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----
-title: Tutorial
-layout: documentation
-documentation: true
----
-In this tutorial, you'll learn how to create Storm topologies and deploy them to a Storm cluster. Java will be the main language used, but a few examples will use Python to illustrate Storm's multi-language capabilities.
-
-## Preliminaries
-
-This tutorial uses examples from the [storm-starter](https://github.com/apache/storm/blob/master/examples/storm-starter) project. It's recommended that you clone the project and follow along with the examples. Read [Setting up a development environment](Setting-up-development-environment.html) and [Creating a new Storm project](Creating-a-new-Storm-project.html) to get your machine set up.
-
-## Components of a Storm cluster
-
-A Storm cluster is superficially similar to a Hadoop cluster. Whereas on Hadoop you run "MapReduce jobs", on Storm you run "topologies". "Jobs" and "topologies" themselves are very different -- one key difference is that a MapReduce job eventually finishes, whereas a topology processes messages forever (or until you kill it).
-
-There are two kinds of nodes on a Storm cluster: the master node and the worker nodes. The master node runs a daemon called "Nimbus" that is similar to Hadoop's "JobTracker". Nimbus is responsible for distributing code around the cluster, assigning tasks to machines, and monitoring for failures.
-
-Each worker node runs a daemon called the "Supervisor". The supervisor listens for work assigned to its machine and starts and stops worker processes as necessary based on what Nimbus has assigned to it. Each worker process executes a subset of a topology; a running topology consists of many worker processes spread across many machines.
-
-
-
-All coordination between Nimbus and the Supervisors is done through a [Zookeeper](http://zookeeper.apache.org/) cluster. Additionally, the Nimbus daemon and Supervisor daemons are fail-fast and stateless; all state is kept in Zookeeper or on local disk. This means you can kill -9 Nimbus or the Supervisors and they'll start back up like nothing happened. This design leads to Storm clusters being incredibly stable.
-
-## Topologies
-
-To do realtime computation on Storm, you create what are called "topologies". A topology is a graph of computation. Each node in a topology contains processing logic, and links between nodes indicate how data should be passed around between nodes.
-
-Running a topology is straightforward. First, you package all your code and dependencies into a single jar. Then, you run a command like the following:
-
-```
-storm jar all-my-code.jar backtype.storm.MyTopology arg1 arg2
-```
-
-This runs the class `backtype.storm.MyTopology` with the arguments `arg1` and `arg2`. The main function of the class defines the topology and submits it to Nimbus. The `storm jar` part takes care of connecting to Nimbus and uploading the jar.
-
-Since topology definitions are just Thrift structs, and Nimbus is a Thrift service, you can create and submit topologies using any programming language. The above example is the easiest way to do it from a JVM-based language. See [Running topologies on a production cluster](Running-topologies-on-a-production-cluster.html) for more information on starting and stopping topologies.
-
-## Streams
-
-The core abstraction in Storm is the "stream". A stream is an unbounded sequence of tuples. Storm provides the primitives for transforming a stream into a new stream in a distributed and reliable way. For example, you may transform a stream of tweets into a stream of trending topics.
-
-The basic primitives Storm provides for doing stream transformations are "spouts" and "bolts". Spouts and bolts have interfaces that you implement to run your application-specific logic.
-
-A spout is a source of streams. For example, a spout may read tuples off of a [Kestrel](http://github.com/nathanmarz/storm-kestrel) queue and emit them as a stream. Or a spout may connect to the Twitter API and emit a stream of tweets.
-
-A bolt consumes any number of input streams, does some processing, and possibly emits new streams. Complex stream transformations, like computing a stream of trending topics from a stream of tweets, require multiple steps and thus multiple bolts. Bolts can do anything from run functions, filter tuples, do streaming aggregations, do streaming joins, talk to databases, and more.
-
-Networks of spouts and bolts are packaged into a "topology" which is the top-level abstraction that you submit to Storm clusters for execution. A topology is a graph of stream transformations where each node is a spout or bolt. Edges in the graph indicate which bolts are subscribing to which streams. When a spout or bolt emits a tuple to a stream, it sends the tuple to every bolt that subscribed to that stream.
-
-
-
-Links between nodes in your topology indicate how tuples should be passed around. For example, if there is a link between Spout A and Bolt B, a link from Spout A to Bolt C, and a link from Bolt B to Bolt C, then everytime Spout A emits a tuple, it will send the tuple to both Bolt B and Bolt C. All of Bolt B's output tuples will go to Bolt C as well.
-
-Each node in a Storm topology executes in parallel. In your topology, you can specify how much parallelism you want for each node, and then Storm will spawn that number of threads across the cluster to do the execution.
-
-A topology runs forever, or until you kill it. Storm will automatically reassign any failed tasks. Additionally, Storm guarantees that there will be no data loss, even if machines go down and messages are dropped.
-
-## Data model
-
-Storm uses tuples as its data model. A tuple is a named list of values, and a field in a tuple can be an object of any type. Out of the box, Storm supports all the primitive types, strings, and byte arrays as tuple field values. To use an object of another type, you just need to implement [a serializer](Serialization.html) for the type.
-
-Every node in a topology must declare the output fields for the tuples it emits. For example, this bolt declares that it emits 2-tuples with the fields "double" and "triple":
-
-```java
-public class DoubleAndTripleBolt extends BaseRichBolt {
- private OutputCollectorBase _collector;
-
- @Override
- public void prepare(Map conf, TopologyContext context, OutputCollectorBase collector) {
- _collector = collector;
- }
-
- @Override
- public void execute(Tuple input) {
- int val = input.getInteger(0);
- _collector.emit(input, new Values(val*2, val*3));
- _collector.ack(input);
- }
-
- @Override
- public void declareOutputFields(OutputFieldsDeclarer declarer) {
- declarer.declare(new Fields("double", "triple"));
- }
-}
-```
-
-The `declareOutputFields` function declares the output fields `["double", "triple"]` for the component. The rest of the bolt will be explained in the upcoming sections.
-
-## A simple topology
-
-Let's take a look at a simple topology to explore the concepts more and see how the code shapes up. Let's look at the `ExclamationTopology` definition from storm-starter:
-
-```java
-TopologyBuilder builder = new TopologyBuilder();
-builder.setSpout("words", new TestWordSpout(), 10);
-builder.setBolt("exclaim1", new ExclamationBolt(), 3)
- .shuffleGrouping("words");
-builder.setBolt("exclaim2", new ExclamationBolt(), 2)
- .shuffleGrouping("exclaim1");
-```
-
-This topology contains a spout and two bolts. The spout emits words, and each bolt appends the string "!!!" to its input. The nodes are arranged in a line: the spout emits to the first bolt which then emits to the second bolt. If the spout emits the tuples ["bob"] and ["john"], then the second bolt will emit the words ["bob!!!!!!"] and ["john!!!!!!"].
-
-This code defines the nodes using the `setSpout` and `setBolt` methods. These methods take as input a user-specified id, an object containing the processing logic, and the amount of parallelism you want for the node. In this example, the spout is given id "words" and the bolts are given ids "exclaim1" and "exclaim2".
-
-The object containing the processing logic implements the [IRichSpout](/javadoc/apidocs/backtype/storm/topology/IRichSpout.html) interface for spouts and the [IRichBolt](/javadoc/apidocs/backtype/storm/topology/IRichBolt.html) interface for bolts.
-
-The last parameter, how much parallelism you want for the node, is optional. It indicates how many threads should execute that component across the cluster. If you omit it, Storm will only allocate one thread for that node.
-
-`setBolt` returns an [InputDeclarer](/javadoc/apidocs/backtype/storm/topology/InputDeclarer.html) object that is used to define the inputs to the Bolt. Here, component "exclaim1" declares that it wants to read all the tuples emitted by component "words" using a shuffle grouping, and component "exclaim2" declares that it wants to read all the tuples emitted by component "exclaim1" using a shuffle grouping. "shuffle grouping" means that tuples should be randomly distributed from the input tasks to the bolt's tasks. There are many ways to group data between components. These will be explained in a few sections.
-
-If you wanted component "exclaim2" to read all the tuples emitted by both component "words" and component "exclaim1", you would write component "exclaim2"'s definition like this:
-
-```java
-builder.setBolt("exclaim2", new ExclamationBolt(), 5)
- .shuffleGrouping("words")
- .shuffleGrouping("exclaim1");
-```
-
-As you can see, input declarations can be chained to specify multiple sources for the Bolt.
-
-Let's dig into the implementations of the spouts and bolts in this topology. Spouts are responsible for emitting new messages into the topology. `TestWordSpout` in this topology emits a random word from the list ["nathan", "mike", "jackson", "golda", "bertels"] as a 1-tuple every 100ms. The implementation of `nextTuple()` in TestWordSpout looks like this:
-
-```java
-public void nextTuple() {
- Utils.sleep(100);
- final String[] words = new String[] {"nathan", "mike", "jackson", "golda", "bertels"};
- final Random rand = new Random();
- final String word = words[rand.nextInt(words.length)];
- _collector.emit(new Values(word));
-}
-```
-
-As you can see, the implementation is very straightforward.
-
-`ExclamationBolt` appends the string "!!!" to its input. Let's take a look at the full implementation for `ExclamationBolt`:
-
-```java
-public static class ExclamationBolt implements IRichBolt {
- OutputCollector _collector;
-
- @Override
- public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
- _collector = collector;
- }
-
- @Override
- public void execute(Tuple tuple) {
- _collector.emit(tuple, new Values(tuple.getString(0) + "!!!"));
- _collector.ack(tuple);
- }
-
- @Override
- public void cleanup() {
- }
-
- @Override
- public void declareOutputFields(OutputFieldsDeclarer declarer) {
- declarer.declare(new Fields("word"));
- }
-
- @Override
- public Map<String, Object> getComponentConfiguration() {
- return null;
- }
-}
-```
-
-The `prepare` method provides the bolt with an `OutputCollector` that is used for emitting tuples from this bolt. Tuples can be emitted at anytime from the bolt -- in the `prepare`, `execute`, or `cleanup` methods, or even asynchronously in another thread. This `prepare` implementation simply saves the `OutputCollector` as an instance variable to be used later on in the `execute` method.
-
-The `execute` method receives a tuple from one of the bolt's inputs. The `ExclamationBolt` grabs the first field from the tuple and emits a new tuple with the string "!!!" appended to it. If you implement a bolt that subscribes to multiple input sources, you can find out which component the [Tuple](/javadoc/apidocs/backtype/storm/tuple/Tuple.html) came from by using the `Tuple#getSourceComponent` method.
-
-There's a few other things going on in the `execute` method, namely that the input tuple is passed as the first argument to `emit` and the input tuple is acked on the final line. These are part of Storm's reliability API for guaranteeing no data loss and will be explained later in this tutorial.
-
-The `cleanup` method is called when a Bolt is being shutdown and should cleanup any resources that were opened. There's no guarantee that this method will be called on the cluster: for example, if the machine the task is running on blows up, there's no way to invoke the method. The `cleanup` method is intended for when you run topologies in [local mode](Local-mode.html) (where a Storm cluster is simulated in process), and you want to be able to run and kill many topologies without suffering any resource leaks.
-
-The `declareOutputFields` method declares that the `ExclamationBolt` emits 1-tuples with one field called "word".
-
-The `getComponentConfiguration` method allows you to configure various aspects of how this component runs. This is a more advanced topic that is explained further on [Configuration](Configuration.html).
-
-Methods like `cleanup` and `getComponentConfiguration` are often not needed in a bolt implementation. You can define bolts more succinctly by using a base class that provides default implementations where appropriate. `ExclamationBolt` can be written more succinctly by extending `BaseRichBolt`, like so:
-
-```java
-public static class ExclamationBolt extends BaseRichBolt {
- OutputCollector _collector;
-
- @Override
- public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
- _collector = collector;
- }
-
- @Override
- public void execute(Tuple tuple) {
- _collector.emit(tuple, new Values(tuple.getString(0) + "!!!"));
- _collector.ack(tuple);
- }
-
- @Override
- public void declareOutputFields(OutputFieldsDeclarer declarer) {
- declarer.declare(new Fields("word"));
- }
-}
-```
-
-## Running ExclamationTopology in local mode
-
-Let's see how to run the `ExclamationTopology` in local mode and see that it's working.
-
-Storm has two modes of operation: local mode and distributed mode. In local mode, Storm executes completely in process by simulating worker nodes with threads. Local mode is useful for testing and development of topologies. When you run the topologies in storm-starter, they'll run in local mode and you'll be able to see what messages each component is emitting. You can read more about running topologies in local mode on [Local mode](Local-mode.html).
-
-In distributed mode, Storm operates as a cluster of machines. When you submit a topology to the master, you also submit all the code necessary to run the topology. The master will take care of distributing your code and allocating workers to run your topology. If workers go down, the master will reassign them somewhere else. You can read more about running topologies on a cluster on [Running topologies on a production cluster](Running-topologies-on-a-production-cluster.html)].
-
-Here's the code that runs `ExclamationTopology` in local mode:
-
-```java
-Config conf = new Config();
-conf.setDebug(true);
-conf.setNumWorkers(2);
-
-LocalCluster cluster = new LocalCluster();
-cluster.submitTopology("test", conf, builder.createTopology());
-Utils.sleep(10000);
-cluster.killTopology("test");
-cluster.shutdown();
-```
-
-First, the code defines an in-process cluster by creating a `LocalCluster` object. Submitting topologies to this virtual cluster is identical to submitting topologies to distributed clusters. It submits a topology to the `LocalCluster` by calling `submitTopology`, which takes as arguments a name for the running topology, a configuration for the topology, and then the topology itself.
-
-The name is used to identify the topology so that you can kill it later on. A topology will run indefinitely until you kill it.
-
-The configuration is used to tune various aspects of the running topology. The two configurations specified here are very common:
-
-1. **TOPOLOGY_WORKERS** (set with `setNumWorkers`) specifies how many _processes_ you want allocated around the cluster to execute the topology. Each component in the topology will execute as many _threads_. The number of threads allocated to a given component is configured through the `setBolt` and `setSpout` methods. Those _threads_ exist within worker _processes_. Each worker _process_ contains within it some number of _threads_ for some number of components. For instance, you may have 300 threads specified across all your components and 50 worker processes specified in your config. Each worker process will execute 6 threads, each of which of could belong to a different component. You tune the performance of Storm topologies by tweaking the parallelism for each component and the number of worker processes those threads should run within.
-2. **TOPOLOGY_DEBUG** (set with `setDebug`), when set to true, tells Storm to log every message every emitted by a component. This is useful in local mode when testing topologies, but you probably want to keep this turned off when running topologies on the cluster.
-
-There's many other configurations you can set for the topology. The various configurations are detailed on [the Javadoc for Config](/javadoc/apidocs/backtype/storm/Config.html).
-
-To learn about how to set up your development environment so that you can run topologies in local mode (such as in Eclipse), see [Creating a new Storm project](Creating-a-new-Storm-project.html).
-
-## Stream groupings
-
-A stream grouping tells a topology how to send tuples between two components. Remember, spouts and bolts execute in parallel as many tasks across the cluster. If you look at how a topology is executing at the task level, it looks something like this:
-
-
-
-When a task for Bolt A emits a tuple to Bolt B, which task should it send the tuple to?
-
-A "stream grouping" answers this question by telling Storm how to send tuples between sets of tasks. Before we dig into the different kinds of stream groupings, let's take a look at another topology from [storm-starter](http://github.com/apache/storm/blob/master/examples/storm-starter). This [WordCountTopology](https://github.com/apache/storm/blob/master/examples/storm-starter/src/jvm/storm/starter/WordCountTopology.java) reads sentences off of a spout and streams out of `WordCountBolt` the total number of times it has seen that word before:
-
-```java
-TopologyBuilder builder = new TopologyBuilder();
-
-builder.setSpout("sentences", new RandomSentenceSpout(), 5);
-builder.setBolt("split", new SplitSentence(), 8)
- .shuffleGrouping("sentences");
-builder.setBolt("count", new WordCount(), 12)
- .fieldsGrouping("split", new Fields("word"));
-```
-
-`SplitSentence` emits a tuple for each word in each sentence it receives, and `WordCount` keeps a map in memory from word to count. Each time `WordCount` receives a word, it updates its state and emits the new word count.
-
-There's a few different kinds of stream groupings.
-
-The simplest kind of grouping is called a "shuffle grouping" which sends the tuple to a random task. A shuffle grouping is used in the `WordCountTopology` to send tuples from `RandomSentenceSpout` to the `SplitSentence` bolt. It has the effect of evenly distributing the work of processing the tuples across all of `SplitSentence` bolt's tasks.
-
-A more interesting kind of grouping is the "fields grouping". A fields grouping is used between the `SplitSentence` bolt and the `WordCount` bolt. It is critical for the functioning of the `WordCount` bolt that the same word always go to the same task. Otherwise, more than one task will see the same word, and they'll each emit incorrect values for the count since each has incomplete information. A fields grouping lets you group a stream by a subset of its fields. This causes equal values for that subset of fields to go to the same task. Since `WordCount` subscribes to `SplitSentence`'s output stream using a fields grouping on the "word" field, the same word always goes to the same task and the bolt produces the correct output.
-
-Fields groupings are the basis of implementing streaming joins and streaming aggregations as well as a plethora of other use cases. Underneath the hood, fields groupings are implemented using mod hashing.
-
-There's a few other kinds of stream groupings. You can read more about them on [Concepts](Concepts.html).
-
-## Defining Bolts in other languages
-
-Bolts can be defined in any language. Bolts written in another language are executed as subprocesses, and Storm communicates with those subprocesses with JSON messages over stdin/stdout. The communication protocol just requires an ~100 line adapter library, and Storm ships with adapter libraries for Ruby, Python, and Fancy.
-
-Here's the definition of the `SplitSentence` bolt from `WordCountTopology`:
-
-```java
-public static class SplitSentence extends ShellBolt implements IRichBolt {
- public SplitSentence() {
- super("python", "splitsentence.py");
- }
-
- public void declareOutputFields(OutputFieldsDeclarer declarer) {
- declarer.declare(new Fields("word"));
- }
-}
-```
-
-`SplitSentence` overrides `ShellBolt` and declares it as running using `python` with the arguments `splitsentence.py`. Here's the implementation of `splitsentence.py`:
-
-```python
-import storm
-
-class SplitSentenceBolt(storm.BasicBolt):
- def process(self, tup):
- words = tup.values[0].split(" ")
- for word in words:
- storm.emit([word])
-
-SplitSentenceBolt().run()
-```
-
-For more information on writing spouts and bolts in other languages, and to learn about how to create topologies in other languages (and avoid the JVM completely), see [Using non-JVM languages with Storm](Using-non-JVM-languages-with-Storm.html).
-
-## Guaranteeing message processing
-
-Earlier on in this tutorial, we skipped over a few aspects of how tuples are emitted. Those aspects were part of Storm's reliability API: how Storm guarantees that every message coming off a spout will be fully processed. See [Guaranteeing message processing](Guaranteeing-message-processing.html) for information on how this works and what you have to do as a user to take advantage of Storm's reliability capabilities.
-
-## Transactional topologies
-
-Storm guarantees that every message will be played through the topology at least once. A common question asked is "how do you do things like counting on top of Storm? Won't you overcount?" Storm has a feature called transactional topologies that let you achieve exactly-once messaging semantics for most computations. Read more about transactional topologies [here](Transactional-topologies.html).
-
-## Distributed RPC
-
-This tutorial showed how to do basic stream processing on top of Storm. There's lots more things you can do with Storm's primitives. One of the most interesting applications of Storm is Distributed RPC, where you parallelize the computation of intense functions on the fly. Read more about Distributed RPC [here](Distributed-RPC.html).
-
-## Conclusion
-
-This tutorial gave a broad overview of developing, testing, and deploying Storm topologies. The rest of the documentation dives deeper into all the aspects of using Storm.
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/Understanding-the-parallelism-of-a-Storm-topology.md
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----
-title: Understanding the Parallelism of a Storm Topology
-layout: documentation
-documentation: true
----
-## What makes a running topology: worker processes, executors and tasks
-
-Storm distinguishes between the following three main entities that are used to actually run a topology in a Storm cluster:
-
-1. Worker processes
-2. Executors (threads)
-3. Tasks
-
-Here is a simple illustration of their relationships:
-
-
-
-A _worker process_ executes a subset of a topology. 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. It may run one or more tasks for the same component (spout or bolt).
-
-A _task_ performs the actual data processing — 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.
-
-## Configuring the parallelism of a topology
-
-Note that in Storm’s terminology "parallelism" is specifically used to describe the so-called _parallelism hint_, which means the initial number of executor (threads) of a component. In this document though we use the term "parallelism" in a more general sense to describe how you can configure not only the number of executors but also the number of worker processes and the number of tasks of a Storm topology. We will specifically call out when "parallelism" is used in the normal, narrow definition of Storm.
-
-The following sections give an overview of the various configuration options and how to set them in your code. There is more than one way of setting these options though, and the table lists only some of them. Storm currently has the following [order of precedence for configuration settings](Configuration.html): ``defaults.yaml`` < ``storm.yaml`` < topology-specific configuration < internal component-specific configuration < external component-specific configuration.
-
-### Number of worker processes
-
-* Description: How many worker processes to create _for the topology_ across machines in the cluster.
-* Configuration option: [TOPOLOGY_WORKERS](/javadoc/apidocs/backtype/storm/Config.html#TOPOLOGY_WORKERS)
-* How to set in your code (examples):
- * [Config#setNumWorkers](/javadoc/apidocs/backtype/storm/Config.html)
-
-### Number of executors (threads)
-
-* Description: How many executors to spawn _per component_.
-* Configuration option: None (pass ``parallelism_hint`` parameter to ``setSpout`` or ``setBolt``)
-* How to set in your code (examples):
- * [TopologyBuilder#setSpout()](/javadoc/apidocs/backtype/storm/topology/TopologyBuilder.html)
- * [TopologyBuilder#setBolt()](/javadoc/apidocs/backtype/storm/topology/TopologyBuilder.html)
- * Note that as of Storm 0.8 the ``parallelism_hint`` parameter now specifies the initial number of executors (not tasks!) for that bolt.
-
-### Number of tasks
-
-* Description: How many tasks to create _per component_.
-* Configuration option: [TOPOLOGY_TASKS](/javadoc/apidocs/backtype/storm/Config.html#TOPOLOGY_TASKS)
-* How to set in your code (examples):
- * [ComponentConfigurationDeclarer#setNumTasks()](/javadoc/apidocs/backtype/storm/topology/ComponentConfigurationDeclarer.html)
-
-
-Here is an example code snippet to show these settings in practice:
-
-```java
-topologyBuilder.setBolt("green-bolt", new GreenBolt(), 2)
- .setNumTasks(4)
- .shuffleGrouping("blue-spout");
-```
-
-In the above code we configured Storm to run the bolt ``GreenBolt`` with an initial number of two executors and four associated tasks. Storm will run two tasks per executor (thread). If you do not explicitly configure the number of tasks, Storm will run by default one task per executor.
-
-## Example of a running topology
-
-The following illustration shows how a simple topology would look like in operation. The topology consists of three components: one spout called ``BlueSpout`` and two bolts called ``GreenBolt`` and ``YellowBolt``. The components are linked such that ``BlueSpout`` sends its output to ``GreenBolt``, which in turns sends its own output to ``YellowBolt``.
-
-
-
-The ``GreenBolt`` was configured as per the code snippet above whereas ``BlueSpout`` and ``YellowBolt`` only set the parallelism hint (number of executors). Here is the relevant code:
-
-```java
-Config conf = new Config();
-conf.setNumWorkers(2); // use two worker processes
-
-topologyBuilder.setSpout("blue-spout", new BlueSpout(), 2); // set parallelism hint to 2
-
-topologyBuilder.setBolt("green-bolt", new GreenBolt(), 2)
- .setNumTasks(4)
- .shuffleGrouping("blue-spout");
-
-topologyBuilder.setBolt("yellow-bolt", new YellowBolt(), 6)
- .shuffleGrouping("green-bolt");
-
-StormSubmitter.submitTopology(
- "mytopology",
- conf,
- topologyBuilder.createTopology()
- );
-```
-
-And of course Storm comes with additional configuration settings to control the parallelism of a topology, including:
-
-* [TOPOLOGY_MAX_TASK_PARALLELISM](/javadoc/apidocs/backtype/storm/Config.html#TOPOLOGY_MAX_TASK_PARALLELISM): This setting puts a ceiling on the number of executors that can be spawned for a single component. It is typically used during testing to limit the number of threads spawned when running a topology in local mode. You can set this option via e.g. [Config#setMaxTaskParallelism()](/javadoc/apidocs/backtype/storm/Config.html#setMaxTaskParallelism(int)).
-
-## How to change the parallelism of a running topology
-
-A nifty feature of Storm is that you can increase or decrease the number of worker processes and/or executors without being required to restart the cluster or the topology. The act of doing so is called rebalancing.
-
-You have two options to rebalance a topology:
-
-1. Use the Storm web UI to rebalance the topology.
-2. Use the CLI tool storm rebalance as described below.
-
-Here is an example of using the CLI tool:
-
-```
-## Reconfigure the topology "mytopology" to use 5 worker processes,
-## the spout "blue-spout" to use 3 executors and
-## the bolt "yellow-bolt" to use 10 executors.
-
-$ storm rebalance mytopology -n 5 -e blue-spout=3 -e yellow-bolt=10
-```
-
-## References
-
-* [Concepts](Concepts.html)
-* [Configuration](Configuration.html)
-* [Running topologies on a production cluster](Running-topologies-on-a-production-cluster.html)]
-* [Local mode](Local-mode.html)
-* [Tutorial](Tutorial.html)
-* [Storm API documentation](/javadoc/apidocs/), most notably the class ``Config``
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/Using-non-JVM-languages-with-Storm.md
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diff --git a/docs/documentation/Using-non-JVM-languages-with-Storm.md b/docs/documentation/Using-non-JVM-languages-with-Storm.md
deleted file mode 100644
index 2e5c67a..0000000
--- a/docs/documentation/Using-non-JVM-languages-with-Storm.md
+++ /dev/null
@@ -1,52 +0,0 @@
----
-layout: documentation
----
-- two pieces: creating topologies and implementing spouts and bolts in other languages
-- creating topologies in another language is easy since topologies are just thrift structures (link to storm.thrift)
-- implementing spouts and bolts in another language is called a "multilang components" or "shelling"
- - Here's a specification of the protocol: [Multilang protocol](Multilang-protocol.html)
- - the thrift structure lets you define multilang components explicitly as a program and a script (e.g., python and the file implementing your bolt)
- - In Java, you override ShellBolt or ShellSpout to create multilang components
- - note that output fields declarations happens in the thrift structure, so in Java you create multilang components like the following:
- - declare fields in java, processing code in the other language by specifying it in constructor of shellbolt
- - multilang uses json messages over stdin/stdout to communicate with the subprocess
- - storm comes with ruby, python, and fancy adapters that implement the protocol. show an example of python
- - python supports emitting, anchoring, acking, and logging
-- "storm shell" command makes constructing jar and uploading to nimbus easy
- - makes jar and uploads it
- - calls your program with host/port of nimbus and the jarfile id
-
-## Notes on implementing a DSL in a non-JVM language
-
-The right place to start is src/storm.thrift. Since Storm topologies are just Thrift structures, and Nimbus is a Thrift daemon, you can create and submit topologies in any language.
-
-When you create the Thrift structs for spouts and bolts, the code for the spout or bolt is specified in the ComponentObject struct:
-
-```
-union ComponentObject {
- 1: binary serialized_java;
- 2: ShellComponent shell;
- 3: JavaObject java_object;
-}
-```
-
-For a non-JVM DSL, you would want to make use of "2" and "3". ShellComponent lets you specify a script to run that component (e.g., your python code). And JavaObject lets you specify native java spouts and bolts for the component (and Storm will use reflection to create that spout or bolt).
-
-There's a "storm shell" command that will help with submitting a topology. Its usage is like this:
-
-```
-storm shell resources/ python topology.py arg1 arg2
-```
-
-storm shell will then package resources/ into a jar, upload the jar to Nimbus, and call your topology.py script like this:
-
-```
-python topology.py arg1 arg2 {nimbus-host} {nimbus-port} {uploaded-jar-location}
-```
-
-Then you can connect to Nimbus using the Thrift API and submit the topology, passing {uploaded-jar-location} into the submitTopology method. For reference, here's the submitTopology definition:
-
-```
-void submitTopology(1: string name, 2: string uploadedJarLocation, 3: string jsonConf, 4: StormTopology topology)
- throws (1: AlreadyAliveException e, 2: InvalidTopologyException ite);
-```
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/Windowing.md
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diff --git a/docs/documentation/Windowing.md b/docs/documentation/Windowing.md
deleted file mode 100644
index 803e5ca..0000000
--- a/docs/documentation/Windowing.md
+++ /dev/null
@@ -1,235 +0,0 @@
-# Windowing support in core storm
-
-Storm core has support for processing a group of tuples that falls within a window. Windows are specified with the
-following two parameters,
-
-1. Window length - the length or duration of the window
-2. Sliding interval - the interval at which the windowing slides
-
-## Sliding Window
-
-Tuples are grouped in windows and window slides every sliding interval. A tuple can belong to more than one window.
-
-For example a time duration based sliding window with length 10 secs and sliding interval of 5 seconds.
-
-```
-| e1 e2 | e3 e4 e5 e6 | e7 e8 e9 |...
-0 5 10 15 -> time
-
-|<------- w1 -------->|
- |------------ w2 ------->|
-```
-
-The window is evaluated every 5 seconds and some of the tuples in the first window overlaps with the second one.
-
-
-## Tumbling Window
-
-Tuples are grouped in a single window based on time or count. Any tuple belongs to only one of the windows.
-
-For example a time duration based tumbling window with length 5 secs.
-
-```
-| e1 e2 | e3 e4 e5 e6 | e7 e8 e9 |...
-0 5 10 15 -> time
- w1 w2 w3
-```
-
-The window is evaluated every five seconds and none of the windows overlap.
-
-Storm supports specifying the window length and sliding intervals as a count of the number of tuples or as a time duration.
-
-The bolt interface `IWindowedBolt` is implemented by bolts that needs windowing support.
-
-```java
-public interface IWindowedBolt extends IComponent {
- void prepare(Map stormConf, TopologyContext context, OutputCollector collector);
- /**
- * Process tuples falling within the window and optionally emit
- * new tuples based on the tuples in the input window.
- */
- void execute(TupleWindow inputWindow);
- void cleanup();
-}
-```
-
-Every time the window activates, the `execute` method is invoked. The TupleWindow parameter gives access to the current tuples
-in the window, the tuples that expired and the new tuples that are added since last window was computed which will be useful
-for efficient windowing computations.
-
-Bolts that needs windowing support typically would extend `BaseWindowedBolt` which has the apis for specifying the
-window length and sliding intervals.
-
-E.g.
-
-```java
-public class SlidingWindowBolt extends BaseWindowedBolt {
- private OutputCollector collector;
-
- @Override
- public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {
- this.collector = collector;
- }
-
- @Override
- public void execute(TupleWindow inputWindow) {
- for(Tuple tuple: inputWindow.get()) {
- // do the windowing computation
- ...
- }
- // emit the results
- collector.emit(new Values(computedValue));
- }
-}
-
-public static void main(String[] args) {
- TopologyBuilder builder = new TopologyBuilder();
- builder.setSpout("spout", new RandomSentenceSpout(), 1);
- builder.setBolt("slidingwindowbolt",
- new SlidingWindowBolt().withWindow(new Count(30), new Count(10)),
- 1).shuffleGrouping("spout");
- Config conf = new Config();
- conf.setDebug(true);
- conf.setNumWorkers(1);
-
- StormSubmitter.submitTopologyWithProgressBar(args[0], conf, builder.createTopology());
-
-}
-```
-
-The following window configurations are supported.
-
-```java
-withWindow(Count windowLength, Count slidingInterval)
-Tuple count based sliding window that slides after `slidingInterval` number of tuples.
-
-withWindow(Count windowLength)
-Tuple count based window that slides with every incoming tuple.
-
-withWindow(Count windowLength, Duration slidingInterval)
-Tuple count based sliding window that slides after `slidingInterval` time duration.
-
-withWindow(Duration windowLength, Duration slidingInterval)
-Time duration based sliding window that slides after `slidingInterval` time duration.
-
-withWindow(Duration windowLength)
-Time duration based window that slides with every incoming tuple.
-
-withWindow(Duration windowLength, Count slidingInterval)
-Time duration based sliding window configuration that slides after `slidingInterval` number of tuples.
-
-withTumblingWindow(BaseWindowedBolt.Count count)
-Count based tumbling window that tumbles after the specified count of tuples.
-
-withTumblingWindow(BaseWindowedBolt.Duration duration)
-Time duration based tumbling window that tumbles after the specified time duration.
-
-```
-
-## Tuple timestamp and out of order tuples
-By default the timestamp tracked in the window is the time when the tuple is processed by the bolt. The window calculations
-are performed based on the processing timestamp. Storm has support for tracking windows based on the source generated timestamp.
-
-```java
-/**
-* Specify a field in the tuple that represents the timestamp as a long value. If this
-* field is not present in the incoming tuple, an {@link IllegalArgumentException} will be thrown.
-*
-* @param fieldName the name of the field that contains the timestamp
-*/
-public BaseWindowedBolt withTimestampField(String fieldName)
-```
-
-The value for the above `fieldName` will be looked up from the incoming tuple and considered for windowing calculations.
-If the field is not present in the tuple an exception will be thrown. Along with the timestamp field name, a time lag parameter
-can also be specified which indicates the max time limit for tuples with out of order timestamps.
-
-E.g. If the lag is 5 secs and a tuple `t1` arrived with timestamp `06:00:05` no tuples may arrive with tuple timestamp earlier than `06:00:00`. If a tuple
-arrives with timestamp 05:59:59 after `t1` and the window has moved past `t1`, it will be treated as a late tuple and not processed. Currently the late
- tuples are just logged in the worker log files at INFO level.
-
-```java
-/**
-* Specify the maximum time lag of the tuple timestamp in milliseconds. It means that the tuple timestamps
-* cannot be out of order by more than this amount.
-*
-* @param duration the max lag duration
-*/
-public BaseWindowedBolt withLag(Duration duration)
-```
-
-### Watermarks
-For processing tuples with timestamp field, storm internally computes watermarks based on the incoming tuple timestamp. Watermark is
-the minimum of the latest tuple timestamps (minus the lag) across all the input streams. At a higher level this is similar to the watermark concept
-used by Flink and Google's MillWheel for tracking event based timestamps.
-
-Periodically (default every sec), the watermark timestamps are emitted and this is considered as the clock tick for the window calculation if
-tuple based timestamps are in use. The interval at which watermarks are emitted can be changed with the below api.
-
-```java
-/**
-* Specify the watermark event generation interval. For tuple based timestamps, watermark events
-* are used to track the progress of time
-*
-* @param interval the interval at which watermark events are generated
-*/
-public BaseWindowedBolt withWatermarkInterval(Duration interval)
-```
-
-
-When a watermark is received, all windows up to that timestamp will be evaluated.
-
-For example, consider tuple timestamp based processing with following window parameters,
-
-`Window length = 20s, sliding interval = 10s, watermark emit frequency = 1s, max lag = 5s`
-
-```
-|-----|-----|-----|-----|-----|-----|-----|
-0 10 20 30 40 50 60 70
-````
-
-Current ts = `09:00:00`
-
-Tuples `e1(6:00:03), e2(6:00:05), e3(6:00:07), e4(6:00:18), e5(6:00:26), e6(6:00:36)` are received between `9:00:00` and `9:00:01`
-
-At time t = `09:00:01`, watermark w1 = `6:00:31` is emitted since no tuples earlier than `6:00:31` can arrive.
-
-Three windows will be evaluated. The first window end ts (06:00:10) is computed by taking the earliest event timestamp (06:00:03)
-and computing the ceiling based on the sliding interval (10s).
-
-1. `5:59:50 - 06:00:10` with tuples e1, e2, e3
-2. `6:00:00 - 06:00:20` with tuples e1, e2, e3, e4
-3. `6:00:10 - 06:00:30` with tuples e4, e5
-
-e6 is not evaluated since watermark timestamp `6:00:31` is older than the tuple ts `6:00:36`.
-
-Tuples `e7(8:00:25), e8(8:00:26), e9(8:00:27), e10(8:00:39)` are received between `9:00:01` and `9:00:02`
-
-At time t = `09:00:02` another watermark w2 = `08:00:34` is emitted since no tuples earlier than `8:00:34` can arrive now.
-
-Three windows will be evaluated,
-
-1. `6:00:20 - 06:00:40` with tuples e5, e6 (from earlier batch)
-2. `6:00:30 - 06:00:50` with tuple e6 (from earlier batch)
-3. `8:00:10 - 08:00:30` with tuples e7, e8, e9
-
-e10 is not evaluated since the tuple ts `8:00:39` is beyond the watermark time `8:00:34`.
-
-The window calculation considers the time gaps and computes the windows based on the tuple timestamp.
-
-## Guarentees
-The windowing functionality in storm core currently provides at-least once guarentee. The values emitted from the bolts
-`execute(TupleWindow inputWindow)` method are automatically anchored to all the tuples in the inputWindow. The downstream
-bolts are expected to ack the received tuple (i.e the tuple emitted from the windowed bolt) to complete the tuple tree.
-If not the tuples will be replayed and the windowing computation will be re-evaluated.
-
-The tuples in the window are automatically acked when the expire, i.e. when they fall out of the window after
-`windowLength + slidingInterval`. Note that the configuration `topology.message.timeout.secs` should be sufficiently more
-than `windowLength + slidingInterval` for time based windows; otherwise the tuples will timeout and get replayed and can result
-in duplicate evaluations. For count based windows, the configuration should be adjusted such that `windowLength + slidingInterval`
-tuples can be received within the timeout period.
-
-## Example topology
-An example toplogy `SlidingWindowTopology` shows how to use the apis to compute a sliding window sum and a tumbling window
-average.
-
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/distcache-blobstore.md
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diff --git a/docs/documentation/distcache-blobstore.md b/docs/documentation/distcache-blobstore.md
deleted file mode 100644
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-# Storm Distributed Cache API
-
-The distributed cache feature in storm is used to efficiently distribute files
-(or blobs, which is the equivalent terminology for a file in the distributed
-cache and is used interchangeably in this document) that are large and can
-change during the lifetime of a topology, such as geo-location data,
-dictionaries, etc. Typical use cases include phrase recognition, entity
-extraction, document classification, URL re-writing, location/address detection
-and so forth. Such files may be several KB to several GB in size. For small
-datasets that don't need dynamic updates, including them in the topology jar
-could be fine. But for large files, the startup times could become very large.
-In these cases, the distributed cache feature can provide fast topology startup,
-especially if the files were previously downloaded for the same submitter and
-are still in the cache. This is useful with frequent deployments, sometimes few
-times a day with updated jars, because the large cached files will remain available
-without changes. The large cached blobs that do not change frequently will
-remain available in the distributed cache.
-
-At the starting time of a topology, the user specifies the set of files the
-topology needs. Once a topology is running, the user at any time can request for
-any file in the distributed cache to be updated with a newer version. The
-updating of blobs happens in an eventual consistency model. If the topology
-needs to know what version of a file it has access to, it is the responsibility
-of the user to find this information out. The files are stored in a cache with
-Least-Recently Used (LRU) eviction policy, where the supervisor decides which
-cached files are no longer needed and can delete them to free disk space. The
-blobs can be compressed, and the user can request the blobs to be uncompressed
-before it accesses them.
-
-## Motivation for Distributed Cache
-* Allows sharing blobs among topologies.
-* Allows updating the blobs from the command line.
-
-## Distributed Cache Implementations
-The current BlobStore interface has the following two implementations
-* LocalFsBlobStore
-* HdfsBlobStore
-
-Appendix A contains the interface for blobstore implementation.
-
-## LocalFsBlobStore
-
-
-Local file system implementation of Blobstore can be depicted in the above timeline diagram.
-
-There are several stages from blob creation to blob download and corresponding execution of a topology.
-The main stages can be depicted as follows
-
-### Blob Creation Command
-Blobs in the blobstore can be created through command line using the following command.
-
-```
-storm blobstore create --file README.txt --acl o::rwa --repl-fctr 4 key1
-```
-
-The above command creates a blob with a key name “key1” corresponding to the file README.txt.
-The access given to all users being read, write and admin with a replication factor of 4.
-
-### Topology Submission and Blob Mapping
-Users can submit their topology with the following command. The command includes the
-topology map configuration. The configuration holds two keys “key1” and “key2” with the
-key “key1” having a local file name mapping named “blob_file” and it is not compressed.
-
-```
-storm jar /home/y/lib/storm-starter/current/storm-starter-jar-with-dependencies.jar
-storm.starter.clj.word_count test_topo -c topology.blobstore.map='{"key1":{"localname":"blob_file", "uncompress":"false"},"key2":{}}'
-```
-
-### Blob Creation Process
-The creation of the blob takes place through the interface “ClientBlobStore”. Appendix B contains the “ClientBlobStore” interface.
-The concrete implementation of this interface is the “NimbusBlobStore”. In the case of local file system the client makes a
-call to the nimbus to create the blobs within the local file system. The nimbus uses the local file system implementation to create these blobs.
-When a user submits a topology, the jar, configuration and code files are uploaded as blobs with the help of blobstore.
-Also, all the other blobs specified by the topology are mapped to it with the help of topology.blobstore.map configuration.
-
-### Blob Download by the Supervisor
-Finally, the blobs corresponding to a topology are downloaded by the supervisor once it receives the assignments from the nimbus through
-the same “NimbusBlobStore” thrift client that uploaded the blobs. The supervisor downloads the code, jar and conf blobs by calling the
-“NimbusBlobStore” client directly while the blobs specified in the topology.blobstore.map are downloaded and mapped locally with the help
-of the Localizer. The Localizer talks to the “NimbusBlobStore” thrift client to download the blobs and adds the blob compression and local
-blob name mapping logic to suit the implementation of a topology. Once all the blobs have been downloaded the workers are launched to run
-the topologies.
-
-## HdfsBlobStore
-
-
-The HdfsBlobStore functionality has a similar implementation and blob creation and download procedure barring how the replication
-is handled in the two blobstore implementations. The replication in HDFS blobstore is obvious as HDFS is equipped to handle replication
-and it requires no state to be stored inside the zookeeper. On the other hand, the local file system blobstore requires the state to be
-stored on the zookeeper in order for it to work with nimbus HA. Nimbus HA allows the local filesystem to implement the replication feature
-seamlessly by storing the state in the zookeeper about the running topologies and syncing the blobs on various nimbuses. On the supervisor’s
-end, the supervisor and localizer talks to HdfsBlobStore through “HdfsClientBlobStore” implementation.
-
-## Additional Features and Documentation
-```
-storm jar /home/y/lib/storm-starter/current/storm-starter-jar-with-dependencies.jar storm.starter.clj.word_count test_topo
--c topology.blobstore.map='{"key1":{"localname":"blob_file", "uncompress":"false"},"key2":{}}'
-```
-
-### Compression
-The blobstore allows the user to specify the “uncompress” configuration to true or false. This configuration can be specified
-in the topology.blobstore.map mentioned in the above command. This allows the user to upload a compressed file like a tarball/zip.
-In local file system blobstore, the compressed blobs are stored on the nimbus node. The localizer code takes the responsibility to
-uncompress the blob and store it on the supervisor node. Symbolic links to the blobs on the supervisor node are created within the worker
-before the execution starts.
-
-### Local File Name Mapping
-Apart from compression the blobstore helps to give the blob a name that can be used by the workers. The localizer takes
-the responsibility of mapping the blob to a local name on the supervisor node.
-
-## Additional Blobstore Implementation Details
-Blobstore uses a hashing function to create the blobs based on the key. The blobs are generally stored inside the directory specified by
-the blobstore.dir configuration. By default, it is stored under “storm.local.dir/nimbus/blobs” for local file system and a similar path on
-hdfs file system.
-
-Once a file is submitted, the blobstore reads the configs and creates a metadata for the blob with all the access control details. The metadata
-is generally used for authorization while accessing the blobs. The blob key and version contribute to the hash code and there by the directory
-under “storm.local.dir/nimbus/blobs/data” where the data is placed. The blobs are generally placed in a positive number directory like 193,822 etc.
-
-Once the topology is launched and the relevant blobs have been created, the supervisor downloads blobs related to the storm.conf, storm.ser
-and storm.code first and all the blobs uploaded by the command line separately using the localizer to uncompress and map them to a local name
-specified in the topology.blobstore.map configuration. The supervisor periodically updates blobs by checking for the change of version.
-This allows updating the blobs on the fly and thereby making it a very useful feature.
-
-For a local file system, the distributed cache on the supervisor node is set to 10240 MB as a soft limit and the clean up code attempts
-to clean anything over the soft limit every 600 seconds based on LRU policy.
-
-The HDFS blobstore implementation handles load better by removing the burden on the nimbus to store the blobs, which avoids it becoming a bottleneck. Moreover, it provides seamless replication of blobs. On the other hand, the local file system blobstore is not very efficient in
-replicating the blobs and is limited by the number of nimbuses. Moreover, the supervisor talks to the HDFS blobstore directly without the
-involvement of the nimbus and thereby reduces the load and dependency on nimbus.
-
-## Highly Available Nimbus
-### Problem Statement:
-Currently the storm master aka nimbus, is a process that runs on a single machine under supervision. In most cases, the
-nimbus failure is transient and it is restarted by the process that does supervision. However sometimes when disks fail and networks
-partitions occur, nimbus goes down. Under these circumstances, the topologies run normally but no new topologies can be
-submitted, no existing topologies can be killed/deactivated/activated and if a supervisor node fails then the
-reassignments are not performed resulting in performance degradation or topology failures. With this project we intend,
-to resolve this problem by running nimbus in a primary backup mode to guarantee that even if a nimbus server fails one
-of the backups will take over.
-
-### Requirements for Highly Available Nimbus:
-* Increase overall availability of nimbus.
-* Allow nimbus hosts to leave and join the cluster at will any time. A newly joined host should auto catch up and join
-the list of potential leaders automatically.
-* No topology resubmissions required in case of nimbus fail overs.
-* No active topology should ever be lost.
-
-#### Leader Election:
-The nimbus server will use the following interface:
-
-```java
-public interface ILeaderElector {
- /**
- * queue up for leadership lock. The call returns immediately and the caller
- * must check isLeader() to perform any leadership action.
- */
- void addToLeaderLockQueue();
-
- /**
- * Removes the caller from the leader lock queue. If the caller is leader
- * also releases the lock.
- */
- void removeFromLeaderLockQueue();
-
- /**
- *
- * @return true if the caller currently has the leader lock.
- */
- boolean isLeader();
-
- /**
- *
- * @return the current leader's address , throws exception if noone has has lock.
- */
- InetSocketAddress getLeaderAddress();
-
- /**
- *
- * @return list of current nimbus addresses, includes leader.
- */
- List<InetSocketAddress> getAllNimbusAddresses();
-}
-```
-Once a nimbus comes up it calls addToLeaderLockQueue() function. The leader election code selects a leader from the queue.
-If the topology code, jar or config blobs are missing, it would download the blobs from any other nimbus which is up and running.
-
-The first implementation will be Zookeeper based. If the zookeeper connection is lost/reset resulting in loss of lock
-or the spot in queue the implementation will take care of updating the state such that isLeader() will reflect the
-current status. The leader like actions must finish in less than minimumOf(connectionTimeout, SessionTimeout) to ensure
-the lock was held by nimbus for the entire duration of the action (Not sure if we want to just state this expectation
-and ensure that zk configurations are set high enough which will result in higher failover time or we actually want to
-create some sort of rollback mechanism for all actions, the second option needs a lot of code). If a nimbus that is not
-leader receives a request that only a leader can perform, it will throw a RunTimeException.
-
-### Nimbus state store:
-
-To achieve fail over from primary to backup servers nimbus state/data needs to be replicated across all nimbus hosts or
-needs to be stored in a distributed storage. Replicating the data correctly involves state management, consistency checks
-and it is hard to test for correctness. However many storm users do not want to take extra dependency on another replicated
-storage system like HDFS and still need high availability. The blobstore implementation along with the state storage helps
-to overcome the failover scenarios in case a leader nimbus goes down.
-
-To support replication we will allow the user to define a code replication factor which would reflect number of nimbus
-hosts to which the code must be replicated before starting the topology. With replication comes the issue of consistency.
-The topology is launched once the code, jar and conf blob files are replicated based on the "topology.min.replication" config.
-Maintaining state for failover scenarios is important for local file system. The current implementation makes sure one of the
-available nimbus is elected as a leader in the case of a failure. If the topology specific blobs are missing, the leader nimbus
-tries to download them as and when they are needed. With this current architecture, we do not have to download all the blobs
-required for a topology for a nimbus to accept leadership. This helps us in case the blobs are very large and avoid causing any
-inadvertant delays in electing a leader.
-
-The state for every blob is relevant for the local blobstore implementation. For HDFS blobstore the replication
-is taken care by the HDFS. For handling the fail over scenarios for a local blobstore we need to store the state of the leader and
-non-leader nimbuses within the zookeeper.
-
-The state is stored under /storm/blobstore/key/nimbusHostPort:SequenceNumber for the blobstore to work to make nimbus highly available.
-This state is used in the local file system blobstore to support replication. The HDFS blobstore does not have to store the state inside the
-zookeeper.
-
-* NimbusHostPort: This piece of information generally contains the parsed string holding the hostname and port of the nimbus.
- It uses the same class “NimbusHostPortInfo” used earlier by the code-distributor interface to store the state and parse the data.
-
-* SequenceNumber: This is the blob sequence number information. The SequenceNumber information is implemented by a KeySequenceNumber class.
-The sequence numbers are generated for every key. For every update, the sequence numbers are assigned based ona global sequence number
-stored under /storm/blobstoremaxsequencenumber/key. For more details about how the numbers are generated you can look at the java docs for KeySequenceNumber.
-
-
-
-The sequence diagram proposes how the blobstore works and the state storage inside the zookeeper makes the nimbus highly available.
-Currently, the thread to sync the blobs on a non-leader is within the nimbus. In the future, it will be nice to move the thread around
-to the blobstore to make the blobstore coordinate the state change and blob download as per the sequence diagram.
-
-## Thrift and Rest API
-In order to avoid workers/supervisors/ui talking to zookeeper for getting master nimbus address we are going to modify the
-`getClusterInfo` API so it can also return nimbus information. getClusterInfo currently returns `ClusterSummary` instance
-which has a list of `supervisorSummary` and a list of `topologySummary` instances. We will add a list of `NimbusSummary`
-to the `ClusterSummary`. See the structures below:
-
-```thrift
-struct ClusterSummary {
- 1: required list<SupervisorSummary> supervisors;
- 3: required list<TopologySummary> topologies;
- 4: required list<NimbusSummary> nimbuses;
-}
-
-struct NimbusSummary {
- 1: required string host;
- 2: required i32 port;
- 3: required i32 uptime_secs;
- 4: required bool isLeader;
- 5: required string version;
-}
-```
-
-This will be used by StormSubmitter, Nimbus clients, supervisors and ui to discover the current leaders and participating
-nimbus hosts. Any nimbus host will be able to respond to these requests. The nimbus hosts can read this information once
-from zookeeper and cache it and keep updating the cache when the watchers are fired to indicate any changes,which should
-be rare in general case.
-
-Note: All nimbus hosts have watchers on zookeeper to be notified immediately as soon as a new blobs is available for download, the callback may or may not download
-the code. Therefore, a background thread is triggered to download the respective blobs to run the topologies. The replication is achieved when the blobs are downloaded
-onto non-leader nimbuses. So you should expect your topology submission time to be somewhere between 0 to (2 * nimbus.code.sync.freq.secs) for any
-nimbus.min.replication.count > 1.
-
-## Configuration
-
-```
-blobstore.dir: The directory where all blobs are stored. For local file system it represents the directory on the nimbus
-node and for HDFS file system it represents the hdfs file system path.
-
-supervisor.blobstore.class: This configuration is meant to set the client for the supervisor in order to talk to the blobstore.
-For a local file system blobstore it is set to “backtype.storm.blobstore.NimbusBlobStore” and for the HDFS blobstore it is set
-to “backtype.storm.blobstore.HdfsClientBlobStore”.
-
-supervisor.blobstore.download.thread.count: This configuration spawns multiple threads for from the supervisor in order download
-blobs concurrently. The default is set to 5
-
-supervisor.blobstore.download.max_retries: This configuration is set to allow the supervisor to retry for the blob download.
-By default it is set to 3.
-
-supervisor.localizer.cache.target.size.mb: The jvm opts provided to workers launched by this supervisor. All "%ID%" substrings
-are replaced with an identifier for this worker. Also, "%WORKER-ID%", "%STORM-ID%" and "%WORKER-PORT%" are replaced with
-appropriate runtime values for this worker. The distributed cache target size in MB. This is a soft limit to the size
-of the distributed cache contents. It is set to 10240 MB.
-
-supervisor.localizer.cleanup.interval.ms: The distributed cache cleanup interval. Controls how often it scans to attempt to
-cleanup anything over the cache target size. By default it is set to 600000 milliseconds.
-
-nimbus.blobstore.class: Sets the blobstore implementation nimbus uses. It is set to "backtype.storm.blobstore.LocalFsBlobStore"
-
-nimbus.blobstore.expiration.secs: During operations with the blobstore, via master, how long a connection is idle before nimbus
-considers it dead and drops the session and any associated connections. The default is set to 600.
-
-storm.blobstore.inputstream.buffer.size.bytes: The buffer size it uses for blobstore upload. It is set to 65536 bytes.
-
-client.blobstore.class: The blobstore implementation the storm client uses. The current implementation uses the default
-config "backtype.storm.blobstore.NimbusBlobStore".
-
-blobstore.replication.factor: It sets the replication for each blob within the blobstore. The “topology.min.replication.count”
-ensures the minimum replication the topology specific blobs are set before launching the topology. You might want to set the
-“topology.min.replication.count <= blobstore.replication”. The default is set to 3.
-
-topology.min.replication.count : Minimum number of nimbus hosts where the code must be replicated before leader nimbus
-can mark the topology as active and create assignments. Default is 1.
-
-topology.max.replication.wait.time.sec: Maximum wait time for the nimbus host replication to achieve the nimbus.min.replication.count.
-Once this time is elapsed nimbus will go ahead and perform topology activation tasks even if required nimbus.min.replication.count is not achieved.
-The default is 60 seconds, a value of -1 indicates to wait for ever.
-* nimbus.code.sync.freq.secs: Frequency at which the background thread on nimbus which syncs code for locally missing blobs. Default is 2 minutes.
-```
-
-## Using the Distributed Cache API, Command Line Interface (CLI)
-
-### Creating blobs
-
-To use the distributed cache feature, the user first has to "introduce" files
-that need to be cached and bind them to key strings. To achieve this, the user
-uses the "blobstore create" command of the storm executable, as follows:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore create [-f|--file FILE] [-a|--acl ACL1,ACL2,...] [--repl-fctr NUMBER] [keyname]
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The contents come from a FILE, if provided by -f or --file option, otherwise
-from STDIN.
-The ACLs, which can also be a comma separated list of many ACLs, is of the
-following format:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-> [u|o]:[username]:[r-|w-|a-|_]
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-where:
-
-* u = user
-* o = other
-* username = user for this particular ACL
-* r = read access
-* w = write access
-* a = admin access
-* _ = ignored
-
-The replication factor can be set to a value greater than 1 using --repl-fctr.
-
-Note: The replication right now is configurable for a hdfs blobstore but for a
-local blobstore the replication always stays at 1. For a hdfs blobstore
-the default replication is set to 3.
-
-###### Example:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore create --file README.txt --acl o::rwa --repl-fctr 4 key1
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In the above example, the *README.txt* file is added to the distributed cache.
-It can be accessed using the key string "*key1*" for any topology that needs
-it. The file is set to have read/write/admin access for others, a.k.a world
-everything and the replication is set to 4.
-
-###### Example:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore create mytopo:data.tgz -f data.tgz -a u:alice:rwa,u:bob:rw,o::r
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The above example createss a mytopo:data.tgz key using the data stored in
-data.tgz. User alice would have full access, bob would have read/write access
-and everyone else would have read access.
-
-### Making dist. cache files accessible to topologies
-
-Once a blob is created, we can use it for topologies. This is generally achieved
-by including the key string among the configurations of a topology, with the
-following format. A shortcut is to add the configuration item on the command
-line when starting a topology by using the **-c** command:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--c topology.blobstore.map='{"[KEY]":{"localname":"[VALUE]", "uncompress":"[true|false]"}}'
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Note: Please take care of the quotes.
-
-The cache file would then be accessible to the topology as a local file with the
-name [VALUE].
-The localname parameter is optional, if omitted the local cached file will have
-the same name as [KEY].
-The uncompress parameter is optional, if omitted the local cached file will not
-be uncompressed. Note that the key string needs to have the appropriate
-file-name-like format and extension, so it can be uncompressed correctly.
-
-###### Example:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm jar /home/y/lib/storm-starter/current/storm-starter-jar-with-dependencies.jar storm.starter.clj.word_count test_topo -c topology.blobstore.map='{"key1":{"localname":"blob_file", "uncompress":"false"},"key2":{}}'
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Note: Please take care of the quotes.
-
-In the above example, we start the *word_count* topology (stored in the
-*storm-starter-jar-with-dependencies.jar* file), and ask it to have access
-to the cached file stored with key string = *key1*. This file would then be
-accessible to the topology as a local file called *blob_file*, and the
-supervisor will not try to uncompress the file. Note that in our example, the
-file's content originally came from *README.txt*. We also ask for the file
-stored with the key string = *key2* to be accessible to the topology. Since
-both the optional parameters are omitted, this file will get the local name =
-*key2*, and will not be uncompressed.
-
-### Updating a cached file
-
-It is possible for the cached files to be updated while topologies are running.
-The update happens in an eventual consistency model, where the supervisors poll
-Nimbus every 30 seconds, and update their local copies. In the current version,
-it is the user's responsibility to check whether a new file is available.
-
-To update a cached file, use the following command. Contents come from a FILE or
-STDIN. Write access is required to be able to update a cached file.
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore update [-f|--file NEW_FILE] [KEYSTRING]
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-###### Example:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore update -f updates.txt key1
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In the above example, the topologies will be presented with the contents of the
-file *updates.txt* instead of *README.txt* (from the previous example), even
-though their access by the topology is still through a file called
-*blob_file*.
-
-### Removing a cached file
-
-To remove a file from the distributed cache, use the following command. Removing
-a file requires write access.
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore delete [KEYSTRING]
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-### Listing Blobs currently in the distributed cache blobstore
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore list [KEY...]
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-lists blobs currently in the blobstore
-
-### Reading the contents of a blob
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore cat [-f|--file FILE] KEY
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-read a blob and then either write it to a file, or STDOUT. Reading a blob
-requires read access.
-
-### Setting the access control for a blob
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-set-acl [-s ACL] KEY
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma separated list
-(requires admin access).
-
-### Update the replication factor for a blob
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore replication --update --repl-fctr 5 key1
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-### Read the replication factor of a blob
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm blobstore replication --read key1
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-### Command line help
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-storm help blobstore
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-## Using the Distributed Cache API from Java
-
-We start by getting a ClientBlobStore object by calling this function:
-
-``` java
-Config theconf = new Config();
-theconf.putAll(Utils.readStormConfig());
-ClientBlobStore clientBlobStore = Utils.getClientBlobStore(theconf);
-```
-
-The required Utils package can by imported by:
-
-```java
-import backtype.storm.utils.Utils;
-```
-
-ClientBlobStore and other blob-related classes can be imported by:
-
-```java
-import backtype.storm.blobstore.ClientBlobStore;
-import backtype.storm.blobstore.AtomicOutputStream;
-import backtype.storm.blobstore.InputStreamWithMeta;
-import backtype.storm.blobstore.BlobStoreAclHandler;
-import backtype.storm.generated.*;
-```
-
-### Creating ACLs to be used for blobs
-
-```java
-String stringBlobACL = "u:username:rwa";
-AccessControl blobACL = BlobStoreAclHandler.parseAccessControl(stringBlobACL);
-List<AccessControl> acls = new LinkedList<AccessControl>();
-acls.add(blobACL); // more ACLs can be added here
-SettableBlobMeta settableBlobMeta = new SettableBlobMeta(acls);
-settableBlobMeta.set_replication_factor(4); // Here we can set the replication factor
-```
-
-The settableBlobMeta object is what we need to create a blob in the next step.
-
-### Creating a blob
-
-```java
-AtomicOutputStream blobStream = clientBlobStore.createBlob("some_key", settableBlobMeta);
-blobStream.write("Some String or input data".getBytes());
-blobStream.close();
-```
-
-Note that the settableBlobMeta object here comes from the last step, creating ACLs.
-It is recommended that for very large files, the user writes the bytes in smaller chunks (for example 64 KB, up to 1 MB chunks).
-
-### Updating a blob
-
-Similar to creating a blob, but we get the AtomicOutputStream in a different way:
-
-```java
-String blobKey = "some_key";
-AtomicOutputStream blobStream = clientBlobStore.updateBlob(blobKey);
-```
-
-Pass a byte stream to the returned AtomicOutputStream as before.
-
-### Updating the ACLs of a blob
-
-```java
-String blobKey = "some_key";
-AccessControl updateAcl = BlobStoreAclHandler.parseAccessControl("u:USER:--a");
-List<AccessControl> updateAcls = new LinkedList<AccessControl>();
-updateAcls.add(updateAcl);
-SettableBlobMeta modifiedSettableBlobMeta = new SettableBlobMeta(updateAcls);
-clientBlobStore.setBlobMeta(blobKey, modifiedSettableBlobMeta);
-
-//Now set write only
-updateAcl = BlobStoreAclHandler.parseAccessControl("u:USER:-w-");
-updateAcls = new LinkedList<AccessControl>();
-updateAcls.add(updateAcl);
-modifiedSettableBlobMeta = new SettableBlobMeta(updateAcls);
-clientBlobStore.setBlobMeta(blobKey, modifiedSettableBlobMeta);
-```
-
-### Updating and Reading the replication of a blob
-
-```java
-String blobKey = "some_key";
-BlobReplication replication = clientBlobStore.updateBlobReplication(blobKey, 5);
-int replication_factor = replication.get_replication();
-```
-
-Note: The replication factor gets updated and reflected only for hdfs blobstore
-
-### Reading a blob
-
-```java
-String blobKey = "some_key";
-InputStreamWithMeta blobInputStream = clientBlobStore.getBlob(blobKey);
-BufferedReader r = new BufferedReader(new InputStreamReader(blobInputStream));
-String blobContents = r.readLine();
-```
-
-### Deleting a blob
-
-```java
-String blobKey = "some_key";
-clientBlobStore.deleteBlob(blobKey);
-```
-
-### Getting a list of blob keys already in the blobstore
-
-```java
-Iterator <String> stringIterator = clientBlobStore.listKeys();
-```
-
-## Appendix A
-
-```java
-public abstract void prepare(Map conf, String baseDir);
-
-public abstract AtomicOutputStream createBlob(String key, SettableBlobMeta meta, Subject who) throws AuthorizationException, KeyAlreadyExistsException;
-
-public abstract AtomicOutputStream updateBlob(String key, Subject who) throws AuthorizationException, KeyNotFoundException;
-
-public abstract ReadableBlobMeta getBlobMeta(String key, Subject who) throws AuthorizationException, KeyNotFoundException;
-
-public abstract void setBlobMeta(String key, SettableBlobMeta meta, Subject who) throws AuthorizationException, KeyNotFoundException;
-
-public abstract void deleteBlob(String key, Subject who) throws AuthorizationException, KeyNotFoundException;
-
-public abstract InputStreamWithMeta getBlob(String key, Subject who) throws AuthorizationException, KeyNotFoundException;
-
-public abstract Iterator<String> listKeys(Subject who);
-
-public abstract BlobReplication getBlobReplication(String key, Subject who) throws Exception;
-
-public abstract BlobReplication updateBlobReplication(String key, int replication, Subject who) throws AuthorizationException, KeyNotFoundException, IOException
-```
-
-## Appendix B
-
-```java
-public abstract void prepare(Map conf);
-
-protected abstract AtomicOutputStream createBlobToExtend(String key, SettableBlobMeta meta) throws AuthorizationException, KeyAlreadyExistsException;
-
-public abstract AtomicOutputStream updateBlob(String key) throws AuthorizationException, KeyNotFoundException;
-
-public abstract ReadableBlobMeta getBlobMeta(String key) throws AuthorizationException, KeyNotFoundException;
-
-protected abstract void setBlobMetaToExtend(String key, SettableBlobMeta meta) throws AuthorizationException, KeyNotFoundException;
-
-public abstract void deleteBlob(String key) throws AuthorizationException, KeyNotFoundException;
-
-public abstract InputStreamWithMeta getBlob(String key) throws AuthorizationException, KeyNotFoundException;
-
-public abstract Iterator<String> listKeys();
-
-public abstract void watchBlob(String key, IBlobWatcher watcher) throws AuthorizationException;
-
-public abstract void stopWatchingBlob(String key) throws AuthorizationException;
-
-public abstract BlobReplication getBlobReplication(String Key) throws AuthorizationException, KeyNotFoundException;
-
-public abstract BlobReplication updateBlobReplication(String Key, int replication) throws AuthorizationException, KeyNotFoundException
-```
-
-## Appendix C
-
-``` thrift
-service Nimbus {
-...
-string beginCreateBlob(1: string key, 2: SettableBlobMeta meta) throws (1: AuthorizationException aze, 2: KeyAlreadyExistsException kae);
-
-string beginUpdateBlob(1: string key) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-
-void uploadBlobChunk(1: string session, 2: binary chunk) throws (1: AuthorizationException aze);
-
-void finishBlobUpload(1: string session) throws (1: AuthorizationException aze);
-
-void cancelBlobUpload(1: string session) throws (1: AuthorizationException aze);
-
-ReadableBlobMeta getBlobMeta(1: string key) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-
-void setBlobMeta(1: string key, 2: SettableBlobMeta meta) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-
-BeginDownloadResult beginBlobDownload(1: string key) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-
-binary downloadBlobChunk(1: string session) throws (1: AuthorizationException aze);
-
-void deleteBlob(1: string key) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-
-ListBlobsResult listBlobs(1: string session);
-
-BlobReplication getBlobReplication(1: string key) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-
-BlobReplication updateBlobReplication(1: string key, 2: i32 replication) throws (1: AuthorizationException aze, 2: KeyNotFoundException knf);
-...
-}
-
-struct BlobReplication {
-1: required i32 replication;
-}
-
-exception AuthorizationException {
- 1: required string msg;
-}
-
-exception KeyNotFoundException {
- 1: required string msg;
-}
-
-exception KeyAlreadyExistsException {
- 1: required string msg;
-}
-
-enum AccessControlType {
- OTHER = 1,
- USER = 2
- //eventually ,GROUP=3
-}
-
-struct AccessControl {
- 1: required AccessControlType type;
- 2: optional string name; //Name of user or group in ACL
- 3: required i32 access; //bitmasks READ=0x1, WRITE=0x2, ADMIN=0x4
-}
-
-struct SettableBlobMeta {
- 1: required list<AccessControl> acl;
- 2: optional i32 replication_factor
-}
-
-struct ReadableBlobMeta {
- 1: required SettableBlobMeta settable;
- //This is some indication of a version of a BLOB. The only guarantee is
- // if the data changed in the blob the version will be different.
- 2: required i64 version;
-}
-
-struct ListBlobsResult {
- 1: required list<string> keys;
- 2: required string session;
-}
-
-struct BeginDownloadResult {
- //Same version as in ReadableBlobMeta
- 1: required i64 version;
- 2: required string session;
- 3: optional i64 data_size;
-}
-```
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/dynamic-log-level-settings.md
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-Dynamic Log Level Settings
-==========================
-
-We have added the ability to set log level settings for a running topology using the Storm UI and the Storm CLI.
-
-The log level settings apply the same way as you'd expect from log4j, as all we are doing is telling log4j to set the level of the logger you provide. If you set the log level of a parent logger, the children loggers start using that level (unless the children have a more restrictive level already). A timeout can optionally be provided (except for DEBUG mode, where it’s required in the UI), if workers should reset log levels automatically.
-
-This revert action is triggered using a polling mechanism (every 30 seconds, but this is configurable), so you should expect your timeouts to be the value you provided plus anywhere between 0 and the setting's value.
-
-Using the Storm UI
--------------
-
-In order to set a level, click on a running topology, and then click on “Change Log Level” in the Topology Actions section.
-
-
-
-Next, provide the logger name, select the level you expect (e.g. WARN), and a timeout in seconds (or 0 if not needed). Then click on “Add”.
-
-
-
-To clear the log level click on the “Clear” button. This reverts the log level back to what it was before you added the setting. The log level line will disappear from the UI.
-
-While there is a delay resetting log levels back, setting the log level in the first place is immediate (or as quickly as the message can travel from the UI/CLI to the workers by way of nimbus and zookeeper).
-
-Using the CLI
--------------
-
-Using the CLI, issue the command:
-
-`./bin/storm set_log_level [topology name] -l [logger name]=[LEVEL]:[TIMEOUT]`
-
-For example:
-
-`./bin/storm set_log_level my_topology -l ROOT=DEBUG:30`
-
-Sets the ROOT logger to DEBUG for 30 seconds.
-
-`./bin/storm set_log_level my_topology -r ROOT`
-
-Clears the ROOT logger dynamic log level, resetting it to its original value.
-
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/dynamic-worker-profiling.md
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-Dynamic Worker Profiling
-==========================
-
-In multi-tenant mode, storm launches long-running JVMs across cluster without sudo access to user. Self-serving of Java heap-dumps, jstacks and java profiling of these JVMs would improve users' ability to analyze and debug issues when monitoring it actively.
-
-The storm dynamic profiler lets you dynamically take heap-dumps, jprofile or jstack for a worker jvm running on stock cluster. It let user download these dumps from the browser and use your favorite tools to analyze it The UI component page provides list workers for the component and action buttons. The logviewer lets you download the dumps generated by these logs. Please see the screenshots for more information.
-
-Using the Storm UI
--------------
-
-In order to request for heap-dump, jstack, start/stop/dump jprofile or restart a worker, click on a running topology, then click on specific component, then you can select workers by checking the box of any of the worker's executors in the Executors table, and then click on “Start","Heap", "Jstack" or "Restart Worker" in the "Profiling and Debugging" section.
-
-
-
-In the Executors table, click the checkbox in the Actions column next to any executor, and any other executors belonging to the same worker are automatically selected. When the action has completed, any output files created will available at the link in the Actions column.
-
-
-
-For start jprofile, provide a timeout in minutes (or 10 if not needed). Then click on “Start”.
-
-
-
-To stop the jprofile logging click on the “Stop” button. This dumps the jprofile stats and stops the profiling. Refresh the page for the line to disappear from the UI.
-
-Click on "My Dump Files" to go the logviewer UI for list of worker specific dump files.
-
-
-
-Configuration
--------------
-
-The "worker.profiler.command" can be configured to point to specific pluggable profiler, heapdump commands. The "worker.profiler.enabled" can be disabled if plugin is not available or jdk does not support Jprofile flight recording so that worker JVM options will not have "worker.profiler.childopts". To use different profiler plugin, you can change these configuration.
-
http://git-wip-us.apache.org/repos/asf/storm/blob/93e0d028/docs/documentation/images/ack_tree.png
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