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From Schubert Zhang <zson...@gmail.com>
Subject Re: Cassandra vs HBase
Date Thu, 03 Sep 2009 03:27:38 GMT
JG and stack,

Thanks for your comments.

I am not talking about how HBase is good or not, and not talking about how
to improve HBase's performance. May this is not a good thread about my
topic, since I do not consider Cassandra.

I want to get advice here from concept and theory level, and you may have
such advice with your expert experiences.

I want to share some of my application requirements here to let the question
clear:

- My dataset:
  (1) structured data, about 200-500 bytes (<1KB) each row.
  (2) time-series, but we cannot ensure the incoming data always in order by
timestamp. maybe, the later coming input file with some rows which timestamp
are before the preceding file.
  (3) data set is very big and fast continually. (e.g. 4 billion rows/2TB
daily)

- Processing
  (1) Use mapreduce to ingest input raw data files, periodically. But we
cannot ensure the current processing data is always later than the preceding
processing.
  (2) We want do analytical query(based on mapreduce) on time-ranges of
these data.
  (3) We also want to query (random) few a small set of rows from the
dataset, with low latency (e.g. < or ~ 1 second). So we must use indexing
(primary and/or secondary).

Schubert

On Thu, Sep 3, 2009 at 2:32 AM, Jonathan Gray <jlist@streamy.com> wrote:

> @Sylvain
>
> If you describe your use case, perhaps we can help you to understand what
> others are doing / have done similarly.  Event logging is certainly
> something many of us have done.
>
> If you're wondering about how much load HBase can handle, provide some
> numbers of what you expect.  How much data in bytes are associated with each
> event, how many events per hour, and what operations do you want to do on
> it?  We could help you determine how big of a cluster you might need and the
> kind of write/read throughput you might see.
>
> @Schubert
>
> You do not need to partition your tables by stamp.  One possibility is to
> put the stamp as the first part of your rowkeys, and in that way you will
> have the table sorted by time.  Using Scan's start/stop keys, you can
> prevent doing a full table scan.
>
It would not work. Since our data comes fastly. In the method only one
region(server) are busy for writing. The throughput is bad for writing.


>
> For both of you... If you are storing massive amounts of streaming log-type
> data, do you need full random read access to it?  If you just need to
> process on subsets of time, that's easily partitioned by file. HBase should
> be used if you need to *read* from it randomly, not streaming.  If you have
> processing that HBase's inherent sorting, grouping, and indexing can benefit
> from, then it also can make sense to use HBase in order to avoid full-scans
> of data.
>

I know it is a contradiction between random-access and batch processing. But
the features of HBase(sorting, distributed b-tree, merge/compaction) are
very attractive.


>
> HBase is not the answer because of lack of HDFS append.  You could buffer
> in something outside HDFS, close files after a certain size/time (this his
> what hbase does now, we can have data loss because of no
> appends as well), etc...
>
> Reads/writes of lots of streaming data to HBase will always be slower than
> HDFS.  HBase adds additional buffering, and the compaction/split processes
> actually mean you copy the same data multiple times (probably 3-4 times avg
> which lines up with the 3-4x slowdown you see).
>
>
> And there is currently a patch in development (that works at least
> partially) to do direct-to-hdfs imports to HBase which would then be nearly
> as fast as a normal HDFS writing job.
>
> Issue here:  https://issues.apache.org/jira/browse/HBASE-48
>
>
> JG
>
>
> Sylvain Hellegouarch wrote:
>
>> I must admit, I'm left as puzzled as you are. Our current use case at work
>> involve large amount of small event log writing. Of course HDFS was quickly
>> out of question since it's not there yet to append to a file and more
>> generally to handle large amount of small write ops.
>>
>> So we decided with HBase because we trust the Hadoop/HBase infrastructure
>> will offer us the robustness and reliability we need. That being said, I'm
>> not feeling at ease in regards to the capacity of HBase to handle the
>> potential load we are looking at inputing.
>>
>> In fact, it's a common treat of such systems, they've been designed with a
>> certain use case in mind and sometimes I feel like their design and
>> implementation leak way too much on our infrastructure, leading us down the
>> path of a virtual lock-in.
>>
>> Now I am not accusing anyone here, just observing that I find it really
>> hard to locate any industrial story of those systems in a similar use case
>> we have at hand.
>>
>> The number of nodes this or that company has doesn't quite interest me as
>> much as the way they are actually using HBase and Hadoop.
>>
>> RDBMS don't scale as well but they've got a long history and people do
>> know how to optimise, use and manage them. It seems column-oriented database
>> systems are still young :)
>>
>> - Sylvain
>>
>> Schubert Zhang a écrit :
>>
>>> Regardless Cassandra, I want to discuss some questions about
>>> HBase/Bigtable.  Any advices are expected.
>>>
>>> Regards runing MapReduce to scan/analyze big data in HBase.
>>>
>>> Compared to sequentially reading data from HDFS files directly,
>>> scan/sequential-reading data from HBase is slower. (As my test, at least
>>> 3:1
>>> or 4:1).
>>>
>>> For the data in HBase, it is diffcult to only analyze specified part of
>>> data. For example, it is diffcult to only analyze the recent one day of
>>> data. In my application, I am considering partition data into different
>>> HBase tables (e.g. one day - one table), then, I can only touch one table
>>> for analyze via MapReduce.
>>> In Google's Bigtable paper, in the "8.1 Google Analytics", they also
>>> discribe this usage, but I don't know how.
>>>
>>> It is also slower to put flooding data into HBase table than writing to
>>> files. (As my test, at least 3:1 or 4:1 too). So, maybe in the future,
>>> HBase
>>> can provide a bulk-load feature, like PNUTS?
>>>
>>> Many people suggest us to only store metadata into HBase tables, and
>>> leave
>>> data in HDFS files, because our time-series dataset is very big.  I
>>> understand this idea make sense for some simple application requirements.
>>> But usually, I want different indexes to the raw data. It is diffcult to
>>> build such indexes if the the raw data files (which are raw or are
>>> reconstructed via MapReduce  periodically on recent data ) are not
>>> totally
>>> sorted.  .... HBase can provide us many expected features: sorted,
>>> distributed b-tree, compact/merge.
>>>
>>> So, it is very difficult for me to make trade-off.
>>> If I store data in HDFS files (may be partitioned), and metadata/index in
>>> HBase. The metadata/index is very difficult to be build.
>>> If I rely on HBase totally, the performance of ingesting-data and
>>> scaning-data is not good. Is it reasonable to do MapReduce on HBase? We
>>> know
>>> the goal of HBase is to provide random access over HDFS, and it is a
>>> extention or adaptor over HDFS.
>>>
>>> ----
>>> Many a time, I am thinking, maybe we need a data storage engine, which
>>> need
>>> not so strong consistency, and it can provide better writing and
>>> reading throughput like HDFS. Maybe, we can design another system like a
>>> simpler HBase ?
>>>
>>> Schubert
>>>
>>> On Wed, Sep 2, 2009 at 8:56 AM, Andrew Purtell <apurtell@apache.org>
>>> wrote:
>>>
>>>
>>>
>>>> To be precise, S3. http://status.aws.amazon.com/s3-20080720.html
>>>>
>>>>  - Andy
>>>>
>>>>
>>>>
>>>>
>>>> ________________________________
>>>> From: Andrew Purtell <apurtell@apache.org>
>>>> To: hbase-user@hadoop.apache.org
>>>> Sent: Tuesday, September 1, 2009 5:53:09 PM
>>>> Subject: Re: Cassandra vs HBase
>>>>
>>>>
>>>> Right... I recall an incident in AWS where a malformed gossip packet
>>>> took
>>>> down all of Dynamo. Seems that even P2P doesn't mitigate against corner
>>>> cases.
>>>>
>>>>
>>>> On Tue, Sep 1, 2009 at 3:12 PM, Jonathan Ellis <jbellis@gmail.com>
>>>> wrote:
>>>>
>>>>
>>>>
>>>>> The big win for Cassandra is that its p2p distribution model -- which
>>>>> drives the consistency model -- means there is no single point of
>>>>> failure.  SPF can be mitigated by failover but it's really, really
>>>>> hard to get all the corner cases right with that approach.  Even
>>>>> Google with their 3 year head start and huge engineering resources
>>>>> still has trouble with that occasionally.  (See e.g.
>>>>> http://groups.google.com/group/google-appengine/msg/ba95ded980c8c179.)
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>>
>>
>>

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