Even though 12.1.0.2 is "only" a patch set, it introduces a number of very interesting performance features. In-Memory Column Store is the most well known in this area. But, be aware, a number of additional features that, for example, helps optimizing the physical storage and the caching of data are also available. The aim of this session is to explain and demonstrate how these new features work.

1. 2014 © Trivadis
BASEL BERN BRUGG LAUSANNE ZUERICH DUESSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MUNICH STUTTGART VIENNA
Oracle Database 12.1.0.2 New Performance Features
DOAG 2014, Nürnberg (DE) Christian Antognini
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@ChrisAntognini
Senior principal consultant, trainer and partner at Trivadis in Zurich (CH)
christian.antognini@trivadis.com
http://antognini.ch
Focus: get the most out of Oracle Database
Logical and physical database design
Query optimizer
Application performance management
Author of Troubleshooting Oracle Performance (Apress, 2008/2014)
OakTable Network, Oracle ACE Director

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1.Zone Maps
2.Attribute Clustering
3.In-Memory Column Store
4.In-Memory Aggregation
5.Approximate Count Distinct
6.Automatic Big Table Caching
7.Full Database Caching
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AGENDA

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Zone Maps
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A zone map is a redundant access structure associated to a table.
At most one zone map per table can be created.
Zone maps are intended to reduce the number of logical/physical I/O during table scans.
Zone pruning
Partition pruning
Requirements to use zone maps:
Oracle Partitioning option
Exadata or Supercluster 
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Zone Maps

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The target table is divided in zones.
SYS_OP_ZONE_ID(ROWID,SCALE)
A zone consists of a specific number of blocks.
The SCALE attribute controls it
#푏푙표푐푘푠=2푠푐푎푙푒
For every zone, the zone map stores the minimum and maximum values of several columns.
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How Is a Zone Map Built and What Does It Contain?
Zone 1 – Min 4 / Max 34
Zone 2 – Min 1 / Max 666
Zone 3 – Min 56 / Max 145
Zone 4 – Min 54 / Max 111
Zone 5 – Min 95 / Max 101

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A basic zone map stores information about the columns of a single table.
A zone map is a materialized view with some particular properties.
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Basic Zone Map
CREATE MATERIALIZED ZONEMAP p_bzm ON p (id, n1, n2)
SQL> SELECT object_type, object_name
2 FROM user_objects
3 WHERE object_name LIKE '%P_BZM';
OBJECT_TYPE OBJECT_NAME
----------------- -------------
MATERIALIZED VIEW P_BZM
TABLE P_BZM
INDEX I_ZMAP$_P_BZM

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Basic Zone Maps – Execution Plan
SELECT count(*) FROM p WHERE n1 = 42
--------------------------------------------------------
| Id | Operation | Name |
--------------------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | SORT AGGREGATE | |
|* 2 | TABLE ACCESS STORAGE FULL WITH ZONEMAP| P |
--------------------------------------------------------
2 - storage("N1"=42)
filter((SYS_ZMAP_FILTER('/* ZM_PRUNING */ SELECT "ZONE_ID$",
CASE WHEN BITAND(zm."ZONE_STATE$",1)=1 THEN 1 ELSE CASE
WHEN (zm."MIN_2_N1" > :1 OR zm."MAX_2_N1" < :2) THEN 3
ELSE 2 END END FROM "CHRIS"."P_ZM" zm
WHERE zm."ZONE_LEVEL$"=0 ORDER BY zm."ZONE_ID$"',
SYS_OP_ZONE_ID(ROWID),42,42)<3 AND "N1"=42))

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A join zone map stores information about the columns of several tables.
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Join Zone Maps
CREATE MATERIALIZED ZONEMAP p_jzm AS
SELECT sys_op_zone_id(p.rowid) AS zone_id$,
min(p.n1) AS min_p_n1, max(p.n1) AS max_p_n1,
min(p.n2) AS min_p_n2, max(p.n2) AS max_p_n2,
min(c.n1) AS min_c_n1, max(c.n1) AS max_c_n1,
min(c.n2) AS min_c_n2, max(c.n2) AS max_c_n2
FROM p LEFT OUTER JOIN c ON p.id = c.p_id
GROUP BY sys_op_zone_id(p.rowid)

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Join Zone Maps – Execution Plan
SELECT count(*) FROM p JOIN c ON p.id = c.p_id WHERE c.n2 = 42
---------------------------------------------------------
| Id | Operation | Name |
---------------------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | SORT AGGREGATE | |
|* 2 | HASH JOIN | |
|* 3 | TABLE ACCESS STORAGE FULL | C |
|* 4 | TABLE ACCESS STORAGE FULL WITH ZONEMAP| P |
---------------------------------------------------------
2 - access("P"."ID"="C"."P_ID")
3 - storage("C"."N2"=42)
filter("C"."N2"=42)
4 - filter(SYS_ZMAP_FILTER('/* ZM_PRUNING */ SELECT "ZONE_ID$",
CASE WHEN BITAND(zm."ZONE_STATE$",1)=1 THEN 1 ELSE … )

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Zone maps are redundant access structures that are not always up-to-date.
Basic zone maps:
DML  selective invalidation
Direct-path insert  automatic maintenance
Join zone maps (created on the parent table):
DML on parent  selective invalidation
Direct-path insert on parent  automatic maintenance
DML or direct-path insert on child  full invalidation
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Staleness of Zone Maps

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To refresh a zone map two techniques are available:
ALTER MATERIALIZED ZONEMAP REBUILD statement
REFRESH procedure of DBMS_MVIEW package
Complete as well as fast refreshes are available.
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Refreshing Zone Maps

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Attribute Clustering
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Clustering data that is processed together is an old optimization technique.
Index and hash clusters
Heap table reorganization for improving the clustering factor of an important index
Clustered data makes a number of features more effective
B-tree index range scans
Compression
Exadata/In-Memory storage indexes
Zone maps pruning
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Clustering Data

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Attribute clustering is a new table directive that specifies
on which column(s) data has to be clustered
how data is ordered (linear, interleaved)
when clustering takes place
Keeping data clustered may be expensive. Therefore, the database engine doesn’t enforce it.
It only takes place in case of direct-path inserts and data movement operations.
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Attribute Clustering
ALTER TABLE t CLUSTERING BY LINEAR ORDER (id) YES ON LOAD

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It’s also possible to cluster data based on column(s) of another table
The joined table must have a PK or UK (ORA-65418)
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Join Attribute Clustering
CREATE TABLE p (id NUMBER,
n NUMBER,
pad VARCHAR2(100),
CONSTRAINT p_pk PRIMARY KEY (id))
CREATE TABLE c (id NUMBER,
p_id NUMBER,
p_n NUMBER,
pad VARCHAR2(100))
CLUSTERING c JOIN p ON (c.p_id = p.id) BY LINEAR ORDER (p.n)

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It is possible to combine attribute clustering with zone maps
Both clustering and the zone map are based on the same columns
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Attribute Clustering and Zone Maps
CREATE TABLE c (
id NUMBER,
p_id NUMBER,
p_n NUMBER,
pad VARCHAR2(100)
)
CLUSTERING c JOIN p ON (c.p_id = p.id) BY LINEAR ORDER (p.n)
WITH MATERIALIZED ZONEMAP (c_zm)

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In-Memory Aggregation
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To optimize queries against a star schema, the query optimizer should do the following:
Start evaluating each dimension table that has restrictions on it.
Assemble a list with the resulting dimension keys.
Use this list to extract the matching rows from the fact table.
This approach cannot be implemented with regular joins.
The query optimizer can join only two data sets at one time.
Joining two dimension tables leads to a Cartesian product.
To solve this problem, Oracle Database implements two query transformations:
Star transformation
Vector transformation (new in 12.1.0.2 – requires the In-Memory option)
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The Star Schema Challenge

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Star Transformation vs. Vector Transformation
Selectivity
Elapsed Time

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In the documentation Oracle refers to it as In-Memory Aggregation
It isn’t only about aggregation
It requires the In-Memory option because it takes advantage of some of its features
INMEMORY_SIZE must be greater than 0
The query optimizer considers it when
equijoins are used
an aggregate function is applied to a column of the fact table
the query contains a GROUP BY clause
-ROLLUP, CUBE and GROUPING SETS are not yet supported
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Vector Transformation (1)

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It’s a cost-based query transformation
It can be controlled through the (NO_)VECTOR_TRANSFORM hints
As with the star transformation, sometimes is not possible to force it
It introduces new row source operations
KEY VECTOR CREATE
KEY VECTOR USE
VECTOR GROUP BY
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Vector Transformation (2)

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1.For every dimension table with a filter on it the following operations take place
Access table and filter data
Create key vector
Aggregate data
Create temporary table
2.Access the fact table through a FTS and filter data by applying the key vectors
On Exadata the filter is not yet offloaded
3.Aggregate data with either a vector or hash aggregation
4.Join data from the fact table to temporary tables
5.Join dimension tables without a filter on them
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Vector Transformation – Processing Steps

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Example
SELECT c.customer_class, sum(o.order_total)
FROM orders o, customers c, warehouses w
WHERE o.customer_id = c.customer_id
AND o.warehouse_id = w.warehouse_id
AND c.dob BETWEEN :b1 AND :b2
AND w.warehouse_name BETWEEN :b3 AND :b4
GROUP BY c.customer_class
customers
orders
warehouses

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Execution Plan – Access Dimension Tables, Create Key Vectors, and Create Temporary Tables
-------------------------------------------------------------------------
| Id | Operation | Name |
-------------------------------------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | TEMP TABLE TRANSFORMATION | |
| 2 | LOAD AS SELECT | SYS_TEMP_0FD9DE69C_28565764 |
| 3 | VECTOR GROUP BY | |
| 4 | KEY VECTOR CREATE BUFFERED | :KV0000 |
|* 5 | TABLE ACCESS STORAGE FULL | CUSTOMERS |
| 6 | LOAD AS SELECT | SYS_TEMP_0FD9DE69D_28565764 |
| 7 | VECTOR GROUP BY | |
| 8 | HASH GROUP BY | |
| 9 | KEY VECTOR CREATE BUFFERED | :KV0001 |
|* 10 | TABLE ACCESS STORAGE FULL | WAREHOUSES |
...

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Execution Plan – Access Fact Table by Applying Key Vectors, and Join Temporary Tables
...
| 11 | HASH GROUP BY | |
|* 12 | HASH JOIN | |
| 13 | MERGE JOIN CARTESIAN | |
| 14 | TABLE ACCESS STORAGE FULL | SYS_TEMP_0FD9DE69D_28565764 |
| 15 | BUFFER SORT | |
| 16 | TABLE ACCESS STORAGE FULL | SYS_TEMP_0FD9DE69C_28565764 |
| 17 | VIEW | VW_VT_72AE2D8F |
| 18 | VECTOR GROUP BY | |
| 19 | HASH GROUP BY | |
| 20 | KEY VECTOR USE | :KV0001 |
| 21 | KEY VECTOR USE | :KV0000 |
|* 22 | TABLE ACCESS STORAGE FULL| ORDERS |
-------------------------------------------------------------------------
...
22 - filter(SYS_OP_KEY_VECTOR_FILTER("O"."CUSTOMER_ID",:KV0000) AND
SYS_OP_KEY_VECTOR_FILTER("O"."WAREHOUSE_ID",:KV0001))

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Approximate Count Distinct
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Computing the number of distinct values can be resource intensive
In some situation performance is more important than precision
Many algorithms that estimate the number of distinct values have been developed
Oracle implemented one of this algorithms (HyperLogLog)
In case an estimated number of distinct values is acceptable, you can replace COUNT(DISTINCT expr) with APPROX_COUNT_DISTINCT(expr)
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APPROX_COUNT_DISTINCT Function

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100’000’000 rows
12.7 GB
24 columns
Number of distinct values is 2m, where 푚∈ℕ∗푚<25
Test queries
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Test Case – Setup
SELECT count(DISTINCT n_m) FROM t
SELECT approx_count_distinct(n_m) FROM t

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Test Case – Elapsed Time
0
10
20
30
40
2
4
8
16
32
64
128
256
512
1024
2048
4096
8192
16384
32768
65536
131072
262144
524288
1048576
2097152
4194304
8388608
16777216
Elapsed Time [s]
Number of Distinct Values
COUNT(DISTINCT)
APPROX_COUNT_DISTINCT()

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Test Case – PGA Utilization
1
10
100
1000
2
4
8
16
32
64
128
256
512
1024
2048
4096
8192
16384
32768
65536
131072
262144
524288
1048576
2097152
4194304
8388608
16777216
PGA [MB]
Number of Distinct Values
COUNT(DISTINCT)
APPROX_COUNT_DISTINCT()

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Test Case – Precision
-4
-2
0
2
4
2
4
8
16
32
64
128
256
512
1024
2048
4096
8192
16384
32768
65536
131072
262144
524288
1048576
2097152
4194304
8388608
16777216
Error in Percent
Number of Distinct Values

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Core Messages
Zone Maps
Interesting concept, but mostly useless because of licensing rule
Attribute Clustering
Declarative way to implement old but good technique for “load-once read-many” data
In-Memory Aggregation
Very interesting concept, complements the star transformation
Approximate Count Distinct
Useful feature, sound implementation
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Questions and answers ...
BASEL BERN BRUGG LAUSANNE ZUERICH DUESSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MUNICH STUTTGART VIENNA
Christian Antognini Principal Senior Consultant
christian.antognini@trivadis.com
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