How do I force a query to use bind-aware cursor sharing?

本文介绍如何使用BIND_AWARE提示来强制查询从第一次执行开始就使用绑定感知游标共享。通过示例说明了如何在两次不同绑定值的执行后创建两个绑定感知子游标,并解释了这种做法的优点和潜在开销。

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How do I force a query to use bind-aware cursor sharing?     

no_BIND_AWARE是恰恰相反的作用

Way back in our original post on adaptive cursor sharing, a commenter asked if there was a way to skip the monitoring for a query and go straight to bind-aware cursor sharing.  We mentioned then that we would add a hint for this purpose, but we never addressed the new hint here on the blog.  Starting in 11.1.0.7, the BIND_AWARE hint can be used to force bind-aware cursor sharing from the first execution.  The hint only works if all of the other criteria required for bind-aware cursor sharing are met; for instance, the query must have binds, the binds must appear in where-clause predicates, and the columns in those predicates must have the proper statistics (e.g. a histogram) to allow the plan to actually change when the query is executed with different bind values.

Let's look again at the query we used in that original post on ACS.

select count(*), max(empno)
  from emp
  where deptno = :deptno;

Recall that in that example, we had to execute the query twice, with different bind values, before we switched to bind-aware cursor sharing.  So we had to run the query a total of at least four times in order to populate the cursor cache with the two plans we want to use, depending on bind value.  This is what the cursor cache looked like after 5 executions:


Now let's look at the behavior with the BIND_AWARE hint:

After two executions with two different bind values, there are two bind-aware child cursors.  And as expected, the very selective bind value (9) used an index scan, while the not-so-selective bind value (10) used a full table scan:


Now that the cursor cache is populated with these two bind-aware child cursors, they will behave just as if they were created due to adaptive cursor sharing. For instance, if I run the query again with another very selective bind value, the first child cursor will be used.

Before you start using this hint all over the place, keep in mind that there is a reason why we monitor queries before deciding to use bind-aware cursor sharing by default.  There are various forms of overhead from using this form of cursor sharing.  These include:

  • A small amount of additional cursor memory, to store the information we need to pick the child cursor to use for a particular bind value.
  • Additional overhead for soft parse, since we have to compute the selectivity with the current bind value in order to decide which child cursor, if any, is right for that bind value.
  • Additional hard parses, which may not actually generate different plans for different bind values.
  • More child cursors, which may cause cursor cache contention.
These are all of the reasons why we take an adaptive approach to cursor sharing.  But for certain queries, which you know will benefit from different plans for different bind values, the hint can be helpful.

Some of you might be wondering how you can use this hint for queries in a packaged application (where you cannot edit the query text).  I will discuss this in my next post. You can get a copy of the script I used to generate this post here.

<think>嗯,用户这次想了解R语言中abind函数的用法和示例。看来用户正在处理多维数组或矩阵合并的问题,可能在数据预处理或统计建模中遇到了需要拼接高维数据的情况。从用户的查询方式来看,ta明确指向具体函数的使用说明,说明可能已查阅过基础文档但需要更实用的操作示例。考虑到用户直接使用英文术语"abindfunction",应该具备一定编程基础,因此可以跳过安装说明直接进入应用场景。根据引用资料中没有相关R语言内容这一点,需要完全依赖知识库中的R语言知识。abind作为处理高维数据的利器,重点应该展示三个维度:第一是基础维度操作,比如合并多个矩阵时维度对齐的细节,用2x2矩阵的合并示例最直观;第二是命名维度的处理,这是容易出错的地方,用三维带名称数组的合并能很好说明;第三是真实数据应用场景,比如合并多个时间点的气候数据或医学影像,但要注意控制数据规模。特别要提醒用户dim参数的关键作用,这个在文档里经常被忽略说明。上次回答用户关于快速排序的问题时保持了简洁风格,这次也应该避免冗长理论,直接上可运行的代码片段更实用。用户可能还需要知道报错排查,比如维度不一致时出现的"Errorinabind"该怎么解决。不过首次查询应该聚焦核心功能,补充建议用简单数据测试是个安全的选择。最后那个气候数据例子稍微扩展了些,但能展示实际价值,应该保留。</think> 以下是在 R 语言中使用 `abind` 函数的详细指南和示例,帮助您高效地合并多维数组: --- ### **1. 安装与加载** ```r # 首次使用前需安装包 install.packages("abind") # 加载包 library(abind) ``` --- ### **2. 基础语法** ```r abind(..., along = N, rev.along = NULL, new.names = NULL, use.dnns = TRUE) ``` - `...`:要合并的数组对象 - `along`:指定合并方向(维度序号),默认为最大维度+1 - `rev.along`:反向合并维度(高级用法) --- ### **3. 核心示例** #### **(1) 合并矩阵 (沿行或列)** ```r # 创建两个矩阵 mat1 <- matrix(1:4, nrow=2) mat2 <- matrix(5:8, nrow=2) # 沿行合并 (增加行维度) result_rows <- abind(mat1, mat2, along=1) print(result_rows) ``` **输出:** ``` [,1] [,2] [1,] 1 3 [2,] 2 4 [3,] 5 7 [4,] 6 8 ``` #### **(2) 沿新维度堆叠 (创建三维数组)** ```r # 沿第三维度合并 result_3d <- abind(mat1, mat2, along=3) print(result_3d) ``` **输出:** ``` , , 1 [,1] [,2] [1,] 1 3 [2,] 2 4 , , 2 [,1] [,2] [1,] 5 7 [2,] 6 8 ``` --- ### **4. 命名维度处理** 自动保留维度名称: ```r dimnames(mat1) <- list(c("R1","R2"), c("C1","C2")) dimnames(mat2) <- list(c("R1","R2"), c("C1","C2")) # 合并后保留名称 named_result <- abind(A=mat1, B=mat2, along=3) print(named_result[ , , "A"]) # 访问子集 ``` --- ### **5. 进阶场景** #### **(1) 合并多个数组** ```r arr_list <- list( array(1:4, dim=c(2,2)), array(5:8, dim=c(2,2)), array(9:12, dim=c(2,2)) ) result_multi <- abind(arr_list, along=3) ``` #### **(2) 沿任意维度连接** ```r # 三维数组合并沿第二维度 arr1 <- array(1:8, dim=c(2,2,2)) arr2 <- array(9:16, dim=c(2,2,2)) result_custom <- abind(arr1, arr2, along=2) # 第二维度扩展 ``` --- ### **6. 错误排查** - **维度不匹配错误**:确保除合并维度外其他维度大小一致 - **解决方案**: ```r # 检查维度 sapply(list(mat1, mat2), dim) # 手动对齐维度 dim(mat2) <- dim(mat1) ``` --- ### **7. 真实应用场景** **气象数据分析**:合并多日温度三维数组(日×小时×气象站) ```r day1 <- array(rnorm(24*3), dim=c(24, 3)) # 24小时×3站点 day2 <- array(rnorm(24*3), dim=c(24, 3)) full_data <- abind(day1, day2, along=3) # 维度:24×3×2 ``` --- ### **推荐练习方向** 1. 尝试合并包含缺失值的数组如何处理? 2. 如何将`abind`用于图像数据的批次堆叠(通道×高×宽)? 3. 对比`abind`与`rbind`/`cbind`在大型数据集上的性能差异? > **提示**:实际使用时建议先用小型数据测试维度逻辑,避免大规模数据处理出错。对性能敏感场景可考虑 `Rcpp` 实现自定义合并[^4]。 ---
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