Impdp Parallel Index Creation always creates indexes with degree 1. (文档 ID 1289032.1)

本文探讨了在Oracle 11.2.0.2及以上版本中使用导入功能创建索引时遇到的问题,即索引总是以并行度1创建。文章解释了这种现象的原因,并提供了调整索引创建并行度的方法。


APPLIES TO:

Oracle Database - Enterprise Edition - Version 11.2.0.2 to 11.2.0.4 [Release 11.2]
Information in this document applies to any platform.
***Checked for relevance on 23-Nov-2016***

GOAL

Indexes seem always created with parallel degree 1 during import as seen from a sqlfile.

The sql file shows content like:

CREATE INDEX "AP"."AP_INVOICE_DISTRIBUTIONS_N2" ON
"AP"."AP_INVOICE_DISTRIBUTION
S_ALL" ("POSTED_FLAG")
PCTFREE 10 INITRANS 2 MAXTRANS 255 NOLOGGING
STORAGE(INITIAL 262144 NEXT 262144 MINEXTENTS 1 MAXEXTENTS 2147483645
PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE
DE
FAULT CELL_FLASH_CACHE DEFAULT)
TABLESPACE "APPS_TS_TX_IDX"
PARALLEL 1 ;

ALTER INDEX "AP"."AP_INVOICE_DISTRIBUTIONS_N2" PARALLEL 24;


The database is version 11.2.0.2 or above where Bug 8604502 has been fixed.

SOLUTION

The issue is discussed in
Bug 10408313 - INDEXES ARE CREATED WITH PARALLEL DEGREE 1 DURING IMPORT 
closed with status 'Not a Bug'.

The import job with SQLFILE parameter option cannot use multiple execution streams. 
It always executed with parallel 1. Hence its showing the PARALLEL 1 in generated sqlfile and this is an expected behavior.

When you run the import with parallel value and without sqlfile option, then you can see the actual index creation statement with correct parallel value in dw(worker) traces

REFERENCES

BUG:8604502  - INDEXES ARE ALWAYS CREATED WITH PARALLEL DEGREE 1 DURING IMPORT

BUG:10408313  - INDEXES ARE CREATED WITH PARALLEL DEGREE 1 DURING IMPORT
BUG:9710511  - DEGREE OF PARALLELISM IS NOT USED FOR CREATING INDEXES DURING IMPDP OF APPS DB
### Degree Centrality In the context of graph theory, degree centrality measures the number of direct connections a node has within a network. For an undirected graph, this is simply the count of edges incident upon a node[^1]. The formula to calculate degree centrality \( C_D(v) \) for a vertex \( v \) can be expressed as: ```python def degree_centrality(G, v): return G.degree[v] ``` For directed graphs, two separate metrics are often considered: indegree (number of incoming links) and outdegree (number of outgoing links). ### Betweenness Centrality Betweenness centrality quantifies the importance of nodes based on their role in connecting other parts of the network through shortest paths. Specifically, it reflects how frequently each node appears along all possible geodesics between pairs of vertices not including itself[^2]. Mathematically, betweenness centrality \( C_B(v) \) for a given vertex \( v \) is defined by summing over all distinct unordered pairs s,t ≠ v : \[ C_B(v)=\sum _{{s\neq t\neq v}}{\frac {\sigma _{st}(v)}{\sigma _{st}}} \] where \( σ_{st} \) represents total number of shortest paths from source \( s \) to target \( t \), while \( σ_{st}(v) \) denotes those passing via intermediate point \( v \). An efficient algorithm exists that computes these values without explicitly enumerating every path. ### Closeness Centrality Closeness centrality evaluates proximity or accessibility among entities inside networks; higher scores indicate closer average distances relative to others. It inversely correlates with farness—the summation of lengths across minimum routes linking focal element i towards remaining members j≠i . Formally stated, \[ C_C(i)=(n-1)\left(\sum _{j=1,j\neq i}^{N}{d(i,j)^{-1}}\right)^{-1}, \] with d representing distance function measuring separation extent amidst dyads under scrutiny here. ```python import networkx as nx G = nx.Graph() # Example graph initialization closeness_centralities = nx.closeness_centrality(G) print(closeness_centralities) ```
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