Oracle Analytic SQL

NCOUG 2008

By: Ron Warshawsky CTO DBA InfoPower, Inc.

Oracle Analytic SQL

Note: “standard” name is “Window” functions

When? – Starting 8i

Why? – Simple Solution of Complex Problems

Why Exactly? – advanced ranking, aggregation, row comparison, statistics, “what if” scenarios

Order of Evaluation in SQL: Prior to “ORDER BY” clause

Oracle Analytic SQL

Syntax:

Analytic-Function(<Argument>,<Argument>,...)OVER (  <Query-Partition-Clause>  <Order-By-Clause>  <Windowing-Clause>)

PARTITION BY – aggregates result set into groups

ORDER BY – orders data within a partition

WINDOWING – rows or ranges (logical offset)

Oracle Analytic SQL

More Windowing:

<ROWS|RANGE> BETWEEN ... AND UNBOUNDED PRECEDING – start of partition UNBOUNDED FOLLOWING – end of partition CURRENT ROW value_expr < PRECEDING| FOLLOWING>

Examples:

ROWS BETWEEN 1 PRECEDING and 1 FOLLOWING

ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

RANGE BETWEEN INTERVAL '7' day PRECEDING AND CURRENT ROW

Oracle Analytic SQL

Major Functions:

STANDARD AGGREGATE FUNCTIONSROW_NUMBER()LAG()LEAD()RANK()DENSE_RANK()PERCENT_RANK()NTILE()FIRST_VALUE()LAST_VALUE()FIRST()LAST()STATISTICAL FUNCTIONS

Oracle Analytic SQL

Practical Examples

Oracle Analytic SQL

Selecting the Top n Records

select sql_id, BUFFER_GETS_DELTAfrom ( select sql_id, BUFFER_GETS_DELTA, dense_rank() over (order by BUFFER_GETS_DELTA desc) dr from DBA_HIST_SQLSTAT) x where dr <= 5;

Oracle Analytic SQL

Calculate 3 period Moving Average

select snap_id ,sql_id ,BUFFER_GETS_DELTA ,avg(BUFFER_GETS_DELTA) over (order by snap_id rows between 1 preceding and 1 following ) MA_3snap_BGfrom DBA_HIST_SQLSTAT where sql_id = '0h6b2sajwb74n';

Oracle Analytic SQLCalculate 3 Day Moving Average

select BEGIN_INTERVAL_TIME ,sql_id ,BUFFER_GETS_DELTA ,avg(BUFFER_GETS_DELTA) over ( order by BEGIN_INTERVAL_TIME RANGE between interval '1' day preceding and interval '1' day following ) MA_3day_BGfrom DBA_HIST_SQLSTAT a ,dba_hist_snapshot bwhere a.INSTANCE_NUMBER = b.INSTANCE_NUMBER and a.SNAP_ID = b.SNAP_ID and sql_id = '0h6b2sajwb74n';

Oracle Analytic SQL

Sort Set by Most Frequently Occurring Items

select sql_id, CNT, dense_rank() over (order by cnt desc) as rnk from ( select sql_id,count(*) as cnt from DBA_HIST_SQLSTATgroup by sql_id) x

Oracle Analytic SQL

Identify Percentage of Total

select distinct sql_id, (sql_bg/total)*100 as pctfrom (select sql_id, sum(BUFFER_GETS_DELTA) over () total, sum(BUFFER_GETS_DELTA) over (partition by sql_id) sql_bgfrom DBA_HIST_SQLSTAT) xorder by 2;

Oracle Analytic SQL

Reduce Skewing (ignore highest and lowest value within a set)

select sql_id, avg(BUFFER_GETS_DELTA ) avg_bgfrom (select sql_id, BUFFER_GETS_DELTA, min(BUFFER_GETS_DELTA) over (partition by sql_id) min_bg, max(BUFFER_GETS_DELTA) over (partition by sql_id) max_bgfrom DBA_HIST_SQLSTAT) xwhere BUFFER_GETS_DELTA not in (min_bg, max_bg)group by sql_id;

Oracle Analytic SQL

Convert Rows into Columns

select max(case when object_type='TABLE‘ then object_name else null end) as TAB_NAME,max(case when object_type='INDEX‘ then object_name else null end) as IDX_NAMEfrom (select object_type ,object_name ,row_number() over (partition by object_type order by object_name) rnfrom dba_objectswhere object_type in ('TABLE', 'INDEX' )) xgroup by rnorder by 1;

Oracle Analytic SQL

Create fix size “buckets” of data

select

segment_name

ceil(row_number() over (order by segment_name)/50) grp,

from DBA_SEGMENTS

;

Oracle Analytic SQL

Create pre-defined number of data “buckets”

select

ntile(10) over (order by segment_name) grp,

segment_name

from DBA_SEGMENTS

;

Oracle Analytic SQL

Mix Aggregates from multiple groups

select sql_id, module, sum(BUFFER_GETS_DELTA ) over(partition by module) module_cnt, PARSING_SCHEMA_NAME, sum(BUFFER_GETS_DELTA ) over(partition by PARSING_SCHEMA_NAME) PARSING_SCHEMA_NAME_cnt, sum(BUFFER_GETS_DELTA ) over() totalfrom DBA_HIST_SQLSTAT;

Oracle Analytic SQL

Aggregating across moving historical ranges

select distinct x.*from (select trunc(BEGIN_INTERVAL_TIME ) SNAP_Date ,sum(BUFFER_GETS_DELTA) over (partition by trunc(BEGIN_INTERVAL_TIME ) ) snap_bg ,sum(BUFFER_GETS_DELTA ) over(order by trunc(BEGIN_INTERVAL_TIME ) range between 14 preceding and current row) prior_2week_bgfrom DBA_HIST_SQLSTAT a ,dba_hist_snapshot bwhere a.INSTANCE_NUMBER = b.INSTANCE_NUMBER and a.SNAP_ID = b.SNAP_ID) x;;

Oracle Analytic SQL

Identify Consecutive Ranges of Time

select a.sql_id, b.BEGIN_INTERVAL_TIME snap_time, lead(b.BEGIN_INTERVAL_TIME) over (order by a.snap_id) next_snap_timefrom DBA_HIST_SQLSTAT a ,dba_hist_snapshot bwhere a.INSTANCE_NUMBER = b.INSTANCE_NUMBER and a.SNAP_ID = b.SNAP_ID and sql_id = '83taa7kaw59c1'

Oracle Analytic SQL

Access Future and Historical rows

select snap_id ,BUFFER_GETS_DELTA ,nvl(lead(BUFFER_GETS_DELTA) over (order by snap_id, BUFFER_GETS_DELTA ) , null ) forward ,nvl(lag (BUFFER_GETS_DELTA) over (order by snap_id, BUFFER_GETS_DELTA ) , null ) backwardfrom ( select snap_id ,sum(BUFFER_GETS_DELTA ) BUFFER_GETS_DELTAfrom DBA_HIST_SQLSTAT group by snap_id) xorder by 1;

Oracle Analytic SQL

Select every N-th row

select SNAP_IDfrom (select snap_id ,row_number( ) over (order by snap_id) rnfrom dba_hist_snapshot ) xwhere mod(rn,5) = 1;

Oracle Analytic SQL

Remove Duplicate without use of Distinct

select sql_idfrom (select sql_id, row_number( ) over (partition by sql_id order by sql_id) rnfrom DBA_HIST_SQLSTAT ) xwhere rn = 1;

Oracle Analytic SQL

Q&A