36. On the number of column families

HBase currently does not do well with anything above two or three column families so keep the number of column families in your schema low. Currently, flushing and compactions are done on a per Region basis so if one column family is carrying the bulk of the data bringing on flushes, the adjacent families will also be flushed even though the amount of data they carry is small. When many column families exist the flushing and compaction interaction can make for a bunch of needless i/o (To be addressed by changing flushing and compaction to work on a per column family basis). For more information on compactions, see Compaction.

HBase现在还不能很好的处理超过2、3个列族的情况，所以尽可能保持较少的列族数量。目前，flush和compact是基于region的，所以如果其中一个列族由于数据过多触发flush，其它列族即使数据较少，也会一起被flush。当许多列族同时进行flush和compact，会造成大量不必要的i/o(待通过修改为基于列族进行flush和compact来解决)。关于compact的更多信息，请查看Compaction章节。

Try to make do with one column family if you can in your schemas. Only introduce a second and third column family in the case where data access is usually column scoped; i.e. you query one column family or the other but usually not both at the one time.

可能的话，尝试只使用一个列族。只有当数据的访问总是涉及一定范围的列时可以考虑引入第二个或第三个列族；比如，你会查询这个列或另一个列，而不会同时查询。

36.1. 列族基数(Cardinality of ColumnFamilies)

Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of rows). If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows, ColumnFamilyA’s data will likely be spread across many, many regions (and RegionServers). This makes mass scans for ColumnFamilyA less efficient.

如果表包含多个列族，需要注意基数问题(比如，行数)。如果ColumnFamilyA包含100万行而ColumnFamilyB包含10亿行，那么ColumnFamilyA的数据会被分散到很多很多region(以及RegionServer)中。这会使对ColumnFamilyA的大规模scan比较低效。

37. Rowkey Design

37.1. 热点(Hotspotting)

Rows in HBase are sorted lexicographically by row key. This design optimizes for scans, allowing you to store related rows, or rows that will be read together, near each other. However, poorly designed row keys are a common source of hotspotting. Hotspotting occurs when a large amount of client traffic is directed at one node, or only a few nodes, of a cluster. This traffic may represent reads, writes, or other operations. The traffic overwhelms the single machine responsible for hosting that region, causing performance degradation and potentially leading to region unavailability. This can also have adverse effects on other regions hosted by the same region server as that host is unable to service the requested load. It is important to design data access patterns such that the cluster is fully and evenly utilized.

HBase中的行按照rowkey的字典序存储。这种设计优化了scan，允许你把有关联的，或者会被一起读取的行放在临近的地方。然而，不良的行键设计是热点的常见来源。当大量的客户端流量被导向集群中的一个或者少数几个节点时，就会出现热点。流量可能是读取、写入，或者其它操作。流量会压垮托管这些region的单个机器，导致性能下降甚至region不可用。由于主机不能够再提供服务，所以这同样会对这些regionServer上的其它region带来负面影响。对数据访问模式进行设计，使集群得到充分和均匀的使用，是很重要的。

To prevent hotspotting on writes, design your row keys such that rows that truly do need to be in the same region are, but in the bigger picture, data is being written to multiple regions across the cluster, rather than one at a time. Some common techniques for avoiding hotspotting are described below, along with some of their advantages and drawbacks.

要避免写热点，需将rowkey设计为，确实需要临近的行才存在于同一个region，总体上看，数据写到集群中的多个region比一个要好。下面是一些常见的避免热点的技术手段，以及它们的优点和缺点。

加盐(Salting)

Salting in this sense has nothing to do with cryptography, but refers to adding random data to the start of a row key. In this case, salting refers to adding a randomly-assigned prefix to the row key to cause it to sort differently than it otherwise would. The number of possible prefixes correspond to the number of regions you want to spread the data across. Salting can be helpful if you have a few "hot" row key patterns which come up over and over amongst other more evenly-distributed rows. Consider the following example, which shows that salting can spread write load across multiple RegionServers, and illustrates some of the negative implications for reads.

这里的加盐与密码学无关，而是关于在rowkey的开头添加随机数据。在本例中，加盐是指通过给rowkey增加随机分配的前缀，来使其排序不同于其它方式。可能的前缀数量与你希望将数据分散到的region数量一致。如果存在一些行，相对于其它分布均匀的行来说，总是反复出现，那么加盐就会有很用。考虑后面这个例子，其展示了加盐能够将写入压力分散到多个RegionServer，同时对读取的一些负面影响。

Example 11. Salting Example

Suppose you have the following list of row keys, and your table is split such that there is one region for each letter of the alphabet. Prefix 'a' is one region, prefix 'b' is another. In this table, all rows starting with 'f' are in the same region. This example focuses on rows with keys like the following:

假设你有下面这个rowkey列表，并且表按照每个首字母对应一个region的方式split。前缀a为一个region，前缀b为另一个region。在这个表中，以f开头的行存在于同一个reigon。这个例子主要关注具有如下键的行:

foo0001
foo0002
foo0003
foo0004

Now, imagine that you would like to spread these across four different regions. You decide to use four different salts: a, b, c, and d. In this scenario, each of these letter prefixes will be on a different region. After applying the salts, you have the following rowkeys instead. Since you can now write to four separate regions, you theoretically have four times the throughput when writing that you would have if all the writes were going to the same region.

现在，想象下你需要将他们分散到不同的region去。你决定使用四种不同的盐：a, b, c, and d。
在这个场景里，每个字母前缀会位于不同的region。使用这些盐之后，取而代之的是以下行键。由于你现在可以写入到四个独立的region，理论上与全部写入到同一个region相比，你获取了四倍的吞吐。

a-foo0003
b-foo0001
c-foo0004
d-foo0002

Then, if you add another row, it will randomly be assigned one of the four possible salt values and end up near one of the existing rows.

然后，如果你增加其它行，它会被随机的分配到四种盐值之一，并且放在现有的行附近。

a-foo0003
b-foo0001
c-foo0003
c-foo0004
d-foo0002

Since this assignment will be random, you will need to do more work if you want to retrieve the rows in lexicographic order. In this way, salting attempts to increase throughput on writes, but has a cost during reads.

由于分配是随机的，你需要做一些额外的工作来恢复行的字典顺序。在这个方法中，加盐尝试增加写入的吞吐能力，但是增加了读取时的代价。

哈希(Hashing)

Instead of a random assignment, you could use a one-way hash that would cause a given row to always be "salted" with the same prefix, in a way that would spread the load across the RegionServers, but allow for predictability during reads. Using a deterministic hash allows the client to reconstruct the complete rowkey and use a Get operation to retrieve that row as normal.

你可以用单向哈希使给定的行总是以相同的前缀加盐，来取代随机分配，这个方法可以将压力分散到各个regionServer，同时在读取的时候能够预知前缀。使用一个确定的哈希，客户端能够重新构造完整的rowkey，然后使用一个普通的get操作去获取行。

Example 12. Hashing Example

Given the same situation in the salting example above, you could instead apply a one-way hash that would cause the row with key foo0003 to always, and predictably, receive the a prefix. Then, to retrieve that row, you would already know the key. You could also optimize things so that certain pairs of keys were always in the same region, for instance.

上面加盐的例子中，你可以换用一个单向哈希来使foo0003总是能够得到a这个前缀。这样的话，你已经知道了用什么key去获取行。你还可以做一些优化，例如，使特定的一些key总是位于同样的region。

反转键(Reversing the Key)

A third common trick for preventing hotspotting is to reverse a fixed-width or numeric row key so that the part that changes the most often (the least significant digit) is first. This effectively randomizes row keys, but sacrifices row ordering properties.

第三种常见的避免热点的方法是将固定长度或数字类型的rowkey进行反转，这样变化频繁的部分就会到前面。这使得rowkey变得随机，不过会失去顺序性。

See https://communities.intel.com/community/itpeernetwork/datastack/blog/2013/11/10/discussion-on-designing-hbase-tables, and article on Salted Tables from the Phoenix project, and the discussion in the comments of HBASE-11682 for more information about avoiding hotspotting.

查看https://communities.intel.com/community/itpeernetwork/datastack/blog/2013/11/10/discussion-on-designing-hbase-tables, 和Phoenix项目中其它加盐表相关的文章，以及HBASE-11682中评论的讨论，以了解更多关于避免热点的信息。

37.2. 递增rowkey/时序数据(Monotonically Increasing Row Keys/Timeseries Data)

In the HBase chapter of Tom White’s book Hadoop: The Definitive Guide (O’Reilly) there is a an optimization note on watching out for a phenomenon where an import process walks in lock-step with all clients in concert pounding one of the table’s regions (and thus, a single node), then moving onto the next region, etc.

With monotonically increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan on why monotonically increasing row keys are problematic in BigTable-like datastores: monotonically increasing values are bad.

The pile-up on a single region brought on by monotonically increasing keys can be mitigated by randomizing the input records to not be in sorted order, but in general it’s best to avoid using a timestamp or a sequence (e.g. 1, 2, 3) as the row-key.

在Tom White关于Hadoop的书中的HBase章节中：在权威指南里面有一个优化说明，其中指出要注意这样一种现象，所有客户端的写入操作全部集中在表的某一个region(也即，单个节点)，然后转换到下一个region，一直这样。

使用单向递增的rowkey时(例如，使用时间戳)，这就会发生。参考IKai Lan的连载，关于为什么在BigTable类的数据库中单向递增的rowkey会是问题：monotonically increasing values are bad。

可以通过将输入记录随机化而变得无序来缓解单向递增key带来的单region压力，不过通常更好的做法是避免使用时间戳或是一个序列(比如：1，2，3)作为rowkey。

If you do need to upload time series data into HBase, you should study OpenTSDB as a successful example. It has a page describing the schema it uses in HBase. The key format in OpenTSDB is effectively metric_type, which would appear at first glance to contradict the previous advice about not using a timestamp as the key. However, the difference is that the timestamp is not in the lead position of the key, and the design assumption is that there are dozens or hundreds (or more) of different metric types. Thus, even with a continual stream of input data with a mix of metric types, the Puts are distributed across various points of regions in the table.

如果你需要将时序数据存入HBase，你应该将OpenTSDB作为一个成功案例去学习。它有个页面描述其在HBase中使用的模式。OpenTSDB使用
metric_type作为key的格式，乍一看与之前建议的避免使用时间戳作为key相矛盾。不过，区别在于时间戳没有处于key的前导位，并且该设计假设会有几十或几百个不同的指标类型。因此，即使有连续的混杂不同指标类型的输入数据，写入也会分布到表的不同region中去。

See schema.casestudies for some rowkey design examples.

更多关于rowkey设计的示例可查看schema.casestudies。

37.3. 尽可能最小化row和column大小(Try to minimize row and column sizes)

In HBase, values are always freighted with their coordinates; as a cell value passes through the system, it’ll be accompanied by its row, column name, and timestamp - always. If your rows and column names are large, especially compared to the size of the cell value, then you may run up against some interesting scenarios. One such is the case described by Marc Limotte at the tail of HBASE-3551 (recommended!). Therein, the indices that are kept on HBase storefiles (StoreFile (HFile)) to facilitate random access may end up occupying large chunks of the HBase allotted RAM because the cell value coordinates are large. Mark in the above cited comment suggests upping the block size so entries in the store file index happen at a larger interval or modify the table schema so it makes for smaller rows and column names. Compression will also make for larger indices. See the thread a question storefileIndexSize up on the user mailing list.

在HBase中,value总是带有其坐标；cell的value在系统中处理时总是携带着row，column名称，以及时间戳。如果你的row和column名称很大，尤其是相对于value来说，那么你可能会碰到一些有意思的情景。在HBASE-3551的末尾Marc Limotte描述了这样的一个案例。
其中，由于cell的value坐标过大，storefiles中存储的用来加速随机访问的索引数据占用了大量的HBase可用内存。在之前的回复中，Mark建议增加block的大小，使得store file中能以更大的间隔产生index，或者修改表设计，使用更小的row和column名称。压缩也能够带来较大的索引。查看用户邮件列表中的这个主题：a question storefileIndexSize。

Most of the time small inefficiencies don’t matter all that much. Unfortunately, this is a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be repeated several billion times in your data.

多数时候，细微的低效并不重要。不幸的是，该案例中正是由此导致的。无论选择怎样的列族、属性、和行键，它们总是会在你的数据中重复数十亿次。

See keyvalue for more information on HBase stores data internally to see why this is important.

查看keyvalue章节，了解关于HBase内部数据存储的更多信息，来理解为什么这很重要。

37.3.1. 列族(Column Families)

Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for data/default).

See KeyValue for more information on HBase stores data internally to see why this is important.

尝试让列族名称尽可能短，最好是一个字符。
查看keyvalue章节，了解关于HBase内部数据存储的更多信息，来理解为什么这很重要。

37.3.2. 属性(Attributes)

Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer shorter attribute names (e.g., "via") to store in HBase.

See keyvalue for more information on HBase stores data internally to see why this is important.

虽然详细的属性名称容易阅读，但是短一些更有利于存储到HBase中。
查看keyvalue章节，了解关于HBase内部数据存储的更多信息，来理解为什么这很重要。

37.3.3. 行键长度(Rowkey Length)

Keep them as short as is reasonable such that they can still be useful for required data access (e.g. Get vs. Scan). A short key that is useless for data access is not better than a longer key with better get/scan properties. Expect tradeoffs when designing rowkeys.

使其合理的简短而不丧失数据访问时的可用性。一个简短但对数据访问来说无用的键，并不比一个长一些的键更好。在设计行键的时候需要进行权衡。

37.3.4. 字节模式(Byte Patterns)

A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight bytes. If you stored this number as a String — presuming a byte per character — you need nearly 3x the bytes.

long类型占8个字节。你可以存储一个小于18,446,744,073,709,551,615的数字。如果你将该数字存储为字符串-假设每个字符一个字节-
你需要三倍的字节数。

Not convinced? Below is some sample code that you can run on your own.

不相信吗？下面是一些示例代码，你可以自己运行看看。

// long
//
long l = 1234567890L;
byte[] lb = Bytes.toBytes(l);
System.out.println("long bytes length: " + lb.length);   // returns 8

String s = String.valueOf(l);
byte[] sb = Bytes.toBytes(s);
System.out.println("long as string length: " + sb.length);    // returns 10

// hash
//
MessageDigest md = MessageDigest.getInstance("MD5");
byte[] digest = md.digest(Bytes.toBytes(s));
System.out.println("md5 digest bytes length: " + digest.length);    // returns 16

String sDigest = new String(digest);
byte[] sbDigest = Bytes.toBytes(sDigest);
System.out.println("md5 digest as string length: " + sbDigest.length);    // returns 26

Unfortunately, using a binary representation of a type will make your data harder to read outside of your code. For example, this is what you will see in the shell when you increment a value:

不幸的是，用二进制类型会导致你的数据在代码之外难以理解。例如，当你incr一个值时你会在shell中看到这些东西。

hbase(main):001:0> incr 't', 'r', 'f:q', 1
COUNTER VALUE = 1

hbase(main):002:0> get 't', 'r'
COLUMN                                        CELL
 f:q                                          timestamp=1369163040570, value=\x00\x00\x00\x00\x00\x00\x00\x01
1 row(s) in 0.0310 seconds

The shell makes a best effort to print a string, and it this case it decided to just print the hex. The same will happen to your row keys inside the region names. It can be okay if you know what’s being stored, but it might also be unreadable if arbitrary data can be put in the same cells. This is the main trade-off.

shell会尽可能的打印出字符串，但在该示例中它决定只是打印十六进制。这同样会发生在你的region名称中的行键。如果你知道所存储的东西，这可以接受，但在cell中放入任意数据可能会失去可读性。这是主要的权衡点。

37.4.反转时间戳(Reverse Timestamps)

反向scan接口(Reverse Scan API)

HBASE-4811 implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning. This feature is available in HBase 0.98 and later. See Scan.setReversed() for more information.

HBASE-4811实现了一个可以反向scan表或其中一个范围的接口，减少你因为正向或反向扫描而进行模式优化的需要。该功能在HBase 0.98或更高版本中可用。更多信息可查看Scan.setReversed()。

A common problem in database processing is quickly finding the most recent version of a value. A technique using reverse timestamps as a part of the key can help greatly with a special case of this problem. Also found in the HBase chapter of Tom White’s book Hadoop: The Definitive Guide (O’Reilly), the technique involves appending (Long.MAX_VALUE - timestamp) to the end of any key, e.g. key.

The most recent value for [key] in a table can be found by performing a Scan for [key] and obtaining the first record. Since HBase keys are in sorted order, this key sorts before any older row-keys for [key] and thus is first.

This technique would be used instead of using Number of Versions where the intent is to hold onto all versions "forever" (or a very long time) and at the same time quickly obtain access to any other version by using the same Scan technique.

数据库处理中有这样一个常见的问题，快速找到最新版本的一个值。在特定的与此有关的案例中，把反转时间戳作为key的一部分，会有很大的帮助。Tom White的hadoop书籍的HBase章节：权威指南，关于在任意key的末尾添加(Long.MAX_VALUE - timestamp)的技巧。

一个表中键的最新值可通过执行一个对该键的scan并获取第一个记录得到。由于HBase中的键是有序的，该键会排在更老的行键之前，因此是第一个。

这个技巧可被用来替代意图永久保留所有版本(或一个较长的时期)的多版本技术，并且同时可使用同样的扫描方式来快速获取任意其它版本数据。

37.5. 行键和列族(Rowkeys and ColumnFamilies)

Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision.

行键的作用域是列族。因此，相同的行键可以存在于表的每个列族中而不会冲突。

37.6. 行键的不变性(Immutability of Rowkeys)

Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row is deleted and then re-inserted. This is a fairly common question on the HBase dist-list so it pays to get the rowkeys right the first time (and/or before you’ve inserted a lot of data).

行键是不可变的。唯一使它们“改变”的的方法使先删除再重新插入。这是一个很常见的问题，因此有必要一开始就使用正确的行键(在你插入很多数据之前)。

37.7. 行键和region分片的关系(Relationship Between RowKeys and Region Splits)

If you pre-split your table, it is critical to understand how your rowkey will be distributed across the region boundaries. As an example of why this is important, consider the example of using displayable hex characters as the lead position of the key (e.g., "0000000000000000" to "ffffffffffffffff"). Running those key ranges through Bytes.split (which is the split strategy used when creating regions in Admin.createTable(byte[] startKey, byte[] endKey, numRegions) for 10 regions will generate the following splits…​

如果你预拆分你的表，理解你的行键在region边界如何分布非常重要。考虑这个使用可见十六进制字符作为先导位的行键(比如，"0000000000000000" to "ffffffffffffffff")的例子，用来说明为什么这很重要。运行Bytes.split(使用Admin.createTable(byte[] startKey, byte[] endKey, numRegions )创建region时使用的分片策略)将该范围的行键分为10个region，会得到下面这些分片。

(note: the lead byte is listed to the right as a comment.) Given that the first split is a '0' and the last split is an 'f', everything is great, right? Not so fast.

(注：首字节作为注释在右边列出)。假设第一个分片是'0'，最后一个分片是'f'，一切都挺好，是吗？先别急。

The problem is that all the data is going to pile up in the first 2 regions and the last region thus creating a "lumpy" (and possibly "hot") region problem. To understand why, refer to an ASCII Table. '0' is byte 48, and 'f' is byte 102, but there is a huge gap in byte values (bytes 58 to 96) that will never appear in this keyspace because the only values are [0-9] and [a-f]. Thus, the middle regions will never be used. To make pre-splitting work with this example keyspace, a custom definition of splits (i.e., and not relying on the built-in split method) is required.

问题在于，所有数据会集中在前2个region以及最后1个region，因而带来了热点region问题。参考ASCII表来理解为什么。'0' 对应字节的值为48,'f'对应字节的值为102，但由于可能的值只有[0-9]和[a-f]，其中有很大一部分字节值(58-96)不会出现在行键区间中，此时需要一个自定义分片策略(比如，不依赖内置的分片方法)。

Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split them in such a way that all the regions are accessible in the keyspace. While this example demonstrated the problem with a hex-key keyspace, the same problem can happen with any keyspace. Know your data.

经验1:预拆分表通常是一个最佳实践，但你需要以一种所有region都会被访问的方式去拆分。这个例子只是演示了使用十六进制行键时的问题，使用其它任意行键都可能有类似问题。理解你的数据。

Lesson #2: While generally not advisable, using hex-keys (and more generally, displayable data) can still work with pre-split tables as long as all the created regions are accessible in the keyspace.

经验2：虽然通常不建议使用十六进制行键(通常采用可见字符)，但只要能够使所有region都能被访问，就可以进行预拆分。

To conclude this example, the following is an example of how appropriate splits can be pre-created for hex-keys:.

作为总结，下面是一个如何为十六进制行键进行适当的预拆分的示例:

public static boolean createTable(Admin admin, HTableDescriptor table, byte[][] splits)
throws IOException {
  try {
    admin.createTable( table, splits );
    return true;
  } catch (TableExistsException e) {
    logger.info("table " + table.getNameAsString() + " already exists");
    // the table already exists...
    return false;
  }
}

public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
  byte[][] splits = new byte[numRegions-1][];
  BigInteger lowestKey = new BigInteger(startKey, 16);
  BigInteger highestKey = new BigInteger(endKey, 16);
  BigInteger range = highestKey.subtract(lowestKey);
  BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
  lowestKey = lowestKey.add(regionIncrement);
  for(int i=0; i < numRegions-1;i++) {
    BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
    byte[] b = String.format("%016x", key).getBytes();
    splits[i] = b;
  }
  return splits;
}

38. Number of Versions

38.1. 最大版本数(Maximum Number of Versions)

The maximum number of row versions to store is configured per column family via HColumnDescriptor. The default for max versions is 1. This is an important parameter because as described in Data Model section HBase does not overwrite row values, but rather stores different values per row by time (and qualifier). Excess versions are removed during major compactions. The number of max versions may need to be increased or decreased depending on application needs.

行的最大保存版本数通过HColumnDescriptor为每个列族配置。默认最大版本数为1.这是个很重要的参数，正如数据模型章节所述，HBase不会覆盖数据，而是按时间(和限定符)为每行保存不同的值。多余的版本会在major合并时删除。最大版本数可根据应用需要增大或减小。

It is not recommended setting the number of max versions to an exceedingly high level (e.g., hundreds or more) unless those old values are very dear to you because this will greatly increase StoreFile size.

不建议将最大版本数设置的过大(比如，几百或更多)，因为这会大幅增加StoreFile的大小，除非那些旧数据对你来说很有价值。

38.2. 最小版本数(Minimum Number of Versions)

Like maximum number of row versions, the minimum number of row versions to keep is configured per column family via HColumnDescriptor. The default for min versions is 0, which means the feature is disabled. The minimum number of row versions parameter is used together with the time-to-live parameter and can be combined with the number of row versions parameter to allow configurations such as "keep the last T minutes worth of data, at most N versions, but keep at least M versions around" (where M is the value for minimum number of row versions, M

与最大版本数一样，最小版本数也通过HColumnDescriptor为每个列族配置。默认最小版本数为0，意味着该功能未启用。最小版本数可以与存活时间以及最大版本数一起使用，来进行"保留最近T分钟内的数据，最多N个版本，但最少要保留M个版本"(M代表最小版本数，M

39. Supported Datatypes

HBase supports a "bytes-in/bytes-out" interface via Put and Result, so anything that can be converted to an array of bytes can be stored as a value. Input could be strings, numbers, complex objects, or even images as long as they can rendered as bytes.

HBase通过Put和Result支持"字节输入/字节输出"接口，所以可被转换为字节数组的任意东西都能够被作为值存储。输入可以是字符串、数字、组合对象或者甚至图片也可以只要它们可以被表示为字节。

There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would probably be too much to ask); search the mailing list for conversations on this topic. All rows in HBase conform to the Data Model, and that includes versioning. Take that into consideration when making your design, as well as block size for the ColumnFamily.

实际上对于值大小有一些限制(比如，在HBase中存储10-50MB的对象可能要求太高)；搜索邮件列表来查看与此话题相关的讨论。HBase中的所有行都需要遵循数据模型，包括版本控制。与列族的块大小一样，你需要在设计时考虑这些。

39.1. Counters

One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See Increment in Table.

特别值得一提的一种数据类型是"计数器"(用于实现数值原子递增)。See Increment in Table.

Synchronization on counters are done on the RegionServer, not in the client.

对计数器的同步是在RegionServer完成，而不是客户端。

40. Joins

If you have multiple tables, don’t forget to factor in the potential for Joins into the schema design.

如果你有多个表，不要忘记将连接的潜力考虑到模式设计中。

41. Time To Live (TTL)

ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the expiration time is reached. This applies to all versions of a row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC.

列族可以设置以秒为单位的存活时间，HBase会在过期时自动删除这些行。这将应用到行的所有版本-甚至当前那个。存活时间在HBase中采用UTC进行编码。

Store files which contains only expired rows are deleted on minor compaction. Setting hbase.store.delete.expired.storefile to false disables this feature. Setting minimum number of versions to other than 0 also disables this.

See HColumnDescriptor for more information.

只包含已过期数据的Store files会在minor合并的时候 被删除。可将hbase.store.delete.expired.storefile设置为false来禁用此功能。也可以将最小版本数设置为大于0的值来禁用。
更多信息可查看HColumnDescriptor.

Recent versions of HBase also support setting time to live on a per cell basis. See HBASE-10560 for more information. Cell TTLs are submitted as an attribute on mutation requests (Appends, Increments, Puts, etc.) using Mutation#setTTL. If the TTL attribute is set, it will be applied to all cells updated on the server by the operation. There are two notable differences between cell TTL handling and ColumnFamily TTLs:

Cell TTLs are expressed in units of milliseconds instead of seconds.

A cell TTLs cannot extend the effective lifetime of a cell beyond a ColumnFamily level TTL setting.

最近版本的HBase支持基于每个cell设置存活时间。更新信息查看HBASE-10560。cell的存活时间，通过Mutation#setTTL方法，将其作为mutation请求的一个属性进行提交。如果设置了存活时间属性，则会应用到被此操作更新的所有cell。cell的存活时间和列族的存活时间有2个明显的不同：

cell的存活时间单位是毫秒而不是秒。

cell的存活时间不能超过列族的存活时间而延长cell的有效寿命。

42. Keeping Deleted Cells

By default, delete markers extend back to the beginning of time. Therefore, Get or Scan operations will not see a deleted cell (row or column), even when the Get or Scan operation indicates a time range before the delete marker was placed.

默认情况下，删除标记会作用至最开始的时间。因此，Get或Scan操作将不会看到已删除的cell(行或列)，即使其指定了早于删除标记的时间范围。

ColumnFamilies can optionally keep deleted cells. In this case, deleted cells can still be retrieved, as long as these operations specify a time range that ends before the timestamp of any delete that would affect the cells. This allows for point-in-time queries even in the presence of deletes.

列族可以选择保留已删除cell。这种情况下，已删除的cell可以被获取，只要操作所指定的时间范围，早于这些cell的删除操作的时间点。这允许在存在删除的情况下，进行任意时间点的查询。

Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells. A new "raw" scan options returns all deleted rows and the delete markers.

已删除的cell依然受存活时间和最大版本数的约束。一个新的"raw"scan选项可返回所有已删除的行和删除标记。

通过shell修改KEEP_DELETED_CELLS的值

hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true

通过api修改KEEP_DELETED_CELLS的值

...
HColumnDescriptor.setKeepDeletedCells(true);
...

Let us illustrate the basic effect of setting the KEEP_DELETED_CELLS attribute on a table.
First, without:
举例说明一下给表设置KEEP_DELETED_CELLS属性后的基本影响。

首先，未设置：

create 'test', {NAME=>'e', VERSIONS=>2147483647}
put 'test', 'r1', 'e:c1', 'value', 10
put 'test', 'r1', 'e:c1', 'value', 12
put 'test', 'r1', 'e:c1', 'value', 14
delete 'test', 'r1', 'e:c1',  11

hbase(main):017:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                              COLUMN+CELL
 r1                                              column=e:c1, timestamp=14, value=value
 r1                                              column=e:c1, timestamp=12, value=value
 r1                                              column=e:c1, timestamp=11, type=DeleteColumn
 r1                                              column=e:c1, timestamp=10, value=value
1 row(s) in 0.0120 seconds

hbase(main):018:0> flush 'test'
0 row(s) in 0.0350 seconds

hbase(main):019:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                              COLUMN+CELL
 r1                                              column=e:c1, timestamp=14, value=value
 r1                                              column=e:c1, timestamp=12, value=value
 r1                                              column=e:c1, timestamp=11, type=DeleteColumn
1 row(s) in 0.0120 seconds

hbase(main):020:0> major_compact 'test'
0 row(s) in 0.0260 seconds

hbase(main):021:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                              COLUMN+CELL
 r1                                              column=e:c1, timestamp=14, value=value
 r1                                              column=e:c1, timestamp=12, value=value
1 row(s) in 0.0120 seconds

Notice how delete cells are let go.

注意被删除的cell是如何消失的。

Now let’s run the same test only with KEEP_DELETED_CELLS set on the table (you can do table or per-column-family):

现在只给表增加KEEP_DELETED_CELLS设置(可以在表上或者列族上)，并重新运行同样的测试：

hbase(main):005:0> create 'test', {NAME=>'e', VERSIONS=>2147483647, KEEP_DELETED_CELLS => true}
0 row(s) in 0.2160 seconds

=> Hbase::Table - test
hbase(main):006:0> put 'test', 'r1', 'e:c1', 'value', 10
0 row(s) in 0.1070 seconds

hbase(main):007:0> put 'test', 'r1', 'e:c1', 'value', 12
0 row(s) in 0.0140 seconds

hbase(main):008:0> put 'test', 'r1', 'e:c1', 'value', 14
0 row(s) in 0.0160 seconds

hbase(main):009:0> delete 'test', 'r1', 'e:c1',  11
0 row(s) in 0.0290 seconds

hbase(main):010:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                                                                          COLUMN+CELL
 r1                                                                                          column=e:c1, timestamp=14, value=value
 r1                                                                                          column=e:c1, timestamp=12, value=value
 r1                                                                                          column=e:c1, timestamp=11, type=DeleteColumn
 r1                                                                                          column=e:c1, timestamp=10, value=value
1 row(s) in 0.0550 seconds

hbase(main):011:0> flush 'test'
0 row(s) in 0.2780 seconds

hbase(main):012:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                                                                          COLUMN+CELL
 r1                                                                                          column=e:c1, timestamp=14, value=value
 r1                                                                                          column=e:c1, timestamp=12, value=value
 r1                                                                                          column=e:c1, timestamp=11, type=DeleteColumn
 r1                                                                                          column=e:c1, timestamp=10, value=value
1 row(s) in 0.0620 seconds

hbase(main):013:0> major_compact 'test'
0 row(s) in 0.0530 seconds

hbase(main):014:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                                                                          COLUMN+CELL
 r1                                                                                          column=e:c1, timestamp=14, value=value
 r1                                                                                          column=e:c1, timestamp=12, value=value
 r1                                                                                          column=e:c1, timestamp=11, type=DeleteColumn
 r1                                                                                          column=e:c1, timestamp=10, value=value
1 row(s) in 0.0650 seconds

KEEP_DELETED_CELLS is to avoid removing Cells from HBase when the only reason to remove them is the delete marker. So with KEEP_DELETED_CELLS enabled deleted cells would get removed if either you write more versions than the configured max, or you have a TTL and Cells are in excess of the configured timeout, etc.

KEEP_DELETED_CELLS用来避免删除那些只是被删除标记所删除的cell。因此KEEP_DELETED_CELLS启用时，如果超出最大版本数，或者超出了配置的存活时间，被delete的cell还是会被真正删除掉。

43. Secondary Indexes and Alternate Query Paths

This section could also be titled "what if my table rowkey looks like this but I also want to query my table like that." A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are reporting requirements on activity across users for certain time ranges. Thus, selecting by user is easy because it is in the lead position of the key, but time is not.

这个章节也可以使用"如果我的表行键是这样但是希望以那样的方式去查询"的标题。问题列表中常见的一个例子是行键的格式是"用户-时间戳"，但存在按照特定时间范围查询用户活动的报表需求。此时，按用户查询很容易，因为它位于行键的先导位，但按时间查询就比较难。

There is no single answer on the best way to handle this because it depends on…​

对于如何以最好的方式去解决该问题并没有单一的答案，因为这取决于...

• Number of users
• Data size and data arrival rate
• Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges)
• Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others)

• 用户数量
• 数据大小和到达速率
• 报表需求的复杂度(比如，完全*的日期选择 vs 预先配置范围)
• 查询所需的执行速度(比如，对于一个ad-hoc报表，90秒可能是合理的，但是对于其它情况就太久了)

and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution. Common techniques are in sub-sections below. This is a comprehensive, but not exhaustive, list of approaches.

而且解决方案也受集群大小和能够投入的处理器多少的影响。后面的子章节列出了常用的技术手段。这是一份全面而并不详尽的方法列表。

It should not be a surprise that secondary indexes require additional cluster space and processing. This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update. RDBMS products are more advanced in this regard to handle alternative index management out of the box. However, HBase scales better at larger data volumes, so this is a feature trade-off.

毫无疑问二级索引需要额外的集群空间和处理.这就是关系型数据库中所发生的，因为创建额外索引既需要空间也需要花时间去更新。在开箱即用的索引管理方面，关系型数据库更为先进。然而，HBase在更大数据量是具备更好的扩展性，因此这是一个功能上的权衡。

Pay attention to Apache HBase Performance Tuning when implementing any of these approaches.

在实现那些方法时，请注意"性能调优"。

Additionally, see the David Butler response in this dist-list thread HBase, mail # user - Stargate+hbase

此外，可查看David Butler在问题列表中的回复，HBase, mail # user - Stargate+hbase。

43.1. (过滤器查询)Filter Query

Depending on the case, it may be appropriate to use Client Request Filters. In this case, no secondary index is created. However, don’t try a full-scan on a large table like this from an application (i.e., single-threaded client).

根据具体情况，使用客户端过滤器进行请求可能时合适的。但是，不要尝试从应用程序中对一个大表进行全扫描(比如，单线程客户端)。

43.2. (周期性更新二级索引)Periodic-Update Secondary Index

A secondary index could be created in another table which is periodically updated via a MapReduce job. The job could be executed intra-day, but depending on load-strategy it could still potentially be out of sync with the main data table.

See mapreduce.example.readwrite for more information.

二级索引可通过另外一张表创建，通过MapReduce作业周期性更新。该作业可以当天运行，不过取决于负载策略，它仍然可能与主表不同步。

更多信息查看mapreduce.example.readwrite。

43.3. (多写二级索引)Dual-Write Secondary Index

Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to data table, write to index table). If this is approach is taken after a data table already exists, then bootstrapping will be needed for the secondary index with a MapReduce job (see secondary.indexes.periodic).

另一个策略是在写入数据到集群的时候构建二级索引(比如，写入数据表，然后写入索引表)。如果是对已存在的表采用该方法，则需要先执行一个MapReduce作业来进行初始化(查看secondary.indexes.periodic)。

43.4. (汇总表)Summary Tables

Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous, summary tables are a common approach. These would be generated with MapReduce jobs into another table.

See mapreduce.example.summary for more information.

在时间范围很长且数据量很大时，汇总表是常用的方法。可通过MapReduce作业将其生成为另外一个表。

更多信息查看mapreduce.example.summary。

43.5. (协处理器二级索引)Coprocessor Secondary Index

Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see coprocessors

协处理器类似关系型数据库中的触发器。在0.92版本中加入。更多信息，查看coprocessors。

44. Constraints

HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for Constraints is in enforcing business rules for attributes in the table (e.g. make sure values are in the range 1-10). Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled. Extensive documentation on using Constraints can be found at Constraint since version 0.94.

HBase现在支持传统数据库所说的"约束"。约束用来强制表中的属性遵守业务规则(比如，确保值在1-10之间)。约束也可以用来强制参照完整性，但是由于它会显著降低写吞吐，因此强烈不赞成使用。在0.94版本之后，关于如何使用约束，可查看扩展文档Constraint。

45. Schema Design Case Studies

The following will describe some typical data ingestion use-cases with HBase, and how the rowkey design and construction can be approached. Note: this is just an illustration of potential approaches, not an exhaustive list. Know your data, and know your processing requirements.

以下会描述一些使用HBase进行数据获取的用户案例，以及如何进行行键设计和构造的方法。注：这里只是对可能的方法的说明，并非一个详尽的列表。理解你的数据，以及你的处理需求。

It is highly recommended that you read the rest of the HBase and Schema Design first, before reading these case studies.

强烈推荐你在阅读这些学习案例之前，先读一读HBase and Schema Design的剩余内容。

The following case studies are described:

• Log Data / Timeseries Data
• Log Data / Timeseries on Steroids
• Customer/Order
• Tall/Wide/Middle Schema Design
• List Data

以下描述的是这些案例：

• 日志数据 / 时序数据
• 日志数据 / 聚合时序数据
• 客户/订单
• 高/宽/中等 模式设计
• 列表数据

45.1. 案例学习-日志和时序数据(Case Study - Log Data and Timeseries Data)

Assume that the following data elements are being collected.

• Hostname
• Timestamp
• Log event
• Value/message

假设收集到的是以下数据元素

• 主机名
• 时间戳
• 日志事件
• 值/消息

We can store them in an HBase table called LOG_DATA, but what will the rowkey be? From these attributes the rowkey will be some combination of hostname, timestamp, and log-event - but what specifically?

我们可以将它们存储在一个叫做LOG_DATA的表中，但是行键是什么呢？由这些属性可知，应该是主机、时间戳和日志事件的一些组合，但具体是什么？

45.1.1. 时间戳位于前导位(Timestamp In The Rowkey Lead Position)

The rowkey timestamp[log-event] suffers from the monotonically increasing rowkey problem described in Monotonically Increasing Row Keys/Timeseries Data.

timestamp[log-event]组成的行键会遇到Monotonically Increasing Row Keys/Timeseries Data中所描述的单调递增行键问题。

There is another pattern frequently mentioned in the dist-lists about "bucketing" timestamps, by performing a mod operation on the timestamp. If time-oriented scans are important, this could be a useful approach. Attention must be paid to the number of buckets, because this will require the same number of scans to return results.

还有另一种dist-lists中经常提到的，对时间戳取模进行分桶的模式。如果基于时间的扫描比较重要，这会是一个有用的方法。注意桶的数量，因为这会带来同样数量的scan，以返回结果。

long bucket = timestamp % numBuckets;  
to construct:  
[bucket][timestamp][hostname][log-event]

As stated above, to select data for a particular timerange, a Scan will need to be performed for each bucket. 100 buckets, for example, will provide a wide distribution in the keyspace but it will require 100 Scans to obtain data for a single timestamp, so there are trade-offs.

如上所述，要获取一个特定时间范围的数据，需要对每个桶执行一个scan。比如100个桶，能够对键空间提供一个广泛的分布，但在获取某个时间戳范围的数据时需要100个scan，因此需要做权衡。

45.1.2. 主机名位于前导位(Host In The Rowkey Lead Position)

The rowkey hostname[timestamp] is a candidate if there is a large-ish number of hosts to spread the writes and reads across the keyspace. This approach would be useful if scanning by hostname was a priority.

如果有很多的节点来分散对键空间的写入和读取，hostname[timestamp]也是个可选项。这个方法在主要以主机进行扫描时会比较有效。

45.1.3. 时间戳，或反转时间戳(Timestamp, or Reverse Timestamp?)

If the most important access path is to pull most recent events, then storing the timestamps as reverse-timestamps (e.g., timestamp = Long.MAX_VALUE – timestamp) will create the property of being able to do a Scan on hostname to obtain the most recently captured events.

如果最重要的访问方式是得到最新的事件，那么以反转时间戳的方式存储的话(e.g., timestamp = Long.MAX_VALUE – timestamp)，将产生这样的特性：在对hostname进行scan时可以获取最近得到的事件。

Neither approach is wrong, it just depends on what is most appropriate for the situation.

方法无所谓对错，只取决于对具体情况是否最为适合。

Reverse Scan API
HBASE-4811 implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning. This feature is available in HBase 0.98 and later. See Scan.setReversed() for more information.

反转scan接口
HBASE-4811实现了一个接口，用来反向扫描一个表或其中一个范围，以减少为能够反向扫描而所需的设计优化。在HBase 0.98及其后版本可用。See Scan.setReversed() for more information。

45.1.4. 变长 或 定长行键(Variable Length or Fixed Length Rowkeys?)

It is critical to remember that rowkeys are stamped on every column in HBase. If the hostname is a and the event type is e1 then the resulting rowkey would be quite small. However, what if the ingested hostname is myserver1.mycompany.com and the event type is com.package1.subpackage2.subsubpackage3.ImportantService?

务必要记得，在HBase中行键会重复存在于每个列。如果主机名和事件类型分别是a和e1，行键就会非常小，但如果主机名和事件类型是myserver1.mycompany.com和com.package1.subpackage2.subsubpackage3.ImportantService呢？

It might make sense to use some substitution in the rowkey. There are at least two approaches: hashed and numeric. In the Hostname In The Rowkey Lead Position example, it might look like this:

对行键进行一些替换也许是有意义的。至少有2种方法：哈希和数字。在主机名作为行键前导位的例子中，看起来是这样：

Composite Rowkey With Hashes:

• [MD5 hash of hostname] = 16 bytes
• [MD5 hash of event-type] = 16 bytes
• [timestamp] = 8 bytes

使用哈希的组合行键

• [MD5 hash of hostname] = 16 bytes
• [MD5 hash of event-type] = 16 bytes
• [timestamp] = 8 bytes

Composite Rowkey With Numeric Substitution:

For this approach another lookup table would be needed in addition to LOG_DATA, called LOG_TYPES. The rowkey of LOG_TYPES would be:

• type
• [bytes] variable length bytes for raw hostname or event-type.

A column for this rowkey could be a long with an assigned number, which could be obtained by using an HBase counter

So the resulting composite rowkey would be:

• [substituted long for hostname] = 8 bytes
• [substituted long for event type] = 8 bytes
• [timestamp] = 8 bytes

使用数字的组合行键

这个方法在LOG_DATA之外，还需要另一张叫做LOG_TYPE的查找表。LOG_TYPE表的行键是：

• type
• [bytes] 代表原始主机名和事件的定长字节数组

该行键的列可以是通过计数器获取到的一个数值。

因此最终的组合行键是这样：

• [substituted long for hostname] = 8 bytes
• [substituted long for event type] = 8 bytes
• [timestamp] = 8 bytes

In either the Hash or Numeric substitution approach, the raw values for hostname and event-type can be stored as columns.

无论是用哈希还是数字的替换方法，主机名和事件类型的原始值都可以作为列进行存储。

45.2. 案例学习 - 日志数据和聚合时序数据(Case Study - Log Data and Timeseries Data on Steroids)

This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack rows into columns for certain time-periods. For a detailed explanation, see: http://opentsdb.net/schema.html, and Lessons Learned from OpenTSDB from HBaseCon2012.

这实际上就是OpenTSDB采用的方法。它把数据进行重写并按照一定的时间周期将行打包成列。对其细节的解释, see: http://opentsdb.net/schema.html, and Lessons Learned from OpenTSDB from HBaseCon2012.

But this is how the general concept works: data is ingested, for example, in this manner…​

hostname[timestamp1]
hostname[timestamp2]
hostname[timestamp3]
with separate rowkeys for each detailed event, but is re-written like this…​

hostname[timerange]
and each of the above events are converted into columns stored with a time-offset relative to the beginning timerange (e.g., every 5 minutes). This is obviously a very advanced processing technique, but HBase makes this possible.

不过这里展示了大概的工作原理：比如，数据以下面的方式被获取：

[hostname][log-event][timestamp1]  
[hostname][log-event][timestamp2]  
[hostname][log-event][timestamp3]

每一个明细事件作为一个行键，但会被重写成这样：

[hostname][log-event][timerange]

并且以上的每个事件，都会转换为一个列，存储着相对于起始时间范围的一个时间偏移(比如，每5分钟)。这显然是一个非常先进的处理技术，但是HBase使之成为可能。

45.3. (案例学习 - 客户/订单)Case Study - Customer/Order

Assume that HBase is used to store customer and order information. There are two core record-types being ingested: a Customer record type, and Order record type.

The Customer record type would include all the things that you’d typically expect:

假设使用HBase存储客户和订单信息。会获取到两种主要的记录类型：客户记录，和订单记录。

客户记录会包含如下内容：

• Customer number
• Customer name
• Address (e.g., city, state, zip)
• Phone numbers, etc.

订单记录会包含如下内容：

• Customer number
• Order number
• Sales date
• A series of nested objects for shipping locations and line-items (see Order Object Design for details)

Assuming that the combination of customer number and sales order uniquely identify an order, these two attributes will compose the rowkey, and specifically a composite key such as:

假设客户号和订单号的组合唯一标识一个订单，对于订单表，将会由这2个属性组成行键，如下：

[customer number][order number]

for an ORDER table.

However, there are more design decisions to make: are the raw values the best choices for rowkeys?

当然，还有更多设计决策需要去做：原始值对行键来说是不是
最好的选择？

The same design questions in the Log Data use-case confront us here. What is the keyspace of the customer number, and what is the format (e.g., numeric? alphanumeric?) As it is advantageous to use fixed-length keys in HBase, as well as keys that can support a reasonable spread in the keyspace, similar options appear:

日志数据案例中遇到的设计问题，这里一样存在。客户号的键空间是怎样的，格式如何(比如，数字？字符串？)在HBase中使用定长以及能够合理分布的行键是有益的，类似这样：

Composite Rowkey With Hashes:

• [MD5 of customer number] = 16 bytes
• [MD5 of order number] = 16 bytes

Composite Numeric/Hash Combo Rowkey:

• [substituted long for customer number] = 8 bytes
• [MD5 of order number] = 16 bytes

哈希方式组合行键:

• [MD5 of customer number] = 16 bytes
• [MD5 of order number] = 16 bytes

混合数字和哈希的方式组合行键:

• [substituted long for customer number] = 8 bytes
• [MD5 of order number] = 16 bytes

45.3.1. (单个表？多个表？)Single Table? Multiple Tables?

A traditional design approach would have separate tables for CUSTOMER and SALES. Another option is to pack multiple record types into a single table (e.g., CUSTOMER++).

一个典型的设计方法是将客户和销售分为独立的表。另一个选项是将多种记录类型放到一个表中(比如，CUSTOMER++)。

Customer Record Type Rowkey:

[customer-id]

[type] = type indicating `1' for customer record type

Order Record Type Rowkey:

[customer-id]

[type] = type indicating `2' for order record type

[order]

The advantage of this particular CUSTOMER++ approach is that organizes many different record-types by customer-id (e.g., a single scan could get you everything about that customer). The disadvantage is that it’s not as easy to scan for a particular record-type.

这种独特的CUSTOMER++方法的优势是将多种不同的记录类型通过客户id进行组织(比如，单个scan就可以获取该客户的所有数据)。劣势是对于特定的记录类型进行扫描不太容易。

45.3.2. (订单对象设计)Order Object Design

Now we need to address how to model the Order object. Assume that the class structure is as follows:

Order
(an Order can have multiple ShippingLocations

LineItem
(a ShippingLocation can have multiple LineItems

there are multiple options on storing this data.

选择我们需要解决如何对订单对象建模。假设类结构如下：
订单
(一个订单可以包含多个物流地址)
明细项
(一个物流地址可以含有多个明细项)
对此类数据的存储有多种选择。

完全标准化(Completely Normalized)

With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and LINE_ITEM.

在这个方法中，将会分为ORDER, SHIPPING_LOCATION, and LINE_ITEM等独立的表。

The ORDER table’s rowkey was described above: schema.casestudies.custorder

The SHIPPING_LOCATION’s composite rowkey would be something like this:

[order-rowkey]

shipping location number

The LINE_ITEM table’s composite rowkey would be something like this:

[order-rowkey]

shipping location number

line item number

ORDER表的行键如上所述：schema.casestudies.custorder

SHIPPING_LOCATION表的组合主键是：

[order-rowkey]

[shipping location number](e.g., 1st location, 2nd, etc.)

LINE_ITEM表的组合主键是：

[order-rowkey]

[shipping location number](e.g., 1st location, 2nd, etc.)

[line item number](e.g., 1st lineitem, 2nd, etc.)

Such a normalized model is likely to be the approach with an RDBMS, but that’s not your only option with HBase. The cons of such an approach is that to retrieve information about any Order, you will need:

Get on the ORDER table for the Order

Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances

Scan on the LINE_ITEM for each ShippingLocation

granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in HBase you’re just more aware of this fact.

RDBMS中常会采用这样的一个标准模型，但在HBase中却非唯一选择。这种方法的缺点是，要检索任意订单的信息，你需要：

从ORDER表中获取订单信息

扫描SHIPPING_LOCATION表获取该订单的物流地址信息

扫描LINE_ITEM表获取每个物流地址的物品项

当然，这就是RDBMS底层实际所做的，但由于HBase不支持join，所以你更理解了这个事实。

带有记录类型的单个表(Single Table With Record Types)

With this approach, there would exist a single table ORDER that would contain

在这个方法中，将会存在单个表ORDER，包含

The Order rowkey was described above: schema.casestudies.custorder

[order-rowkey]

[ORDER record type]

The ShippingLocation composite rowkey would be something like this:

[order-rowkey]

[SHIPPING record type]

shipping location number

The LineItem composite rowkey would be something like this:

[order-rowkey]

[LINE record type]

shipping location number

line item number

ORDER表的行键如上所述：schema.casestudies.custorder

[order-rowkey]

[ORDER record type]

ShippingLocation表的组合行键是：

[order-rowkey]

[SHIPPING record type]

[shipping location number](e.g., 1st location, 2nd, etc.)

LineItem表的组合行键是：

[order-rowkey]

[LINE record type]

[shipping location number](e.g., 1st location, 2nd, etc.)

[line item number](e.g., 1st lineitem, 2nd, etc.)

非规范化(Denormalized)

A variant of the Single Table With Record Types approach is to denormalize and flatten some of the object hierarchy, such as collapsing the ShippingLocation attributes onto each LineItem instance.

对带记录类型的单个表的一个变化，是将对象结构扁平化，比如将ShippingLocation属性放到每个明细项去。

LineItem表的组合行键是：

[order-rowkey]

[LINE record type]

[line item number](e.g., 1st lineitem, 2nd, etc., care must be taken that there are unique across the entire order)

LineItem表的列是：

itemNumber

quantity

price

shipToLine1 (denormalized from ShippingLocation)

shipToLine2 (denormalized from ShippingLocation)

shipToCity (denormalized from ShippingLocation)

shipToState (denormalized from ShippingLocation)

shipToZip (denormalized from ShippingLocation)

The pros of this approach include a less complex object hierarchy, but one of the cons is that updating gets more complicated in case any of this information changes.

这个方法的优点是可以包含一些复杂对象结构，缺点是一旦信息有变将难以更新。

Object BLOB

With this approach, the entire Order object graph is treated, in one way or another, as a BLOB. For example, the ORDER table’s rowkey was described above: schema.casestudies.custorder, and a single column called "order" would contain an object that could be deserialized that contained a container Order, ShippingLocations, and LineItems.

这个方法中，整个订单对象图，以这样或那样的方式，处理为BLOB。例如，订单表的行键如上所述：schema.casestudies.custorder，然后单个的称为order的列会包含一个可被反序列化的对象，包含Order, ShippingLocations, and LineItems.

There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, etc. All of them are variants of the same approach: encode the object graph to a byte-array. Care should be taken with this approach to ensure backward compatibility in case the object model changes such that older persisted structures can still be read back out of HBase.

有多种选项：JSON, XML, Java Serialization, Avro, Hadoop Writables, 等等。它们都可以做到：将对象图编码为字节数组。对于该方法，需要注意的是，确保向后兼容，旧的数据结构在对象模型变化之后仍然能够从HBase中读取。

Pros are being able to manage complex object graphs with minimal I/O (e.g., a single HBase Get per Order in this example), but the cons include the aforementioned warning about backward compatibility of serialization, language dependencies of serialization (e.g., Java Serialization only works with Java clients), the fact that you have to deserialize the entire object to get any piece of information inside the BLOB, and the difficulty in getting frameworks like Hive to work with custom objects like this.

优点是可以通过很小的IO管理复杂的对象图(比如, 在该例中单个get请求就可以获取整个订单信息 )，但缺点如前所述，需要小心序列化方面的向后兼容，序列化的语言依赖(比如，java的序列化只能通过java的客户端)，获取一点点数据也需要反序列化整个对象，以及类似Hive这样的框架难以处理此类自定义对象。

45.4. Case Study - "Tall/Wide/Middle" Schema Design Smackdown

This section will describe additional schema design questions that appear on the dist-list, specifically about tall and wide tables. These are general guidelines and not laws - each application must consider its own needs.

这个章节将描述出现在dist-list中的另外一些设计问题，特别是关于高表和宽表。这些是一般性的指南而不是法律 - 每个应用必须考虑其自身所需。

45.4.1. 行 vs 版本(Rows vs. Versions)

A common question is whether one should prefer rows or HBase’s built-in-versioning. The context is typically where there are "a lot" of versions of a row to be retained (e.g., where it is significantly above the HBase default of 1 max versions). The rows-approach would require storing a timestamp in some portion of the rowkey so that they would not overwrite with each successive update.

Preference: Rows (generally speaking).

一个常见的问题是使用行还是内置的版本。典型的情况是那些一个行有很多版本需要保存(比如，明显需要超过默认的最大一个版本)。行的方式需要在行键的某个部分存储一个时间戳，从而不会覆盖每次更新。

优先：行(通常来说)

45.4.2. 行 vs 列(Rows vs. Columns)

Another common question is whether one should prefer rows or columns. The context is typically in extreme cases of wide tables, such as having 1 row with 1 million attributes, or 1 million rows with 1 columns apiece.

Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely wide cases, not in the standard use-case where one needs to store a few dozen or hundred columns. But there is also a middle path between these two options, and that is "Rows as Columns."

另一个常见的问题是使用行还是列。典型的情况是较为极端的宽表，比如一行含有一百万列，或者一百万行各自含一个列。

优先：行(通常来说)。澄清一下，该准则针对极端宽表的情况，而不是常规的只需要存储几十或几百个列的使用场景。但在这两个选项之间还有一个中间选则，即"行作为列"。

45.4.3. 行作为列(Rows as Columns)

The middle path between Rows vs. Columns is packing data that would be a separate row into columns, for certain rows. OpenTSDB is the best example of this case where a single row represents a defined time-range, and then discrete events are treated as columns. This approach is often more complex, and may require the additional complexity of re-writing your data, but has the advantage of being I/O efficient. For an overview of this approach, see schema.casestudies.log-steroids.

行vs列的中间选择是针对一些特定的行，将其数据打包作为列。 OpenTSDB 就是一个最好的例子，单个行表示一个既定的时间范围，而离散的事件作为列。这种方法通常会更复杂，并且需要额外的复杂度去重写你的数据，但在I/O性能上有优势。对方法的概要说明，查看schema.casestudies.log-steroids。

45.5. 案例学习 - 列表数据(Case Study - List Data)

The following is an exchange from the user dist-list regarding a fairly common question: how to handle per-user list data in Apache HBase.

以下是来自用户dist-list的关于一个常见问题的交流：如何用HBase处理用户列表数据。

• QUESTION *

We’re looking at how to store a large amount of (per-user) list data in HBase, and we were trying to figure out what kind of access pattern made the most sense. One option is store the majority of the data in a key, so we could have something like:

我们在研究如何在HBase中存储大量列表数据，并尝试找出最有意义的访问模式。一个选项是将数据的主要部分存为一个键，看起来是这样：

<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
<FixedWidthUserName><FixedWidthValueId3>:"" (no value)

The other option we had was to do this entirely using:

另一个选项是完全使用：

<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...

where each row would contain multiple values. So in one case reading the first thirty values would be:

每行会包含多个值。因此读取前三十个值的话前者可以这样：

scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}

And in the second case it would be

而后者是这样：

get 'FixedWidthUserName\x00\x00\x00\x00'

The general usage pattern would be to read only the first 30 values of these lists, with infrequent access reading deeper into the lists. Some users would have ⇐ 30 total values in these lists, and some users would have millions (i.e. power-law distribution)

常见的使用方式是只从列表中读取前30行，较少去读取更多。有些用户的列表共有30行，而有些用户则有百万行。

The single-value format seems like it would take up more space on HBase, but would offer some improved retrieval / pagination flexibility. Would there be any significant performance advantages to be able to paginate via gets vs paginating with scans?

单个值的格式在HBase中看起来会占用更多空间，但能够提供更优的检索/分页灵活性。通过gets分页是否比scans分页有明显的性能优势？

My initial understanding was that doing a scan should be faster if our paging size is unknown (and caching is set appropriately), but that gets should be faster if we’ll always need the same page size. I’ve ended up hearing different people tell me opposite things about performance. I assume the page sizes would be relatively consistent, so for most use cases we could guarantee that we only wanted one page of data in the fixed-page-length case. I would also assume that we would have infrequent updates, but may have inserts into the middle of these lists (meaning we’d need to update all subsequent rows).

Thanks for help / suggestions / follow-up questions.

我最初的理解是，如果分页大小未知的话，执行一个scan会比较快，但如果总是需要同样的分页大小，那么gets会更快。我听到其他人对于性能有不同看法。我假设分页大小会相对一致，因此对于大多用例，我们可以保证我们只获取固定大小的一页数据。我也假设我们很少更新，但在列表的中间插入数据(意味着我们需要更新所有后续的行)。

• ANSWER *

If I understand you correctly, you’re ultimately trying to store triples in the form "user, valueid, value", right? E.g., something like:

如果我理解的没错，你本质上是想存储"user, valueid, value"的元组？类似这样：

"user123, firstname, Paul",
"user234, lastname, Smith"

(But the usernames are fixed width, and the valueids are fixed width).

(不过usernames为定长，并且valueids也是定长)。

And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with valueid Y". Is that right? And these values should be returned sorted by valueid?

The tl;dr version is that you should probably go with one row per user+value, and not build a complicated intra-row pagination scheme on your own unless you’re really sure it is needed.

并且，你的访问模式是"对于用户x，列出从Y开始的30个值"。是这样吗？另外，这些值需要以valueid顺序返回？

tl;dr版本是，你或许应该每个user+value作为一行，而不是去亲自构建一个行内分页模式，除非你确定这是需要的。

Your two options mirror a common question people have when designing HBase schemas: should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so there are many rows in the table for each user; the row key is user + valueid, and there would be (presumably) a single column qualifier that means "the value". This is great if you want to scan over rows in sorted order by row key (thus my question above, about whether these ids are sorted correctly). You can start a scan at any user+valueid, read the next 30, and be done. What you’re giving up is the ability to have transactional guarantees around all the rows for one user, but it doesn’t sound like you need that. Doing it this way is generally recommended (see here https://hbase.apache.org/book.html#schema.smackdown).

你的两个选项反映了人们在设计HBase模式时的一个常见问题：应该用高表还是宽表？你第一个模式时高表：每一行代表一个用户的一个值；行键是user + valueid，且只有一个列限定符叫做"the value"。如果你想基于有序行键进行扫描的话，这很不错。你可以从任意的user+valueid开始一个scan，读取接下来的30行，就可以了。你所放弃的是对于某个用户所有行的事务保证方面的能力，但貌似你并不需要这个。这是通常所推荐的方式(看这里:https://hbase.apache.org/book.html#schema.smackdown)。

Your second option is "wide": you store a bunch of values in one row, using different qualifiers (where the qualifier is the valueid). The simple way to do that would be to just store ALL values for one user in a single row. I’m guessing you jumped to the "paginated" version because you’re assuming that storing millions of columns in a single row would be bad for performance, which may or may not be true; as long as you’re not trying to do too much in a single request, or do things like scanning over and returning all of the cells in the row, it shouldn’t be fundamentally worse. The client has methods that allow you to get specific slices of columns.

你的第二个选项是宽表：你在一行中存储一批值，用不同的限定符(这里使用valueid)。要做到这样只需要简单的将单个用户的数据存为一行。我猜你想到了分页版本，因为你假定在一行中存储百万列性能会比较差，但未必是这样；只要你没有试图在单个请求中获取过多数据，或扫描并返回行的所有cell，实际上就不会更差。客户端有一些方法，允许你指定部分列。

Note that neither case fundamentally uses more disk space than the other; you’re just "shifting" part of the identifying information for a value either to the left (into the row key, in option one) or to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores the whole row key, and column family name. (If this is a bit confusing, take an hour and watch Lars George’s excellent video about understanding HBase schema design: http://www.youtube.com/watch?v=_HLoH_PgrLk).

注意，没有哪个选项会占用更多的空间；你只是将值的标识信息放在左边(行键中)或右边(列限定符)。在底层，每个键值对仍然会存储整个行键和列名称。(如果有一些困惑，花一个小时看下Lars George关于理解HBase模式设计的视频：http://www.youtube.com/watch?v=_HLoH_PgrLk)

A manually paginated version has lots more complexities, as you note, like having to keep track of how many things are in each page, re-shuffling if new values are inserted, etc. That seems significantly more complex. It might have some slight speed advantages (or disadvantages!) at extremely high throughput, and the only way to really know that would be to try it out. If you don’t have time to build it both ways and compare, my advice would be to start with the simplest option (one row per user+value). Start simple and iterate! )

手工分页的版本更为复杂，如你所知，比如需要跟踪每页有多少内容，有新的数据插入时需要重新调整，等等。这看起来明显更为复杂。也许在极端高吞吐情况下，它会有微小的速度优势(或劣势)，但只能通过测试来知道真实情况。如果你没时间去构建它们并比较，我的建议是从最简单的选项开始(每个user+value作为一行)。从简单开始然后迭代！)

46. Operational and Performance Configuration Options

46.1. 优化HBase 服务端RPC处理(Tune HBase Server RPC Handling)

• Set hbase.regionserver.handler.count (in hbase-site.xml) to cores x spindles for concurrency.

• Optionally, split the call queues into separate read and write queues for differentiated service. The parameter hbase.ipc.server.callqueue.handler.factor specifies the number of call queues:

  • 0 means a single shared queue
  • 1 means one queue for each handler.
  • A value between 0 and 1 allocates the number of queues proportionally to the number of handlers. For instance, a value of .5 shares one queue between each two handlers.

• Use hbase.ipc.server.callqueue.read.ratio (hbase.ipc.server.callqueue.read.share in 0.98) to split the call queues into read and write queues:

  • 0.5 means there will be the same number of read and write queues
  • < 0.5 for more read than write
  • > 0.5 for more write than read

• Set hbase.ipc.server.callqueue.scan.ratio (HBase 1.0+) to split read call queues into small-read and long-read queues:

  • 0.5 means that there will be the same number of short-read and long-read queues
  • < 0.5 for more short-read
  • > 0.5 for more long-read

• 将hbase.regionserver.handler.count设置为cpu数量的倍数.

• 可选的，针对不同服务将请求队列进行隔离，hbase.ipc.server.callqueue.handler.factor参数定义了请求队列的数量:

  • 0 代表共用1个队列。
  • 1 代表每个handler对应1个队列。
  • 0-1中间的值，代表根据handler的数量，按比例分配队列。比如，0.5意味着2个handler共用1个队列。

• 使用hbase.ipc.server.callqueue.read.ratio将请求队列拆分为读和写队列:

  • 0.5 代表读队列和写队列数量一样
  • < 0.5 代表读队列更多
  • > 0.5 代表写队列更多

• 配置hbase.ipc.server.callqueue.scan.ratio (HBase 1.0+) 将读队列拆分为short-read和long-read队列:

  • 0.5 代表short-read和long-read队列数量一样
  • < 0.5 代表short-read队列更多
  • > 0.5 代表long-read队列更多

46.2. 对RPC禁用Nagle(Disable Nagle for RPC)

Disable Nagle’s algorithm. Delayed ACKs can add up to ~200ms to RPC round trip time. Set the following parameters:

• In Hadoop’s core-site.xml:

  • ipc.server.tcpnodelay = true
  • ipc.client.tcpnodelay = true

• In HBase’s hbase-site.xml:

  • hbase.ipc.client.tcpnodelay = true
  • hbase.ipc.server.tcpnodelay = true

禁用Nagle算法. 延迟的ACKs会将RPC往返时间最多增加到200ms。 Set the following parameters:

• In Hadoop’s core-site.xml:

  • ipc.server.tcpnodelay = true
  • ipc.client.tcpnodelay = true

• In HBase’s hbase-site.xml:

  • hbase.ipc.client.tcpnodelay = true
  • hbase.ipc.server.tcpnodelay = true

46.3. 限制服务端错误影响(Limit Server Failure Impact)

Detect regionserver failure as fast as reasonable. Set the following parameters:

• In hbase-site.xml, set zookeeper.session.timeout to 30 seconds or less to bound failure detection (20-30 seconds is a good start).

  • Notice: the sessionTimeout of zookeeper is limited between 2 times and 20 times the tickTime(the basic time unit in milliseconds used by ZooKeeper.the default value is 2000ms.It is used to do heartbeats and the minimum session timeout will be twice the tickTime).

• Detect and avoid unhealthy or failed HDFS DataNodes: in hdfs-site.xml and hbase-site.xml, set the following parameters:

  • dfs.namenode.avoid.read.stale.datanode = true
  • dfs.namenode.avoid.write.stale.datanode = true

在合理范围内尽快发现regionserver的错误. 配置以下参数:

• 在hbase-site.xml中, 将zookeeper.session.timeout设置为30秒或更少 (20-30秒是个不错的开始)。

  • 注意: zookeeper的会话超时时间被限制为tickTime的2倍到20倍之间(ZooKeeper使用的一个基本时间单位.默认值是2000ms.它被用来发送心跳，且最小的会话过期时间应2倍于此值)。

• 发现和避免非健康或失败的HDFS节点： in hdfs-site.xml and hbase-site.xml, set the following parameters:

  • dfs.namenode.avoid.read.stale.datanode = true
  • dfs.namenode.avoid.write.stale.datanode = true

46.4. Optimize on the Server Side for Low Latency

Skip the network for local blocks when the RegionServer goes to read from HDFS by exploiting HDFS’s Short-Circuit Local Reads facility. Note how setup must be done both at the datanode and on the dfsclient ends of the conneciton — i.e. at the RegionServer and how both ends need to have loaded the hadoop native .so library. After configuring your hadoop setting dfs.client.read.shortcircuit to true and configuring the dfs.domain.socket.path path for the datanode and dfsclient to share and restarting, next configure the regionserver/dfsclient side.

当RegionServer从HDFS读取时，利用HDFS的短路读特性，针对本地块可以跳过网络。注意需要在datanode和dfsclient中同时配置，并且都需要加载Hadoop的本地.so库。将hadoop的dfs.client.read.shortcircuit设置为true，并且配置dfs.domain.socket.path用来共享，然后重启，接下来配置regionserver端。

• In hbase-site.xml, set the following parameters:

  • dfs.client.read.shortcircuit = true
  • dfs.client.read.shortcircuit.skip.checksum = true so we don’t double checksum (HBase does its own checksumming to save on i/os. See hbase.regionserver.checksum.verify for more on this.
  • dfs.domain.socket.path to match what was set for the datanodes.
  • dfs.client.read.shortcircuit.buffer.size = 131072 Important to avoid OOME — hbase has a default it uses if unset, see hbase.dfs.client.read.shortcircuit.buffer.size; its default is 131072.
• Ensure data locality. In hbase-site.xml, set hbase.hstore.min.locality.to.skip.major.compact = 0.7 (Meaning that 0.7 <= n <= 1)

• Make sure DataNodes have enough handlers for block transfers. In hdfs-site.xml, set the following parameters:

  • dfs.datanode.max.xcievers >= 8192
  • dfs.datanode.handler.count = number of spindles

Check the RegionServer logs after restart. You should only see complaint if misconfiguration. Otherwise, shortcircuit read operates quietly in background. It does not provide metrics so no optics on how effective it is but read latencies should show a marked improvement, especially if good data locality, lots of random reads, and dataset is larger than available cache.

重启之后检查RegionServer的日志。如果配置错误会看到异常日志。否则，短路读并不会有显式的输出。它并未提供监控指标，所以效果如何不好看出，但是读取延迟应该会有显著提升，尤其是如果数据有较好的本地性，大量的随机读取，且数据集远大于可用缓存。

Other advanced configurations that you might play with, especially if shortcircuit functionality is complaining in the logs, include dfs.client.read.shortcircuit.streams.cache.size and dfs.client.socketcache.capacity. Documentation is sparse on these options. You’ll have to read source code.

另一个你可能需要处理的高级配置，尤其是当日志里出现关于短路功能异常时，包含dfs.client.read.shortcircuit.streams.cache.size 和 dfs.client.socketcache.capacity。它们的配置文档比较分散。你可能需要阅读源码。

For more on short-circuit reads, see Colin’s old blog on rollout, How Improved Short-Circuit Local Reads Bring Better Performance and Security to Hadoop. The HDFS-347 issue also makes for an interesting read showing the HDFS community at its best (caveat a few comments).

更多关于短路读的信息，可以查看Colin的旧博客，How Improved Short-Circuit Local Reads Bring Better Performance and Security to Hadoop。有兴趣的话可以阅读HDFS-347，其展示了HDFS社区在这上面的努力(一些评论值得关注)。

46.5. JVM Tuning

46.5.1. Tune JVM GC for low collection latencies

Use the CMS collector: -XX:+UseConcMarkSweepGC

Keep eden space as small as possible to minimize average collection time. Example:

-XX:CMSInitiatingOccupancyFraction=70
Optimize for low collection latency rather than throughput: -Xmn512m

Collect eden in parallel: -XX:+UseParNewGC

Avoid collection under pressure: -XX:+UseCMSInitiatingOccupancyOnly

Limit per request scanner result sizing so everything fits into survivor space but doesn’t tenure. In hbase-site.xml, set hbase.client.scanner.max.result.size to 1/8th of eden space (with -Xmn512m this is ~51MB )

Set max.result.size x handler.count less than survivor space

使用CMS收集器：-XX:+UseConcMarkSweepGC

使eden区尽可能的小，来最小化平均收集时间。例如：
-XX:CMSInitiatingOccupancyFraction=70

为低延迟而不是吞吐进行优化：-Xmn512m

eden区使用并行收集：-XX:+UseParNewGC

避免在压力大时收集：
-XX:+UseCMSInitiatingOccupancyOnly

限制单个请求的结果大小，从而都可以放到survivor区而不是tenure区。在hbase-site.xml中，配置hbase.client.scanner.max.result.size为eden区的八分之一(with -Xmn512m this is ~51MB)

使max.result.size x handler.count小于survivor区。

46.5.2. OS-Level Tuning

Turn transparent huge pages (THP) off:

echo never > /sys/kernel/mm/transparent_hugepage/enabled
echo never > /sys/kernel/mm/transparent_hugepage/defrag
Set vm.swappiness = 0

Set vm.min_free_kbytes to at least 1GB (8GB on larger memory systems)

Disable NUMA zone reclaim with vm.zone_reclaim_mode = 0x

47. Special Cases

47.1. 对于那些希望快速失败而非等待的应用(For applications where failing quickly is better than waiting)

In hbase-site.xml on the client side, set the following parameters:

Set hbase.client.pause = 1000

Set hbase.client.retries.number = 3

If you want to ride over splits and region moves, increase hbase.client.retries.number substantially (>= 20)

Set the RecoverableZookeeper retry count: zookeeper.recovery.retry = 1 (no retry)

In hbase-site.xml on the server side, set the Zookeeper session timeout for detecting server failures: zookeeper.session.timeout ⇐ 30 seconds (20-30 is good).

47.2. 对于那些能够容忍稍微过时信息的应用(For applications that can tolerate slightly out of date information)

HBase timeline consistency (HBASE-10070) With read replicas enabled, read-only copies of regions (replicas) are distributed over the cluster. One RegionServer services the default or primary replica, which is the only replica that can service writes. Other RegionServers serve the secondary replicas, follow the primary RegionServer, and only see committed updates. The secondary replicas are read-only, but can serve reads immediately while the primary is failing over, cutting read availability blips from seconds to milliseconds. Phoenix supports timeline consistency as of 4.4.0 Tips:

• Deploy HBase 1.0.0 or later.
• Enable timeline consistent replicas on the server side.

• Use one of the following methods to set timeline consistency:

  • Use ALTER SESSION SET CONSISTENCY = 'TIMELINE’
  • Set the connection property Consistency to timeline in the JDBC connect string

HBase时间线一致性(HBase -10070)在启用读副本的情况下，region的只读副本分布在集群中。 一个RegionServer提供默认的或主副本服务, 写服务只能由该副本提供. 其它RegionServers提供从副本服务, 跟进主RegionServer, 只对已提交的更新可见.从副本是只读的，但当主副本挂掉时，能够立即提供读服务，将读不可用的时间从秒级减少到毫秒级。 Phoenix从4.4.0开始支持时间线一致性:

• 部署HBase 1.0.0之后的版本。
• 在服务端启用时间线一致性.

• 使用下述的方法之一来配置时间线一致性:

  • Use ALTER SESSION SET CONSISTENCY = 'TIMELINE’
  • Set the connection property Consistency to timeline in the JDBC connect string