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From j...@apache.org
Subject [5/7] helix git commit: Prepare website for 0.6.9 release and add 0.6.9 release notes.
Date Tue, 10 Oct 2017 00:02:36 GMT
http://git-wip-us.apache.org/repos/asf/helix/blob/2d1d16e4/website/0.6.9/src/site/markdown/tutorial_task_framework.md
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+<!---
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+<head>
+  <title>Tutorial - Task Framework</title>
+</head>
+
+## [Helix Tutorial](./Tutorial.html): Task Framework
+
+Task framework, in Helix, provides executable task scheduling and workflow management. In Helix, three layers of task abstraction have been offered to user for defining their logics of dependencies. The graph shows the relationships between three layers. Workflow can contain multiple jobs. One job can depend on other one. Multiple tasks, including same task different partition and different task different partition, can be added in one job.
+Task framework not only can abstract three layers task logics but also helps doing task assignment and rebalancing. User can create a workflow (or a job queue) at first beginning. Then jobs can be added into workflow. Those jobs contain the executable tasks implemented by user. Once workflow is completed, Helix will schedule the works based on the condition user provided.
+
+![Task Framework flow chart](./images/TaskFrameworkLayers.png)
+
+### Key Concepts
+* Task is the basic unit in Helix task framework. It can represents the a single runnable logics that user prefer to execute for each partition (distributed units).
+* Job defines one time operation across all the partitions. It contains multiple Tasks and configuration of tasks, such as how many tasks, timeout per task and so on.
+* Workflow is directed acyclic graph represents the relationships and running orders of Jobs. In addition, a workflow can also provide customized configuration, for example, Job dependencies.
+* JobQueue is another type of Workflow. Different from normal one, JobQueue is not terminated until user kill it. Also JobQueue can keep accepting newly coming jobs.
+
+### Implement Your Task
+
+#### [Task Interface](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/Task.java)
+
+The task interface contains two methods: run and cancel. User can implement his or her own logic in run function and cancel / roll back logic in cancel function.
+
+```
+public class MyTask implements Task {
+  @Override
+  TaskResult run() {
+    // Task logic
+  }
+ 
+  @Override
+  void cancel() {
+    // Cancel logic
+  }
+}
+```
+
+#### [TaskConfig](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/TaskConfig.java)
+
+In helix, usually an object config represents the abstraction of that object, such as TaskConfig, JobConfig and WorkflowConfig. TaskConfig contains configurable task conditions. TaskConfig does not require to have any input to create a new object:
+
+```
+TaskConfig taskConfig = new TaskConfig(null, null, null, null);
+```
+
+For these four fields:
+* Command: The task command, will use Job command if this is null
+* ID: Task unique id, will generate a new ID for this task if input is null
+* TaskTargetPartition: Target partition of a target. Could be null
+* ConfigMap: Task property key-value map containing all other property stated above, such as command, ID.
+
+#### Share Content Across Tasks and Jobs
+
+Task framework also provides a feature that user can store the key-value data per task, job and workflow. The content stored at workflow layer can shared by different jobs belong to this workflow. Similarly content persisted at job layer can shared by different tasks nested in this job. Currently, user can extend the abstract class [UserContentStore](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/UserContentStore.java) and use two methods putUserContent and getUserContent. It will similar to hash map put and get method except a Scope.  The Scope will define which layer this key-value pair to be persisted.
+
+```
+public class MyTask extends UserContentStore implements Task {
+  @Override
+  TaskResult run() {
+    putUserContent("KEY", "WORKFLOWVALUE", SCOPE.WORKFLOW);
+    putUserContent("KEY", "JOBVALUE", SCOPE.JOB);
+    putUserContent("KEY", "TASKVALUE", SCOPE.TASK);
+    String taskValue = getUserContent("KEY", SCOPE.TASK);
+  }
+ ...
+}
+```
+
+#### Return [Task Results](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/TaskResult.java)
+
+User can define the TaskResult for a task once it is at final stage (complete or failed). The TaskResult contains two fields: status and info. Status is current Task Status including COMPLETED, CANCELLED, FAILED and FATAL_FAILED. The difference between FAILED and FATAL_FAILED is that once the task defined as FATAL_FAILED, helix will not do the retry for this task and abort it. The other field is information, which is a String type. User can pass any information including error message, description and so on.
+
+```
+TaskResult run() {
+    ....
+    return new TaskResult(TaskResult.Status.FAILED, "ERROR MESSAGE OR OTHER INFORMATION");
+}
+```
+
+#### Task Retry and Abort
+
+Helix provides retry logics to users. User can specify the how many times allowed to tolerant failure of tasks under a job. It is a method will be introduced in Following Job Section. Another choice offered to user that if user thinks a task is very critical and do not want to do the retry once it is failed, user can return a TaskResult stated above with FATAL_FAILED status. Then Helix will not do the retry for that task.
+
+```
+return new TaskResult(TaskResult.Status.FATAL_FAILED, "DO NOT WANT TO RETRY, ERROR MESSAGE");
+```
+
+#### [TaskDriver](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/TaskDriver.java)
+
+All the control operation related to workflow and job are based on TaskDriver object. TaskDriver offers several APIs to controller, modify and track the tasks. Those APIs will be introduced in each section when they are necessary. TaskDriver object can be created either by [HelixManager](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/HelixManager.java) or [ZkClient](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/manager/zk/ZkClient.java) with cluster name:
+
+```
+HelixManager manager = new ZKHelixManager(CLUSTER_NAME, INSTANCE_NAME, InstanceType.PARTICIPANT, ZK_ADDRESS);
+TaskDriver taskDriver1 = new TaskDriver(manager);
+ 
+TaskDriver taskDriver2 = new TaskDriver(zkclient, CLUSTER_NAME);
+```
+
+#### Propagate Task Error Message to Helix
+
+When task encounter an error, it could be returned by TaskResult. Unfortunately, user can not get this TaskResult object directly. But Helix provides error messages persistent. Thus user can fetch the error messages from Helix via TaskDriver, which introduced above. The error messages will be stored in Info field per Job. Thus user have to get JobContext, which is the job status and result object.
+
+```
+taskDriver.getJobContext("JOBNAME").getInfo();
+```
+
+### Creating a Workflow
+
+#### One-time Workflow
+
+As common use, one-time workflow will be the default workflow as user created. The first step is to create a WorkflowConfig.Builder object with workflow name. Then all configs can be set in WorkflowConfig.Builder. Once the configuration is done, [WorkflowConfig](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/WorkflowConfig.java) object can be got from WorkflowConfig.Builder object.
+We have two rules to validate the Workflow configuration:
+* Expiry time should not be less than 0
+* Schedule config should be valid either one-time or a positive interval magnitude (Recurrent workflow)
+Example:
+
+```
+Workflow.Builder myWorkflowBuilder = new Workflow.Builder("MyWorkflow");
+myWorkflowBuilder.setExpiry(5000L);
+Workflow myWorkflow = myWorkflowBuilder.build();
+```
+
+#### Recurrent Workflow
+
+Recurrent workflow is the workflow scheduled periodically. The only config different from One-time workflow is to set a recurrent [ScheduleConfig](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/ScheduleConfig.java). There two methods in ScheduleConfig can help you to create a ScheduleConfig object: recurringFromNow and recurringFromDate. Both of them needs recurUnit (time unit for recurrent) and recurInteval (magnitude of recurrent interval). Here's the example:
+
+```
+ScheduleConfig myConfig1 = ScheduleConfig.recurringFFromNow(TimeUnit.MINUTES, 5L);
+ScheduleConfig myConfig2 = ScheduleConfig.recurringFFromDate(Calendar.getInstance.getTime, TimeUnit.HOURS, 10L);
+```
+
+Once this schedule config is created. It could be set in the workflow config:
+
+```
+Workflow.Builder myWorkflowBuilder = new Workflow.Builder("MyWorkflow");
+myWorkflowBuilder.setExpiry(2000L)
+                 .setScheduleConfig(ScheduleConfig.recurringFromNow(TimeUnit.DAYS, 5));
+Workflow myWorkflow = myWorkflowBuilder.build();
+```
+
+#### Start a Workflow
+
+Start a workflow is just using taskdrive to start it. Since this is an async call, after start the workflow, user can keep doing actions.
+
+```
+taskDriver.start(myWorkflow);
+```
+
+#### Stop a Workflow
+
+Stop workflow can be executed via TaskDriver:
+
+```
+taskDriver.stop(myWorkflow);
+```
+
+#### Resume a Workflow
+
+Once the workflow is stopped, it does not mean the workflow is gone. Thus user can resume the workflow that has been stopped. Using TaskDriver resume the workflow:
+
+```
+taskDriver.resume(myWorkflow);
+```
+
+#### Delete a Workflow
+
+Simliar to start, stop and resume, delete operation is supported by TaskDriver.
+
+```
+taskDriver.delete(myWorkflow);
+```
+
+#### Add a Job
+
+WARNING: Job can only be added to WorkflowConfig.Builder. Once WorkflowConfig built, no job can be added! For creating a Job, please refering following section (Create a Job)
+
+```
+myWorkflowBuilder.addJob("JobName", jobConfigBuilder);
+```
+
+#### Add a Job dependency
+
+Jobs can have dependencies. If one job2 depends job1, job2 will not be scheduled until job1 finished.
+
+```
+myWorkflowBuilder.addParentChildDependency(ParentJobName, ChildJobName);
+```
+
+#### Additional Workflow Options
+
+| Additional Config Options | Detail |
+| ------------------------- | ------ |
+| _setJobDag(JobDag v)_ | If user already defined the job DAG, it could be set with this method. |
+| _setExpiry(long v, TimeUnit unit)_ | Set the expiration time for this workflow. |
+| _setFailureThreshold(int failureThreshold)_ | Set the failure threshold for this workflow, once job failures reach this number, the workflow will be failed. |
+| _setWorkflowType(String workflowType)_ | Set the user defined workflowType for this workflow. |
+| _setTerminable(boolean isTerminable)_ | Set the whether this workflow is terminable or not. |
+| _setCapacity(int capacity)_ | Set the number of jobs that workflow can hold before reject further jobs. Only used when workflow is not terminable. |
+| _setTargetState(TargetState v)_ | Set the final state of this workflow. |
+
+### Creating a Queue
+
+[Job queue](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/JobQueue.java) is another shape of workflow. Here listed different between a job queue and workflow:
+
+| Property | Workflow | Job Queue |
+| -------- | -------- | --------- |
+| Existing time | Workflow will be deleted after it is done. | Job queue will be there until user delete it. |
+| Add jobs | Once workflow is build, no job can be added. | Job queue can keep accepting jobs. |
+| Parallel run | Allows parallel run for jobs without dependencies | No parallel run allowed except setting _ParallelJobs_ |
+
+For creating a job queue, user have to provide queue name and workflow config (please refer above Create a Workflow). Similar to other task object, create a JobQueue.Builder first. Then JobQueue can be validated and generated via build function.
+
+```
+WorkflowConfig.Builder myWorkflowCfgBuilder = new WorkflowConfig.Builder().setWorkFlowType("MyType");
+JobQueue jobQueue = new JobQueue.Builder("MyQueueName").setWorkflowConfig(myWorkflowCfgBuilder.build()).build();
+```
+
+####Append Job to Queue
+
+WARNING:Different from normal workflow, job for JobQueue can be append even in anytime. Similar to workflow add a job, job can be appended via enqueueJob function via TaskDriver.
+
+```
+jobQueueBuilder.enqueueJob("JobName", jobConfigBuilder);
+```
+
+####Delete Job from Queue
+
+Helix allowed user to delete a job from existing queue. We offers delete API in TaskDriver to do this. Delete job from queue and this queue has to be stopped. Then user can resume the job once delete success.
+
+```
+taskDriver.stop("QueueName");
+taskDriver.deleteJob("QueueName", "JobName");
+taskDriver.resume("QueueName");
+```
+
+####Additional Option for JobQueue
+
+_setParallelJobs(int parallelJobs)_ : Set the how many jobs can parallel running, except there is any dependencies.
+
+###Create a Job
+
+Before generate a [JobConfig](https://github.com/apache/helix/blob/helix-0.6.x/helix-core/src/main/java/org/apache/helix/task/JobConfig.java) object, user still have to use JobConfig.Builder to build JobConfig.
+
+```
+JobConfig.Builder myJobCfgBuilder = new JobConfig.Builder();
+JobConfig myJobCfg = myJobCfgBuilder.build();
+```
+
+Helix has couple rules to validate a job:
+* Each job must at least have one task to execute. For adding tasks and task rules please refer following section Add Tasks.
+* Task timeout should not less than zero.
+* Number of concurrent tasks per instances should not less than one.
+* Maximum attempts per task should not less than one
+* There must be a workflow name
+
+#### Add Tasks
+
+There are two ways of adding tasks:
+* Add by TaskConfig. Tasks can be added via adding TaskConfigs. User can create a List of TaskConfigs or add TaskConfigMap, which is a task id to TaskConfig mapping.
+
+```
+TaskConfig taskCfg = new TaskConfig(null, null, null, null);
+List<TaskConfig> taskCfgs = new ArrayList<TaskConfig>();
+myJobCfg.addTaskConfigs(taskCfgs);
+ 
+Map<String, TaskConfig> taskCfgMap = new HashMap<String, TaskConfig>();
+taskCfgMap.put(taskCfg.getId(), taskCfg);
+myJobCfg.addTaskConfigMap(taskCfgMap);
+```
+
+* Add by Job command. If user does not want to specify each TaskConfig, we can create identical tasks based on Job command with number of tasks.
+
+```
+myJobCfg.setCommand("JobCommand").setNumberOfTasks(10);
+```
+WARNING: Either user provides TaskConfigs / TaskConfigMap or both of Job command and number tasks (except Targeted Job, refer following section) . Otherwise, validation will be failed.
+
+#### Generic Job
+
+Generic Job is the default job created. It does not have targeted resource. Thus this generic job could be assigned to one of eligble instances.
+
+#### Targeted Job
+
+Targeted Job has set up the target resource. For this kind of job, Job command is necessary, but number of tasks is not. The tasks will depends on the partion number of targeted resource. To set target resource, just put target resource name to JobConfig.Builder.
+
+```
+myJobCfgBuilder.setTargetResource("TargetResourceName");
+```
+
+In addition, user can specify the instance target state. For example, if user want to run the Task on "Master" state instance, setTargetPartitionState method can help to set the partition to assign to specific instance.
+
+```
+myJobCfgBuilder.setTargetPartitionState(Arrays.asList(new String[]{"Master", "Slave"}));
+```
+
+#### Instance Group
+
+Grouping jobs with targeted group of instances feature has been supported. User firstly have to define the instance group tag for instances, which means label some instances with specific tag. Then user can put those tags to a job that only would like to assigned to those instances. For example, customer data only available on instance 1, 2, 3. These three instances can be tagged as "CUSTOMER" and  customer data related jobs can set  the instance group tag "CUSTOMER". Thus customer data related jobs will only assign to instance 1, 2, 3. 
+To add instance group tag, just set it in JobConfig.Builder:
+
+```
+jobCfg.setInstanceGroupTag("INSTANCEGROUPTAG");
+```
+
+#### Additional Job Options
+
+| Operation | Detail |
+| --------- | ------ |
+| _setWorkflow(String workflowName)_ | Set the workflow that this job belongs to |
+| _setTargetPartions(List\<String\> targetPartionNames)_ | Set list of partition names |
+| _setTargetPartionStates(Set\<String\>)_ | Set the partition states |
+| _setCommand(String command)_ | Set the job command |
+| _setJobCommandConfigMap(Map\<String, String\> v)_ | Set the job command config maps |
+| _setTimeoutPerTask(long v)_ | Set the timeout for each task |
+| _setNumConcurrentTasksPerInstance(int v)_ | Set number of tasks can concurrent run on same instance |
+| _setMaxAttemptsPerTask(int v)_ | Set times of retry for a task |
+| _setFailureThreshold(int v)_ | Set failure tolerance of tasks for this job |
+| _setTaskRetryDelay(long v)_ | Set the delay time before a task retry |
+| _setIgnoreDependentJobFailure(boolean ignoreDependentJobFailure)_ | Set whether ignore the job failure of parent job of this job |
+| _setJobType(String jobType)_ | Set the job type of this job |
+
+### Monitor the status of your job
+As we introduced the excellent util TaskDriver in Workflow Section, we have extra more functionality that provided to user. The user can synchronized wait Job or Workflow until it reaches certain STATES. The function Helix have API pollForJobState and pollForWorkflowState. For pollForJobState, it accepts arguments:
+* Workflow name, required
+* Job name, required
+* Timeout, not required, will be three minutes if user choose function without timeout argument. Time unit is milisecond.
+* TaskStates, at least one state. This function can accept multiple TaskState, will end function until one of those TaskState reaches.
+For example:
+
+```
+taskDriver.pollForJobState("MyWorkflowName", "MyJobName", 180000L, TaskState.FAILED, TaskState.FATAL_FAILED);
+taskDriver.pollForJobState("MyWorkflowName", "MyJobName", TaskState.COMPLETED);
+```
+
+For pollForWorkflowState, it accepts similar arguments except Job name. For example:
+
+```
+taskDriver.pollForWorkflowState("MyWorkflowName", 180000L, TaskState.FAILED, TaskState.FATAL_FAILED);
+taskDriver.pollForWorkflowState("MyWorkflowName", TaskState.COMPLETED);
+```

http://git-wip-us.apache.org/repos/asf/helix/blob/2d1d16e4/website/0.6.9/src/site/markdown/tutorial_throttling.md
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+<!---
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+<head>
+  <title>Tutorial - Throttling</title>
+</head>
+
+## [Helix Tutorial](./Tutorial.html): Throttling
+
+In this chapter, we\'ll learn how to control the parallel execution of cluster tasks.  Only a centralized cluster manager with global knowledge (i.e. Helix) is capable of coordinating this decision.
+
+### Throttling
+
+Since all state changes in the system are triggered through transitions, Helix can control the number of transitions that can happen in parallel. Some of the transitions may be lightweight, but some might involve moving data, which is quite expensive from a network and IOPS perspective.
+
+Helix allows applications to set a threshold on transitions. The threshold can be set at multiple scopes:
+
+* MessageType e.g STATE_TRANSITION
+* TransitionType e.g SLAVE-MASTER
+* Resource e.g database
+* Node i.e per-node maximum transitions in parallel
+
+

http://git-wip-us.apache.org/repos/asf/helix/blob/2d1d16e4/website/0.6.9/src/site/markdown/tutorial_user_def_rebalancer.md
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+<!---
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+<head>
+  <title>Tutorial - User-Defined Rebalancing</title>
+</head>
+
+## [Helix Tutorial](./Tutorial.html): User-Defined Rebalancing
+
+Even though Helix can compute both the location and the state of replicas internally using a default fully-automatic rebalancer, specific applications may require rebalancing strategies that optimize for different requirements. Thus, Helix allows applications to plug in arbitrary rebalancer algorithms that implement a provided interface. One of the main design goals of Helix is to provide maximum flexibility to any distributed application. Thus, it allows applications to fully implement the rebalancer, which is the core constraint solver in the system, if the application developer so chooses.
+
+Whenever the state of the cluster changes, as is the case when participants join or leave the cluster, Helix automatically calls the rebalancer to compute a new mapping of all the replicas in the resource. When using a pluggable rebalancer, the only required step is to register it with Helix. Subsequently, no additional bootstrapping steps are necessary. Helix uses reflection to look up and load the class dynamically at runtime. As a result, it is also technically possible to change the rebalancing strategy used at any time.
+
+The Rebalancer interface is as follows:
+
+```
+void init(HelixManager manager);
+
+IdealState computeNewIdealState(String resourceName, IdealState currentIdealState,
+    final CurrentStateOutput currentStateOutput, final ClusterDataCache clusterData);
+```
+The first parameter is the resource to rebalance, the second is pre-existing ideal mappings, the third is a snapshot of the actual placements and state assignments, and the fourth is a full cache of all of the cluster data available to Helix. Internally, Helix implements the same interface for its own rebalancing routines, so a user-defined rebalancer will be cognizant of the same information about the cluster as an internal implementation. Helix strives to provide applications the ability to implement algorithms that may require a large portion of the entire state of the cluster to make the best placement and state assignment decisions possible.
+
+An IdealState is a full representation of the location of each replica of each partition of a given resource. This is a simple representation of the placement that the algorithm believes is the best possible. If the placement meets all defined constraints, this is what will become the actual state of the distributed system.
+
+### Specifying a Rebalancer
+For implementations that set up the cluster through existing code, the following HelixAdmin calls will update the Rebalancer class:
+
+```
+IdealState idealState = helixAdmin.getResourceIdealState(clusterName, resourceName);
+idealState.setRebalanceMode(RebalanceMode.USER_DEFINED);
+idealState.setRebalancerClassName(className);
+helixAdmin.setResourceIdealState(clusterName, resourceName, idealState);
+```
+
+There are two key fields to set to specify that a pluggable rebalancer should be used. First, the rebalance mode should be set to USER_DEFINED, and second the rebalancer class name should be set to a class that implements Rebalancer and is within the scope of the project. The class name is a fully-qualified class name consisting of its package and its name. Without specification of the USER_DEFINED mode, the user-defined rebalancer class will not be used even if specified. Furthermore, Helix will not attempt to rebalance the resources through its standard routines if its mode is USER_DEFINED, regardless of whether or not a rebalancer class is registered.
+
+### Example
+
+In the next release (0.7.0), we will provide a full recipe of a user-defined rebalancer in action.
+
+Consider the case where partitions are locks in a lock manager and 6 locks are to be distributed evenly to a set of participants, and only one participant can hold each lock. We can define a rebalancing algorithm that simply takes the modulus of the lock number and the number of participants to evenly distribute the locks across participants. Helix allows capping the number of partitions a participant can accept, but since locks are lightweight, we do not need to define a restriction in this case. The following is a succinct implementation of this algorithm.
+
+```
+@Override
+IdealState computeNewIdealState(String resourceName, IdealState currentIdealState,
+    final CurrentStateOutput currentStateOutput, final ClusterDataCache clusterData) {
+  // Get the list of live participants in the cluster
+  List<String> liveParticipants = new ArrayList<String>(clusterData.getLiveInstances().keySet());
+
+  // Count the number of participants allowed to lock each lock (in this example, this is 1)
+  int lockHolders = Integer.parseInt(currentIdealState.getReplicas());
+
+  // Fairly assign the lock state to the participants using a simple mod-based sequential
+  // assignment. For instance, if each lock can be held by 3 participants, lock 0 would be held
+  // by participants (0, 1, 2), lock 1 would be held by (1, 2, 3), and so on, wrapping around the
+  // number of participants as necessary.
+  int i = 0;
+  for (String partition : currentIdealState.getPartitionSet()) {
+    List<String> preferenceList = new ArrayList<String>();
+    for (int j = i; j < i + lockHolders; j++) {
+      int participantIndex = j % liveParticipants.size();
+      String participant = liveParticipants.get(participantIndex);
+      // enforce that a participant can only have one instance of a given lock
+      if (!preferenceList.contains(participant)) {
+        preferenceList.add(participant);
+      }
+    }
+    currentIdealState.setPreferenceList(partition, preferenceList);
+    i++;
+  }
+  return assignment;
+}
+```
+
+Here are the IdealState preference lists emitted by the user-defined rebalancer for a 3-participant system whenever there is a change to the set of participants.
+
+* Participant_A joins
+
+```
+{
+  "lock_0": ["Participant_A"],
+  "lock_1": ["Participant_A"],
+  "lock_2": ["Participant_A"],
+  "lock_3": ["Participant_A"],
+  "lock_4": ["Participant_A"],
+  "lock_5": ["Participant_A"],
+}
+```
+
+A preference list is a mapping for each resource of partition to the participants serving each replica. The state model is a simple LOCKED/RELEASED model, so participant A holds all lock partitions in the LOCKED state.
+
+* Participant_B joins
+
+```
+{
+  "lock_0": ["Participant_A"],
+  "lock_1": ["Participant_B"],
+  "lock_2": ["Participant_A"],
+  "lock_3": ["Participant_B"],
+  "lock_4": ["Participant_A"],
+  "lock_5": ["Participant_B"],
+}
+```
+
+Now that there are two participants, the simple mod-based function assigns every other lock to the second participant. On any system change, the rebalancer is invoked so that the application can define how to redistribute its resources.
+
+* Participant_C joins (steady state)
+
+```
+{
+  "lock_0": ["Participant_A"],
+  "lock_1": ["Participant_B"],
+  "lock_2": ["Participant_C"],
+  "lock_3": ["Participant_A"],
+  "lock_4": ["Participant_B"],
+  "lock_5": ["Participant_C"],
+}
+```
+
+This is the steady state of the system. Notice that four of the six locks now have a different owner. That is because of the naïve modulus-based assignmemt approach used by the user-defined rebalancer. However, the interface is flexible enough to allow you to employ consistent hashing or any other scheme if minimal movement is a system requirement.
+
+* Participant_B fails
+
+```
+{
+  "lock_0": ["Participant_A"],
+  "lock_1": ["Participant_C"],
+  "lock_2": ["Participant_A"],
+  "lock_3": ["Participant_C"],
+  "lock_4": ["Participant_A"],
+  "lock_5": ["Participant_C"],
+}
+```
+
+On any node failure, as in the case of node addition, the rebalancer is invoked automatically so that it can generate a new mapping as a response to the change. Helix ensures that the Rebalancer has the opportunity to reassign locks as required by the application.
+
+* Participant_B (or the replacement for the original Participant_B) rejoins
+
+```
+{
+  "lock_0": ["Participant_A"],
+  "lock_1": ["Participant_B"],
+  "lock_2": ["Participant_C"],
+  "lock_3": ["Participant_A"],
+  "lock_4": ["Participant_B"],
+  "lock_5": ["Participant_C"],
+}
+```
+
+The rebalancer was invoked once again and the resulting IdealState preference lists reflect the steady state.
+
+### Caveats
+- The rebalancer class must be available at runtime, or else Helix will not attempt to rebalance at all
+- The Helix controller will only take into account the preference lists in the new IdealState for this release. In 0.7.0, Helix rebalancers will be able to compute the full resource assignment, including the states.
+- Helix does not currently persist the new IdealState computed by the user-defined rebalancer. However, the Helix property store is available for saving any computed state. In 0.7.0, Helix will persist the result of running the rebalancer.

http://git-wip-us.apache.org/repos/asf/helix/blob/2d1d16e4/website/0.6.9/src/site/markdown/tutorial_yaml.md
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diff --git a/website/0.6.9/src/site/markdown/tutorial_yaml.md b/website/0.6.9/src/site/markdown/tutorial_yaml.md
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@@ -0,0 +1,102 @@
+<!---
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+<head>
+  <title>Tutorial - YAML Cluster Setup</title>
+</head>
+
+## [Helix Tutorial](./Tutorial.html): YAML Cluster Setup
+
+As an alternative to using Helix Admin to set up the cluster, its resources, constraints, and the state model, Helix supports bootstrapping a cluster configuration based on a YAML file. Below is an annotated example of such a file for a simple distributed lock manager where a lock can only be LOCKED or RELEASED, and each lock only allows a single participant to hold it in the LOCKED state.
+
+```
+clusterName: lock-manager-custom-rebalancer # unique name for the cluster (required)
+resources:
+  - name: lock-group # unique resource name (required)
+    rebalancer: # required
+      mode: USER_DEFINED # required - USER_DEFINED means we will provide our own rebalancer
+      class: org.apache.helix.userdefinedrebalancer.LockManagerRebalancer # required for USER_DEFINED
+    partitions:
+      count: 12 # number of partitions for the resource (default is 1)
+      replicas: 1 # number of replicas per partition (default is 1)
+    stateModel:
+      name: lock-unlock # model name (required)
+      states: [LOCKED, RELEASED, DROPPED] # the list of possible states (required if model not built-in)
+      transitions: # the list of possible transitions (required if model not built-in)
+        - name: Unlock
+          from: LOCKED
+          to: RELEASED
+        - name: Lock
+          from: RELEASED
+          to: LOCKED
+        - name: DropLock
+          from: LOCKED
+          to: DROPPED
+        - name: DropUnlock
+          from: RELEASED
+          to: DROPPED
+        - name: Undrop
+          from: DROPPED
+          to: RELEASED
+      initialState: RELEASED # (required if model not built-in)
+    constraints:
+      state:
+        counts: # maximum number of replicas of a partition that can be in each state (required if model not built-in)
+          - name: LOCKED
+            count: "1"
+          - name: RELEASED
+            count: "-1"
+          - name: DROPPED
+            count: "-1"
+        priorityList: [LOCKED, RELEASED, DROPPED] # states in order of priority (all priorities equal if not specified)
+      transition: # transitions priority to enforce order that transitions occur
+        priorityList: [Unlock, Lock, Undrop, DropUnlock, DropLock] # all priorities equal if not specified
+participants: # list of nodes that can serve replicas (optional if dynamic joining is active, required otherwise)
+  - name: localhost_12001
+    host: localhost
+    port: 12001
+  - name: localhost_12002
+    host: localhost
+    port: 12002
+  - name: localhost_12003
+    host: localhost
+    port: 12003
+```
+
+Using a file like the one above, the cluster can be set up either with the command line:
+
+```
+helix/helix-core/target/helix-core/pkg/bin/YAMLClusterSetup.sh localhost:2199 lock-manager-config.yaml
+```
+
+or with code:
+
+```
+YAMLClusterSetup setup = new YAMLClusterSetup(zkAddress);
+InputStream input =
+    Thread.currentThread().getContextClassLoader()
+        .getResourceAsStream("lock-manager-config.yaml");
+YAMLClusterSetup.YAMLClusterConfig config = setup.setupCluster(input);
+```
+
+Some notes:
+
+- A rebalancer class is only required for the USER_DEFINED mode. It is ignored otherwise.
+
+- Built-in state models, like OnlineOffline, LeaderStandby, and MasterSlave, or state models that have already been added only require a name for stateModel. If partition and/or replica counts are not provided, a value of 1 is assumed.

http://git-wip-us.apache.org/repos/asf/helix/blob/2d1d16e4/website/0.6.9/src/site/resources/.htaccess
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diff --git a/website/0.6.9/src/site/resources/.htaccess b/website/0.6.9/src/site/resources/.htaccess
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+++ b/website/0.6.9/src/site/resources/.htaccess
@@ -0,0 +1,20 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+Redirect /download.html /download.cgi

http://git-wip-us.apache.org/repos/asf/helix/blob/2d1d16e4/website/0.6.9/src/site/resources/css/bootstrap-responsive.min.css
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diff --git a/website/0.6.9/src/site/resources/css/bootstrap-responsive.min.css b/website/0.6.9/src/site/resources/css/bootstrap-responsive.min.css
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--- /dev/null
+++ b/website/0.6.9/src/site/resources/css/bootstrap-responsive.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Bootstrap Responsive v2.2.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
+ */@-ms-viewport{width:device-width}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.hidden{display:none;visibility:hidden}.visible-phone{display:none!important}.visible-tablet{display:none!important}.hidden-desktop{display:none!important}.visible-desktop{display:inherit!important}@media(min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit!important}.visible-desktop{display:none!important}.visible-tablet{display:inherit!important}.hidden-tablet{display:none!important}}@media(max-width:767px){.hidden-desktop{display:inherit!important}.visible-desktop{display:none!important}.visible-phone{display:inherit!important}.hidden-phone{display:none!important}}@media(min-width:1
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