Enable auto creation of topic on the server. There are pros and cons to both approaches. A string that uniquely identifies the group of consumer processes to which this consumer belongs. This file is rolled over to a fresh file when it reaches a configurable size (say 1GB). There are many remaining details that each algorithm must handle (such as precisely defined what makes a log more complete, ensuring log consistency during leader failure or changing the set of servers in the replica set) but we will ignore these for now. The amount of time the broker will wait trying to meet the request.required.acks requirement before sending back an error to the client. For some applications, however, the dependence on zookeeper is inappropriate. Producers, on the other hand, have the option of either waiting for the message to be committed or not, depending on their preference for tradeoff between latency and durability. The first node to be restarted was the controller. For now let's assume a perfect, lossless broker and try to understand the guarantees to the producer and consumer. The fundamental guarantee a log replication algorithm must provide is that if we tell the client a message is committed, and the leader fails, the new leader we elect must also have that message. 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 socket timeout for network requests. When recovering from a crash for any log segment not known to be fsync'd Kafka will check the integrity of each message by checking its CRC and also rebuild the accompanying offset index file as part of the recovery process executed on startup. The I/O scheduler will batch together consecutive small writes into bigger physical writes which improves throughput. the replication factor is greater than 1 and at least one of these replicas is alive). The low-level "simple" API maintains a connection to a single broker and has a close correspondence to the network requests sent to the server. Batching is one of the big drivers of efficiency, and to enable batching the Kafka producer has an asynchronous mode that accumulates data in memory and sends out larger batches in a single request. The small I/O problem happens both between the client and the server and in the server's own persistent operations. This makes the state about what has been consumed very small, just one number for each partition. Since storage systems mix very fast cached operations with very slow physical disk operations, the observed performance of tree structures is often superlinear as data increases with fixed cache--i.e. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster. As expected, when it came back up it took some time re-syncing it's partitions, but eventually came back and all was well. This API is completely stateless, with the offset being passed in on every request, allowing the user to maintain this metadata however they choose. State change are executed by different listeners concurrently. You could imagine other possible designs which would be only pull, end-to-end. For Kafka node liveness has two conditions. This definition above actually makes no reference to the notion of consumers or partitions. Tricky problems must be dealt with, like what to do with messages that are sent but never acknowledged. In this case the process that took over processing would start at the saved position even though a few messages prior to that position had not been processed. When the leader does die we need to choose a new leader from among the followers. The topic level properties have the format of csv (e.g., "xyz.per.topic=topic1:value1,topic2:value2") and they override the default value for the specified topics. So what about exactly once semantics (i.e. Clearly there are multiple possible message delivery guarantees that could be provided: Many systems claim to provide "exactly once" delivery semantics, but it is important to read the fine print, most of these claims are misleading (i.e. However the producer can also specify that it wants to perform the send completely asynchronously or that it wants to wait only until the leader (but not necessarily the followers) have the message. all partitions consumed, "kafka.consumer":name="([-.\w]+)-MaxLag",type="ConsumerFetcherManager", The min fetch rate among all fetchers to brokers in a consumer, "kafka.consumer":name="([-.\w]+)-MinFetch",type="ConsumerFetcherManager", Kafka maintains feeds of messages in categories called, We'll call processes that publish messages to a Kafka topic, We'll call processes that subscribe to topics and process the feed of published messages, Kafka is run as a cluster comprised of one or more servers each of which is called a. The default is the no-op kafka.serializer.DefaultEncoder. BTrees are the most versatile data structure available, and make it possible to support a wide variety of transactional and non-transactional semantics in the messaging system. The memory overhead of objects is very high, often doubling the size of the data stored (or worse). It can read the messages, then save its position in the log, and finally process the messages. This allows the accumulation of more bytes to send, and few larger I/O operations on the servers. By being very fast we help ensure that the application will tip-over under load before the infrastructure. The following gives the zookeeper structures and algorithms used for co-ordination between consumers and brokers. The socket timeout for commands from the partition management controller to the replicas. Setting this higher will improve performance a lot but will increase the window of data at risk in the event of a crash (though that is usually best addressed through replication). The leader handles all read and write requests for the partition while the followers passively replicate the leader. Note that we will always pre-allocate a sparse file with this much space and shrink it down when the log rolls. çäºä¸æ¥å¿ï¼å°±çå°ä»shutdownäºï¼å ³é®æ¯ä¹æ²¡äººæä½å [2016-07-21 11:04:10,285] INFO [Group Metadata Manager on Broker 0]: Removed 0 expired offsets in 0 milliseconds. Since leader election takes a bit of time, this property specifies the amount of time that the producer waits before refreshing the metadata. This combination of features means that Kafka consumers are very cheap—they can come and go without much impact on the cluster or on other consumers. It would need to deal gracefully with large data backlogs to be able to support periodic data loads from offline systems. In effect this just means that it is transferred into the kernel's pagecache. It is important that this property be in sync with the maximum fetch size your consumers use or else an unruly producer will be able to publish messages too large for consumers to consume. Although we cannot be sure of what happened in the case of a network error, it is possible to allow the producer to generate a sort of "primary key" that makes retrying the produce request idempotent. The amount of data to retain in the log for each topic-partitions. The amount of time to block before dropping messages when running in async mode and the buffer has reached queue.buffering.max.messages. 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 API that wraps the 2 low-level producers - kafka.producer.SyncProducer and kafka.producer.async.AsyncProducer. The search is done as a simple binary search variation against an in-memory range maintained for each file. A similar type of "store-and-forward" producer is often proposed. Using the zero-copy optimization above, data is copied into pagecache exactly once and reused on each consumption instead of being stored in memory and copied out to kernel space every time it is read. An embedded controller (EC) is a microcontroller in computers that handles various system tasks that the operating system does not handle. A pull-based system has the nicer property that the consumer simply falls behind and catches up when it can. Consumer instances can be in separate processes or on separate machines. The message log maintained by the broker is itself just a directory of files, each populated by a sequence of message sets that have been written to disk in the same format used by the producer and consumer. Valid values are (1) async for asynchronous send and (2) sync for synchronous send. Reference information for Kafka Broker Metrics. This leads to a great deal of flexibility for consumers, as we will describe. To do this give a connection string in the form hostname1:port1,hostname2:port2,hostname3:port3/chroot/path which would put all this cluster's data under the path /chroot/path. This allows the file-backed message set to use the more efficient transferTo implementation instead of an in-process buffered write. First let's review some basic messaging terminology: 1. When a broker joins, it registers itself under the broker node registry directory and writes information about its host name and port. If the consumer fails to heartbeat to zookeeper for this period of time it is considered dead and a rebalance will occur. This property will cause the producer to automatically retry a failed send request. This parameter allows you to set whether compression should be turned on for particular topics. Once a published message is committed it will not be lost as long as one broker that replicates the partition to which this message was written remains "alive". 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. The frequency with which each replica saves its high watermark to disk to handle recovery. We are Leaving the payload opaque is the right decision: there is a great deal of progress being made on serialization libraries right now, and any particular choice is unlikely to be right for all uses. 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