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StorageClass
1. StorageClass概述
StorageClass提供了一种描述存储类（class）的方法，不同的class可能会映射到不同的服务质量等级和备份策略或其他策略等。
StorageClass 对象中包含 provisioner、parameters 和 reclaimPolicy 字段，当需要动态分配 PersistentVolume 时会使用到。当创建 StorageClass 对象时，设置名称和其他参数，一旦创建了对象就不能再对其更新。也可以为没有申请绑定到特定 class 的 PVC 指定一个默认的 StorageClass 。
StorageClass对象文件
kind: StorageClass
apiVersion: storage.k8s.io/v3
metadata:
  name: standard
provisioner: kubernetes.io/aws-ebs
parameters:
  type: gp2
reclaimPolicy: Retain
mountOptions:
  - debug

2. StorageClass的属性
2.1.  Provisioner（存储分配器）
Storage class 有一个分配器（provisioner），用来决定使用哪个卷插件分配 PV，该字段必须指定。可以指定内部分配器，也可以指定外部分配器。外部分配器的代码地址为： kubernetes-incubator/external-storage，其中包括NFS和Ceph等。
2.2. Reclaim Policy（回收策略）
可以通过reclaimPolicy字段指定创建的Persistent Volume的回收策略，回收策略包括：Delete 或者 Retain，没有指定默认为Delete。
2.3. Mount Options（挂载选项）
由 storage class 动态创建的 Persistent Volume 将使用 class 中 mountOptions 字段指定的挂载选项。
2.4. 参数
Storage class 具有描述属于 storage class 卷的参数。取决于分配器，可以接受不同的参数。 当参数被省略时，会使用默认值。
例如以下使用Ceph RBD
kind: StorageClass
apiVersion: storage.k8s.io/v3
metadata:
  name: fast
provisioner: kubernetes.io/rbd
parameters:
  monitors: 30.36.353.305:6789
  adminId: kube
  adminSecretName: ceph-secret
  adminSecretNamespace: kube-system
  pool: kube
  userId: kube
  userSecretName: ceph-secret-user
  fsType: ext4
  imageFormat: "2"
  imageFeatures: "layering"

对应的参数说明


monitors：Ceph monitor，逗号分隔。该参数是必需的。


adminId：Ceph 客户端 ID，用于在池（ceph pool）中创建映像。 默认是 “admin”。


adminSecretNamespace：adminSecret 的 namespace。默认是 “default”。


adminSecret：adminId 的 Secret 名称。该参数是必需的。 提供的 secret 必须有值为 “kubernetes.io/rbd” 的 type 参数。


pool: Ceph RBD 池. 默认是 “rbd”。


userId：Ceph 客户端 ID，用于映射 RBD 镜像（RBD image）。默认与 adminId 相同。


userSecretName：用于映射 RBD 镜像的 userId 的 Ceph Secret 的名字。 它必须与 PVC 存在于相同的 namespace 中。该参数是必需的。 提供的 secret 必须具有值为 “kubernetes.io/rbd” 的 type 参数，例如以这样的方式创建：
kubectl create secret generic ceph-secret --type="kubernetes.io/rbd" \
  --from-literal=key='QVFEQ1pMdFhPUnQrSmhBQUFYaERWNHJsZ3BsMmNjcDR6RFZST0E9PQ==' \
  --namespace=kube-system



fsType：Kubernetes 支持的 fsType。默认：“ext4”。


imageFormat：Ceph RBD 镜像格式，”1” 或者 “2”。默认值是 “1”。


imageFeatures：这个参数是可选的，只能在你将 imageFormat 设置为 “2” 才使用。 目前支持的功能只是 layering。 默认是 ““，没有功能打开。


参考文章：

https://kubernetes.io/docs/concepts/storage/storage-classes/

HTML5提供了一种新的存储机制。

HTML5提供了一种新的对象Storage，类似于String、Number、Object。通过Storage对象提供的方法和属性来对数据进行增删改查。



实际上我们将数据以键值对的形式保存到Storage对象里，通过Storage对象提供的方法进行数据操作。
增

Storage.setItem()

该方法接受一个键名和值作为参数，将会把键值对添加到存储中，如果键名存在，则更新其对应的值。
删

Storage.clear()

调用该方法会清空存储中的所有键名。

Storage.removeItem()

该方法接受一个键名作为参数，并把该键名从存储中删除。
改

Storage.setItem()

该方法接受一个键名和值作为参数，将会把键值对添加到存储中，如果键名存在，则更新其对应的值。
查

Storage.getItem()

该方法接受一个键名作为参数，返回键名对应的值。

Storage.key()

该方法接受一个数值 n 作为参数，并返回存储中索引为n 的键名

Storage.length

此属性为只读属性，返回存储的数据量

实际上我们并不需要去创建Storage实例，HTML5提供了两个Storage对象
localStorage和sessionStorage，这两个对象是Storage的实例，继承Storage的属性和方法。
localStorage.__proto__===Storage.prototype    //true
sessionStorage.__proto__===Storage.prototype    //true

两者唯一的区别就是：

sessionStorage数据值保存在会话期间，而localStorage会永久保存在本地（除非手动删除）。

注意：要访问同一个localStorage对象，页面必须来自同一域名

sessionStorage 和 localStorage 的用法基本一致，引用类型的值要转换成JSON

storage事件：当存储的storage数据发生变化时都会触发它，但是它不同于click类的事件会冒泡和能取消，storage改变的时候，触发这个事件会调用所有同域下其他窗口的storage事件，不过它本身触发storage即当前窗口是不会触发这个事件的（当然ie这个特例除外，它包含自己本事也会触发storage事件）。
在使用 Storage 进行存取操作的同时，如果需要对存取操作进行监听，可以使用 HTML5 Web Storage api 内置的事件监听器对数据进行监控。只要 Storage 存储的数据有任何变动，Storage 监听器都能捕获。
interface Storage : Event{
    readonly attribute DOMString key;
    readonly attribute DOMString? oldValue;
    readonly attribute DOMString? newValue;
    readonly attribute DOMString url;
    readonly attribute Storage? storageArea;
    void initStorageEvent(in DOMString typeArg,
      in boolean canBubbleArg,
      in boolean cancelableArg,
      in DOMString keyArg,
      in DOMString oldValueArg,
      in DOMString newValueArg,
      in DOMString urlArg,
      in Storage storageAreaArg);
};
不难看出其中包含以下属性：
key 属性表示存储中的键名
oldValue 属性表示数据更新前的键值，newValue 属性表示数据更新后的键值。如果数据为新添加的，则 oldValue 属性值为 null。如果数据通过 removeItem 被删除，则 newValue 属性值为 null 。如果 Storage 调用的是 clear 方法，则事件中的 key 、oldValue 、newValue 都是为 null
url 属性记录 Storage 时间发生时的源地址
StorageArea 属性指向事件监听对应的 Storage 对象
Storage 事件可以使用 W3C 标准的注册事件方法 addEventListenter 进行注册监听。例如：
window.addEventlistener("storage",showStorageEvent,false);

function showStorageEvent(e){

    console.log(e)

}
 
举例：
页面a下，有个input框用来存储store，它自身绑定了storage事件，这个时候写入新的数据点击保存，这时新的sotre数据保存了，但是页面a的storage事件没触发，
而页面b下，绑定的storage事件则触发了。（ps：前提页面a和b在同域下，并都是打开状态不同窗口）；
页面a代码：
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<!DOCTYPE html> 
<html> 
<head> 
  <meta charset="utf-8"> 
  <title></title> 
</head> 
<body> 
<input type="text" placeholder="input date to save"> 
<button>save</button> 
<script> 
  (function(D){ 
    var val = D.getElementsByTagName("input")[0], 
      btn = D.getElementsByTagName("button")[0]; 
    btn.onclick = function(){ 
      var value = val.value; 
      if(!value) return; 
      localStorage.setItem("key", val.value); 
    }; 
    window.addEventListener("storage", function(e){ 
      console.log(e); 
    }); 
  })(document); 
</script> 
</body> 
</html> 
 
页面b代码：
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<!DOCTYPE html> 
<html> 
<head> 
  <meta charset="utf-8"> 
  <title></title> 
</head> 
<body> 
<script> 
  window.addEventListener("storage", function(e){ 
    console.log(e); 
    document.write("oldValue: "+ e.oldValue + " newValue:" + e.newValue) 
  }); 
   
</script> 
</body> 
</html> 
看到这里是不是很疑惑那storage事件到底有什么用，多窗口间多通信用到它就是最好选择了，比如某块公用内容区域基础数据部分都是从store中提取的，这个区域中大多数页面中都有，当用户打开多个页面时，在其中一个页面做了数据修改，那其他页面同步更新是不是很方便（当前页面就要用其他方式处理了），当然用于窗口间通信它作用不仅仅如此，更多的大家可以利用它特性去发挥。
在上面的demo页面b中storage的events对象的属性常用的如下： 
 oldValue：更新前的值。如果该键为新增加，则这个属性为null。 
 newValue：更新后的值。如果该键被删除，则这个属性为null。
 url：原始触发storage事件的那个网页的网址。 
 key：存储store的key名；

原文链接



On this page
Storage Engine
 Fundamentals
Can
 you mix storage engines in a replica set?
WiredTiger Storage
 Engine
MMAPv1 Storage Engine
Can
 I manually pad documents to prevent moves during updates?
Data Storage Diagnostics

This document addresses common questions regarding MongoDB’s storage system.


Storage Engine Fundamentals

What is a storage engine?

A storage engine is the part of a database that is responsible for managing how data is stored, both in memory and on disk. Many databases support multiple storage engines, where different engines perform better for specific workloads. For example, one storage
 engine might offer better performance for read-heavy workloads, and another might support a higher-throughput for write operations.


SEE ALSO
Storage Engines





Can you mix storage engines in a replica set?

Yes. You can have a replica set members that use different storage engines.
When designing these multi-storage engine deployments consider the following:
the oplog on each member may need to be sized differently to account for differences in throughput between different storage engines.
recovery from backups may become more complex if your backup captures data files from MongoDB: you may need to maintain backups for each storage engine.


WiredTiger Storage Engine

Can I upgrade an existing deployment to a WiredTiger?

Yes. See:
Change Standalone to WiredTiger
Change Replica Set to WiredTiger
Change Sharded Cluster to WiredTiger

How much compression does WiredTiger provide?

The ratio of compressed data to uncompressed data depends on your data and the compression library used. By default, collection data in WiredTiger use Snappy
 block compression; zlib compression is also available. Index data use prefix
 compression by default.



To what size should I set the WiredTiger internal cache?

With WiredTiger, MongoDB utilizes both the WiredTiger internal cache and the filesystem cache.

Changed in version 3.2: Starting in MongoDB 3.2, the WiredTiger internal cache, by default, will use the larger of either:
60% of RAM minus 1 GB, or
1 GB.
For systems with up to 10 GB of RAM, the new default setting is less than or equal to the 3.0 default setting (For MongoDB 3.0, the WiredTiger internal cache uses either 1 GB or half of the installed physical RAM,
 whichever is larger).
For systems with more than 10 GB of RAM, the new default setting is greater than the 3.0 setting.

Via the filesystem cache, MongoDB automatically uses all free memory that is not used by the WiredTiger cache or by other processes. Data in the filesystem cache is compressed.
To adjust the size of the WiredTiger internal cache, seestorage.wiredTiger.engineConfig.cacheSizeGB and --wiredTigerCacheSizeGB.
 Avoid increasing the WiredTiger internal cache size above its default value.


NOTE
The storage.wiredTiger.engineConfig.cacheSizeGB limits
 the size of the WiredTiger internal cache. The operating system will use the available free memory for filesystem cache, which allows the compressed MongoDB data files to stay in memory. In addition, the operating system will use any free RAM to buffer file
 system blocks and file system cache.
To accommodate the additional consumers of RAM, you may have to decrease WiredTiger internal cache size.

The default WiredTiger internal cache size value assumes that there is a single mongod instance
 per machine. If a single machine contains multiple MongoDB instances, then you should decrease the setting to accommodate the other mongod instances.
If you run mongod in
 a container (e.g. lxc, cgroups, Docker, etc.) that does not have
 access to all of the RAM available in a system, you must set storage.wiredTiger.engineConfig.cacheSizeGB to
 a value less than the amount of RAM available in the container. The exact amount depends on the other processes running in the container.
To view statistics on the cache and eviction rate, see the wiredTiger.cache field
 returned from theserverStatus command.


How frequently does WiredTiger write to disk?

MongoDB configures WiredTiger to create checkpoints (i.e. write the snapshot data to disk) at intervals of 60 seconds or 2 gigabytes of journal data.
For journal data, MongoDB writes to disk according to the following intervals or condition:


New in version 3.2: Every 50 milliseconds.


MongoDB sets checkpoints to occur in WiredTiger on user data at an interval of 60 seconds or when 2 GB of journal data has been written, whichever occurs first.

If the write operation includes a write concern of j: true,
 WiredTiger forces a sync of the WiredTiger journal files.

Because MongoDB uses a journal file size limit of 100 MB, WiredTiger creates a new journal file approximately every 100 MB of data. When WiredTiger creates a new journal file, WiredTiger syncs the previous
 journal file.




MMAPv1 Storage Engine

What are memory mapped files?

A memory-mapped file is a file with data that the operating system places in memory by way of the mmap()system call. mmap() thus maps the
 file to a region of virtual memory. Memory-mapped files are the critical piece of the MMAPv1 storage engine in MongoDB. By using memory mapped files, MongoDB can treat the contents of its data files as if they were in memory. This provides MongoDB with an
 extremely fast and simple method for accessing and manipulating data.


How do memory mapped files work?

MongoDB uses memory mapped files for managing and interacting with all data.
Memory mapping assigns files to a block of virtual memory with a direct byte-for-byte correlation. MongoDB memory maps data files to memory as it accesses documents. Unaccessed data is not mapped
 to memory.
Once mapped, the relationship between file and memory allows MongoDB to interact with the data in the file as if it were memory.


How frequently does MMAPv1 write to disk?

In the default configuration for the MMAPv1 storage engine, MongoDB writes to the data files on disk every
 60 seconds and writes to the journal files roughly every 100 milliseconds.
To change the interval for writing to the data files, use the storage.syncPeriodSecs setting.
 For the journal files, see storage.journal.commitIntervalMs setting.
These values represent the maximum amount of time between the completion of a write operation and when MongoDB writes to the data files or to the journal files. In many cases MongoDB and the
 operating system flush data to disk more frequently, so that the above values represents a theoretical maximum.



Why are the files in my data directory larger than the data in my database?

The data files in your data directory, which is the /data/db directory in default configurations, might be larger than the data set inserted into the database.
 Consider the following possible causes:

Preallocated data files

MongoDB preallocates its data files to avoid filesystem fragmentation, and because of this, the size of these files do not necessarily reflect the size of your data.
The storage.mmapv1.smallFiles option
 will reduce the size of these files, which may be useful if you have many small databases on disk.


The oplog

If this mongod is
 a member of a replica set, the data directory includes the oplog.rs file, which is a
 preallocated capped collection in the local database.
The default allocation is approximately 5% of disk space on 64-bit installations. In most cases, you should not need to resize the oplog. See Oplog
 Sizing for more information.


The journal

The data directory contains the journal files, which store write operations on disk before MongoDB applies them to databases. See Journaling.


Empty records

MongoDB maintains lists of empty records in data files as it deletes documents and collections. MongoDB can reuse this space, but will not, by default, return this space to the operating system.
To allow MongoDB to more effectively reuse the space, you can de-fragment your data. To de-fragment, use the compact command.
 The compact requires
 up to 2 gigabytes of extra disk space to run. Do not usecompact if
 you are critically low on disk space. For more information on its behavior and other considerations, see compact.
compact only
 removes fragmentation from MongoDB data files within a collection and does not return any disk space to the operating system. To return disk space to the operating system, see How
 do I reclaim disk space?.



How do I reclaim disk space?

The following provides some options to consider when reclaiming disk space.


NOTE
You do not need to reclaim disk space for MongoDB to reuse freed space. See Empty
 records for information on reuse of freed space.


repairDatabase

You can use repairDatabase on
 a database to rebuilds the database, de-fragmenting the associated storage in the process.
repairDatabase requires
 free disk space equal to the size of your current data set plus 2 gigabytes. If the volume that holds dbpath lacks sufficient space, you can mount a separate volume and use that for the repair. For additional information and considerations, see repairDatabase.


WARNING
Do not use repairDatabase if
 you are critically low on disk space.
repairDatabase will
 block all other operations and may take a long time to complete.

You can only run repairDatabase on
 a standalone mongod instance.
You can also run the repairDatabase operation
 for all databases on the server by restarting your mongodstandalone
 instance with the --repair and --repairpath options.
 All databases on the server will be unavailable during this operation.


Resync the Member of the Replica Set

For a secondary member of a replica set, you can perform a resync of the member by:
 stopping the secondary member to resync, deleting all data and subdirectories from the member’s data directory, and restarting.
For details, see Resync a Member of a
 Replica Set.



What is the working set?

Working set represents the total body of data that the application uses in the course of normal operation. Often this is a subset of the total data size, but the specific size of the working set depends on actual moment-to-moment use of the database.
If you run a query that requires MongoDB to scan every document in a collection, the working set will expand to include every document. Depending on physical memory size, this may cause documents in the working
 set to “page out,” or to be removed from physical memory by the operating system. The next time MongoDB needs to access these documents, MongoDB may incur a hard page fault.
For best performance, the majority of your active set should fit in RAM.


What are page faults?

With the MMAPv1 storage engine, page faults can occur as MongoDB reads from or writes data to parts of its data files that are not currently located in physical memory. In contrast, operating system page faults happen when physical memory is exhausted and pages
 of physical memory are swapped to disk.
If there is free memory, then the operating system can find the page on disk and load it to memory directly. However, if there is no free memory, the operating system must:
find a page in memory that is stale or no longer needed, and write the page to disk.
read the requested page from disk and load it into memory.
This process, on an active system, can take a long time, particularly in comparison to reading a page that is already in memory.
See Page
 Faults for more information.


What is the difference between soft and hard page faults?

Page faults occur when MongoDB, with the MMAP storage engine, needs access to data
 that isn’t currently in active memory. A “hard” page fault refers to situations when MongoDB must access a disk to access the data. A “soft” page fault, by contrast, merely moves memory pages from one list to another, such as from an operating system file
 cache.
See Page
 Faults for more information.





Can I manually pad documents to prevent moves during updates?

Changed in version 3.0.0.

With the MMAPv1 storage engine, an update can cause a document
 to move on disk if the document grows in size. To minimize document movements, MongoDB uses padding.
You should not have to pad manually because by default, MongoDB uses Power
 of 2 Sized Allocations to add padding automatically. The Power
 of 2 Sized Allocations ensures that MongoDB allocates document space in sizes that are powers of 2, which helps ensure that MongoDB can efficiently reuse free space created by document deletion or relocation as well as reduce the occurrences of reallocations
 in many cases.
However, if you must pad a document manually, you can add a temporary field to the document and then$unset the
 field, as in the following example.


WARNING
Do not manually pad documents in a capped collection. Applying manual padding to a document in a capped collection can break replication. Also, the padding is not preserved if you re-sync the MongoDB
 instance.



var myTempPadding = [ "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
                      "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
                      "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
                      "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"];

db.myCollection.insert( { _id: 5, paddingField: myTempPadding } );

db.myCollection.update( { _id: 5 },
                        { $unset: { paddingField: "" } }
                      )

db.myCollection.update( { _id: 5 },
                        { $set: { realField: "Some text that I might have needed padding for" } }
                      )





SEE ALSO
Record Allocation Strategies




Data Storage Diagnostics

How can I check the size of a collection?

To view the statistics for a collection, including the data size, use the db.collection.stats() method
 from the mongo shell.
 The following example issues db.collection.stats() for
 the orders collection:


db.orders.stats();



MongoDB also provides the following methods to return specific sizes for the collection:
db.collection.dataSize() to
 return data size in bytes for the collection.
db.collection.storageSize() to
 return allocation size in bytes, including unused space.
db.collection.totalSize() to
 return the data size plus the index size in bytes.
db.collection.totalIndexSize() to
 return the index size in bytes.
The following script prints the statistics for each database:


db._adminCommand("listDatabases").databases.forEach(function (d) {
   mdb = db.getSiblingDB(d.name);
   printjson(mdb.stats());
})



The following script prints the statistics for each collection in each database:


db._adminCommand("listDatabases").databases.forEach(function (d) {
   mdb = db.getSiblingDB(d.name);
   mdb.getCollectionNames().forEach(function(c) {
      s = mdb[c].stats();
      printjson(s);
   })
})





How can I check the size of indexes for a collection?

To view the size of the data allocated for an index, use the db.collection.stats() method
 and check the indexSizes field
 in the returned document.



How can I get information on the storage use of a database?

The db.stats() method
 in the mongo shell
 returns the current state of the “active” database. For the description of the returned fields, see dbStats
 Output.