实时查询/聚集数百万条记录 - hadoop？ hbase？ cassandra？

问题描述

我有一个可以并行化的解决方案，但是我没有hadoop / nosql的经验，我不知道哪个解决方案最适合我的需要。在理论上，如果我有无限的CPU，我的结果应该立即返回。所以，任何帮助将不胜感激。非常感谢！

以下是我的功能：

• >
  
• 数据集键：
  
  
  
  • 所有数据集都具有相同的键
  
  
  • 10或20百万）
  
  


• 数据集列：
  
  
  
  
  • 具有相同的列
  
  
  • 10到20列
  
  
  • 大多数列都是我们需要聚合的数值（avg，stddev，以计算统计信息）
  
  
  • 几列是type_id列，因为在特定查询中我们可能
    只想包含某些type_ids
  
  
  
  
• 网络应用程序
  
  
  
  
  • 用户可以选择他们感兴趣的数据集（从15到1000） / li>
    
  
  
  
  
  
  
  • >更新数据：
    
    
    
    • 整个数据集可以添加，删除或替换/更新
    
    
    • 添加列。
    
    
    • 从不向数据集添加行/键，因此不需要具有大量快速写入的系统
    
    
  
  
  • 基础架构：
    
    
    
    
    • 目前有两台机器，每组24个核心
    $ b $
    
    
    
    

    I无法预先计算我的聚合值，但由于每个键是独立的，这应该是易于扩展。目前，我有一个postgres数据库中的数据，其中每个数据集在其自己的分区。

    
    
    
    
    
    • 分区很好， / drop>替换分区
    
    
    • 数据库适用于基于type_id的过滤
    
    
    • 数据库不容易写入并行查询
    
    
    • 数据库适用于结构化数据，而且我的数据不是结构化的
    
    
    
    
    
    
    
    
    
    • 为每个特定type_id的数据集创建了一个制表符分隔文件
    
    
    • 上传到hdfs
    • 
      
    • map：检索每个键的值/列
    
    
    • reduce：计算的平均值和标准偏差
    
    
    
    
    

    从我粗略的概念验证，我可以看到这将缩放很好，但我可以看到hadoop / hdfs有延迟我读过，它通常不用于实时查询（即使我确定，在5秒内将结果返回给用户）。

    
    
    

    有关如何处理这个问题的任何建议？我正在想试着HBase接下来得到一个感觉。我应该看看Hive吗？ Cassandra？ Voldemort？

    
    
    

    感谢！

    解决方案

    似乎他们会帮助你。基本上每个人都编译到一个或多个map / reduce作业，所以响应不能在5秒内。

    
    
    

    HBase可能工作，虽然你的基础设施有点小最佳性能。我不明白为什么你不能预计算每个列的汇总统计。您应该查找计算运行平均值，以便您不必执行重量减少。

    
    
    

    查看

    
    
    

    stddev（X）= sqrt （E [X ^ 2] - （E [X]）^ 2）

    
    
    

    这意味着你可以通过

    $ b得到AB的stddev
    $ b p sqrt（E [AB ^ 2] - （E [AB]）^ 2）。 E [AB ^ 2]是（sum（A ^ 2）+ sum（B ^ 2））/（| A | + | B |）
    
    

    I have a solution that can be parallelized, but I don't (yet) have experience with hadoop/nosql, and I'm not sure which solution is best for my needs. In theory, if I had unlimited CPUs, my results should return back instantaneously. So, any help would be appreciated. Thanks!

    Here's what I have:

    • 1000s of datasets
    • dataset keys:
      • all datasets have the same keys
      • 1 million keys (this may later be 10 or 20 million)
    • dataset columns:
      
      • each dataset has the same columns
      • 10 to 20 columns
      • most columns are numerical values for which we need to aggregate on (avg, stddev, and use R to calculate statistics)
      • a few columns are "type_id" columns, since in a particular query we maywant to only include certain type_ids
    • web application
      
      • user can choose which datasets they are interested in (anywhere from 15 to 1000)
      • application needs to present: key, and aggregated results (avg, stddev) of each column
    • updates of data:
      • an entire dataset can be added, dropped, or replaced/updated
      • would be cool to be able to add columns. But, if required, can just replace the entire dataset.
      • never add rows/keys to a dataset - so don't need a system with lots of fast writes
    • infrastructure:
      
      • currently two machines with 24 cores each
      • eventually, want ability to also run this on amazon

    I can't precompute my aggregated values, but since each key is independent, this should be easily scalable. Currently, I have this data in a postgres database, where each dataset is in its own partition.

    • partitions are nice, since can easily add/drop/replace partitions
    • database is nice for filtering based on type_id
    • databases aren't easy for writing parallel queries
    • databases are good for structured data, and my data is not structured

    As a proof of concept I tried out hadoop:

    • created a tab separated file per dataset for a particular type_id
    • uploaded to hdfs
    • map: retrieved a value/column for each key
    • reduce: computed average and standard deviation

    From my crude proof-of-concept, I can see this will scale nicely, but I can see hadoop/hdfs has latency I've read that that it's generally not used for real time querying (even though I'm ok with returning results back to users in 5 seconds).

    Any suggestion on how I should approach this? I was thinking of trying HBase next to get a feel for that. Should I instead look at Hive? Cassandra? Voldemort?

    thanks!

    解决方案

    Hive or Pig don't seem like they would help you. Essentially each of them compiles down to one or more map/reduce jobs, so the response cannot be within 5 seconds

    HBase may work, although your infrastructure is a bit small for optimal performance. I don't understand why you can't pre-compute summary statistics for each column. You should look up computing running averages so that you don't have to do heavy weight reduces.

    check out http://en.wikipedia.org/wiki/Standard_deviation

    stddev(X) = sqrt(E[X^2]- (E[X])^2)

    this implies that you can get the stddev of AB by doing

    sqrt(E[AB^2]-(E[AB])^2). E[AB^2] is (sum(A^2) + sum(B^2))/(|A|+|B|)

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