1. ITIS 5160
Indexing

2. Indexing datacubes Objective: speed queries up.
Traditional databases (OLTP): B-Trees
Time and space logarithmic to the amount of indexed keys.
Dynamic, stable and exhibit good performance under updates. (But OLAP is not about updates….)
Bitmaps:
Space efficient
Difficult to update (but we don’t care in DW).
Can effectively prune searches before looking at data.

3. Bitmaps R = (…., A,….., M) R (A) B8 B7 B6 B5 B4 B3 B2 B1 B0

4. Query optimization
Consider a high-selectivity-factor query with predicates on two attributes.
Query optimizer: builds plans (P1) Full relation scan (filter as you go). (P2) Index scan on the predicate with lower selectivity factor, followed by temporary relation scan, to filter out non-qualifying tuples, using the other predicate. (Works well if data is clustered on the first index key). (P3) Index scan for each predicate (separately), followed by merge of RID.

5. Query optimization (continued)tn
Index Pred1
Blocks of data
(P2)
Tuple list1
(P3)
Merged list
Pred. 2 t1 tn
Index Pred2
Tuple list2
answer

6. Query optimization (continued)
When using bitmap indexes (P3) can be an easy winner!
CPU operations in bitmaps (AND, OR, XOR, etc.) are more efficient than regular RID merges: just apply the binary operations to the bitmaps
(In B-trees, you would have to scan the two lists and select tuples in both -- merge operation--)
Of course, you can build B-trees on the compound key, but we would need one for every compound predicate (exponential number of trees…).

7. Bitmaps and predicates
A = a1 AND B = b2
Bitmap for a1
Bitmap for b2
Bitmap for a1 and b2 =
AND

8. Tradeoffs Dimension cardinality small dense bitmaps
Dimension cardinality large sparse bitmaps
Compression
(decompression)

9. Star-Joins Select F.S, D1.A1, D2.A2, …. Dn.An Likely strategy:
from F,D1,D2,Dn where F.A1 = D1.A1 F.A2 = D2.A2 … F.An = Dn.An and D1.B1 = ‘c1’ D2.B2 = ‘p2’ ….
Likely strategy:
For each Di find suitable values of Ai such that Di.Bi = ‘xi’ (unless you have a bitmap index for Bi). Use bitmap index on Ai’ values to form a bitmap for related rows of F (OR-ing the bitmaps).
At this stage, you have n such bitmaps, the result can be found AND-ing them.

10. Bitmaps R = (…., A,….., M) value-list index
R (A) B8 B B6 B5 B4 B3 B2 B1 B0

11. Example sequence <3,3> value-list index (equality)
R (A) B22 B12 B02 B21 B11 B01
(1x3+0)

12. Encoding scheme
Equality encoding: all bits to 0 except the one that corresponds to the value
Range Encoding: the vi rightmost bits to 0, the remaining to 1

13. Range encoding single component, base-9
R (A) B8 B B6 B5 B4 B3 B2 B1 B0

14. RangeEval
Evaluates each range predicate by computing two bitmaps: BEQ bitmap and either BGT or BLT
RangeEval-Opt uses only <=
A < v is the same as A <= v-1
A > v is the same as Not( A <= v)
A >= v is the same as Not (A <= v-1)

15. Example (revisited) sequence <3,3> value-list index(Equality)
R (A) B22 B12 B02 B21 B11 B01
(1x3+0)

16. Example sequence <3,3> range-encoded index
R (A) B12 B B11 B01

17. RangeEval-OPT