I and you have the same problem to solve,and I also research recently.
and I heard that:
<Lucene in a cluster >Lucene is a highly optimized inverted index search
engine. It stored a number of inverted indexes in a custom file format that is
highly optimized to ensure that the indexes can be loaded by searchers quickly
and searched efficiently. These structures are create so that they are almost
completely pre-computed. To store the index, Lucene uses an implementation of a
'Directory' interface, not to be confused with anything in java.io. . The
standard implementation if FSDirectory that stored the search index on a file
system. There a number of other implementations that can bused including ones
to split the index on the filesystem into smaller chunks, and ones to
distribute the index throughout a cluster using Map Reduce (see google). There
is additionally a database implementation that stored the index as blocks in a
database. Lucene derives its speed from this index structure, and to work
really well it needs to be able to seek efficiently into the blocks o
f the segments that make up the index. This is trivial where the underlying
storage mechanism supports seek, but less trivial if the storage mechanism does
not. The FSDirectory is based on files, and is efficient in this area. If the
files are on a local file system, pure seeks can be used. If the index is on a
shared file system , there will always be some latency and potentially
increased IO traffic. The Database implementation is highly dependent on the
the blob implementation in the target database and will nearly always be slower
than the FSDirectory. Some databases support seekable blobs (Oracle), some
emulate this behavior (MySQL with emulateLocators=true), others just don't
support it and so are really slow. (and I mean really slow) All of this impacts
how Lucene works in a cluster. Each node performing the search needs access to
the index. To make search work in a clustered environment we must provide this.
There are 3 ways of doing this. Use a shared file system be
tween all nodes, and use FSDirectory. Use indexes on the nodes local file
system and a synchronization strategy. Use a database using JDBCDirectory Use a
distributed file system (eg Google File System, Nutch Distributed File System)
Use a local cache with backup in the Database Shared filesystem There are a
number of issues with a shared file system. Performance is lower than a local
file system (obviously), unless a SAN is used, but a SAN shared file system
must be a true SAN file system (eg Redhat Global File System, Apple XSan) as
modifications to the file system blocks must be mirrored instantly in the block
cache of all connected nodes, otherwise they will see a corrupted file system.
Remember a SAN is just a networked block device, that without additional help
cannot be shared by multiple compute nodes at the same time. Provided the
performance of the shared file system is sufficient, Lucene works well like
this with no modifications using the FSDirectory implementatio
n. The implementation of the lock managed in the Sakai Search component
eliminates problems with locks reported by the Lucene community. This mechanism
is available now in Sakai Search. Synchronized Local indexes. Where the
architecture of the cluster is a shared nothing architecture, the Lucene
indexes can be written to local disk and synchronized at the end of each index
cycle. This is an optimal deployment of Lucene in a cluster as it ensures that
all the IO is from the local disk and is hence fast. To ensure that there is
always a back up copy of the index, the synchronization would also target a
backup location. The difficulty with this approach is that without support in
the implementation of the search engine, it requires some deployment support.
This may involve include making hard link mirrors to speed up the
synchronization process. Lucene indexes are suitable for synchronizing with
rsync which is a block based synchronization mechanism. The main drawback of
this a
pproach is that the full index is present on the local machine. In large
search environments, this duplication will be wastefully, however in search
engine terms, a single deployment of Sakai will probably never get into the
large space ( large > 100M documents, 2TB index) This mechanism is available,
but requires local configuration Database hosted search index. Where a simple
cluster setup is required, a database hosted search index is straightforward
option. There are however significant drawbacks with this approach, most
notable being the drop in performance. The index is stored as blocks in blobs
inside the database. These blobs are stored in a block structure to eliminate
most of the unnecessary loading however each blob bypasses any local disk block
cache on the local machine and has to be streamed over the network. If the
database supports seekable blobs, within the database itself, it is possible to
minimize unnecessary network traffic. Oracle has this support. Howe
ver where the database only emulated this behavior (MySQL) the performance is
poor as the complete blob needs to be streamed over the network. In addition to
this the speed of access is slower since a SQL statement has to be executed for
each data access. The net result is slower performance. This mechanims is
available, but performance is probably unacceptable Distributed File System
Real Search Engines use a distributed file system that provides a self healing
file system where the data itself is distributed across multiple nodes in such
a way that the file system can recover from the loss of one or more nodes. The
original file system of this form is the Google File System and the Nutch
Distributed File System is modeled on Google File System. Both implementations
use a gather scatter algorithm detailed by Google in Map-Reduce (see Google
labs). This approach results in every node containing a part of the file
system. Where the index size has grown to such an extent to ma
ke the storage of the complete index on every node in the cluster, this
approach becomes more attractive. At the moment there are no plans to provide
an implementation of a distributed file system within Sakai. Database Clustered
Local Search In this approach, indexes are used from local disk, but backed up
to the database as Lucene Segments. A cluster app node is installed, it
synconizes the local copy of the search index with the database. When new
content is added by one of the cluster app nodes, it updates the backup copy in
the database. On reciept of the index reload events, all cluster app nodes
resyncronize the with the database downloading changed and new search segments.
This mechanism in in the process of being tested, I exhibits the same
performance as a local basaed search for a 200MB index with 80,000 documents.
Once this mechanism is completely tested it will become the default OOTB
mechanism, as it works where there is a single cluster node or more than one c
luster node. The added advantage of this mechanism is that the index is stored
in the database. It will also be possible to implement this mechanism with a
shared filestore acting as the backup location.
> Date: Mon, 9 Jun 2008 19:21:06 +0530> From: [EMAIL PROTECTED]> To:
> java-user@lucene.apache.org> Subject: Running Lucene in a Clustered
> Environment> > Hi all,> > I'm new to Lucene. I need to run Lucene in a
> clustered environment. So> creating the index in the local file system is not
> an option and it is> better if I can create the index in the database as all
> nodes can share it.> > Can anyone of you please suggest me a way to do this?
> I got to know about> org.apache.lucene.store.jdbc.JdbcDirectory from mailing
> list archives.> However, since it's not part of the Lucene release itself I'd
> be pleased if> someone can point me where to find an implementation of it.> >
> Additionally, instead of keeping the index inside the database, is there any>
> other way to work Lucene in a clustering environment?> > Thanks in advance> >
> Kalani> > > > -- > Kalani Ruwanpathirana> Department of Computer Science &
> Engineering> University of Moratuwa
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