# Storm worker 并行度等理解

Storm 调优是非常重要的, 仅次于写出正确的代码, 好在Storm官网上有关于worker executors tasks的介绍, http://storm.incubator.apache.org/documentation/Understanding-the-parallelism-of-a-Storm-topology.html

# What makes a running topology: worker processes, executors and tasks

worker process executes a subset of a topology, and runs in its own JVM. A worker process belongs to a specific topology and may run one or more executors for one or more components (spouts or bolts) of this topology. A running topology consists of many such processes running on many machines within a Storm cluster.

An executor is a thread that is spawned by a worker process and runs within the worker’s JVM. An executor may run one or more tasks for the same component (spout or bolt). An executor always has one thread that it uses for all of its tasks, which means that tasks run serially on an executor.

task performs the actual data processing and is run within its parent executor’s thread of execution. Each spout or bolt that you implement in your code executes as many tasks across the cluster. The number of tasks for a component is always the same throughout the lifetime of a topology, but the number of executors (threads) for a component can change over time. This means that the following condition holds true: #threads <= #tasks. By default, the number of tasks is set to be the same as the number of executors, i.e. Storm will run one task per thread (which is usually what you want anyways).

Also be aware that:

• The number of executor threads can be changed after the topology has been started (see storm rebalance command below).
• The number of tasks of a topology is static.

See Understanding the Internal Message Buffers of Storm for another view on the various threads that are running within the lifetime of a worker process and its associated executors and tasks.