Tag Archives: hash clusters

CLUSTERING FACTOR DEMYSTIFIED : PART – III

April 10, 2013Database, Tuning, Uncategorizedclustering factor, hash clusters, high clustering factor, index clusters, single table hash clusters, single table index clustersAnju Garg

How to resolve the performance issues due to high clustering factor?

In my earlier post, Clustering Factor Demystified : Part – I, I had discussed that to improve the Clustering Factor, the table must be rebuilt (and reordered). The data retrieval can be considerably speeded up by physically sequencing the rows in the same order as the key column. If we can group together the rows for a key value, we can get all of the row with a single block read because the rows are together. To achieve this goal, various methods may be used. In the post Clustering Factor Demystified : Part -II, I had demonstrated Manual Row Re-sequenciung (CTAS with order by) which pre-orders data to avoid expensive disk sorts after retrieval. In this post, I will demonstrate the use of Single table hash clusters and Single table index clusters which clusters related rows together onto the same data block .

Overview:

Create a table organized which contains two columns - id(number) and txt (char)- Populate the table insert 34 records for each value of id where id ranges from 1 to 100- In this case as records are added sequentially, records for a key value are stored together
Create another table unorganized which is a replica of the ‘organized’ table but records are inserted in a random manner so that records for a key value may be scattered across different blocks .
Create a single table index cluster table from ‘unorganized’ table using CTAS.
Create a single table hash cluster table from ‘unorganized’ table using CTAS
Trace the query using exact match on three tables and verify that hash cluster table gives the best performance .
Trace the query using range scan on three tables and verify that index cluster table gives the best performance .
Verify that index and hash cluster tables have better clustering factor .

Implementation:

- Create a table organized which contains two columns – id(number) and txt (char)
– Populate the table insert 34 records for each value of id where id ranges from 1 to 100
– In this case as records are added sequentially, records for a key value are stored together

SQL> drop table organized purge;
           create table organized (id number(3), txt char(900));
           begin
           for i in 1..100 loop
               insert into organized select i, lpad(‘x’, 900, ‘x’)
               from    dba_objects where rownum < 35;
           end loop;
          end;
          /

- create another table unorganized which is a replica of the ‘organized’ table but records are inserted in a random manner so that records for a key value may be scattered across different blocks (order by dbms_random.random).

SQL> drop table unorganized purge;
create table unorganized as select * from organized order by dbms_random.random;

create index unorganized_idx on unorganized(id);

exec dbms_stats.gather_table_stats(USER, ‘unorganized’, estimate_percent => 100, method_opt=> ‘for all indexed columns size 254′);

– Create a single table index cluster table from ‘unorganized’ table using CTAS.

— Create a cluster with size = blocksize = 8k and index it

SQL> drop cluster index_cluster including tables;
create cluster index_cluster
( id number(3) )
size 8192;

create index index_cluster_idx
on cluster index_cluster;
drop table index_cluster_tab purge;

create table index_cluster_tab
cluster index_cluster( id )
as select * from unorganized ;

– Create a single table hash cluster table from ‘unorganized’ table using CTAS

SQL>drop tablespace mssm including contents and datafiles;

Create tablespace mssm datafile ‘/u01/app/oracle/oradata/orcl/mssm01.dbf’ size 100m segment space management manual;

drop cluster hash_cluster including tables;
create cluster Hash_cluster
( id number(3) )
size 8192 single table hash is id hashkeys 100 tablespace mssm;

drop table hash_cluster_tab purge;

create table hash_cluster_tab cluster hash_cluster(id)

as select * from unorganized;

begin
dbms_stats.gather_table_stats
( user, ‘UNORGANIZED’, cascade=>true ); dbms_stats.gather_table_stats
( user, ‘INDEX_CLUSTER_TAB’, cascade=>true ); dbms_stats.gather_table_stats
( user, ‘HASH_CLUSTER_TAB’, cascade=>true );
End;
/

- Find out no. of blocks across which records of a key value are spread in the two tables.- Note that in ‘unorganized’ table, records for an id are scattered across more than 30 blocks whereas in index_cluster_tab and hash_cluster_tab tables, records for each id are clustered i.e. records for each key value are spread across 5 blocks only.

SQL> select unorg.id id, unorg.cnt unorganized_blocks,
idx_tab.cnt index_cluster_blocks, hash_tab.cnt hash_cluster_blocks
from
( select id, count(distinct(dbms_rowid.ROWID_BLOCK_NUMBER(rowid))) cnt
from unorganized
group by id) unorg,
( select id, count(distinct(dbms_rowid.ROWID_BLOCK_NUMBER(rowid))) cnt
from index_cluster_tab
group by id) idx_tab,
( select id, count(distinct(dbms_rowid.ROWID_BLOCK_NUMBER(rowid))) cnt
from hash_cluster_tab
group by id) hash_tab
where idx_tab.id = unorg.id
and hash_tab.id = unorg.id
order by id;

ID UNORGANIZED_BLOCKS INDEX_CLUSTER_BLOCKS HASH_CLUSTER_BLOCKS

———- —————— ——————– ——————-

1 33 5 5

2 34 5 5

3 33 5 5

4 33 5 5

5 33 5 5

6 32 5 5

7 34 5 5

8 34 5 5

9 33 5 5

10 32 5 5

…

90 34 5 5

91 34 5 5

92 30 5 5

93 34 5 5

94 34 5 5

95 34 5 5

96 34 5 5

97 33 5 5

98 33 5 5

99 34 5 5

100 34 5 5

- Trace the query using exact match on three tables and verify that hash cluster table gives the best performance .

– Let’s compare the statistics when rows for all the id’s are retrieved in succession from the three tables

conn / as sysdba

alter session set tracefile_identifier = ‘cluster_factor';

alter session set sql_trace=true;

declare
type tab_row is table of unorganized%rowtype;
tab_rows tab_row;
type id_val is table of unorganized.id%type;
id_vals id_val;

begin
select distinct id bulk collect into id_vals
from unorganized; for k in id_vals.first .. id_vals.last
loop
select * bulk collect into tab_rows
from unorganized
where id = k;
end loop;
end;
/

declare
type tab_row is table of index_cluster_tab%rowtype;
tab_rows tab_row;
type id_val is table of index_cluster_tab.id%type;
id_vals id_val; begin
select distinct id bulk collect into id_vals
from index_cluster_tab; for k in id_vals.first .. id_vals.last
loop
select * bulk collect into tab_rows
from index_cluster_tab
where id = k;
end loop;
end;
/declare
type tab_row is table of hash_cluster_tab%rowtype;
tab_rows tab_row;
type id_val is table of hash_cluster_tab.id%type;
id_vals id_val; begin
select distinct id bulk collect into id_vals
from hash_cluster_tab; for k in id_vals.first .. id_vals.last
loop
select * bulk collect into tab_rows
from hash_cluster_tab
where id = k;
end loop;
end;
/
– Find out the name of trace file generatedSQL> col trace_file for a100
select value trace_file from v$diag_info
where upper(name) like ‘%TRACE FILE%';
TRACE_FILE
—————————————————————————————————
/u01/app/oracle/diag/rdbms/orcl1/orcl1/trace/orcl1_ora_24963_cluster_factor.trc– Run tkprof utility on the trace file generated $cd /u01/app/oracle/diag/rdbms/orcl/orcl/trace
rm cluster_factor.out tkprof /u01/app/oracle/diag/rdbms/orcl1/orcl1/trace/orcl1_ora_8403_cluster_factor.trc cluster_factor.out

vi cluster_factor.out

********************************************************************************
Here are the contents of the trace file:

In case of unorganized table, it can be seen that no. of blocks visited (3517) is approaches the number of rows (3400) in the table as rows for an id are scattered across a large no. of blocks.

SQL ID: 0npa78p7jkfa5

Plan Hash: 1120857569

SELECT *
FROM
UNORGANIZED WHERE ID = :B1

call     count       cpu    elapsed       disk      query    current        rows
——- —— ——– ———- ———- ———- ———- ———-
Parse        1      0.00       0.00          0          0          0           0
Execute    100      0.00       0.00          0          0          0           0
Fetch      100      0.01       0.02          0       3517          0        3400
——- —— ——– ———- ———- ———- ———- ———-
total      201      0.01       0.02          0       3517          0        3400

*******************************************************************************
In case of single table index cluster,
Total I/O’s = I/O’s against the table + I/O’s against the table

i/O’s against the table = no. of table blocks across which various records for different id’s are stored

Since we saw earlier that records for each key value are scattered across 5 blocks,

I/O’s against the table = no. of distinct key values (id’s) * no. of blocks occupied by records for an id
= 100 * 5
= 500

Rest 100 I/O’s are made against against the index ( one I/O for each key value)

Hence total I/O’s = 100 + 500 = 600
********************************************************************************

SQL ID: 6qy378ww4729s
Plan Hash: 3651720007
SELECT *
FROM
INDEX_CLUSTER_TAB WHERE ID = :B1

call     count       cpu    elapsed       disk      query    current        rows
——- —— ——– ———- ———- ———- ———- ———-
Parse        1      0.00       0.00          0          0          0           0
Execute    100      0.00       0.00          0          0          0           0
Fetch      100      0.00       0.00          0        600          0        3400
——- —— ——– ———- ———- ———- ———- ———-
total      201      0.00       0.00          0        600          0        3400

*******************************************************************************
In case of single table hash cluster, as index access is not needed,
Total I/O’s = I/O’s against the table
= no. of distinct key values (id’s) * no. of blocks occupied by records for an id
= 100 * 5
= 500
********************************************************************************

SQL ID: ctnu91v20p2x2

Plan Hash: 3860562250

SELECT *
FROM
HASH_CLUSTER_TAB WHERE ID = :B1

call     count       cpu    elapsed       disk      query    current        rows
——- —— ——– ———- ———- ———- ———- ———-
Parse        1      0.00       0.00          0          0          0           0
Execute    100      0.00       0.00          0          0          0           0
Fetch      100      0.00       0.00          0        500          0        3400
——- —— ——– ———- ———- ———- ———- ———-
total      201      0.00       0.00          0        500          0        3400

*******************************************************************************

Summarizing the above results :

unorganized index_cluster_tab hash_cluster_tab
total CPU 0.01 0.00 0.00

elapsed 0.01 0.02 0.00
time

I/O’s 3517 600 500

Hence, it can be concluded that for exact match queries hash clusters give the best performance since least no. of I/O’s are made.

- Trace the query using range scan on three tables and verify that index cluster table gives the best performance .

– Let’s compare the statistics when rows for entire range of id’s are retrieved from the three tables

conn / as sysdba

alter session set
tracefile_identifier = ‘cluster_factor';

alter session set
sql_trace=true;

declare

type tab_row is table of
unorganized%rowtype;

tab_rows tab_row;

type id_val is table of
unorganized.id%type;

id_vals id_val;

begin

select distinct id bulk collect into id_vals

from unorganized;

for j in id_vals.first..id_vals.first loop

for k in id_vals.last .. id_vals.last loop

select * bulk collect into tab_rows

from unorganized

where id >= j and id <= k;

end loop;

end;

declare

type tab_row is table of index_cluster_tab%rowtype;

tab_rows tab_row;

type id_val is table of index_cluster_tab.id%type;

id_vals id_val;

begin

select distinct id bulk collect into id_vals

from index_cluster_tab;

for j in id_vals.first..id_vals.first loop

for k in id_vals.last .. id_vals.last loop

select * bulk collect into tab_rows

from index_cluster_tab

where id >= j and id <= k;

end loop;

end;

declare

type tab_row is table of hash_cluster_tab%rowtype;

tab_rows tab_row;

type id_val is table of hash_cluster_tab.id%type;

id_vals id_val;

begin

select distinct id bulk collect into id_vals

from hash_cluster_tab;

for j in id_vals.first..id_vals.first loop

for k in id_vals.last .. id_vals.last loop

select * bulk collect into tab_rows

from hash_cluster_tab

where id >= j and id <= k;

end loop;

end;

– Find out the name of trace file generated

SQL> col
trace_file for a100

select value trace_file from v$diag_info

where upper(name) like ‘%TRACE
FILE%';

TRACE_FILE

—————————————————————————————————-

/u01/app/oracle/diag/rdbms/orcl1/orcl1/trace/orcl1_ora_24963_cluster_factor.trc

– Run tkprof utility on the trace file generated

$cd /u01/app/oracle/diag/rdbms/orcl/orcl/trace

rm cluster_factor.out

tkprof /u01/app/oracle/diag/rdbms/orcl1/orcl1/trace /orcl1_ora_8403_cluster_factor.trc cluster_factor.out

vi cluster_factor.out

Here are the contents of the trace file:

In case of unorganized table, it can be seen that Full table scan is done and

total I/O’s = Physical I/O’s + logical I/O’s

= 486 + 489 = 975

CPU usage = 0.01

elapsed time = 0.01

cost = 127

********************************************************************************

SQL ID:
dpg9s5v7jannv

Plan Hash:
3859503019

SELECT *

FROM

UNORGANIZED WHERE ID >= :B2 AND ID <=
:B1

call     count
cpu    elapsed       disk
query    current        rows

——-
—— ——– ———- ———-
———- ———- ———-

Parse 1
0.00 0.00 0 0 0 0

Execute 1
0.00 0.00 0 0 0 0

Fetch 1
0.01 0.01 486 489 0 3400

——-
—— ——– ———- ———-
———- ———- ———-

total 3
0.01 0.01 486 489 0 3400

Rows Row Source Operation

——-
—————————————————

3400
FILTER (cr=489 pr=486 pw=0
time=3524 us)

3400
TABLE ACCESS FULL UNORGANIZED (cr=489 pr=486 pw=0 time=1888 us cost=127
size=3073600 card=3400)

********************************************************************************

In case of index cluster table, it can be seen that index range scan is performed on cluster index followed by table access cluster.

total I/O’s = Physical I/O’s + logical I/O’s

= 385 + (501 + 1)

= 887

CPU usage = 0.01

elapsed time = 0.01

cost = 101

*******************************************************************************

SQL ID:
22k91ut1b18nj

Plan Hash:
533030663

SELECT *

FROM

INDEX_CLUSTER_TAB WHERE ID >= :B2 AND ID
<= :B1

call     count
cpu    elapsed       disk
query    current        rows

——-
—— ——– ———- ———-
———- ———- ———-

Parse 1
0.00 0.00 0 0 0 0

Execute 1
0.00 0.00 0 0 0 0

Fetch 1
0.01 0.01 385 501 0 3400

——-
—— ——– ———- ———-
———- ———- ———-

total 3 0.01
0.01 385 501 0 3400

Misses in library
cache during parse: 1

Optimizer mode:
ALL_ROWS

Parsing user id:
SYS (recursive depth: 1)

Rows Row Source Operation

——- —————————————————

3400
FILTER (cr=501 pr=385 pw=0
time=9441 us)

3400
TABLE ACCESS CLUSTER INDEX_CLUSTER_TAB (cr=501 pr=385 pw=0 time=7679 us
cost=101 size=3073600 card=3400)

100
INDEX RANGE SCAN INDEX_CLUSTER_IDX (cr=1 pr=0 pw=0 time=99 us cost=1
size=0 card=1)(object id 80458)

********************************************************************************

In case of hash cluster table, it can be seen that hash access is made to the table.

total I/O’s = Physical I/O’s + logical I/O’s

= 503 + 506

= 1009

CPU usage = 0.04

elapsed time = 0.04

cost = 132

*******************************************************************************

SQL ID:
c2www0m7npkqp

Plan Hash:
4115468836

SELECT *

FROM

HASH_CLUSTER_TAB WHERE ID >= :B2 AND ID
<= :B1

call     count
cpu    elapsed       disk
query    current        rows

——-
—— ——– ———- ———-
———- ———- ———-

Parse 1
0.00 0.00 0 0 0 0

Execute 1
0.00 0.00 0 0 0 0

Fetch 1
0.04 0.04 503 506 0 3400

——-
—— ——– ———- ———-
———- ———- ———-

total 3
0.04 0.04 503 506 0 3400

Misses in library
cache during parse: 1

Optimizer mode:
ALL_ROWS

Parsing user id:
SYS (recursive depth: 1)

Rows Row Source Operation

——-
—————————————————

3400
FILTER (cr=506 pr=503 pw=0
time=39906 us)

3400
TABLE ACCESS FULL HASH_CLUSTER_TAB (cr=506 pr=503 pw=0 time=37389 us
cost=132 size=3073600 card=3400)

********************************************************************************

Summarizing above results :

unorganized index_cluster_tab hash_cluster_tab

total I/O’s 975 887 1009

CPU usage 0.01 0.01 0.04

elapsed time 0.01 0.01 0.04

cost 127 101 132

Hence, to search a range of values, single table index cluster is the best choice.

CPU usage, I/O’s and cost is the maximum in case of single table hash cluster table.

- Verify that index and hash cluster tables have better clustering factor .

Let’s compare clustering factor of indexes on the three tables.

Tables unorganized and index_cluster_tab already have index.

– Let’s create index on hash_cluster_tab and gather statistics .

SQL>create index hash_cluster_idx on hash_cluster_tab(id);

exec dbms_stats.gather_index_stats(USER, ‘HASH_CLUSTER_IDX’);

exec dbms_stats.gather_index_stats(USER, ‘INDEX_CLUSTER_IDX’);

– Find out clustering factor of the three tables.

SQL> select index_name, clustering_factor

from user_indexes

where index_name in (‘UNORGANIZED_IDX’, ‘INDEX_CLUSTER_IDX’, ‘HASH_CLUSTER_IDX’);

INDEX_NAME CLUSTERING_FACTOR

—————————— —————–

HASH_CLUSTER_IDX 500

INDEX_CLUSTER_IDX 100

UNORGANIZED_IDX 3311

– Note that

– clustering factor of index on unorganized table approaches no. of rows in the table (3400).

– clustering factor of index on hash_cluster_tab table = 500 . As entries for each id are spread across 5 blocks, 500 blocks need to be accessed to get all the rows and index is aware of this information.

– clustering factor of index on index_cluster_tab table = 100 as there are 100 entries (one for each id) in the index. Here also 500 table blocks need to be accessed to get all the rows but index contains information about only the first(or may be the last) data block for an id. Rest 4 blocks containing records for that id are chained to it and index does not have that information and clustering factor of an index is computed on the basis of the information available in the index. That’s why clustering factor in this case = no. of index entries.

SUMMARY:

Clustered tables cannot be truncated.
Choosing the Key :Choosing the correct cluster key is dependent on the most common types of queries issued against the clustered tables. The cluster key should be on the column against which queries are most commonly issued.

HASH CLUSTERS

A hash cluster stores related rows together in the same data blocks. Rows in a hash cluster are stored together based on their hash value.

– Hash clusters are a great way to reduce IO on some tables, but they have their downside.

*If too little space is reserved for each
key (small SIZE value), or if the cluster is created with too few hash keys (small HASHKEYS), then each key will split across multiple blocks negating the benefits of the cluster.When creating a hash cluster, it is important to choose the cluster key correctly and set the HASH IS, SIZE, and HASHKEYS parameters so that performance and space use are optimal.
* If too much space is reserved for each key (large SIZE value), or if the cluster is created with too many hash keys (large
HASHKEYS), then the cluster will contain thousands of empty blocks that slow down full table scans . A SIZE value much larger results in wasted space.

Hash clusters reduce contention and I/O since index is not accessed .When you use an index range scan + table access by index rowid, the root index block becomes a “hot block” causing contention for the cache buffers chains (cbc) latch and hence an increase in CPU usage.
Hashing works best for exact match searches i.e. SELECT … WHERE cluster_key = …;

A properly sized hash cluster for a lookup table gives pretty much a SINGLE IO for a keyed lookup.

Hash clusters should only really be used for tables which are static in size so that you can determine the number of rows and amount of space required for the tables in the cluster. If tables in a hash cluster require more space than the initial allocation for the cluster, performance degradation can be substantial because overflow blocks are required.
Hash clusters should only really be used for tables which have mostly read-only data. The hash cluster will take marginally longer
to insert into since the data now has a “place” to go and maintaining this structure will take longer then maintaining a HEAP table .Updates do not provide much overhead unless the hashkey is being updated.
Hash clusters should not be used in applications where most queries on the table retrieve rows over a range of cluster key values where a hash function cannot be used to determine the location of specific hash keys and instead, the equivalent of a full table scan must be done to fetch the rows for the query:
Hash clusters should not be used in applications where hash key is updated. The hashing values can not be recalculated and thus serious overflow can result.
Hash clusters should not be used for tables which are not static and continually growing. If a table grows without limit, the space required over the life of the table (its cluster) cannot be predetermined.
Hash clusters should not be used for when you cannot afford to pre-allocate the space that the hash cluster will eventually
need.
Hash clusters allocate all the storage for all the hash buckets when the cluster is created, so they may waste space.
Full scans on single table hash clusters will cost as much as they would in a heap table.

INDEX CLUSTERS

In an indexed cluster, Oracle stores together rows having the same cluster key value. Each distinct cluster key value is stored only once in each data block, regardless of the number of tables and rows in which it occurs. This saves disk space and improves performance for many operations.

Index clusters should be used for the apllications where most queries on the table retrieve rows over a range of cluster key values. For example, in full table scans or queries such as the following:

SELECT . . . WHERE cluster_key < . . . ;

With an index, key values are ordered in the index, so cluster key values that satisfy the WHERE clause of a query can be found with relatively few I/Os.
Index clusters should be used for the tables which are not static, but instead are continually growing and the space required over the life of the table (its cluster) cannot be predetermined.
Index clusters should be used for applications which frequently perform full-table scans on the table and the table is sparsely populated. A full-table scan in this situation takes longer under hashing.
Cluster index has one entry per cluster key and not for each row. Therefore, the index is smaller and less costly to access for finding multiple rows.

References:

http://books.google.co.in/books?id=HPJDlGmecwcC&pg=PA394&lpg=PA394&dq=hash+clusters+in+oracle&source=bl&ots=VUP9IDzGvG&sig=KACw6NNqFxZCY1kZrtR_D4n4qPs&hl=en&sa=X&ei=qy0yUdKUEo3irAef04C4CQ&ved=0CHoQ6AEwCDgK#v=onepage&q=hash%20clusters%20in%20oracle&f=false

http://books.google.co.in/books?id=EmvWP1vgTsYC&pg=PA100&lpg=PA100&dq=hash+clusters+in+oracle&source=bl&ots=ifkHqq_9Vb&sig=3AGOhD-e3Yfikk6aRjKh6mNPOr0&hl=en&sa=X&ei=qy0yUdKUEo3irAef04C4CQ&ved=0CH0Q6AEwCTgK#v=onepage&q=hash%20clusters%20in%20oracle&f=false

http://www.geekinterview.com/question_details/3965#sthash.WuPc5ebj.dpuf

http://asktom.oracle.com/pls/asktom/f?p=100:11:0::::P11_QUESTION_ID:4536286321836

http://www.dba-oracle.com/demo/oracle_hashing.htm

http://richardfoote.wordpress.com/2013/05/08/important-clustering-factor-calculation-improvement-fix-you/

Datapump And Clustering Factor

————————————————————–

Related links:

Home

Tuning Index

Buffer Cache Wait Events

Clustering Factor Demystified Part-I

Clustering Factor Demystified Part-II

Direct Read Enhancements in 11g

Single Table Index Clusters

SingleTable Hash Clusters

SQL Profile Demystified : Part – I

SQL Profile Demystified Part – II

Undocumented Parameters in Oracle 11g

———————–

SINGLE TABLE HASH CLUSTERS DEMYSTIFIED

March 21, 2013Database, Uncategorizedclustering factor, cluster_key, hash clusters, hash is, hashkeys, single table hash clustersAnju Garg

In this post, I will discuss single table hash clusters which we can fetch a row requiring only a single block get most of the time. Hash clusters are quite similar to index clusters, except that the cluster index is missing. The data in the cluster itself acts like an index.

First, let us see how is the data stored in a hash cluster.

Let’s say we have a hash cluster table with two columns id (number) and text (char) where id is the key column. On inserting a record with id = 1 into the table- The key column value (1 here) is hashed and converted into a diskaddress.- The record is placed at the disk address returned above.Now, if another record is inserted with id = 2, , the key column value 2 is hashed to return a different address. Thus records having different key values will be placed at different disk addresses whereas all the records with same key value will go to the same address on the disk.

Now, let’s see how the data is retrieved from a hash cluster as compared to a conventional indexed cluster table.In a conventional indexed heap table , if there is a unique index on the column being queried, the algorithm followed is :- Perform unique scan of the index to locate the key and get the rowid (i.e. at least 2 I/O’s – one for index root block, one for index leaf block and may be more as blevel of the index increases)

– Using the rowid obtained above, access the table by rowid (i.e. one I/O to get the data)

whereas, the procedure followed in a hash cluster is :

– Hash the primary key to get the physical location of the record

– Perform single I/O to read the block.

Thus, hash clusters can reduce I/O on tables.

How to create a single table hash cluster?

create cluster <cluster_name> (cluster_key <datatype>)

size <size_number> single table hashkeys <hash_keys_number> hash is
<expr>;

Let’s understand various clauses in create cluster command:

CLUSTER_KEY <datatype>

The cluster key should have a single column containing only integers.

Hash clusters having composite cluster keys or cluster keys made up of non integer columns use the internal hash function.

If a non-integer cluster key value is supplied and internal hash function is bypassed , the operation (INSERT or UPDATE statement) is rolled back and an error is returned.

SIZE <size_number>

Specifies the amount of space in bytes reserved in a block to store all rows having the same cluster key value or the same hash value. This space decides the maximum number of cluster or hash values stored in a data block.

SINGLE TABLE

SINGLE TABLE indicates that this hash cluster can contain only one
table.. However, you may drop the table and create another table in the
same cluster.

HASHKEYS <hash_keys_number>

Specify the HASHKEYS clause to create a hash cluster and specify the number of hash values for the hash cluster.The HASHKEYS value specifies and limits the number of unique hash values that can be generated by the hash function used by the cluster. (how many distinct values you anticipate for the cluster key over time)Oracle Database rounds up the HASHKEYS value to the nearest prime number to obtain the actual number of hash values. The minimum value for this parameter
is 2. If you omit the HASHKEYS parameter, the database creates an indexed cluster by default.

When you create a hash cluster, the database immediately allocates space for the cluster based on the values of the SIZE and HASHKEYS parameters.

It allocates a hash table to hold HASHKEY number of cluster keys of SIZE bytes each.

HASH IS <expr>

Specify an expression to be used as the hash function for the hash cluster. Must evaluate to a positive value

If you omit the HASH IS clause, then Oracle Database uses an internal hash function for the hash cluster.

—- Overview –

– create two single table hash clusters with
size = 8k , hashkeys = 4

cluster HSH_CLUSTER_ROWSIZ_8K and HSH_CLUSTER_ROWSIZ_4K

— Create table hash_cluster_tab_ROWSIZE_8k in cluster HSH_CLUSTER_ROWSIZ_8K with row size such that only one record fits one block

— Create table hash_cluster_tab_ROWSIZE_4k in cluster HSH_CLUSTER_ROWSIZ_4K with row size such that two records fit one block

— insert records for 5 distinct key values to both the tables

— Each record goes to a different block as hashkeys has been set to 5 (next prime no.) and each block can have records with only one hash key.

— Try to access record with a key value – single I/O

— Insert records for 6th key values although provision has made for 5 keys only and check that hash collision takes place as id = 6 is mapped to one of the already existing hash values (id=1)

— Add another record for id = 1 and check that multiple blocks containing rows with same hash value are chained together.

– Create 3 clusters with size = 2K, hashkeys = 4

HSH_CLUSTER_SIZ_2K_ROW_1K, HSH_CLUSTER_SIZ_2K_ROW_2K and HSH_CLUSTER_SIZ_2K_ROW_4K

— Create a table HSH_TAB_SIZ_2K_ROWSIZ_1K in cluster HSH_CLUSTER_SIZ_2K_ROW_1K with row size such that row size =1/2( specified size) = 1/2(2K) = 1K

— Create a table HSH_TAB_SIZ_2K_ROWSIZ_2K in cluster HSH_CLUSTER_SIZ_2K_ROW_2Kwith row size such that row size = specified size 2K

— Create a table HSH_TAB_SIZ_2K_ROWSIZ_4K in cluster HSH_CLUSTER_SIZ_2K_ROW_4K with row size such that row size = 2( specified size) = 2(2K) = 4K

— Insert records for 5 distinct key values in the three tables

— Check that each block contains records for a maximum of 3 hashkeys.
— Insert 3 records with id = 1 in all the 3 tables

— Add records for id’s = 6 to 9 i.e. four keys more than what we have defined the cluster for (5).

— Note that it takes time to add these records

— Check that there is collision for hash keys – Multiple key values correspond to the same hash key – the hash chain for a hash key becomes longer and contains records with different key values.

— Check that overallocation takes place i.e. a block holds rows for hashkeys more than it is expected to hold (3).
– Implementation –

– create two single table hash clusters with
size = 8k , hashkeys = 4

cluster HSH_CLUSTER_ROWSIZ_8K and HSH_CLUSTER_ROWSIZ_4K

– The cluster key column for the clusters is id. The column in table in this cluster does not have to be called ID, but it must be NUMBER(2), to match this definition.

– Also we have specified a SIZE 8K option which means that we expect about 8K bytes of data to be associated with each cluster key value. Oracle will use it to compute the maximum number of cluster keys that could fit per block. Here, maximum no. of hash keys per block = Block size/SIZE = 8K/8K = 1 i.e. each block can contain records having only one hash key.

– The value of HASHKEYS limits the number of unique hash values that can be generated by the hash function used for the cluster. Oracle rounds the number you specify for HASHKEYS to the nearest prime number (5 here as we have set HASHKEYS to 4 and 5 is the next prime number) i.e. for any cluster key value, the hash function generates a maximum of 5 values.

SQL>Create tablespace mssm datafile
‘/u01/app/oracle/oradata/orcl/mssm01.dbf’ size 100m
segment space management manual;

alter system set deferred_segment_creation=FALSE;

drop cluster HSH_CLUSTER_ROWSIZ_8K including tables;

create cluster HSH_CLUSTER_ROWSIZ_8K ( id number(2) )
size 8192 single table hash is id hashkeys 4 tablespace mssm;

drop cluster HSH_CLUSTER_ROWSIZ_4K including tables;

create cluster HSH_CLUSTER_ROWSIZ_4K ( id number(2) )
size 8192 single table hash is id hashkeys 4 tablespace mssm;

Let’s create tables in these clusters ..

– Create table
hash_cluster_tab_ROWSIZE_8k in cluster HSH_CLUSTER_ROWSIZ_8K with row
size such that only one record fits one block

drop table hash_cluster_tab_rowsize_8k purge;

CREATE TABLE HASH_CLUSTER_TAB_ROWSIZE_8K
(id          number(2) ,
txt1        char(2000),
txt2        char(2000),
txt3        char(2000)
) CLUSTER HSH_CLUSTER_ROWSIZ_8K( id );

– Create table
hash_cluster_tab_ROWSIZE_4k in cluster HSH_CLUSTER_ROWSIZ_4K with row
size such that two records fit one block

drop table hash_cluster_tab_rowsize_4k purge;

CREATE TABLE hash_cluster_tab_rowsize_4k
(id          number(2) ,
txt1        char(2000),
txt2        char(1000)) CLUSTER HSH_CLUSTER_ROWSIZ_4K( id );

– check the blocks assigned to the clusters –

SQL> col segment_name for a30
select segment_name, extent_id, block_id, blocks
from dba_extents where segment_name like ‘%HASH_CLUSTER%';

SEGMENT_NAME EXTENT_ID BLOCK_ID BLOCKS

—————————— ———- ———- ———-

HSH_CLUSTER_ROWSIZ_8K 0 128 8

HSH_CLUSTER_ROWSIZ_4K 0 136 8

– Insert 5 records each with a distinct
key value to both the tables

SQL>Begin
for i in 1..5 loop
insert into hash_cluster_tab_rowsize_8k values (i, ‘x’, ‘x’, ‘x’);
insert into hash_cluster_tab_rowsize_4k values (i, ‘x’, ‘x’);
end loop;
commit;
end;
/

Let’s find out the blocks the records have
gone to ..

– Each record goes to a different block as hashkeys has been set to 5 (next prime no.)
First block assigned to the cluster(128, 136) is used to store header info.

Records occupy blocks second block onwards

Note that record for id=5 goes to the first block as mod (id, hashkeys) decides the block no. for a key and mod (5,5) = 0

0 denotes the first block to accommodate records for the table

Note that records for id= 1 to 4 go to the subsequent blocks as mod (1,5) = 1, mod(2,5) = 2 …

In hash_cluster_tab_rowsize_4k, even though two rows can fit one block, still each row has gone to a different block as one block has been assigned to a hash value. and

SQL>select ‘HASH_CLUSTER_TABLE_8K’
TAB_NAME , id, dbms_rowid.rowid_block_number(t1.rowid) hash_cluster_block
from hash_cluster_tab_rowsize_8k t1
union
select ‘HASH_CLUSTER_TABLE_4K’, id,
dbms_rowid.rowid_block_number(t2.rowid) hash_cluster_block
from hash_cluster_tab_rowsize_4k t2
order by 1,2,3;

TAB_NAME
ID HASH_CLUSTER_BLOCK

——————— ———- ——————

HASH_CLUSTER_TABLE_4K
1
138

HASH_CLUSTER_TABLE_4K
2
139

HASH_CLUSTER_TABLE_4K
3
140

HASH_CLUSTER_TABLE_4K
4
141

HASH_CLUSTER_TABLE_4K
5
137

HASH_CLUSTER_TABLE_8K
1
130

HASH_CLUSTER_TABLE_8K
2
131

HASH_CLUSTER_TABLE_8K
3
132

HASH_CLUSTER_TABLE_8K
4
133

HASH_CLUSTER_TABLE_8K
5
129

– Try to access record with a key value –
single I/O is needed (cr=1) as hash access is used

SQL>conn / as sysdba

alter session set tracefile_identifier=’hash_cluster';

alter session set sql_trace=true;

select * from hash_cluster_tab_rowsize_8k where id = 1;

select * from hash_cluster_tab_rowsize_4k where id = 1;

alter session set sql_trace=false;

col value for a75

select value from v$diag_info where upper(name) like ‘%TRACE FILE%';

VALUE

—————————————————————————

/u01/app/oracle/diag/rdbms/orcl/orcl/trace/orcl_ora_14452_hash_cluster.trc

ho
rm hash_cluster.out

ho tkprof
/u01/app/oracle/diag/rdbms/orcl/orcl/trace/orcl_ora_14452_hash_cluster.trc
hash_cluster.out

ho vi hash_cluster.out

********************************************************************************

select * from

hash_cluster_tab_rowsize_8k where id = 1

call     count
cpu    elapsed
disk      query
current        rows

——- —— ——– ———- ———- ———-
———- ———-

Parse
1      0.00
0.00
0          2
0           0

Execute 1
0.00
0.00
0
0
0 0

Fetch
2      0.00
0.00
0          1
0           1

——- —— ——– ———- ———- ———-
———- ———-

total
4      0.00
0.01
0          3
0           1

Rows Row Source Operation

——- —————————————————

1 TABLE ACCESS HASH
HASH_CLUSTER_TAB_ROWSIZE_8K (cr=1 pr=0
pw=0 time=0 us)

********************************************************************************

select * from

hash_cluster_tab_rowsize_4k where id = 1

call     count
cpu    elapsed
disk      query
current        rows

——- —— ——– ———- ———- ———-
———- ———-

Parse
1      0.00
0.00
0          2
0           0

Execute 1 0.00
0.00
0
0
0 0

Fetch
2      0.00
0.00
0          1
0           1

——- —— ——– ———- ———- ———-
———- ———-

total
4 0.00 0.00
0
3
0 1

Rows Row Source Operation

——- —————————————————

1 TABLE ACCESS HASH
HASH_CLUSTER_TAB_ROWSIZE_4K (cr=1 pr=0
pw=0 time=0 us)

********************************************************************************

– Insert records for 6th key values although
provision has made for 5 keys only

SQL> insert into
hash_cluster_tab_rowsize_8k values (6, ‘x’, ‘x’, ‘x’);

insert into hash_cluster_tab_rowsize_4k values (6,
‘x’, ‘x’);

commit;

– Let’s find out the blocks the records have
gone to .

– In hash_cluster_tab_rowsize_4k, id = 6 is hashed
to one of the existing hash values and record is placed in the same block (138)
as the earlier containing record with same hash key as two records can fit one
block. Hence block 138 becomes overflow block as it contains more hashvalues
than it is configured for.

Here id = 6 has been mappped to same hash
value as id = 1 and has occupied the same block as id = 1

– In hash_cluster_tab_rowsize_8k, since one row can
fit one block, Newly added rows have gone to a new block (147) as
the earlier blocks can’t accommodate new rows.

But it has again been mapped to one of the
already existing hash values and is chained to the blocks containing those key
values.

SQL>select ‘HASH_CLUSTER_TABLE_8K’
TAB_NAME , id, dbms_rowid.rowid_block_number(t1.rowid) hash_cluster_block

from hash_cluster_tab_rowsize_8k t1

union

select ‘HASH_CLUSTER_TABLE_4K’, id,
dbms_rowid.rowid_block_number(t2.rowid) hash_cluster_block

from hash_cluster_tab_rowsize_4k t2

order by 1,2,3;

TAB_NAME
ID HASH_CLUSTER_BLOCK

——————— ———- ——————

HASH_CLUSTER_TABLE_4K
1
138

HASH_CLUSTER_TABLE_4K
2
139

HASH_CLUSTER_TABLE_4K
3
140

HASH_CLUSTER_TABLE_4K
4
141

HASH_CLUSTER_TABLE_4K
5
137

HASH_CLUSTER_TABLE_4K
6
138

HASH_CLUSTER_TABLE_8K
1
130

HASH_CLUSTER_TABLE_8K
2
131

HASH_CLUSTER_TABLE_8K
3
132

HASH_CLUSTER_TABLE_8K
4
133

HASH_CLUSTER_TABLE_8K
5
129

HASH_CLUSTER_TABLE_8K
6
147

– Let’s add another record for id = 1

In hash_cluster_tab_rowsize_2k, although we have space for another row
in blocks containing id = 2,3,4,5 , the new record goes to a new block(142) as
one block can contain only one hashvalue. From now onwards, a block which
contains id = 1 may also contain an entry for id=6 and vice versa since both of
them hash to the same value.

In hash_cluster_tab_rowsize_8k also , the new record goes to a new
block (135) as a block can contain only one row. Presuming that id = 6 and 1
hash to the same value, blocks containing id = 1 (130 and 135) have been
chained to block containing id=6 (135).

From now onwards, when we search for id =1 or id = 6 we will have to scan a
larger no. of blocks.Hence, if actual no. of cluster keys exceeds the specified
value for HASHKEYS, the likelihood of a collision (two cluster
key values having the same hash value) increases and performance degrades.

SQL>

insert into
hash_cluster_tab_rowsize_8k values (1, ‘x’, ‘x’, ‘x’);

insert into
hash_cluster_tab_rowsize_4k values (1, ‘x’, ‘x’);

commit;

select ‘HASH_CLUSTER_TABLE_8K’
TAB_NAME , id, dbms_rowid.rowid_block_number(t1.rowid) hash_cluster_block

from hash_cluster_tab_rowsize_8k t1

union all

select ‘HASH_CLUSTER_TABLE_4K’, id,
dbms_rowid.rowid_block_number(t2.rowid) hash_cluster_block

from hash_cluster_tab_rowsize_4k t2

order by 1,2,3;

TAB_NAME
ID HASH_CLUSTER_BLOCK

——————— ———- ——————

HASH_CLUSTER_TABLE_4K 1
138

HASH_CLUSTER_TABLE_4K 1
142

HASH_CLUSTER_TABLE_4K
2
139

HASH_CLUSTER_TABLE_4K
3
140

HASH_CLUSTER_TABLE_4K
4
141

HASH_CLUSTER_TABLE_4K
5
137

HASH_CLUSTER_TABLE_4K 6
138

HASH_CLUSTER_TABLE_8K 1
130

HASH_CLUSTER_TABLE_8K 1
135

HASH_CLUSTER_TABLE_8K
2
131

HASH_CLUSTER_TABLE_8K
3
132

HASH_CLUSTER_TABLE_8K
4
133

HASH_CLUSTER_TABLE_8K
5
129

HASH_CLUSTER_TABLE_8K
6
134

Therefore, the distribution of rows in a hash cluster is directly
controlled by the value set for the HASHKEYS parameter. With a larger number of
hash keys for a given number of rows, the likelihood of
a collision (two cluster key values having the same hash value)
decreases. Minimizing the number of collisions is important because overflow
blocks (thus extra I/O) might be necessary to store rows with hash values that
collide.

Now let’s play around with SIZE clause...

The maximum number of hash keys assigned per data block is determined by the
SIZE parameter of the CREATE CLUSTER command. SIZE is an estimate of the total
amount of space in bytes required to store the average number of rows associated
with each hash value. For example, if the available free space per data block
is 1700 bytes and SIZE is set to 500 bytes, three hash keys (round(1700/500))
are assigned per data block.

Note: The importance of the SIZE parameter of hash clusters is analogous
to that of the SIZE parameter for index clusters. However, with index clusters,
SIZE applies to rows with the same cluster key value instead of the same hash
value.

Although the maximum number of hash key values per data block is determined
by SIZE, Oracle does not actually reserve space for each hash key value in the
block. For example, if SIZE determines that three hash key values are allowed
per block, this does not prevent rows for one hash key value from taking up all
of the available space in the block. If there are more rows for a given hash
key value than can fit in a single block, the block is chained, as necessary.

– Create 3 clusters with size = 2K, hashkeys
= 4

HSH_CLUSTER_SIZ_2K_ROW_1K, HSH_CLUSTER_SIZ_2K_ROW_2Kand
HSH_CLUSTER_SIZ_2K_ROW_4K

— Available free space is slightly less that blocksize of 8K

— No. of hashkeys that can be stored in the block < 4 (blocksize/size =
8k/2K)

= 3

SQL> drop cluster HSH_CLUSTER_SIZ_2K_ROW_1K
including tables;

create cluster HSH_CLUSTER_SIZ_2K_ROW_1K

( id number(2) )

size 2K single table hash is id hashkeys 4 tablespace mssm;

drop cluster
HSH_CLUSTER_SIZ_2K_ROW_2Kincluding tables;

create cluster hash_cluster_size_2k_row_2k

( id number(2) )

size 2K single table hash is id hashkeys 4 tablespace mssm;

drop cluster
HSH_CLUSTER_SIZ_2K_ROW_4K including tables;

create cluster HSH_CLUSTER_SIZ_2K_ROW_4K

( id number(2) )

size 2K single table hash is id hashkeys 4 tablespace mssm;

– Create a table HSH_TAB_SIZ_2K_ROWSIZ_1K in
cluster HSH_CLUSTER_SIZ_2K_ROW_1K with row size such that row size =1/2(
specified size) = 1/2(2K) = 1K

SQL>drop table HSH_TAB_SIZ_2K_ROWSIZ_1K
purge;

CREATE TABLE HSH_TAB_SIZ_2K_ROWSIZ_1K

(
id number(2) ,

txt1 char(400),

txt2 char(400)

)

CLUSTER HSH_CLUSTER_SIZ_2K_ROW_1K( id );

– Create a table HSH_TAB_SIZ_2K_ROWSIZ_2K in
cluster HSH_CLUSTER_SIZ_2K_ROW_2Kwith row size such that row size =
specified size 2K

SQL>drop table HSH_TAB_SIZ_2K_ROWSIZ_2K
purge;

CREATE TABLE HSH_TAB_SIZ_2K_ROWSIZ_2K

(
id number(2) ,

txt1 char(900),

txt2 char(900)

)

CLUSTER HASH_CLUSTER_SIZE_2K_row_2k( id );
– Create a table HSH_TAB_SIZ_2K_ROWSIZ_4K in
cluster HSH_CLUSTER_SIZ_2K_ROW_4K with row size such that row size = 2(
specified size) = 2(2K) = 4K

SQL>drop table HSH_TAB_SIZ_2K_ROWSIZ_4K
purge;

CREATE TABLE HSH_TAB_SIZ_2K_ROWSIZ_4K

(
id number(2) ,

txt1 char(1900),

txt2 char(1900)

)

CLUSTER HSH_CLUSTER_SIZ_2K_ROW_4K( id );

– Insert records for 5 distinct key values in
the three tables

SQL>Begin

for i in 1..5 loop

insert into
HSH_TAB_SIZ_2K_ROWSIZ_1K values (i, ‘x’, ‘x’);

insert into
HSH_TAB_SIZ_2K_ROWSIZ_2K values (i, ‘x’, ‘x’);

insert into
HSH_TAB_SIZ_2K_ROWSIZ_4K values (i, ‘x’, ‘x’);

end
loop;

commit;

end;

– Check that each block contains records for
a maximum of 3 hashkeys.
– For row size = 1K

8 rows can fit one block

Max no. of hash keys per block = 3

Max no. of records of distinct key values per block = 3

Records for id = 1,2 and 5 (3 hash keys) go to block 161

Records for id = 3 and 4 (only records left) go to block
162

– For row size = 2K

3 rows can fit one block

Max no. of hash keys per block = 3

Max no. of records of distinct key values per block = 3

Records for id = 1,2 and 5 (3 hash keys) go to block 169

Records for id = 3 and 4 (only records left) go to block
170

– For row size = 4K

1 row can fit one block

Max no. of hash keys per block = 3

Max no. of records possible per block = 1

Records for each id go to different block

SQL>select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t1.rowid) hash_cluster_block

from HSH_TAB_SIZ_2K_ROWSIZ_1K t1

union

select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t2.rowid) hash_cluster_block

from HSH_TAB_SIZ_2K_ROWSIZ_2K t2

union

select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t3.rowid) hash_cluster_block

from HSH_TAB_SIZ_2K_ROWSIZ_4K t3

order by 1,2,3;

TABLE_NAME
ID HASH_CLUSTER_BLOCK

————————————- ———- ——————

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K 1
161

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
2
161

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
3
162

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
4
162

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
5
161

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
1
169

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
2
169

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
3
170

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
4
170

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
5
169

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
177

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
2
179

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
3
178

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
4
180

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
5
181

— Let’s insert 3 records with id = 1 in
all the 3 tables

- For row size = 1K

No. of rows already there = 3 in block 161 containing id = 1

Since 8 rows can fit one block, all the 3 rows inserted go to
block 161

Now block 161 has 6 rows

i.e. although the maximum number of hash key values per data
block as determined by SIZE is 3, Oracle does not actually reserve space for
each hash key value in the block. Rows for one hash key value(id=1) can take up
all of the available space in the block.

– For row size = 2K

3 rows can fit one block

Block 169 containing id=1 already has 3 rows

Although 1 more row can fit in block 170 , newly added
rows for id=1 go to anothet block 172

i.e. when key value is inserted for the first time, its
blockmate keys are decided or let’s say that it is decided
records for which hash values will stay together. From then onwards, records
for its blockmate keys will always reside on the same block. Hence, once hash
values occupying a block have been grouped (1,2,5 and 3,4) further records will
occupy only the blocks along with their blockmates.

Block 172 is chained to block 169 as both contain id=1.

– For row size = 4K

1 row can fit one block

Each newly added record goes to new block

All the blocks containing id=1 (177, 184, 185, 186) will
be chained

SQL>select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t1.rowid) hash_cluster_block, count(*)

from HSH_TAB_SIZ_2K_ROWSIZ_1K t1

group by id,
dbms_rowid.rowid_block_number(t1.rowid)

union

select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t2.rowid) hash_cluster_block, count(*)

from HSH_TAB_SIZ_2K_ROWSIZ_2K t2

group by id,
dbms_rowid.rowid_block_number(t2.rowid)

union

select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t3.rowid) hash_cluster_block, count(*)

from HSH_TAB_SIZ_2K_ROWSIZ_4K t3

group by id,
dbms_rowid.rowid_block_number(t3.rowid)

order by 1,3,2;

TABLE_NAME
ID HASH_CLUSTER_BLOCK COUNT(*)

————————————- ———- ——————
———-

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
1
161 4

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
2
161 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
5
161 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
3
162 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
4
162 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
1
169 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
2
169 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
5
169 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
3
170 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
4
170 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
1
172 3

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
177 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
3
178 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
2
179 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
4
180 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
5
181 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
184 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
185 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
186 1
– Add records for id’s = 6 to 9 i.e. four
keys more than what we have defined the cluster for (5).

— Note that it takes time to add these records

SQL>Begin

for i in 6..9 loop

insert into
HSH_TAB_SIZ_2K_ROWSIZ_1K values (i, ‘x’, ‘x’);

insert into
HSH_TAB_SIZ_2K_ROWSIZ_2K values (i, ‘x’, ‘x’);

insert into
HSH_TAB_SIZ_2K_ROWSIZ_4K values (i, ‘x’, ‘x’);

end
loop;

commit;

end;

– Check that there is collision for hash keys
- Multiple key values correspond to the same hash key – the
hash chain for a hash key becomes longer and contains records with different
key values.

– Check that overallocation takes place i.e.
a block holds rows for hashkeys more than it is expected to hold (3).

– For row size = 1K

In block 161, no. of rows already there = 6 containing id
= 1

Since 8 rows can fit one block, rows for id = 6 and 7 go to block
161 i.e. now block 161 contains records for id = 1,2,5,6,7

i.e. it contains 2 hashkeys more than it has configured to hold
. This is called overallocation and block 161 is called overflow block.

From now onwards whenever a record is added with any of id =
1,2,5,6,7, it will be chained to block 161 .

All the blocks chained to block 161 will be searched whenever any of
these id’s is searched.

– For row size = 2K

3 rows can fit one block

Block 169 containing id=1 already has 3 rows with id =
1,2,5

Block 172 has 3 rows with id = 1 – It is already full –
it is chained to block 169

Block 170 has 2 rows with id = 3,4

Record for id=8 goes to block 170 i.e. from now onwards all
the blocks containing of id=3,4,8 will be chained to block 170.

All the blocks chained to block 170 will be
searched whenever any of these id’s (3,4,8) is searched.

Rows with id’s 7 and 9 go to block 173 . This block
will be chained with block 172 or 170

– For row size = 4K

1 row can fit one block

Each newly added record goes to new block

Blocks with id’s > 5 will be chained with any of the
earlier blocks with matching hash value.

e.g. mod(1,5) = mod (6,5) = 1

Hence block containing id = 6 i.e. block 183 will
chain with the all the blocks containing id = 1 (177, 184, 185, 186)

i.e. to search for id = 6, 5 blocks will be searched.

– With a larger number of hash keys for a given number of rows, the
likelihood of a collision (two cluster key values having the same
hash value) decreases.

SQL>select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t1.rowid) hash_cluster_block, count(*)

from HSH_TAB_SIZ_2K_ROWSIZ_1K t1

group by id,
dbms_rowid.rowid_block_number(t1.rowid)

union

select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t2.rowid) hash_cluster_block, count(*)

from HSH_TAB_SIZ_2K_ROWSIZ_2K t2

group by id,
dbms_rowid.rowid_block_number(t2.rowid)

union

select
‘HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K’ TABLE_NAME, id,
dbms_rowid.rowid_block_number(t3.rowid) hash_cluster_block, count(*)

from HSH_TAB_SIZ_2K_ROWSIZ_4K t3

group by id,
dbms_rowid.rowid_block_number(t3.rowid)

order by 1,3,2;

TABLE_NAME
ID HASH_CLUSTER_BLOCK COUNT(*)

————————————- ———- ——————
———-

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
1
161 4

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
2
161 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
5
161 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
6
161 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
7
161 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
3
162 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
4
162 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
8
162 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_1K
9
162 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
1
169 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
2
169 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
5
169 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
3
170 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
4
170 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
8
170 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
6
171 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
1
172 3

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
7
173 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_2K
9
173 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
177 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
3
178 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
2
179 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
4
180 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
5
181 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
9
182 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
6
183 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
184 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
185 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
1
186 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
7
187 1

HASH_CLUSTER_TABLE_SIZE_2K_ROWSIZE_4K
8
188 1
Specifying a User-Defined Hash Function

You can also specify any SQL expression as the hash function for a hash
cluster. If your cluster key values are not evenly distributed among the
cluster, you should consider creating your own hash function that more
efficiently distributes cluster rows among the hash values.

For example, if you have a hash cluster containing employee information and
the cluster key is the employee’s home area code, it is likely that many
employees will hash to the same hash value. To alleviate this problem, you can
place the following expression in the HASH IS clause of the CREATE CLUSTER
command:

MOD((emp.home_area_code + emp.home_prefix + emp.home_suffix), 101)

The expression takes the area code column and adds the phone prefix and
suffix columns, divides by the number of hash values (in this case 101), and
then uses the remainder as the hash value. The result is cluster rows more
evenly distributed among the various hash values.

CREATE CLUSTER address

(postal_code NUMBER, country_id CHAR(2))

HASHKEYS 20

HASH IS MOD(postal_code + country_id, 101);

Summary:

— Hash Cluster tables are appropriate for data that is read frequently via
an equality comparison on the key. If an index scan is used for a key
value, as more no. of users search for the same record, they hit the same index
block which becomes the “hot block” leading to more contention for
the cache buffers chains (cbc) latch. Replacing indexed tables with hash cluster
tables in this case can resolve the problem of contention for CBC latches.

— you cannot range scan a table in a hash cluster without adding a
conventional index to the table. In an index cluster, the query for range of values will be able to make use of the cluster key index to find these rows. In a hash cluster, this query would result in a full table scan unless you had an index on the key column. Only exact equality searches (including in lists and subqueries) may be made on the hash key without using an index that supports range scans.

— When you create a hash cluster table, you must determine in advance, the number of hash keys your table will ever have. The number of HASHKEYs in a hash cluster is a fixed size. You cannot change the size of the hash table without a rebuild of the cluster. This does not in any way limit the amount of data you can store in this cluster; it simply limits the number of unique hash keys that can be generated for this cluster. That may affect performance due to unintended hash collisions if the value was set too low. Getting the size of the HASHKEYs and SIZE parameters right is crucial to avoid a rebuild.

— With a hash cluster, the tables will start out big and will take longer
to create, as Oracle must initialize each block, an action that normally takes
place as data is added to the table. They have the potential to have data in
their first block and their last block, with nothing in between. Full scanning
a virtually empty hash cluster will take as long as full scanning a full hash cluster.

• The hash cluster is allocated right from the beginning. Oracle will take
your HASHKEYS/ trunc(blocksize/SIZE) and allocate and format that space right away. As soon as the first table is put in that cluster, any full scan will hit every allocated block. This is different from every other table in this respect.

• Updates to hash cluster tables do not introduce significant overhead,
unless you update the HASHKEY, which would not be a good idea, as it would
cause the row to migrate

. Hash clusters allocate all the storage for all the hash buckets when the cluster is created, so they may waste space.. Full scans on single table hash clusters will cost as much as they would in a heap table.

In my post Clustering Factor Demystified : Part – III, I have demonstrated the use of single table index and hash clusters to improve the clustering factor of an unorganized table.

——————————–

References :

http://www.relationaldbdesign.com/extended-database-features/module3/oracle-hash-cluster.php

http://docs.oracle.com/cd/B19306_01/server.102/b14231/hash.htm

http://simpleoracledba.blogspot.in/2009/05/oracle-single-table-hash-clusters-are.html

http://jonathanlewis.wordpress.com/2009/10/05/hash-clusters-2/

http://www.iselfschooling.com/mc4articles/mc4cluster.htm

http://dbaforums.org/oracle/index.php?showtopic=18947

http://www.orafaq.com/tuningguide/bad%20hash%20cluster.html

http://www.riddle.ru/mirrors/oracledocs/server/scn73/ch507.html

http://www.dba-oracle.com/t_hash_tables_clusters.htm

http://docs.oracle.com/cd/E11882_01/server.112/e25789/tablecls.htm