We have a cloud base Java application that uses Oracle DB and Apache Solr for document indexing/searching. I need to implement an ontology and I intend to use Apache Jena. It's an uncharted territory for me. According to the docs, seems that using TDB, we can use Oracle DB for storage/query, but it's not clear to me if we can use Apache Solr for the same purpose. Is that possible? What are the pros/cons? Can you give me e brief comparison between TDB and Solr in regard with that?
tl;dr You can do this, but it's obviously not meant to be this way.
The base question here is: Can we store ontological data in something as flat a s Lucene/Solr index. Well, with enough work and dedication you can do this. I wrote a Lucene-based store for Topic Maps data several years ago. It earned me a masters degree in Comp.Sci. But that is not what you want, I suppose.
The Apache Jena extension TDB is a database of its own, designed for easy use in Jena. As far as I'm concerned, there is no such connector for Solr to use as a store. If you insist on using Solr as the datastore you will have to a) think hard about how to flatten the ontological data into index tables and b) implement the connector by yourself.
I'd say, go with TDB and if you want to do text search with Jena, use something like the TEXT QUERY extension.
Related
I am trying to compare data ingested into both Accumulo and Solr from the same source XML. The data ingested into Accumulo is legacy code while Solr is new code. I can easily extract out data from Solr using SolrCloud and choosing CSV or JSON, which is easily readable. But I'm at a loss for how to easily view the data in Accumulo. I used scan to view the data, but it is not easily readable. Is there a way to export the data in Accumulo to a CSV or something similar so it will be easy to read/compare with other datasets?
As I understand it, Apache Solr is a document store which uses Lucene indexes to make search fast via a web-based REST interface. On the other hand, Apache Accumulo is a massively scalable sorted key-value store, which stores arbitrary key-value pairs with cell-level security labels, in accordance with the user's application, queryable with a Java API. It makes no sense to compare the two. They are entirely different applications. Accumulo is a low-level infrastructure application, upon which you can build complex systems, such as a search engine comparable to Solr, but it is not directly comparable to Solr because Accumulo is not a search engine.
To answer your question about how to view data in Accumulo, the answer is to use its Java API. I recommend starting with the Tour on its web page, for some examples of how to query it. As for how the data is presented, and in what form, that depends on the application which ingested it in the first place. It can be arbitrary binary data in byte arrays and may not be directly viewable; that depends on the application. Accumulo is agnostic to the nature of the data stored in its key-value pairs.
What you were probably referring to in your question, when you said "I used scan to view the data", you were probably referring to the scan command in Accumulo's shell. You should probably be aware that the shell is not the primary interface for query. It is intended for system administration and triage of data ingest. The Java API is the primary means of querying.
The Accumulo open source community is pretty responsive to questions. If you're having trouble figuring out how best to use it for your needs, I would advise to ask on their community mailing lists, which can be found at their website. StackOverflow is more suitable for very specific questions than generalized "getting started" kinds of tutorials.
I'm using cloudant which I could use mapreduce to project view of data and also it could search document with lucene
But these 2 feature is separate and cannot be used together
Suppose I make a game with userdata like this
{
name: ""
items:[]
}
Each user has item. Then I want to let user find all swords with quality +10. With cloudant I might project type and quality as key and use query key=["sword",10]
But it cannot make query more complex than that like lucene could do. To do lucene I need to normalize all items to be document and reference it with owner
I really wish I could do a lucene search on a key of data projection. I mean, instead of normalization, I could store nested document as I want and use map/reduce to project data inside document so I could search for items directly
PS. If that database has partial update by scripting and inherently has transaction update feature that would be the best
I'd suggest trying out elasticsearch.
Seems like your use case should be covered by the search api
If you need to do more complex analytics elasticsearch supports aggregations.
I am not at all sure that I got the question correctly, but you may want to take a look at riak. It offers a solr-based search, which is quite well documented. I have used it in the past for distributed search over a distributed key-value index and it was quite fast.
If you use this, you will also need to take a look at the syntax of solr queries, so I add it here to save you some time. However, keep in mind that not all of those solr query functionalities were available in riak (at least that was when I used it).
There are several solutions that would do the job. I can give my 2 cents proposing the well established MongoDB. With MongoDB you can create a text-Index on a given field and then do a full text Search as explained here. The feature is in MongoDb since version 2.4 and the syntax is well documented on MongoDB docs.
I'm lost in: Hadoop, Hbase, Lucene, Carrot2, Cloudera, Tika, ZooKeeper, Solr, Katta, Cascading, POI...
When you read about the one you can be often sure that each of the others tools is going to be mentioned.
I don't expect you to explain every tool to me - sure not. If you could help me to narrow this set for my particular scenario it would be great. So far I'm not sure which of the above will fit and it looks like (as always) there are more then one way of doing what's to be done.
The scenario is: 500GB - ~20 TB of documents stored in Hadoop. Text documents in multiple formats: email, doc, pdf, odt. Metadata about those documents stored in SQL db (sender, recipients, date, department etc.) Main source of documents will be ExchangeServer (emails and attachments), but not only. Now to the search: User needs to be able to do complex full-text searches over those documents. Basicaly he'll be presented with some search-config panel (java desktop application, not webapp) - he'll set date range, document types, senders/recipients, keywords etc. - fire the search and get the resulting list of the documents (and for each document info why its included in search results i.e. which keywords are found in document).
Which tools I should take into consideration and which not? The point is to develop such solution with only minimal required "glue"-code. I'm proficient in SQLdbs but quite uncomfortable with Apache-and-related technologies.
Basic workflow looks like this: ExchangeServer/other source -> conversion from doc/pdf/... -> deduplication -> Hadopp + SQL (metadata) -> build/update an index <- search through the docs (and do it fast) -> present search results
Thank you!
Going with solr is a good option. I have used it for similar scenario you described above. You can use solr for real huge data as its a distributed index server.
But to get the meta data about all of these documents formats you should be using some other tool. Basically your workflow will be this.
1) Use hadoop cluster to store data.
2) Extract data in hadoop cluster using map/redcue
3) Do document identification( identify document type)
4) Extract meta data from these document.
5) Index metadata in solr server, store other ingestion information in database
6) Solr server is distributed index server, so for each ingestion you could create a new shard or index.
7) When search is required search on all the indexs.
8) Solr supports all the complex searches , so you don't have to make your own search engine.
9) It also does paging for you as well.
We've done exactly this for some of our clients by using Solr as a "secondary indexer" to HBase. Updates to HBase are sent to Solr, and you can query against it. Typically folks start with HBase, and then graft search on. Sounds like you know from the get go that search is what you want, so you can probably embed the secondary indexing in from your pipeline that feeds HBase.
You may find though that just using Solr does everything you need.
Another project to look at is Lily, http://www.lilyproject.org/lily/index.html, which has already done the work of integrating Solr with a distributed database.
Also, I do not see why you would not want to use a browser for this application. You are describing exactly what faceted search is. While you certainly could set up a desktop app that communicates with the server (parses JSON) and displays the results in a thick client GUI, all of this work is already done for you in the browser. And, Solr comes with a free faceted search system out of the box: just follow along the tutorial.
Going with Solr (http://lucene.apache.org/solr) is a good solution, but be ready to have to deal with some non-obvious things. First is planning your indexes properly. Multiple terabytes of data will almost definitely need multiple shards on Solr for any level of reasonable performance and you'll be in charge of managing those yourself. It does provide distributed search (doing the queries off multiple shards), but that is only half the battle.
ElasticSearch (http://www.elasticsearch.org/) is another popular alternative, but i don't have much experience with it regarding scale. It uses the same Lucene engine so i'd expect the search feature-set to be similar.
Another type of solution is something like SenseiDB - open sourced from LinkedIn - which gives the full-text search functionality (also Lucene-based) as well as proven scale for large amounts of data:
http://senseidb.com
They've definitely done a lot of work on search over there and my casual use of it is pretty promising.
Assuming all your data is already in Hadoop, you could write some custom MR jobs that pull the data in a consistent schema-friendly format into SenseiDB. SenseiDB already provides a Hadoop MR indexer which you can look at.
The only caveat is it is a little more complex to setup, but will save you with the scaling issues many times over - especially around indexing performance and faceting functionality. It also provides clustering support if HA is important to you - which is still in Alpha for Solr (Solr 4.x is alpha atm).
Hope that helps and good luck!
Update:
I asked a friend who is more versed in ElasticSearch than me and it does have the advantage of clustering and rebalancing based on the # of machines and shards you have. This is a definite win over Solr - especially if you're dealing with TBs of data. The only downside is the current state of documentation on ElasticSearch leaves a lot to be desired.
As a side note, you can't say the documents are stored in Hadoop, they are stored in a distributed file system (most probably HDFS since you mentioned Hadoop).
Regarding searching/indexing: Lucene is the tool to use for your scenario. You can use it for both indexing and searching. It's a java library. There is also an associated project (called Solr) which allows you to access the indexing/searching system through WebServices. So you should also take a look at Solr as it allows the handling of different types of documents (Lucene puts the responsability of interpreting the document (PDF, Word, etc) on your shoulders but you, probably, can already do that)
I would like to be able to search a CouchDB database using Solr. Are there any projects that provide such an integration?
I am also aware of CouchDB-Lucene. Is there a way to hook Solr into that?
Thanks!
It would make more sense to roll your own, given how wasy it easy. First you need to decide what kind of SOLR schema to use and how to map your CouchDB documents onto that schema. Then simple iterate through all the documents in a db Pagination in CouchDB? and generate SOLR <add> documents.
People do this all the time with all kinds of data sources. Since SOLR is essentially searching a single table, the hard work is often figuring out how to map your database format onto a single table. Read up on what you can do with the SOLR schema, and you may be surprised at how easy this is.
There is a CouchDB integration for ElasticSearch available, apart from feeding ElasticSearch with JSON on your own. Both work with schema-less JSON, so it's very easy to integrate them.
In terms of features, ElasticSearch would offer a comparable set to Solr (in addition to some unique features, of course.)
According to this
http://wiki.apache.org/couchdb/Related_Projects
there was a CouchDB-Solr2 project (scroll down to the end), which is no longer maintained.
where do I find a howto to set up elasticSearch using Postgres?
My field sizes will be about 350mb, yes, MB, each in size. I have a
text output of all of the US Code and all decisions from all the courts,
the Statutes at Large, pretty much everything you would find in a library,
and I need to be able to do full text searches and return the exact point
in the field to the app to return the exact page in PDF form. Postgres
can easily handle the datastore, but I've never used elasticSearch and
have no idea of how it integrates into the indexing, etc.
As of 2015, there's ZomboDB (https://github.com/zombodb/zombodb). As the author, I'm a bit biased, but it's quite powerful. ;)
It's a Postgres extension and Elasticsearch plugin that allows you to "CREATE INDEX"s that use a remote Elasticsearch cluster, and it exposes a fairly powerful query language for performing full-text searches.
Because it's an actual index in Postgres, the ES cluster is automatically synchronized as you INSERT/UPDATE/DELETE records. As such, there's no need for asynchronous synchronization processes.
Additionally, because it's an actual index, it is transaction-safe, which means concurrent Postgres sessions will only see results that are consistent with their current transaction.
Here's a link to ZomboDB's tutorial. It should give you an idea of how easy ZomboDB is to use.
There is an application that you can use to import SQL Server, Oracle, Postgresql MySQL, etc. in to an ElasticSearch index.
http://code.google.com/p/ogr2elasticsearch/
Please let me know if you have any trouble building or using it. ~Adam
You can explore using pgsync.
PGSync is an open-source middleware (written in python) for syncing data from Postgres to Elasticsearch effortlessly. It allows you to keep Postgres as your source of truth and expose structured denormalized documents in Elasticsearch.
Githib link: https://github.com/toluaina/pgsync
Its possible to insert/update/delete postgres data in elasticsearch without middle ware other than the pgsql_http extension. Using triggers you can get a pretty much real-time index update.
You can also query elasticsearch and use the results within postgres to do joins etc with other tables/data in your database.
See the elasticsearch examples: https://github.com/sysadminmike/pgsql-http_examples