1 / 18

Index tuning

Index tuning. Hash Index. overview. 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. Static Hashing.

pcollett
Télécharger la présentation

Index tuning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Index tuning Hash Index

  2. overview

  3. Introduction • Hash-basedindexes 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 = ‘x1 x2 … xn’ n byte character string Have b buckets h: add x1 + x2 + ….. xn 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. Within a bucket: Do we keep keys sorted? • Yes, if CPU time critical • & Inserts/Deletes not too frequent CS 245 Notes 5 6

  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) = 0 d a e c b 0 1 2 3 h(e) = 1

  9. d maybe move “g” up EXAMPLE: deletion Delete:ef 0 1 2 3 a b d c c e f g CS 245 Notes 5 9

  10. 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 CS 245 Notes 5 10

  11. How do we cope with overflows? Periodically rehashing: reorganization Dynamic hashing • Extensible • Linear CS 245 Notes 5 11

  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 10100. 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 overand `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

More Related