Hi Timothy,
Thank you for your answer - it is really helpful. Just to clarify - when using
ValueSource then flow is something like this:
- user sends query
- solr calls ValueSource to prepare values for every document (this part is
cached in ExternalFileField implementation I guess)
- solr runs query
And above flow is valid in every use case of ValueSource? There are no
pre-calculated values, etc (just asking to make it clear)? What caching
scenario is recommended here to make sure you won't end up with different
cached entry for every query (I think I would follow the example of
ExternalFileField)?
Another thing is that in most cases array of values created in this process is
rather sparse.. so I was thinking if there are no other solutions to store them
with associatnion to documents index...
--
Maciej Liżewski
-Original Message-
From: Timothy Potter [mailto:thelabd...@gmail.com]
Sent: Saturday, April 20, 2013 2:02 AM
To: solr-user@lucene.apache.org
Subject: Re: external values source
Hi Maciek,
I think a custom ValueSource is definitely what you want because you need to
compute some derived value based on an indexed field and some external value.
The trick is figuring how to make the lookup to the external data very, very
fast. Here's a rough sketch of what we do:
We have a table in a database that contains a numeric value for a user and an
organization, such as query:
select num from table where userId='bob' and orgId=123 (similar to what you
stated in question #4)
On the Solr side, documents are indexed with user_id_s field, which is half of
what I need to do my lookup. The orgId is determined by the Solr client at
query construction time, so is passed to my custom ValueSource (aka function)
in the query. In our app, users can be associated with many different orgIds
and changes frequently so we can't index the association.
To do the lookup to the database, we have a custom ValueSource, something like:
dbLookup(user_id_s, 123)
(note: user_id_s is the name of the field holding my userID values in the index
and 123 is the orgId)
Behind the scenes, the ValueSource will have access to the user_id_s field
values using FieldCache, something like:
final BinaryDocValues dv =
FieldCache.DEFAULT.getTerms(reader.reader(), user_id_s);
This gives us fast access to the user_id_s value for any given doc (question #1
above) So now we can return an IntDocValues instance by
doing:
@Override
public FunctionValues getValues(Map context, AtomicReaderContext
reader) throws IOException {
final BytesRef br = new BytesRef();
final BinaryDocValues dv =
FieldCache.DEFAULT.getTerms(reader.reader(), fieldName);
return new IntDocValues(this) {
@Override
public int intVal(int doc) {
dv.get(doc,br);
if (br.length == 0)
return 0;
final String user_id_s = br.utf8ToString(); // the indexed
userID for doc
int val = 0;
// todo: do custom lookup with orgID and user_id_s to compute
int value for doc
return val;
}
}
...
}
In this code, fieldName is set in the constructor (not shown) by parsing it out
of the parameters, something like:
this.fieldName =
((org.apache.solr.schema.StrFieldSource)source).getField();
The user_id_s field comes into your ValueSource as a StrFieldSource (or
whatever type you use) ... here is how the ValueSource gets constructed at
query time:
public class MyValueSourceParser extends ValueSourceParser {
public void init(NamedList namedList) {}
public ValueSource parse(FunctionQParser fqp) throws SyntaxError {
return new MyValueSource(fqp.parseValueSource(), fqp.parseArg());
}
}
There is one instance of your ValueSourceParser created per core. The parse
method gets called for every query that uses the ValueSource.
At query time, I might use the ValueSource to return this computed value in my
fl list, such as:
fl=id,looked_up:dbLookup(user_id_l,123),...
Or to sort by:
sort=dbLookup(user_id_s,123) desc
The data in our table doesn't change that frequently, so we export it to a flat
file in S3 and our custom ValueSource downloads from S3, transforms it into an
in-memory HashMap for fast lookups. We thought about just issuing a query to
load the data from the db directly but we have many nodes and the query is
expensive and result set is large so we didn't want to hammer our database with
N Solr nodes querying for the same data at roughly the same time. So we do it
once and post the compressed results to a shared location. The data in the
table is sparse as compared to the number of documents and userIds we have.
We simply poll S3 for changes every few minutes, which is good enough for us.
This happens from many nodes in a large Solr Cloud cluster running in EC2 so S3
works well for us as a distribution mechanism
