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Index tuning Hash Index. overview Introduction Hash-based indexes are best for equality selections. –Can efficiently support index nested joins –Cannot.

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Presentation on theme: "Index tuning Hash Index. overview Introduction Hash-based indexes are best for equality selections. –Can efficiently support index nested joins –Cannot."— Presentation transcript:

1 Index tuning Hash Index

2 overview

3 Introduction Hash-based indexes are best for equality selections. –Can efficiently support index nested joins –Cannot support range searches. Static and dynamic hashing techniques exist; trade-offs similar to ISAM vs. B+ trees.

4 Static Hashing # primary pages fixed, allocated sequentially, never de-allocated; overflow pages if needed. h(k) mod M = bucket to which data entry with key k belongs. (M = # of buckets) Buckets contain data entries. Long overflow chains can develop and degrade performance. Search needs one disk I/O, insert and delete needs two I/Os

5 Example hash function Key = ‘x 1 x 2 … x n ’ n byte character string Have b buckets h: add x 1 + x 2 + ….. x n – compute sum modulo b Good hash function: –Expected number of keys/bucket is the same for all buckets Read Knuth Vol. 3 if you really need to select a good function.

6 CS 245Notes 56 Within a bucket: Do we keep keys sorted? Yes, if CPU time critical & Inserts/Deletes not too frequent

7 Next: example to illustrate inserts, overflows, deletes h(K)

8 EXAMPLE 2 records/bucket INSERT: h(a) = 1 h(b) = 2 h(c) = 1 h(d) = d a c b h(e) = 1 e

9 CS 245Notes a b c e d EXAMPLE: deletion Delete: e f f g maybe move “g” up c d

10 CS 245Notes 510 Rule of thumb: Try to keep space utilization between 50% and 80% Utilization = # keys used total # keys that fit If < 50%, wasting space If > 80%, overflows significant depends on how good hash function is & on # keys/bucket

11 CS 245Notes 511 How do we cope with overflows? Periodically rehashing: reorganization Dynamic hashing Extensible Linear

12 Extendible Hashing Situation: Bucket (primary page) becomes full. Why not re-organize file by doubling # of buckets? –Reading and writing all pages is expensive! –Idea: Use directory of pointers to buckets, double # of buckets by doubling the directory, splitting just the bucket that overflowed! Directory much smaller than file, so doubling it is much cheaper. Only one page of data entries is split. –No overflow page! Trick lies in how hash function is adjusted!

13 Example Directory is array of size 4. To find bucket for r, take last `global depth’ # bits of h(r); we denote r by h(r). –If h(r) = 5 = binary 101, it is in bucket pointed to by 01. Insert: If bucket is full, split it (allocate new page, re- distribute). –If necessary, double the directory. (As we will see, splitting a bucket does not always require doubling; we can tell by comparing global depth with local depth for the split bucket.) –When directory doubled, global depth +1 –When a bucket is split, local depth+1

14 Insert h(r)=20 (Causes Doubling)

15 Points to note 20 = binary Last 2 bits (00) tell us r belongs in A or A2. Last 3 bits needed to tell which. –Global depth of directory: Max # of bits needed to tell which bucket an entry belongs to. –Local depth of a bucket: # of bits used to determine if an entry belongs to this bucket. When does bucket split cause directory doubling? –Before insert, local depth of bucket = global depth. Insert causes local depth to become > global depth; directory is doubled by copying it over and `fixing’ pointer to split image page. (Use of least significant bits enables efficient doubling via copying of directory!)

16 Directory Doubling Why use least significant bits in directory? Allows for doubling via copying!

17 Comments on extendible hashing If directory fits in memory, equality search answered with one disk access; else two. –100MB file, 100 bytes/rec, 4K page size, which contains 1,000,000 records (as data entries), each page/bucket has about 40 data entries, so there are about 25,000 directory elements; chances are high that directory will fit in memory. Directory grows in spurts, and, if the distribution of hash values is skewed, directory can grow large. Multiple entries with same hash value cause problems!, needs to be handled specially, e.g., overflow page Delete: If removal of data entry makes bucket empty, can be merged with `split image’. If each directory element points to same bucket as its split image, can halve directory.

18 summarize


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