hbase-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Schubert Zhang <zson...@gmail.com>
Subject Re: Cassandra vs HBase
Date Thu, 03 Sep 2009 06:37:27 GMT
in line.

On Thu, Sep 3, 2009 at 12:46 PM, stack <stack@duboce.net> wrote:

>
> How many machines can you use for this job?


Tens.  (e.g. 10~20)


>
> Do you need to keep it all?  Does some data expire (or can it be moved
> offline)?
>
Yes, we need remove old data which expire.



> I see why you have timestamp as part of the key in your current hbase
> cluster -- i.e. tall tables -- as you have no other choice currently.


> It might make sense premaking the regions in the table.  Look at how many
> regions were made the day before and go ahead and premake them to save
> yourself having to ride over splits (I can show you how to write a little
> script to do this).
>
> Does the time-series data arrive roughly on time -- e.g. all instruments
> emit the 4 o'clock readings at 4 o'clock or is there some flux in here?  In
> other words, do you have a write rate of thousands of updates per second,
> all carrying the same timestamp?


The data will arrive with a minutes delay.
Usually, we need to write/ingest tens of thousands of new rows. Many rows
with the same timestamp.


>
> St.Ack
>
>
>
>
> > 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
> > .)
> > >>>>>
> > >>>>>
> > >>>>
> > >>>>
> > >>>>
> > >>>>
> > >>>
> > >>>
> > >>>
> > >>
> > >>
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message