ackers

corresponding Acker. For example When a tuple is created in a topology, whether in a spout or a bolt, it is given a random 64 bit ID. these IDS are used by ackers to track the tuple Dag for every spout tuple. Every tuple knows the IDs of all the spout tuples for which it exists in their tuple trees. when you emit a new tuple in a bolt, the spout tuple IDs from the tuple's anchors are copied into the new tuple. when a tuple is acked, it sends a messag

is set to true, storm records each piece of information sent. Topology. Does optimize master optimize topologies by running multiple tasks in a single thread at the appropriate time. The number of processes that should be started when topology. Workers executes the topology cluster. Each process internally executes a certain number of tasks in the thread mode. The topology component is combined with the parameter and concurrency prompt to optimize the performance. Topology. number of Acker t

. Refresh. Poll. secs The frequency of link synchronization between tasks and other tasks. (if the task is re-allocated, other tasks need to refresh the connection to send messages to it ). generally, other tasks will be notified when the reallocation occurs. This configuration is only used to prevent notifications. Topology. Debug If it is set to true, storm records each piece of information that is sent. Topology. Optimize Whether the master can optimize topologies b

total, and 12 ackers are allocated first to ensure that they can be placed in different processes and each supervisor has 2 acker; 18 bolt-tasks are assigned, the six tasks are in the same process as the acker, And the last 10 spout-tasks are allocated. They should be in the same process as the bolt-task)3. Custom Task Scheduling Jstorm enables custom scheduling. The ConfigExtension. setUserDefineAssignment interface is provided to you. You only need

be 6 threads in each process (150/25 = 6).2)config.topology_ackers: This configuration sets the number of Acker threads. Ackers is part of the Storm's reliability API, and the reliability API for Storm can be seen: How Twitter storm ensures that messages are not lost.3)config.topology_max_spout_pending: This setting is a SPOUT task up to the maximum number of non-processed tuple (no ack/failed) reply, we recommend you set this configuration, To preve

I have some questions about this. I have carefully reviewed it and recorded it. The prerequisite for enabling the storm tracker mechanism is, 1. When spout emit tuple is added, the 3rd parameter messageid must be added.2. In the configuration, the number of ackers must be at least 1.3. Add the second anchor tuple parameter to bolt emit to maintain the tracker link. Process, 1. When tuple has a messageid, spout adds the tuple to the pending lis

) (let[out-tasks (ifout-task-id (tasks-fnout-task-id out-stream-id values) (tasks-fnout-stream-id values)) rooted? (andmessage-id has-ackers?) root-id (ifrooted? (MessageId/generateId rand))

time the processing is finished. The Acker to be aggregated tells the root node that all the tuple processing is done). We can set the number of ACKER by Config.topology_acker_executors. Storm default Acker is the same number as the number of worker. --when we need to deal with a lot of messages, we may need to increase the amount of this Acker. The best way to understand the reliability of storm is to study the life cycle of tuples and the tuple dag. When a tuple is generated in topology, eith

topology. For example, if you set it to 25, there will be a total of 25 Java processes in the cluster to execute all of the topology's tasks. If all the components in your topology add up to a total of 150 parallelism, then there will be 6 threads in each process (150/25 = 6). 2)config.topology_ackers: This configuration sets the number of Acker threads. Ackers is part of the Storm's reliability API, and the reliability API for Storm can be seen: How