閱讀779 返回首頁    go 阿裏雲 go 技術社區[雲棲]

《kafka中文手冊》-快速開始（三）

Step 7: Use Kafka Connect to import/export data

Writing data from the console and writing it back to the console is a convenient place to start, but you’ll probably want to use data from other sources or export data from Kafka to other systems. For many systems, instead of writing custom integration code you can use Kafka Connect to import or export data.

從控製太寫入和讀取數據方便大家開始學習, 但是你可能需要從其他係統導入到kafka或從kafka導出數據到其它係統. 對於很多係統, 你不需要寫寫特定的整合代碼, 隻需要使用Kafka Connect提供的功能進行導入導出

Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs connectors, which implement the custom logic for interacting with an external system. In this quickstart we’ll see how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic and export data from a Kafka topic to a file.

Kafka Connect工具包含數據的導入和導出, 它可以是一個外部工具connectors, 和外部係統交互實現特定的業務邏輯後. 在下麵的例子中我們會看到一個簡單的連接器, 從文件中讀取數據寫入到kafka, 和從kafka的topic中導出數據到文件中去

First, we’ll start by creating some seed data to test with:

首先, 我們先創建一些隨機的測試數據

> echo -e "foo\nbar" > test.txt

Next, we’ll start two connectors running in standalone mode, which means they run in a single, local, dedicated process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data. The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector class to instantiate, and any other configuration required by the connector.

接下來, 我們啟動兩個獨立連接器, 這意味著它們以單一的，本地的，專用的進程進行運行. 我們以參數的形式提供三個配置文件, 第一個參數是kafka連接器常用的一些配置, 包含kafka連接的服務器器地址, 數據的序列化格式等. 剩下的配置文件各自包含一個要創建的連接器, 這些文件包含一個唯一的連接器名稱, 連接器對於的啟動類, 還任何連接器需要的其他配置信息

> bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties

These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file.

在這些樣例包含kafka之前啟動的默認本地的集群, 和啟動兩個連接器, 第一個輸入連接器負責從一個文件按行讀取數據, 並發布到kafka的topic上去, 第二個是一個輸入連接器, 負責從kafka的topic中讀取數據, 並按行輸出到文件中

During startup you’ll see a number of log messages, including some indicating that the connectors are being instantiated. Once the Kafka Connect process has started, the source connector should start reading lines from test.txt and producing them to the topic connect-test, and the sink connector should start reading messages from the topic connect-test and write them to the file test.sink.txt. We can verify the data has been delivered through the entire pipeline by examining the contents of the output file:

在啟動的時候, 你會看到一些日誌信息, 包括連接器初始化的啟動信息. 一旦kafka連接器啟動起來, 源連接器從test.txt 文件中讀取行記錄, 並發布到connect-test的主題中, 輸出連接器開始從connect-test主題中讀取數據, 並寫入到 test.sink.txt 文件中, 我們可以根據輸出文件的內容, 檢測這個管道的數據傳送是否正常

> cat test.sink.txt
foo
bar

Note that the data is being stored in the Kafka topic connect-test, so we can also run a console consumer to see the data in the topic (or use custom consumer code to process it):

現在數據已經保存到connect-test主題中, 所以, 我們可以跑一個終端消費者來查看主題中的數據(或者編寫特定的消費進行處理)

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic connect-test --from-beginning
{"schema":{"type":"string","optional":false},"payload":"foo"}
{"schema":{"type":"string","optional":false},"payload":"bar"}
...

The connectors continue to process data, so we can add data to the file and see it move through the pipeline:

連接器持續在處理數據, 所以我們可以把數據寫入到文件中, 查看到數據是否通過這個管道處理

 

> echo "Another line" >> test.txt

 

You should see the line appear in the console consumer output and in the sink file.

你可以看到那些從消費者終端輸入的行, 都會寫入到輸出文件中

Step 8: Use Kafka Streams to process data

Kafka Streams is a client library of Kafka for real-time stream processing and analyzing data stored in Kafka brokers. This quickstart example will demonstrate how to run a streaming application coded in this library. Here is the gist of the WordCountDemo example code (converted to use Java 8 lambda expressions for easy reading).

Kafka Streams 是kafka提供用於對存儲到kafka服務器中的數據進行實時分析和數據處理的庫. 這裏展示了怎麼使用該庫進行流處理的一個樣例, 這裏是提供了WorkCountDemo樣例的部分代碼(為了方便閱讀使用java 8 lambda表達式方式)

KTable wordCounts = textLines
    // Split each text line, by whitespace, into words.
    .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))

    // Ensure the words are available as record keys for the next aggregate operation.
    .map((key, value) -> new KeyValue<>(value, value))

    // Count the occurrences of each word (record key) and store the results into a table named "Counts".
    .countByKey("Counts")

It implements the WordCount algorithm, which computes a word occurrence histogram from the input text. However, unlike other WordCount examples you might have seen before that operate on bounded data, the WordCount demo application behaves slightly differently because it is designed to operate on an infinite, unbounded stream of data. Similar to the bounded variant, it is a stateful algorithm that tracks and updates the counts of words. However, since it must assume potentially unbounded input data, it will periodically output its current state and results while continuing to process more data because it cannot know when it has processed “all” the input data.

這裏實現從輸入文本中統計單詞出現次數的算法. 和其他的有限輸入的單詞計數算法不一樣, 這個樣例有點不一樣, 因為它的輸入流是無限, 沒有邊界的數據流數據, 類似有邊界的輸入, 這裏包含有狀態的算法用於跟蹤和更新單詞技術. 當然, 由於處理的數據是沒有邊界的, 處理程序並不知道什麼時候已經處理完全部的輸入數據, 因此, 它在處理數據的同時, 並定時輸出當前的狀態和結果,

We will now prepare input data to a Kafka topic, which will subsequently be processed by a Kafka Streams application.

我們現在為流處理應用準備點數據到kafka的主題中

> echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" > file-input.txt

Or on Windows:

> echo all streams lead to kafka> file-input.txt
> echo hello kafka streams>> file-input.txt
> echo|set /p=join kafka summit>> file-input.txt

Next, we send this input data to the input topic named streams-file-input using the console producer (in practice, stream data will likely be flowing continuously into Kafka where the application will be up and running):

接下來, 我們使用終端生產者把數據輸入到streams-file-input主題中(實際生產中, 在應用啟動時, 流數據將會持續輸入到kafka中)

> bin/kafka-topics.sh --create \
            --zookeeper localhost:2181 \
            --replication-factor 1 \
            --partitions 1 \
            --topic streams-file-input

> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-file-input < file-input.txt

We can now run the WordCount demo application to process the input data: 現在我們跑下WordCount 樣例來處理這些數據.

> bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo

There won’t be any STDOUT output except log entries as the results are continuously written back into another topic named streams-wordcount-output in Kafka. The demo will run for a few seconds and then, unlike typical stream processing applications, terminate automatically.

We can now inspect the output of the WordCount demo application by reading from its output topic:

這裏不會有任何日誌輸出到標準輸出,  然後, 有持續的結果輸出到kafka另外一個主題叫做 streams-wordcount-output , 和其它的流處理係統不一樣, 樣例隻會跑幾秒鍾然後自動停止了. 我們可以從輸出主題中讀取數據來檢測WordCount樣例的數據處理結果.

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 \
            --topic streams-wordcount-output \
            --from-beginning \
            --formatter kafka.tools.DefaultMessageFormatter \
            --property print.key=true \
            --property print.value=true \
            --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \
            --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer

with the following output data being printed to the console: 將會有如下的數據輸出到控製台上

all     1
lead    1
to      1
hello   1
streams 2
join    1
kafka   3
summit  1

Here, the first column is the Kafka message key, and the second column is the message value, both in in java.lang.String format. Note that the output is actually a continuous stream of updates, where each data record (i.e. each line in the original output above) is an updated count of a single word, aka record key such as “kafka”. For multiple records with the same key, each later record is an update of the previous one.

這裏, 第一欄是kafka消息的鍵值, 第二欄是消息值, 都是使用java.lang.String的格式, 注意, 輸出實際是上持續數據流的更新, 每條記錄都是對每個單詞的持續更新計數, 比如 “kafka”這個單詞的記錄. 如果多條記錄有相同的鍵, 則每條記錄都會更新計數器.

Now you can write more input messages to the streams-file-input topic and observe additional messages added to streams-wordcount-output topic, reflecting updated word counts (e.g., using the console producer and the console consumer, as described above).

You can stop the console consumer via Ctrl-C.

現在, 你可以寫入更多的消息到streams-file-input 主題中, 然後觀察另外輸出到streams-wordcount-output主題中的記錄中被更新的單詞計數(e.g, 使用前麵提到的終端生產者和終端消費者)

1.4 Ecosystem

There are a plethora of tools that integrate with Kafka outside the main distribution. The ecosystem page lists many of these, including stream processing systems, Hadoop integration, monitoring, and deployment tools.

在kafka發行的主版本外, 還有大量整合了kafka的工具. 這裏 有羅列大量的工具, 包括流的在線處理係統, hadoop整合, 監控 和部署工具

1.5 Upgrading From Previous Versions

Upgrading from 0.8.x, 0.9.x or 0.10.0.X to 0.10.1.0

0.10.1.0 has wire protocol changes. By following the recommended rolling upgrade plan below, you guarantee no downtime during the upgrade. However, please notice the Potential breaking changes in 0.10.1.0 before upgrade.
Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients (i.e. 0.10.1.x clients only support 0.10.1.x or later brokers while 0.10.1.x brokers also support older clients).

For a rolling upgrade:

  1. Update server.properties file on all brokers and add the following properties:
  2. Upgrade the brokers one at a time: shut down the broker, update the code, and restart it.
  3. Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.1.0.
  4. If your previous message format is 0.10.0, change log.message.format.version to 0.10.1 (this is a no-op as the message format is the same for both 0.10.0 and 0.10.1). If your previous message format version is lower than 0.10.0, do not change log.message.format.version yet – this parameter should only change once all consumers have been upgraded to 0.10.0.0 or later.
  5. Restart the brokers one by one for the new protocol version to take effect.
  6. If log.message.format.version is still lower than 0.10.0 at this point, wait until all consumers have been upgraded to 0.10.0 or later, then change log.message.format.version to 0.10.1 on each broker and restart them one by one.

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential breaking changes in 0.10.1.0
  • The log retention time is no longer based on last modified time of the log segments. Instead it will be based on the largest timestamp of the messages in a log segment.
  • The log rolling time is no longer depending on log segment create time. Instead it is now based on the timestamp in the messages. More specifically. if the timestamp of the first message in the segment is T, the log will be rolled out when a new message has a timestamp greater than or equal to T + log.roll.ms
  • The open file handlers of 0.10.0 will increase by ~33% because of the addition of time index files for each segment.
  • The time index and offset index share the same index size configuration. Since each time index entry is 1.5x the size of offset index entry. User may need to increase log.index.size.max.bytes to avoid potential frequent log rolling.
  • Due to the increased number of index files, on some brokers with large amount the log segments (e.g. >15K), the log loading process during the broker startup could be longer. Based on our experiment, setting the num.recovery.threads.per.data.dir to one may reduce the log loading time.
Notable changes in 0.10.1.0
  • The new Java consumer is no longer in beta and we recommend it for all new development. The old Scala consumers are still supported, but they will be deprecated in the next release and will be removed in a future major release.
  • The --new-consumer/--new.consumer switch is no longer required to use tools like MirrorMaker and the Console Consumer with the new consumer; one simply needs to pass a Kafka broker to connect to instead of the ZooKeeper ensemble. In addition, usage of the Console Consumer with the old consumer has been deprecated and it will be removed in a future major release.
  • Kafka clusters can now be uniquely identified by a cluster id. It will be automatically generated when a broker is upgraded to 0.10.1.0. The cluster id is available via the kafka.server:type=KafkaServer,name=ClusterId metric and it is part of the Metadata response. Serializers, client interceptors and metric reporters can receive the cluster id by implementing the ClusterResourceListener interface.
  • The BrokerState “RunningAsController” (value 4) has been removed. Due to a bug, a broker would only be in this state briefly before transitioning out of it and hence the impact of the removal should be minimal. The recommended way to detect if a given broker is the controller is via the kafka.controller:type=KafkaController,name=ActiveControllerCount metric.
  • The new Java Consumer now allows users to search offsets by timestamp on partitions.
  • The new Java Consumer now supports heartbeating from a background thread. There is a new configuration max.poll.interval.ms which controls the maximum time between poll invocations before the consumer will proactively leave the group (5 minutes by default). The value of the configurationrequest.timeout.ms must always be larger than max.poll.interval.ms because this is the maximum time that a JoinGroup request can block on the server while the consumer is rebalancing, so we have changed its default value to just above 5 minutes. Finally, the default value of session.timeout.ms has been adjusted down to 10 seconds, and the default value of max.poll.records has been changed to 500.
  • When using an Authorizer and a user doesn’t have Describe authorization on a topic, the broker will no longer return TOPIC_AUTHORIZATION_FAILED errors to requests since this leaks topic names. Instead, the UNKNOWN_TOPIC_OR_PARTITION error code will be returned. This may cause unexpected timeouts or delays when using the producer and consumer since Kafka clients will typically retry automatically on unknown topic errors. You should consult the client logs if you suspect this could be happening.
  • Fetch responses have a size limit by default (50 MB for consumers and 10 MB for replication). The existing per partition limits also apply (1 MB for consumers and replication). Note that neither of these limits is an absolute maximum as explained in the next point.
  • Consumers and replicas can make progress if a message larger than the response/partition size limit is found. More concretely, if the first message in the first non-empty partition of the fetch is larger than either or both limits, the message will still be returned.
  • Overloaded constructors were added to kafka.api.FetchRequest and kafka.javaapi.FetchRequest to allow the caller to specify the order of the partitions (since order is significant in v3). The previously existing constructors were deprecated and the partitions are shuffled before the request is sent to avoid starvation issues.
New Protocol Versions
  • ListOffsetRequest v1 supports accurate offset search based on timestamps.
  • MetadataResponse v2 introduces a new field: “cluster_id”.
  • FetchRequest v3 supports limiting the response size (in addition to the existing per partition limit), it returns messages bigger than the limits if required to make progress and the order of partitions in the request is now significant.
  • JoinGroup v1 introduces a new field: “rebalance_timeout”.

Upgrading from 0.8.x or 0.9.x to 0.10.0.0

0.10.0.0 has potential breaking changes (please review before upgrading) and possible performance impact following the upgrade. By following the recommended rolling upgrade plan below, you guarantee no downtime and no performance impact during and following the upgrade.
Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients.

Notes to clients with version 0.9.0.0: Due to a bug introduced in 0.9.0.0, clients that depend on ZooKeeper (old Scala high-level Consumer and MirrorMaker if used with the old consumer) will not work with 0.10.0.x brokers. Therefore, 0.9.0.0 clients should be upgraded to 0.9.0.1 before brokers are upgraded to 0.10.0.x. This step is not necessary for 0.8.X or 0.9.0.1 clients.

For a rolling upgrade:

  1. Update server.properties file on all brokers and add the following properties:
  2. Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it.
  3. Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.0.0. NOTE: You shouldn’t touch log.message.format.version yet – this parameter should only change once all consumers have been upgraded to 0.10.0.0
  4. Restart the brokers one by one for the new protocol version to take effect.
  5. Once all consumers have been upgraded to 0.10.0, change log.message.format.version to 0.10.0 on each broker and restart them one by one.

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential performance impact following upgrade to 0.10.0.0

The message format in 0.10.0 includes a new timestamp field and uses relative offsets for compressed messages. The on disk message format can be configured through log.message.format.version in the server.properties file. The default on-disk message format is 0.10.0. If a consumer client is on a version before 0.10.0.0, it only understands message formats before 0.10.0. In this case, the broker is able to convert messages from the 0.10.0 format to an earlier format before sending the response to the consumer on an older version. However, the broker can’t use zero-copy transfer in this case. Reports from the Kafka community on the performance impact have shown CPU utilization going from 20% before to 100% after an upgrade, which forced an immediate upgrade of all clients to bring performance back to normal. To avoid such message conversion before consumers are upgraded to 0.10.0.0, one can set log.message.format.version to 0.8.2 or 0.9.0 when upgrading the broker to 0.10.0.0. This way, the broker can still use zero-copy transfer to send the data to the old consumers. Once consumers are upgraded, one can change the message format to 0.10.0 on the broker and enjoy the new message format that includes new timestamp and improved compression. The conversion is supported to ensure compatibility and can be useful to support a few apps that have not updated to newer clients yet, but is impractical to support all consumer traffic on even an overprovisioned cluster. Therefore, it is critical to avoid the message conversion as much as possible when brokers have been upgraded but the majority of clients have not.

For clients that are upgraded to 0.10.0.0, there is no performance impact.

Note: By setting the message format version, one certifies that all existing messages are on or below that message format version. Otherwise consumers before 0.10.0.0 might break. In particular, after the message format is set to 0.10.0, one should not change it back to an earlier format as it may break consumers on versions before 0.10.0.0.

Note: Due to the additional timestamp introduced in each message, producers sending small messages may see a message throughput degradation because of the increased overhead. Likewise, replication now transmits an additional 8 bytes per message. If you’re running close to the network capacity of your cluster, it’s possible that you’ll overwhelm the network cards and see failures and performance issues due to the overload.

Note: If you have enabled compression on producers, you may notice reduced producer throughput and/or lower compression rate on the broker in some cases. When receiving compressed messages, 0.10.0 brokers avoid recompressing the messages, which in general reduces the latency and improves the throughput. In certain cases, however, this may reduce the batching size on the producer, which could lead to worse throughput. If this happens, users can tune linger.ms and batch.size of the producer for better throughput. In addition, the producer buffer used for compressing messages with snappy is smaller than the one used by the broker, which may have a negative impact on the compression ratio for the messages on disk. We intend to make this configurable in a future Kafka release.

 

Potential breaking changes in 0.10.0.0
  • Starting from Kafka 0.10.0.0, the message format version in Kafka is represented as the Kafka version. For example, message format 0.9.0 refers to the highest message version supported by Kafka 0.9.0.
  • Message format 0.10.0 has been introduced and it is used by default. It includes a timestamp field in the messages and relative offsets are used for compressed messages.
  • ProduceRequest/Response v2 has been introduced and it is used by default to support message format 0.10.0
  • FetchRequest/Response v2 has been introduced and it is used by default to support message format 0.10.0
  • MessageFormatter interface was changed from def writeTo(key: Array[Byte], value: Array[Byte], output: PrintStream) todef writeTo(consumerRecord: ConsumerRecord[Array[Byte], Array[Byte]], output: PrintStream)
  • MessageReader interface was changed from def readMessage(): KeyedMessage[Array[Byte], Array[Byte]] to def readMessage(): ProducerRecord[Array[Byte], Array[Byte]]
  • MessageFormatter’s package was changed from kafka.tools to kafka.common
  • MessageReader’s package was changed from kafka.tools to kafka.common
  • MirrorMakerMessageHandler no longer exposes the handle(record: MessageAndMetadata[Array[Byte], Array[Byte]]) method as it was never called.
  • The 0.7 KafkaMigrationTool is no longer packaged with Kafka. If you need to migrate from 0.7 to 0.10.0, please migrate to 0.8 first and then follow the documented upgrade process to upgrade from 0.8 to 0.10.0.
  • The new consumer has standardized its APIs to accept java.util.Collection as the sequence type for method parameters. Existing code may have to be updated to work with the 0.10.0 client library.
  • LZ4-compressed message handling was changed to use an interoperable framing specification (LZ4f v1.5.1). To maintain compatibility with old clients, this change only applies to Message format 0.10.0 and later. Clients that Produce/Fetch LZ4-compressed messages using v0/v1 (Message format 0.9.0) should continue to use the 0.9.0 framing implementation. Clients that use Produce/Fetch protocols v2 or later should use interoperable LZ4f framing. A list of interoperable LZ4 libraries is available at https://www.lz4.org/
Notable changes in 0.10.0.0
  • Starting from Kafka 0.10.0.0, a new client library named Kafka Streams is available for stream processing on data stored in Kafka topics. This new client library only works with 0.10.x and upward versioned brokers due to message format changes mentioned above. For more information please read this section.
  • The default value of the configuration parameter receive.buffer.bytes is now 64K for the new consumer.
  • The new consumer now exposes the configuration parameter exclude.internal.topics to restrict internal topics (such as the consumer offsets topic) from accidentally being included in regular expression subscriptions. By default, it is enabled.
  • The old Scala producer has been deprecated. Users should migrate their code to the Java producer included in the kafka-clients JAR as soon as possible.
  • The new consumer API has been marked stable.

Upgrading from 0.8.0, 0.8.1.X or 0.8.2.X to 0.9.0.0

0.9.0.0 has potential breaking changes (please review before upgrading) and an inter-broker protocol change from previous versions. This means that upgraded brokers and clients may not be compatible with older versions. It is important that you upgrade your Kafka cluster before upgrading your clients. If you are using MirrorMaker downstream clusters should be upgraded first as well.

For a rolling upgrade:

  1. Update server.properties file on all brokers and add the following property: inter.broker.protocol.version=0.8.2.X
  2. Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it.
  3. Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.9.0.0.
  4. Restart the brokers one by one for the new protocol version to take effect

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential breaking changes in 0.9.0.0
  • Java 1.6 is no longer supported.
  • Scala 2.9 is no longer supported.
  • Broker IDs above 1000 are now reserved by default to automatically assigned broker IDs. If your cluster has existing broker IDs above that threshold make sure to increase the reserved.broker.max.id broker configuration property accordingly.
  • Configuration parameter replica.lag.max.messages was removed. Partition leaders will no longer consider the number of lagging messages when deciding which replicas are in sync.
  • Configuration parameter replica.lag.time.max.ms now refers not just to the time passed since last fetch request from replica, but also to time since the replica last caught up. Replicas that are still fetching messages from leaders but did not catch up to the latest messages in replica.lag.time.max.ms will be considered out of sync.
  • Compacted topics no longer accept messages without key and an exception is thrown by the producer if this is attempted. In 0.8.x, a message without key would cause the log compaction thread to subsequently complain and quit (and stop compacting all compacted topics).
  • MirrorMaker no longer supports multiple target clusters. As a result it will only accept a single –consumer.config parameter. To mirror multiple source clusters, you will need at least one MirrorMaker instance per source cluster, each with its own consumer configuration.
  • Tools packaged under org.apache.kafka.clients.tools.* have been moved to org.apache.kafka.tools.*. All included scripts will still function as usual, only custom code directly importing these classes will be affected.
  • The default Kafka JVM performance options (KAFKA_JVM_PERFORMANCE_OPTS) have been changed in kafka-run-class.sh.
  • The kafka-topics.sh script (kafka.admin.TopicCommand) now exits with non-zero exit code on failure.
  • The kafka-topics.sh script (kafka.admin.TopicCommand) will now print a warning when topic names risk metric collisions due to the use of a ‘.’ or ‘_’ in the topic name, and error in the case of an actual collision.
  • The kafka-console-producer.sh script (kafka.tools.ConsoleProducer) will use the Java producer instead of the old Scala producer be default, and users have to specify ‘old-producer’ to use the old producer.
  • By default, all command line tools will print all logging messages to stderr instead of stdout.
Notable changes in 0.9.0.1
  • The new broker id generation feature can be disabled by setting broker.id.generation.enable to false.
  • Configuration parameter log.cleaner.enable is now true by default. This means topics with a cleanup.policy=compact will now be compacted by default, and 128 MB of heap will be allocated to the cleaner process via log.cleaner.dedupe.buffer.size. You may want to review log.cleaner.dedupe.buffer.size and the other log.cleaner configuration values based on your usage of compacted topics.
  • Default value of configuration parameter fetch.min.bytes for the new consumer is now 1 by default.
Deprecations in 0.9.0.0
  • Altering topic configuration from the kafka-topics.sh script (kafka.admin.TopicCommand) has been deprecated. Going forward, please use the kafka-configs.sh script (kafka.admin.ConfigCommand) for this functionality.
  • The kafka-consumer-offset-checker.sh (kafka.tools.ConsumerOffsetChecker) has been deprecated. Going forward, please use kafka-consumer-groups.sh (kafka.admin.ConsumerGroupCommand) for this functionality.
  • The kafka.tools.ProducerPerformance class has been deprecated. Going forward, please use org.apache.kafka.tools.ProducerPerformance for this functionality (kafka-producer-perf-test.sh will also be changed to use the new class).
  • The producer config block.on.buffer.full has been deprecated and will be removed in future release. Currently its default value has been changed to false. The KafkaProducer will no longer throw BufferExhaustedException but instead will use max.block.ms value to block, after which it will throw a TimeoutException. If block.on.buffer.full property is set to true explicitly, it will set the max.block.ms to Long.MAX_VALUE and metadata.fetch.timeout.ms will not be honoured

Upgrading from 0.8.1 to 0.8.2

0.8.2 is fully compatible with 0.8.1. The upgrade can be done one broker at a time by simply bringing it down, updating the code, and restarting it.

Upgrading from 0.8.0 to 0.8.1

0.8.1 is fully compatible with 0.8. The upgrade can be done one broker at a time by simply bringing it down, updating the code, and restarting it.

Upgrading from 0.7

Release 0.7 is incompatible with newer releases. Major changes were made to the API, ZooKeeper data structures, and protocol, and configuration in order to add replication (Which was missing in 0.7). The upgrade from 0.7 to later versions requires a special tool for migration. This migration can be done without downtime.

轉載自 並發編程網 - ifeve.com

最後更新：2017-05-19 10:25:04

  上一篇：go  《Spring Boot官方文檔》16. 自動配置
  下一篇：go  《kafka中文手冊》-快速開始（二）