Design nested Index

Hi,
I want to design nested Index .Is it possible or not? Is it possible to join 2 cursors that are join cursors .one example like this:
the sample record like this:
rec={'tradelead':({'offer_id':1256,'sell_buy':'s','title':'sell foods'},
{'offer_id':1257,'sell_buy':'b','title':'buy foods'},
{'offer_id':1258,'sell_buy':'s','title':'sell foods'}),
'products':({'product_id':2049,'product_name':'pressure pipes'}
,{'product_id':2050,'product_name':'pressure pipes'}
,{'product_id':2051,'product_name':'flatsheets'})}
import bsddb3
db1=bsddb3.db.DB()
class Index:
def productid(self,db2,key,data):
data1=data['products']
for Info in data1:
try:
proid=Info['product_id']
db2.put(str(proid),key)
except:
pass
def productname(self,db2,data):
data1=data['products']
for Info in data1:
try:
proid=Info['product_id']
proname=Info['product_name']
db2.put(str(proname),str(proid))
except:
pass
def offerid(self,db2,key,data):
data1=data['tradelead']
for Info in data1:
try:
offerid=Info['offer_id']
db2.put(str(offerid),key)
except:
pass
def sellbuy(self,db2,key,data):
data1=data['tradelead']
for Info in data1:
try:
offerid=Info['offer_id']
sb=Info['sell_buy']
db2.put(str(sb),str(offerid))
except:
pass
db1=bsddb3.db.DB()
db1.open("E:\\main.db",bsddb3.db.DB_BTREE,bsddb3.db.DB_CREATE)
db2=bsddb3.db.DB()
db2.set_flags(bsddb3.db.DB_DUPSORT)
db2.open("E:\\productid.db",bsddb3.db.DB_BTREE,bsddb3.db.DB_CREATE)
db3=bsddb3.db.DB()
db3.set_flags(bsddb3.db.DB_DUPSORT)
db3.open("E:\\productname.db",bsddb3.db.DB_BTREE,bsddb3.db.DB_CREATE)
db4=bsddb3.db.DB()
db4.set_flags(bsddb3.db.DB_DUPSORT)
db4.open("E:\\offerid.db",bsddb3.db.DB_BTREE,bsddb3.db.DB_CREATE)
db5=bsddb3.db.DB()
db5.set_flags(bsddb3.db.DB_DUPSORT)
db5.open("E:\\sell_buy.db",bsddb3.db.DB_BTREE,bsddb3.db.DB_CREATE)
Info=Index()
#sample code for import data
db1.put("1",str(rec))
Info.productid(db2,"1",rec)
Info.productname(db3,rec)
Info.offerid(db4,"1",rec)
Info.sellbuy(db5,"1",rec)
cur1=db3.cursor()
cur1.get("pressure pipes",bsddb3.db.DB_SET)
curlist1=[cur1]
join_cur1=db2.join(curlist1,0)
data=join_cur1.get(0)
cur2.get("s",bsddb3.db.DB_SET)
curlist2=[cur2]
join_cur2=db4.join(curlist2,0)
# Is it possible join join_curs?
curlist=[join_cur1,join_cur2]
join_cur=db1.join(curlist,0)
data=join_cur.get(0)
while data:
print data
data=join_cur.get(0)
thanks alot
regards,
saeed

Hi Saeed,
The join won't work if one of the cursors is a join cursor. To workaround this, you could take the keys returned by a join cursor and perform an ordinary get() or c_get() (on a normal cursor), or you may join a larger number of cursors on secondaries of a primary database. This is a normal consequence of the way join cursors are implemented in Berkeley DB. A join cursor can only retrieve the results of a join sequentially; unlike regular cursors, they're returning a result that is determined dinamically (by looking up corresponding keys in different cursors' lists), rather by pointing at entries in the database.
Regards,
Andrei

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    Elapsed: 00:01:43.60So despite "+processing+" over 2 billion rows, the count is significantly faster than the count of the very much smaller ALL_OBJECTS view.
    Why? Because it took less I/O to count those 2 billion rows (using a single index) than the nearly 52 thousand objects in the data dictionary (using several indexes and tables).
    The rules that governs performance do not change with the number of rows in a table. The same rules and principles apply. What does change is the "+error margin+" - you are going to see a performance problem quite a lot sooner on a large table than a very small table. You may not notice that a 1ms overhead per row is a performance problem on a 100 row table. You will definitely notice it on a million row table.
    So forget about the number of rows in these tables. Make sure that the SQL is designed logically correctly. Make sure that the table/db design provides optimal I/O paths for that SQL.
    And catersian merge joins, nested loops with full table scans and so on.. that points to both these as being potential problems. SQL designed incorrectly (like forgetting join criteria). Table design lacking (requiring nested loop full table scans as oppose to index range scans or hash joins).
    Oh yeah - and as already mentioned, forget the cost that the execution plan shows. Unless you know what unit that cost is expressed in and how to interpret it?

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