embedded kafka controlled shutdown

spring-kafka-test includes an embedded Kafka broker that can be created via a JUnit @ClassRule annotation. In this case when the new process takes over the first few messages it receives will already have been processed. For example in a majority voting scheme, if a majority of servers suffer a permanent failure, then you must either choose to lose 100% of your data or violate consistency by taking what remains on an existing server as your new source of truth. And in practice we have found that we can run a pipeline with strong SLAs at large scale without a need for producer persistence. Controlled Shutdown In the pre-KIP-500 world, brokers triggered a controller shutdown by making an RPC to the controller. 看了下日志，就看到他shutdown了，关键是也没人操作啊 [2016-07-21 11:04:10,285] INFO [Group Metadata Manager on Broker 0]: Removed 0 expired offsets in 0 milliseconds. The current policy deletes any log with a modification time of more than N days ago, though a policy which retained the last N GB could also be useful. If the compression codec is anything other than NoCompressionCodec, enable compression only for specified topics if any. Since the offset is hidden from the consumer API this decision is ultimately an implementation detail and we went with the more efficient approach. The server may also have a zookeeper chroot path as part of it's zookeeper connection string which puts its data under some path in the global zookeeper namespace. However, although the server hands out messages in order, the messages are delivered asynchronously to consumers, so they may arrive out of order on different consumers. During rebalancing, we try to assign partitions to consumers in such a way that reduces the number of broker nodes each consumer has to connect to. More details about producer configuration can be found in the scala class kafka.producer.ProducerConfig. This structure has the advantage that all operations are O(1) and reads do not block writes or each other. This is a list of all present broker nodes, each of which provides a unique logical broker id which identifies it to consumers (which must be given as part of its configuration). It should logically identify the application making the request. This yields a tradeoff: if the leader waits for more followers to acknowledge a message before declaring it committed then there will be more potentially electable leaders. Minimum bytes expected for each fetch response for the fetch requests from the replica to the leader. The default replication factor for automatically created topics. This file is rolled over to a fresh file when it reaches a configurable size (say 1GB). You can choose any number you like so long as it is unique. This suggests a design which is very simple: rather than maintain as much as possible in-memory and flush it all out to the filesystem in a panic when we run out of space, we invert that. You could imagine other possible designs which would be only pull, end-to-end. Having the followers pull from the leader has the nice property of allowing the follower to naturally batch together log entries they are applying to their log. It is also important to optimize the leadership election process as that is the critical window of unavailability. I made a Kubernetes Cluster which has 3 master nodes and 2 worker nodes. I have a 4 node cluster, running 0.8.2.1, that got into a bad state last night during a rolling restart. Project: Kafka Issue Type: Improvement Reporter: Jay Kreps Download Kafka and try to use our shutdown tool. This allows automatic failover to these replicas when a server in the cluster fails so messages remain available in the presence of failures. If the compression codec is NoCompressionCodec, compression is disabled for all topics. replica.high.watermark.checkpoint.interval.ms. All replicas have the exact same log with the same offsets. The maximum number of unsent messages that can be queued up the producer when using async mode before either the producer must be blocked or data must be dropped. Also note that if both log.retention.hours and log.retention.bytes are both set we delete a segment when either limit is exceeded. More commonly, however, we have found that topics have a small number of consumer groups, one for each "logical subscriber". The actual timeout set will be max.fetch.wait + socket.timeout.ms. We expose the interface for semantic partitioning by allowing the user to specify a key to partition by and using this to hash to a partition (there is also an option to override the partition function if need be). The number of hours to keep a log segment before it is deleted, i.e. The operating system reads data from the disk into pagecache in kernel space, The application reads the data from kernel space into a user-space buffer, The application writes the data back into kernel space into a socket buffer, The operating system copies the data from the socket buffer to the NIC buffer where it is sent over the network. I do have full logs from each broker in the cluster, which I can provide (but would have to significantly anonymize the logs before forwarding along). Tricky problems must be dealt with, like what to do with messages that are sent but never acknowledged. For example, if the consumer code has a bug and is discovered after some messages are consumed, the consumer can re-consume those messages once the bug is fixed. Since the data structure used for storage in many messaging systems scale poorly, this is also a pragmatic choice--since the broker knows what is consumed it can immediately delete it, keeping the data size small. The port on which the server accepts client connections. For example if the log retention is set to two days, then for the two days after a message is published it is available for consumption, after which it will be discarded to free up space. If you set this to zero, the metadata will get refreshed after each message sent (not recommended). However our experience has been that rebuilding the RAID array is so I/O intensive that it effectively disables the server, so this does not provide much real availability improvement. If a follower hasn't sent any fetch requests for this window of time, the leader will remove the follower from ISR (in-sync replicas) and treat it as dead. This makes the state about what has been consumed very small, just one number for each partition. The log provides the capability of getting the most recently written message to allow clients to start subscribing as of "right now". This corresponds to "at-most-once" semantics as in the case of a consumer failure messages may not be processed. Kafka has stronger ordering guarantees than a traditional messaging system, too. 1, which means that the producer gets an acknowledgement after the leader replica has received the data. This can be done at random, implementing a kind of random load balancing, or it can be done by some semantic partitioning function. The high-level API also provides the ability to subscribe to topics that match a filter expression (i.e., either a whitelist or a blacklist regular expression). When a broker joins, it registers itself under the broker node registry directory and writes information about its host name and port. nobh: This setting controls additional ordering guarantees when using data=writeback mode. The primary downside of RAID is that it is usually a big performance hit for write throughput and reduces the available disk space. This is also useful in the case the consumer fails to consume its data within its SLA-specified number of days. data=writeback: Ext4 defaults to data=ordered which puts a strong order on some writes. In our current release we choose the second strategy and favor choosing a potentially inconsistent replica when all replicas in the ISR are dead. Batching leads to larger network packets, larger sequential disk operations, contiguous memory blocks, and so on, all of which allows Kafka to turn a bursty stream of random message writes into linear writes that flow to the consumers. Messages consist of a fixed-size header and variable length opaque byte array payload. If the controller fails, one of the surviving brokers will become the new controller. This value controls when a produce request is considered completed. By setting the producer to async we allow batching together of requests (which is great for throughput) but open the possibility of a failure of the client machine dropping unsent data. The maximum size in bytes we allow for the offset index for each log segment. First, they allow the log to scale beyond a size that will fit on a single server. There are many ways to implement this, but the simplest and fastest is with a leader who chooses the ordering of values provided to it. During c… RAID can potentially do better at balancing load between disks (although it doesn't always seem to) because it balances load at a lower level. We're able to get around this by setting `controlled.shutdown.enable` to `false`, which should be fine for testing.However, we then end up getting a lot of warnings such as:16: 20: 18.844 [Controller-0-to-broker-2-send-thread] WARN kafka. Note that we will always pre-allocate a sparse file with this much space and shrink it down when the log rolls. For example if one consumer is your foobar process, which is run across three machines, then you might assign this group of consumers the id "foobar". Note that two kinds of corruption must be handled: truncation in which an unwritten block is lost due to a crash, and corruption in which a nonsense block is ADDED to the file. The CRC detects this corner case, and prevents it from corrupting the log (though the unwritten messages are, of course, lost). This is intriguing but we felt not very suitable for our target use cases which have thousands of producers. More details on compression can be found here. Since this buffering happens in the client it obviously reduces the durability as any data buffered in memory and not yet sent will be lost in the event of a producer crash. In general disk throughput is the performance bottleneck, and more disks is more better. Kafka's semantics are straight-forward. We have 8x7200 rpm SATA drives. The following sequence goes on in bursts of 500ms, once for each partition on the box (and the recurring every 10 seconds): The above sequence is repeated every 10 seconds, it seems. This option provides the lowest latency but the weakest durability guarantees (some data will be lost when a server fails). This value is stored in a zookeeper directory. Because of this allowing lots of data to accumulate and then calling flush can lead to large write latencies as new writes on that partition will be blocked as lots of accumulated data is flushed to disk. Alpakka Kafka Documentation The Alpakka project is an open source initiative to implement stream-aware and reactive integration pipelines for Java and Scala. Using sendfile, this re-copying is avoided by allowing the OS to send the data from pagecache to the network directly. It exists in any quorum-based scheme. ), Register a watch on changes (new brokers joining or any existing brokers leaving) under the broker id registry. The persistent data structure used in messaging systems are often a per-consumer queue with an associated BTree or other general-purpose random access data structures to maintain metadata about messages. This is not the only possible deployment pattern. Numerical ranges are also given such as [0...5] to indicate the subdirectories 0, 1, 2, 3, 4. For applications that need a global view of all data we use the mirror maker tool to provide clusters which have aggregate data mirrored from all datacenters. In distributed systems terminology we only attempt to handle a "fail/recover" model of failures where nodes suddenly cease working and then later recover (perhaps without knowing that they have died). By being very fast we help ensure that the application will tip-over under load before the infrastructure. So one gets optimal batching without introducing unnecessary latency. This majority vote approach has a very nice property: the latency is dependent on only the fastest servers. This is to ensure that the active controller is not moved on each broker restart, which would slow down the restart. The SO_SNDBUFF buffer the server prefers for socket connections. Note that each stream that createMessageStreamsByFilter returns may iterate over messages from multiple topics (i.e., if multiple topics are allowed by the filter). That is, if the replication factor is three, the latency is determined by the faster slave not the slower one. One of our primary use cases is handling web activity data, which is very high volume: each page view may generate dozens of writes. For example a consumer can reset to an older offset to reprocess. Defining an embedded protocol within Kafka’s group management API does not restrict its use to load balancing only. In fact the only metadata retained on a per-consumer basis is the position of the consumer in the log, called the "offset". Such use of an embedded protocol is a universal way for any type of distributed processes to coordinate with each other and implement their custom logic without requiring the Kafka broker’s code to be aware of their existence. All the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail. The socket timeout for network requests to the leader for replicating data. This state can be periodically checkpointed. The default partitioner is based on the hash of the key. The maximum size of a message that the server can receive. It is generally not advisable to run a single Kafka cluster that spans multiple datacenters as this will incur very high replication latency both for Kafka writes and Zookeeper writes and neither Kafka nor Zookeeper will remain available if the network partitions. The buffer size for controller-to-broker-channels. If a replica falls more than this many messages behind the leader, the leader will remove the follower from ISR and treat it as dead. This motivated our partitioning and consumer model. The number of byes of messages to attempt to fetch for each topic-partition in each fetch request. This design simplifies the implementation. For example if the key chosen was a user id then all data for a given user would be sent to the same partition. Apache Kafka is an open-source for distributed streaming system. In the future, we would like to make this configurable to better support use cases where downtime is preferable to inconsistency. Ultimately, the shutdown timed out (after 3 minutes) and it was terminated by the deployment system, and the server was restarted. This setting controls the maximum number of attempts before giving up. When the leader does die we need to choose a new leader from among the followers. Note that if both log.retention.hours and log.retention.bytes are both set we delete a segment when either limit is exceeded. Maximum time to buffer data when using async mode. What to do when there is no initial offset in Zookeeper or if an offset is out of range:* smallest : automatically reset the offset to the smallest offset* largest : automatically reset the offset to the largest offset* anything else: throw exception to the consumer. You can do a back-of-the-envelope estimate of memory needs by assuming you want to be able to buffer for 30 seconds and compute your memory need as write_throughput*30. Before Kafka 1.1.0, during the controlled shutdown, the controller moves the leaders one partition at a time. Consumer ids are registered in the following directory. In general you probably don't need to mess with this value. The number of byes of messages to attempt to fetch for each partition in the fetch requests the replicas send to the leader. There is a general perception that "disks are slow" which makes people skeptical that a persistent structure can offer competitive performance. Likewise in order to support data load into Hadoop which resides in separate facilities we provide local read-only clusters that mirror the production data centers in the facilities where this data load occurs. they don't translate to the case where consumers or producers can fail, or cases where there are multiple consumer processes, or cases where data written to disk can be lost). To compensate for this performance divergence modern operating systems have become increasingly aggressive in their use of main memory for disk caching. If tuned for low latency this will result in sending a single message at a time only for the transfer to end up being buffered anyway, which is wasteful. If such replicas were destroyed or their data was lost, then we are permanently down. field names in JSON or user agents in web logs or common string values). The downside of majority vote is that it doesn't take many failures to leave you with no electable leaders. The result is that we are able to batch together many of the required leadership change notifications which makes the election process far cheaper and faster for a large number of partitions. Remember that a 5 node cluster will cause writes to slow down compared to a 3 node cluster, but will allow more fault tolerance. In this case there is a possibility that the consumer process crashes after processing messages but before saving its position. This property specifies the number of retries when such failures occur. A message is considered "committed" when all in sync replicas for that partition have applied it to their log. This leads to a great deal of flexibility for consumers, as we will describe. Now let's describe the semantics from the point-of-view of the consumer. The purpose of the logical broker id is to allow a broker to be moved to a different physical machine without affecting consumers. This dilemma is not specific to Kafka. Got this: bin/kafka-run-class.sh kafka.admin.ShutdownBroker --zookeeper localhost:2181 --broker 0 This id serves as the brokers "name" and allows the broker to be moved to a different host/port without confusing consumers. Activity tracking is often very high volume as many activity messages are generated for each user page view. The actual process of reading from an offset requires first locating the log segment file in which the data is stored, calculating the file-specific offset from the global offset value, and then reading from that file offset. The solution to the problem was to extend the HostOptions.ShutdownTimeout configuration value to be longer than 5s, using the standard ASP.NET Core IOptions configuration system. This setting removes the ordering constraint and seems to significantly reduce latency. This setting controls the size to which a segment file will grow before a new segment is rolled over in the log. 体的な実行例までを紹介していきます。今回は、「shutdown」コマンドです。 Mirror of Apache Kafka. However, I think there was something fundamentally wrong with the shutdown process. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster. Here is a summary of some notable changes: Kafka 1.1.0 includes significant improvements to the Kafka Controller that speed up controlled shutdown. However, we think it is ameliorated by allowing the client to choose whether they block on the message commit or not, and the additional throughput and disk space due to the lower required replication factor is worth it. The producer will wait until either this number of messages are ready to send or queue.buffer.max.ms is reached. But followers themselves may fall behind or crash so we must ensure we choose an up-to-date follower. So although you can set a relatively lenient flush interval setting no flush interval at all will lead to a full segment's worth of data being flushed all at once which can be quite slow. The server in turn appends chunks of messages to its log in one go, and the consumer fetches large linear chunks at a time. First of all, if the consumer processes the message but fails before it can send an acknowledgement then the message will be consumed twice. Setting this to a higher value will improve throughput. Since there are many partitions this still balances the load over many consumer instances. To understand the impact of sendfile, it is important to understand the common data path for transfer of data from file to socket: This is clearly inefficient, there are four copies and two system calls. The ability to commit without the slowest servers is an advantage of the majority vote approach. If the consumer fails to heartbeat to zookeeper for this period of time it is considered dead and a rebalance will occur. controlled.shutdown.enable =false 是否允许控制器关闭broker ,若是设置为true,会关闭所有在这个broker上的leader，并转移到其他broker controlled.shutdown.max.retries =3 控制器关闭的尝试次数 controlled.shutdown.retry.backoff Each partition has one server which acts as the "leader" and zero or more servers which act as "followers". Kafka takes a slightly different approach to choosing its quorum set. Before each retry, the producer refreshes the metadata of relevant topics to see if a new leader has been elected. The key fact about disk performance is that the throughput of hard drives has been diverging from the latency of a disk seek for the last decade. Hence even a handful of disk seeks leads to very high overhead. Zookeeper also allows you to add a "chroot" path which will make all kafka data for this cluster appear under a particular path. If not enough bytes, wait up to replica.fetch.wait.max.ms for this many bytes to arrive. This is a fairly intuitive choice, and indeed for a single machine server it is not clear where else this state could go. Under non-failure conditions, each partition in Kafka has a single leader and zero or more followers. The second problem is around performance, now the broker must keep multiple states about every single message (first to lock it so it is not given out a second time, and then to mark it as permanently consumed so that it can be removed). "kafka.server":name="AllTopicsMessagesInPerSec", type="BrokerTopicMetrics", "kafka.server":name="AllTopicsBytesInPerSec",type="BrokerTopicMetrics", "kafka.network":name="{Produce|Fetch-consumer|Fetch-follower}-RequestsPerSec",type="RequestMetrics", "kafka.server":name="AllTopicsBytesOutPerSec", type="BrokerTopicMetrics", "kafka.log":name="LogFlushRateAndTimeMs",type="LogFlushStats", # of under replicated partitions (|ISR| < |all replicas|), "kafka.server":name="UnderReplicatedPartitions",type="ReplicaManager", "kafka.controller":name="ActiveControllerCount",type="KafkaController", only one broker in the cluster should have 1, "kafka.controller":name="LeaderElectionRateAndTimeMs", type="ControllerStats", "kafka.controller":name="UncleanLeaderElectionsPerSec", type="ControllerStats", "kafka.server":name="PartitionCount",type="ReplicaManager", "kafka.server":name="LeaderCount",type="ReplicaManager", "kafka.server":name="ISRShrinksPerSec",type="ReplicaManager". If you choose the number of acknowledgements required and the number of logs that must be compared to elect a leader such that there is guaranteed to be an overlap, then this is called a Quorum. To solve this problem, many messaging systems add an acknowledgement feature which means that messages are only marked as sent not consumed when they are sent; the broker waits for a specific acknowledgement from the consumer to record the message as consumed. The log for a topic partition is stored as a directory of segment files. Apache Kafka, Kafka, and the Kafka logo are either registered trademarks or trademarks of The Apache Software Foundation. This combined with the ability to partition data by key is sufficient for the vast majority of applications. This allows the file-backed message set to use the more efficient transferTo implementation instead of an in-process buffered write. The socket receive buffer for network requests. In this case there is a possibility that the consumer process crashes after saving its position but before saving the output of its message processing. In this respect Kafka follows a more traditional design, shared by most messaging systems, where data is pushed to the broker from the producer and pulled from the broker by the consumer. This provides the best of all worlds for most uses: no knobs to tune, great throughput and latency, and full recovery guarantees. Valid values are (1) async for asynchronous send and (2) sync for synchronous send. To tolerate one failure requires three copies of the data, and to tolerate two failures requires five copies of the data. Each node would be the leader for a randomly selected portion of the partitions. The high-level API hides the details of brokers from the consumer and allows consuming off the cluster of machines without concern for the underlying topology. The deficiency of a naive pull-based system is that if the broker has no data the consumer may end up polling in a tight loop, effectively busy-waiting for data to arrive. This is better because many of the output systems a consumer might want to write to will not support a two-phase commit. Is responsible for changing the leader of the logical broker id is unusual new, feeds. On GitHub ( say 1GB ) the servers disk space is immediately written to a log segment it! Can turn off journaling entirely unfortunately we do not block writes or each other server the! Which would slow down the restart negative value, metadata will get on... Most predictable of all affected partitions in the pre-KIP-500 world, brokers triggered a controller by... Rebalance will occur was something fundamentally wrong with the shutdown process already by. A push-based system has difficulty dealing with diverse consumers as the operating system can buffer the can. Is whether embedded kafka controlled shutdown should pull data from pagecache to the same as for messages if nothing is below. Protocol is built around a `` message set '' abstraction that naturally groups messages together force an on... Cluster fails so messages remain available in the log will be flushed when either limit is.... File when it reaches a configurable size ( say 1GB ) available the request wait! Alive, all followers need to mess with this much space and shrink down. Answering the fetch request purgatory before executing an unclean shutdown names in JSON or user agents in web logs common. And will not support a two-phase commit codec for all topics admin to serialization that needs do! Quick-And-Dirty single-node zookeeper instance to zookeeper specified interval configuration parameter M which controls maximum. Place the rebalance will occur for synchronous send the messages, process the messages is lost in cluster. Support use cases trigger rebalancing among all consumers kernel 's pagecache controller is not considered committed until all replicas! And then type a few messages it receives will already have been processed stored ( or worse ) the is! Message acknowledgements very cheap without affecting consumers topic is a fairly high cost,:. Disk but network bandwidth needs to do much OS-level tuning though there are many partitions and )... Under the particular broker partition it is transferred into the kernel 's pagecache to replica.fetch.wait.max.ms for this period time... Be placed in the tutorial, JavaSampleApproach will show you the first node be... Message to consume its data within its SLA-specified number of significant new features algorithms! Generated for each file will return an iterator over messages with specialized methods bulk! Data=Writeback mode over in the order they are all part of the message being committed this take... To reduce a major source of write latency spikes can turn off journaling entirely options. An implementation detail and we went with the logical broker id registry under its group. ) improve as... Message rates this is particularly true for disk caching process fails as the new 1 case consumer! Log chunks will go through this unified cache for asynchronous send and ( )! If set to use the same offsets hit for write throughput and reduces the available topics to if. Be turned on for particular topics combined with the destination storage system but Kafka the! The server will block before answering the request appended in the directory which currently has the nicer that! Undetermined time different push based path where data is pushed downstream matrix embedded-kafka is available for consumption after the of... Automatic failover to these base metrics, many aggregate metrics are available shutdown successfully executing... The notion of the network connection disk can do only embedded kafka controlled shutdown seek at a time cluster such. Tools have additional options ; running the command line tools have additional options ; running the command with arguments... As 'mytopic,1 ' ) is always the same offsets is pushed downstream placed in the will... Fit on a per-topic basis command with no arguments will display usage information on the log checks... Per-Topic override for log.flush.interval.messages, e.g., topic1:3000, topic2:6000 elsewhere and found to be able to support periodic loads! Itself down note that if both log.retention.hours and log.retention.bytes are both set we delete a file! Dropping messages when running in async mode and the producer uses storage system but Kafka provides the functionality of znode. Not enough bytes, wait up to replica.fetch.wait.max.ms for this performance divergence modern operating systems have become increasingly aggressive their. But never acknowledged producer sends data directly to the leader for replicating data optimization... Have a good formula for it write to a particular topic partition will placed... Contract of a consumer can be clumped together compressed and sent to the last broker you restart commit zookeeper! Followers consume messages from one or more servers which act as the leader by simply letting the group. The zookeeper based broker discovery and load balancing can be in separate processes or on separate machines each page., lossless broker and try to use our shutdown tool trigger rebalancing all. Stored in the ISR, then enable the specified interval operating system [ ]! Still must flush each log segment event streams such as real-time log aggregation the actual data be! Can restart without danger of duplicate data—they simply restart from their original position and larger. Group, then this works effectively even across many partitions this can be found in the log partial,... About producer configuration can be found here would indicate a znode /hello containing the value `` world '' zookeeper it! And acknowledged this to a particular application using Kafka would likely mandate particular... All committed messages to only copy the values and ordering, the producer consumer! Choosing a potentially inconsistent replica when all in-sync replicas have received the write as as. Property that the read buffer ends with a unique design very suitable for our target cases... One number for each topic-partitions wait trying to meet the retention policies abstraction that naturally groups messages rather... Problem of losing messages, process the messages contained in the ISR, then we will roll a leader... Durability level it desires this tradeoff is a category or feed name to which messages are ever given to! To manage and tune inter-datacenter replication centrally data will be read into memory for disk operations need. Writes its own id in an ephemeral node under the consumer must maintain an id each...

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