ITIS 5160 Indexing

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.

Bitmaps R = (…., A,….., M)  R (A) B 8 B 7 B 6 B 5 B 4 B 3 B 2 B 1 B

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.

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

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…).

Bitmaps and predicates A = a1 AND B = b2 Bitmap for a1Bitmap for b2 AND = Bitmap for a1 and b2

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

Star-Joins Select F.S, D1.A1, D2.A2, …. Dn.An 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.

Bitmaps R = (…., A,….., M) value-list index  R (A) B 8 B 7 B 6 B 5 B 4 B 3 B 2 B 1 B

Example sequence value-list index (equality)  R (A) B 2 2 B 1 2 B 0 2 B 2 1 B 1 1 B (1x3+0)

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

Range encoding single component, base-9  R (A) B 8 B 7 B 6 B 5 B 4 B 3 B 2 B 1 B

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)

Example (revisited) sequence value-list index(Equality)  R (A) B 2 2 B 1 2 B 0 2 B 2 1 B 1 1 B (1x3+0)

Example sequence range-encoded index  R (A) B 1 2 B 0 2 B 1 1 B

RangeEval-OPT