Kafka producer used by the Kafka sink. You can use any producer properties supported by Kafka. Prepend the producer property name with the prefix kafka. (for example, kafka.compression.codec). See the Kafka documentation for the full list of Kafka producer properties. |
Kafka sink 使用 topic 以及 key properties from the FlumeEvent headers to determine where to send events in Kafka. If the header contains the topic property, that event is sent to the designated topic, overriding the configured topic. If the header contains the key property, that key is used to partition events within the topic. Events with the same key are sent to the same partition. If the key parameter is not specified, events are distributed randomly to partitions. Use these properties to control the topics and partitions to which events are sent through the Flume source or interceptor.
CDH 5.3 以及更高的版本包含一個Kafka channel to Flume in addition to the existing memory and file channels. 可以使用Kafka channel:
To write to Hadoop directly from Kafka without using a source.不使用source，從Kafka直接向hadoop中寫數(shù)據(jù)。
To write to Kafka directly from Flume sources without additional buffering.不使用額外的緩沖區(qū)直接從Flume source向Kafka寫數(shù)據(jù)。
As a reliable and highly available channel for any source/sink combination.可以與任何source/sink結(jié)合。
如下的 Flume 配置使用了一個Kafka channel 以及一個exec source 和 hdfs sink:
tier1.sources = source1 tier1.channels = channel1 tier1.sinks = sink1 tier1.sources.source1.type = exec tier1.sources.source1.command = /usr/bin/vmstat 1 tier1.sources.source1.channels = channel1 tier1.channels.channel1.type = org.apache.flume.channel.kafka.KafkaChannel tier1.channels.channel1.capacity = 10000 tier1.channels.channel1.transactionCapacity = 1000 tier1.channels.channel1.brokerList = kafka02.example.com:9092,kafka03.example.com:9092 tier1.channels.channel1.topic = channel2 tier1.channels.channel1.zookeeperConnect = zk01.example.com:2181 tier1.channels.channel1.parseAsFlumeEvent = true tier1.sinks.sink1.type = hdfs tier1.sinks.sink1.hdfs.path = /tmp/kafka/channel tier1.sinks.sink1.hdfs.rollInterval = 5 tier1.sinks.sink1.hdfs.rollSize = 0 tier1.sinks.sink1.hdfs.rollCount = 0 tier1.sinks.sink1.hdfs.fileType = DataStream tier1.sinks.sink1.channel = channel1
下面的列表描述了Kafka channel 所支持的參數(shù); 粗體為必要參數(shù).
Table 3. Kafka Channel Properties
| Property Name | Default Value | Description |
|---|---|---|
| type | 必須設(shè)置為：org.apache.flume.channel.kafka.KafkaChannel. | |
| brokerList | The brokers the Kafka channel uses to discover topic partitions, formatted as a comma-separated list of hostname:port entries. You do not need to specify the entire list of brokers, but Cloudera recommends that you specify at least two for high availability. | |
| zookeeperConnect | The URI of the ZooKeeper server or quorum used by Kafka. This can be a single node (for example, zk01.example.com:2181) or a comma-separated list of nodes in a ZooKeeper quorum (for example, zk01.example.com:2181,zk02.example.com:2181, zk03.example.com:2181). | |
| topic | flume-channel | The Kafka topic the channel will use. |
| groupID | flume | The unique identifier of the Kafka consumer group the channel uses to register with Kafka. |
| parseAsFlumeEvent | true | Set to true if a Flume source is writing to the channel and expects AvroDataums with the FlumeEvent schema (org.apache.flume.source.avro.AvroFlumeEvent) in the channel. Set to false if other producers are writing to the topic that the channel is using. |
| readSmallestOffset | false | If true, reads all data in the topic. If false, reads only data written after the channel has started. Only used when parseAsFlumeEvent is false. |
| kafka.consumer.timeout.ms | 100 | 當(dāng)向sink寫數(shù)據(jù)時輪詢的間隔時間. |
| 其他Kafka producer所支持的屬性 | Used to configure the Kafka producer. You can use any producer properties supported by Kafka. Prepend the producer property name with the prefix kafka. (for example, kafka.compression.codec). See the Kafka documentation for the full list of Kafka producer properties. |
| << Using Kafka with Spark Streaming | ?2015 Cloudera, Inc. All rights reserved | Additional Information >> |
| Terms and Conditions Privacy Policy |
以上是“Flume如何整合kafka”這篇文章的所有內(nèi)容，感謝各位的閱讀！相信大家都有了一定的了解，希望分享的內(nèi)容對大家有所幫助，如果還想學(xué)習(xí)更多知識，歡迎關(guān)注億速云行業(yè)資訊頻道！
免責(zé)聲明：本站發(fā)布的內(nèi)容（圖片、視頻和文字）以原創(chuàng)、轉(zhuǎn)載和分享為主，文章觀點不代表本網(wǎng)站立場，如果涉及侵權(quán)請聯(lián)系站長郵箱：is@yisu.com進行舉報，并提供相關(guān)證據(jù)，一經(jīng)查實，將立刻刪除涉嫌侵權(quán)內(nèi)容。
億速云公眾號
手機網(wǎng)站二維碼