Clave De Registro Para Easy Drive Data Recovery 3.0

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Clave De Registro Para Easy Drive Data Recovery 3.0

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Third, the NVMe interface can transfer much larger amounts of data per I/O, and in some cases may be able to support more outstanding I/O requests, compared to the Xen PV block interface. This can cause higher I/O latency if very large I/Os or a large number of I/O requests are issued to volumes designed to support throughput workloads like EBS Throughput Optimized HDD (st1) and Cold HDD (sc1) volumes. This I/O latency is normal for throughput optimized volumes in these scenarios, but may cause I/O timeouts in NVMe drivers. The I/O timeout can be adjusted in the Linux driver by specifying a larger value for the nvme_core.io_timeout kernel module parameter.

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Topics are added and modified using the topic tool: > bin/kafka-topics.sh --bootstrap-server broker_host:port --create --topic my_topic_name \ --partitions 20 --replication-factor 3 --config x=y The replication factor controls how many servers will replicate each message that is written. If you have a replication factor of 3 then up to 2 servers can fail before you will lose access to your data. We recommend you use a replication factor of 2 or 3 so that you can transparently bounce machines without interrupting data consumption. The partition count controls how many logs the topic will be sharded into. There are several impacts of the partition count. First each partition must fit entirely on a single server. So if you have 20 partitions the full data set (and read and write load) will be handled by no more than 20 servers (not counting replicas). Finally the partition count impacts the maximum parallelism of your consumers. This is discussed in greater detail in the concepts section. Each sharded partition log is placed into its own folder under the Kafka log directory. The name of such folders consists of the topic name, appended by a dash (-) and the partition id. Since a typical folder name can not be over 255 characters long, there will be a limitation on the length of topic names. We assume the number of partitions will not ever be above 100,000. Therefore, topic names cannot be longer than 249 characters. This leaves just enough room in the folder name for a dash and a potentially 5 digit long partition id. The configurations added on the command line override the default settings the server has for things like the length of time data should be retained. The complete set of per-topic configurations is documented here. Modifying topics You can change the configuration or partitioning of a topic using the same topic tool. To add partitions you can do > bin/kafka-topics.sh --bootstrap-server broker_host:port --alter --topic my_topic_name \ --partitions 40 Be aware that one use case for partitions is to semantically partition data, and adding partitions doesn't change the partitioning of existing data so this may disturb consumers if they rely on that partition. That is if data is partitioned by hash(key) % number_of_partitions then this partitioning will potentially be shuffled by adding partitions but Kafka will not attempt to automatically redistribute data in any way. To add configs: > bin/kafka-configs.sh --bootstrap-server broker_host:port --entity-type topics --entity-name my_topic_name --alter --add-config x=y To remove a config: > bin/kafka-configs.sh --bootstrap-server broker_host:port --entity-type topics --entity-name my_topic_name --alter --delete-config x And finally deleting a topic: > bin/kafka-topics.sh --bootstrap-server broker_host:port --delete --topic my_topic_name Kafka does not currently support reducing the number of partitions for a topic. Instructions for changing the replication factor of a topic can be found here. Graceful shutdown The Kafka cluster will automatically detect any broker shutdown or failure and elect new leaders for the partitions on that machine. This will occur whether a server fails or it is brought down intentionally for maintenance or configuration changes. For the latter cases Kafka supports a more graceful mechanism for stopping a server than just killing it. When a server is stopped gracefully it has two optimizations it will take advantage of: It will sync all its logs to disk to avoid needing to do any log recovery when it restarts (i.e. validating the checksum for all messages in the tail of the log). Log recovery takes time so this speeds up intentional restarts. It will migrate any partitions the server is the leader for to other replicas prior to shutting down. This will make the leadership transfer faster and minimize the time each partition is unavailable to a few milliseconds. Syncing the logs will happen automatically whenever the server is stopped other than by a hard kill, but the controlled leadership migration requires using a special setting: controlled.shutdown.enable=true Note that controlled shutdown will only succeed if all the partitions hosted on the broker have replicas (i.e. the replication factor is greater than 1 and at least one of these replicas is alive). This is generally what you want since shutting down the last replica would make that topic partition unavailable. Balancing leadership Whenever a broker stops or crashes, leadership for that broker's partitions transfers to other replicas. When the broker is restarted it will only be a follower for all its partitions, meaning it will not be used for client reads and writes. To avoid this imbalance, Kafka has a notion of preferred replicas. If the list of replicas for a partition is 1,5,9 then node 1 is preferred as the leader to either node 5 or 9 because it is earlier in the replica list. By default the Kafka cluster will try to restore leadership to the preferred replicas. This behaviour is configured with: auto.leader.rebalance.enable=true You can also set this to false, but you will then need to manually restore leadership to the restored replicas by running the command: > bin/kafka-leader-election.sh --bootstrap-server broker_host:port --election-type preferred --all-topic-partitions Balancing Replicas Across Racks The rack awareness feature spreads replicas of the same partition across different racks. This extends the guarantees Kafka provides for broker-failure to cover rack-failure, limiting the risk of data loss should all the brokers on a rack fail at once. The feature can also be applied to other broker groupings such as availability zones in EC2. 2ff7e9595c