Valid values are "none", "gzip" and "snappy".
The default is the no-op kafka.serializer.DefaultEncoder. This is a fairly intuitive choice, and indeed for a single machine server it is not clear where else this state could go. The most similar academic publication we are aware of to Kafka's actual implementation is PacificA from Microsoft. 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. For example /hello -> world would indicate a znode /hello containing the value "world". The partition reassignment tool can run in 3 mutually exclusive modes -, For instance, the following example will move all partitions for topics foo1,foo2 to the new set of brokers 5,6. A common approach to this tradeoff is to use a majority vote for both the commit decision and the leader election. This is because among any f+1 replicas, there must be at least one replica that contains all committed messages. A pull-based system has the nicer property that the consumer simply falls behind and catches up when it can. The average number of requests sent per second. The average size of all requests in the window. If so the consumer should use the same chroot path in its connection string. We upped the max socket buffer size to enable high-performance data transfer between data centers. The API encourages creating many topic streams in a single call in order to minimize this rebalancing. (Each change triggers rebalancing among all consumers in all consumer groups. This is a way to setup multiple Kafka clusters or other applications on the same ZooKeeper cluster. Using pagecache has several advantages over an in-process cache for storing data that will be written out to disk: It is not necessary to tune these settings, however those wanting to optimize performance have a few knobs that will help: The easiest way to see the available metrics to fire up jconsole and point it at a running kafka client or server; this will all browsing all metrics with JMX. This represents an SLA on how soon consumers must read their data. The broker also register the list of existing topics and their logical partitions in the broker topic registry. The protocol is kept quite simple to allow for future implementation of clients in other languages. This is an important factor for Kafka's usage model where there are many partitions and ensuring leadership balance is important. Important note: the refresh happen only AFTER the message is sent, so if the producer never sends a message the metadata is never refreshed.
Run the producer and then type a few messages into the console to send to the server. If the leader fails, one of the followers will automatically become the new leader. The minimum amount of data the server should return for a fetch request. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster. This setting accomplishes this by adding a small amount of artificial delaythat is, rather than immediately sending out a record the producer will wait for up to the given delay to allow other records to be sent so that the sends can be batched together. This means relying on the background flush done by the OS and Kafka's own background flush. Another advantage of a pull-based system is that it lends itself to aggressive batching of data sent to the consumer. Each consumer in the same group is given a shared group_id. These are not the strongest possible semantics for publishers. For example a setting of 100 will try to batch together 100ms of messages to send at once. Compaction will never re-order messages, just remove some. The client id is a user-specified string sent in each request to help trace calls. The average size of all requests in the window for a node. If your disk usage favors linear reads then read-ahead is effectively pre-populating this cache with useful data on each disk read. This dilemma is not specific to Kafka. For example in HDFS the namenode's high-availability feature is built on a majority-vote-based journal, but this more expensive approach is not used for the data itself. We have seen a few issues running on Windows and Windows is not currently a well supported platform though we would be happy to change that. Hostname of broker. Each consumer does the following during rebalancing: When rebalancing is triggered at one consumer, rebalancing should be triggered in other consumers within the same group about the same time. The client controls which partition it publishes messages to. More details about broker configuration can be found in the scala class kafka.server.KafkaConfig. Setting this to. Messages sent by a producer to a particular topic partition will be appended in the order they are sent. 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.
This corresponds to the "at-least-once" semantics in the case of consumer failure. The average number of bytes sent per partition per-request. Supporting these uses led use to a design with a number of unique elements, more akin to a database log then a traditional messaging system. Kafka is meant to be used with replication by defaultin fact we implement un-replicated topics as replicated topics where the replication factor is one. If you set this to a negative value, metadata will only get refreshed on failure. The status can be either of successfully completed, failed or in progress. First each partition must fit entirely on a single server. -1, The producer gets an acknowledgement after all in-sync replicas have received the data. It is the permanent identifier for a position in the log. The period of time we hold log files around after they are removed from the in-memory segment index. You should not need to change this. The average number of network operations (reads or writes) on all connections per second. Multiple consumers can form a group and jointly consume a single topic. The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions. 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). This parameter allows you to specify the compression codec for all data generated by this producer. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. The number of network threads that the server uses for handling network requests. Register a watch on changes (new consumers joining or any existing consumers leaving) under the consumer id registry. The drawback of using application level flush settings are that this is less efficient in it's disk usage pattern (it gives the OS less leeway to re-order writes) and it can introduce latency as fsync in most Linux filesystems blocks writes to the file whereas the background flushing does much more granular page-level locking. Specifically, how many other brokers must have committed the data to their log and acknowledged this to the leader? At low message rates this is not an issue, but under load the impact is significant.
kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec, kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec, kafka.network:type=RequestMetrics,name=RequestsPerSec,request={Produce|FetchConsumer|FetchFollower}, kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec, kafka.log:type=LogFlushStats,name=LogFlushRateAndTimeMs, # of under replicated partitions (|ISR| < |all replicas|), kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions, kafka.controller:type=KafkaController,name=ActiveControllerCount, only one broker in the cluster should have 1, kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs, kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec, kafka.server:type=ReplicaManager,name=PartitionCount, kafka.server:type=ReplicaManager,name=LeaderCount, kafka.server:type=ReplicaManager,name=IsrShrinksPerSec. When used together, min.insync.replicas and request.required.acks allow you to enforce greater durability guarantees. The threading model is a single acceptor thread and N processor threads which handle a fixed number of connections each. This setting controls the size to which a segment file will grow before a new segment is rolled over in the log. The average compression rate of record batches for a topic. It would need to deal gracefully with large data backlogs to be able to support periodic data loads from offline systems. Segment size for the offsets topic. This is required during migration from zookeeper-based offset storage to kafka-based offset storage. A consumer can look up its offset manager by issuing a ConsumerMetadataRequest to any Kafka broker and reading the ConsumerMetadataResponse which will contain the offset manager. 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. Typical values are. This majority vote approach has a very nice property: the latency is dependent on only the fastest servers. This retention policy can be set per-topic, so a single cluster can have some topics where retention is enforced by size or time and other topics where retention is enforced by compaction. expand-cluster-reassignment.json) to be input to the tool with the --execute option as follows-, Finally, the --verify option can be used with the tool to check the status of the partition reassignment. The average number of outgoing bytes sent per second for a node. In general you don't need to do any low-level tuning of the filesystem, but in the next few sections we will go over some of this in case it is useful. If the compression codec is anything other than NoCompressionCodec, enable compression only for specified topics if any.
The interval between checks to see if any logs need cleaning. Each broker is uniquely identified by a non-negative integer id. Usage information on the hadoop consumer can be found here. These linear reads and writes are the most predictable of all usage patterns, and are heavily optimized by the operating system. Possible values: range, roundrobin. This batch of messages will be written in compressed form and will remain compressed in the log and will only be decompressed by the consumer. Kafka does it better. i.e., any consumer instance in that consumer group should send its offset commits and fetches to that offset manager (broker). In general disk throughput is the performance bottleneck, and more disks is more better. 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. There are a rich variety of algorithms in this family including ZooKeeper's Zab, Raft, and Viewstamped Replication. The number of threads to use for cleaning logs in log compaction. When a window expires we erase and overwrite the oldest window.
Responses received sent per second for a node. Intuitively a persistent queue could be built on simple reads and appends to files as is commonly the case with logging solutions. No attempt will be made to batch records larger than this size.
In case the offset manager moves, the consumer will need to rediscover the offset manager. To avoid this we employ a standardized binary message format that is shared by the producer, the broker, and the consumer (so data chunks can be transferred without modification between them).
The default is the no-op kafka.serializer.DefaultEncoder. This is a fairly intuitive choice, and indeed for a single machine server it is not clear where else this state could go. The most similar academic publication we are aware of to Kafka's actual implementation is PacificA from Microsoft. 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. For example /hello -> world would indicate a znode /hello containing the value "world". The partition reassignment tool can run in 3 mutually exclusive modes -, For instance, the following example will move all partitions for topics foo1,foo2 to the new set of brokers 5,6. A common approach to this tradeoff is to use a majority vote for both the commit decision and the leader election. This is because among any f+1 replicas, there must be at least one replica that contains all committed messages. A pull-based system has the nicer property that the consumer simply falls behind and catches up when it can. The average number of requests sent per second. The average size of all requests in the window. If so the consumer should use the same chroot path in its connection string. We upped the max socket buffer size to enable high-performance data transfer between data centers. The API encourages creating many topic streams in a single call in order to minimize this rebalancing. (Each change triggers rebalancing among all consumers in all consumer groups. This is a way to setup multiple Kafka clusters or other applications on the same ZooKeeper cluster. Using pagecache has several advantages over an in-process cache for storing data that will be written out to disk: It is not necessary to tune these settings, however those wanting to optimize performance have a few knobs that will help: The easiest way to see the available metrics to fire up jconsole and point it at a running kafka client or server; this will all browsing all metrics with JMX. This represents an SLA on how soon consumers must read their data. The broker also register the list of existing topics and their logical partitions in the broker topic registry. The protocol is kept quite simple to allow for future implementation of clients in other languages. This is an important factor for Kafka's usage model where there are many partitions and ensuring leadership balance is important. Important note: the refresh happen only AFTER the message is sent, so if the producer never sends a message the metadata is never refreshed.
Run the producer and then type a few messages into the console to send to the server. If the leader fails, one of the followers will automatically become the new leader. The minimum amount of data the server should return for a fetch request. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster. This setting accomplishes this by adding a small amount of artificial delaythat is, rather than immediately sending out a record the producer will wait for up to the given delay to allow other records to be sent so that the sends can be batched together. This means relying on the background flush done by the OS and Kafka's own background flush. Another advantage of a pull-based system is that it lends itself to aggressive batching of data sent to the consumer. Each consumer in the same group is given a shared group_id. These are not the strongest possible semantics for publishers. For example a setting of 100 will try to batch together 100ms of messages to send at once. Compaction will never re-order messages, just remove some. The client id is a user-specified string sent in each request to help trace calls. The average size of all requests in the window for a node. If your disk usage favors linear reads then read-ahead is effectively pre-populating this cache with useful data on each disk read. This dilemma is not specific to Kafka. For example in HDFS the namenode's high-availability feature is built on a majority-vote-based journal, but this more expensive approach is not used for the data itself. We have seen a few issues running on Windows and Windows is not currently a well supported platform though we would be happy to change that. Hostname of broker. Each consumer does the following during rebalancing: When rebalancing is triggered at one consumer, rebalancing should be triggered in other consumers within the same group about the same time. The client controls which partition it publishes messages to. More details about broker configuration can be found in the scala class kafka.server.KafkaConfig. Setting this to. Messages sent by a producer to a particular topic partition will be appended in the order they are sent. 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.
This corresponds to the "at-least-once" semantics in the case of consumer failure. The average number of bytes sent per partition per-request. Supporting these uses led use to a design with a number of unique elements, more akin to a database log then a traditional messaging system. Kafka is meant to be used with replication by defaultin fact we implement un-replicated topics as replicated topics where the replication factor is one. If you set this to a negative value, metadata will only get refreshed on failure. The status can be either of successfully completed, failed or in progress. First each partition must fit entirely on a single server. -1, The producer gets an acknowledgement after all in-sync replicas have received the data. It is the permanent identifier for a position in the log. The period of time we hold log files around after they are removed from the in-memory segment index. You should not need to change this. The average number of network operations (reads or writes) on all connections per second. Multiple consumers can form a group and jointly consume a single topic. The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions. 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). This parameter allows you to specify the compression codec for all data generated by this producer. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. The number of network threads that the server uses for handling network requests. Register a watch on changes (new consumers joining or any existing consumers leaving) under the consumer id registry. The drawback of using application level flush settings are that this is less efficient in it's disk usage pattern (it gives the OS less leeway to re-order writes) and it can introduce latency as fsync in most Linux filesystems blocks writes to the file whereas the background flushing does much more granular page-level locking. Specifically, how many other brokers must have committed the data to their log and acknowledged this to the leader? At low message rates this is not an issue, but under load the impact is significant.
kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec, kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec, kafka.network:type=RequestMetrics,name=RequestsPerSec,request={Produce|FetchConsumer|FetchFollower}, kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec, kafka.log:type=LogFlushStats,name=LogFlushRateAndTimeMs, # of under replicated partitions (|ISR| < |all replicas|), kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions, kafka.controller:type=KafkaController,name=ActiveControllerCount, only one broker in the cluster should have 1, kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs, kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec, kafka.server:type=ReplicaManager,name=PartitionCount, kafka.server:type=ReplicaManager,name=LeaderCount, kafka.server:type=ReplicaManager,name=IsrShrinksPerSec. When used together, min.insync.replicas and request.required.acks allow you to enforce greater durability guarantees. The threading model is a single acceptor thread and N processor threads which handle a fixed number of connections each. This setting controls the size to which a segment file will grow before a new segment is rolled over in the log. The average compression rate of record batches for a topic. It would need to deal gracefully with large data backlogs to be able to support periodic data loads from offline systems. Segment size for the offsets topic. This is required during migration from zookeeper-based offset storage to kafka-based offset storage. A consumer can look up its offset manager by issuing a ConsumerMetadataRequest to any Kafka broker and reading the ConsumerMetadataResponse which will contain the offset manager. 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. Typical values are. This majority vote approach has a very nice property: the latency is dependent on only the fastest servers. This retention policy can be set per-topic, so a single cluster can have some topics where retention is enforced by size or time and other topics where retention is enforced by compaction. expand-cluster-reassignment.json) to be input to the tool with the --execute option as follows-, Finally, the --verify option can be used with the tool to check the status of the partition reassignment. The average number of outgoing bytes sent per second for a node. In general you don't need to do any low-level tuning of the filesystem, but in the next few sections we will go over some of this in case it is useful. If the compression codec is anything other than NoCompressionCodec, enable compression only for specified topics if any.
The interval between checks to see if any logs need cleaning. Each broker is uniquely identified by a non-negative integer id. Usage information on the hadoop consumer can be found here. These linear reads and writes are the most predictable of all usage patterns, and are heavily optimized by the operating system. Possible values: range, roundrobin. This batch of messages will be written in compressed form and will remain compressed in the log and will only be decompressed by the consumer. Kafka does it better. i.e., any consumer instance in that consumer group should send its offset commits and fetches to that offset manager (broker). In general disk throughput is the performance bottleneck, and more disks is more better. 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. There are a rich variety of algorithms in this family including ZooKeeper's Zab, Raft, and Viewstamped Replication. The number of threads to use for cleaning logs in log compaction. When a window expires we erase and overwrite the oldest window.
Responses received sent per second for a node. Intuitively a persistent queue could be built on simple reads and appends to files as is commonly the case with logging solutions. No attempt will be made to batch records larger than this size.
In case the offset manager moves, the consumer will need to rediscover the offset manager. To avoid this we employ a standardized binary message format that is shared by the producer, the broker, and the consumer (so data chunks can be transferred without modification between them).