>> Solr does not provide a complex enough support to rank.
I believe Solr has a bunch of plug-ability to write your own custom ranking
approach. If you think you can't do your desired ranking with Solr, you're
probably wrong and need to ask for help from the Solr community.
>> retrieving data by keyword is one of them. I think Solr is a proper
The key to keyword retrieval is the construction of the data. Among other
things, this is one of the key things that Solr is very good at: creating a
very efficient organization of the data so that you can retrieve quickly.
At their core, Solr, ElasticSearch, Lily and Katta all use Lucene to
construct this data. HBase is bad at this.
>> how HBase support high performance when it needs to keep consistency in
a large scale distributed system
HBase is primarily built for retrieving a single row at a time based on a
predetermined and known location (the key). It is also very efficient at
splitting massive datasets across multiple machines and allowing sequential
batch analyses of these datasets. HBase can maintain high performance in
this way because consistency only ever exists at the row level. This is
what HBase is good at.
You need to focus what you're doing and then write it out. Figure out how
you think the pieces should work together. Read the documentation. Then,
ask specific questions where you feel like the documentation is unclear or
you feel confused. Your general questions are very difficult to answer in
any kind of really helpful way.
On Wed, Feb 22, 2012 at 9:51 AM, Bing Li <[EMAIL PROTECTED]> wrote:
> Mr Gupta,
> Thanks so much for your reply!
> In my use cases, retrieving data by keyword is one of them. I think Solr
> is a proper choice.
> However, Solr does not provide a complex enough support to rank. And,
> frequent updating is also not suitable in Solr. So it is difficult to
> retrieve data randomly based on the values other than keyword frequency in
> text. In this case, I attempt to use HBase.
> But I don't know how HBase support high performance when it needs to keep
> consistency in a large scale distributed system.
> Now both of them are used in my system.
> I will check out ElasticSearch.
> Best regards,
> On Thu, Feb 23, 2012 at 1:35 AM, T Vinod Gupta <[EMAIL PROTECTED]>wrote:
>> Its a classic battle on whether to use solr or hbase or a combination of
>> both. both systems are very different but there is some overlap in the
>> utility. they also differ vastly when it compares to computation power,
>> storage needs, etc. so in the end, it all boils down to your use case. you
>> need to pick the technology that it best suited to your needs.
>> im still not clear on your use case though.
>> btw, if you haven't started using solr yet - then you might want to
>> checkout ElasticSearch. I spent over a week researching between solr and ES
>> and eventually chose ES due to its cool merits.
>> On Wed, Feb 22, 2012 at 9:31 AM, Ted Yu <[EMAIL PROTECTED]> wrote:
>>> There is no secondary index support in HBase at the moment.
>>> It's on our road map.
>>> On Wed, Feb 22, 2012 at 9:28 AM, Bing Li <[EMAIL PROTECTED]> wrote:
>>> > Jacques,
>>> > Yes. But I still have questions about that.
>>> > In my system, when users search with a keyword arbitrarily, the query
>>> > forwarded to Solr. No any updating operations but appending new indexes
>>> > exist in Solr managed data.
>>> > When I need to retrieve data based on ranking values, HBase is used.
>>> > the ranking values need to be updated all the time.
>>> > Is that correct?
>>> > My question is that the performance must be low if keeping consistency
>>> in a
>>> > large scale distributed environment. How does HBase handle this issue?
>>> > Thanks so much!
>>> > Bing
>>> > On Thu, Feb 23, 2012 at 1:17 AM, Jacques <[EMAIL PROTECTED]> wrote: